import MySQLdb succeeded Import error (python version) python version = 3 warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data list_job_run_as_list : ['mask_detection', 'datou', 'CacheModelData_queries', 'CachePhotoData_queries', 'test_fork', 'prepare_maskdata', 'portfolio_queries', 'sla_mensuel'] python version used : 3 liste_fichiers : [('tests/mask_test', True, 'Test mask-detection ', 'mask_detection'), ('tests/datou_test', True, 'Datou All Test', 'datou', 'all'), ('mtr/database_queries/CacheModelData_queries', True, 'Test Cache Model Data', 'CacheModelData_queries'), ('tests/cache_photo_data_test', True, 'Test local_cache_photo ', 'CachePhotoData_queries'), ('mtr/mask_rcnn/prepare_maskdata', True, 'test prepare mask data', 'prepare_maskdata', 'all'), ('mtr/database_queries/portfolio_queries', True, 'test portfolio queries', 'portfolio_queries'), ('prod/memo/memo', True, 'SLA Mensuel', 'sla_mensuel', 'all')] #&_# BEGIN OF TEST : tests/mask_test #&_# /home/admin/workarea/git/Velours/python/tests/mask_test.py Test mask-detection python version used : 3 ############################### TEST memory used ################################ free memory at begining : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 10593 run mask_detect Inside batchDatouExec : verbose : False # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : mask_detect list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.17866230010986328 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 : False number of steps : 1 step1:mask_detect Tue May 6 21:35:27 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 : 10593 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 /home/admin/workarea/git/Velours/python/tests/python_tests.py:11: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses import imp 2025-05-06 21:35:30.359930: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-05-06 21:35:30.387070: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-05-06 21:35:30.389437: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fced8000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-05-06 21:35:30.389493: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-05-06 21:35:30.393528: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-05-06 21:35:30.611440: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1300c060 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-05-06 21:35:30.611496: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-05-06 21:35:30.612758: 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 2025-05-06 21:35:30.613232: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-06 21:35:30.616271: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-06 21:35:30.618845: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-06 21:35:30.619254: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-06 21:35:30.622186: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-06 21:35:30.623668: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-06 21:35:30.628317: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-06 21:35:30.629742: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-06 21:35:30.629817: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-06 21:35:30.630591: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-06 21:35:30.630607: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-06 21:35:30.630617: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-06 21:35:30.631916: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9815 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. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl454 thcls : [{'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3473 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21)) {'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] 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 mask_coco_origin NUM_CLASSES 81 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 2025-05-06 21:35:31.191714: 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 2025-05-06 21:35:31.191813: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-06 21:35:31.191842: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-06 21:35:31.191868: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-06 21:35:31.191892: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-06 21:35:31.191917: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-06 21:35:31.191961: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-06 21:35:31.191987: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-06 21:35:31.194183: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-06 21:35:31.195901: 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 2025-05-06 21:35:31.195962: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-06 21:35:31.195993: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-06 21:35:31.196020: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-06 21:35:31.196048: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-06 21:35:31.196075: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-06 21:35:31.196102: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-06 21:35:31.196129: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-06 21:35:31.198288: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-06 21:35:31.198328: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-06 21:35:31.198343: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-06 21:35:31.198357: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-06 21:35:31.200607: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9815 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. 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 : mask_coco_origin model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-05-06 21:35:38.488580: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-06 21:35:38.671057: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-06 21:35:40.626387: E tensorflow/stream_executor/cuda/cuda_driver.cc:910] failed to synchronize the stop event: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2025-05-06 21:35:40.626478: E tensorflow/stream_executor/gpu/gpu_timer.cc:55] Internal: Error destroying CUDA event: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2025-05-06 21:35:40.626490: E tensorflow/stream_executor/gpu/gpu_timer.cc:60] Internal: Error destroying CUDA event: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2025-05-06 21:35:40.626524: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 8B (8 bytes) from device: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2025-05-06 21:35:40.626538: E tensorflow/stream_executor/stream.cc:5485] Internal: Failed to enqueue async memset operation: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2025-05-06 21:35:40.626556: W tensorflow/core/kernels/gpu_utils.cc:69] Failed to check cudnn convolutions for out-of-bounds reads and writes with an error message: 'Failed to load in-memory CUBIN: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered'; skipping this check. This only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once. 2025-05-06 21:35:40.626565: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 8B (8 bytes) from device: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2025-05-06 21:35:40.626577: I tensorflow/stream_executor/stream.cc:4963] [stream=0x142a4fc0,impl=0x142a3fb0] did not memzero GPU location; source: 0x7fcd2f7fc020 2025-05-06 21:35:40.627044: F ./tensorflow/core/kernels/reduction_gpu_kernels.cu.h:731] Non-OK-status: GpuLaunchKernel(RowReduceKernel, num_blocks, threads_per_block, 0, cu_stream, in, out, num_rows, num_cols, op, init) status: Internal: an illegal memory access was encountered max_time_sub_proc : 3600 Useless call to update_current_state in case -12 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! ERROR : mask output needs to be a dictionnary now ! No output to save, continue without doing anything ! save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : -12 free memory after detection : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 7035 error , can't release the memory or there are other process who occupe the free memory ERROR test release memory FAILED ############################### TEST detect object ################################ run mask_detect Inside batchDatouExec : verbose : False Catched exception ! Connect or reconnect ! # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : mask_detect list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.2597472667694092 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 : False number of steps : 1 step1:mask_detect Tue May 6 22:35:31 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 : 7035 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-05-06 22:35:36.446862: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-05-06 22:35:36.483150: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-05-06 22:35:36.485836: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fcee0000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-05-06 22:35:36.485893: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-05-06 22:35:36.494012: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-05-06 22:35:36.778717: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x138bd6a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-05-06 22:35:36.778772: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-05-06 22:35:36.780098: 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 2025-05-06 22:35:36.780414: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-06 22:35:36.782262: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-06 22:35:36.799871: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-06 22:35:36.800376: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-06 22:35:36.832754: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-06 22:35:36.838114: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-06 22:35:36.900874: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-06 22:35:36.902701: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-06 22:35:36.903185: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-06 22:35:36.906131: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-06 22:35:36.906165: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-06 22:35:36.906176: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-06 22:35:36.908071: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6470 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl454 thcls : [{'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3473 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21)) {'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] 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 mask_coco_origin NUM_CLASSES 81 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 2025-05-06 22:35:37.724400: 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 2025-05-06 22:35:37.724496: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-06 22:35:37.724524: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-06 22:35:37.724549: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-06 22:35:37.724574: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-06 22:35:37.724597: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-06 22:35:37.724622: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-06 22:35:37.724649: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-06 22:35:37.726408: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-06 22:35:37.728008: 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 2025-05-06 22:35:37.728084: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-06 22:35:37.728114: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-06 22:35:37.728142: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-06 22:35:37.728169: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-06 22:35:37.728196: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-06 22:35:37.728223: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-06 22:35:37.728251: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-06 22:35:37.730017: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-06 22:35:37.730060: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-06 22:35:37.730074: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-06 22:35:37.730088: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-06 22:35:37.731917: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6470 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. 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 : mask_coco_origin model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-05-06 22:35:47.299077: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-06 22:35:47.500699: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (720, 1280, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 1280.00000 nb d'objets trouves : 4 Detection mask done ! Trying to reset tf kernel 2343476 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1525 tf kernel not reseted sub process len(results) : 1 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 1 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 : 6814 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl454 Catched exception ! Connect or reconnect ! thcls : [{'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3473 ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] time for calcul the mask position with numpy : 0.00045037269592285156 nb_pixel_total : 16902 time to create 1 rle with old method : 0.019316434860229492 length of segment : 107 time for calcul the mask position with numpy : 0.015085220336914062 nb_pixel_total : 480740 time to create 1 rle with new method : 0.02859973907470703 length of segment : 632 time for calcul the mask position with numpy : 0.0004832744598388672 nb_pixel_total : 36641 time to create 1 rle with old method : 0.0422205924987793 length of segment : 133 time for calcul the mask position with numpy : 9.608268737792969e-05 nb_pixel_total : 4794 time to create 1 rle with old method : 0.005801677703857422 length of segment : 51 time spent for convertir_results : 1.0069684982299805 time spend for datou_step_exec : 23.75443410873413 time spend to save output : 4.076957702636719e-05 total time spend for step 1 : 23.754474878311157 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 423 chid ids of type : 445 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.015671968460083008 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'917855882': [[(917855882, 492601069, 445, 1092, 1280, 0, 108, 0.99883693, [(1205, 1, 58), (1165, 2, 105), (1159, 3, 113), (1149, 4, 124), (1113, 5, 161), (1100, 6, 174), (1097, 7, 177), (1095, 8, 179), (1095, 9, 179), (1095, 10, 179), (1095, 11, 179), (1095, 12, 179), (1095, 13, 179), (1095, 14, 178), (1095, 15, 178), (1095, 16, 178), (1095, 17, 178), (1095, 18, 177), (1095, 19, 177), (1095, 20, 177), (1095, 21, 177), (1095, 22, 177), (1095, 23, 178), (1095, 24, 178), (1095, 25, 178), (1095, 26, 179), (1095, 27, 179), (1095, 28, 180), (1095, 29, 181), (1095, 30, 182), (1095, 31, 183), (1095, 32, 183), (1095, 33, 184), (1095, 34, 184), (1096, 35, 183), (1096, 36, 183), (1096, 37, 184), (1097, 38, 183), (1097, 39, 183), (1097, 40, 183), (1098, 41, 182), (1098, 42, 182), (1098, 43, 182), (1099, 44, 181), (1099, 45, 181), (1099, 46, 181), (1100, 47, 180), (1100, 48, 180), (1101, 49, 179), (1101, 50, 179), (1102, 51, 178), (1102, 52, 178), (1103, 53, 177), (1103, 54, 177), (1104, 55, 176), (1104, 56, 176), (1104, 57, 176), (1104, 58, 176), (1105, 59, 175), (1105, 60, 175), (1105, 61, 175), (1105, 62, 175), (1105, 63, 175), (1106, 64, 174), (1106, 65, 174), (1106, 66, 174), (1106, 67, 174), (1106, 68, 174), (1106, 69, 174), (1106, 70, 174), (1106, 71, 174), (1106, 72, 174), (1106, 73, 174), (1107, 74, 173), (1107, 75, 173), (1107, 76, 173), (1107, 77, 173), (1107, 78, 173), (1107, 79, 173), (1108, 80, 172), (1108, 81, 172), (1109, 82, 171), (1110, 83, 170), (1110, 84, 170), (1111, 85, 169), (1112, 86, 168), (1113, 87, 166), (1114, 88, 165), (1115, 89, 164), (1117, 90, 162), (1120, 91, 159), (1138, 92, 141), (1146, 93, 133), (1154, 94, 125), (1167, 95, 112), (1177, 96, 102), (1183, 97, 95), (1185, 98, 93), (1187, 99, 90), (1188, 100, 55), (1264, 100, 12), (1190, 101, 50), (1191, 102, 46), (1194, 103, 40), (1197, 104, 34), (1202, 105, 25), (1207, 106, 16)], ['1222,106,1207,106,1206,105,1197,104,1191,102,1182,96,1176,95,1167,95,1166,94,1154,94,1153,93,1146,93,1145,92,1137,91,1120,91,1115,89,1110,84,1107,79,1106,73,1106,64,1104,55,1099,46,1095,34,1095,8,1100,6,1112,6,1113,5,1148,5,1149,4,1158,4,1165,2,1204,2,1205,1,1262,1,1269,2,1273,5,1273,13,1271,18,1271,22,1273,27,1277,31,1279,37,1279,86,1278,87,1278,96,1275,100,1264,100,1263,99,1243,99,1230,104']), (917855882, 492601069, 445, 52, 1128, 16, 668, 0.9977495, [(711, 22, 22), (925, 22, 47), (608, 23, 146), (894, 23, 103), (598, 24, 234), (850, 24, 158), (590, 25, 427), (582, 26, 444), (575, 27, 458), (569, 28, 466), (565, 29, 472), (560, 30, 480), (556, 31, 486), (550, 32, 495), (544, 33, 503), (538, 34, 512), (532, 35, 520), (527, 36, 527), (523, 37, 534), (518, 38, 541), (514, 39, 548), (510, 40, 554), (506, 41, 561), (503, 42, 566), (499, 43, 572), (496, 44, 577), (493, 45, 582), (491, 46, 585), (488, 47, 590), (487, 48, 592), (485, 49, 595), (483, 50, 598), (482, 51, 600), (481, 52, 602), (480, 53, 603), (479, 54, 605), (478, 55, 606), (476, 56, 608), (475, 57, 610), (474, 58, 611), (473, 59, 613), (472, 60, 614), (470, 61, 616), (469, 62, 618), (468, 63, 619), (466, 64, 621), (465, 65, 623), (464, 66, 624), (462, 67, 626), (461, 68, 628), (459, 69, 630), (458, 70, 631), (456, 71, 633), (455, 72, 635), (453, 73, 637), (452, 74, 638), (451, 75, 639), (449, 76, 641), (448, 77, 642), (447, 78, 643), (446, 79, 644), (445, 80, 645), (444, 81, 646), (442, 82, 648), (441, 83, 649), (440, 84, 650), (439, 85, 651), (438, 86, 652), (437, 87, 653), (436, 88, 654), (435, 89, 655), (434, 90, 656), (433, 91, 657), (432, 92, 658), (431, 93, 659), (430, 94, 660), (429, 95, 661), (428, 96, 662), (427, 97, 663), (425, 98, 665), (423, 99, 667), (421, 100, 669), (419, 101, 671), (417, 102, 673), (413, 103, 677), (410, 104, 680), (405, 105, 685), (401, 106, 689), (397, 107, 693), (392, 108, 698), (387, 109, 703), (382, 110, 708), (377, 111, 713), (373, 112, 717), (369, 113, 721), (365, 114, 725), (362, 115, 728), (358, 116, 732), (356, 117, 734), (353, 118, 737), (351, 119, 739), (349, 120, 741), (346, 121, 744), (344, 122, 746), (341, 123, 749), (338, 124, 752), (335, 125, 755), (331, 126, 759), (327, 127, 763), (323, 128, 767), (319, 129, 770), (314, 130, 775), (308, 131, 781), (303, 132, 786), (294, 133, 795), (287, 134, 802), (279, 135, 810), (273, 136, 816), (267, 137, 822), (262, 138, 827), (258, 139, 831), (255, 140, 834), (252, 141, 837), (250, 142, 839), (247, 143, 842), (245, 144, 844), (242, 145, 847), (240, 146, 849), (237, 147, 852), (234, 148, 855), (230, 149, 859), (226, 150, 863), (220, 151, 869), (213, 152, 876), (207, 153, 882), (200, 154, 889), (193, 155, 896), (187, 156, 902), (184, 157, 905), (181, 158, 908), (178, 159, 911), (176, 160, 913), (174, 161, 915), (172, 162, 917), (170, 163, 919), (168, 164, 921), (167, 165, 922), (165, 166, 924), (164, 167, 925), (162, 168, 927), (161, 169, 928), (159, 170, 930), (157, 171, 932), (155, 172, 934), (153, 173, 935), (151, 174, 937), (149, 175, 939), (146, 176, 942), (144, 177, 944), (142, 178, 946), (140, 179, 948), (139, 180, 949), (137, 181, 951), (136, 182, 952), (134, 183, 954), (133, 184, 955), (132, 185, 956), (131, 186, 957), (130, 187, 958), (129, 188, 959), (128, 189, 960), (127, 190, 960), (126, 191, 961), (126, 192, 961), (125, 193, 962), (124, 194, 963), (123, 195, 964), (122, 196, 965), (122, 197, 965), (121, 198, 966), (120, 199, 967), (119, 200, 968), (118, 201, 969), (117, 202, 970), (116, 203, 971), (114, 204, 973), (113, 205, 973), (112, 206, 974), (111, 207, 975), (109, 208, 977), (108, 209, 978), (107, 210, 979), (106, 211, 980), (105, 212, 981), (104, 213, 982), (103, 214, 983), (102, 215, 984), (101, 216, 985), (101, 217, 984), (100, 218, 985), (100, 219, 985), (99, 220, 986), (98, 221, 987), (98, 222, 987), (97, 223, 988), (97, 224, 987), (96, 225, 988), (96, 226, 988), (95, 227, 989), (95, 228, 989), (94, 229, 990), (94, 230, 990), (94, 231, 990), (93, 232, 990), (93, 233, 990), (92, 234, 991), (92, 235, 991), (92, 236, 991), (91, 237, 992), (91, 238, 991), (91, 239, 991), (91, 240, 990), (91, 241, 990), (90, 242, 991), (90, 243, 990), (90, 244, 990), (90, 245, 989), (90, 246, 989), (89, 247, 990), (89, 248, 989), (89, 249, 989), (89, 250, 988), (89, 251, 988), (88, 252, 988), (88, 253, 988), (88, 254, 987), (88, 255, 986), (88, 256, 986), (87, 257, 986), (87, 258, 985), (87, 259, 985), (87, 260, 984), (87, 261, 983), (86, 262, 983), (86, 263, 982), (86, 264, 982), (86, 265, 981), (85, 266, 981), (85, 267, 980), (85, 268, 980), (85, 269, 979), (84, 270, 979), (84, 271, 979), (84, 272, 978), (83, 273, 979), (83, 274, 978), (83, 275, 977), (82, 276, 978), (82, 277, 977), (82, 278, 977), (81, 279, 977), (81, 280, 977), (81, 281, 977), (80, 282, 977), (80, 283, 977), (80, 284, 976), (79, 285, 977), (79, 286, 976), (79, 287, 976), (78, 288, 976), (78, 289, 976), (78, 290, 975), (77, 291, 976), (77, 292, 975), (77, 293, 975), (76, 294, 975), (76, 295, 975), (76, 296, 974), (75, 297, 975), (75, 298, 974), (74, 299, 975), (74, 300, 974), (74, 301, 974), (73, 302, 974), (73, 303, 974), (72, 304, 974), (72, 305, 974), (71, 306, 974), (71, 307, 973), (71, 308, 972), (70, 309, 972), (70, 310, 971), (70, 311, 970), (70, 312, 968), (69, 313, 968), (69, 314, 966), (69, 315, 964), (69, 316, 962), (68, 317, 961), (68, 318, 959), (68, 319, 958), (68, 320, 956), (67, 321, 955), (67, 322, 954), (67, 323, 952), (67, 324, 951), (66, 325, 951), (66, 326, 950), (66, 327, 948), (66, 328, 947), (66, 329, 946), (65, 330, 946), (65, 331, 946), (65, 332, 945), (65, 333, 944), (65, 334, 942), (65, 335, 941), (65, 336, 940), (65, 337, 939), (65, 338, 938), (65, 339, 936), (64, 340, 936), (64, 341, 934), (64, 342, 932), (64, 343, 930), (64, 344, 928), (64, 345, 926), (64, 346, 925), (64, 347, 923), (64, 348, 922), (64, 349, 920), (64, 350, 919), (63, 351, 919), (63, 352, 918), (63, 353, 917), (63, 354, 916), (63, 355, 915), (63, 356, 914), (63, 357, 912), (63, 358, 911), (63, 359, 910), (63, 360, 909), (63, 361, 908), (63, 362, 906), (63, 363, 905), (63, 364, 904), (63, 365, 902), (63, 366, 901), (63, 367, 899), (63, 368, 898), (63, 369, 896), (62, 370, 895), (62, 371, 893), (62, 372, 891), (62, 373, 890), (62, 374, 888), (62, 375, 887), (62, 376, 886), (62, 377, 885), (62, 378, 884), (62, 379, 883), (63, 380, 880), (63, 381, 879), (63, 382, 878), (63, 383, 877), (63, 384, 876), (63, 385, 875), (63, 386, 874), (63, 387, 873), (63, 388, 872), (64, 389, 870), (64, 390, 869), (64, 391, 868), (64, 392, 867), (64, 393, 865), (64, 394, 864), (64, 395, 863), (65, 396, 861), (65, 397, 860), (65, 398, 859), (65, 399, 858), (65, 400, 857), (65, 401, 856), (65, 402, 854), (65, 403, 853), (65, 404, 851), (65, 405, 850), (65, 406, 848), (66, 407, 846), (66, 408, 844), (66, 409, 843), (66, 410, 842), (66, 411, 841), (66, 412, 840), (66, 413, 838), (66, 414, 837), (66, 415, 836), (66, 416, 835), (66, 417, 835), (66, 418, 834), (66, 419, 833), (67, 420, 831), (67, 421, 830), (67, 422, 829), (67, 423, 829), (67, 424, 828), (67, 425, 827), (67, 426, 826), (67, 427, 825), (67, 428, 824), (68, 429, 822), (68, 430, 820), (68, 431, 819), (68, 432, 818), (68, 433, 816), (68, 434, 815), (68, 435, 813), (68, 436, 811), (69, 437, 809), (69, 438, 807), (69, 439, 806), (69, 440, 804), (69, 441, 803), (69, 442, 802), (69, 443, 800), (70, 444, 798), (70, 445, 797), (70, 446, 796), (70, 447, 796), (71, 448, 794), (71, 449, 794), (72, 450, 792), (72, 451, 792), (73, 452, 790), (73, 453, 789), (74, 454, 788), (74, 455, 787), (75, 456, 786), (75, 457, 785), (76, 458, 784), (76, 459, 783), (77, 460, 782), (77, 461, 781), (77, 462, 781), (78, 463, 779), (78, 464, 779), (79, 465, 777), (79, 466, 777), (79, 467, 776), (80, 468, 775), (80, 469, 774), (80, 470, 774), (81, 471, 772), (81, 472, 771), (82, 473, 770), (82, 474, 769), (83, 475, 767), (83, 476, 766), (83, 477, 766), (84, 478, 764), (84, 479, 763), (85, 480, 761), (85, 481, 760), (85, 482, 759), (86, 483, 757), (86, 484, 755), (87, 485, 753), (87, 486, 752), (87, 487, 750), (88, 488, 748), (88, 489, 747), (88, 490, 746), (89, 491, 744), (89, 492, 743), (90, 493, 741), (90, 494, 741), (91, 495, 739), (91, 496, 738), (92, 497, 736), (93, 498, 735), (94, 499, 733), (94, 500, 733), (95, 501, 731), (96, 502, 729), (97, 503, 728), (98, 504, 726), (99, 505, 724), (99, 506, 724), (100, 507, 722), (101, 508, 721), (102, 509, 719), (104, 510, 717), (105, 511, 715), (106, 512, 714), (107, 513, 712), (108, 514, 711), (110, 515, 708), (111, 516, 707), (113, 517, 704), (114, 518, 703), (115, 519, 701), (117, 520, 698), (118, 521, 697), (119, 522, 695), (121, 523, 693), (122, 524, 691), (124, 525, 689), (125, 526, 687), (126, 527, 685), (128, 528, 683), (129, 529, 681), (131, 530, 678), (132, 531, 676), (134, 532, 673), (135, 533, 672), (137, 534, 669), (138, 535, 667), (140, 536, 664), (141, 537, 662), (143, 538, 659), (144, 539, 657), (146, 540, 654), (148, 541, 651), (149, 542, 649), (151, 543, 645), (153, 544, 642), (154, 545, 640), (156, 546, 638), (158, 547, 635), (159, 548, 633), (161, 549, 630), (162, 550, 628), (164, 551, 625), (166, 552, 623), (167, 553, 621), (169, 554, 618), (170, 555, 617), (171, 556, 615), (173, 557, 613), (174, 558, 611), (176, 559, 608), (177, 560, 607), (178, 561, 605), (180, 562, 603), (181, 563, 601), (183, 564, 599), (185, 565, 597), (186, 566, 595), (189, 567, 592), (192, 568, 589), (195, 569, 585), (198, 570, 582), (201, 571, 579), (204, 572, 575), (206, 573, 573), (209, 574, 569), (212, 575, 566), (215, 576, 563), (218, 577, 559), (221, 578, 556), (223, 579, 553), (226, 580, 550), (228, 581, 547), (230, 582, 545), (232, 583, 542), (234, 584, 540), (235, 585, 539), (237, 586, 536), (238, 587, 534), (240, 588, 531), (242, 589, 528), (243, 590, 526), (245, 591, 523), (247, 592, 520), (249, 593, 516), (251, 594, 513), (253, 595, 510), (256, 596, 505), (258, 597, 501), (261, 598, 497), (263, 599, 493), (267, 600, 488), (271, 601, 482), (274, 602, 478), (278, 603, 473), (281, 604, 468), (284, 605, 464), (287, 606, 460), (290, 607, 456), (292, 608, 453), (295, 609, 449), (297, 610, 446), (300, 611, 442), (303, 612, 438), (305, 613, 434), (307, 614, 431), (310, 615, 427), (312, 616, 423), (315, 617, 418), (317, 618, 415), (320, 619, 410), (322, 620, 406), (325, 621, 401), (327, 622, 396), (330, 623, 390), (333, 624, 384), (335, 625, 379), (338, 626, 374), (341, 627, 369), (345, 628, 362), (349, 629, 356), (353, 630, 350), (357, 631, 344), (360, 632, 340), (364, 633, 334), (368, 634, 328), (373, 635, 320), (378, 636, 313), (383, 637, 305), (389, 638, 295), (395, 639, 282), (401, 640, 270), (408, 641, 256), (416, 642, 240), (431, 643, 217), (448, 644, 193), (465, 645, 169), (480, 646, 148), (495, 647, 126), (511, 648, 104), (526, 649, 82), (565, 650, 9)], ['526,649,416,642,341,627,289,606,263,599,220,577,186,566,102,509,91,496,70,447,63,388,65,330,85,269,91,237,101,216,134,183,187,156,225,151,252,141,343,123,358,116,416,103,449,76,493,45,527,36,608,23,754,24,893,24,925,22,996,23,1032,27,1066,41,1082,52,1089,72,1088,172,1082,237,1045,305,1019,322,1002,338,950,373,885,432,865,446,851,473,830,493,810,528,786,554,773,585,740,612,683,638,607,649']), (917855882, 492601069, 445, 0, 440, 0, 116, 0.99194497, [(127, 1, 141), (94, 2, 206), (384, 2, 2), (59, 3, 273), (340, 3, 57), (22, 4, 381), (19, 5, 387), (16, 6, 392), (15, 7, 394), (14, 8, 396), (14, 9, 397), (13, 10, 399), (12, 11, 400), (12, 12, 400), (11, 13, 402), (10, 14, 403), (11, 15, 403), (11, 16, 404), (12, 17, 403), (12, 18, 404), (12, 19, 405), (12, 20, 405), (12, 21, 406), (12, 22, 406), (12, 23, 407), (12, 24, 407), (12, 25, 408), (12, 26, 408), (12, 27, 408), (12, 28, 408), (12, 29, 409), (12, 30, 409), (12, 31, 409), (12, 32, 409), (12, 33, 409), (12, 34, 410), (12, 35, 410), (12, 36, 410), (12, 37, 410), (12, 38, 410), (12, 39, 410), (12, 40, 410), (12, 41, 411), (12, 42, 411), (12, 43, 411), (12, 44, 411), (12, 45, 411), (12, 46, 410), (12, 47, 410), (12, 48, 410), (12, 49, 410), (12, 50, 410), (12, 51, 410), (12, 52, 409), (12, 53, 408), (12, 54, 408), (12, 55, 407), (12, 56, 406), (12, 57, 404), (12, 58, 403), (11, 59, 403), (11, 60, 402), (11, 61, 401), (11, 62, 400), (11, 63, 400), (11, 64, 399), (11, 65, 398), (11, 66, 397), (11, 67, 397), (11, 68, 396), (11, 69, 395), (11, 70, 395), (11, 71, 394), (11, 72, 394), (11, 73, 394), (11, 74, 393), (11, 75, 393), (11, 76, 393), (11, 77, 393), (11, 78, 393), (11, 79, 393), (11, 80, 392), (10, 81, 394), (10, 82, 394), (10, 83, 395), (9, 84, 396), (9, 85, 262), (279, 85, 126), (9, 86, 75), (98, 86, 28), (142, 86, 117), (292, 86, 112), (9, 87, 71), (152, 87, 103), (294, 87, 110), (8, 88, 68), (161, 88, 91), (296, 88, 107), (8, 89, 63), (177, 89, 72), (297, 89, 106), (7, 90, 61), (205, 90, 40), (298, 90, 104), (7, 91, 57), (299, 91, 103), (6, 92, 54), (300, 92, 102), (6, 93, 50), (301, 93, 100), (7, 94, 46), (303, 94, 97), (7, 95, 44), (306, 95, 92), (7, 96, 42), (308, 96, 89), (7, 97, 40), (310, 97, 86), (7, 98, 38), (312, 98, 83), (8, 99, 34), (314, 99, 79), (8, 100, 32), (317, 100, 75), (8, 101, 29), (319, 101, 71), (13, 102, 19), (324, 102, 63), (20, 103, 6), (330, 103, 51), (337, 104, 37), (344, 105, 22), (352, 106, 3)], ['344,105,319,101,301,93,291,85,259,85,244,90,205,90,204,89,177,89,176,88,161,88,160,87,142,86,141,85,125,86,98,86,84,85,56,92,36,101,26,102,8,101,6,92,11,80,11,59,12,58,12,17,10,14,16,6,22,4,58,4,59,3,93,3,94,2,126,2,127,1,267,1,268,2,396,3,407,6,419,25,421,34,421,51,410,62,404,71,402,80,404,85,401,92,394,98,386,102,365,105']), (917855882, 492601069, 445, 390, 550, 0, 54, 0.93913287, [(414, 0, 7), (441, 0, 60), (508, 0, 28), (402, 1, 142), (401, 2, 146), (402, 3, 145), (404, 4, 143), (406, 5, 140), (408, 6, 137), (410, 7, 134), (411, 8, 132), (412, 9, 130), (413, 10, 127), (414, 11, 125), (415, 12, 123), (415, 13, 122), (416, 14, 120), (417, 15, 117), (417, 16, 116), (418, 17, 114), (418, 18, 113), (418, 19, 111), (418, 20, 109), (419, 21, 107), (419, 22, 105), (419, 23, 103), (419, 24, 102), (419, 25, 100), (420, 26, 97), (420, 27, 95), (420, 28, 94), (421, 29, 91), (421, 30, 90), (422, 31, 88), (422, 32, 88), (422, 33, 87), (423, 34, 84), (423, 35, 82), (423, 36, 81), (424, 37, 79), (424, 38, 77), (424, 39, 75), (424, 40, 73), (424, 41, 71), (425, 42, 67), (425, 43, 66), (426, 44, 62), (426, 45, 6), (433, 45, 52), (443, 46, 30), (450, 47, 1)], ['450,47,449,46,443,46,442,45,426,45,424,41,424,37,423,36,422,31,419,25,419,21,418,20,418,17,417,15,409,6,402,3,402,1,413,1,414,0,420,0,421,1,440,1,441,0,500,0,501,1,507,1,508,0,535,0,536,1,543,1,546,2,546,4,542,8,530,18,527,19,525,21,522,22,520,24,512,28,508,33,505,34,502,37,494,41,492,41,490,43,488,43,484,45,473,45,472,46,451,46'])], 'temp/1746563730_2124625_917855882_da0fa7b7e6b5b551fe26c0ba8713276d.jpg']} ############################### TEST POLYGON ################################ Inside batchDatouExec : verbose : False # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : mask_detect list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.1717395782470703 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 : False number of steps : 1 step1:mask_detect Tue May 6 22:35:55 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 : 6814 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-05-06 22:35:58.681260: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-05-06 22:35:58.715127: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-05-06 22:35:58.717451: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fcee4000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-05-06 22:35:58.717520: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-05-06 22:35:58.723157: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-05-06 22:35:58.881733: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x13b93360 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-05-06 22:35:58.881786: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-05-06 22:35:58.882472: 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 2025-05-06 22:35:58.883462: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-06 22:35:58.903300: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-06 22:35:58.915879: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-06 22:35:58.918745: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-06 22:35:58.944046: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-06 22:35:58.949674: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-06 22:35:58.994375: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-06 22:35:58.995718: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-06 22:35:58.996136: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-06 22:35:58.996764: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-06 22:35:58.996779: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-06 22:35:58.996788: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-06 22:35:58.997903: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6263 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. 2025-05-06 22:35:59.247449: 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 2025-05-06 22:35:59.247602: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-06 22:35:59.247641: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-06 22:35:59.247673: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-06 22:35:59.247697: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-06 22:35:59.247729: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-06 22:35:59.247759: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-06 22:35:59.247799: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-06 22:35:59.249010: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-06 22:35:59.250550: 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 2025-05-06 22:35:59.250588: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-06 22:35:59.250607: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-06 22:35:59.250635: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-06 22:35:59.250657: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-06 22:35:59.250676: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-06 22:35:59.250695: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-06 22:35:59.250722: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-06 22:35:59.251930: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-06 22:35:59.251982: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-06 22:35:59.251992: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-06 22:35:59.251999: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-06 22:35:59.253245: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6263 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 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21)) {'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] 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 mask_coco_origin NUM_CLASSES 81 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 : mask_coco_origin model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-05-06 22:36:09.661472: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-06 22:36:09.949807: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (2448, 2448, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 2448.00000 nb d'objets trouves : 1 Detection mask done ! Trying to reset tf kernel 2344631 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1525 tf kernel not reseted sub process len(results) : 1 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 1 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 : 6814 list_Values should be empty [] ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] time for calcul the mask position with numpy : 0.29966092109680176 nb_pixel_total : 3689099 time to create 1 rle with new method : 0.4493865966796875 length of segment : 2037 time spent for convertir_results : 1.763918161392212 time spend for datou_step_exec : 22.728816032409668 time spend to save output : 4.863739013671875e-05 total time spend for step 1 : 22.728864669799805 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Catched exception ! Connect or reconnect ! Number saved : None batch 1 Loaded 722 chid ids of type : 445 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.01963496208190918 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'917877156': [[(917877156, 492601069, 445, 7, 2268, 122, 2241, 0.98500615, [(523, 124, 445), (1143, 124, 284), (501, 125, 950), (481, 126, 993), (461, 127, 1035), (443, 128, 1091), (427, 129, 1149), (411, 130, 1178), (396, 131, 1201), (382, 132, 1224), (370, 133, 1243), (367, 134, 1254), (364, 135, 1264), (362, 136, 1273), (359, 137, 1282), (357, 138, 1291), (354, 139, 1300), (352, 140, 1308), (350, 141, 1315), (347, 142, 1322), (345, 143, 1328), (343, 144, 1334), (341, 145, 1340), (340, 146, 1344), (338, 147, 1350), (336, 148, 1355), (334, 149, 1361), (333, 150, 1365), (331, 151, 1370), (329, 152, 1375), (328, 153, 1379), (326, 154, 1384), (325, 155, 1388), (324, 156, 1392), (322, 157, 1396), (321, 158, 1400), (320, 159, 1404), (318, 160, 1408), (317, 161, 1411), (316, 162, 1415), (315, 163, 1419), (313, 164, 1423), (312, 165, 1427), (311, 166, 1431), (309, 167, 1435), (308, 168, 1439), (307, 169, 1442), (305, 170, 1446), (303, 171, 1450), (302, 172, 1454), (300, 173, 1458), (299, 174, 1462), (297, 175, 1466), (295, 176, 1471), (293, 177, 1476), (291, 178, 1481), (289, 179, 1485), (287, 180, 1490), (284, 181, 1497), (281, 182, 1503), (279, 183, 1508), (276, 184, 1515), (273, 185, 1521), (271, 186, 1527), (268, 187, 1534), (265, 188, 1541), (263, 189, 1547), (260, 190, 1554), (257, 191, 1561), (254, 192, 1569), (252, 193, 1577), (249, 194, 1586), (246, 195, 1596), (243, 196, 1605), (240, 197, 1614), (237, 198, 1623), (235, 199, 1631), (232, 200, 1640), (229, 201, 1648), (226, 202, 1657), (223, 203, 1666), (220, 204, 1675), (217, 205, 1683), (214, 206, 1691), (211, 207, 1696), (208, 208, 1701), (206, 209, 1705), (204, 210, 1708), (203, 211, 1711), (201, 212, 1715), (199, 213, 1718), (198, 214, 1721), (196, 215, 1724), (195, 216, 1727), (193, 217, 1730), (192, 218, 1732), (190, 219, 1735), (189, 220, 1738), (188, 221, 1740), (186, 222, 1743), (185, 223, 1745), (184, 224, 1747), (183, 225, 1749), (182, 226, 1751), (181, 227, 1753), (180, 228, 1755), (178, 229, 1758), (177, 230, 1760), (176, 231, 1762), (176, 232, 1763), (175, 233, 1765), (174, 234, 1767), (173, 235, 1768), (172, 236, 1770), (171, 237, 1772), (170, 238, 1774), (169, 239, 1775), (168, 240, 1777), (167, 241, 1779), (167, 242, 1780), (166, 243, 1781), (165, 244, 1783), (164, 245, 1785), (163, 246, 1787), (161, 247, 1790), (160, 248, 1792), (159, 249, 1794), (158, 250, 1796), (157, 251, 1797), (156, 252, 1799), (155, 253, 1801), (154, 254, 1804), (152, 255, 1807), (151, 256, 1809), (150, 257, 1811), (148, 258, 1814), (147, 259, 1816), (146, 260, 1818), (144, 261, 1822), (143, 262, 1824), (141, 263, 1827), (140, 264, 1830), (138, 265, 1833), (137, 266, 1835), (135, 267, 1839), (133, 268, 1842), (131, 269, 1846), (130, 270, 1849), (128, 271, 1852), (126, 272, 1856), (125, 273, 1859), (124, 274, 1862), (122, 275, 1865), (121, 276, 1868), (120, 277, 1871), (119, 278, 1873), (117, 279, 1877), (116, 280, 1879), (115, 281, 1881), (114, 282, 1884), (113, 283, 1886), (112, 284, 1888), (111, 285, 1890), (110, 286, 1893), (109, 287, 1895), (108, 288, 1897), (107, 289, 1899), (107, 290, 1900), (106, 291, 1902), (105, 292, 1904), (104, 293, 1906), (103, 294, 1908), (103, 295, 1909), (102, 296, 1911), (101, 297, 1913), (100, 298, 1915), (100, 299, 1915), (99, 300, 1917), (98, 301, 1919), (98, 302, 1920), (97, 303, 1922), (97, 304, 1922), (96, 305, 1924), (95, 306, 1926), (95, 307, 1926), (94, 308, 1928), (94, 309, 1929), (93, 310, 1930), (93, 311, 1931), (92, 312, 1932), (92, 313, 1933), (92, 314, 1933), (92, 315, 1934), (91, 316, 1935), (91, 317, 1936), (91, 318, 1936), (91, 319, 1937), (90, 320, 1939), (90, 321, 1939), (90, 322, 1940), (89, 323, 1941), (89, 324, 1942), (89, 325, 1942), (89, 326, 1943), (88, 327, 1944), (88, 328, 1945), (88, 329, 1946), (88, 330, 1946), (87, 331, 1948), (87, 332, 1948), (87, 333, 1949), (87, 334, 1950), (86, 335, 1951), (86, 336, 1952), (86, 337, 1953), (85, 338, 1954), (85, 339, 1955), (85, 340, 1956), (85, 341, 1956), (84, 342, 1958), (84, 343, 1959), (84, 344, 1960), (83, 345, 1961), (83, 346, 1962), (83, 347, 1963), (83, 348, 1964), (82, 349, 1966), (82, 350, 1966), (82, 351, 1967), (81, 352, 1969), (81, 353, 1970), (81, 354, 1971), (81, 355, 1972), (80, 356, 1974), (80, 357, 1975), (80, 358, 1976), (79, 359, 1978), (79, 360, 1979), (79, 361, 1980), (78, 362, 1982), (78, 363, 1983), (78, 364, 1984), (78, 365, 1985), (77, 366, 1987), (77, 367, 1988), (77, 368, 1989), (76, 369, 1991), (76, 370, 1992), (76, 371, 1993), (75, 372, 1995), (75, 373, 1996), (75, 374, 1997), (74, 375, 1999), (74, 376, 2000), (74, 377, 2001), (73, 378, 2003), (73, 379, 2004), (73, 380, 2005), (72, 381, 2006), (72, 382, 2007), (72, 383, 2008), (71, 384, 2010), (71, 385, 2010), (71, 386, 2011), (71, 387, 2012), (70, 388, 2013), (70, 389, 2014), (70, 390, 2014), (69, 391, 2016), (69, 392, 2016), (69, 393, 2017), (68, 394, 2018), (68, 395, 2019), (68, 396, 2019), (67, 397, 2021), (67, 398, 2021), (67, 399, 2022), (66, 400, 2023), (66, 401, 2024), (66, 402, 2024), (65, 403, 2026), (65, 404, 2026), (65, 405, 2027), (64, 406, 2028), (64, 407, 2029), (63, 408, 2030), (63, 409, 2031), (63, 410, 2031), (62, 411, 2033), (62, 412, 2033), (62, 413, 2033), (61, 414, 2035), (61, 415, 2035), (60, 416, 2037), (60, 417, 2037), (59, 418, 2039), (59, 419, 2039), (59, 420, 2039), (58, 421, 2041), (58, 422, 2041), (57, 423, 2043), (57, 424, 2043), (56, 425, 2045), (56, 426, 2045), (56, 427, 2045), (55, 428, 2047), (55, 429, 2047), (54, 430, 2049), (54, 431, 2049), (53, 432, 2050), (53, 433, 2051), (52, 434, 2052), (52, 435, 2052), (51, 436, 2054), (51, 437, 2054), (50, 438, 2056), (50, 439, 2056), (49, 440, 2057), (49, 441, 2058), (48, 442, 2059), (47, 443, 2060), (47, 444, 2061), (46, 445, 2062), (46, 446, 2063), (45, 447, 2064), (45, 448, 2064), (44, 449, 2066), (44, 450, 2066), (43, 451, 2067), (43, 452, 2068), (42, 453, 2069), (42, 454, 2069), (42, 455, 2070), (41, 456, 2071), (41, 457, 2071), (40, 458, 2073), (40, 459, 2073), (39, 460, 2074), (39, 461, 2075), (38, 462, 2076), (38, 463, 2076), (38, 464, 2077), (38, 465, 2077), (38, 466, 2077), (38, 467, 2077), (37, 468, 2079), (37, 469, 2079), (37, 470, 2079), (37, 471, 2080), (37, 472, 2080), (37, 473, 2080), (36, 474, 2082), (36, 475, 2082), (36, 476, 2082), (36, 477, 2083), (36, 478, 2083), (36, 479, 2083), (36, 480, 2084), (35, 481, 2085), (35, 482, 2086), (35, 483, 2086), (35, 484, 2086), (35, 485, 2087), (35, 486, 2087), (34, 487, 2088), (34, 488, 2089), (34, 489, 2089), (34, 490, 2090), (34, 491, 2090), (34, 492, 2090), (34, 493, 2091), (33, 494, 2092), (33, 495, 2093), (33, 496, 2093), (33, 497, 2094), (33, 498, 2094), (33, 499, 2094), (33, 500, 2095), (32, 501, 2096), (32, 502, 2097), (32, 503, 2097), (32, 504, 2098), (32, 505, 2098), (32, 506, 2099), (32, 507, 2099), (31, 508, 2101), (31, 509, 2101), (31, 510, 2102), (31, 511, 2102), (31, 512, 2103), (31, 513, 2103), (31, 514, 2104), (31, 515, 2104), (30, 516, 2106), (30, 517, 2106), (30, 518, 2107), (30, 519, 2108), (30, 520, 2108), (30, 521, 2109), (30, 522, 2109), (29, 523, 2111), (29, 524, 2112), (29, 525, 2112), (29, 526, 2113), (29, 527, 2114), (29, 528, 2114), (29, 529, 2115), (29, 530, 2116), (28, 531, 2117), (28, 532, 2118), (28, 533, 2119), (28, 534, 2120), (28, 535, 2120), (28, 536, 2121), (28, 537, 2122), (28, 538, 2122), (28, 539, 2122), (28, 540, 2123), (27, 541, 2124), (27, 542, 2124), (27, 543, 2125), (27, 544, 2125), (27, 545, 2125), (27, 546, 2126), (27, 547, 2126), (27, 548, 2126), (27, 549, 2127), (27, 550, 2127), (27, 551, 2127), (27, 552, 2127), (27, 553, 2128), (27, 554, 2128), (27, 555, 2128), (27, 556, 2129), (27, 557, 2129), (27, 558, 2129), (27, 559, 2129), (27, 560, 2130), (26, 561, 2131), (26, 562, 2131), (26, 563, 2131), (26, 564, 2132), (26, 565, 2132), (26, 566, 2132), (26, 567, 2132), (26, 568, 2133), (26, 569, 2133), (26, 570, 2133), (26, 571, 2133), (26, 572, 2134), (26, 573, 2134), (26, 574, 2134), (26, 575, 2134), (26, 576, 2135), (26, 577, 2135), (26, 578, 2135), (26, 579, 2135), (26, 580, 2136), (25, 581, 2137), (25, 582, 2137), (25, 583, 2137), (25, 584, 2138), (25, 585, 2138), (25, 586, 2138), (25, 587, 2138), (25, 588, 2138), (25, 589, 2139), (25, 590, 2139), (25, 591, 2139), (25, 592, 2139), (25, 593, 2140), (25, 594, 2140), (25, 595, 2140), (25, 596, 2140), (25, 597, 2140), (25, 598, 2141), (25, 599, 2141), (25, 600, 2141), (24, 601, 2142), (24, 602, 2142), (24, 603, 2143), (24, 604, 2143), (24, 605, 2143), (24, 606, 2143), (24, 607, 2143), (24, 608, 2144), (24, 609, 2144), (24, 610, 2144), (24, 611, 2144), (24, 612, 2144), (24, 613, 2145), (24, 614, 2145), (24, 615, 2145), (24, 616, 2145), (24, 617, 2145), (24, 618, 2145), (24, 619, 2145), (24, 620, 2145), (24, 621, 2145), (24, 622, 2145), (24, 623, 2145), (23, 624, 2146), (23, 625, 2146), (23, 626, 2146), (23, 627, 2146), (23, 628, 2146), (23, 629, 2147), (23, 630, 2147), (23, 631, 2147), (23, 632, 2147), (23, 633, 2147), (23, 634, 2147), (23, 635, 2147), (23, 636, 2147), (23, 637, 2147), (23, 638, 2147), (23, 639, 2147), (23, 640, 2147), (23, 641, 2147), (23, 642, 2147), (23, 643, 2147), (23, 644, 2147), (23, 645, 2147), (23, 646, 2148), (23, 647, 2148), (22, 648, 2149), (22, 649, 2149), (22, 650, 2149), (22, 651, 2149), (22, 652, 2149), (22, 653, 2149), (22, 654, 2149), (22, 655, 2149), (22, 656, 2149), (22, 657, 2149), (22, 658, 2149), (22, 659, 2149), (22, 660, 2149), (22, 661, 2149), (22, 662, 2149), (22, 663, 2149), (22, 664, 2150), (22, 665, 2150), (22, 666, 2150), (22, 667, 2150), (22, 668, 2150), (22, 669, 2150), (22, 670, 2150), (22, 671, 2150), (21, 672, 2151), (21, 673, 2151), (21, 674, 2151), (21, 675, 2151), (21, 676, 2151), (21, 677, 2151), (21, 678, 2151), (21, 679, 2151), (21, 680, 2151), (21, 681, 2151), (21, 682, 2151), (21, 683, 2152), (21, 684, 2152), (21, 685, 2152), (21, 686, 2152), (21, 687, 2152), (21, 688, 2152), (21, 689, 2152), (21, 690, 2152), (21, 691, 2152), (21, 692, 2152), (21, 693, 2152), (21, 694, 2152), (21, 695, 2152), (21, 696, 2152), (21, 697, 2152), (21, 698, 2151), (22, 699, 2150), (22, 700, 2150), (22, 701, 2150), (22, 702, 2150), (22, 703, 2150), (22, 704, 2150), (22, 705, 2150), (22, 706, 2150), (22, 707, 2150), (22, 708, 2150), (22, 709, 2150), (22, 710, 2150), (22, 711, 2150), (23, 712, 2149), (23, 713, 2149), (23, 714, 2149), (23, 715, 2149), (23, 716, 2149), (23, 717, 2149), (23, 718, 2149), (23, 719, 2148), (23, 720, 2148), (23, 721, 2148), (23, 722, 2148), (23, 723, 2148), (24, 724, 2147), (24, 725, 2147), (24, 726, 2147), (24, 727, 2147), (24, 728, 2147), (24, 729, 2147), (24, 730, 2147), (24, 731, 2147), (24, 732, 2147), (24, 733, 2147), (24, 734, 2147), (24, 735, 2147), (25, 736, 2146), (25, 737, 2146), (25, 738, 2146), (25, 739, 2146), (25, 740, 2145), (25, 741, 2145), (25, 742, 2145), (25, 743, 2145), (25, 744, 2145), (25, 745, 2145), (25, 746, 2145), (25, 747, 2145), (26, 748, 2144), (26, 749, 2144), (26, 750, 2144), (26, 751, 2144), (26, 752, 2144), (26, 753, 2144), (26, 754, 2144), (26, 755, 2144), (26, 756, 2144), (26, 757, 2144), (26, 758, 2144), (27, 759, 2143), (27, 760, 2142), (27, 761, 2142), (27, 762, 2142), (27, 763, 2142), (27, 764, 2142), (27, 765, 2142), (27, 766, 2142), (27, 767, 2142), (27, 768, 2142), (27, 769, 2142), (27, 770, 2142), (27, 771, 2142), (27, 772, 2142), (27, 773, 2142), (27, 774, 2142), (27, 775, 2142), (27, 776, 2142), (27, 777, 2142), (27, 778, 2142), (27, 779, 2141), (27, 780, 2141), (27, 781, 2141), (27, 782, 2141), (27, 783, 2141), (27, 784, 2141), (27, 785, 2141), (27, 786, 2141), (27, 787, 2141), (27, 788, 2141), (27, 789, 2141), (27, 790, 2141), (27, 791, 2141), (27, 792, 2141), (27, 793, 2141), (27, 794, 2141), (27, 795, 2141), (27, 796, 2141), (27, 797, 2140), (26, 798, 2141), (26, 799, 2141), (26, 800, 2141), (26, 801, 2141), (26, 802, 2141), (26, 803, 2141), (26, 804, 2141), (26, 805, 2141), (26, 806, 2141), (26, 807, 2141), (26, 808, 2141), (26, 809, 2141), (26, 810, 2141), (26, 811, 2141), (26, 812, 2141), (26, 813, 2141), (26, 814, 2141), (26, 815, 2140), (26, 816, 2140), (26, 817, 2140), (26, 818, 2140), (26, 819, 2140), (26, 820, 2140), (26, 821, 2140), (26, 822, 2140), (26, 823, 2140), (26, 824, 2140), (26, 825, 2140), (26, 826, 2140), (26, 827, 2140), (26, 828, 2140), (26, 829, 2140), (26, 830, 2140), (26, 831, 2140), (26, 832, 2139), (26, 833, 2139), (26, 834, 2139), (26, 835, 2139), (26, 836, 2139), (26, 837, 2139), (26, 838, 2139), (26, 839, 2139), (26, 840, 2139), (26, 841, 2139), (26, 842, 2138), (26, 843, 2138), (26, 844, 2138), (26, 845, 2137), (26, 846, 2137), (26, 847, 2136), (26, 848, 2136), (26, 849, 2136), (26, 850, 2135), (26, 851, 2135), (26, 852, 2134), (26, 853, 2134), (26, 854, 2134), (27, 855, 2132), (27, 856, 2132), (27, 857, 2131), (27, 858, 2131), (27, 859, 2130), (27, 860, 2130), (27, 861, 2129), (27, 862, 2129), (27, 863, 2128), (27, 864, 2128), (27, 865, 2127), (27, 866, 2127), (27, 867, 2126), (27, 868, 2126), (27, 869, 2125), (27, 870, 2125), (27, 871, 2124), (27, 872, 2124), (27, 873, 2123), (27, 874, 2123), (28, 875, 2121), (28, 876, 2120), (28, 877, 2120), (28, 878, 2119), (28, 879, 2119), (28, 880, 2118), (28, 881, 2117), (28, 882, 2117), (28, 883, 2116), (28, 884, 2116), (28, 885, 2115), (28, 886, 2115), (28, 887, 2114), (28, 888, 2114), (28, 889, 2113), (28, 890, 2112), (28, 891, 2112), (28, 892, 2111), (29, 893, 2110), (29, 894, 2109), (29, 895, 2109), (29, 896, 2108), (29, 897, 2108), (29, 898, 2108), (29, 899, 2107), (29, 900, 2107), (29, 901, 2106), (29, 902, 2106), (29, 903, 2105), (29, 904, 2105), (29, 905, 2104), (29, 906, 2104), (29, 907, 2104), (29, 908, 2103), (29, 909, 2103), (29, 910, 2102), (29, 911, 2102), (30, 912, 2101), (30, 913, 2100), (30, 914, 2100), (30, 915, 2099), (30, 916, 2099), (30, 917, 2099), (30, 918, 2098), (30, 919, 2098), (30, 920, 2098), (30, 921, 2098), (30, 922, 2097), (30, 923, 2097), (30, 924, 2097), (29, 925, 2098), (29, 926, 2097), (29, 927, 2097), (29, 928, 2097), (29, 929, 2097), (29, 930, 2097), (29, 931, 2096), (29, 932, 2096), (29, 933, 2096), (29, 934, 2096), (29, 935, 2095), (29, 936, 2095), (29, 937, 2095), (29, 938, 2095), (29, 939, 2094), (29, 940, 2094), (29, 941, 2094), (29, 942, 2094), (29, 943, 2094), (29, 944, 2093), (29, 945, 2093), (29, 946, 2093), (29, 947, 2093), (29, 948, 2093), (29, 949, 2092), (29, 950, 2092), (29, 951, 2092), (29, 952, 2092), (28, 953, 2092), (28, 954, 2092), (28, 955, 2092), (28, 956, 2092), (28, 957, 2092), (28, 958, 2091), (28, 959, 2091), (28, 960, 2091), (28, 961, 2091), (28, 962, 2091), (28, 963, 2091), (28, 964, 2090), (28, 965, 2090), (28, 966, 2090), (28, 967, 2090), (28, 968, 2090), (28, 969, 2089), (28, 970, 2089), (28, 971, 2089), (28, 972, 2089), (28, 973, 2089), (28, 974, 2088), (28, 975, 2088), (28, 976, 2088), (28, 977, 2088), (28, 978, 2088), (28, 979, 2088), (28, 980, 2087), (28, 981, 2087), (28, 982, 2087), (27, 983, 2088), (27, 984, 2088), (27, 985, 2088), (27, 986, 2087), (27, 987, 2087), (27, 988, 2087), (27, 989, 2087), (27, 990, 2087), (27, 991, 2087), (27, 992, 2086), (27, 993, 2086), (27, 994, 2086), (27, 995, 2085), (27, 996, 2085), (27, 997, 2085), (27, 998, 2084), (27, 999, 2084), (27, 1000, 2084), (28, 1001, 2082), (28, 1002, 2082), (28, 1003, 2082), (28, 1004, 2081), (28, 1005, 2081), (28, 1006, 2081), (28, 1007, 2080), (28, 1008, 2080), (28, 1009, 2080), (28, 1010, 2079), (28, 1011, 2079), (28, 1012, 2079), (28, 1013, 2078), (28, 1014, 2078), (28, 1015, 2078), (28, 1016, 2077), (28, 1017, 2077), (28, 1018, 2076), (28, 1019, 2076), (28, 1020, 2076), (28, 1021, 2075), (28, 1022, 2075), (28, 1023, 2075), (28, 1024, 2074), (28, 1025, 2074), (28, 1026, 2073), (28, 1027, 2073), (28, 1028, 2073), (28, 1029, 2072), (29, 1030, 2071), (29, 1031, 2070), (29, 1032, 2070), (29, 1033, 2069), (29, 1034, 2069), (29, 1035, 2069), (29, 1036, 2068), (29, 1037, 2068), (29, 1038, 2067), (29, 1039, 2067), (29, 1040, 2066), (29, 1041, 2066), (29, 1042, 2066), (29, 1043, 2065), (29, 1044, 2065), (29, 1045, 2064), (29, 1046, 2064), (29, 1047, 2063), (29, 1048, 2063), (29, 1049, 2062), (29, 1050, 2062), (29, 1051, 2061), (29, 1052, 2061), (29, 1053, 2060), (29, 1054, 2060), (29, 1055, 2059), (29, 1056, 2059), (29, 1057, 2058), (30, 1058, 2057), (30, 1059, 2056), (30, 1060, 2056), (30, 1061, 2055), (30, 1062, 2055), (30, 1063, 2054), (30, 1064, 2054), (30, 1065, 2053), (30, 1066, 2052), (30, 1067, 2052), (30, 1068, 2051), (30, 1069, 2051), (30, 1070, 2050), (30, 1071, 2049), (30, 1072, 2048), (30, 1073, 2047), (30, 1074, 2047), (30, 1075, 2046), (30, 1076, 2045), (30, 1077, 2044), (30, 1078, 2043), (30, 1079, 2042), (30, 1080, 2041), (30, 1081, 2040), (30, 1082, 2039), (30, 1083, 2038), (30, 1084, 2037), (30, 1085, 2036), (29, 1086, 2036), (29, 1087, 2035), (29, 1088, 2034), (29, 1089, 2033), (29, 1090, 2032), (29, 1091, 2031), (29, 1092, 2030), (29, 1093, 2029), (29, 1094, 2028), (29, 1095, 2027), (29, 1096, 2026), (29, 1097, 2026), (29, 1098, 2025), (29, 1099, 2024), (29, 1100, 2023), (29, 1101, 2022), (29, 1102, 2022), (29, 1103, 2021), (29, 1104, 2020), (29, 1105, 2019), (29, 1106, 2019), (29, 1107, 2018), (29, 1108, 2017), (29, 1109, 2016), (29, 1110, 2016), (29, 1111, 2015), (29, 1112, 2014), (29, 1113, 2014), (29, 1114, 2013), (29, 1115, 2013), (29, 1116, 2012), (29, 1117, 2011), (29, 1118, 2011), (29, 1119, 2010), (29, 1120, 2010), (29, 1121, 2009), (29, 1122, 2009), (29, 1123, 2008), (29, 1124, 2008), (29, 1125, 2007), (29, 1126, 2006), (29, 1127, 2006), (29, 1128, 2005), (29, 1129, 2005), (29, 1130, 2005), (29, 1131, 2004), (29, 1132, 2004), (29, 1133, 2003), (29, 1134, 2003), (29, 1135, 2002), (29, 1136, 2002), (29, 1137, 2001), (29, 1138, 2001), (28, 1139, 2001), (28, 1140, 2001), (28, 1141, 2001), (28, 1142, 2000), (28, 1143, 2000), (28, 1144, 2000), (28, 1145, 1999), (28, 1146, 1999), (28, 1147, 1999), (28, 1148, 1998), (28, 1149, 1998), (28, 1150, 1998), (29, 1151, 1996), (29, 1152, 1996), (29, 1153, 1996), (29, 1154, 1995), (29, 1155, 1995), (29, 1156, 1995), (29, 1157, 1994), (29, 1158, 1994), (29, 1159, 1994), (29, 1160, 1993), (29, 1161, 1993), (29, 1162, 1992), (29, 1163, 1992), (29, 1164, 1992), (29, 1165, 1991), (29, 1166, 1991), (29, 1167, 1991), (29, 1168, 1990), (29, 1169, 1990), (29, 1170, 1989), (29, 1171, 1989), (29, 1172, 1989), (29, 1173, 1988), (29, 1174, 1988), (29, 1175, 1987), (29, 1176, 1987), (29, 1177, 1987), (29, 1178, 1986), (29, 1179, 1986), (29, 1180, 1985), (29, 1181, 1985), (29, 1182, 1985), (29, 1183, 1984), (29, 1184, 1984), (29, 1185, 1983), (29, 1186, 1983), (29, 1187, 1982), (29, 1188, 1982), (29, 1189, 1981), (29, 1190, 1981), (29, 1191, 1980), (29, 1192, 1980), (29, 1193, 1980), (29, 1194, 1979), (29, 1195, 1979), (29, 1196, 1978), (29, 1197, 1978), (29, 1198, 1977), (29, 1199, 1977), (29, 1200, 1976), (29, 1201, 1976), (29, 1202, 1975), (29, 1203, 1975), (29, 1204, 1974), (29, 1205, 1973), (29, 1206, 1973), (29, 1207, 1972), (29, 1208, 1972), (29, 1209, 1971), (29, 1210, 1971), (29, 1211, 1970), (29, 1212, 1970), (29, 1213, 1969), (29, 1214, 1968), (29, 1215, 1968), (29, 1216, 1967), (29, 1217, 1967), (29, 1218, 1966), (29, 1219, 1965), (29, 1220, 1965), (29, 1221, 1964), (29, 1222, 1963), (29, 1223, 1963), (29, 1224, 1962), (29, 1225, 1961), (29, 1226, 1960), (29, 1227, 1960), (29, 1228, 1959), (29, 1229, 1958), (29, 1230, 1957), (29, 1231, 1956), (29, 1232, 1955), (29, 1233, 1955), (29, 1234, 1954), (29, 1235, 1953), (29, 1236, 1952), (29, 1237, 1951), (29, 1238, 1951), (30, 1239, 1949), (30, 1240, 1948), (30, 1241, 1947), (30, 1242, 1947), (30, 1243, 1946), (30, 1244, 1945), (30, 1245, 1945), (30, 1246, 1944), (30, 1247, 1943), (30, 1248, 1943), (30, 1249, 1942), (30, 1250, 1942), (30, 1251, 1941), (30, 1252, 1940), (30, 1253, 1940), (30, 1254, 1939), (30, 1255, 1939), (30, 1256, 1938), (30, 1257, 1937), (30, 1258, 1937), (30, 1259, 1936), (30, 1260, 1936), (30, 1261, 1935), (30, 1262, 1935), (30, 1263, 1934), (30, 1264, 1934), (30, 1265, 1933), (30, 1266, 1933), (30, 1267, 1932), (30, 1268, 1932), (30, 1269, 1931), (30, 1270, 1931), (30, 1271, 1931), (30, 1272, 1930), (30, 1273, 1930), (30, 1274, 1929), (30, 1275, 1929), (30, 1276, 1928), (30, 1277, 1928), (30, 1278, 1928), (30, 1279, 1927), (30, 1280, 1927), (30, 1281, 1926), (30, 1282, 1926), (30, 1283, 1926), (30, 1284, 1925), (30, 1285, 1925), (30, 1286, 1925), (30, 1287, 1924), (30, 1288, 1924), (30, 1289, 1923), (30, 1290, 1923), (30, 1291, 1923), (30, 1292, 1922), (30, 1293, 1922), (30, 1294, 1922), (30, 1295, 1922), (30, 1296, 1921), (30, 1297, 1921), (30, 1298, 1921), (30, 1299, 1921), (30, 1300, 1920), (30, 1301, 1920), (30, 1302, 1920), (30, 1303, 1920), (30, 1304, 1919), (30, 1305, 1919), (30, 1306, 1919), (30, 1307, 1919), (30, 1308, 1918), (30, 1309, 1918), (30, 1310, 1918), (30, 1311, 1918), (31, 1312, 1916), (31, 1313, 1916), (31, 1314, 1916), (31, 1315, 1916), (31, 1316, 1915), (31, 1317, 1915), (31, 1318, 1915), (31, 1319, 1915), (31, 1320, 1914), (31, 1321, 1914), (31, 1322, 1914), (31, 1323, 1914), (31, 1324, 1913), (31, 1325, 1913), (31, 1326, 1913), (31, 1327, 1913), (31, 1328, 1912), (31, 1329, 1912), (31, 1330, 1912), (31, 1331, 1912), (31, 1332, 1911), (31, 1333, 1911), (31, 1334, 1911), (31, 1335, 1910), (31, 1336, 1910), (31, 1337, 1910), (31, 1338, 1910), (31, 1339, 1909), (32, 1340, 1908), (32, 1341, 1908), (32, 1342, 1907), (32, 1343, 1907), (32, 1344, 1907), (32, 1345, 1907), (32, 1346, 1906), (32, 1347, 1906), (32, 1348, 1906), (32, 1349, 1905), (32, 1350, 1905), (32, 1351, 1905), (32, 1352, 1905), (32, 1353, 1904), (32, 1354, 1904), (32, 1355, 1904), (32, 1356, 1903), (32, 1357, 1903), (32, 1358, 1903), (32, 1359, 1902), (32, 1360, 1902), (32, 1361, 1902), (32, 1362, 1901), (32, 1363, 1901), (32, 1364, 1901), (32, 1365, 1901), (32, 1366, 1900), (33, 1367, 1899), (33, 1368, 1899), (33, 1369, 1898), (33, 1370, 1898), (33, 1371, 1897), (33, 1372, 1897), (33, 1373, 1896), (33, 1374, 1896), (33, 1375, 1895), (33, 1376, 1895), (33, 1377, 1894), (33, 1378, 1894), (33, 1379, 1893), (33, 1380, 1893), (33, 1381, 1892), (33, 1382, 1892), (34, 1383, 1890), (34, 1384, 1889), (34, 1385, 1889), (34, 1386, 1888), (34, 1387, 1887), (34, 1388, 1887), (34, 1389, 1886), (34, 1390, 1886), (34, 1391, 1885), (34, 1392, 1884), (34, 1393, 1883), (34, 1394, 1883), (34, 1395, 1882), (34, 1396, 1881), (35, 1397, 1880), (35, 1398, 1879), (35, 1399, 1878), (35, 1400, 1877), (35, 1401, 1876), (35, 1402, 1876), (35, 1403, 1875), (35, 1404, 1874), (35, 1405, 1873), (35, 1406, 1872), (35, 1407, 1871), (35, 1408, 1870), (35, 1409, 1869), (36, 1410, 1867), (36, 1411, 1866), (36, 1412, 1865), (36, 1413, 1865), (36, 1414, 1864), (36, 1415, 1863), (36, 1416, 1862), (36, 1417, 1861), (36, 1418, 1861), (36, 1419, 1860), (36, 1420, 1859), (36, 1421, 1858), (36, 1422, 1858), (37, 1423, 1856), (37, 1424, 1855), (37, 1425, 1854), (37, 1426, 1854), (37, 1427, 1853), (37, 1428, 1852), (37, 1429, 1852), (37, 1430, 1851), (37, 1431, 1851), (37, 1432, 1850), (37, 1433, 1849), (37, 1434, 1849), (37, 1435, 1848), (38, 1436, 1847), (38, 1437, 1846), (38, 1438, 1846), (38, 1439, 1845), (38, 1440, 1844), (38, 1441, 1844), (38, 1442, 1843), (38, 1443, 1843), (38, 1444, 1842), (38, 1445, 1842), (38, 1446, 1841), (38, 1447, 1841), (38, 1448, 1841), (38, 1449, 1841), (38, 1450, 1841), (38, 1451, 1840), (38, 1452, 1840), (39, 1453, 1839), (39, 1454, 1839), (39, 1455, 1838), (39, 1456, 1838), (39, 1457, 1838), (39, 1458, 1838), (39, 1459, 1838), (39, 1460, 1837), (39, 1461, 1837), (39, 1462, 1837), (39, 1463, 1837), (39, 1464, 1837), (39, 1465, 1836), (39, 1466, 1836), (39, 1467, 1836), (39, 1468, 1836), (39, 1469, 1836), (39, 1470, 1835), (39, 1471, 1835), (39, 1472, 1835), (39, 1473, 1835), (39, 1474, 1835), (39, 1475, 1834), (39, 1476, 1834), (39, 1477, 1834), (39, 1478, 1834), (39, 1479, 1834), (39, 1480, 1834), (39, 1481, 1833), (39, 1482, 1833), (39, 1483, 1833), (39, 1484, 1833), (39, 1485, 1833), (39, 1486, 1832), (39, 1487, 1832), (39, 1488, 1832), (39, 1489, 1832), (39, 1490, 1832), (39, 1491, 1831), (39, 1492, 1831), (39, 1493, 1831), (39, 1494, 1831), (40, 1495, 1830), (40, 1496, 1830), (40, 1497, 1829), (40, 1498, 1829), (40, 1499, 1829), (40, 1500, 1829), (40, 1501, 1829), (40, 1502, 1829), (40, 1503, 1828), (40, 1504, 1828), (40, 1505, 1828), (40, 1506, 1828), (40, 1507, 1828), (40, 1508, 1828), (40, 1509, 1827), (40, 1510, 1827), (40, 1511, 1827), (40, 1512, 1827), (40, 1513, 1827), (40, 1514, 1827), (40, 1515, 1826), (40, 1516, 1826), (40, 1517, 1826), (40, 1518, 1826), (40, 1519, 1826), (40, 1520, 1826), (40, 1521, 1825), (40, 1522, 1825), (40, 1523, 1825), (40, 1524, 1825), (40, 1525, 1825), (40, 1526, 1825), (40, 1527, 1825), (40, 1528, 1825), (40, 1529, 1824), (40, 1530, 1824), (40, 1531, 1824), (40, 1532, 1824), (40, 1533, 1824), (40, 1534, 1824), (40, 1535, 1824), (40, 1536, 1824), (40, 1537, 1823), (41, 1538, 1822), (41, 1539, 1822), (41, 1540, 1822), (41, 1541, 1822), (41, 1542, 1822), (41, 1543, 1822), (41, 1544, 1822), (41, 1545, 1822), (41, 1546, 1821), (41, 1547, 1821), (41, 1548, 1821), (41, 1549, 1821), (41, 1550, 1821), (41, 1551, 1821), (41, 1552, 1821), (41, 1553, 1820), (41, 1554, 1820), (41, 1555, 1820), (41, 1556, 1820), (41, 1557, 1820), (41, 1558, 1820), (41, 1559, 1820), (41, 1560, 1820), (41, 1561, 1819), (41, 1562, 1819), (41, 1563, 1819), (41, 1564, 1819), (41, 1565, 1819), (41, 1566, 1819), (41, 1567, 1819), (41, 1568, 1819), (41, 1569, 1818), (41, 1570, 1818), (41, 1571, 1818), (41, 1572, 1818), (41, 1573, 1818), (41, 1574, 1818), (41, 1575, 1818), (41, 1576, 1818), (41, 1577, 1817), (41, 1578, 1817), (41, 1579, 1817), (41, 1580, 1817), (41, 1581, 1817), (41, 1582, 1817), (41, 1583, 1817), (42, 1584, 1816), (42, 1585, 1815), (42, 1586, 1815), (42, 1587, 1815), (42, 1588, 1815), (42, 1589, 1815), (42, 1590, 1815), (42, 1591, 1815), (42, 1592, 1814), (42, 1593, 1814), (42, 1594, 1814), (42, 1595, 1814), (42, 1596, 1814), (42, 1597, 1814), (42, 1598, 1814), (42, 1599, 1814), (42, 1600, 1813), (42, 1601, 1813), (42, 1602, 1813), (42, 1603, 1813), (42, 1604, 1813), (41, 1605, 1814), (41, 1606, 1814), (41, 1607, 1814), (41, 1608, 1814), (41, 1609, 1813), (41, 1610, 1813), (41, 1611, 1813), (41, 1612, 1813), (41, 1613, 1813), (41, 1614, 1813), (41, 1615, 1813), (41, 1616, 1813), (41, 1617, 1813), (41, 1618, 1812), (41, 1619, 1812), (41, 1620, 1812), (41, 1621, 1812), (41, 1622, 1812), (41, 1623, 1812), (41, 1624, 1812), (41, 1625, 1812), (41, 1626, 1811), (41, 1627, 1811), (40, 1628, 1812), (40, 1629, 1812), (40, 1630, 1812), (40, 1631, 1812), (40, 1632, 1812), (40, 1633, 1812), (40, 1634, 1811), (40, 1635, 1811), (40, 1636, 1811), (40, 1637, 1811), (40, 1638, 1811), (40, 1639, 1811), (40, 1640, 1811), (40, 1641, 1811), (40, 1642, 1810), (40, 1643, 1810), (40, 1644, 1810), (40, 1645, 1810), (40, 1646, 1810), (40, 1647, 1810), (40, 1648, 1810), (40, 1649, 1809), (40, 1650, 1809), (40, 1651, 1809), (39, 1652, 1810), (39, 1653, 1810), (39, 1654, 1810), (39, 1655, 1810), (39, 1656, 1810), (39, 1657, 1809), (39, 1658, 1809), (39, 1659, 1809), (39, 1660, 1809), (39, 1661, 1809), (39, 1662, 1809), (39, 1663, 1809), (39, 1664, 1808), (39, 1665, 1808), (39, 1666, 1808), (39, 1667, 1808), (39, 1668, 1808), (39, 1669, 1808), (39, 1670, 1808), (39, 1671, 1807), (39, 1672, 1807), (39, 1673, 1807), (39, 1674, 1807), (39, 1675, 1807), (39, 1676, 1806), (39, 1677, 1806), (39, 1678, 1806), (40, 1679, 1805), (40, 1680, 1804), (40, 1681, 1804), (40, 1682, 1804), (40, 1683, 1804), (40, 1684, 1803), (41, 1685, 1802), (41, 1686, 1802), (41, 1687, 1801), (41, 1688, 1801), (41, 1689, 1801), (41, 1690, 1801), (42, 1691, 1799), (42, 1692, 1799), (42, 1693, 1799), (42, 1694, 1799), (42, 1695, 1798), (42, 1696, 1798), (43, 1697, 1797), (43, 1698, 1796), (43, 1699, 1796), (43, 1700, 1796), (43, 1701, 1795), (43, 1702, 1795), (44, 1703, 1794), (44, 1704, 1794), (44, 1705, 1793), (44, 1706, 1793), (44, 1707, 1793), (44, 1708, 1792), (45, 1709, 1791), (45, 1710, 1791), (45, 1711, 1790), (45, 1712, 1790), (45, 1713, 1790), (46, 1714, 1788), (46, 1715, 1788), (46, 1716, 1788), (46, 1717, 1787), (46, 1718, 1787), (46, 1719, 1787), (47, 1720, 1785), (47, 1721, 1785), (47, 1722, 1785), (47, 1723, 1784), (47, 1724, 1784), (48, 1725, 1782), (48, 1726, 1782), (48, 1727, 1782), (48, 1728, 1781), (48, 1729, 1781), (49, 1730, 1780), (49, 1731, 1779), (49, 1732, 1779), (49, 1733, 1778), (50, 1734, 1777), (50, 1735, 1777), (50, 1736, 1776), (50, 1737, 1776), (50, 1738, 1775), (51, 1739, 1774), (51, 1740, 1773), (51, 1741, 1773), (51, 1742, 1773), (51, 1743, 1772), (52, 1744, 1771), (52, 1745, 1770), (52, 1746, 1770), (52, 1747, 1769), (52, 1748, 1769), (53, 1749, 1767), (53, 1750, 1767), (53, 1751, 1767), (53, 1752, 1766), (53, 1753, 1766), (53, 1754, 1765), (53, 1755, 1765), (53, 1756, 1765), (53, 1757, 1764), (53, 1758, 1764), (53, 1759, 1764), (53, 1760, 1763), (53, 1761, 1763), (53, 1762, 1762), (53, 1763, 1762), (53, 1764, 1762), (53, 1765, 1761), (53, 1766, 1761), (53, 1767, 1761), (53, 1768, 1760), (53, 1769, 1760), (53, 1770, 1760), (53, 1771, 1759), (53, 1772, 1759), (53, 1773, 1758), (53, 1774, 1758), (53, 1775, 1758), (53, 1776, 1757), (53, 1777, 1757), (53, 1778, 1757), (53, 1779, 1756), (53, 1780, 1756), (53, 1781, 1756), (53, 1782, 1755), (53, 1783, 1755), (53, 1784, 1755), (53, 1785, 1754), (53, 1786, 1754), (53, 1787, 1754), (53, 1788, 1754), (53, 1789, 1753), (53, 1790, 1753), (53, 1791, 1753), (53, 1792, 1752), (53, 1793, 1752), (53, 1794, 1752), (53, 1795, 1751), (53, 1796, 1751), (53, 1797, 1751), (53, 1798, 1750), (53, 1799, 1750), (53, 1800, 1750), (53, 1801, 1750), (53, 1802, 1749), (53, 1803, 1749), (53, 1804, 1749), (53, 1805, 1748), (53, 1806, 1748), (54, 1807, 1747), (54, 1808, 1747), (54, 1809, 1746), (54, 1810, 1746), (54, 1811, 1746), (54, 1812, 1745), (54, 1813, 1745), (54, 1814, 1745), (54, 1815, 1745), (54, 1816, 1744), (54, 1817, 1744), (54, 1818, 1744), (54, 1819, 1744), (54, 1820, 1743), (54, 1821, 1743), (54, 1822, 1743), (54, 1823, 1743), (54, 1824, 1742), (54, 1825, 1742), (55, 1826, 1741), (55, 1827, 1740), (56, 1828, 1739), (56, 1829, 1739), (57, 1830, 1737), (57, 1831, 1737), (58, 1832, 1736), (58, 1833, 1735), (58, 1834, 1735), (59, 1835, 1734), (59, 1836, 1733), (60, 1837, 1732), (60, 1838, 1732), (61, 1839, 1730), (61, 1840, 1730), (62, 1841, 1729), (62, 1842, 1728), (63, 1843, 1727), (63, 1844, 1727), (64, 1845, 1725), (64, 1846, 1725), (65, 1847, 1724), (65, 1848, 1723), (66, 1849, 1722), (66, 1850, 1721), (66, 1851, 1721), (67, 1852, 1720), (67, 1853, 1719), (68, 1854, 1718), (68, 1855, 1718), (69, 1856, 1716), (69, 1857, 1716), (70, 1858, 1714), (70, 1859, 1714), (71, 1860, 1713), (71, 1861, 1712), (71, 1862, 1712), (72, 1863, 1711), (72, 1864, 1710), (73, 1865, 1709), (73, 1866, 1708), (74, 1867, 1707), (74, 1868, 1707), (75, 1869, 1705), (75, 1870, 1705), (75, 1871, 1704), (76, 1872, 1703), (76, 1873, 1703), (77, 1874, 1701), (77, 1875, 1701), (78, 1876, 1699), (78, 1877, 1699), (78, 1878, 1699), (79, 1879, 1697), (79, 1880, 1697), (80, 1881, 1695), (80, 1882, 1695), (81, 1883, 1694), (81, 1884, 1693), (81, 1885, 1693), (82, 1886, 1691), (82, 1887, 1691), (83, 1888, 1689), (83, 1889, 1689), (84, 1890, 1687), (84, 1891, 1687), (84, 1892, 1687), (85, 1893, 1685), (85, 1894, 1685), (86, 1895, 1683), (86, 1896, 1683), (87, 1897, 1681), (87, 1898, 1681), (87, 1899, 1680), (88, 1900, 1679), (88, 1901, 1679), (89, 1902, 1677), (89, 1903, 1677), (89, 1904, 1676), (90, 1905, 1675), (90, 1906, 1674), (90, 1907, 1674), (91, 1908, 1672), (91, 1909, 1672), (92, 1910, 1670), (92, 1911, 1670), (92, 1912, 1669), (93, 1913, 1667), (93, 1914, 1667), (94, 1915, 1665), (94, 1916, 1665), (94, 1917, 1664), (95, 1918, 1663), (95, 1919, 1662), (96, 1920, 1661), (96, 1921, 1660), (97, 1922, 1658), (97, 1923, 1658), (98, 1924, 1656), (98, 1925, 1656), (99, 1926, 1654), (99, 1927, 1654), (100, 1928, 1652), (100, 1929, 1651), (101, 1930, 1650), (101, 1931, 1649), (102, 1932, 1647), (102, 1933, 1647), (103, 1934, 1645), (103, 1935, 1644), (104, 1936, 1643), (104, 1937, 1642), (105, 1938, 1640), (105, 1939, 1640), (106, 1940, 1638), (106, 1941, 1637), (107, 1942, 1636), (108, 1943, 1634), (108, 1944, 1633), (109, 1945, 1631), (109, 1946, 1630), (110, 1947, 1629), (111, 1948, 1627), (111, 1949, 1626), (112, 1950, 1624), (113, 1951, 1622), (113, 1952, 1622), (114, 1953, 1620), (115, 1954, 1618), (115, 1955, 1617), (116, 1956, 1616), (117, 1957, 1614), (118, 1958, 1612), (118, 1959, 1611), (119, 1960, 1610), (120, 1961, 1608), (121, 1962, 1606), (121, 1963, 82), (212, 1963, 1515), (122, 1964, 69), (219, 1964, 1507), (123, 1965, 57), (226, 1965, 1499), (124, 1966, 46), (233, 1966, 1491), (125, 1967, 35), (240, 1967, 1484), (125, 1968, 26), (247, 1968, 1476), (126, 1969, 17), (254, 1969, 1468), (127, 1970, 8), (261, 1970, 1461), (269, 1971, 1452), (277, 1972, 1443), (284, 1973, 1436), (291, 1974, 1428), (296, 1975, 1422), (301, 1976, 1416), (305, 1977, 1411), (310, 1978, 1405), (316, 1979, 1398), (321, 1980, 1392), (327, 1981, 1385), (333, 1982, 1378), (339, 1983, 1371), (345, 1984, 1364), (352, 1985, 1356), (360, 1986, 1347), (367, 1987, 1339), (373, 1988, 1331), (377, 1989, 1326), (381, 1990, 1321), (385, 1991, 1316), (389, 1992, 1311), (393, 1993, 1305), (397, 1994, 1300), (402, 1995, 1294), (406, 1996, 1288), (411, 1997, 1282), (415, 1998, 1277), (420, 1999, 1270), (425, 2000, 1264), (430, 2001, 1257), (435, 2002, 1251), (440, 2003, 1244), (445, 2004, 1238), (451, 2005, 1230), (456, 2006, 1224), (460, 2007, 1218), (465, 2008, 1211), (470, 2009, 1205), (476, 2010, 1197), (481, 2011, 1190), (486, 2012, 1183), (491, 2013, 1176), (497, 2014, 1168), (502, 2015, 1162), (508, 2016, 1092), (513, 2017, 1065), (519, 2018, 1055), (525, 2019, 1045), (531, 2020, 1035), (536, 2021, 1026), (541, 2022, 1017), (546, 2023, 1008), (551, 2024, 999), (556, 2025, 991), (560, 2026, 983), (565, 2027, 974), (569, 2028, 967), (574, 2029, 958), (578, 2030, 951), (583, 2031, 942), (587, 2032, 935), (591, 2033, 928), (595, 2034, 920), (599, 2035, 913), (603, 2036, 906), (607, 2037, 899), (611, 2038, 892), (614, 2039, 882), (617, 2040, 869), (620, 2041, 855), (622, 2042, 843), (625, 2043, 830), (627, 2044, 819), (629, 2045, 807), (632, 2046, 795), (634, 2047, 785), (636, 2048, 780), (638, 2049, 774), (640, 2050, 769), (642, 2051, 764), (644, 2052, 759), (646, 2053, 754), (647, 2054, 750), (649, 2055, 745), (651, 2056, 740), (653, 2057, 735), (655, 2058, 730), (657, 2059, 724), (658, 2060, 720), (660, 2061, 714), (662, 2062, 709), (665, 2063, 702), (667, 2064, 696), (669, 2065, 691), (671, 2066, 685), (673, 2067, 679), (676, 2068, 672), (678, 2069, 666), (681, 2070, 658), (683, 2071, 651), (686, 2072, 643), (688, 2073, 636), (691, 2074, 628), (694, 2075, 619), (699, 2076, 609), (703, 2077, 599), (708, 2078, 588), (712, 2079, 579), (717, 2080, 568), (721, 2081, 558), (726, 2082, 547), (730, 2083, 537), (734, 2084, 526), (739, 2085, 516), (743, 2086, 506), (747, 2087, 497), (751, 2088, 488), (756, 2089, 478), (760, 2090, 469), (764, 2091, 460), (768, 2092, 452), (772, 2093, 443), (776, 2094, 434), (779, 2095, 427), (782, 2096, 420), (786, 2097, 411), (789, 2098, 404), (792, 2099, 397), (795, 2100, 390), (799, 2101, 382), (802, 2102, 375), (805, 2103, 370), (808, 2104, 364), (811, 2105, 359), (815, 2106, 353), (818, 2107, 348), (821, 2108, 342), (824, 2109, 337), (827, 2110, 332), (830, 2111, 327), (833, 2112, 322), (836, 2113, 317), (840, 2114, 311), (843, 2115, 306), (846, 2116, 301), (849, 2117, 296), (852, 2118, 291), (855, 2119, 287), (858, 2120, 282), (861, 2121, 277), (864, 2122, 272), (867, 2123, 268), (870, 2124, 263), (872, 2125, 260), (875, 2126, 255), (878, 2127, 250), (881, 2128, 246), (884, 2129, 241), (887, 2130, 236), (890, 2131, 231), (893, 2132, 226), (896, 2133, 221), (899, 2134, 216), (903, 2135, 210), (906, 2136, 204), (910, 2137, 198), (913, 2138, 193), (917, 2139, 187), (920, 2140, 181), (924, 2141, 175), (928, 2142, 166), (932, 2143, 155), (936, 2144, 143), (946, 2145, 125), (956, 2146, 107), (966, 2147, 89), (977, 2148, 69), (988, 2149, 50), (1000, 2150, 29), (1011, 2151, 9)], ['1000,2150,899,2134,775,2093,694,2075,624,2042,555,2024,211,1962,127,1970,54,1825,39,1678,41,1538,29,1238,29,893,21,698,27,541,38,462,92,312,117,279,204,210,286,181,370,133,523,124,1426,124,1575,129,1680,145,1823,193,1904,206,2014,298,2094,411,2149,537,2168,613,2171,718,2164,841,2128,915,2113,991,2080,1069,2028,1139,1966,1257,1931,1368,1878,1446,1846,1670,1774,1883,1752,1927,1663,2015,1578,2016,1495,2039,1419,2046,1351,2067,1172,2103,1093,2142'])], 'temp/1746563755_2124625_917877156_a9c2d4b99270c9302def4ed40606e685.jpg']} nb pixel non reg : 3692295 nb pixel common : 3685926 proportion of common points : 0.998275056570507 [('test release memory', 'FAILURE', False), ('test detect objet', 'SUCCESS', True), ('test polygone', 'SUCCESS', True)] res_total : False #&_# TEST FAILED #&_# : tests/mask_test #&_# /home/admin/workarea/git/Velours/python/tests/python_tests.py refs/heads/master_6b796098f0a7c88b7d6a90fb4c0df56eec821fbf SQL :INSERT INTO MTRAdmin.monitor_sys (name, type, server, version_code, result_str, result_bool, lien , test_group ,test_name) VALUES ('python_test3','1','marlene','refs/heads/master_6b796098f0a7c88b7d6a90fb4c0df56eec821fbf','{"mask_detection": "fail"}','0','http://marlene.fotonower-preprod.com/job/2025/May/06052025/python_test3//data_2/data_log/job/2025/May/06052025/python_test3/log-python3----short_python3--v--marlene-21:35:00.txt','mask_detection','unknown'); #&_# END OF TEST #&_# : tests/mask_test #&_# #&_# BEGIN OF TEST : tests/datou_test #&_# /home/admin/workarea/git/Velours/python/tests/datou_test.py Datou All Test python version used : 3 ############################### TEST sam ################################ TEST SAM Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4573 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4573 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4573 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4573 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : sam list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1189321094) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 1189321094 download finish for photo 1189321094 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.23105382919311523 #### fin chargement data Blocking on flush ? No conitnuing About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! WARNING : we have an input that is not a photo, we should get rid of it Calling datou_exec Inside datou_exec : verbose : True number of steps : 1 step1:sam Tue May 6 22:36:23 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746563783_2124625_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png': 1189321094} map_photo_id_path_extension : {1189321094: {'path': 'temp/1746563783_2124625_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png', 'extension': 'png'}} map_subphoto_mainphoto : {} Beginning of datou step sam ! pht : 4677 Inside sam : nb paths : 1 (640, 960, 3) time for calcul the mask position with numpy : 0.0056192874908447266 nb_pixel_total : 3767 time to create 1 rle with old method : 0.005330085754394531 time for calcul the mask position with numpy : 0.0018630027770996094 nb_pixel_total : 16094 time to create 1 rle with old method : 0.025689363479614258 time for calcul the mask position with numpy : 0.0015180110931396484 nb_pixel_total : 5625 time to create 1 rle with old method : 0.00656437873840332 time for calcul the mask position with numpy : 0.0015091896057128906 nb_pixel_total : 16354 time to create 1 rle with old method : 0.018285512924194336 time for calcul the mask position with numpy : 0.0014142990112304688 nb_pixel_total : 2371 time to create 1 rle with old method : 0.0027174949645996094 time for calcul the mask position with numpy : 0.001436471939086914 nb_pixel_total : 3934 time to create 1 rle with old method : 0.004799604415893555 time for calcul the mask position with numpy : 0.0018727779388427734 nb_pixel_total : 13935 time to create 1 rle with old method : 0.016101360321044922 time for calcul the mask position with numpy : 0.001986980438232422 nb_pixel_total : 83697 time to create 1 rle with old method : 0.09558987617492676 time for calcul the mask position with numpy : 0.001567840576171875 nb_pixel_total : 10824 time to create 1 rle with old method : 0.01264500617980957 time for calcul the mask position with numpy : 0.0016756057739257812 nb_pixel_total : 38734 time to create 1 rle with old method : 0.045412540435791016 time for calcul the mask position with numpy : 0.0015408992767333984 nb_pixel_total : 14719 time to create 1 rle with old method : 0.01723480224609375 time for calcul the mask position with numpy : 0.0018801689147949219 nb_pixel_total : 3320 time to create 1 rle with old method : 0.004719972610473633 time for calcul the mask position with numpy : 0.0016887187957763672 nb_pixel_total : 9898 time to create 1 rle with old method : 0.011733293533325195 time for calcul the mask position with numpy : 0.0016636848449707031 nb_pixel_total : 29477 time to create 1 rle with old method : 0.03455495834350586 time for calcul the mask position with numpy : 0.0015108585357666016 nb_pixel_total : 2938 time to create 1 rle with old method : 0.003500223159790039 time for calcul the mask position with numpy : 0.0014634132385253906 nb_pixel_total : 2332 time to create 1 rle with old method : 0.0027840137481689453 time for calcul the mask position with numpy : 0.0015196800231933594 nb_pixel_total : 4273 time to create 1 rle with old method : 0.0049970149993896484 time for calcul the mask position with numpy : 0.0014617443084716797 nb_pixel_total : 1223 time to create 1 rle with old method : 0.0014996528625488281 time for calcul the mask position with numpy : 0.0014300346374511719 nb_pixel_total : 692 time to create 1 rle with old method : 0.0008978843688964844 time for calcul the mask position with numpy : 0.0014128684997558594 nb_pixel_total : 596 time to create 1 rle with old method : 0.0007810592651367188 time for calcul the mask position with numpy : 0.001445770263671875 nb_pixel_total : 6622 time to create 1 rle with old method : 0.00816488265991211 time for calcul the mask position with numpy : 0.0014939308166503906 nb_pixel_total : 4248 time to create 1 rle with old method : 0.005255222320556641 time for calcul the mask position with numpy : 0.0014410018920898438 nb_pixel_total : 2339 time to create 1 rle with old method : 0.002801656723022461 time for calcul the mask position with numpy : 0.001497030258178711 nb_pixel_total : 13119 time to create 1 rle with old method : 0.01605510711669922 time for calcul the mask position with numpy : 0.0015950202941894531 nb_pixel_total : 2079 time to create 1 rle with old method : 0.0024831295013427734 time for calcul the mask position with numpy : 0.0015323162078857422 nb_pixel_total : 12027 time to create 1 rle with old method : 0.014786243438720703 time for calcul the mask position with numpy : 0.0016951560974121094 nb_pixel_total : 1637 time to create 1 rle with old method : 0.0020287036895751953 time for calcul the mask position with numpy : 0.0014963150024414062 nb_pixel_total : 7637 time to create 1 rle with old method : 0.008577585220336914 time for calcul the mask position with numpy : 0.0013782978057861328 nb_pixel_total : 5497 time to create 1 rle with old method : 0.006350994110107422 time for calcul the mask position with numpy : 0.0014462471008300781 nb_pixel_total : 1508 time to create 1 rle with old method : 0.0017964839935302734 time for calcul the mask position with numpy : 0.0014314651489257812 nb_pixel_total : 1616 time to create 1 rle with old method : 0.0020029544830322266 time for calcul the mask position with numpy : 0.0014324188232421875 nb_pixel_total : 1421 time to create 1 rle with old method : 0.001855611801147461 time for calcul the mask position with numpy : 0.0015716552734375 nb_pixel_total : 3515 time to create 1 rle with old method : 0.004098415374755859 time for calcul the mask position with numpy : 0.0014910697937011719 nb_pixel_total : 8610 time to create 1 rle with old method : 0.009659528732299805 time for calcul the mask position with numpy : 0.0014142990112304688 nb_pixel_total : 2803 time to create 1 rle with old method : 0.0033621788024902344 time for calcul the mask position with numpy : 0.0013628005981445312 nb_pixel_total : 2448 time to create 1 rle with old method : 0.0028638839721679688 time for calcul the mask position with numpy : 0.0013585090637207031 nb_pixel_total : 1321 time to create 1 rle with old method : 0.001689910888671875 time for calcul the mask position with numpy : 0.0014886856079101562 nb_pixel_total : 3931 time to create 1 rle with old method : 0.004670381546020508 time for calcul the mask position with numpy : 0.0014233589172363281 nb_pixel_total : 2731 time to create 1 rle with old method : 0.0032052993774414062 time for calcul the mask position with numpy : 0.0014379024505615234 nb_pixel_total : 2407 time to create 1 rle with old method : 0.002849578857421875 time for calcul the mask position with numpy : 0.0013947486877441406 nb_pixel_total : 13011 time to create 1 rle with old method : 0.016040325164794922 time for calcul the mask position with numpy : 0.0014765262603759766 nb_pixel_total : 1025 time to create 1 rle with old method : 0.00141143798828125 time for calcul the mask position with numpy : 0.0015091896057128906 nb_pixel_total : 5542 time to create 1 rle with old method : 0.0067255496978759766 time for calcul the mask position with numpy : 0.001485586166381836 nb_pixel_total : 8712 time to create 1 rle with old method : 0.010057687759399414 time for calcul the mask position with numpy : 0.0013878345489501953 nb_pixel_total : 8434 time to create 1 rle with old method : 0.009607076644897461 time for calcul the mask position with numpy : 0.0013556480407714844 nb_pixel_total : 342 time to create 1 rle with old method : 0.0004177093505859375 time for calcul the mask position with numpy : 0.0013463497161865234 nb_pixel_total : 3369 time to create 1 rle with old method : 0.0038673877716064453 time for calcul the mask position with numpy : 0.0014486312866210938 nb_pixel_total : 4113 time to create 1 rle with old method : 0.004867076873779297 time for calcul the mask position with numpy : 0.0014026165008544922 nb_pixel_total : 1227 time to create 1 rle with old method : 0.0013856887817382812 time for calcul the mask position with numpy : 0.0014920234680175781 nb_pixel_total : 10625 time to create 1 rle with old method : 0.011914253234863281 time for calcul the mask position with numpy : 0.0014348030090332031 nb_pixel_total : 13068 time to create 1 rle with old method : 0.014538288116455078 time for calcul the mask position with numpy : 0.0014889240264892578 nb_pixel_total : 27553 time to create 1 rle with old method : 0.03231239318847656 time for calcul the mask position with numpy : 0.0015897750854492188 nb_pixel_total : 2387 time to create 1 rle with old method : 0.003294229507446289 time for calcul the mask position with numpy : 0.0016477108001708984 nb_pixel_total : 4172 time to create 1 rle with old method : 0.005335330963134766 time for calcul the mask position with numpy : 0.0015614032745361328 nb_pixel_total : 872 time to create 1 rle with old method : 0.0012903213500976562 time for calcul the mask position with numpy : 0.0015947818756103516 nb_pixel_total : 875 time to create 1 rle with old method : 0.0013127326965332031 time for calcul the mask position with numpy : 0.001676321029663086 nb_pixel_total : 1075 time to create 1 rle with old method : 0.001463174819946289 time for calcul the mask position with numpy : 0.0015599727630615234 nb_pixel_total : 1708 time to create 1 rle with old method : 0.002302408218383789 time for calcul the mask position with numpy : 0.0016281604766845703 nb_pixel_total : 4150 time to create 1 rle with old method : 0.005693912506103516 time for calcul the mask position with numpy : 0.0015354156494140625 nb_pixel_total : 889 time to create 1 rle with old method : 0.0011169910430908203 time for calcul the mask position with numpy : 0.001445770263671875 nb_pixel_total : 1672 time to create 1 rle with old method : 0.0019404888153076172 time for calcul the mask position with numpy : 0.001547098159790039 nb_pixel_total : 39047 time to create 1 rle with old method : 0.04526042938232422 time for calcul the mask position with numpy : 0.0015375614166259766 nb_pixel_total : 337 time to create 1 rle with old method : 0.0007586479187011719 time for calcul the mask position with numpy : 0.001638650894165039 nb_pixel_total : 577 time to create 1 rle with old method : 0.0007367134094238281 time for calcul the mask position with numpy : 0.0015308856964111328 nb_pixel_total : 274 time to create 1 rle with old method : 0.0003933906555175781 time for calcul the mask position with numpy : 0.0016789436340332031 nb_pixel_total : 2768 time to create 1 rle with old method : 0.0036988258361816406 time for calcul the mask position with numpy : 0.0014240741729736328 nb_pixel_total : 1206 time to create 1 rle with old method : 0.001520395278930664 time for calcul the mask position with numpy : 0.001382589340209961 nb_pixel_total : 592 time to create 1 rle with old method : 0.0007288455963134766 time for calcul the mask position with numpy : 0.0015587806701660156 nb_pixel_total : 1056 time to create 1 rle with old method : 0.0014448165893554688 time for calcul the mask position with numpy : 0.0015552043914794922 nb_pixel_total : 7402 time to create 1 rle with old method : 0.009015083312988281 time for calcul the mask position with numpy : 0.0015156269073486328 nb_pixel_total : 3093 time to create 1 rle with old method : 0.004199504852294922 time for calcul the mask position with numpy : 0.0015342235565185547 nb_pixel_total : 16702 time to create 1 rle with old method : 0.020441770553588867 time for calcul the mask position with numpy : 0.0014667510986328125 nb_pixel_total : 1823 time to create 1 rle with old method : 0.0022766590118408203 time for calcul the mask position with numpy : 0.0014286041259765625 nb_pixel_total : 1745 time to create 1 rle with old method : 0.0023224353790283203 time for calcul the mask position with numpy : 0.0015101432800292969 nb_pixel_total : 268 time to create 1 rle with old method : 0.0003528594970703125 time for calcul the mask position with numpy : 0.0016655921936035156 nb_pixel_total : 9081 time to create 1 rle with old method : 0.011377096176147461 time for calcul the mask position with numpy : 0.001592874526977539 nb_pixel_total : 710 time to create 1 rle with old method : 0.0010182857513427734 time for calcul the mask position with numpy : 0.0015778541564941406 nb_pixel_total : 968 time to create 1 rle with old method : 0.0012402534484863281 time for calcul the mask position with numpy : 0.001669168472290039 nb_pixel_total : 9507 time to create 1 rle with old method : 0.011597394943237305 time for calcul the mask position with numpy : 0.001561880111694336 nb_pixel_total : 1337 time to create 1 rle with old method : 0.0018181800842285156 time for calcul the mask position with numpy : 0.001447439193725586 nb_pixel_total : 7376 time to create 1 rle with old method : 0.008663654327392578 time for calcul the mask position with numpy : 0.0013990402221679688 nb_pixel_total : 3166 time to create 1 rle with old method : 0.0036270618438720703 time for calcul the mask position with numpy : 0.0014047622680664062 nb_pixel_total : 294 time to create 1 rle with old method : 0.00043582916259765625 time for calcul the mask position with numpy : 0.0015118122100830078 nb_pixel_total : 18541 time to create 1 rle with old method : 0.02267003059387207 time for calcul the mask position with numpy : 0.0018236637115478516 nb_pixel_total : 974 time to create 1 rle with old method : 0.0012316703796386719 time for calcul the mask position with numpy : 0.0013835430145263672 nb_pixel_total : 248 time to create 1 rle with old method : 0.0003504753112792969 time for calcul the mask position with numpy : 0.0013933181762695312 nb_pixel_total : 221 time to create 1 rle with old method : 0.00032067298889160156 time for calcul the mask position with numpy : 0.0014264583587646484 nb_pixel_total : 615 time to create 1 rle with old method : 0.0007877349853515625 time for calcul the mask position with numpy : 0.0013644695281982422 nb_pixel_total : 735 time to create 1 rle with old method : 0.0010044574737548828 time for calcul the mask position with numpy : 0.0014023780822753906 nb_pixel_total : 1502 time to create 1 rle with old method : 0.0017740726470947266 time for calcul the mask position with numpy : 0.0013926029205322266 nb_pixel_total : 1634 time to create 1 rle with old method : 0.0019533634185791016 time for calcul the mask position with numpy : 0.0014133453369140625 nb_pixel_total : 914 time to create 1 rle with old method : 0.0011208057403564453 time for calcul the mask position with numpy : 0.0014095306396484375 nb_pixel_total : 1435 time to create 1 rle with old method : 0.0018115043640136719 time for calcul the mask position with numpy : 0.0014824867248535156 nb_pixel_total : 7500 time to create 1 rle with old method : 0.008325815200805664 time for calcul the mask position with numpy : 0.0013735294342041016 nb_pixel_total : 595 time to create 1 rle with old method : 0.0011925697326660156 time for calcul the mask position with numpy : 0.0016467571258544922 nb_pixel_total : 1132 time to create 1 rle with old method : 0.0013852119445800781 time for calcul the mask position with numpy : 0.001443624496459961 nb_pixel_total : 916 time to create 1 rle with old method : 0.0011534690856933594 time for calcul the mask position with numpy : 0.0013422966003417969 nb_pixel_total : 2195 time to create 1 rle with old method : 0.0028116703033447266 time for calcul the mask position with numpy : 0.0014524459838867188 nb_pixel_total : 945 time to create 1 rle with old method : 0.0013322830200195312 time for calcul the mask position with numpy : 0.0013637542724609375 nb_pixel_total : 8397 time to create 1 rle with old method : 0.00975799560546875 time for calcul the mask position with numpy : 0.0014281272888183594 nb_pixel_total : 778 time to create 1 rle with old method : 0.001066446304321289 time for calcul the mask position with numpy : 0.0014293193817138672 nb_pixel_total : 947 time to create 1 rle with old method : 0.0012135505676269531 time for calcul the mask position with numpy : 0.0014281272888183594 nb_pixel_total : 885 time to create 1 rle with old method : 0.0011599063873291016 time for calcul the mask position with numpy : 0.0014286041259765625 nb_pixel_total : 1614 time to create 1 rle with old method : 0.0020296573638916016 time for calcul the mask position with numpy : 0.0014452934265136719 nb_pixel_total : 956 time to create 1 rle with old method : 0.0012500286102294922 insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) batch 1 Loaded 105 chid ids of type : 4677 Number RLEs to save : 9795 INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) first line : ('3787213899', '202', '535', '8') ... last line : ('3787214003', '868', '202', '5') INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) TO DO : save crop sub photo not yet done ! After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : sam we use saveGeneral [1189321094] map_info['map_portfolio_photo'] : {} final : True mtd_id 4573 list_pids : [1189321094] Looping around the photos to save general results len do output : 1 /1189321094Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4573', None, None, None, None, None, None, None, None) ('4573', None, '1189321094', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4573', None, '1189321094', 'None', None, None, None, None, None)] time used for this insertion : 0.019568443298339844 save_final save missing photos in datou_result : time spend for datou_step_exec : 10.900320053100586 time spend to save output : 0.01999378204345703 total time spend for step 1 : 10.920313835144043 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1189321094': [[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'temp/1746563783_2124625_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']} nb_objects detect : 105 ############################### TEST frcnn ################################ test frcnn Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4184 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4184 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4184 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4184 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : frcnn list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (917754606) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 917754606 download finish for photo 917754606 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.1319737434387207 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:frcnn Tue May 6 22:36:34 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746563794_2124625_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg': 917754606} map_photo_id_path_extension : {917754606: {'path': 'temp/1746563794_2124625_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Faster rcnn ! classes : ['background', 'plaque'] pht : 4370 caffemodel_name (should be vgg16_immat_307 but not used because net loaded outside in the fonction) : {'id': 3375, 'mtr_user_id': 31, 'name': 'detection_plaque_valcor_010622', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,plaque', 'svm_portfolios_learning': '0,0', 'photo_hashtag_type': 4370, 'photo_desc_type': 5676, 'type_classification': 'caffe_faster_rcnn', 'hashtag_id_list': '0,0'} To loadFromThcl() model_param file didn't exist model_name : detection_plaque_valcor_010622 model_type : caffe_faster_rcnn list file need : ['caffemodel', 'test.prototxt'] file exist in s3 : ['caffemodel', 'test.prototxt'] file manque in s3 : [] local folder : /data/models_weight/detection_plaque_valcor_010622 /data/models_weight/detection_plaque_valcor_010622/caffemodel size_local : 349723073 size in s3 : 349723073 create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:56 caffemodel already exist and didn't need to update /data/models_weight/detection_plaque_valcor_010622/test.prototxt size_local : 7163 size in s3 : 7163 create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:55 test.prototxt already exist and didn't need to update prototxt : /data/models_weight/detection_plaque_valcor_010622/test.prototxt caffemodel : /data/models_weight/detection_plaque_valcor_010622/caffemodel Loaded network /data/models_weight/detection_plaque_valcor_010622/caffemodel About to compute detect_faster_rcnn : len(args) : 1 Inside frcnn step exec : nb paths : 1 image_path : temp/1746563794_2124625_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg image_size (600, 800, 3) [[[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] [[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] [[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] ... [[ 14 16 16] [ 13 15 15] [ 11 13 13] ... [198 206 205] [198 206 205] [198 206 205]] [[ 16 18 18] [ 14 16 16] [ 11 13 13] ... [206 214 213] [206 214 213] [206 214 213]] [[ 13 15 15] [ 12 14 14] [ 9 11 11] ... [210 218 217] [210 218 217] [210 218 217]]] Detection took 0.108s for 300 object proposals c : plaque list_crops.shape (72, 5) proba : 0.06384116 (374.12708, 293.919, 430.81012, 317.8083) proba : 0.052221414 (382.1769, 297.18842, 552.35767, 344.65796) proba : 0.012271304 (345.35672, 272.42975, 468.85794, 320.72418) We are managing local photo_id len de result frcnn : 1 After datou_step_exec type output : time spend for datou_step_exec : 5.126070499420166 time spend to save output : 0.00010514259338378906 total time spend for step 1 : 5.12617564201355 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True Inside saveFrcnn : final : True verbose : True threshold to save the result : 0.1 output flattener : [(0, 493029425, 4370, 374, 430, 293, 317, 0.06384116, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.052221414, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012271304, None)] Warning : no hashtag_ids to insert in the database final : True begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4184', None, '917754606', '0', 0, '0', 493061979, '0', None)] time used for this insertion : 0.01660299301147461 [917754606] map_info['map_portfolio_photo'] : {} final : True mtd_id 4184 list_pids : [917754606] Looping around the photos to save general results len do output : 1 /0 before output type Managing all output in save final without adding information in the mtr_datou_result ('4184', None, None, None, None, None, None, None, None) ('4184', None, '917754606', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4184', None, '917754606', None, None, None, None, None, None)] time used for this insertion : 0.02039933204650879 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {0: [[(0, 493029425, 4370, 374, 430, 293, 317, 0.06384116, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.052221414, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012271304, None)], 'temp/1746563794_2124625_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg']} ############################### TEST thcl ################################ TEST THCL Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=2 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 2 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=2 # 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) 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 : step 1 thcl is not linked in the step_by_step architecture ! WARNING : step 2 argmax is not linked in the step_by_step architecture ! 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 ! DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : thcl, argmax list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (916235064) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 916235064 download finish for photo 916235064 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.13190984725952148 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 2 step1:thcl Tue May 6 22:36:39 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746563799_2124625_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064} map_photo_id_path_extension : {916235064: {'path': 'temp/1746563799_2124625_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Thcl ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'355': 1} we are using the classfication for only one thcl 355 In convert_file_to_np l 337 : 1 l343 1 l357 after caffe.io.load_image dimension du image : (3, (66, 66, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.020711898803710938 time to convert the images to numpy array : 0.0011928081512451172 total time to convert the images to numpy array : 0.02226090431213379 list photo_ids error: [] list photo_ids correct : [916235064] number of photos to traite : 1 try to delete the photos incorrect in DB tagging for thcl : 355 To do loadFromThcl(), then load ParamDescType : thcl355 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (355) thcls : [{'id': 355, 'mtr_user_id': 31, 'name': 'car_360_1027', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'c_elysee_1027_gao__port_506302,mokka_1027_gao__port_506374,captur_1027_gao__port_506399,sorento_1027_gao__port_506192,navara_1027_gao__port_506205,xc90_1027_gao__port_506350,saxo_1027_gao__port_506052,trafic_1027_gao__port_506295,punto_evo_1027_gao__port_506066,5_1027_gao__port_506117,250_1027_gao__port_506065,d_max_1027_gao__port_506125,panamera_1027_gao__port_506387,alhambra_1027_gao__port_506381,x6_1027_gao__port_506349,vitara_1027_gao__port_506328,fiesta_1027_gao__port_506377,qashqai_1027_gao__port_506286,147_1027_gao__port_506124,c5_1027_gao__port_506172,q5_1027_gao__port_506206,giulia_1027_gao__port_506178,karl_1027_gao__port_506371,mehari_1027_gao__port_506076,911_1027_gao__port_506114,508_1027_gao__port_506329,idea_1027_gao__port_506122,megane_1027_gao__port_506220,ghibli_1027_gao__port_506174,touareg_1027_gao__port_506224,i10_1027_gao__port_506232,jumper_1027_gao__port_506234,classe_clk_1027_gao__port_506173,kuga_1027_gao__port_506181,ct_1027_gao__port_506323,leon_1027_gao__port_506326,ds5_1027_gao__port_506376,cordoba_1027_gao__port_506048,classe_cla_1027_gao__port_506400,jumpy_1027_gao__port_506179,avensis_1027_gao__port_506311,juke_1027_gao__port_506325,4008_1027_gao__port_506402,190_series_1027_gao__port_506051,serie_3_1027_gao__port_506294,q7_1027_gao__port_506318,glc_1027_gao__port_506303,grand_vitara_1027_gao__port_506175,s40_1027_gao__port_506099,toledo_1027_gao__port_506061,5008_1027_gao__port_506337,continental_1027_gao__port_506250,coupe_1027_gao__port_506082,iq_1027_gao__port_506166,407_1027_gao__port_506133,touran_1027_gao__port_506308,300c_1027_gao__port_506078,classe_gl_1027_gao__port_506340,vivaro_1027_gao__port_506310,sl_1027_gao__port_506100,elise_1027_gao__port_506121,1007_1027_gao__port_506070,i40_1027_gao__port_506218,bipper_tepee_1027_gao__port_506227,focus_1027_gao__port_506272,primera_1027_gao__port_506147,r4_1027_gao__port_506160,a8_1027_gao__port_506265,boxer_1027_gao__port_506202,s5_1027_gao__port_506222,r21_1027_gao__port_506093,c3_1027_gao__port_506257,santa_fe_1027_gao__port_506208,m4_1027_gao__port_506344,safrane_1027_gao__port_506077,classe_gle_1027_gao__port_506395,0_1027_gao__port_506094,ix35_1027_gao__port_506219,carens_1027_gao__port_506298,classe_a_1027_gao__port_506339,ix20_1027_gao__port_506343,note_1027_gao__port_506365,a5_1027_gao__port_506200,sx4_1027_gao__port_506348,sandero_1027_gao__port_506198,3008_1027_gao__port_506385,q50_1027_gao__port_506239,latitude_1027_gao__port_506236,v40_1027_gao__port_506391,xsara_1027_gao__port_506087,grand_c_max_1027_gao__port_506342,swift_1027_gao__port_506149,serie_1_1027_gao__port_506184,xc70_1027_gao__port_506393,master_1027_gao__port_506203,clio_1027_gao__port_506280,duster_1027_gao__port_506216,traveller_1027_gao__port_506403,tipo_1027_gao__port_506355,rav_4_1027_gao__port_506332,coccinelle_1027_gao__port_506259,spacetourer_1027_gao__port_506401,xe_1027_gao__port_506357,ds3_1027_gao__port_506324,mx_5_1027_gao__port_506098,land_cruiser_1027_gao__port_506315,classe_b_1027_gao__port_506335,806_1027_gao__port_506088,rx_8_1027_gao__port_506046,spark_1027_gao__port_506185,6_1027_gao__port_506171,bravo_1027_gao__port_506080,nx_1027_gao__port_506345,sharan_1027_gao__port_506347,x_type_1027_gao__port_506067,jimny_1027_gao__port_506233,wrangler_1027_gao__port_506225,c_crosser_1027_gao__port_506312,v70_1027_gao__port_506278,classe_e_1027_gao__port_506300,classe_v_1027_gao__port_506258,m3_1027_gao__port_506182,abarth_500_1027_gao__port_506226,serie_6_1027_gao__port_506262,modus_1027_gao__port_506146,3_1027_gao__port_506113,405_1027_gao__port_506108,allroad_1027_gao__port_506297,auris_1027_gao__port_506322,galaxy_1027_gao__port_506143,giulietta_1027_gao__port_506363,106_1027_gao__port_506073,classe_m_1027_gao__port_506154,espace_1027_gao__port_506313,panda_1027_gao__port_506189,rcz_1027_gao__port_506197,4007_1027_gao__port_506162,classe_cl_1027_gao__port_506249,leaf_1027_gao__port_506139,octavia_1027_gao__port_506237,ds4_1027_gao__port_506336,freelander_1027_gao__port_506084,evasion_1027_gao__port_506109,punto_1027_gao__port_506106,2cv_1027_gao__port_506045,x4_1027_gao__port_506392,antara_1027_gao__port_506247,murano_1027_gao__port_506316,alto_1027_gao__port_506201,meriva_1027_gao__port_506353,orlando_1027_gao__port_506305,new_beetle_1027_gao__port_506050,306_1027_gao__port_506145,tiguan_1027_gao__port_506362,s_type_1027_gao__port_506101,c1_1027_gao__port_506128,vectra_1027_gao__port_506044,outlander_1027_gao__port_506317,307_1027_gao__port_506074,a6_s6_1027_gao__port_506134,nemo_combi_1027_gao__port_506196,berlingo_1027_gao__port_506194,partner_1027_gao__port_506285,cayenne_1027_gao__port_506177,quattroporte_1027_gao__port_506240,c_max_1027_gao__port_506282,fabia_1027_gao__port_506396,cx_3_1027_gao__port_506281,x_trail_1027_gao__port_506264,scirocco_1027_gao__port_506276,matiz_1027_gao__port_506144,tigra_1027_gao__port_506069,escort_1027_gao__port_506091,c2_1027_gao__port_506081,mini_1027_gao__port_506168,i30_1027_gao__port_506291,picanto_1027_gao__port_506238,mito_1027_gao__port_506072,impreza_1027_gao__port_506085,kangoo_1027_gao__port_506235,a4_1027_gao__port_506193,cayman_1027_gao__port_506268,sportage_1027_gao__port_506148,up_1027_gao__port_506356,optima_1027_gao__port_506386,defender_1027_gao__port_506229,serie_2_1027_gao__port_506256,edge_1027_gao__port_506187,r19_1027_gao__port_506110,jetta_1027_gao__port_506304,eos_1027_gao__port_506115,accord_1027_gao__port_506214,yaris_1027_gao__port_506334,classe_cls_1027_gao__port_506289,polo_1027_gao__port_506361,serie_4_1027_gao__port_506366,mini_cabriolet_1027_gao__port_506204,prius_1027_gao__port_506190,lodgy_1027_gao__port_506188,serie_7_1027_gao__port_506307,c15_1027_gao__port_506055,kadjar_1027_gao__port_506389,insignia_1027_gao__port_506364,308_1027_gao__port_506279,roomster_1027_gao__port_506241,80_1027_gao__port_506057,309_1027_gao__port_506063,tucson_1027_gao__port_506320,x3_1027_gao__port_506212,xf_1027_gao__port_506263,2008_1027_gao__port_506394,passat_1027_gao__port_506306,compass_1027_gao__port_506260,twingo_1027_gao__port_506309,micra_1027_gao__port_506221,golf_1027_gao__port_506155,soul_1027_gao__port_506176,rapid_1027_gao__port_506398,forester_1027_gao__port_506360,slk_1027_gao__port_506210,forfour_1027_gao__port_506341,serie_5_1027_gao__port_506209,xj_1027_gao__port_506170,pajero_1027_gao__port_506097,agila_1027_gao__port_506119,a6_1027_gao__port_506163,fox_1027_gao__port_506092,boxster_1027_gao__port_506267,altea_1027_gao__port_506246,samurai_1027_gao__port_506047,trax_1027_gao__port_506296,getz_1027_gao__port_506058,cherokee_1027_gao__port_506269,koleos_1027_gao__port_506378,z_series_1027_gao__port_506123,ecosport_1027_gao__port_506271,space_star_1027_gao__port_506277,rs3_sportback_1027_gao__port_506207,civic_1027_gao__port_506141,talisman_1027_gao__port_506390,f_pace_1027_gao__port_506314,classe_c_1027_gao__port_506299,tt_1027_gao__port_506075,pathfinder_1027_gao__port_506183,156_1027_gao__port_506157,cx_5_1027_gao__port_506228,scenic_1027_gao__port_506255,yeti_1027_gao__port_506358,mustang_1027_gao__port_506053,stilo_1027_gao__port_506060,ateca_1027_gao__port_506382,fiorino_1027_gao__port_506217,classe_glk_1027_gao__port_506290,fortwo_1027_gao__port_506230,cruze_1027_gao__port_506186,107_1027_gao__port_506213,aygo_1027_gao__port_506248,rx_1027_gao__port_506354,500_1027_gao__port_506245,bora_1027_gao__port_506104,transit_1027_gao__port_506111,pt_cruiser_1027_gao__port_506054,patrol_1027_gao__port_506068,r8_1027_gao__port_506156,xm_1027_gao__port_506102,s60_1027_gao__port_506191,aveo_1027_gao__port_506158,captiva_1027_gao__port_506159,ax_1027_gao__port_506153,rexton_1027_gao__port_506107,camaro_1027_gao__port_506056,ypsilon_1027_gao__port_506131,delta_1027_gao__port_506165,c4_1027_gao__port_506370,zx_1027_gao__port_506161,verso_1027_gao__port_506242,superb_1027_gao__port_506327,r5_1027_gao__port_506253,caddy_1027_gao__port_506330,x5_1027_gao__port_506243,f_type_1027_gao__port_506231,fusion_1027_gao__port_506096,dokker_1027_gao__port_506331,205_1027_gao__port_506062,macan_1027_gao__port_506195,tourneo_1027_gao__port_506369,108_1027_gao__port_506384,9_3_1027_gao__port_506071,mondeo_1027_gao__port_506116,cr_v_1027_gao__port_506164,c30_1027_gao__port_506090,pulsar_1027_gao__port_506397,ibiza_1027_gao__port_506273,a1_1027_gao__port_506338,matrix_1027_gao__port_506140,carnival_1027_gao__port_506136,xantia_1027_gao__port_506086,terrano_1027_gao__port_506083,q3_1027_gao__port_506275,hr_v_1027_gao__port_506283,expert_1027_gao__port_506142,multivan_1027_gao__port_506383,venga_1027_gao__port_506380,scudo_1027_gao__port_506129,laguna_1027_gao__port_506368,vel_satis_1027_gao__port_506130,b_max_1027_gao__port_506367,ignis_1027_gao__port_506292,159_1027_gao__port_506064,grande_punto_1027_gao__port_506138,logan_1027_gao__port_506167,s_max_1027_gao__port_506223,caravelle_1027_gao__port_506351,adam_1027_gao__port_506079,406_1027_gao__port_506132,q30_1027_gao__port_506293,almera_1027_gao__port_506089,corsa_1027_gao__port_506095,corolla_1027_gao__port_506120,xc60_1027_gao__port_506388,viano_1027_gao__port_506211,pro_cee_d_1027_gao__port_506274,a3_1027_gao__port_506321,v50_1027_gao__port_506150,voyager_1027_gao__port_506169,corvette_1027_gao__port_506049,rio_1027_gao__port_506379,jazz_1027_gao__port_506252,200_1027_gao__port_506112,tts_1027_gao__port_506199,zafira_1027_gao__port_506287,asx_1027_gao__port_506266,607_1027_gao__port_506118,207_1027_gao__port_506103,classe_s_1027_gao__port_506301,c6_1027_gao__port_506105,express_1027_gao__port_506137,classe_gla_1027_gao__port_506352,v60_1027_gao__port_506333,ka_1027_gao__port_506180,range_rover_1027_gao__port_506254,discovery_1027_gao__port_506375,classe_r_1027_gao__port_506270,transporter_1027_gao__port_506319,cee_d_1027_gao__port_506288,zoe_1027_gao__port_506244,i20_1027_gao__port_506284,gtv_1027_gao__port_506059,s4_avant_1027_gao__port_506261,x1_1027_gao__port_506372,autres_1027_gao__port_506127,208_1027_gao__port_506359,c8_1027_gao__port_506135,astra_1027_gao__port_506215,2_1027_gao__port_506151,doblo_1027_gao__port_506251,807_1027_gao__port_506152,206_1027_gao__port_506126,a7_1027_gao__port_506373,renegade_1027_gao__port_506346', 'svm_portfolios_learning': '506302,506374,506399,506192,506205,506350,506052,506295,506066,506117,506065,506125,506387,506381,506349,506328,506377,506286,506124,506172,506206,506178,506371,506076,506114,506329,506122,506220,506174,506224,506232,506234,506173,506181,506323,506326,506376,506048,506400,506179,506311,506325,506402,506051,506294,506318,506303,506175,506099,506061,506337,506250,506082,506166,506133,506308,506078,506340,506310,506100,506121,506070,506218,506227,506272,506147,506160,506265,506202,506222,506093,506257,506208,506344,506077,506395,506094,506219,506298,506339,506343,506365,506200,506348,506198,506385,506239,506236,506391,506087,506342,506149,506184,506393,506203,506280,506216,506403,506355,506332,506259,506401,506357,506324,506098,506315,506335,506088,506046,506185,506171,506080,506345,506347,506067,506233,506225,506312,506278,506300,506258,506182,506226,506262,506146,506113,506108,506297,506322,506143,506363,506073,506154,506313,506189,506197,506162,506249,506139,506237,506336,506084,506109,506106,506045,506392,506247,506316,506201,506353,506305,506050,506145,506362,506101,506128,506044,506317,506074,506134,506196,506194,506285,506177,506240,506282,506396,506281,506264,506276,506144,506069,506091,506081,506168,506291,506238,506072,506085,506235,506193,506268,506148,506356,506386,506229,506256,506187,506110,506304,506115,506214,506334,506289,506361,506366,506204,506190,506188,506307,506055,506389,506364,506279,506241,506057,506063,506320,506212,506263,506394,506306,506260,506309,506221,506155,506176,506398,506360,506210,506341,506209,506170,506097,506119,506163,506092,506267,506246,506047,506296,506058,506269,506378,506123,506271,506277,506207,506141,506390,506314,506299,506075,506183,506157,506228,506255,506358,506053,506060,506382,506217,506290,506230,506186,506213,506248,506354,506245,506104,506111,506054,506068,506156,506102,506191,506158,506159,506153,506107,506056,506131,506165,506370,506161,506242,506327,506253,506330,506243,506231,506096,506331,506062,506195,506369,506384,506071,506116,506164,506090,506397,506273,506338,506140,506136,506086,506083,506275,506283,506142,506383,506380,506129,506368,506130,506367,506292,506064,506138,506167,506223,506351,506079,506132,506293,506089,506095,506120,506388,506211,506274,506321,506150,506169,506049,506379,506252,506112,506199,506287,506266,506118,506103,506301,506105,506137,506352,506333,506180,506254,506375,506270,506319,506288,506244,506284,506059,506261,506372,506127,506359,506135,506215,506151,506251,506152,506126,506373,506346', 'photo_hashtag_type': 332, 'photo_desc_type': 3390, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 355, 'mtr_user_id': 31, 'name': 'car_360_1027', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'c_elysee_1027_gao__port_506302,mokka_1027_gao__port_506374,captur_1027_gao__port_506399,sorento_1027_gao__port_506192,navara_1027_gao__port_506205,xc90_1027_gao__port_506350,saxo_1027_gao__port_506052,trafic_1027_gao__port_506295,punto_evo_1027_gao__port_506066,5_1027_gao__port_506117,250_1027_gao__port_506065,d_max_1027_gao__port_506125,panamera_1027_gao__port_506387,alhambra_1027_gao__port_506381,x6_1027_gao__port_506349,vitara_1027_gao__port_506328,fiesta_1027_gao__port_506377,qashqai_1027_gao__port_506286,147_1027_gao__port_506124,c5_1027_gao__port_506172,q5_1027_gao__port_506206,giulia_1027_gao__port_506178,karl_1027_gao__port_506371,mehari_1027_gao__port_506076,911_1027_gao__port_506114,508_1027_gao__port_506329,idea_1027_gao__port_506122,megane_1027_gao__port_506220,ghibli_1027_gao__port_506174,touareg_1027_gao__port_506224,i10_1027_gao__port_506232,jumper_1027_gao__port_506234,classe_clk_1027_gao__port_506173,kuga_1027_gao__port_506181,ct_1027_gao__port_506323,leon_1027_gao__port_506326,ds5_1027_gao__port_506376,cordoba_1027_gao__port_506048,classe_cla_1027_gao__port_506400,jumpy_1027_gao__port_506179,avensis_1027_gao__port_506311,juke_1027_gao__port_506325,4008_1027_gao__port_506402,190_series_1027_gao__port_506051,serie_3_1027_gao__port_506294,q7_1027_gao__port_506318,glc_1027_gao__port_506303,grand_vitara_1027_gao__port_506175,s40_1027_gao__port_506099,toledo_1027_gao__port_506061,5008_1027_gao__port_506337,continental_1027_gao__port_506250,coupe_1027_gao__port_506082,iq_1027_gao__port_506166,407_1027_gao__port_506133,touran_1027_gao__port_506308,300c_1027_gao__port_506078,classe_gl_1027_gao__port_506340,vivaro_1027_gao__port_506310,sl_1027_gao__port_506100,elise_1027_gao__port_506121,1007_1027_gao__port_506070,i40_1027_gao__port_506218,bipper_tepee_1027_gao__port_506227,focus_1027_gao__port_506272,primera_1027_gao__port_506147,r4_1027_gao__port_506160,a8_1027_gao__port_506265,boxer_1027_gao__port_506202,s5_1027_gao__port_506222,r21_1027_gao__port_506093,c3_1027_gao__port_506257,santa_fe_1027_gao__port_506208,m4_1027_gao__port_506344,safrane_1027_gao__port_506077,classe_gle_1027_gao__port_506395,0_1027_gao__port_506094,ix35_1027_gao__port_506219,carens_1027_gao__port_506298,classe_a_1027_gao__port_506339,ix20_1027_gao__port_506343,note_1027_gao__port_506365,a5_1027_gao__port_506200,sx4_1027_gao__port_506348,sandero_1027_gao__port_506198,3008_1027_gao__port_506385,q50_1027_gao__port_506239,latitude_1027_gao__port_506236,v40_1027_gao__port_506391,xsara_1027_gao__port_506087,grand_c_max_1027_gao__port_506342,swift_1027_gao__port_506149,serie_1_1027_gao__port_506184,xc70_1027_gao__port_506393,master_1027_gao__port_506203,clio_1027_gao__port_506280,duster_1027_gao__port_506216,traveller_1027_gao__port_506403,tipo_1027_gao__port_506355,rav_4_1027_gao__port_506332,coccinelle_1027_gao__port_506259,spacetourer_1027_gao__port_506401,xe_1027_gao__port_506357,ds3_1027_gao__port_506324,mx_5_1027_gao__port_506098,land_cruiser_1027_gao__port_506315,classe_b_1027_gao__port_506335,806_1027_gao__port_506088,rx_8_1027_gao__port_506046,spark_1027_gao__port_506185,6_1027_gao__port_506171,bravo_1027_gao__port_506080,nx_1027_gao__port_506345,sharan_1027_gao__port_506347,x_type_1027_gao__port_506067,jimny_1027_gao__port_506233,wrangler_1027_gao__port_506225,c_crosser_1027_gao__port_506312,v70_1027_gao__port_506278,classe_e_1027_gao__port_506300,classe_v_1027_gao__port_506258,m3_1027_gao__port_506182,abarth_500_1027_gao__port_506226,serie_6_1027_gao__port_506262,modus_1027_gao__port_506146,3_1027_gao__port_506113,405_1027_gao__port_506108,allroad_1027_gao__port_506297,auris_1027_gao__port_506322,galaxy_1027_gao__port_506143,giulietta_1027_gao__port_506363,106_1027_gao__port_506073,classe_m_1027_gao__port_506154,espace_1027_gao__port_506313,panda_1027_gao__port_506189,rcz_1027_gao__port_506197,4007_1027_gao__port_506162,classe_cl_1027_gao__port_506249,leaf_1027_gao__port_506139,octavia_1027_gao__port_506237,ds4_1027_gao__port_506336,freelander_1027_gao__port_506084,evasion_1027_gao__port_506109,punto_1027_gao__port_506106,2cv_1027_gao__port_506045,x4_1027_gao__port_506392,antara_1027_gao__port_506247,murano_1027_gao__port_506316,alto_1027_gao__port_506201,meriva_1027_gao__port_506353,orlando_1027_gao__port_506305,new_beetle_1027_gao__port_506050,306_1027_gao__port_506145,tiguan_1027_gao__port_506362,s_type_1027_gao__port_506101,c1_1027_gao__port_506128,vectra_1027_gao__port_506044,outlander_1027_gao__port_506317,307_1027_gao__port_506074,a6_s6_1027_gao__port_506134,nemo_combi_1027_gao__port_506196,berlingo_1027_gao__port_506194,partner_1027_gao__port_506285,cayenne_1027_gao__port_506177,quattroporte_1027_gao__port_506240,c_max_1027_gao__port_506282,fabia_1027_gao__port_506396,cx_3_1027_gao__port_506281,x_trail_1027_gao__port_506264,scirocco_1027_gao__port_506276,matiz_1027_gao__port_506144,tigra_1027_gao__port_506069,escort_1027_gao__port_506091,c2_1027_gao__port_506081,mini_1027_gao__port_506168,i30_1027_gao__port_506291,picanto_1027_gao__port_506238,mito_1027_gao__port_506072,impreza_1027_gao__port_506085,kangoo_1027_gao__port_506235,a4_1027_gao__port_506193,cayman_1027_gao__port_506268,sportage_1027_gao__port_506148,up_1027_gao__port_506356,optima_1027_gao__port_506386,defender_1027_gao__port_506229,serie_2_1027_gao__port_506256,edge_1027_gao__port_506187,r19_1027_gao__port_506110,jetta_1027_gao__port_506304,eos_1027_gao__port_506115,accord_1027_gao__port_506214,yaris_1027_gao__port_506334,classe_cls_1027_gao__port_506289,polo_1027_gao__port_506361,serie_4_1027_gao__port_506366,mini_cabriolet_1027_gao__port_506204,prius_1027_gao__port_506190,lodgy_1027_gao__port_506188,serie_7_1027_gao__port_506307,c15_1027_gao__port_506055,kadjar_1027_gao__port_506389,insignia_1027_gao__port_506364,308_1027_gao__port_506279,roomster_1027_gao__port_506241,80_1027_gao__port_506057,309_1027_gao__port_506063,tucson_1027_gao__port_506320,x3_1027_gao__port_506212,xf_1027_gao__port_506263,2008_1027_gao__port_506394,passat_1027_gao__port_506306,compass_1027_gao__port_506260,twingo_1027_gao__port_506309,micra_1027_gao__port_506221,golf_1027_gao__port_506155,soul_1027_gao__port_506176,rapid_1027_gao__port_506398,forester_1027_gao__port_506360,slk_1027_gao__port_506210,forfour_1027_gao__port_506341,serie_5_1027_gao__port_506209,xj_1027_gao__port_506170,pajero_1027_gao__port_506097,agila_1027_gao__port_506119,a6_1027_gao__port_506163,fox_1027_gao__port_506092,boxster_1027_gao__port_506267,altea_1027_gao__port_506246,samurai_1027_gao__port_506047,trax_1027_gao__port_506296,getz_1027_gao__port_506058,cherokee_1027_gao__port_506269,koleos_1027_gao__port_506378,z_series_1027_gao__port_506123,ecosport_1027_gao__port_506271,space_star_1027_gao__port_506277,rs3_sportback_1027_gao__port_506207,civic_1027_gao__port_506141,talisman_1027_gao__port_506390,f_pace_1027_gao__port_506314,classe_c_1027_gao__port_506299,tt_1027_gao__port_506075,pathfinder_1027_gao__port_506183,156_1027_gao__port_506157,cx_5_1027_gao__port_506228,scenic_1027_gao__port_506255,yeti_1027_gao__port_506358,mustang_1027_gao__port_506053,stilo_1027_gao__port_506060,ateca_1027_gao__port_506382,fiorino_1027_gao__port_506217,classe_glk_1027_gao__port_506290,fortwo_1027_gao__port_506230,cruze_1027_gao__port_506186,107_1027_gao__port_506213,aygo_1027_gao__port_506248,rx_1027_gao__port_506354,500_1027_gao__port_506245,bora_1027_gao__port_506104,transit_1027_gao__port_506111,pt_cruiser_1027_gao__port_506054,patrol_1027_gao__port_506068,r8_1027_gao__port_506156,xm_1027_gao__port_506102,s60_1027_gao__port_506191,aveo_1027_gao__port_506158,captiva_1027_gao__port_506159,ax_1027_gao__port_506153,rexton_1027_gao__port_506107,camaro_1027_gao__port_506056,ypsilon_1027_gao__port_506131,delta_1027_gao__port_506165,c4_1027_gao__port_506370,zx_1027_gao__port_506161,verso_1027_gao__port_506242,superb_1027_gao__port_506327,r5_1027_gao__port_506253,caddy_1027_gao__port_506330,x5_1027_gao__port_506243,f_type_1027_gao__port_506231,fusion_1027_gao__port_506096,dokker_1027_gao__port_506331,205_1027_gao__port_506062,macan_1027_gao__port_506195,tourneo_1027_gao__port_506369,108_1027_gao__port_506384,9_3_1027_gao__port_506071,mondeo_1027_gao__port_506116,cr_v_1027_gao__port_506164,c30_1027_gao__port_506090,pulsar_1027_gao__port_506397,ibiza_1027_gao__port_506273,a1_1027_gao__port_506338,matrix_1027_gao__port_506140,carnival_1027_gao__port_506136,xantia_1027_gao__port_506086,terrano_1027_gao__port_506083,q3_1027_gao__port_506275,hr_v_1027_gao__port_506283,expert_1027_gao__port_506142,multivan_1027_gao__port_506383,venga_1027_gao__port_506380,scudo_1027_gao__port_506129,laguna_1027_gao__port_506368,vel_satis_1027_gao__port_506130,b_max_1027_gao__port_506367,ignis_1027_gao__port_506292,159_1027_gao__port_506064,grande_punto_1027_gao__port_506138,logan_1027_gao__port_506167,s_max_1027_gao__port_506223,caravelle_1027_gao__port_506351,adam_1027_gao__port_506079,406_1027_gao__port_506132,q30_1027_gao__port_506293,almera_1027_gao__port_506089,corsa_1027_gao__port_506095,corolla_1027_gao__port_506120,xc60_1027_gao__port_506388,viano_1027_gao__port_506211,pro_cee_d_1027_gao__port_506274,a3_1027_gao__port_506321,v50_1027_gao__port_506150,voyager_1027_gao__port_506169,corvette_1027_gao__port_506049,rio_1027_gao__port_506379,jazz_1027_gao__port_506252,200_1027_gao__port_506112,tts_1027_gao__port_506199,zafira_1027_gao__port_506287,asx_1027_gao__port_506266,607_1027_gao__port_506118,207_1027_gao__port_506103,classe_s_1027_gao__port_506301,c6_1027_gao__port_506105,express_1027_gao__port_506137,classe_gla_1027_gao__port_506352,v60_1027_gao__port_506333,ka_1027_gao__port_506180,range_rover_1027_gao__port_506254,discovery_1027_gao__port_506375,classe_r_1027_gao__port_506270,transporter_1027_gao__port_506319,cee_d_1027_gao__port_506288,zoe_1027_gao__port_506244,i20_1027_gao__port_506284,gtv_1027_gao__port_506059,s4_avant_1027_gao__port_506261,x1_1027_gao__port_506372,autres_1027_gao__port_506127,208_1027_gao__port_506359,c8_1027_gao__port_506135,astra_1027_gao__port_506215,2_1027_gao__port_506151,doblo_1027_gao__port_506251,807_1027_gao__port_506152,206_1027_gao__port_506126,a7_1027_gao__port_506373,renegade_1027_gao__port_506346', 'svm_portfolios_learning': '506302,506374,506399,506192,506205,506350,506052,506295,506066,506117,506065,506125,506387,506381,506349,506328,506377,506286,506124,506172,506206,506178,506371,506076,506114,506329,506122,506220,506174,506224,506232,506234,506173,506181,506323,506326,506376,506048,506400,506179,506311,506325,506402,506051,506294,506318,506303,506175,506099,506061,506337,506250,506082,506166,506133,506308,506078,506340,506310,506100,506121,506070,506218,506227,506272,506147,506160,506265,506202,506222,506093,506257,506208,506344,506077,506395,506094,506219,506298,506339,506343,506365,506200,506348,506198,506385,506239,506236,506391,506087,506342,506149,506184,506393,506203,506280,506216,506403,506355,506332,506259,506401,506357,506324,506098,506315,506335,506088,506046,506185,506171,506080,506345,506347,506067,506233,506225,506312,506278,506300,506258,506182,506226,506262,506146,506113,506108,506297,506322,506143,506363,506073,506154,506313,506189,506197,506162,506249,506139,506237,506336,506084,506109,506106,506045,506392,506247,506316,506201,506353,506305,506050,506145,506362,506101,506128,506044,506317,506074,506134,506196,506194,506285,506177,506240,506282,506396,506281,506264,506276,506144,506069,506091,506081,506168,506291,506238,506072,506085,506235,506193,506268,506148,506356,506386,506229,506256,506187,506110,506304,506115,506214,506334,506289,506361,506366,506204,506190,506188,506307,506055,506389,506364,506279,506241,506057,506063,506320,506212,506263,506394,506306,506260,506309,506221,506155,506176,506398,506360,506210,506341,506209,506170,506097,506119,506163,506092,506267,506246,506047,506296,506058,506269,506378,506123,506271,506277,506207,506141,506390,506314,506299,506075,506183,506157,506228,506255,506358,506053,506060,506382,506217,506290,506230,506186,506213,506248,506354,506245,506104,506111,506054,506068,506156,506102,506191,506158,506159,506153,506107,506056,506131,506165,506370,506161,506242,506327,506253,506330,506243,506231,506096,506331,506062,506195,506369,506384,506071,506116,506164,506090,506397,506273,506338,506140,506136,506086,506083,506275,506283,506142,506383,506380,506129,506368,506130,506367,506292,506064,506138,506167,506223,506351,506079,506132,506293,506089,506095,506120,506388,506211,506274,506321,506150,506169,506049,506379,506252,506112,506199,506287,506266,506118,506103,506301,506105,506137,506352,506333,506180,506254,506375,506270,506319,506288,506244,506284,506059,506261,506372,506127,506359,506135,506215,506151,506251,506152,506126,506373,506346', 'photo_hashtag_type': 332, 'photo_desc_type': 3390, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3390 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (3390) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27)) To loadFromThcl() : net_3390 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 2717 max_wait_temp : 1 max_wait : 0 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (3390) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27)) param : , param.caffemodel : car_360_1027 None mean_file_type : mean_file_path : prototxt_file_path : model : car_360_1027 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : car_360_1027 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : [] local folder : /data/models_weight/car_360_1027 /data/models_weight/car_360_1027/caffemodel size_local : 542944640 size in s3 : 542944640 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 caffemodel already exist and didn't need to update /data/models_weight/car_360_1027/deploy_conv_normal.prototxt size_local : 4626 size in s3 : 4626 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42 deploy_conv_normal.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/deploy_fc.prototxt size_local : 1132 size in s3 : 1132 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 deploy_fc.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/deploy.prototxt size_local : 5654 size in s3 : 5654 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42 deploy.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/mean.npy size_local : 1572944 size in s3 : 1572944 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:55 mean.npy already exist and didn't need to update /data/models_weight/car_360_1027/synset_words.txt size_local : 13687 size in s3 : 13687 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /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/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/car_360_1027/deploy.prototxt caffemodel_filename : /data/models_weight/car_360_1027/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 2717 max_wait_temp : 1 max_wait : 0 dict_keys(['pool5', 'prob']) time used to do the prepocess of the images : 0.012535333633422852 time used to do the prediction : 0.07380104064941406 save descriptor for thcl : 355 (1, 512, 7, 7) Got the blobs of the net to insert : [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] code_as_byte_string:b'0000000000'| time to traite the descriptors : 0.05160641670227051 Testing : ['916235064'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (916235064) Catched exception ! Connect or reconnect ! result : {916235064: {'photo_id': 916235064, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2017/10/14/6293d1bb790dc6902450e7c572b7d10b.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': None}} list_photo_exists : [916235064] storage_type for insertDescriptorsMulti : 1 To insert : 916235064 time to insert the descriptors : 0.7709386348724365 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : False verbose : True time used to find the portfolios of the photos select button_legend_list from MTRDatou.classification_theme where id = 355 SAVE THCL, output : {'916235064': [[('916235064', 'c_elysee_1027_gao__port_506302', 0.0018815151, 332, '355'), ('916235064', 'mokka_1027_gao__port_506374', 0.001163533, 332, '355'), ('916235064', 'captur_1027_gao__port_506399', 0.0008157765, 332, '355'), ('916235064', 'sorento_1027_gao__port_506192', 0.0011773085, 332, '355'), ('916235064', 'navara_1027_gao__port_506205', 0.0025851368, 332, '355'), ('916235064', 'xc90_1027_gao__port_506350', 0.0041701864, 332, '355'), ('916235064', 'saxo_1027_gao__port_506052', 0.0034807967, 332, '355'), ('916235064', 'trafic_1027_gao__port_506295', 0.0073671555, 332, '355'), ('916235064', 'punto_evo_1027_gao__port_506066', 0.0021888835, 332, '355'), ('916235064', '5_1027_gao__port_506117', 0.0005797825, 332, '355'), ('916235064', '250_1027_gao__port_506065', 0.004591136, 332, '355'), ('916235064', 'd_max_1027_gao__port_506125', 0.0031587104, 332, '355'), ('916235064', 'panamera_1027_gao__port_506387', 0.0022507766, 332, '355'), ('916235064', 'alhambra_1027_gao__port_506381', 0.00532046, 332, '355'), ('916235064', 'x6_1027_gao__port_506349', 0.0010999391, 332, '355'), ('916235064', 'vitara_1027_gao__port_506328', 0.005402243, 332, '355'), ('916235064', 'fiesta_1027_gao__port_506377', 0.003918998, 332, '355'), ('916235064', 'qashqai_1027_gao__port_506286', 0.0014787986, 332, '355'), ('916235064', '147_1027_gao__port_506124', 0.0019778237, 332, '355'), ('916235064', 'c5_1027_gao__port_506172', 0.001244143, 332, '355'), ('916235064', 'q5_1027_gao__port_506206', 0.0015049267, 332, '355'), ('916235064', 'giulia_1027_gao__port_506178', 0.002169477, 332, '355'), ('916235064', 'karl_1027_gao__port_506371', 0.002708164, 332, '355'), ('916235064', 'mehari_1027_gao__port_506076', 0.0047034114, 332, '355'), ('916235064', '911_1027_gao__port_506114', 0.0019417792, 332, '355'), ('916235064', '508_1027_gao__port_506329', 0.000958546, 332, '355'), ('916235064', 'idea_1027_gao__port_506122', 0.0007699649, 332, '355'), ('916235064', 'megane_1027_gao__port_506220', 0.0019468933, 332, '355'), ('916235064', 'ghibli_1027_gao__port_506174', 0.0013725213, 332, '355'), ('916235064', 'touareg_1027_gao__port_506224', 0.001620228, 332, '355'), ('916235064', 'i10_1027_gao__port_506232', 0.0013924856, 332, '355'), ('916235064', 'jumper_1027_gao__port_506234', 0.010044554, 332, '355'), ('916235064', 'classe_clk_1027_gao__port_506173', 0.0010792508, 332, '355'), ('916235064', 'kuga_1027_gao__port_506181', 0.00084468414, 332, '355'), ('916235064', 'ct_1027_gao__port_506323', 0.0012520803, 332, '355'), ('916235064', 'leon_1027_gao__port_506326', 0.002584419, 332, '355'), ('916235064', 'ds5_1027_gao__port_506376', 0.0012430026, 332, '355'), ('916235064', 'cordoba_1027_gao__port_506048', 0.0028648537, 332, '355'), ('916235064', 'classe_cla_1027_gao__port_506400', 0.0012949446, 332, '355'), ('916235064', 'jumpy_1027_gao__port_506179', 0.010338841, 332, '355'), ('916235064', 'avensis_1027_gao__port_506311', 0.0018766422, 332, '355'), ('916235064', 'juke_1027_gao__port_506325', 0.0011343785, 332, '355'), ('916235064', '4008_1027_gao__port_506402', 0.0015757899, 332, '355'), ('916235064', '190_series_1027_gao__port_506051', 0.0039800275, 332, '355'), ('916235064', 'serie_3_1027_gao__port_506294', 0.0028740803, 332, '355'), ('916235064', 'q7_1027_gao__port_506318', 0.0023354844, 332, '355'), ('916235064', 'glc_1027_gao__port_506303', 0.0012106914, 332, '355'), ('916235064', 'grand_vitara_1027_gao__port_506175', 0.0011447719, 332, '355'), ('916235064', 's40_1027_gao__port_506099', 0.0022337122, 332, '355'), ('916235064', 'toledo_1027_gao__port_506061', 0.0017464333, 332, '355'), ('916235064', '5008_1027_gao__port_506337', 0.0046993354, 332, '355'), ('916235064', 'continental_1027_gao__port_506250', 0.0021914914, 332, '355'), ('916235064', 'coupe_1027_gao__port_506082', 0.0022630768, 332, '355'), ('916235064', 'iq_1027_gao__port_506166', 0.0018174133, 332, '355'), ('916235064', '407_1027_gao__port_506133', 0.000905645, 332, '355'), ('916235064', 'touran_1027_gao__port_506308', 0.0020403517, 332, '355'), ('916235064', '300c_1027_gao__port_506078', 0.0025335404, 332, '355'), ('916235064', 'classe_gl_1027_gao__port_506340', 0.004489264, 332, '355'), ('916235064', 'vivaro_1027_gao__port_506310', 0.0034254892, 332, '355'), ('916235064', 'sl_1027_gao__port_506100', 0.0031352043, 332, '355'), ('916235064', 'elise_1027_gao__port_506121', 0.0010255014, 332, '355'), ('916235064', '1007_1027_gao__port_506070', 0.001535459, 332, '355'), ('916235064', 'i40_1027_gao__port_506218', 0.00059145596, 332, '355'), ('916235064', 'bipper_tepee_1027_gao__port_506227', 0.004029792, 332, '355'), ('916235064', 'focus_1027_gao__port_506272', 0.0011585763, 332, '355'), ('916235064', 'primera_1027_gao__port_506147', 0.001215686, 332, '355'), ('916235064', 'r4_1027_gao__port_506160', 0.0149666285, 332, '355'), ('916235064', 'a8_1027_gao__port_506265', 0.001132015, 332, '355'), ('916235064', 'boxer_1027_gao__port_506202', 0.010545438, 332, '355'), ('916235064', 's5_1027_gao__port_506222', 0.0011984914, 332, '355'), ('916235064', 'r21_1027_gao__port_506093', 0.0041854326, 332, '355'), ('916235064', 'c3_1027_gao__port_506257', 0.002363612, 332, '355'), ('916235064', 'santa_fe_1027_gao__port_506208', 0.0016323745, 332, '355'), ('916235064', 'm4_1027_gao__port_506344', 0.0015567646, 332, '355'), ('916235064', 'safrane_1027_gao__port_506077', 0.0013958007, 332, '355'), ('916235064', 'classe_gle_1027_gao__port_506395', 0.0021979164, 332, '355'), ('916235064', '0_1027_gao__port_506094', 0.00882796, 332, '355'), ('916235064', 'ix35_1027_gao__port_506219', 0.0014615158, 332, '355'), ('916235064', 'carens_1027_gao__port_506298', 0.0008824966, 332, '355'), ('916235064', 'classe_a_1027_gao__port_506339', 0.002471465, 332, '355'), ('916235064', 'ix20_1027_gao__port_506343', 0.0010092822, 332, '355'), ('916235064', 'note_1027_gao__port_506365', 0.0015962754, 332, '355'), ('916235064', 'a5_1027_gao__port_506200', 0.001533037, 332, '355'), ('916235064', 'sx4_1027_gao__port_506348', 0.0014917458, 332, '355'), ('916235064', 'sandero_1027_gao__port_506198', 0.0014586445, 332, '355'), ('916235064', '3008_1027_gao__port_506385', 0.0056458, 332, '355'), ('916235064', 'q50_1027_gao__port_506239', 0.0011165532, 332, '355'), ('916235064', 'latitude_1027_gao__port_506236', 0.00080193515, 332, '355'), ('916235064', 'v40_1027_gao__port_506391', 0.0017146348, 332, '355'), ('916235064', 'xsara_1027_gao__port_506087', 0.000982233, 332, '355'), ('916235064', 'grand_c_max_1027_gao__port_506342', 0.0017958145, 332, '355'), ('916235064', 'swift_1027_gao__port_506149', 0.0015019681, 332, '355'), ('916235064', 'serie_1_1027_gao__port_506184', 0.0015139561, 332, '355'), ('916235064', 'xc70_1027_gao__port_506393', 0.00361946, 332, '355'), ('916235064', 'master_1027_gao__port_506203', 0.007957677, 332, '355'), ('916235064', 'clio_1027_gao__port_506280', 0.0029576162, 332, '355'), ('916235064', 'duster_1027_gao__port_506216', 0.0007444019, 332, '355'), ('916235064', 'traveller_1027_gao__port_506403', 0.004294281, 332, '355'), ('916235064', 'tipo_1027_gao__port_506355', 0.0010929897, 332, '355'), ('916235064', 'rav_4_1027_gao__port_506332', 0.0013603792, 332, '355'), ('916235064', 'coccinelle_1027_gao__port_506259', 0.0034949046, 332, '355'), ('916235064', 'spacetourer_1027_gao__port_506401', 0.0030974299, 332, '355'), ('916235064', 'xe_1027_gao__port_506357', 0.0014470303, 332, '355'), ('916235064', 'ds3_1027_gao__port_506324', 0.001309373, 332, '355'), ('916235064', 'mx_5_1027_gao__port_506098', 0.002588819, 332, '355'), ('916235064', 'land_cruiser_1027_gao__port_506315', 0.009531677, 332, '355'), ('916235064', 'classe_b_1027_gao__port_506335', 0.0017215749, 332, '355'), ('916235064', '806_1027_gao__port_506088', 0.0025616165, 332, '355'), ('916235064', 'rx_8_1027_gao__port_506046', 0.0036220248, 332, '355'), ('916235064', 'spark_1027_gao__port_506185', 0.0010076998, 332, '355'), ('916235064', '6_1027_gao__port_506171', 0.0011182382, 332, '355'), ('916235064', 'bravo_1027_gao__port_506080', 0.0014649852, 332, '355'), ('916235064', 'nx_1027_gao__port_506345', 0.0013682871, 332, '355'), ('916235064', 'sharan_1027_gao__port_506347', 0.0050928122, 332, '355'), ('916235064', 'x_type_1027_gao__port_506067', 0.0007802882, 332, '355'), ('916235064', 'jimny_1027_gao__port_506233', 0.0046063676, 332, '355'), ('916235064', 'wrangler_1027_gao__port_506225', 0.0017996939, 332, '355'), ('916235064', 'c_crosser_1027_gao__port_506312', 0.0015927608, 332, '355'), ('916235064', 'v70_1027_gao__port_506278', 0.0019676122, 332, '355'), ('916235064', 'classe_e_1027_gao__port_506300', 0.0017369051, 332, '355'), ('916235064', 'classe_v_1027_gao__port_506258', 0.012730968, 332, '355'), ('916235064', 'm3_1027_gao__port_506182', 0.0023371682, 332, '355'), ('916235064', 'abarth_500_1027_gao__port_506226', 0.004043627, 332, '355'), ('916235064', 'serie_6_1027_gao__port_506262', 0.0011314746, 332, '355'), ('916235064', 'modus_1027_gao__port_506146', 0.0018293725, 332, '355'), ('916235064', '3_1027_gao__port_506113', 0.0015082044, 332, '355'), ('916235064', '405_1027_gao__port_506108', 0.00805465, 332, '355'), ('916235064', 'allroad_1027_gao__port_506297', 0.0010597688, 332, '355'), ('916235064', 'auris_1027_gao__port_506322', 0.0011524364, 332, '355'), ('916235064', 'galaxy_1027_gao__port_506143', 0.0025148091, 332, '355'), ('916235064', 'giulietta_1027_gao__port_506363', 0.00086511625, 332, '355'), ('916235064', '106_1027_gao__port_506073', 0.008269703, 332, '355'), ('916235064', 'classe_m_1027_gao__port_506154', 0.0030020438, 332, '355'), ('916235064', 'espace_1027_gao__port_506313', 0.001064669, 332, '355'), ('916235064', 'panda_1027_gao__port_506189', 0.009030152, 332, '355'), ('916235064', 'rcz_1027_gao__port_506197', 0.001129348, 332, '355'), ('916235064', '4007_1027_gao__port_506162', 0.0006793034, 332, '355'), ('916235064', 'classe_cl_1027_gao__port_506249', 0.0010859503, 332, '355'), ('916235064', 'leaf_1027_gao__port_506139', 0.0018038432, 332, '355'), ('916235064', 'octavia_1027_gao__port_506237', 0.0018601122, 332, '355'), ('916235064', 'ds4_1027_gao__port_506336', 0.0024159718, 332, '355'), ('916235064', 'freelander_1027_gao__port_506084', 0.0023473857, 332, '355'), ('916235064', 'evasion_1027_gao__port_506109', 0.0031141117, 332, '355'), ('916235064', 'punto_1027_gao__port_506106', 0.0019495311, 332, '355'), ('916235064', '2cv_1027_gao__port_506045', 0.007973814, 332, '355'), ('916235064', 'x4_1027_gao__port_506392', 0.0017950581, 332, '355'), ('916235064', 'antara_1027_gao__port_506247', 0.0012468314, 332, '355'), ('916235064', 'murano_1027_gao__port_506316', 0.0006088502, 332, '355'), ('916235064', 'alto_1027_gao__port_506201', 0.009231417, 332, '355'), ('916235064', 'meriva_1027_gao__port_506353', 0.0013765121, 332, '355'), ('916235064', 'orlando_1027_gao__port_506305', 0.0018461943, 332, '355'), ('916235064', 'new_beetle_1027_gao__port_506050', 0.0011637141, 332, '355'), ('916235064', '306_1027_gao__port_506145', 0.0035068137, 332, '355'), ('916235064', 'tiguan_1027_gao__port_506362', 0.0026826072, 332, '355'), ('916235064', 's_type_1027_gao__port_506101', 0.0011382176, 332, '355'), ('916235064', 'c1_1027_gao__port_506128', 0.0027514454, 332, '355'), ('916235064', 'vectra_1027_gao__port_506044', 0.0011988629, 332, '355'), ('916235064', 'outlander_1027_gao__port_506317', 0.0017122066, 332, '355'), ('916235064', '307_1027_gao__port_506074', 0.0020010031, 332, '355'), ('916235064', 'a6_s6_1027_gao__port_506134', 0.0016569144, 332, '355'), ('916235064', 'nemo_combi_1027_gao__port_506196', 0.0022663984, 332, '355'), ('916235064', 'berlingo_1027_gao__port_506194', 0.0046630995, 332, '355'), ('916235064', 'partner_1027_gao__port_506285', 0.003941473, 332, '355'), ('916235064', 'cayenne_1027_gao__port_506177', 0.0037981598, 332, '355'), ('916235064', 'quattroporte_1027_gao__port_506240', 0.0024441616, 332, '355'), ('916235064', 'c_max_1027_gao__port_506282', 0.0013124277, 332, '355'), ('916235064', 'fabia_1027_gao__port_506396', 0.0052993386, 332, '355'), ('916235064', 'cx_3_1027_gao__port_506281', 0.0014462394, 332, '355'), ('916235064', 'x_trail_1027_gao__port_506264', 0.001831588, 332, '355'), ('916235064', 'scirocco_1027_gao__port_506276', 0.0047910335, 332, '355'), ('916235064', 'matiz_1027_gao__port_506144', 0.001755949, 332, '355'), ('916235064', 'tigra_1027_gao__port_506069', 0.0008541573, 332, '355'), ('916235064', 'escort_1027_gao__port_506091', 0.004839589, 332, '355'), ('916235064', 'c2_1027_gao__port_506081', 0.0014903682, 332, '355'), ('916235064', 'mini_1027_gao__port_506168', 0.0011921348, 332, '355'), ('916235064', 'i30_1027_gao__port_506291', 0.00063255744, 332, '355'), ('916235064', 'picanto_1027_gao__port_506238', 0.0029747516, 332, '355'), ('916235064', 'mito_1027_gao__port_506072', 0.0015077423, 332, '355'), ('916235064', 'impreza_1027_gao__port_506085', 0.0020190754, 332, '355'), ('916235064', 'kangoo_1027_gao__port_506235', 0.0065848897, 332, '355'), ('916235064', 'a4_1027_gao__port_506193', 0.0019873064, 332, '355'), ('916235064', 'cayman_1027_gao__port_506268', 0.0018140828, 332, '355'), ('916235064', 'sportage_1027_gao__port_506148', 0.0014275897, 332, '355'), ('916235064', 'up_1027_gao__port_506356', 0.0068636513, 332, '355'), ('916235064', 'optima_1027_gao__port_506386', 0.00089179265, 332, '355'), ('916235064', 'defender_1027_gao__port_506229', 0.006721837, 332, '355'), ('916235064', 'serie_2_1027_gao__port_506256', 0.002227493, 332, '355'), ('916235064', 'edge_1027_gao__port_506187', 0.0008747886, 332, '355'), ('916235064', 'r19_1027_gao__port_506110', 0.0049422462, 332, '355'), ('916235064', 'jetta_1027_gao__port_506304', 0.003619592, 332, '355'), ('916235064', 'eos_1027_gao__port_506115', 0.0038920238, 332, '355'), ('916235064', 'accord_1027_gao__port_506214', 0.002012607, 332, '355'), ('916235064', 'yaris_1027_gao__port_506334', 0.0032335655, 332, '355'), ('916235064', 'classe_cls_1027_gao__port_506289', 0.0007851526, 332, '355'), ('916235064', 'polo_1027_gao__port_506361', 0.0043107513, 332, '355'), ('916235064', 'serie_4_1027_gao__port_506366', 0.0011475546, 332, '355'), ('916235064', 'mini_cabriolet_1027_gao__port_506204', 0.00083775906, 332, '355'), ('916235064', 'prius_1027_gao__port_506190', 0.0011488897, 332, '355'), ('916235064', 'lodgy_1027_gao__port_506188', 0.0020173092, 332, '355'), ('916235064', 'serie_7_1027_gao__port_506307', 0.0012477484, 332, '355'), ('916235064', 'c15_1027_gao__port_506055', 0.01771253, 332, '355'), ('916235064', 'kadjar_1027_gao__port_506389', 0.0012505465, 332, '355'), ('916235064', 'insignia_1027_gao__port_506364', 0.0016433944, 332, '355'), ('916235064', '308_1027_gao__port_506279', 0.0021235417, 332, '355'), ('916235064', 'roomster_1027_gao__port_506241', 0.0018014562, 332, '355'), ('916235064', '80_1027_gao__port_506057', 0.004613243, 332, '355'), ('916235064', '309_1027_gao__port_506063', 0.013524463, 332, '355'), ('916235064', 'tucson_1027_gao__port_506320', 0.0021237098, 332, '355'), ('916235064', 'x3_1027_gao__port_506212', 0.0008977101, 332, '355'), ('916235064', 'xf_1027_gao__port_506263', 0.001116576, 332, '355'), ('916235064', '2008_1027_gao__port_506394', 0.0026409847, 332, '355'), ('916235064', 'passat_1027_gao__port_506306', 0.0014975751, 332, '355'), ('916235064', 'compass_1027_gao__port_506260', 0.0032561528, 332, '355'), ('916235064', 'twingo_1027_gao__port_506309', 0.006498971, 332, '355'), ('916235064', 'micra_1027_gao__port_506221', 0.0035862061, 332, '355'), ('916235064', 'golf_1027_gao__port_506155', 0.0031933056, 332, '355'), ('916235064', 'soul_1027_gao__port_506176', 0.0012889866, 332, '355'), ('916235064', 'rapid_1027_gao__port_506398', 0.0025914772, 332, '355'), ('916235064', 'forester_1027_gao__port_506360', 0.0022762162, 332, '355'), ('916235064', 'slk_1027_gao__port_506210', 0.0015844833, 332, '355'), ('916235064', 'forfour_1027_gao__port_506341', 0.002179801, 332, '355'), ('916235064', 'serie_5_1027_gao__port_506209', 0.0013694839, 332, '355'), ('916235064', 'xj_1027_gao__port_506170', 0.0026009392, 332, '355'), ('916235064', 'pajero_1027_gao__port_506097', 0.0052110446, 332, '355'), ('916235064', 'agila_1027_gao__port_506119', 0.004849486, 332, '355'), ('916235064', 'a6_1027_gao__port_506163', 0.0018909838, 332, '355'), ('916235064', 'fox_1027_gao__port_506092', 0.0008465379, 332, '355'), ('916235064', 'boxster_1027_gao__port_506267', 0.0015941741, 332, '355'), ('916235064', 'altea_1027_gao__port_506246', 0.0021390996, 332, '355'), ('916235064', 'samurai_1027_gao__port_506047', 0.0062515694, 332, '355'), ('916235064', 'trax_1027_gao__port_506296', 0.0019439455, 332, '355'), ('916235064', 'getz_1027_gao__port_506058', 0.0016384852, 332, '355'), ('916235064', 'cherokee_1027_gao__port_506269', 0.002980976, 332, '355'), ('916235064', 'koleos_1027_gao__port_506378', 0.0015591203, 332, '355'), ('916235064', 'z_series_1027_gao__port_506123', 0.0016562075, 332, '355'), ('916235064', 'ecosport_1027_gao__port_506271', 0.0013230166, 332, '355'), ('916235064', 'space_star_1027_gao__port_506277', 0.002114385, 332, '355'), ('916235064', 'rs3_sportback_1027_gao__port_506207', 0.0019115944, 332, '355'), ('916235064', 'civic_1027_gao__port_506141', 0.0026897863, 332, '355'), ('916235064', 'talisman_1027_gao__port_506390', 0.0007613807, 332, '355'), ('916235064', 'f_pace_1027_gao__port_506314', 0.0016165625, 332, '355'), ('916235064', 'classe_c_1027_gao__port_506299', 0.0017941078, 332, '355'), ('916235064', 'tt_1027_gao__port_506075', 0.0013935058, 332, '355'), ('916235064', 'pathfinder_1027_gao__port_506183', 0.0016514608, 332, '355'), ('916235064', '156_1027_gao__port_506157', 0.0015444324, 332, '355'), ('916235064', 'cx_5_1027_gao__port_506228', 0.001441379, 332, '355'), ('916235064', 'scenic_1027_gao__port_506255', 0.001608517, 332, '355'), ('916235064', 'yeti_1027_gao__port_506358', 0.002091482, 332, '355'), ('916235064', 'mustang_1027_gao__port_506053', 0.010050034, 332, '355'), ('916235064', 'stilo_1027_gao__port_506060', 0.0010831165, 332, '355'), ('916235064', 'ateca_1027_gao__port_506382', 0.001701098, 332, '355'), ('916235064', 'fiorino_1027_gao__port_506217', 0.0091991555, 332, '355'), ('916235064', 'classe_glk_1027_gao__port_506290', 0.0017016568, 332, '355'), ('916235064', 'fortwo_1027_gao__port_506230', 0.0016010382, 332, '355'), ('916235064', 'cruze_1027_gao__port_506186', 0.0010052888, 332, '355'), ('916235064', '107_1027_gao__port_506213', 0.0016276019, 332, '355'), ('916235064', 'aygo_1027_gao__port_506248', 0.0032433325, 332, '355'), ('916235064', 'rx_1027_gao__port_506354', 0.0010632505, 332, '355'), ('916235064', '500_1027_gao__port_506245', 0.0016354987, 332, '355'), ('916235064', 'bora_1027_gao__port_506104', 0.003816519, 332, '355'), ('916235064', 'transit_1027_gao__port_506111', 0.0048620636, 332, '355'), ('916235064', 'pt_cruiser_1027_gao__port_506054', 0.0019165624, 332, '355'), ('916235064', 'patrol_1027_gao__port_506068', 0.004239815, 332, '355'), ('916235064', 'r8_1027_gao__port_506156', 0.0012722027, 332, '355'), ('916235064', 'xm_1027_gao__port_506102', 0.0022676457, 332, '355'), ('916235064', 's60_1027_gao__port_506191', 0.0031990109, 332, '355'), ('916235064', 'aveo_1027_gao__port_506158', 0.00383995, 332, '355'), ('916235064', 'captiva_1027_gao__port_506159', 0.0017189627, 332, '355'), ('916235064', 'ax_1027_gao__port_506153', 0.006895359, 332, '355'), ('916235064', 'rexton_1027_gao__port_506107', 0.001302058, 332, '355'), ('916235064', 'camaro_1027_gao__port_506056', 0.0024901887, 332, '355'), ('916235064', 'ypsilon_1027_gao__port_506131', 0.0019543373, 332, '355'), ('916235064', 'delta_1027_gao__port_506165', 0.0014002112, 332, '355'), ('916235064', 'c4_1027_gao__port_506370', 0.0013008604, 332, '355'), ('916235064', 'zx_1027_gao__port_506161', 0.004593518, 332, '355'), ('916235064', 'verso_1027_gao__port_506242', 0.00077213196, 332, '355'), ('916235064', 'superb_1027_gao__port_506327', 0.0019941404, 332, '355'), ('916235064', 'r5_1027_gao__port_506253', 0.0095456755, 332, '355'), ('916235064', 'caddy_1027_gao__port_506330', 0.013827021, 332, '355'), ('916235064', 'x5_1027_gao__port_506243', 0.0011203794, 332, '355'), ('916235064', 'f_type_1027_gao__port_506231', 0.0008299294, 332, '355'), ('916235064', 'fusion_1027_gao__port_506096', 0.0012669672, 332, '355'), ('916235064', 'dokker_1027_gao__port_506331', 0.0053577786, 332, '355'), ('916235064', '205_1027_gao__port_506062', 0.006684574, 332, '355'), ('916235064', 'macan_1027_gao__port_506195', 0.0015573638, 332, '355'), ('916235064', 'tourneo_1027_gao__port_506369', 0.0064015426, 332, '355'), ('916235064', '108_1027_gao__port_506384', 0.005264302, 332, '355'), ('916235064', '9_3_1027_gao__port_506071', 0.00083740166, 332, '355'), ('916235064', 'mondeo_1027_gao__port_506116', 0.001439583, 332, '355'), ('916235064', 'cr_v_1027_gao__port_506164', 0.0016413805, 332, '355'), ('916235064', 'c30_1027_gao__port_506090', 0.0017484962, 332, '355'), ('916235064', 'pulsar_1027_gao__port_506397', 0.0012018774, 332, '355'), ('916235064', 'ibiza_1027_gao__port_506273', 0.003723191, 332, '355'), ('916235064', 'a1_1027_gao__port_506338', 0.0012346507, 332, '355'), ('916235064', 'matrix_1027_gao__port_506140', 0.0007077144, 332, '355'), ('916235064', 'carnival_1027_gao__port_506136', 0.0022813834, 332, '355'), ('916235064', 'xantia_1027_gao__port_506086', 0.002195957, 332, '355'), ('916235064', 'terrano_1027_gao__port_506083', 0.0020294236, 332, '355'), ('916235064', 'q3_1027_gao__port_506275', 0.0011264707, 332, '355'), ('916235064', 'hr_v_1027_gao__port_506283', 0.0017805458, 332, '355'), ('916235064', 'expert_1027_gao__port_506142', 0.0073694247, 332, '355'), ('916235064', 'multivan_1027_gao__port_506383', 0.0065045957, 332, '355'), ('916235064', 'venga_1027_gao__port_506380', 0.00080038985, 332, '355'), ('916235064', 'scudo_1027_gao__port_506129', 0.0055925874, 332, '355'), ('916235064', 'laguna_1027_gao__port_506368', 0.00071346643, 332, '355'), ('916235064', 'vel_satis_1027_gao__port_506130', 0.0027262995, 332, '355'), ('916235064', 'b_max_1027_gao__port_506367', 0.0017246644, 332, '355'), ('916235064', 'ignis_1027_gao__port_506292', 0.0043562045, 332, '355'), ('916235064', '159_1027_gao__port_506064', 0.0010782719, 332, '355'), ('916235064', 'grande_punto_1027_gao__port_506138', 0.0023633686, 332, '355'), ('916235064', 'logan_1027_gao__port_506167', 0.004397062, 332, '355'), ('916235064', 's_max_1027_gao__port_506223', 0.0012527143, 332, '355'), ('916235064', 'caravelle_1027_gao__port_506351', 0.0030295239, 332, '355'), ('916235064', 'adam_1027_gao__port_506079', 0.0010538445, 332, '355'), ('916235064', '406_1027_gao__port_506132', 0.0013573177, 332, '355'), ('916235064', 'q30_1027_gao__port_506293', 0.000971569, 332, '355'), ('916235064', 'almera_1027_gao__port_506089', 0.001023948, 332, '355'), ('916235064', 'corsa_1027_gao__port_506095', 0.0025203691, 332, '355'), ('916235064', 'corolla_1027_gao__port_506120', 0.0026821145, 332, '355'), ('916235064', 'xc60_1027_gao__port_506388', 0.0018986552, 332, '355'), ('916235064', 'viano_1027_gao__port_506211', 0.002694277, 332, '355'), ('916235064', 'pro_cee_d_1027_gao__port_506274', 0.00083199603, 332, '355'), ('916235064', 'a3_1027_gao__port_506321', 0.003738007, 332, '355'), ('916235064', 'v50_1027_gao__port_506150', 0.0007919398, 332, '355'), ('916235064', 'voyager_1027_gao__port_506169', 0.0030527418, 332, '355'), ('916235064', 'corvette_1027_gao__port_506049', 0.0037230046, 332, '355'), ('916235064', 'rio_1027_gao__port_506379', 0.0017739855, 332, '355'), ('916235064', 'jazz_1027_gao__port_506252', 0.0015305382, 332, '355'), ('916235064', '200_1027_gao__port_506112', 0.0040870695, 332, '355'), ('916235064', 'tts_1027_gao__port_506199', 0.0011862123, 332, '355'), ('916235064', 'zafira_1027_gao__port_506287', 0.002695364, 332, '355'), ('916235064', 'asx_1027_gao__port_506266', 0.0011407186, 332, '355'), ('916235064', '607_1027_gao__port_506118', 0.0012528675, 332, '355'), ('916235064', '207_1027_gao__port_506103', 0.0015147522, 332, '355'), ('916235064', 'classe_s_1027_gao__port_506301', 0.0031655135, 332, '355'), ('916235064', 'c6_1027_gao__port_506105', 0.0017348051, 332, '355'), ('916235064', 'express_1027_gao__port_506137', 0.016726553, 332, '355'), ('916235064', 'classe_gla_1027_gao__port_506352', 0.0018255885, 332, '355'), ('916235064', 'v60_1027_gao__port_506333', 0.0021459695, 332, '355'), ('916235064', 'ka_1027_gao__port_506180', 0.0014151839, 332, '355'), ('916235064', 'range_rover_1027_gao__port_506254', 0.0020551905, 332, '355'), ('916235064', 'discovery_1027_gao__port_506375', 0.0022965914, 332, '355'), ('916235064', 'classe_r_1027_gao__port_506270', 0.0013943345, 332, '355'), ('916235064', 'transporter_1027_gao__port_506319', 0.011969407, 332, '355'), ('916235064', 'cee_d_1027_gao__port_506288', 0.0010547932, 332, '355'), ('916235064', 'zoe_1027_gao__port_506244', 0.0020714067, 332, '355'), ('916235064', 'i20_1027_gao__port_506284', 0.001786936, 332, '355'), ('916235064', 'gtv_1027_gao__port_506059', 0.0057224543, 332, '355'), ('916235064', 's4_avant_1027_gao__port_506261', 0.0027663556, 332, '355'), ('916235064', 'x1_1027_gao__port_506372', 0.0017145163, 332, '355'), ('916235064', 'autres_1027_gao__port_506127', 0.0048251925, 332, '355'), ('916235064', '208_1027_gao__port_506359', 0.0018686975, 332, '355'), ('916235064', 'c8_1027_gao__port_506135', 0.001257964, 332, '355'), ('916235064', 'astra_1027_gao__port_506215', 0.0012625798, 332, '355'), ('916235064', '2_1027_gao__port_506151', 0.00092448515, 332, '355'), ('916235064', 'doblo_1027_gao__port_506251', 0.007466826, 332, '355'), ('916235064', '807_1027_gao__port_506152', 0.00072900235, 332, '355'), ('916235064', '206_1027_gao__port_506126', 0.00103854, 332, '355'), ('916235064', 'a7_1027_gao__port_506373', 0.0006911185, 332, '355'), ('916235064', 'renegade_1027_gao__port_506346', 0.0021418543, 332, '355')]]} begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 0 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 1.5497207641601562e-05 save missing photos in datou_result : time spend for datou_step_exec : 6.959770202636719 time spend to save output : 1.743344783782959 total time spend for step 1 : 8.703114986419678 step2:argmax Tue May 6 22:36:48 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746563799_2124625_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064} map_photo_id_path_extension : {916235064: {'path': 'temp/1746563799_2124625_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 355 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : True verbose : True photo_id : 916235064 output[photo_id] : [('916235064', 'c15_1027_gao__port_506055', 0.01771253, 332, '355'), 'temp/1746563799_2124625_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) first line : ('916235064', '2049863950', '332') ... last line : ('916235064', '2049863950', '332') time used for this insertion : 0.013123512268066406 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 0.022318601608276367 len list_finale : 1, len picture : 1 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('2', None, '916235064', 'c15_1027_gao__port_506055', None, None, '2049863950', '0.01771253', None)] time used for this insertion : 0.017156124114990234 saving photo_ids in datou_result photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [] time used for this insertion : 2.86102294921875e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.0004627704620361328 time spend to save output : 0.05294680595397949 total time spend for step 2 : 0.053409576416015625 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'916235064': [('916235064', 'c15_1027_gao__port_506055', 0.01771253, 332, '355'), 'temp/1746563799_2124625_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg']} ############################### TEST tfhub2 ################################ TEST TFHUB2 ######################## test with use_multi_inputs=0 ######################## Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4567 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4567 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4567 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4567 # 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) 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 : step 12835 tfhub_classification2 is not linked in the step_by_step architecture ! WARNING : step 12836 argmax is not linked in the step_by_step architecture ! 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 ! DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1171252784,1171252764,1171252487) Found this number of photos: 3 ##### Call download_photos : nb_thread : 5 begin to download photo : 1171252487 begin to download photo : 1171252764 begin to download photo : 1171252784 download finish for photo 1171252487 download finish for photo 1171252764 download finish for photo 1171252784 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 ##### After load_data_input time to download the photos : 0.18742775917053223 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 2 step1:tfhub_classification2 Tue May 6 22:36:48 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746563808_2124625_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg': 1171252487, 'temp/1746563808_2124625_1171252764_29d5179a892cc50aadc9d67245534b59.jpg': 1171252764, 'temp/1746563808_2124625_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg': 1171252784} map_photo_id_path_extension : {1171252487: {'path': 'temp/1746563808_2124625_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg', 'extension': 'jpg'}, 1171252764: {'path': 'temp/1746563808_2124625_1171252764_29d5179a892cc50aadc9d67245534b59.jpg', 'extension': 'jpg'}, 1171252784: {'path': 'temp/1746563808_2124625_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step TFHub with tf2 ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'3609': 1} we are using the classfication for only one thcl 3609 begin to check gpu status inside check gpu memory inside check gpu memory inside check gpu memory inside check gpu memory inside check gpu memory inside check gpu memory 2025-05-06 22:36:57.738752: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-05-06 22:36:57.739421: 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 2025-05-06 22:36:57.739511: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-06 22:36:57.739562: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-06 22:36:57.748267: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-06 22:36:57.748527: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-06 22:36:57.753351: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-06 22:36:57.755290: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-06 22:36:57.762259: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-06 22:36:57.763353: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-06 22:36:57.763801: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-05-06 22:36:57.795273: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-05-06 22:36:57.797604: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fcc4c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-05-06 22:36:57.797690: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-05-06 22:36:57.801630: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x187ac1e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-05-06 22:36:57.801687: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-05-06 22:36:57.802765: 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 2025-05-06 22:36:57.802964: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-06 22:36:57.802999: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-06 22:36:57.803099: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-06 22:36:57.803137: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-06 22:36:57.803186: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-06 22:36:57.803240: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-06 22:36:57.803293: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-06 22:36:57.804629: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-06 22:36:57.804715: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-06 22:36:57.804774: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-06 22:36:57.804791: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-06 22:36:57.804804: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-06 22:36:57.806237: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3096 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) l 3637 free memory gpu now : 2717 max_wait_temp : 6 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3609 To do loadFromThcl(), then load ParamDescType : thcl3609 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (3609) thcls : [{'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'}] thcl {'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'} Update svm_hashtag_type_desc : 5832 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (5832) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5832, 'tfhub_19_06_2023', 1280, 1280, 'tfhub_19_06_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 6, 19, 12, 55, 22), datetime.datetime(2023, 6, 19, 12, 55, 22)) model_name : tfhub_19_06_2023 model_param file didn't exist model_name : tfhub_19_06_2023 model_type : tf_classification2 list file need : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file exist in s3 : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file manque in s3 : [] 2025-05-06 22:37:06.718809: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.02G (3246391296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-06 22:37:06.719674: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.72G (2921752064 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/../../tools/../lib/rpn/proposal_layer.py:28: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. layer_params = yaml.load(self.param_str_) local folder : /data/models_weight/tfhub_19_06_2023 /data/models_weight/tfhub_19_06_2023/Confusion_Matrix.png size_local : 57753 size in s3 : 57753 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15 Confusion_Matrix.png already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_jrm.jpg size_local : 79724 size in s3 : 79724 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_jrm.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pcm.jpg size_local : 83556 size in s3 : 83556 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15 Precision_Recall_pcm.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pcnc.jpg size_local : 74107 size in s3 : 74107 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_pcnc.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pehd.jpg size_local : 72705 size in s3 : 72705 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_pehd.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_tapis_vide.jpg size_local : 70874 size in s3 : 70874 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:15 Precision_Recall_tapis_vide.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Result_Summary.txt size_local : 642 size in s3 : 642 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 Result_Summary.txt already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/checkpoint size_local : 99 size in s3 : 99 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 checkpoint already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00000-of-00002 size_local : 216488 size in s3 : 216488 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00001-of-00002 size_local : 32279708 size in s3 : 32279708 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:21 model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.index size_local : 43546 size in s3 : 43546 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:22 model_checkpoint.ckpt.index already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_weights.h5 size_local : 16499144 size in s3 : 16499144 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:15 model_weights.h5 already exist and didn't need to update desc size : 1280 Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= module (KerasLayer) (None, 1280) 4049564 _________________________________________________________________ tfhub_19_06_2023dense (Dense (None, 5) 6405 ================================================================= Total params: 4,055,969 Trainable params: 6,405 Non-trainable params: 4,049,564 _________________________________________________________________ Loading Weights... time used to create the model : 10.003188848495483 time used to load_weights : 0.1322002410888672 0it [00:00, ?it/s] 3it [00:00, 894.82it/s]2025-05-06 22:37:11.406646: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-06 22:37:11.702663: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR 2025-05-06 22:37:11.721396: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR temp/1746563808_2124625_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg temp/1746563808_2124625_1171252764_29d5179a892cc50aadc9d67245534b59.jpg temp/1746563808_2124625_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg Found 3 images belonging to 1 classes. begin to do the prediction : ERROR in datou_step_exec, will save and exit ! Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[{{node model/module/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/stem_conv2d/StatefulPartitionedCall/Conv2D}}]] [Op:__inference_predict_function_34620] Function call stack: predict_function File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2329, in datou_exec output = datou_step_exec(sNext, args, cache, context, map_info, verbose, mtr_user_id) File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2523, in datou_step_exec return lib_process.datou_step_tfhub2(param, json_param, args, cache, context, map_info, verbose) File "/home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py", line 3147, in datou_step_tfhub2 classes, outputs, features = this_model.predict_image_paths(list_paths, keep_aspect_ratio=keep_aspect_ratio, File "/home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py", line 288, in predict_image_paths Y_pred, F_pred = self.model.predict(valid_generator, validation_steps) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py", line 88, in _method_wrapper return method(self, *args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py", line 1268, in predict tmp_batch_outputs = predict_function(iterator) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py", line 580, in __call__ result = self._call(*args, **kwds) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py", line 650, in _call return self._concrete_stateful_fn._filtered_call(canon_args, canon_kwds) # pylint: disable=protected-access File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py", line 1661, in _filtered_call return self._call_flat( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py", line 1745, in _call_flat return self._build_call_outputs(self._inference_function.call( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py", line 593, in call outputs = execute.execute( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py", line 59, in quick_execute tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, [1171252487, 1171252764, 1171252784] map_info['map_portfolio_photo'] : {} final : True mtd_id 4567 list_pids : [1171252487, 1171252764, 1171252784] begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4567', None, '1171252487', "[>, , , , , ' Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.\\n\\t [[{{node model/module/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/stem_conv2d/StatefulPartitionedCall/Conv2D}}]] [Op:__inference_predict_function_34620]\\n\\nFunction call stack:\\npredict_function\\n']", '-1', '-1.0', '501120777', '1.0', None), ('4567', None, '1171252764', "[>, , , , , ' Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.\\n\\t [[{{node model/module/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/stem_conv2d/StatefulPartitionedCall/Conv2D}}]] [Op:__inference_predict_function_34620]\\n\\nFunction call stack:\\npredict_function\\n']", '-1', '-1.0', '501120777', '1.0', None), ('4567', None, '1171252784', "[>, , , , , ' Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.\\n\\t [[{{node model/module/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/stem_conv2d/StatefulPartitionedCall/Conv2D}}]] [Op:__inference_predict_function_34620]\\n\\nFunction call stack:\\npredict_function\\n']", '-1', '-1.0', '501120777', '1.0', None)] time used for this insertion : 0.016404151916503906 save_final ERROR in last step tfhub_classification2, Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[{{node model/module/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/stem_conv2d/StatefulPartitionedCall/Conv2D}}]] [Op:__inference_predict_function_34620] Function call stack: predict_function time spend for datou_step_exec : 22.851542949676514 time spend to save output : 0.019985675811767578 total time spend for step 0 : 22.87152862548828 need to delete datou_research and reload, so keep current state 1 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : None probably due to empty image bug ERROR expected : {'1171252784': [(1171252784, 'jrm', 0.9677492, 4674, '3609'), 'temp/1687511175_1882837_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'], '1171252764': [(1171252764, 'jrm', 0.9853587, 4674, '3609'), 'temp/1687511175_1882837_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'], '1171252487': [(1171252487, 'jrm', 0.9262757, 4674, '3609'), 'temp/1687511175_1882837_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg']} got : None ######################## test with use_multi_inputs=1 ######################## Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4621 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4621 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4621 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4621 # 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) 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 : step 12927 tfhub_classification2 is not linked in the step_by_step architecture ! WARNING : step 12928 argmax is not linked in the step_by_step architecture ! 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 ! DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1171291875,1171275372,1171275314) Found this number of photos: 3 ##### Call download_photos : nb_thread : 5 begin to download photo : 1171275314 begin to download photo : 1171275372 begin to download photo : 1171291875 download finish for photo 1171291875 download finish for photo 1171275372 download finish for photo 1171275314 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 ##### After load_data_input time to download the photos : 0.18320703506469727 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 2 step1:tfhub_classification2 Tue May 6 22:37:11 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746563831_2124625_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg': 1171291875, 'temp/1746563831_2124625_1171275372_76d81364ff7df843bff095f45c07ba35.jpg': 1171275372, 'temp/1746563831_2124625_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg': 1171275314} map_photo_id_path_extension : {1171291875: {'path': 'temp/1746563831_2124625_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'extension': 'jpg'}, 1171275372: {'path': 'temp/1746563831_2124625_1171275372_76d81364ff7df843bff095f45c07ba35.jpg', 'extension': 'jpg'}, 1171275314: {'path': 'temp/1746563831_2124625_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step TFHub with tf2 ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'3655': 1} we are using the classfication for only one thcl 3655 begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 7 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 7 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 7 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 7 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 9 wait 20 seconds inside check gpu memory l 3637 free memory gpu now : 3786 max_wait_temp : 6 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3655 To do loadFromThcl(), then load ParamDescType : thcl3655 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (3655) thcls : [{'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'}] thcl {'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'} Update svm_hashtag_type_desc : 5862 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (5862) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5862, 'tfhub_18_7_2023', 1280, 1280, 'tfhub_18_7_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 7, 18, 22, 46, 29), datetime.datetime(2023, 7, 18, 22, 46, 29)) model_name : tfhub_18_7_2023 model_param file didn't exist model_name : tfhub_18_7_2023 model_type : tf_classification2 list file need : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file exist in s3 : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file manque in s3 : [] local folder : /data/models_weight/tfhub_18_7_2023 /data/models_weight/tfhub_18_7_2023/Confusion_Matrix.png size_local : 54360 size in s3 : 54360 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:28 Confusion_Matrix.png already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_jrm.jpg size_local : 72583 size in s3 : 72583 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_jrm.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pcm.jpg size_local : 81681 size in s3 : 81681 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:17 Precision_Recall_pcm.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pcnc.jpg size_local : 79510 size in s3 : 79510 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_pcnc.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pehd.jpg size_local : 59936 size in s3 : 59936 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_pehd.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_tapis_vide.jpg size_local : 78974 size in s3 : 78974 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17 Precision_Recall_tapis_vide.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Result_Summary.txt size_local : 642 size in s3 : 642 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 Result_Summary.txt already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/checkpoint size_local : 99 size in s3 : 99 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 checkpoint already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00000-of-00002 size_local : 216529 size in s3 : 216529 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17 model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00001-of-00002 size_local : 32279748 size in s3 : 32279748 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19 model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.index size_local : 43546 size in s3 : 43546 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19 model_checkpoint.ckpt.index already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_weights.h5 size_local : 16500868 size in s3 : 16500868 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:18 model_weights.h5 already exist and didn't need to update desc size : 1280 Model: "model_1" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_2 (InputLayer) [(None, 224, 224, 3) 0 __________________________________________________________________________________________________ input_3 (InputLayer) [(None, 1)] 0 __________________________________________________________________________________________________ module (KerasLayer) (None, 1280) 4049564 input_2[0][0] __________________________________________________________________________________________________ concatenate (Concatenate) (None, 1281) 0 input_3[0][0] module[0][0] __________________________________________________________________________________________________ tfhub_18_7_2023dense (Dense) (None, 5) 6410 concatenate[0][0] ================================================================================================== Total params: 4,055,974 Trainable params: 0 Non-trainable params: 4,055,974 __________________________________________________________________________________________________ Loading Weights... time used to create the model : 8.88857102394104 time used to load_weights : 0.13086295127868652 found 3 data found 0 labels begin to do the prediction : time used to do the prediction : 3.4859793186187744 ['temp/1746563831_2124625_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'temp/1746563831_2124625_1171275372_76d81364ff7df843bff095f45c07ba35.jpg', 'temp/1746563831_2124625_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'] (3,) (3, 5) (3, 1280) shape of features : (3, 1280) shape of new features : (1, 3, 1280) save descriptor for thcl : 3655 (3, 1280) Got the blobs of the net to insert : [0, 1, 0, 0, 11, 0, 2, 2, 0, 0] code_as_byte_string:b'000100000b'| Got the blobs of the net to insert : [0, 0, 0, 0, 14, 0, 1, 4, 0, 0] code_as_byte_string:b'000000000e'| Got the blobs of the net to insert : [0, 0, 0, 0, 8, 0, 0, 0, 3, 0] code_as_byte_string:b'0000000008'| time to traite the descriptors : 0.043540239334106445 Testing : ['1171291875', '1171275372', '1171275314'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (1171291875,1171275372,1171275314) result : {1171275314: {'photo_id': 1171275314, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/23/6e0a72c8fa00d5e4b018bd689b547133.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_23_54_22_6187.jpg'}, 1171275372: {'photo_id': 1171275372, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/23/76d81364ff7df843bff095f45c07ba35.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_23_56_46_6098.jpg'}, 1171291875: {'photo_id': 1171291875, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/23/b62cd9e0d976b143f86fe82d072798c0.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_23_59_04_5803.jpg'}} list_photo_exists : [1171275314, 1171275372, 1171291875] storage_type for insertDescriptorsMulti : 3 To insert : 1171291875 To insert : 1171275372 To insert : 1171275314 time to insert the descriptors : 1.0552732944488525 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : False verbose : True saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171291875, 1171275372, 1171275314] map_info['map_portfolio_photo'] : {} final : False mtd_id 4621 list_pids : [1171291875, 1171275372, 1171275314] Looping around the photos to save general results len do output : 3 /1171291875Didn't retrieve data . /1171275372Didn't retrieve data . /1171275314Didn'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 ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171291875', None, None, None, None, None, None) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275372', None, None, None, None, None, None) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275314', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4621', None, '1171291875', 'None', None, None, None, None, None), ('4621', None, '1171275372', 'None', None, None, None, None, None), ('4621', None, '1171275314', 'None', None, None, None, None, None)] time used for this insertion : 0.016013145446777344 save_final save missing photos in datou_result : time spend for datou_step_exec : 124.61628699302673 time spend to save output : 0.016407489776611328 total time spend for step 1 : 124.63269448280334 step2:argmax Tue May 6 22:39:16 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746563831_2124625_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg': 1171291875, 'temp/1746563831_2124625_1171275372_76d81364ff7df843bff095f45c07ba35.jpg': 1171275372, 'temp/1746563831_2124625_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg': 1171275314} map_photo_id_path_extension : {1171291875: {'path': 'temp/1746563831_2124625_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'extension': 'jpg'}, 1171275372: {'path': 'temp/1746563831_2124625_1171275372_76d81364ff7df843bff095f45c07ba35.jpg', 'extension': 'jpg'}, 1171275314: {'path': 'temp/1746563831_2124625_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 3655 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : True verbose : True photo_id : 1171291875 output[photo_id] : [(1171291875, 'tapis_vide', 0.9706112, 4723, '3655'), 'temp/1746563831_2124625_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'] photo_id : 1171275372 output[photo_id] : [(1171275372, 'tapis_vide', 0.96743727, 4723, '3655'), 'temp/1746563831_2124625_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'] photo_id : 1171275314 output[photo_id] : [(1171275314, 'tapis_vide', 0.9651563, 4723, '3655'), 'temp/1746563831_2124625_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) first line : ('1171291875', '2107748999', '4723') ... last line : ('1171275314', '2107748999', '4723') time used for this insertion : 0.020966291427612305 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 3 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 0.018592357635498047 len list_finale : 3, len picture : 3 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4621', None, '1171291875', 'tapis_vide', None, None, '2107748999', '0.9706112', None), ('4621', None, '1171275372', 'tapis_vide', None, None, '2107748999', '0.96743727', None), ('4621', None, '1171275314', 'tapis_vide', None, None, '2107748999', '0.9651563', None)] time used for this insertion : 0.015080690383911133 saving photo_ids in datou_result photo id not in port photo id not in port photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [] time used for this insertion : 4.76837158203125e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.0002243518829345703 time spend to save output : 0.05942392349243164 total time spend for step 2 : 0.05964827537536621 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171291875': [(1171291875, 'tapis_vide', 0.9706112, 4723, '3655'), 'temp/1746563831_2124625_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'], '1171275372': [(1171275372, 'tapis_vide', 0.96743727, 4723, '3655'), 'temp/1746563831_2124625_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'], '1171275314': [(1171275314, 'tapis_vide', 0.9651563, 4723, '3655'), 'temp/1746563831_2124625_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg']} --------------------- test with use_multi_inputs=1 is succeded ------------------- ERROR tfhub2 FAILED ############################### TEST ordonner ################################ To do loadFromThcl(), then load ParamDescType : thcl358 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (358) thcls : [{'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3392 ['FirstUploadExperveo_vignette__port_505674', 'CAR_EXTERIEUR_Roue__port_503398', 'FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486', 'FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485', 'CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465', 'CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198', 'CAR_EXTERIEUR_Face_avant_axe_droit__port_504451', 'CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235', 'FirstUploadExperveo_vin__port_505675', 'CAR_EXTERIEUR_cote_droite__port_504108', 'CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565', 'FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201', 'cartegrise_orientation__port_505064', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217', 'CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531', 'CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218', 'CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214', 'CAR_EXTERIEUR_Angle_avant_droit__port_504087', 'FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484', 'CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563', 'CAR_EXTERIEUR_Angle_arriere_droit__port_504160', 'CAR_EXTERIEUR_arriere__port_504184', 'CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562', 'INTERIEUR_Compteur_kilometrique__port_503644', 'CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494', 'CAR_EXTERIEUR_Angle_arriere_gauche__port_504170', 'CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226', 'CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202', 'CAR_EXTERIEUR_moteur__port_503704', 'FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487', 'CAR_INTERIEUR_siege_arriere_class_1__port_506551', 'CAR_EXTERIEUR_avant__port_504146', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215', 'CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225', 'CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564', 'FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482', 'CAR_INTERIEUR_coffre__port_503412', 'FirstUploadExperveo_rouetranche__port_505677', 'UploadPhotoImmatBest_class_1__port_505051', 'CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532', 'CAR_EXTERIEUR_angle_avant_gauche__port_504098', 'CAR_EXTERIEUR_face_avant_axe_gauche__port_504236', 'CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540', 'CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233', 'CAR_EXTERIEUR_roue_de_secour__port_503763', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199', 'CAR_EXTERIEUR_cote_gauche__port_504017', 'CAR_INTERIEUR_avant_volant_class_1__port_506503', 'CAR_INTERIEUR_avant_volant_class_2__port_506504', 'CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234'] 51 SELECT hashtag_id,hashtag FROM MTRBack.hashtags where hashtag in ('FirstUploadExperveo_vignette__port_505674','CAR_EXTERIEUR_Roue__port_503398','FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486','FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485','CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465','CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198','CAR_EXTERIEUR_Face_avant_axe_droit__port_504451','CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235','FirstUploadExperveo_vin__port_505675','CAR_EXTERIEUR_cote_droite__port_504108','CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565','FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483','CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201','cartegrise_orientation__port_505064','CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217','CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531','CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218','CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214','CAR_EXTERIEUR_Angle_avant_droit__port_504087','FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484','CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563','CAR_EXTERIEUR_Angle_arriere_droit__port_504160','CAR_EXTERIEUR_arriere__port_504184','CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562','INTERIEUR_Compteur_kilometrique__port_503644','CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494','CAR_EXTERIEUR_Angle_arriere_gauche__port_504170','CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226','CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202','CAR_EXTERIEUR_moteur__port_503704','FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487','CAR_INTERIEUR_siege_arriere_class_1__port_506551','CAR_EXTERIEUR_avant__port_504146','CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215','CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225','CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564','FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482','CAR_INTERIEUR_coffre__port_503412','FirstUploadExperveo_rouetranche__port_505677','UploadPhotoImmatBest_class_1__port_505051','CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532','CAR_EXTERIEUR_angle_avant_gauche__port_504098','CAR_EXTERIEUR_face_avant_axe_gauche__port_504236','CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540','CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233','CAR_EXTERIEUR_roue_de_secour__port_503763','CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199','CAR_EXTERIEUR_cote_gauche__port_504017','CAR_INTERIEUR_avant_volant_class_1__port_506503','CAR_INTERIEUR_avant_volant_class_2__port_506504','CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234'); 51 dict_keys(['cartegrise_orientation__port_505064', 'car_exterieur_angle_arriere_droit_axe_arriere__port_504217', 'car_exterieur_angle_arriere_droit_axe_droit__port_504215', 'car_exterieur_angle_arriere_droit__port_504160', 'car_exterieur_angle_arriere_gauche_axe_arriere__port_504201', 'car_exterieur_angle_arriere_gauche_axe_gauche__port_504199', 'car_exterieur_angle_arriere_gauche__port_504170', 'car_exterieur_angle_avant_droit_axe_arriere__port_504226', 'car_exterieur_angle_avant_droit_axe_droit__port_504225', 'car_exterieur_angle_avant_droit__port_504087', 'car_exterieur_angle_avant_gauche_axe_avant__port_504235', 'car_exterieur_angle_avant_gauche_axe_gauche__port_504234', 'car_exterieur_angle_avant_gauche__port_504098', 'car_exterieur_arriere__port_504184', 'car_exterieur_avant__port_504146', 'car_exterieur_cote_droite__port_504108', 'car_exterieur_cote_droit_axe_arriere__port_504214', 'car_exterieur_cote_droit_axe_avant__port_504465', 'car_exterieur_cote_gauche_axe_arriere__port_504198', 'car_exterieur_cote_gauche_axe_avant__port_504233', 'car_exterieur_cote_gauche__port_504017', 'car_exterieur_face_arriere_axe_droit__port_504218', 'car_exterieur_face_arriere_axe_gauche__port_504202', 'car_exterieur_face_avant_axe_droit__port_504451', 'car_exterieur_face_avant_axe_gauche__port_504236', 'car_exterieur_moteur__port_503704', 'car_exterieur_roue_de_secour__port_503763', 'car_exterieur_roue__port_503398', 'car_interieur_avant_volant_class_1__port_506503', 'car_interieur_avant_volant_class_2__port_506504', 'car_interieur_avant_volant_class_6_boutonrond__port_506562', 'car_interieur_avant_volant_class_6_class_2__port_506563', 'car_interieur_avant_volant_class_6_ecrangrosplan__port_506564', 'car_interieur_avant_volant_class_6_levierdevitesse__port_506565', 'car_interieur_avant_vue-arriere_class_1__port_506531', 'car_interieur_avant_vue-arriere_class_2__port_506532', 'car_interieur_avant_vue_droite_habitacle_class_1__port_506540', 'car_interieur_avant_vue_gauche_habitacle_class_1__port_506494', 'car_interieur_coffre__port_503412', 'car_interieur_siege_arriere_class_1__port_506551', 'firstuploadexperveo_carrosseriegrosplan_carrosserie__port_506483', 'firstuploadexperveo_carrosseriegrosplan_class_6__port_506487', 'firstuploadexperveo_carrosseriegrosplan_morceauderoue__port_506484', 'firstuploadexperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482', 'firstuploadexperveo_carrosseriegrosplan_siegegrosplan__port_506485', 'firstuploadexperveo_carrosseriegrosplan_vindanslamoquette__port_506486', 'firstuploadexperveo_rouetranche__port_505677', 'firstuploadexperveo_vignette__port_505674', 'firstuploadexperveo_vin__port_505675', 'interieur_compteur_kilometrique__port_503644', 'uploadphotoimmatbest_class_1__port_505051']) select photo_hashtag_type from MTRDatou.classification_theme where id = 358 thcl : 358 photo_hashtag_type : 337 SELECT phi.hashtag_id , phi.photo_id FROM MTRBack.photo_hashtag_ids phi, MTRUser.mtr_portfolio_photos mp where phi.type = 337 and phi.photo_id = mp.mtr_photo_id and mp.mtr_portfolio_id =510365; {510365: [(917973295, 1), (917973297, 1), (917973302, 1), (917973293, 7), (917973296, 11), (917973300, 11), (917973286, 13), (917973289, 13), (917973301, 24), (917973285, 29), (917973290, 29), (917973299, 29), (917973304, 35), (917973287, 36), (917973298, 36), (917973305, 36), (917973292, 37), (917973291, 41), (917973303, 41), (917973294, 42), (917973288, 46)]} ############################### TEST rotate ################################ test rotate only Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=230 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=230 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 230 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=230 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : rotate list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (917849322) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 917849322 download finish for photo 917849322 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.13556933403015137 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:rotate Tue May 6 22:39:20 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746563960_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1746563960_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step_rotate ! We are in a linear step without datou_depend ! rotate photos of 90,180,270 degres batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 917849322) and `type` in (0) Loaded 0 chid ids of type : 0 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in () map_chi : {} photo_id in download_rotate_and_save : 917849322 list_chi_loc : 0 Use all angle ! Rotation of photo 917849322 of 90 degree temp/1746563960_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 90 remove_crop_border : False version de PIL : 9.5.0 Needs to change image size ! [[ 6.123234e-17 1.000000e+00] [-1.000000e+00 6.123234e-17]] 90 [[ 6.123234e-17 1.000000e+00] [-1.000000e+00 6.123234e-17]] shrink_image : False image_rotate : image_path : temp/1746563960_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1746563960_2124625_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg image_rotate.mode : RGB Rotation of photo 917849322 of 180 degree temp/1746563960_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 180 remove_crop_border : False version de PIL : 9.5.0 Needs to change image size ! [[-1.0000000e+00 1.2246468e-16] [-1.2246468e-16 -1.0000000e+00]] 180 [[-1.0000000e+00 1.2246468e-16] [-1.2246468e-16 -1.0000000e+00]] shrink_image : False image_rotate : image_path : temp/1746563960_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1746563960_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg image_rotate.mode : RGB Rotation of photo 917849322 of 270 degree temp/1746563960_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 270 remove_crop_border : False version de PIL : 9.5.0 Needs to change image size ! [[-1.8369702e-16 -1.0000000e+00] [ 1.0000000e+00 -1.8369702e-16]] 270 [[-1.8369702e-16 -1.0000000e+00] [ 1.0000000e+00 -1.8369702e-16]] shrink_image : False image_rotate : image_path : temp/1746563960_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1746563960_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg image_rotate.mode : RGB About to upload 3 photos upload in portfolio : 551782 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1746563961_2124625 we have uploaded 3 photos in the portfolio 551782 time of upload the photos Elapsed time : 0.9864373207092285 map_filename_photo_id : 3 map_filename_photo_id : {'temp/1746563960_2124625_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg': 1356481336, 'temp/1746563960_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg': 1356481337, 'temp/1746563960_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg': 1356481338} Len new_chis : 3 Len list_new_chi_with_photo_id : 0 of type : 0 list_new_chi_with_photo_id : [] After datou_step_exec type output : time spend for datou_step_exec : 1.2166571617126465 time spend to save output : 7.724761962890625e-05 total time spend for step 1 : 1.2167344093322754 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [917849322] map_info['map_portfolio_photo'] : {} final : True mtd_id 230 list_pids : [917849322] Looping around the photos to save general results len do output : 3 /1356481336Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481337Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481338Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('230', None, None, None, None, None, None, None, None) ('230', None, '917849322', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 10 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('230', None, '1356481336', 'None', None, None, None, None, None), ('230', None, '1356481337', 'None', None, None, None, None, None), ('230', None, '1356481338', 'None', None, None, None, None, None), ('230', None, '917849322', None, None, None, None, None, None)] time used for this insertion : 0.014579296112060547 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1356481336: ['917849322', 'temp/1746563960_2124625_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', []], 1356481337: ['917849322', 'temp/1746563960_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', []], 1356481338: ['917849322', 'temp/1746563960_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg', []]} test rotate only is a success ! test rotate conditionnel Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=233 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=233 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 233 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=233 # 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 ! 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 ! 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 ! DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : thcl, argmax, rotate list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (917849322) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 917849322 download finish for photo 917849322 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.19862985610961914 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 3 step1:thcl Tue May 6 22:39:22 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746563961_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1746563961_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Thcl ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'500': 1} we are using the classfication for only one thcl 500 In convert_file_to_np l 337 : 1 l343 1 l357 after caffe.io.load_image dimension du image : (3, (2448, 3264, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.00023555755615234375 time to convert the images to numpy array : 1.4464776515960693 total time to convert the images to numpy array : 1.4470326900482178 list photo_ids error: [] list photo_ids correct : [917849322] number of photos to traite : 1 try to delete the photos incorrect in DB tagging for thcl : 500 To do loadFromThcl(), then load ParamDescType : thcl500 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (500) thcls : [{'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'}] thcl {'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'} Update svm_hashtag_type_desc : 3517 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (3517) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34)) To loadFromThcl() : net_3517 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3472 max_wait_temp : 1 max_wait : 0 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (3517) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34)) param : , param.caffemodel : orientation_carte_grise_all_2 None mean_file_type : mean_file_path : prototxt_file_path : model : orientation_carte_grise_all_2 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : orientation_carte_grise_all_2 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : [] local folder : /data/models_weight/orientation_carte_grise_all_2 /data/models_weight/orientation_carte_grise_all_2/caffemodel size_local : 537110520 size in s3 : 537110520 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:17 caffemodel already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy_conv_normal.prototxt size_local : 4626 size in s3 : 4626 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy_conv_normal.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy_fc.prototxt size_local : 1130 size in s3 : 1130 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy_fc.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy.prototxt size_local : 5653 size in s3 : 5653 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:31 mean.npy already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/synset_words.txt size_local : 159 size in s3 : 159 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /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/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/orientation_carte_grise_all_2/deploy.prototxt caffemodel_filename : /data/models_weight/orientation_carte_grise_all_2/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3472 max_wait_temp : 1 max_wait : 0 dict_keys(['pool5', 'prob']) time used to do the prepocess of the images : 4.7228288650512695 time used to do the prediction : 0.21794366836547852 save descriptor for thcl : 500 (1, 512, 7, 7) Got the blobs of the net to insert : [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] code_as_byte_string:b'0000000000'| time to traite the descriptors : 0.051880598068237305 Testing : ['917849322'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (917849322) result : {917849322: {'photo_id': 917849322, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/9/13/2bd260e91e91df8378dde8bb8b8c4548.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_13092022_12_32_19_5566.jpg'}} list_photo_exists : [917849322] storage_type for insertDescriptorsMulti : 1 To insert : 917849322 time to insert the descriptors : 0.6271798610687256 After datou_step_exec type output : time spend for datou_step_exec : 12.97187614440918 time spend to save output : 5.841255187988281e-05 total time spend for step 1 : 12.97193455696106 step2:argmax Tue May 6 22:39:34 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : {'917849322': [[('917849322', 'carteGrisesVerticales__port_549774', 0.9976465, 507, '500'), ('917849322', 'cartegrise_90deg__port_550987', 0.0005042867, 507, '500'), ('917849322', 'cartesGrisesEnvers__port_549765', 0.0003664411, 507, '500'), ('917849322', 'portfolio_270deg__port_550988', 0.0014827707, 507, '500')]]} input_args_next_step : {'917849322': ()} output_args : {'917849322': [[('917849322', 'carteGrisesVerticales__port_549774', 0.9976465, 507, '500'), ('917849322', 'cartegrise_90deg__port_550987', 0.0005042867, 507, '500'), ('917849322', 'cartesGrisesEnvers__port_549765', 0.0003664411, 507, '500'), ('917849322', 'portfolio_270deg__port_550988', 0.0014827707, 507, '500')]]} args : 917849322 depend.output_id : 0 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :1, first value : ([('917849322', 'carteGrisesVerticales__port_549774', 0.9976465, 507, '500'), ('917849322', 'cartegrise_90deg__port_550987', 0.0005042867, 507, '500'), ('917849322', 'cartesGrisesEnvers__port_549765', 0.0003664411, 507, '500'), ('917849322', 'portfolio_270deg__port_550988', 0.0014827707, 507, '500')],) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746563961_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1746563961_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 500 After datou_step_exec type output : time spend for datou_step_exec : 0.0005147457122802734 time spend to save output : 5.650520324707031e-05 total time spend for step 2 : 0.0005712509155273438 step3:rotate Tue May 6 22:39:34 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.9976465, 507, '500'), 'temp/1746563961_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} input_args_next_step : {'917849322': ()} output_args : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.9976465, 507, '500'), 'temp/1746563961_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} args : 917849322 depend.output_id : 1 complete output_args for input 1 : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.9976465, 507, '500'), 'temp/1746563961_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} input_args_next_step : {'917849322': ('temp/1746563961_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg',)} output_args : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.9976465, 507, '500'), 'temp/1746563961_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} args : 917849322 depend.output_id : 0 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :1, first value : ('temp/1746563961_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', ('917849322', 'carteGrisesVerticales__port_549774', 0.9976465, 507, '500')) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746563961_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1746563961_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step_rotate ! We are in a datou with depends ! angle_condi : {'carteGrisesVerticales__port_549774': 0, 'cartegrise_90deg__port_550987': 270, 'portfolio_270deg__port_550988': 90, 'cartesGrisesEnvers__port_549765': 180} rotate photos for hashtag carteGrisesVerticales__port_549774 of 0 degres 1 photos founded : [917849322] batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 917849322) and `type` in (0) Loaded 0 chid ids of type : 0 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in () map_chi : {} photo_id in download_rotate_and_save : 917849322 list_chi_loc : 0 Use all angle ! Rotation of photo 917849322 of 0 degree temp/1746563961_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 0 remove_crop_border : False version de PIL : 9.5.0 Needs to change image size ! [[ 1. 0.] [-0. 1.]] 0 [[ 1. 0.] [-0. 1.]] shrink_image : False image_rotate : image_path : temp/1746563961_2124625_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1746563961_2124625_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg image_rotate.mode : RGB About to upload 1 photos upload in portfolio : 551782 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1746563975_2124625 we have uploaded 1 photos in the portfolio 551782 time of upload the photos Elapsed time : 0.6381566524505615 map_filename_photo_id : 1 map_filename_photo_id : {'temp/1746563961_2124625_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg': 1356481357} Len new_chis : 1 Len list_new_chi_with_photo_id : 0 of type : 0 list_new_chi_with_photo_id : [] rotate photos for hashtag cartegrise_90deg__port_550987 of 270 degres 0 photos founded : [] rotate photos for hashtag portfolio_270deg__port_550988 of 90 degres 0 photos founded : [] rotate photos for hashtag cartesGrisesEnvers__port_549765 of 180 degres 0 photos founded : [] After datou_step_exec type output : time spend for datou_step_exec : 0.7248494625091553 time spend to save output : 6.771087646484375e-05 total time spend for step 3 : 0.7249171733856201 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [917849322] map_info['map_portfolio_photo'] : {} final : True mtd_id 233 list_pids : [917849322] Looping around the photos to save general results len do output : 1 /1356481357Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('233', None, None, None, None, None, None, None, None) ('233', None, '917849322', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('233', None, '1356481357', 'None', None, None, None, None, None), ('233', None, '917849322', None, None, None, None, None, None)] time used for this insertion : 0.015723466873168945 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1356481357: ['917849322', 'temp/1746563961_2124625_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg', []]} ############################### TEST data_augmentation_ellipse_varroa_tile_rotate ################################ SELECT id FROM MTRPhoto.crop_hashtag_ids WHERE photo_id=937852786 AND `type`=520 DELETE FROM MTRPhoto.crop_hashtag_ids WHERE id IN (3786284573,3786284574,3786284575,3786284576) # 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 ! 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 : step 316 crop is not linked in the step_by_step architecture ! Step 318 rotate have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 318 rotate have less outputs used (0) than in the step definition (3) : some outputs may be not used ! 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 ! DataTypes for each output/input checked ! Unexpected type seems boolean for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : DATA AUGMENTATION ELLIPSE VARROA TILE ROTATE Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=243 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=243 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 243 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=243 # 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 ! 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 : step 316 crop is not linked in the step_by_step architecture ! Step 318 rotate have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 318 rotate have less outputs used (0) than in the step definition (3) : some outputs may be not used ! 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 ! DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : crop, tile, rotate list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (937852786) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 937852786 download finish for photo 937852786 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.10099983215332031 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 3 step1:crop Tue May 6 22:39:35 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg': 937852786} map_photo_id_path_extension : {937852786: {'path': 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Crop ! param_json : {'hashtag_id_ellipse': 2087736828, 'photo_hashtag_type_from_ellipse': 520, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'crop_detect_varroa', 'photo_hashtag_type': 407, 'feed_id_new_photos_not_used': 549103, 'host': 'www.fotonower.com', 'margin': 8, 'upload_type': 'python'} margin_type : margin margin_value : [8, 8, 8, 8] Loading chi in step crop with photo_hashtag_type : 407 Loading chi in step crop for list_pids : 1 ! batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 937852786) and `type` in (407) Loaded 4 chid ids of type : 407 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (8165075,8165076,8165077,8165078) +WARNING : Unexpected points, we should remove this data for chi_id : 8165075, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 8165076, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 8165077, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 8165078, for now we just ignore these empty polygon points SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (8165075,8165076,8165077,8165078) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (8165075,8165076,8165077,8165078) select photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type, id from MTRPhoto.photo_sub_photos where photo_id in ( 937852786) WARNING : margin is only used for type bib ! type of cropped photo chosen : we resize croppped photo by 1 on x axis and by 1 on y axis new_file_path_bib_crop : temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg new_file_path_bib_crop : temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg new_file_path_bib_crop : temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg new_file_path_bib_crop : temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg map_result returned by crop_photo_return_map_crop : length : 4 map_result after crop : {8165075: {'crop': 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg', 'photo_id': 937852786, 'sub_photo_id': -1, 'coordonates': (426, 467, 312, 347), 'sub_photo_infos': (418, 475, 304, 355, 1, 1), 'same_chi': False}, 8165076: {'crop': 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg', 'photo_id': 937852786, 'sub_photo_id': -1, 'coordonates': (411, 445, 443, 480), 'sub_photo_infos': (403, 453, 435, 480, 1, 1), 'same_chi': False}, 8165077: {'crop': 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg', 'photo_id': 937852786, 'sub_photo_id': -1, 'coordonates': (103, 138, 358, 396), 'sub_photo_infos': (95, 146, 350, 404, 1, 1), 'same_chi': False}, 8165078: {'crop': 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg', 'photo_id': 937852786, 'sub_photo_id': -1, 'coordonates': (104, 131, 256, 292), 'sub_photo_infos': (96, 139, 248, 300, 1, 1), 'same_chi': False}} Here we crop with rles About to insert : list_path_to_insert length 4 new photo from crops ! About to upload 4 photos https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=crop_detect_varroa&access_token=78d09a0790ec6ecbf119343125a81fdc upload in portfolio : 22735745 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1746563978_2124625 INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `mtr_user_id`, `created_at`) VALUES (22735745, 1356481359, 0, NOW()),(22735745, 1356481360, 0, NOW()),(22735745, 1356481361, 0, NOW()),(22735745, 1356481362, 0, NOW()) 4 we have uploaded 4 photos in the portfolio 22735745 time of upload the photos Elapsed time : 3.0694801807403564 {'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1356481359, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1356481360, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1356481361, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1356481362} list_errors : [] map_result_insert : {'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1356481359, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1356481360, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1356481361, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1356481362} Now we prepare data that will be used for ellipse search ! chi_id found to be used 8165075 path of cropped varroa found to be used to match on an ellipse temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg sub_photo_id found to be used 1356481359 chi_id found to be used 8165076 path of cropped varroa found to be used to match on an ellipse temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg sub_photo_id found to be used 1356481360 chi_id found to be used 8165077 path of cropped varroa found to be used to match on an ellipse temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg sub_photo_id found to be used 1356481361 chi_id found to be used 8165078 path of cropped varroa found to be used to match on an ellipse temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg sub_photo_id found to be used 1356481362 insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(8165075, '1356481359', 31), (8165076, '1356481360', 31), (8165077, '1356481361', 31), (8165078, '1356481362', 31)] map of cropped photos with some data : {'1356481359': ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg', (426, 467, 312, 347)], '1356481360': ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg', (411, 445, 443, 480)], '1356481361': ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg', (103, 138, 358, 396)], '1356481362': ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg', (104, 131, 256, 292)]} About to compute ellipse and record with type : 520 (54, 57) (51, 57) [54, 57] (54, 57) score : 5120 strategy_opt : 5| strategy_opt : [('excentricity', [0.5, 2.0, 0.05]), ('angle', [-90.0, 90.0, 5.0]), ('xc', [14.25, 42.75, 1.78125]), ('yc', [12.75, 38.25, 1.59375]), ('radius', [14.25, 42.75, 1.78125])] {0.5: 18351, 0.55: 16004, 0.6000000000000001: 14127, 0.65: 12575, 0.7000000000000001: 11300, 0.75: 10158, 0.8: 9208, 0.8500000000000001: 8418, 0.9: 7642, 0.9500000000000001: 6925, 1.0: 6364, 1.05: 5843, 1.1: 5353, 1.1500000000000001: 4868, 1.2000000000000002: 4553, 1.25: 4250, 1.3: 3947, 1.35: 3698, 1.4000000000000001: 3438, 1.4500000000000002: 3230, 1.5: 3012, 1.55: 2829, 1.6: 2636, 1.6500000000000001: 2535, 1.7000000000000002: 2460, 1.75: 2388, 1.8: 2372, 1.85: 2354, 1.9000000000000001: 2332, 1.9500000000000002: 2311} [(1.9500000000000002, 2311), (1.9000000000000001, 2332), (1.85, 2354), (1.8, 2372), (1.75, 2388), (1.7000000000000002, 2460), (1.6500000000000001, 2535), (1.6, 2636), (1.55, 2829), (1.5, 3012), (1.4500000000000002, 3230), (1.4000000000000001, 3438), (1.35, 3698), (1.3, 3947), (1.25, 4250), (1.2000000000000002, 4553), (1.1500000000000001, 4868), (1.1, 5353), (1.05, 5843), (1.0, 6364), (0.9500000000000001, 6925), (0.9, 7642), (0.8500000000000001, 8418), (0.8, 9208), (0.75, 10158), (0.7000000000000001, 11300), (0.65, 12575), (0.6000000000000001, 14127), (0.55, 16004), (0.5, 18351)] arg_min reach at : 1.9500000000000002 with value = 2311 | arg_min : 1.9500000000000002 min_score : 2311{-90.0: 3359, -85.0: 3372, -80.0: 3395, -75.0: 3380, -70.0: 3304, -65.0: 3180, -60.0: 2991, -55.0: 2793, -50.0: 2601, -45.0: 2377, -40.0: 2183, -35.0: 2078, -30.0: 1968, -25.0: 1968, -20.0: 2021, -15.0: 2036, -10.0: 2102, -5.0: 2188, 0.0: 2208, 5.0: 2265, 10.0: 2311, 15.0: 2333, 20.0: 2362, 25.0: 2375, 30.0: 2375, 35.0: 2397, 40.0: 2370, 45.0: 2421, 50.0: 2491, 55.0: 2661, 60.0: 2826, 65.0: 2993, 70.0: 3106, 75.0: 3182, 80.0: 3263, 85.0: 3306} [(-30.0, 1968), (-25.0, 1968), (-20.0, 2021), (-15.0, 2036), (-35.0, 2078), (-10.0, 2102), (-40.0, 2183), (-5.0, 2188), (0.0, 2208), (5.0, 2265), (10.0, 2311), (15.0, 2333), (20.0, 2362), (40.0, 2370), (25.0, 2375), (30.0, 2375), (-45.0, 2377), (35.0, 2397), (45.0, 2421), (50.0, 2491), (-50.0, 2601), (55.0, 2661), (-55.0, 2793), (60.0, 2826), (-60.0, 2991), (65.0, 2993), (70.0, 3106), (-65.0, 3180), (75.0, 3182), (80.0, 3263), (-70.0, 3304), (85.0, 3306), (-90.0, 3359), (-85.0, 3372), (-75.0, 3380), (-80.0, 3395)] arg_min reach at : -30.0 with value = 1968 | arg_min : -30.0 min_score : 1968{14.25: 1703, 16.03125: 1654, 17.8125: 1614, 19.59375: 1679, 21.375: 1702, 23.15625: 1712, 24.9375: 1730, 26.71875: 1847, 28.5: 1968, 30.28125: 2177, 32.0625: 2456, 33.84375: 2827, 35.625: 3294, 37.40625: 3723, 39.1875: 4161, 40.96875: 4643} [(17.8125, 1614), (16.03125, 1654), (19.59375, 1679), (21.375, 1702), (14.25, 1703), (23.15625, 1712), (24.9375, 1730), (26.71875, 1847), (28.5, 1968), (30.28125, 2177), (32.0625, 2456), (33.84375, 2827), (35.625, 3294), (37.40625, 3723), (39.1875, 4161), (40.96875, 4643)] arg_min reach at : 17.8125 with value = 1614 | arg_min : 17.8125 min_score : 1614{12.75: 5534, 14.34375: 5352, 15.9375: 5116, 17.53125: 4723, 19.125: 4111, 20.71875: 3506, 22.3125: 2799, 23.90625: 2194, 25.5: 1614, 27.09375: 1339, 28.6875: 1206, 30.28125: 1137, 31.875: 1105, 33.46875: 1339, 35.0625: 1659, 36.65625: 2022} [(31.875, 1105), (30.28125, 1137), (28.6875, 1206), (27.09375, 1339), (33.46875, 1339), (25.5, 1614), (35.0625, 1659), (36.65625, 2022), (23.90625, 2194), (22.3125, 2799), (20.71875, 3506), (19.125, 4111), (17.53125, 4723), (15.9375, 5116), (14.34375, 5352), (12.75, 5534)] arg_min reach at : 31.875 with value = 1105 | arg_min : 31.875 min_score : 1105{14.25: 1942, 16.03125: 1853, 17.8125: 1756, 19.59375: 1651, 21.375: 1533, 23.15625: 1424, 24.9375: 1309, 26.71875: 1199, 28.5: 1105, 30.28125: 1219, 32.0625: 1332, 33.84375: 1547, 35.625: 1881, 37.40625: 2378, 39.1875: 3042, 40.96875: 3824} [(28.5, 1105), (26.71875, 1199), (30.28125, 1219), (24.9375, 1309), (32.0625, 1332), (23.15625, 1424), (21.375, 1533), (33.84375, 1547), (19.59375, 1651), (17.8125, 1756), (16.03125, 1853), (35.625, 1881), (14.25, 1942), (37.40625, 2378), (39.1875, 3042), (40.96875, 3824)] arg_min reach at : 28.5 with value = 1105 | arg_min : 28.5 min_score : 1105{0.5: 17397, 0.55: 15138, 0.6000000000000001: 13352, 0.65: 11859, 0.7000000000000001: 10523, 0.75: 9463, 0.8: 8469, 0.8500000000000001: 7565, 0.9: 6551, 0.9500000000000001: 5592, 1.0: 4856, 1.05: 4291, 1.1: 3654, 1.1500000000000001: 3099, 1.2000000000000002: 2647, 1.25: 2309, 1.3: 1983, 1.35: 1755, 1.4000000000000001: 1594, 1.4500000000000002: 1447, 1.5: 1358, 1.55: 1323, 1.6: 1219, 1.6500000000000001: 1215, 1.7000000000000002: 1201, 1.75: 1131, 1.8: 1134, 1.85: 1142, 1.9000000000000001: 1137, 1.9500000000000002: 1105} [(1.9500000000000002, 1105), (1.75, 1131), (1.8, 1134), (1.9000000000000001, 1137), (1.85, 1142), (1.7000000000000002, 1201), (1.6500000000000001, 1215), (1.6, 1219), (1.55, 1323), (1.5, 1358), (1.4500000000000002, 1447), (1.4000000000000001, 1594), (1.35, 1755), (1.3, 1983), (1.25, 2309), (1.2000000000000002, 2647), (1.1500000000000001, 3099), (1.1, 3654), (1.05, 4291), (1.0, 4856), (0.9500000000000001, 5592), (0.9, 6551), (0.8500000000000001, 7565), (0.8, 8469), (0.75, 9463), (0.7000000000000001, 10523), (0.65, 11859), (0.6000000000000001, 13352), (0.55, 15138), (0.5, 17397)] arg_min reach at : 1.9500000000000002 with value = 1105 arg_min : 1.9500000000000002 min_score : 1105{-90.0: 3514, -85.0: 3396, -80.0: 3238, -75.0: 3017, -70.0: 2764, -65.0: 2549, -60.0: 2339, -55.0: 2105, -50.0: 1912, -45.0: 1701, -40.0: 1467, -35.0: 1266, -30.0: 1105, -25.0: 1110, -20.0: 1114, -15.0: 1111, -10.0: 1110, -5.0: 1121, 0.0: 1120, 5.0: 1118, 10.0: 1109, 15.0: 1109, 20.0: 1099, 25.0: 1088, 30.0: 1108, 35.0: 1355, 40.0: 1649, 45.0: 1925, 50.0: 2257, 55.0: 2542, 60.0: 2845, 65.0: 3137, 70.0: 3370, 75.0: 3500, 80.0: 3578, 85.0: 3540} [(25.0, 1088), (20.0, 1099), (-30.0, 1105), (30.0, 1108), (10.0, 1109), (15.0, 1109), (-25.0, 1110), (-10.0, 1110), (-15.0, 1111), (-20.0, 1114), (5.0, 1118), (0.0, 1120), (-5.0, 1121), (-35.0, 1266), (35.0, 1355), (-40.0, 1467), (40.0, 1649), (-45.0, 1701), (-50.0, 1912), (45.0, 1925), (-55.0, 2105), (50.0, 2257), (-60.0, 2339), (55.0, 2542), (-65.0, 2549), (-70.0, 2764), (60.0, 2845), (-75.0, 3017), (65.0, 3137), (-80.0, 3238), (70.0, 3370), (-85.0, 3396), (75.0, 3500), (-90.0, 3514), (85.0, 3540), (80.0, 3578)] arg_min reach at : 25.0 with value = 1088 arg_min : 25.0 min_score : 1088{14.25: 1174, 16.03125: 1133, 17.8125: 1088, 19.59375: 1051, 21.375: 1026, 23.15625: 997, 24.9375: 979, 26.71875: 1139, 28.5: 1344, 30.28125: 1564, 32.0625: 1949, 33.84375: 2422, 35.625: 2930, 37.40625: 3453, 39.1875: 4006, 40.96875: 4592} [(24.9375, 979), (23.15625, 997), (21.375, 1026), (19.59375, 1051), (17.8125, 1088), (16.03125, 1133), (26.71875, 1139), (14.25, 1174), (28.5, 1344), (30.28125, 1564), (32.0625, 1949), (33.84375, 2422), (35.625, 2930), (37.40625, 3453), (39.1875, 4006), (40.96875, 4592)] arg_min reach at : 24.9375 with value = 979 arg_min : 24.9375 min_score : 979{12.75: 5539, 14.34375: 5239, 15.9375: 4911, 17.53125: 4425, 19.125: 3749, 20.71875: 3181, 22.3125: 2575, 23.90625: 2124, 25.5: 1808, 27.09375: 1478, 28.6875: 1290, 30.28125: 1104, 31.875: 979, 33.46875: 1070, 35.0625: 1368, 36.65625: 1872} [(31.875, 979), (33.46875, 1070), (30.28125, 1104), (28.6875, 1290), (35.0625, 1368), (27.09375, 1478), (25.5, 1808), (36.65625, 1872), (23.90625, 2124), (22.3125, 2575), (20.71875, 3181), (19.125, 3749), (17.53125, 4425), (15.9375, 4911), (14.34375, 5239), (12.75, 5539)] arg_min reach at : 31.875 with value = 979 arg_min : 31.875 min_score : 979{14.25: 1939, 16.03125: 1852, 17.8125: 1755, 19.59375: 1643, 21.375: 1531, 23.15625: 1397, 24.9375: 1264, 26.71875: 1119, 28.5: 979, 30.28125: 1023, 32.0625: 1319, 33.84375: 1771, 35.625: 2432, 37.40625: 3324, 39.1875: 4299, 40.96875: 5405} [(28.5, 979), (30.28125, 1023), (26.71875, 1119), (24.9375, 1264), (32.0625, 1319), (23.15625, 1397), (21.375, 1531), (19.59375, 1643), (17.8125, 1755), (33.84375, 1771), (16.03125, 1852), (14.25, 1939), (35.625, 2432), (37.40625, 3324), (39.1875, 4299), (40.96875, 5405)] arg_min reach at : 28.5 with value = 979 arg_min : 28.5 min_score : 979 yc : 31.875 xc : 24.9375 angle : 25.0 radius : 28.5 excentricity : 1.9500000000000002 yc : 31.875 xc : 24.9375 angle : 25.0 radius : 28.5 excentricity : 1.9500000000000002 x0 : 426 y1 : 347 width : 41, height : 35, area : 1435, score : 1.0 x0 : 432 y1 : 355 width : 35, height : 52, area : 1820, score : 1.0 Now saving polygons points : 1| batch 1 Loaded 1 chid ids of type : 520 CHI and polygons saved ! (47, 50) (45, 50) [47, 50] (47, 50) score : 5362 strategy_opt : 5| strategy_opt : [('excentricity', [0.5, 2.0, 0.05]), ('angle', [-90.0, 90.0, 5.0]), ('xc', [12.5, 37.5, 1.5625]), ('yc', [11.25, 33.75, 1.40625]), ('radius', [12.5, 37.5, 1.5625])] {0.5: 15776, 0.55: 13889, 0.6000000000000001: 12446, 0.65: 11147, 0.7000000000000001: 10181, 0.75: 9228, 0.8: 8441, 0.8500000000000001: 7765, 0.9: 7258, 0.9500000000000001: 6684, 1.0: 6195, 1.05: 5920, 1.1: 5487, 1.1500000000000001: 5099, 1.2000000000000002: 4795, 1.25: 4511, 1.3: 4245, 1.35: 4042, 1.4000000000000001: 3828, 1.4500000000000002: 3629, 1.5: 3467, 1.55: 3235, 1.6: 3126, 1.6500000000000001: 2946, 1.7000000000000002: 2826, 1.75: 2700, 1.8: 2549, 1.85: 2472, 1.9000000000000001: 2381, 1.9500000000000002: 2281} [(1.9500000000000002, 2281), (1.9000000000000001, 2381), (1.85, 2472), (1.8, 2549), (1.75, 2700), (1.7000000000000002, 2826), (1.6500000000000001, 2946), (1.6, 3126), (1.55, 3235), (1.5, 3467), (1.4500000000000002, 3629), (1.4000000000000001, 3828), (1.35, 4042), (1.3, 4245), (1.25, 4511), (1.2000000000000002, 4795), (1.1500000000000001, 5099), (1.1, 5487), (1.05, 5920), (1.0, 6195), (0.9500000000000001, 6684), (0.9, 7258), (0.8500000000000001, 7765), (0.8, 8441), (0.75, 9228), (0.7000000000000001, 10181), (0.65, 11147), (0.6000000000000001, 12446), (0.55, 13889), (0.5, 15776)] arg_min reach at : 1.9500000000000002 with value = 2281 | arg_min : 1.9500000000000002 min_score : 2281{-90.0: 3295, -85.0: 3240, -80.0: 3290, -75.0: 3269, -70.0: 3287, -65.0: 3223, -60.0: 3095, -55.0: 3043, -50.0: 2985, -45.0: 2906, -40.0: 2796, -35.0: 2724, -30.0: 2625, -25.0: 2489, -20.0: 2330, -15.0: 2220, -10.0: 2127, -5.0: 2127, 0.0: 2143, 5.0: 2182, 10.0: 2281, 15.0: 2385, 20.0: 2506, 25.0: 2665, 30.0: 2768, 35.0: 2900, 40.0: 2939, 45.0: 2994, 50.0: 3007, 55.0: 3076, 60.0: 3095, 65.0: 3201, 70.0: 3243, 75.0: 3247, 80.0: 3279, 85.0: 3240} [(-10.0, 2127), (-5.0, 2127), (0.0, 2143), (5.0, 2182), (-15.0, 2220), (10.0, 2281), (-20.0, 2330), (15.0, 2385), (-25.0, 2489), (20.0, 2506), (-30.0, 2625), (25.0, 2665), (-35.0, 2724), (30.0, 2768), (-40.0, 2796), (35.0, 2900), (-45.0, 2906), (40.0, 2939), (-50.0, 2985), (45.0, 2994), (50.0, 3007), (-55.0, 3043), (55.0, 3076), (-60.0, 3095), (60.0, 3095), (65.0, 3201), (-65.0, 3223), (-85.0, 3240), (85.0, 3240), (70.0, 3243), (75.0, 3247), (-75.0, 3269), (80.0, 3279), (-70.0, 3287), (-80.0, 3290), (-90.0, 3295)] arg_min reach at : -10.0 with value = 2127 | arg_min : -10.0 min_score : 2127{12.5: 2171, 14.0625: 2288, 15.625: 2314, 17.1875: 2360, 18.75: 2347, 20.3125: 2300, 21.875: 2249, 23.4375: 2188, 25.0: 2127, 26.5625: 2163, 28.125: 2315, 29.6875: 2476, 31.25: 2756, 32.8125: 2992, 34.375: 3319, 35.9375: 3672} [(25.0, 2127), (26.5625, 2163), (12.5, 2171), (23.4375, 2188), (21.875, 2249), (14.0625, 2288), (20.3125, 2300), (15.625, 2314), (28.125, 2315), (18.75, 2347), (17.1875, 2360), (29.6875, 2476), (31.25, 2756), (32.8125, 2992), (34.375, 3319), (35.9375, 3672)] arg_min reach at : 25.0 with value = 2127 | arg_min : 25.0 min_score : 2127{11.25: 6815, 12.65625: 6505, 14.0625: 5928, 15.46875: 5326, 16.875: 4772, 18.28125: 4095, 19.6875: 3413, 21.09375: 2728, 22.5: 2127, 23.90625: 1694, 25.3125: 1345, 26.71875: 1048, 28.125: 823, 29.53125: 728, 30.9375: 714, 32.34375: 1027} [(30.9375, 714), (29.53125, 728), (28.125, 823), (32.34375, 1027), (26.71875, 1048), (25.3125, 1345), (23.90625, 1694), (22.5, 2127), (21.09375, 2728), (19.6875, 3413), (18.28125, 4095), (16.875, 4772), (15.46875, 5326), (14.0625, 5928), (12.65625, 6505), (11.25, 6815)] arg_min reach at : 30.9375 with value = 714 | arg_min : 30.9375 min_score : 714{12.5: 1275, 14.0625: 1207, 15.625: 1132, 17.1875: 1049, 18.75: 966, 20.3125: 866, 21.875: 784, 23.4375: 729, 25.0: 714, 26.5625: 976, 28.125: 1546, 29.6875: 2147, 31.25: 2981, 32.8125: 3767, 34.375: 4761, 35.9375: 5732} [(25.0, 714), (23.4375, 729), (21.875, 784), (20.3125, 866), (18.75, 966), (26.5625, 976), (17.1875, 1049), (15.625, 1132), (14.0625, 1207), (12.5, 1275), (28.125, 1546), (29.6875, 2147), (31.25, 2981), (32.8125, 3767), (34.375, 4761), (35.9375, 5732)] arg_min reach at : 25.0 with value = 714 | arg_min : 25.0 min_score : 714{0.5: 20107, 0.55: 18155, 0.6000000000000001: 16503, 0.65: 15058, 0.7000000000000001: 13673, 0.75: 12474, 0.8: 11100, 0.8500000000000001: 9667, 0.9: 8374, 0.9500000000000001: 7344, 1.0: 6274, 1.05: 5570, 1.1: 4846, 1.1500000000000001: 4170, 1.2000000000000002: 3623, 1.25: 3211, 1.3: 2832, 1.35: 2475, 1.4000000000000001: 2182, 1.4500000000000002: 1949, 1.5: 1655, 1.55: 1513, 1.6: 1333, 1.6500000000000001: 1182, 1.7000000000000002: 1038, 1.75: 921, 1.8: 841, 1.85: 732, 1.9000000000000001: 733, 1.9500000000000002: 714} [(1.9500000000000002, 714), (1.85, 732), (1.9000000000000001, 733), (1.8, 841), (1.75, 921), (1.7000000000000002, 1038), (1.6500000000000001, 1182), (1.6, 1333), (1.55, 1513), (1.5, 1655), (1.4500000000000002, 1949), (1.4000000000000001, 2182), (1.35, 2475), (1.3, 2832), (1.25, 3211), (1.2000000000000002, 3623), (1.1500000000000001, 4170), (1.1, 4846), (1.05, 5570), (1.0, 6274), (0.9500000000000001, 7344), (0.9, 8374), (0.8500000000000001, 9667), (0.8, 11100), (0.75, 12474), (0.7000000000000001, 13673), (0.65, 15058), (0.6000000000000001, 16503), (0.55, 18155), (0.5, 20107)] arg_min reach at : 1.9500000000000002 with value = 714 arg_min : 1.9500000000000002 min_score : 714{-90.0: 2866, -85.0: 3006, -80.0: 3063, -75.0: 3168, -70.0: 3193, -65.0: 3193, -60.0: 3149, -55.0: 3022, -50.0: 2885, -45.0: 2656, -40.0: 2413, -35.0: 2119, -30.0: 1819, -25.0: 1500, -20.0: 1171, -15.0: 865, -10.0: 714, -5.0: 668, 0.0: 689, 5.0: 734, 10.0: 824, 15.0: 898, 20.0: 1116, 25.0: 1368, 30.0: 1610, 35.0: 1866, 40.0: 2105, 45.0: 2304, 50.0: 2467, 55.0: 2604, 60.0: 2731, 65.0: 2753, 70.0: 2753, 75.0: 2761, 80.0: 2722, 85.0: 2786} [(-5.0, 668), (0.0, 689), (-10.0, 714), (5.0, 734), (10.0, 824), (-15.0, 865), (15.0, 898), (20.0, 1116), (-20.0, 1171), (25.0, 1368), (-25.0, 1500), (30.0, 1610), (-30.0, 1819), (35.0, 1866), (40.0, 2105), (-35.0, 2119), (45.0, 2304), (-40.0, 2413), (50.0, 2467), (55.0, 2604), (-45.0, 2656), (80.0, 2722), (60.0, 2731), (65.0, 2753), (70.0, 2753), (75.0, 2761), (85.0, 2786), (-90.0, 2866), (-50.0, 2885), (-85.0, 3006), (-55.0, 3022), (-80.0, 3063), (-60.0, 3149), (-75.0, 3168), (-70.0, 3193), (-65.0, 3193)] arg_min reach at : -5.0 with value = 668 arg_min : -5.0 min_score : 668{12.5: 1164, 14.0625: 1082, 15.625: 1019, 17.1875: 933, 18.75: 866, 20.3125: 765, 21.875: 713, 23.4375: 655, 25.0: 668, 26.5625: 845, 28.125: 1107, 29.6875: 1374, 31.25: 1691, 32.8125: 2036, 34.375: 2375, 35.9375: 2731} [(23.4375, 655), (25.0, 668), (21.875, 713), (20.3125, 765), (26.5625, 845), (18.75, 866), (17.1875, 933), (15.625, 1019), (14.0625, 1082), (28.125, 1107), (12.5, 1164), (29.6875, 1374), (31.25, 1691), (32.8125, 2036), (34.375, 2375), (35.9375, 2731)] arg_min reach at : 23.4375 with value = 655 arg_min : 23.4375 min_score : 655{11.25: 7209, 12.65625: 6826, 14.0625: 6195, 15.46875: 5638, 16.875: 4941, 18.28125: 4177, 19.6875: 3484, 21.09375: 2750, 22.5: 2183, 23.90625: 1709, 25.3125: 1327, 26.71875: 1008, 28.125: 779, 29.53125: 631, 30.9375: 655, 32.34375: 920} [(29.53125, 631), (30.9375, 655), (28.125, 779), (32.34375, 920), (26.71875, 1008), (25.3125, 1327), (23.90625, 1709), (22.5, 2183), (21.09375, 2750), (19.6875, 3484), (18.28125, 4177), (16.875, 4941), (15.46875, 5638), (14.0625, 6195), (12.65625, 6826), (11.25, 7209)] arg_min reach at : 29.53125 with value = 631 arg_min : 29.53125 min_score : 631{12.5: 1282, 14.0625: 1211, 15.625: 1130, 17.1875: 1050, 18.75: 960, 20.3125: 871, 21.875: 772, 23.4375: 672, 25.0: 631, 26.5625: 666, 28.125: 947, 29.6875: 1472, 31.25: 2184, 32.8125: 3032, 34.375: 3932, 35.9375: 5005} [(25.0, 631), (26.5625, 666), (23.4375, 672), (21.875, 772), (20.3125, 871), (28.125, 947), (18.75, 960), (17.1875, 1050), (15.625, 1130), (14.0625, 1211), (12.5, 1282), (29.6875, 1472), (31.25, 2184), (32.8125, 3032), (34.375, 3932), (35.9375, 5005)] arg_min reach at : 25.0 with value = 631 arg_min : 25.0 min_score : 631 yc : 29.53125 xc : 23.4375 angle : -5.0 radius : 25.0 excentricity : 1.9500000000000002 yc : 29.53125 xc : 23.4375 angle : -5.0 radius : 25.0 excentricity : 1.9500000000000002 x0 : 411 y1 : 480 width : 34, height : 37, area : 1258, score : 1.0 x0 : 419 y1 : 483 width : 26, height : 49, area : 1274, score : 1.0 Now saving polygons points : 1| batch 1 Loaded 2 chid ids of type : 520 + CHI and polygons saved ! (55, 54) (54, 51) [55, 54] (55, 54) score : 4603 strategy_opt : 5| strategy_opt : [('excentricity', [0.5, 2.0, 0.05]), ('angle', [-90.0, 90.0, 5.0]), ('xc', [12.75, 38.25, 1.59375]), ('yc', [13.5, 40.5, 1.6875]), ('radius', [13.5, 40.5, 1.6875])] {0.5: 14831, 0.55: 12756, 0.6000000000000001: 10989, 0.65: 9469, 0.7000000000000001: 8158, 0.75: 7251, 0.8: 6254, 0.8500000000000001: 5559, 0.9: 5119, 0.9500000000000001: 4564, 1.0: 4072, 1.05: 3783, 1.1: 3496, 1.1500000000000001: 3342, 1.2000000000000002: 3228, 1.25: 3161, 1.3: 3127, 1.35: 3087, 1.4000000000000001: 3047, 1.4500000000000002: 3050, 1.5: 2990, 1.55: 2992, 1.6: 2992, 1.6500000000000001: 2985, 1.7000000000000002: 2988, 1.75: 2999, 1.8: 2994, 1.85: 2981, 1.9000000000000001: 3002, 1.9500000000000002: 2995} [(1.85, 2981), (1.6500000000000001, 2985), (1.7000000000000002, 2988), (1.5, 2990), (1.55, 2992), (1.6, 2992), (1.8, 2994), (1.9500000000000002, 2995), (1.75, 2999), (1.9000000000000001, 3002), (1.4000000000000001, 3047), (1.4500000000000002, 3050), (1.35, 3087), (1.3, 3127), (1.25, 3161), (1.2000000000000002, 3228), (1.1500000000000001, 3342), (1.1, 3496), (1.05, 3783), (1.0, 4072), (0.9500000000000001, 4564), (0.9, 5119), (0.8500000000000001, 5559), (0.8, 6254), (0.75, 7251), (0.7000000000000001, 8158), (0.65, 9469), (0.6000000000000001, 10989), (0.55, 12756), (0.5, 14831)] arg_min reach at : 1.85 with value = 2981 | arg_min : 1.85 min_score : 2981{-90.0: 1442, -85.0: 1411, -80.0: 1411, -75.0: 1423, -70.0: 1409, -65.0: 1428, -60.0: 1417, -55.0: 1385, -50.0: 1356, -45.0: 1374, -40.0: 1401, -35.0: 1501, -30.0: 1635, -25.0: 1772, -20.0: 1988, -15.0: 2183, -10.0: 2376, -5.0: 2552, 0.0: 2691, 5.0: 2860, 10.0: 2981, 15.0: 3063, 20.0: 3044, 25.0: 2938, 30.0: 2889, 35.0: 2865, 40.0: 2787, 45.0: 2694, 50.0: 2566, 55.0: 2452, 60.0: 2275, 65.0: 2055, 70.0: 1849, 75.0: 1731, 80.0: 1532, 85.0: 1466} [(-50.0, 1356), (-45.0, 1374), (-55.0, 1385), (-40.0, 1401), (-70.0, 1409), (-85.0, 1411), (-80.0, 1411), (-60.0, 1417), (-75.0, 1423), (-65.0, 1428), (-90.0, 1442), (85.0, 1466), (-35.0, 1501), (80.0, 1532), (-30.0, 1635), (75.0, 1731), (-25.0, 1772), (70.0, 1849), (-20.0, 1988), (65.0, 2055), (-15.0, 2183), (60.0, 2275), (-10.0, 2376), (55.0, 2452), (-5.0, 2552), (50.0, 2566), (0.0, 2691), (45.0, 2694), (40.0, 2787), (5.0, 2860), (35.0, 2865), (30.0, 2889), (25.0, 2938), (10.0, 2981), (20.0, 3044), (15.0, 3063)] arg_min reach at : -50.0 with value = 1356 | arg_min : -50.0 min_score : 1356{12.75: 3483, 14.34375: 3154, 15.9375: 2818, 17.53125: 2499, 19.125: 2212, 20.71875: 1992, 22.3125: 1750, 23.90625: 1554, 25.5: 1356, 27.09375: 1213, 28.6875: 1123, 30.28125: 1079, 31.875: 1312, 33.46875: 1586, 35.0625: 1971, 36.65625: 2464} [(30.28125, 1079), (28.6875, 1123), (27.09375, 1213), (31.875, 1312), (25.5, 1356), (23.90625, 1554), (33.46875, 1586), (22.3125, 1750), (35.0625, 1971), (20.71875, 1992), (19.125, 2212), (36.65625, 2464), (17.53125, 2499), (15.9375, 2818), (14.34375, 3154), (12.75, 3483)] arg_min reach at : 30.28125 with value = 1079 | arg_min : 30.28125 min_score : 1079{13.5: 1299, 15.1875: 1190, 16.875: 1129, 18.5625: 1073, 20.25: 1041, 21.9375: 1025, 23.625: 995, 25.3125: 1002, 27.0: 1079, 28.6875: 1280, 30.375: 1506, 32.0625: 1818, 33.75: 2218, 35.4375: 2606, 37.125: 3013, 38.8125: 3545} [(23.625, 995), (25.3125, 1002), (21.9375, 1025), (20.25, 1041), (18.5625, 1073), (27.0, 1079), (16.875, 1129), (15.1875, 1190), (28.6875, 1280), (13.5, 1299), (30.375, 1506), (32.0625, 1818), (33.75, 2218), (35.4375, 2606), (37.125, 3013), (38.8125, 3545)] arg_min reach at : 23.625 with value = 995 | arg_min : 23.625 min_score : 995{13.5: 1907, 15.1875: 1827, 16.875: 1734, 18.5625: 1635, 20.25: 1520, 21.9375: 1400, 23.625: 1268, 25.3125: 1139, 27.0: 995, 28.6875: 1081, 30.375: 1376, 32.0625: 1837, 33.75: 2365, 35.4375: 3034, 37.125: 3644, 38.8125: 4482} [(27.0, 995), (28.6875, 1081), (25.3125, 1139), (23.625, 1268), (30.375, 1376), (21.9375, 1400), (20.25, 1520), (18.5625, 1635), (16.875, 1734), (15.1875, 1827), (32.0625, 1837), (13.5, 1907), (33.75, 2365), (35.4375, 3034), (37.125, 3644), (38.8125, 4482)] arg_min reach at : 27.0 with value = 995 | arg_min : 27.0 min_score : 995{0.5: 16358, 0.55: 14291, 0.6000000000000001: 12544, 0.65: 11117, 0.7000000000000001: 9800, 0.75: 8586, 0.8: 7533, 0.8500000000000001: 6488, 0.9: 5599, 0.9500000000000001: 4779, 1.0: 4075, 1.05: 3520, 1.1: 3021, 1.1500000000000001: 2582, 1.2000000000000002: 2150, 1.25: 1820, 1.3: 1534, 1.35: 1347, 1.4000000000000001: 1219, 1.4500000000000002: 1130, 1.5: 1073, 1.55: 1037, 1.6: 998, 1.6500000000000001: 961, 1.7000000000000002: 981, 1.75: 984, 1.8: 995, 1.85: 995, 1.9000000000000001: 1023, 1.9500000000000002: 1058} [(1.6500000000000001, 961), (1.7000000000000002, 981), (1.75, 984), (1.8, 995), (1.85, 995), (1.6, 998), (1.9000000000000001, 1023), (1.55, 1037), (1.9500000000000002, 1058), (1.5, 1073), (1.4500000000000002, 1130), (1.4000000000000001, 1219), (1.35, 1347), (1.3, 1534), (1.25, 1820), (1.2000000000000002, 2150), (1.1500000000000001, 2582), (1.1, 3021), (1.05, 3520), (1.0, 4075), (0.9500000000000001, 4779), (0.9, 5599), (0.8500000000000001, 6488), (0.8, 7533), (0.75, 8586), (0.7000000000000001, 9800), (0.65, 11117), (0.6000000000000001, 12544), (0.55, 14291), (0.5, 16358)] arg_min reach at : 1.6500000000000001 with value = 961 arg_min : 1.6500000000000001 min_score : 961{-90.0: 906, -85.0: 871, -80.0: 858, -75.0: 866, -70.0: 852, -65.0: 864, -60.0: 855, -55.0: 853, -50.0: 961, -45.0: 1180, -40.0: 1412, -35.0: 1690, -30.0: 1959, -25.0: 2243, -20.0: 2484, -15.0: 2677, -10.0: 2820, -5.0: 2976, 0.0: 3039, 5.0: 3049, 10.0: 3098, 15.0: 3122, 20.0: 3061, 25.0: 2954, 30.0: 2845, 35.0: 2631, 40.0: 2417, 45.0: 2130, 50.0: 1863, 55.0: 1564, 60.0: 1439, 65.0: 1326, 70.0: 1220, 75.0: 1142, 80.0: 1056, 85.0: 986} [(-70.0, 852), (-55.0, 853), (-60.0, 855), (-80.0, 858), (-65.0, 864), (-75.0, 866), (-85.0, 871), (-90.0, 906), (-50.0, 961), (85.0, 986), (80.0, 1056), (75.0, 1142), (-45.0, 1180), (70.0, 1220), (65.0, 1326), (-40.0, 1412), (60.0, 1439), (55.0, 1564), (-35.0, 1690), (50.0, 1863), (-30.0, 1959), (45.0, 2130), (-25.0, 2243), (40.0, 2417), (-20.0, 2484), (35.0, 2631), (-15.0, 2677), (-10.0, 2820), (30.0, 2845), (25.0, 2954), (-5.0, 2976), (0.0, 3039), (5.0, 3049), (20.0, 3061), (10.0, 3098), (15.0, 3122)] arg_min reach at : -70.0 with value = 852 arg_min : -70.0 min_score : 852{12.75: 4966, 14.34375: 4695, 15.9375: 4364, 17.53125: 3945, 19.125: 3465, 20.71875: 2958, 22.3125: 2415, 23.90625: 1841, 25.5: 1295, 27.09375: 936, 28.6875: 847, 30.28125: 852, 31.875: 883, 33.46875: 997, 35.0625: 1322, 36.65625: 1859} [(28.6875, 847), (30.28125, 852), (31.875, 883), (27.09375, 936), (33.46875, 997), (25.5, 1295), (35.0625, 1322), (23.90625, 1841), (36.65625, 1859), (22.3125, 2415), (20.71875, 2958), (19.125, 3465), (17.53125, 3945), (15.9375, 4364), (14.34375, 4695), (12.75, 4966)] arg_min reach at : 28.6875 with value = 847 arg_min : 28.6875 min_score : 847{13.5: 1489, 15.1875: 1374, 16.875: 1244, 18.5625: 1126, 20.25: 996, 21.9375: 893, 23.625: 847, 25.3125: 933, 27.0: 1153, 28.6875: 1446, 30.375: 1827, 32.0625: 2217, 33.75: 2658, 35.4375: 3137, 37.125: 3653, 38.8125: 4142} [(23.625, 847), (21.9375, 893), (25.3125, 933), (20.25, 996), (18.5625, 1126), (27.0, 1153), (16.875, 1244), (15.1875, 1374), (28.6875, 1446), (13.5, 1489), (30.375, 1827), (32.0625, 2217), (33.75, 2658), (35.4375, 3137), (37.125, 3653), (38.8125, 4142)] arg_min reach at : 23.625 with value = 847 arg_min : 23.625 min_score : 847{13.5: 1870, 15.1875: 1783, 16.875: 1676, 18.5625: 1564, 20.25: 1442, 21.9375: 1302, 23.625: 1153, 25.3125: 997, 27.0: 847, 28.6875: 864, 30.375: 1163, 32.0625: 1613, 33.75: 2300, 35.4375: 3191, 37.125: 4251, 38.8125: 5356} [(27.0, 847), (28.6875, 864), (25.3125, 997), (23.625, 1153), (30.375, 1163), (21.9375, 1302), (20.25, 1442), (18.5625, 1564), (32.0625, 1613), (16.875, 1676), (15.1875, 1783), (13.5, 1870), (33.75, 2300), (35.4375, 3191), (37.125, 4251), (38.8125, 5356)] arg_min reach at : 27.0 with value = 847 arg_min : 27.0 min_score : 847 yc : 23.625 xc : 28.6875 angle : -70.0 radius : 27.0 excentricity : 1.6500000000000001 yc : 23.625 xc : 28.6875 angle : -70.0 radius : 27.0 excentricity : 1.6500000000000001 x0 : 103 y1 : 396 width : 35, height : 38, area : 1330, score : 1.0 x0 : 93 y1 : 396 width : 51, height : 36, area : 1836, score : 1.0 Now saving polygons points : 1| batch 1 Loaded 3 chid ids of type : 520 ++ CHI and polygons saved ! (57, 52) (52, 43) [57, 52] (57, 52) score : 7970 strategy_opt : 5| strategy_opt : [('excentricity', [0.5, 2.0, 0.05]), ('angle', [-90.0, 90.0, 5.0]), ('xc', [10.75, 32.25, 1.34375]), ('yc', [13.0, 39.0, 1.625]), ('radius', [13.0, 39.0, 1.625])] {0.5: 16167, 0.55: 14430, 0.6000000000000001: 12864, 0.65: 11529, 0.7000000000000001: 10346, 0.75: 9256, 0.8: 8418, 0.8500000000000001: 7609, 0.9: 6862, 0.9500000000000001: 6277, 1.0: 5482, 1.05: 4840, 1.1: 4264, 1.1500000000000001: 3773, 1.2000000000000002: 3275, 1.25: 2948, 1.3: 2727, 1.35: 2514, 1.4000000000000001: 2254, 1.4500000000000002: 2155, 1.5: 1986, 1.55: 1892, 1.6: 1846, 1.6500000000000001: 1782, 1.7000000000000002: 1717, 1.75: 1656, 1.8: 1641, 1.85: 1602, 1.9000000000000001: 1587, 1.9500000000000002: 1576} [(1.9500000000000002, 1576), (1.9000000000000001, 1587), (1.85, 1602), (1.8, 1641), (1.75, 1656), (1.7000000000000002, 1717), (1.6500000000000001, 1782), (1.6, 1846), (1.55, 1892), (1.5, 1986), (1.4500000000000002, 2155), (1.4000000000000001, 2254), (1.35, 2514), (1.3, 2727), (1.25, 2948), (1.2000000000000002, 3275), (1.1500000000000001, 3773), (1.1, 4264), (1.05, 4840), (1.0, 5482), (0.9500000000000001, 6277), (0.9, 6862), (0.8500000000000001, 7609), (0.8, 8418), (0.75, 9256), (0.7000000000000001, 10346), (0.65, 11529), (0.6000000000000001, 12864), (0.55, 14430), (0.5, 16167)] arg_min reach at : 1.9500000000000002 with value = 1576 | arg_min : 1.9500000000000002 min_score : 1576{-90.0: 2291, -85.0: 2369, -80.0: 2390, -75.0: 2401, -70.0: 2341, -65.0: 2390, -60.0: 2418, -55.0: 2445, -50.0: 2599, -45.0: 2701, -40.0: 2755, -35.0: 2787, -30.0: 2753, -25.0: 2678, -20.0: 2611, -15.0: 2478, -10.0: 2313, -5.0: 2098, 0.0: 1870, 5.0: 1702, 10.0: 1576, 15.0: 1422, 20.0: 1258, 25.0: 1061, 30.0: 872, 35.0: 752, 40.0: 632, 45.0: 677, 50.0: 872, 55.0: 1070, 60.0: 1274, 65.0: 1477, 70.0: 1637, 75.0: 1895, 80.0: 2049, 85.0: 2193} [(40.0, 632), (45.0, 677), (35.0, 752), (30.0, 872), (50.0, 872), (25.0, 1061), (55.0, 1070), (20.0, 1258), (60.0, 1274), (15.0, 1422), (65.0, 1477), (10.0, 1576), (70.0, 1637), (5.0, 1702), (0.0, 1870), (75.0, 1895), (80.0, 2049), (-5.0, 2098), (85.0, 2193), (-90.0, 2291), (-10.0, 2313), (-70.0, 2341), (-85.0, 2369), (-80.0, 2390), (-65.0, 2390), (-75.0, 2401), (-60.0, 2418), (-55.0, 2445), (-15.0, 2478), (-50.0, 2599), (-20.0, 2611), (-25.0, 2678), (-45.0, 2701), (-30.0, 2753), (-40.0, 2755), (-35.0, 2787)] arg_min reach at : 40.0 with value = 632 | arg_min : 40.0 min_score : 632{10.75: 982, 12.09375: 841, 13.4375: 751, 14.78125: 672, 16.125: 624, 17.46875: 599, 18.8125: 575, 20.15625: 561, 21.5: 632, 22.84375: 895, 24.1875: 1257, 25.53125: 1708, 26.875: 2192, 28.21875: 2686, 29.5625: 3202, 30.90625: 3701} [(20.15625, 561), (18.8125, 575), (17.46875, 599), (16.125, 624), (21.5, 632), (14.78125, 672), (13.4375, 751), (12.09375, 841), (22.84375, 895), (10.75, 982), (24.1875, 1257), (25.53125, 1708), (26.875, 2192), (28.21875, 2686), (29.5625, 3202), (30.90625, 3701)] arg_min reach at : 20.15625 with value = 561 | arg_min : 20.15625 min_score : 561{13.0: 3254, 14.625: 2871, 16.25: 2436, 17.875: 1977, 19.5: 1474, 21.125: 1071, 22.75: 835, 24.375: 629, 26.0: 561, 27.625: 716, 29.25: 937, 30.875: 1286, 32.5: 1738, 34.125: 2226, 35.75: 2782, 37.375: 3291} [(26.0, 561), (24.375, 629), (27.625, 716), (22.75, 835), (29.25, 937), (21.125, 1071), (30.875, 1286), (19.5, 1474), (32.5, 1738), (17.875, 1977), (34.125, 2226), (16.25, 2436), (35.75, 2782), (14.625, 2871), (13.0, 3254), (37.375, 3291)] arg_min reach at : 26.0 with value = 561 | arg_min : 26.0 min_score : 561{13.0: 1371, 14.625: 1301, 16.25: 1217, 17.875: 1127, 19.5: 1028, 21.125: 926, 22.75: 811, 24.375: 683, 26.0: 561, 27.625: 624, 29.25: 1026, 30.875: 1508, 32.5: 2147, 34.125: 2981, 35.75: 3949, 37.375: 5069} [(26.0, 561), (27.625, 624), (24.375, 683), (22.75, 811), (21.125, 926), (29.25, 1026), (19.5, 1028), (17.875, 1127), (16.25, 1217), (14.625, 1301), (13.0, 1371), (30.875, 1508), (32.5, 2147), (34.125, 2981), (35.75, 3949), (37.375, 5069)] arg_min reach at : 26.0 with value = 561 | arg_min : 26.0 min_score : 561{0.5: 13327, 0.55: 12420, 0.6000000000000001: 11540, 0.65: 10560, 0.7000000000000001: 9652, 0.75: 8782, 0.8: 7834, 0.8500000000000001: 7061, 0.9: 6294, 0.9500000000000001: 5633, 1.0: 4916, 1.05: 4424, 1.1: 3865, 1.1500000000000001: 3323, 1.2000000000000002: 2817, 1.25: 2401, 1.3: 1935, 1.35: 1600, 1.4000000000000001: 1218, 1.4500000000000002: 1019, 1.5: 834, 1.55: 693, 1.6: 614, 1.6500000000000001: 568, 1.7000000000000002: 549, 1.75: 529, 1.8: 520, 1.85: 521, 1.9000000000000001: 534, 1.9500000000000002: 561} [(1.8, 520), (1.85, 521), (1.75, 529), (1.9000000000000001, 534), (1.7000000000000002, 549), (1.9500000000000002, 561), (1.6500000000000001, 568), (1.6, 614), (1.55, 693), (1.5, 834), (1.4500000000000002, 1019), (1.4000000000000001, 1218), (1.35, 1600), (1.3, 1935), (1.25, 2401), (1.2000000000000002, 2817), (1.1500000000000001, 3323), (1.1, 3865), (1.05, 4424), (1.0, 4916), (0.9500000000000001, 5633), (0.9, 6294), (0.8500000000000001, 7061), (0.8, 7834), (0.75, 8782), (0.7000000000000001, 9652), (0.65, 10560), (0.6000000000000001, 11540), (0.55, 12420), (0.5, 13327)] arg_min reach at : 1.8 with value = 520 arg_min : 1.8 min_score : 520{-90.0: 2114, -85.0: 2268, -80.0: 2343, -75.0: 2405, -70.0: 2409, -65.0: 2435, -60.0: 2467, -55.0: 2514, -50.0: 2590, -45.0: 2654, -40.0: 2632, -35.0: 2610, -30.0: 2565, -25.0: 2526, -20.0: 2435, -15.0: 2330, -10.0: 2181, -5.0: 2013, 0.0: 1792, 5.0: 1584, 10.0: 1389, 15.0: 1208, 20.0: 1038, 25.0: 843, 30.0: 673, 35.0: 564, 40.0: 520, 45.0: 597, 50.0: 764, 55.0: 963, 60.0: 1158, 65.0: 1390, 70.0: 1573, 75.0: 1800, 80.0: 1969, 85.0: 2070} [(40.0, 520), (35.0, 564), (45.0, 597), (30.0, 673), (50.0, 764), (25.0, 843), (55.0, 963), (20.0, 1038), (60.0, 1158), (15.0, 1208), (10.0, 1389), (65.0, 1390), (70.0, 1573), (5.0, 1584), (0.0, 1792), (75.0, 1800), (80.0, 1969), (-5.0, 2013), (85.0, 2070), (-90.0, 2114), (-10.0, 2181), (-85.0, 2268), (-15.0, 2330), (-80.0, 2343), (-75.0, 2405), (-70.0, 2409), (-65.0, 2435), (-20.0, 2435), (-60.0, 2467), (-55.0, 2514), (-25.0, 2526), (-30.0, 2565), (-50.0, 2590), (-35.0, 2610), (-40.0, 2632), (-45.0, 2654)] arg_min reach at : 40.0 with value = 520 arg_min : 40.0 min_score : 520{10.75: 1004, 12.09375: 902, 13.4375: 775, 14.78125: 655, 16.125: 580, 17.46875: 515, 18.8125: 494, 20.15625: 520, 21.5: 692, 22.84375: 1028, 24.1875: 1466, 25.53125: 2001, 26.875: 2490, 28.21875: 3027, 29.5625: 3629, 30.90625: 4163} [(18.8125, 494), (17.46875, 515), (20.15625, 520), (16.125, 580), (14.78125, 655), (21.5, 692), (13.4375, 775), (12.09375, 902), (10.75, 1004), (22.84375, 1028), (24.1875, 1466), (25.53125, 2001), (26.875, 2490), (28.21875, 3027), (29.5625, 3629), (30.90625, 4163)] arg_min reach at : 18.8125 with value = 494 arg_min : 18.8125 min_score : 494{13.0: 3121, 14.625: 2763, 16.25: 2397, 17.875: 1962, 19.5: 1409, 21.125: 1056, 22.75: 809, 24.375: 589, 26.0: 494, 27.625: 635, 29.25: 987, 30.875: 1406, 32.5: 1937, 34.125: 2442, 35.75: 2987, 37.375: 3537} [(26.0, 494), (24.375, 589), (27.625, 635), (22.75, 809), (29.25, 987), (21.125, 1056), (30.875, 1406), (19.5, 1409), (32.5, 1937), (17.875, 1962), (16.25, 2397), (34.125, 2442), (14.625, 2763), (35.75, 2987), (13.0, 3121), (37.375, 3537)] arg_min reach at : 26.0 with value = 494 arg_min : 26.0 min_score : 494{13.0: 1347, 14.625: 1266, 16.25: 1182, 17.875: 1086, 19.5: 979, 21.125: 865, 22.75: 737, 24.375: 616, 26.0: 494, 27.625: 559, 29.25: 954, 30.875: 1524, 32.5: 2347, 34.125: 3350, 35.75: 4468, 37.375: 5635} [(26.0, 494), (27.625, 559), (24.375, 616), (22.75, 737), (21.125, 865), (29.25, 954), (19.5, 979), (17.875, 1086), (16.25, 1182), (14.625, 1266), (13.0, 1347), (30.875, 1524), (32.5, 2347), (34.125, 3350), (35.75, 4468), (37.375, 5635)] arg_min reach at : 26.0 with value = 494 arg_min : 26.0 min_score : 494 yc : 26.0 xc : 18.8125 angle : 40.0 radius : 26.0 excentricity : 1.8 yc : 26.0 xc : 18.8125 angle : 40.0 radius : 26.0 excentricity : 1.8 x0 : 104 y1 : 292 width : 27, height : 36, area : 972, score : 1.0 x0 : 102 y1 : 288 width : 39, height : 43, area : 1677, score : 1.0 Now saving polygons points : 1| batch 1 Loaded 4 chid ids of type : 520 +++ CHI and polygons saved ! ['temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_ellipsebest.jpg', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_varroa_with_ellipsebest.jpg', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_ellipsebest.jpg', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_varroa_with_ellipsebest.jpg', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_ellipsebest.jpg', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_varroa_with_ellipsebest.jpg', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_ellipsebest.jpg', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_varroa_with_ellipsebest.jpg'] About to upload 8 photos https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc upload in portfolio : 22735746 Result OK ! uploaded one batch 0 Elapsed time : 20.464670181274414 After datou_step_exec type output : time spend for datou_step_exec : 25.019170999526978 time spend to save output : 2.5272369384765625e-05 total time spend for step 1 : 25.019196271896362 step2:tile Tue May 6 22:40:00 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 : ['temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg'] We expect there is only one output and this part is used while all output are not tuple or array 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 input_args_next_step, len :1, first value : [('temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg',)] After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg': 937852786, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1356481359, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1356481360, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1356481361, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1356481362} map_photo_id_path_extension : {937852786: {'path': 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg', 'extension': 'jpg'}, 1356481359: {'path': 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg'}, 1356481360: {'path': 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg'}, 1356481361: {'path': 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg'}, 1356481362: {'path': 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg'}} map_subphoto_mainphoto : {1356481359: 937852786, 1356481360: 937852786, 1356481361: 937852786, 1356481362: 937852786} verbose : True param_json : {'photo_tile_type': 17, 'whiten': True, 'remove_crop_border': True, 'minimal_size_crop_border': 900, 'stride': 240, 'crop_hashtag_type_tiled': 521, 'ETA': 86400, 'new_width': 480, 'new_height': 480, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'tile_taggage_varroa', 'crop_hashtag_type': 520, 'host': 'www.fotonower.com', 'arg_aux_upload': {'type_upload': 'python'}} type(crop_hashtag_type) : type(crop_hashtag_type_tiled) : We consider crop_hashtag_type is an integer ! map_chi_type_to_chi_type_cropped : {520: 521} TO DEPRECATE VR 14-6-18 map_filenames : {937852786: 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg'} list_pids : 1 list_pids : 2 list_subpids to replace list_pids : 0 batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 937852786,937852786) and `type` in (520) Loaded 4 chid ids of type : 520 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (3787225806,3787225807,3787225808,3787225809) ++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (3787225806,3787225807,3787225808,3787225809) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (3787225806,3787225807,3787225808,3787225809) https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=tile_taggage_varroa&access_token=78d09a0790ec6ecbf119343125a81fdc created feed_id_new_photos : 22735765 with name tile_taggage_varroa feed_id_new_photos : 22735765 filename : temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg photo_id : 937852786 height_image_input : 480 width_image_input : 480 new_width : 480 new_height : 480 stride : 240 stride_relative : 0.1 chi to copy from the main photo to the tiled photo input_chi_for_this_image_as_chi : 4 list_bib_to_crops : 1 [(0, 480, 0, 480, 0)] calcul des nouveaux crops pour le tile x0:0,x1:480,y0:0,y1:480 calcul avec la methode originale calcul avec la methode originale calcul avec la methode originale calcul avec la methode originale chi selectionnes : [, , , ] new_crops_tiles : 1 crop_transformed : 4 insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) [(937852786, 2090988864, 17, 0, 480, 0, 480, 1.0)] list_photo_ids_cropped : [937852786] batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 937852786) and `type` in (17) Loaded 1 chid ids of type : 17 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (8165084) SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (8165084) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (8165084) treat the image : temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg , 0 before upload mediasElapsed time : 0.00883173942565918 on upload les photos avec python init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1746564008_2124625 INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `mtr_user_id`, `created_at`) VALUES (22735765, 1356481593, 0, NOW()) 1 we have uploaded 1 photos in the portfolio 22735765 Importing ! upload mediasElapsed time : 0.5687668323516846 , 0insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(8165084, 1356481593, 0)] Saving 4 CHIs. list_chi_tile : [": {'photo_id': 1356481593, 'hashtag_id': 2087736828, 'type': 521, 'x0': 432, 'x1': 467, 'y0': 303, 'y1': 355, 'score': 1.0, 'id': 0, 'points': ['463,352,452,353,439,350,426,342,418,333,417,325,422,319,433,318,446,321,459,328,467,338,469,346', '463,352,452,353,439,350,426,342,418,333,417,325,422,319,433,318,446,321,459,328,467,338,469,346'], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', 'sum_segment': 0}", ": {'photo_id': 1356481593, 'hashtag_id': 2087736828, 'type': 521, 'x0': 419, 'x1': 445, 'y0': 434, 'y1': 480, 'score': 1.0, 'id': 0, 'points': ['451,461,449,467,441,473,429,477,416,477,406,473,402,467,403,461,411,455,423,451,435,452,446,455', '451,461,449,467,441,473,429,477,416,477,406,473,402,467,403,461,411,455,423,451,435,452,446,455'], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', 'sum_segment': 0}", ": {'photo_id': 1356481593, 'hashtag_id': 2087736828, 'type': 521, 'x0': 93, 'x1': 144, 'y0': 360, 'y1': 396, 'score': 1.0, 'id': 0, 'points': ['112,359,120,350,130,348,137,351,141,361,140,374,134,387,126,396,117,399,109,395,105,385,106,372', '112,359,120,350,130,348,137,351,141,361,140,374,134,387,126,396,117,399,109,395,105,385,106,372'], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', 'sum_segment': 0}", ": {'photo_id': 1356481593, 'hashtag_id': 2087736828, 'type': 521, 'x0': 102, 'x1': 141, 'y0': 245, 'y1': 288, 'score': 1.0, 'id': 0, 'points': ['124,293,112,290,102,281,95,271,93,262,96,255,105,254,116,257,127,266,134,276,136,285,132,292', '124,293,112,290,102,281,95,271,93,262,96,255,105,254,116,257,127,266,134,276,136,285,132,292'], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', 'sum_segment': 0}"] insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) batch 1 Loaded 4 chid ids of type : 521 INSERT IGNORE INTO MTRPhoto.crop_polygon_points (`crop_hashtag_id`, `points`) VALUES (%s, %s) Number RLEs to save : 0 INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) TO DO : save crop sub photo not yet done ! end of tileElapsed time : 0.6454157829284668 map_pid_results : {'1356481593': ['temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg']} After datou_step_exec type output : time spend for datou_step_exec : 7.341587066650391 time spend to save output : 0.0001678466796875 total time spend for step 2 : 7.341754913330078 step3:rotate Tue May 6 22:40:08 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 : {'1356481593': ['temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg']} input_args_next_step : {'1356481593': ()} output_args : {'1356481593': ['temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg']} args : 1356481593 depend.output_id : 0 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 input_args_next_step, len :1, first value : ('temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg': 937852786, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1356481359, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1356481360, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1356481361, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1356481362, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg': 1356481593} map_photo_id_path_extension : {937852786: {'path': 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg', 'extension': 'jpg'}, 1356481359: {'path': 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg'}, 1356481360: {'path': 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg'}, 1356481361: {'path': 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg'}, 1356481362: {'path': 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg'}, 1356481593: {'path': 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg'}} map_subphoto_mainphoto : {1356481359: 937852786, 1356481360: 937852786, 1356481361: 937852786, 1356481362: 937852786, 1356481593: 937852786} Beginning of datou_step_rotate ! Warning, new_feed_id is empty ! We are in a datou with depends ! rotate photos of 0,15,30,45,60,75,90,105,120,135,150,165,180,195,210,225,240,255,270,285,300,315,330,345 degres batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 1356481593) and `type` in (521) Loaded 4 chid ids of type : 521 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (3787226011,3787226012,3787226010,3787226009) ++WARNING : duplicated polygon, we should remove this data for chi_id : 3787226009. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3787226010. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3787226011. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3787226012. Ignored now SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (3787226011,3787226012,3787226010,3787226009) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (3787226011,3787226012,3787226010,3787226009) map_chi : {1356481593: [, , , ]} https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=rotate_data_augmentation_varroa_480_ellipse_320&access_token=78d09a0790ec6ecbf119343125a81fdc feed_id_new_photos : 22735843 photo_id in download_rotate_and_save : 1356481593 list_chi_loc : 4 Use all angle ! Rotation of photo 1356481593 of 0 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 0 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 1. 0.] [-0. 1.]] 0 [[ 1. 0.] [-0. 1.]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004901885986328125 nb_pixel_total : 1389 time to create 1 rle with old method : 0.002118825912475586 .time for calcul the mask position with numpy : 0.00038552284240722656 nb_pixel_total : 1157 time to create 1 rle with old method : 0.001806020736694336 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 2 list_crops_rotate : : {'photo_id': -1, 'hashtag_id': 2087736828, 'type': 529, 'x0': 25, 'x1': 61, 'y0': 268, 'y1': 319, 'score': 1.0, 'id': None, 'points': ['32,279,40,270,50,268,57,271,61,281,60,294,54,307,46,316,37,319,29,315,25,305,26,292'], 'sub_photo_id': 0, 'rles': [(-1, 48, 268, 4), (-1, 43, 269, 11), (-1, 40, 270, 16), (-1, 39, 271, 19), (-1, 38, 272, 20), (-1, 37, 273, 22), (-1, 36, 274, 23), (-1, 36, 275, 24), (-1, 35, 276, 25), (-1, 34, 277, 26), (-1, 33, 278, 28), (-1, 32, 279, 29), (-1, 32, 280, 30), (-1, 31, 281, 31), (-1, 31, 282, 31), (-1, 30, 283, 32), (-1, 30, 284, 32), (-1, 29, 285, 33), (-1, 29, 286, 33), (-1, 28, 287, 34), (-1, 28, 288, 33), (-1, 27, 289, 34), (-1, 27, 290, 34), (-1, 26, 291, 35), (-1, 26, 292, 35), (-1, 26, 293, 35), (-1, 26, 294, 35), (-1, 26, 295, 35), (-1, 26, 296, 34), (-1, 26, 297, 34), (-1, 26, 298, 33), (-1, 25, 299, 34), (-1, 25, 300, 33), (-1, 25, 301, 33), (-1, 25, 302, 32), (-1, 25, 303, 32), (-1, 25, 304, 31), (-1, 25, 305, 31), (-1, 25, 306, 30), (-1, 26, 307, 29), (-1, 26, 308, 28), (-1, 27, 309, 26), (-1, 27, 310, 25), (-1, 27, 311, 24), (-1, 28, 312, 23), (-1, 28, 313, 22), (-1, 29, 314, 20), (-1, 29, 315, 19), (-1, 31, 316, 16), (-1, 33, 317, 12), (-1, 35, 318, 7), (-1, 37, 319, 2)], 'hashtag': '', 'sum_segment': 0},: {'photo_id': -1, 'hashtag_id': 2087736828, 'type': 529, 'x0': 13, 'x1': 56, 'y0': 174, 'y1': 213, 'score': 1.0, 'id': None, 'points': ['44,213,32,210,22,201,15,191,13,182,16,175,25,174,36,177,47,186,54,196,56,205,52,212'], 'sub_photo_id': 0, 'rles': [(-1, 21, 174, 6), (-1, 16, 175, 15), (-1, 16, 176, 19), (-1, 15, 177, 22), (-1, 15, 178, 23), (-1, 14, 179, 26), (-1, 14, 180, 27), (-1, 13, 181, 29), (-1, 13, 182, 30), (-1, 13, 183, 31), (-1, 13, 184, 33), (-1, 14, 185, 33), (-1, 14, 186, 34), (-1, 14, 187, 35), (-1, 14, 188, 35), (-1, 15, 189, 35), (-1, 15, 190, 36), (-1, 15, 191, 36), (-1, 16, 192, 36), (-1, 16, 193, 37), (-1, 17, 194, 37), (-1, 18, 195, 36), (-1, 18, 196, 37), (-1, 19, 197, 36), (-1, 20, 198, 35), (-1, 21, 199, 35), (-1, 21, 200, 35), (-1, 22, 201, 34), (-1, 23, 202, 33), (-1, 24, 203, 33), (-1, 25, 204, 32), (-1, 26, 205, 31), (-1, 28, 206, 28), (-1, 29, 207, 27), (-1, 30, 208, 25), (-1, 31, 209, 24), (-1, 32, 210, 22), (-1, 35, 211, 19), (-1, 39, 212, 14), (-1, 43, 213, 6)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 15 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 15 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.96592583 0.25881905] [-0.25881905 0.96592583]] 15 [[ 0.96592583 0.25881905] [-0.25881905 0.96592583]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00042510032653808594 nb_pixel_total : 694 time to create 1 rle with old method : 0.0011687278747558594 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004477500915527344 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0017783641815185547 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -2, 'hashtag_id': 2087736828, 'type': 529, 'x0': 24, 'x1': 72, 'y0': 209, 'y1': 242, 'score': 1.0, 'id': None, 'points': ['62,241,50,242,38,236,28,228,24,220,25,212,34,209,45,209,58,215,67,222,72,231,70,238'], 'sub_photo_id': 0, 'rles': [(-1, 33, 209, 3), (-1, 37, 209, 9), (-1, 30, 210, 18), (-1, 49, 210, 1), (-1, 30, 211, 21), (-1, 26, 212, 27), (-1, 26, 213, 29), (-1, 26, 214, 32), (-1, 25, 215, 35), (-1, 25, 216, 36), (-1, 25, 217, 37), (-1, 25, 218, 38), (-1, 24, 219, 40), (-1, 25, 220, 40), (-1, 25, 221, 42), (-1, 25, 222, 43), (-1, 26, 223, 43), (-1, 27, 224, 42), (-1, 27, 225, 43), (-1, 28, 226, 43), (-1, 29, 227, 42), (-1, 29, 228, 42), (-1, 30, 229, 43), (-1, 30, 230, 43), (-1, 32, 231, 41), (-1, 33, 232, 39), (-1, 34, 233, 39), (-1, 36, 234, 36), (-1, 37, 235, 35), (-1, 38, 236, 34), (-1, 40, 237, 32), (-1, 42, 238, 30), (-1, 43, 239, 28), (-1, 46, 240, 22), (-1, 48, 241, 19), (-1, 50, 242, 15)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 30 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 30 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.8660254 0.5 ] [-0.5 0.8660254]] 30 [[ 0.8660254 0.5 ] [-0.5 0.8660254]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003464221954345703 nb_pixel_total : 221 time to create 1 rle with old method : 0.0003705024719238281 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035381317138671875 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0014653205871582031 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -3, 'hashtag_id': 2087736828, 'type': 529, 'x0': 44, 'x1': 93, 'y0': 238, 'y1': 268, 'score': 1.0, 'id': None, 'points': ['86,264,74,268,61,265,50,260,44,253,43,245,50,240,61,237,75,239,87,245,93,251,93,259'], 'sub_photo_id': 0, 'rles': [(-1, 59, 238, 9), (-1, 55, 239, 17), (-1, 73, 239, 1), (-1, 51, 240, 27), (-1, 49, 241, 30), (-1, 48, 242, 34), (-1, 48, 243, 36), (-1, 46, 244, 40), (-1, 45, 245, 43), (-1, 44, 246, 45), (-1, 44, 247, 46), (-1, 44, 248, 47), (-1, 44, 249, 48), (-1, 44, 250, 49), (-1, 44, 251, 50), (-1, 44, 252, 50), (-1, 44, 253, 50), (-1, 45, 254, 49), (-1, 45, 255, 49), (-1, 47, 256, 47), (-1, 48, 257, 46), (-1, 48, 258, 46), (-1, 50, 259, 44), (-1, 51, 260, 43), (-1, 52, 261, 41), (-1, 54, 262, 37), (-1, 56, 263, 35), (-1, 59, 264, 31), (-1, 60, 265, 28), (-1, 63, 266, 21), (-1, 67, 267, 2), (-1, 70, 267, 11), (-1, 74, 268, 3)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 45 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 45 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.70710678 0.70710678] [-0.70710678 0.70710678]] 45 [[ 0.70710678 0.70710678] [-0.70710678 0.70710678]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003466606140136719 nb_pixel_total : 143 time to create 1 rle with old method : 0.00025725364685058594 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035381317138671875 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0018932819366455078 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -4, 'hashtag_id': 2087736828, 'type': 529, 'x0': 69, 'x1': 121, 'y0': 258, 'y1': 286, 'score': 1.0, 'id': None, 'points': ['115,279,105,285,91,286,79,284,72,279,69,272,74,265,84,259,98,258,110,260,118,265,120,273'], 'sub_photo_id': 0, 'rles': [(-1, 96, 258, 5), (-1, 88, 259, 17), (-1, 84, 260, 26), (-1, 111, 260, 1), (-1, 82, 261, 31), (-1, 81, 262, 34), (-1, 78, 263, 38), (-1, 77, 264, 42), (-1, 75, 265, 45), (-1, 74, 266, 46), (-1, 73, 267, 47), (-1, 72, 268, 48), (-1, 72, 269, 49), (-1, 71, 270, 50), (-1, 70, 271, 51), (-1, 69, 272, 53), (-1, 70, 273, 52), (-1, 70, 274, 51), (-1, 70, 275, 50), (-1, 71, 276, 48), (-1, 71, 277, 49), (-1, 72, 278, 47), (-1, 71, 279, 47), (-1, 72, 280, 45), (-1, 73, 281, 41), (-1, 76, 282, 37), (-1, 77, 283, 34), (-1, 79, 284, 31), (-1, 82, 285, 25), (-1, 85, 286, 21)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 60 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 60 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.5 0.8660254] [-0.8660254 0.5 ]] 60 [[ 0.5 0.8660254] [-0.8660254 0.5 ]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00036263465881347656 nb_pixel_total : 414 time to create 1 rle with old method : 0.0006875991821289062 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00037169456481933594 nb_pixel_total : 1159 time to create 1 rle with old method : 0.001905679702758789 . crop are not in the shrunk photo ! On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -5, 'hashtag_id': 2087736828, 'type': 529, 'x0': 102, 'x1': 152, 'y0': 269, 'y1': 300, 'score': 1.0, 'id': None, 'points': ['148,286,140,295,127,299,115,300,106,297,101,291,105,283,113,275,126,270,139,268,147,271,151,278'], 'sub_photo_id': 0, 'rles': [(-1, 132, 269, 1), (-1, 135, 269, 6), (-1, 126, 270, 18), (-1, 124, 271, 24), (-1, 121, 272, 28), (-1, 118, 273, 32), (-1, 116, 274, 34), (-1, 113, 275, 38), (-1, 112, 276, 39), (-1, 111, 277, 41), (-1, 111, 278, 42), (-1, 109, 279, 44), (-1, 108, 280, 44), (-1, 108, 281, 44), (-1, 106, 282, 45), (-1, 105, 283, 47), (-1, 105, 284, 46), (-1, 104, 285, 46), (-1, 104, 286, 46), (-1, 104, 287, 45), (-1, 104, 288, 44), (-1, 103, 289, 44), (-1, 103, 290, 43), (-1, 102, 291, 43), (-1, 102, 292, 42), (-1, 103, 293, 41), (-1, 104, 294, 38), (-1, 104, 295, 37), (-1, 105, 296, 33), (-1, 106, 297, 28), (-1, 107, 298, 27), (-1, 111, 299, 19), (-1, 112, 300, 1), (-1, 114, 300, 9)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 75 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 75 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.25881905 0.96592583] [-0.96592583 0.25881905]] 75 [[ 0.25881905 0.96592583] [-0.96592583 0.25881905]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00039577484130859375 nb_pixel_total : 1204 time to create 1 rle with old method : 0.001991748809814453 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00034809112548828125 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0019066333770751953 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003330707550048828 nb_pixel_total : 264 time to create 1 rle with old method : 0.00046753883361816406 On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -6, 'hashtag_id': 2087736828, 'type': 529, 'x0': 138, 'x1': 183, 'y0': 271, 'y1': 308, 'score': 1.0, 'id': None, 'points': ['182,285,176,295,164,303,153,307,144,306,138,302,139,293,145,283,156,275,168,270,177,271,183,277'], 'sub_photo_id': 0, 'rles': [(-1, 168, 271, 10), (-1, 165, 272, 14), (-1, 162, 273, 18), (-1, 160, 274, 22), (-1, 157, 275, 25), (-1, 155, 276, 28), (-1, 154, 277, 30), (-1, 153, 278, 31), (-1, 152, 279, 32), (-1, 150, 280, 33), (-1, 148, 281, 36), (-1, 148, 282, 36), (-1, 146, 283, 38), (-1, 145, 284, 38), (-1, 144, 285, 39), (-1, 144, 286, 39), (-1, 143, 287, 39), (-1, 143, 288, 38), (-1, 142, 289, 39), (-1, 141, 290, 40), (-1, 141, 291, 38), (-1, 140, 292, 39), (-1, 140, 293, 39), (-1, 139, 294, 39), (-1, 139, 295, 38), (-1, 139, 296, 38), (-1, 139, 297, 36), (-1, 139, 298, 35), (-1, 139, 299, 33), (-1, 139, 300, 32), (-1, 138, 301, 32), (-1, 138, 302, 30), (-1, 139, 303, 27), (-1, 140, 304, 25), (-1, 142, 305, 19), (-1, 143, 306, 17), (-1, 143, 307, 14), (-1, 150, 308, 1)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 90 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 90 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 6.123234e-17 1.000000e+00] [-1.000000e+00 6.123234e-17]] 90 [[ 6.123234e-17 1.000000e+00] [-1.000000e+00 6.123234e-17]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00038552284240722656 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0024518966674804688 .time for calcul the mask position with numpy : 0.00038814544677734375 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0019073486328125 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 2 list_crops_rotate : : {'photo_id': -7, 'hashtag_id': 2087736828, 'type': 529, 'x0': 268, 'x1': 319, 'y0': 258, 'y1': 294, 'score': 1.0, 'id': None, 'points': ['279,287,270,279,268,269,271,262,281,258,294,259,307,265,316,273,319,282,315,290,305,294,292,293'], 'sub_photo_id': 0, 'rles': [(-1, 280, 258, 8), (-1, 278, 259, 18), (-1, 275, 260, 23), (-1, 273, 261, 27), (-1, 271, 262, 31), (-1, 271, 263, 33), (-1, 270, 264, 36), (-1, 270, 265, 38), (-1, 269, 266, 40), (-1, 269, 267, 41), (-1, 268, 268, 43), (-1, 268, 269, 45), (-1, 268, 270, 46), (-1, 268, 271, 47), (-1, 269, 272, 47), (-1, 269, 273, 48), (-1, 269, 274, 48), (-1, 269, 275, 49), (-1, 269, 276, 49), (-1, 270, 277, 48), (-1, 270, 278, 49), (-1, 270, 279, 49), (-1, 271, 280, 48), (-1, 272, 281, 48), (-1, 273, 282, 47), (-1, 274, 283, 45), (-1, 276, 284, 43), (-1, 277, 285, 41), (-1, 278, 286, 40), (-1, 279, 287, 38), (-1, 281, 288, 36), (-1, 283, 289, 33), (-1, 285, 290, 31), (-1, 287, 291, 27), (-1, 289, 292, 23), (-1, 291, 293, 18), (-1, 299, 294, 8)], 'hashtag': '', 'sum_segment': 0},: {'photo_id': -7, 'hashtag_id': 2087736828, 'type': 529, 'x0': 174, 'x1': 213, 'y0': 263, 'y1': 306, 'score': 1.0, 'id': None, 'points': ['213,275,210,287,201,297,191,304,182,306,175,303,174,294,177,283,186,272,196,265,205,263,212,267'], 'sub_photo_id': 0, 'rles': [(-1, 203, 263, 3), (-1, 199, 264, 9), (-1, 196, 265, 14), (-1, 194, 266, 18), (-1, 193, 267, 20), (-1, 192, 268, 21), (-1, 190, 269, 23), (-1, 189, 270, 24), (-1, 187, 271, 27), (-1, 186, 272, 28), (-1, 185, 273, 29), (-1, 184, 274, 30), (-1, 184, 275, 30), (-1, 183, 276, 31), (-1, 182, 277, 31), (-1, 181, 278, 32), (-1, 180, 279, 33), (-1, 179, 280, 34), (-1, 179, 281, 33), (-1, 178, 282, 34), (-1, 177, 283, 35), (-1, 177, 284, 35), (-1, 176, 285, 35), (-1, 176, 286, 35), (-1, 176, 287, 35), (-1, 176, 288, 34), (-1, 175, 289, 34), (-1, 175, 290, 33), (-1, 175, 291, 32), (-1, 175, 292, 31), (-1, 174, 293, 32), (-1, 174, 294, 31), (-1, 174, 295, 30), (-1, 174, 296, 29), (-1, 174, 297, 28), (-1, 174, 298, 27), (-1, 175, 299, 24), (-1, 175, 300, 23), (-1, 175, 301, 22), (-1, 175, 302, 20), (-1, 175, 303, 19), (-1, 177, 304, 15), (-1, 179, 305, 10), (-1, 181, 306, 4)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 105 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 105 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.25881905 0.96592583] [-0.96592583 -0.25881905]] 105 [[-0.25881905 0.96592583] [-0.96592583 -0.25881905]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003871917724609375 nb_pixel_total : 694 time to create 1 rle with old method : 0.0011684894561767578 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003695487976074219 nb_pixel_total : 1162 time to create 1 rle with old method : 0.001756906509399414 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -8, 'hashtag_id': 2087736828, 'type': 529, 'x0': 209, 'x1': 242, 'y0': 248, 'y1': 296, 'score': 1.0, 'id': None, 'points': ['241,257,242,269,236,281,228,291,220,295,212,294,209,285,209,274,215,261,222,252,231,247,238,249'], 'sub_photo_id': 0, 'rles': [(-1, 229, 248, 3), (-1, 233, 248, 1), (-1, 229, 249, 10), (-1, 226, 250, 14), (-1, 225, 251, 15), (-1, 223, 252, 17), (-1, 222, 253, 19), (-1, 221, 254, 21), (-1, 221, 255, 21), (-1, 220, 256, 23), (-1, 219, 257, 24), (-1, 218, 258, 25), (-1, 217, 259, 26), (-1, 216, 260, 27), (-1, 215, 261, 28), (-1, 215, 262, 28), (-1, 214, 263, 29), (-1, 214, 264, 29), (-1, 214, 265, 29), (-1, 213, 266, 30), (-1, 213, 267, 30), (-1, 212, 268, 31), (-1, 212, 269, 31), (-1, 211, 270, 32), (-1, 210, 271, 32), (-1, 211, 272, 31), (-1, 210, 273, 31), (-1, 210, 274, 31), (-1, 209, 275, 31), (-1, 209, 276, 31), (-1, 209, 277, 31), (-1, 209, 278, 30), (-1, 209, 279, 29), (-1, 209, 280, 29), (-1, 209, 281, 28), (-1, 209, 282, 28), (-1, 209, 283, 27), (-1, 210, 284, 25), (-1, 209, 285, 25), (-1, 209, 286, 25), (-1, 209, 287, 24), (-1, 210, 288, 22), (-1, 210, 289, 21), (-1, 210, 290, 21), (-1, 212, 291, 17), (-1, 212, 292, 15), (-1, 212, 293, 14), (-1, 212, 294, 12), (-1, 215, 295, 8), (-1, 219, 296, 1)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 120 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 120 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.5 0.8660254] [-0.8660254 -0.5 ]] 120 [[-0.5 0.8660254] [-0.8660254 -0.5 ]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003418922424316406 nb_pixel_total : 221 time to create 1 rle with old method : 0.0003829002380371094 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003731250762939453 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0014109611511230469 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -9, 'hashtag_id': 2087736828, 'type': 529, 'x0': 238, 'x1': 268, 'y0': 227, 'y1': 276, 'score': 1.0, 'id': None, 'points': ['264,233,268,245,265,258,260,269,253,275,245,276,240,269,237,258,239,244,245,232,251,226,259,226'], 'sub_photo_id': 0, 'rles': [(-1, 251, 227, 10), (-1, 250, 228, 12), (-1, 249, 229, 13), (-1, 248, 230, 16), (-1, 247, 231, 18), (-1, 246, 232, 19), (-1, 245, 233, 21), (-1, 245, 234, 21), (-1, 244, 235, 22), (-1, 244, 236, 22), (-1, 243, 237, 24), (-1, 243, 238, 24), (-1, 242, 239, 25), (-1, 242, 240, 26), (-1, 242, 241, 26), (-1, 241, 242, 27), (-1, 240, 243, 28), (-1, 240, 244, 29), (-1, 240, 245, 29), (-1, 240, 246, 29), (-1, 239, 247, 29), (-1, 240, 248, 28), (-1, 239, 249, 29), (-1, 239, 250, 29), (-1, 239, 251, 28), (-1, 239, 252, 29), (-1, 238, 253, 30), (-1, 238, 254, 29), (-1, 238, 255, 29), (-1, 238, 256, 29), (-1, 238, 257, 29), (-1, 238, 258, 28), (-1, 238, 259, 28), (-1, 238, 260, 28), (-1, 238, 261, 27), (-1, 239, 262, 25), (-1, 239, 263, 25), (-1, 239, 264, 25), (-1, 239, 265, 24), (-1, 240, 266, 23), (-1, 240, 267, 22), (-1, 240, 268, 22), (-1, 240, 269, 21), (-1, 241, 270, 19), (-1, 241, 271, 18), (-1, 242, 272, 17), (-1, 244, 273, 13), (-1, 244, 274, 12), (-1, 245, 275, 11), (-1, 246, 276, 8)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 135 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 135 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.70710678 0.70710678] [-0.70710678 -0.70710678]] 135 [[-0.70710678 0.70710678] [-0.70710678 -0.70710678]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003495216369628906 nb_pixel_total : 143 time to create 1 rle with old method : 0.0003566741943359375 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00036072731018066406 nb_pixel_total : 1160 time to create 1 rle with old method : 0.001926422119140625 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -10, 'hashtag_id': 2087736828, 'type': 529, 'x0': 258, 'x1': 286, 'y0': 199, 'y1': 251, 'score': 1.0, 'id': None, 'points': ['279,204,285,214,286,228,284,240,279,247,272,250,265,245,259,235,258,221,260,209,265,201,273,199'], 'sub_photo_id': 0, 'rles': [(-1, 272, 199, 2), (-1, 269, 200, 6), (-1, 265, 201, 1), (-1, 267, 201, 9), (-1, 277, 201, 1), (-1, 264, 202, 15), (-1, 264, 203, 16), (-1, 264, 204, 17), (-1, 263, 205, 18), (-1, 262, 206, 19), (-1, 262, 207, 20), (-1, 261, 208, 22), (-1, 260, 209, 23), (-1, 261, 210, 23), (-1, 260, 211, 25), (-1, 260, 212, 25), (-1, 260, 213, 25), (-1, 260, 214, 26), (-1, 260, 215, 27), (-1, 259, 216, 28), (-1, 259, 217, 28), (-1, 259, 218, 28), (-1, 259, 219, 28), (-1, 258, 220, 29), (-1, 258, 221, 29), (-1, 258, 222, 29), (-1, 258, 223, 29), (-1, 258, 224, 29), (-1, 259, 225, 28), (-1, 259, 226, 28), (-1, 259, 227, 28), (-1, 259, 228, 28), (-1, 259, 229, 28), (-1, 259, 230, 28), (-1, 259, 231, 28), (-1, 259, 232, 28), (-1, 260, 233, 27), (-1, 260, 234, 27), (-1, 260, 235, 27), (-1, 260, 236, 26), (-1, 261, 237, 25), (-1, 261, 238, 25), (-1, 262, 239, 23), (-1, 263, 240, 22), (-1, 263, 241, 22), (-1, 263, 242, 21), (-1, 264, 243, 20), (-1, 265, 244, 18), (-1, 265, 245, 17), (-1, 266, 246, 16), (-1, 267, 247, 15), (-1, 268, 248, 13), (-1, 270, 249, 8), (-1, 279, 249, 1), (-1, 271, 250, 5), (-1, 272, 251, 1)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 150 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 150 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.8660254 0.5 ] [-0.5 -0.8660254]] 150 [[-0.8660254 0.5 ] [-0.5 -0.8660254]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00038552284240722656 nb_pixel_total : 414 time to create 1 rle with old method : 0.0007407665252685547 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00037479400634765625 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0016050338745117188 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003731250762939453 nb_pixel_total : 1 time to create 1 rle with old method : 2.2172927856445312e-05 len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -11, 'hashtag_id': 2087736828, 'type': 529, 'x0': 269, 'x1': 300, 'y0': 168, 'y1': 218, 'score': 1.0, 'id': None, 'points': ['286,171,295,179,299,192,300,204,297,213,291,218,283,214,275,206,270,193,268,180,271,172,278,168'], 'sub_photo_id': 0, 'rles': [(-1, 278, 168, 2), (-1, 277, 169, 5), (-1, 283, 169, 1), (-1, 275, 170, 10), (-1, 273, 171, 14), (-1, 272, 172, 16), (-1, 271, 173, 18), (-1, 271, 174, 19), (-1, 271, 175, 20), (-1, 271, 176, 21), (-1, 270, 177, 24), (-1, 270, 178, 24), (-1, 270, 179, 25), (-1, 269, 180, 27), (-1, 269, 181, 27), (-1, 269, 182, 27), (-1, 269, 183, 28), (-1, 269, 184, 28), (-1, 269, 185, 28), (-1, 270, 186, 27), (-1, 270, 187, 29), (-1, 269, 188, 30), (-1, 270, 189, 29), (-1, 270, 190, 29), (-1, 270, 191, 30), (-1, 270, 192, 30), (-1, 270, 193, 30), (-1, 270, 194, 30), (-1, 271, 195, 29), (-1, 271, 196, 29), (-1, 272, 197, 28), (-1, 272, 198, 29), (-1, 272, 199, 29), (-1, 273, 200, 28), (-1, 273, 201, 28), (-1, 273, 202, 28), (-1, 274, 203, 27), (-1, 274, 204, 27), (-1, 275, 205, 26), (-1, 275, 206, 26), (-1, 275, 207, 25), (-1, 276, 208, 25), (-1, 277, 209, 23), (-1, 279, 210, 20), (-1, 279, 211, 20), (-1, 280, 212, 19), (-1, 282, 213, 17), (-1, 282, 214, 16), (-1, 283, 215, 14), (-1, 285, 216, 11), (-1, 289, 217, 5), (-1, 291, 218, 2)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 165 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 165 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.96592583 0.25881905] [-0.25881905 -0.96592583]] 165 [[-0.96592583 0.25881905] [-0.25881905 -0.96592583]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00045871734619140625 nb_pixel_total : 1204 time to create 1 rle with old method : 0.0020189285278320312 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004277229309082031 nb_pixel_total : 1158 time to create 1 rle with old method : 0.0020508766174316406 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00036716461181640625 nb_pixel_total : 264 time to create 1 rle with old method : 0.0004665851593017578 On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -12, 'hashtag_id': 2087736828, 'type': 529, 'x0': 271, 'x1': 308, 'y0': 137, 'y1': 182, 'score': 1.0, 'id': None, 'points': ['285,137,295,143,303,155,307,166,306,175,302,181,293,180,283,174,275,163,270,151,271,142,277,136'], 'sub_photo_id': 0, 'rles': [(-1, 276, 137, 4), (-1, 281, 137, 3), (-1, 276, 138, 11), (-1, 274, 139, 14), (-1, 274, 140, 17), (-1, 273, 141, 18), (-1, 272, 142, 22), (-1, 271, 143, 24), (-1, 271, 144, 26), (-1, 271, 145, 26), (-1, 271, 146, 27), (-1, 271, 147, 28), (-1, 271, 148, 28), (-1, 271, 149, 29), (-1, 271, 150, 30), (-1, 271, 151, 31), (-1, 271, 152, 31), (-1, 272, 153, 31), (-1, 272, 154, 31), (-1, 272, 155, 32), (-1, 273, 156, 32), (-1, 273, 157, 32), (-1, 273, 158, 32), (-1, 274, 159, 31), (-1, 274, 160, 32), (-1, 275, 161, 32), (-1, 275, 162, 32), (-1, 275, 163, 32), (-1, 276, 164, 32), (-1, 276, 165, 32), (-1, 277, 166, 31), (-1, 278, 167, 30), (-1, 279, 168, 29), (-1, 280, 169, 28), (-1, 280, 170, 29), (-1, 281, 171, 27), (-1, 281, 172, 27), (-1, 283, 173, 25), (-1, 283, 174, 25), (-1, 284, 175, 24), (-1, 285, 176, 23), (-1, 287, 177, 21), (-1, 289, 178, 17), (-1, 290, 179, 15), (-1, 292, 180, 13), (-1, 294, 181, 10), (-1, 301, 182, 2)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 180 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 180 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-1.0000000e+00 1.2246468e-16] [-1.2246468e-16 -1.0000000e+00]] 180 [[-1.0000000e+00 1.2246468e-16] [-1.2246468e-16 -1.0000000e+00]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00040435791015625 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0017421245574951172 .time for calcul the mask position with numpy : 0.0003573894500732422 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0014653205871582031 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 2 list_crops_rotate : : {'photo_id': -13, 'hashtag_id': 2087736828, 'type': 529, 'x0': 258, 'x1': 294, 'y0': 1, 'y1': 52, 'score': 1.0, 'id': None, 'points': ['287,41,279,50,269,52,262,49,258,39,259,26,265,13,273,4,282,1,290,5,294,15,293,28'], 'sub_photo_id': 0, 'rles': [(-1, 281, 1, 2), (-1, 278, 2, 7), (-1, 275, 3, 12), (-1, 273, 4, 16), (-1, 272, 5, 19), (-1, 271, 6, 20), (-1, 270, 7, 22), (-1, 269, 8, 23), (-1, 269, 9, 24), (-1, 268, 10, 25), (-1, 267, 11, 26), (-1, 266, 12, 28), (-1, 265, 13, 29), (-1, 265, 14, 30), (-1, 264, 15, 31), (-1, 264, 16, 31), (-1, 263, 17, 32), (-1, 263, 18, 32), (-1, 262, 19, 33), (-1, 262, 20, 33), (-1, 261, 21, 34), (-1, 261, 22, 33), (-1, 260, 23, 34), (-1, 260, 24, 34), (-1, 259, 25, 35), (-1, 259, 26, 35), (-1, 259, 27, 35), (-1, 259, 28, 35), (-1, 259, 29, 35), (-1, 259, 30, 34), (-1, 259, 31, 34), (-1, 259, 32, 33), (-1, 258, 33, 34), (-1, 258, 34, 33), (-1, 258, 35, 33), (-1, 258, 36, 32), (-1, 258, 37, 32), (-1, 258, 38, 31), (-1, 258, 39, 31), (-1, 258, 40, 30), (-1, 259, 41, 29), (-1, 259, 42, 28), (-1, 260, 43, 26), (-1, 260, 44, 25), (-1, 260, 45, 24), (-1, 261, 46, 23), (-1, 261, 47, 22), (-1, 262, 48, 20), (-1, 262, 49, 19), (-1, 264, 50, 16), (-1, 266, 51, 11), (-1, 268, 52, 4)], 'hashtag': '', 'sum_segment': 0},: {'photo_id': -13, 'hashtag_id': 2087736828, 'type': 529, 'x0': 263, 'x1': 306, 'y0': 107, 'y1': 146, 'score': 1.0, 'id': None, 'points': ['275,107,287,110,297,119,304,129,306,138,303,145,294,146,283,143,272,134,265,124,263,115,267,108'], 'sub_photo_id': 0, 'rles': [(-1, 271, 107, 6), (-1, 267, 108, 14), (-1, 266, 109, 19), (-1, 266, 110, 22), (-1, 265, 111, 24), (-1, 265, 112, 25), (-1, 264, 113, 27), (-1, 264, 114, 28), (-1, 263, 115, 31), (-1, 263, 116, 32), (-1, 263, 117, 33), (-1, 264, 118, 33), (-1, 264, 119, 34), (-1, 264, 120, 35), (-1, 264, 121, 35), (-1, 265, 122, 35), (-1, 265, 123, 36), (-1, 265, 124, 37), (-1, 266, 125, 36), (-1, 266, 126, 37), (-1, 267, 127, 37), (-1, 268, 128, 36), (-1, 269, 129, 36), (-1, 269, 130, 36), (-1, 270, 131, 35), (-1, 271, 132, 35), (-1, 271, 133, 35), (-1, 272, 134, 34), (-1, 273, 135, 33), (-1, 274, 136, 33), (-1, 276, 137, 31), (-1, 277, 138, 30), (-1, 278, 139, 29), (-1, 279, 140, 27), (-1, 280, 141, 26), (-1, 282, 142, 23), (-1, 283, 143, 22), (-1, 285, 144, 19), (-1, 289, 145, 15), (-1, 293, 146, 6)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 195 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 195 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.96592583 -0.25881905] [ 0.25881905 -0.96592583]] 195 [[-0.96592583 -0.25881905] [ 0.25881905 -0.96592583]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00039005279541015625 nb_pixel_total : 727 time to create 1 rle with old method : 0.0012383460998535156 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003952980041503906 nb_pixel_total : 1162 time to create 1 rle with old method : 0.001955747604370117 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -14, 'hashtag_id': 2087736828, 'type': 529, 'x0': 248, 'x1': 296, 'y0': 78, 'y1': 111, 'score': 1.0, 'id': None, 'points': ['257,78,269,77,281,83,291,91,295,99,294,107,285,110,274,110,261,104,252,97,247,88,249,81'], 'sub_photo_id': 0, 'rles': [(-1, 256, 78, 15), (-1, 254, 79, 19), (-1, 253, 80, 22), (-1, 250, 81, 28), (-1, 249, 82, 30), (-1, 249, 83, 32), (-1, 249, 84, 34), (-1, 249, 85, 35), (-1, 249, 86, 36), (-1, 248, 87, 39), (-1, 249, 88, 39), (-1, 248, 89, 41), (-1, 248, 90, 43), (-1, 248, 91, 43), (-1, 250, 92, 42), (-1, 250, 93, 42), (-1, 250, 94, 43), (-1, 251, 95, 43), (-1, 252, 96, 42), (-1, 252, 97, 43), (-1, 253, 98, 43), (-1, 254, 99, 42), (-1, 256, 100, 40), (-1, 257, 101, 40), (-1, 258, 102, 38), (-1, 259, 103, 37), (-1, 260, 104, 36), (-1, 261, 105, 35), (-1, 263, 106, 32), (-1, 266, 107, 29), (-1, 268, 108, 27), (-1, 270, 109, 21), (-1, 271, 110, 1), (-1, 273, 110, 18), (-1, 275, 111, 9), (-1, 285, 111, 3)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 210 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 210 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.8660254 -0.5 ] [ 0.5 -0.8660254]] 210 [[-0.8660254 -0.5 ] [ 0.5 -0.8660254]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00042939186096191406 nb_pixel_total : 250 time to create 1 rle with old method : 0.0004570484161376953 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003829002380371094 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0019466876983642578 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -15, 'hashtag_id': 2087736828, 'type': 529, 'x0': 227, 'x1': 276, 'y0': 52, 'y1': 82, 'score': 1.0, 'id': None, 'points': ['233,55,245,51,258,54,269,59,275,66,276,74,269,79,258,82,244,80,232,74,226,68,226,60'], 'sub_photo_id': 0, 'rles': [(-1, 244, 52, 3), (-1, 240, 53, 11), (-1, 252, 53, 2), (-1, 237, 54, 21), (-1, 233, 55, 28), (-1, 231, 56, 31), (-1, 230, 57, 35), (-1, 230, 58, 37), (-1, 228, 59, 41), (-1, 227, 60, 43), (-1, 227, 61, 44), (-1, 227, 62, 46), (-1, 227, 63, 46), (-1, 227, 64, 47), (-1, 227, 65, 49), (-1, 227, 66, 49), (-1, 227, 67, 50), (-1, 227, 68, 50), (-1, 227, 69, 50), (-1, 228, 70, 49), (-1, 229, 71, 48), (-1, 230, 72, 47), (-1, 231, 73, 46), (-1, 232, 74, 45), (-1, 233, 75, 43), (-1, 235, 76, 40), (-1, 237, 77, 36), (-1, 239, 78, 34), (-1, 242, 79, 30), (-1, 243, 80, 27), (-1, 247, 81, 1), (-1, 249, 81, 17), (-1, 253, 82, 9)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 225 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 225 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.70710678 -0.70710678] [ 0.70710678 -0.70710678]] 225 [[-0.70710678 -0.70710678] [ 0.70710678 -0.70710678]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003864765167236328 nb_pixel_total : 169 time to create 1 rle with old method : 0.00032329559326171875 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004019737243652344 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0019314289093017578 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -16, 'hashtag_id': 2087736828, 'type': 529, 'x0': 199, 'x1': 251, 'y0': 34, 'y1': 62, 'score': 1.0, 'id': None, 'points': ['204,40,214,34,228,33,240,35,247,40,250,47,245,54,235,60,221,61,209,59,201,54,199,46'], 'sub_photo_id': 0, 'rles': [(-1, 215, 34, 21), (-1, 214, 35, 25), (-1, 211, 36, 31), (-1, 210, 37, 34), (-1, 208, 38, 37), (-1, 207, 39, 41), (-1, 204, 40, 45), (-1, 203, 41, 47), (-1, 202, 42, 47), (-1, 201, 43, 49), (-1, 202, 44, 48), (-1, 201, 45, 50), (-1, 200, 46, 51), (-1, 199, 47, 52), (-1, 199, 48, 53), (-1, 200, 49, 51), (-1, 200, 50, 50), (-1, 200, 51, 49), (-1, 201, 52, 48), (-1, 201, 53, 47), (-1, 201, 54, 46), (-1, 201, 55, 45), (-1, 202, 56, 42), (-1, 205, 57, 38), (-1, 206, 58, 34), (-1, 208, 59, 31), (-1, 209, 60, 1), (-1, 211, 60, 26), (-1, 216, 61, 17), (-1, 220, 62, 5)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 240 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 240 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.5 -0.8660254] [ 0.8660254 -0.5 ]] 240 [[-0.5 -0.8660254] [ 0.8660254 -0.5 ]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003783702850341797 nb_pixel_total : 450 time to create 1 rle with old method : 0.000774383544921875 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003972053527832031 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0018970966339111328 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00033664703369140625 nb_pixel_total : 1 time to create 1 rle with old method : 2.574920654296875e-05 len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -17, 'hashtag_id': 2087736828, 'type': 529, 'x0': 168, 'x1': 218, 'y0': 20, 'y1': 51, 'score': 1.0, 'id': None, 'points': ['171,33,179,24,192,20,204,19,213,22,218,28,214,36,206,44,193,49,180,51,172,48,168,41'], 'sub_photo_id': 0, 'rles': [(-1, 198, 20, 9), (-1, 208, 20, 1), (-1, 191, 21, 19), (-1, 187, 22, 27), (-1, 187, 23, 28), (-1, 183, 24, 33), (-1, 180, 25, 37), (-1, 179, 26, 38), (-1, 177, 27, 41), (-1, 177, 28, 42), (-1, 176, 29, 43), (-1, 175, 30, 43), (-1, 174, 31, 44), (-1, 173, 32, 44), (-1, 172, 33, 45), (-1, 171, 34, 46), (-1, 171, 35, 46), (-1, 170, 36, 46), (-1, 169, 37, 47), (-1, 170, 38, 45), (-1, 169, 39, 44), (-1, 169, 40, 44), (-1, 168, 41, 44), (-1, 168, 42, 42), (-1, 169, 43, 41), (-1, 170, 44, 39), (-1, 170, 45, 38), (-1, 171, 46, 34), (-1, 171, 47, 32), (-1, 172, 48, 28), (-1, 173, 49, 24), (-1, 177, 50, 18), (-1, 180, 51, 6), (-1, 188, 51, 1)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 255 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 255 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.25881905 -0.96592583] [ 0.96592583 -0.25881905]] 255 [[-0.25881905 -0.96592583] [ 0.96592583 -0.25881905]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.000415802001953125 nb_pixel_total : 1237 time to create 1 rle with old method : 0.002018451690673828 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00039887428283691406 nb_pixel_total : 1158 time to create 1 rle with old method : 0.00200653076171875 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003685951232910156 nb_pixel_total : 234 time to create 1 rle with old method : 0.00041556358337402344 On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -18, 'hashtag_id': 2087736828, 'type': 529, 'x0': 137, 'x1': 182, 'y0': 12, 'y1': 49, 'score': 1.0, 'id': None, 'points': ['137,34,143,24,155,16,166,12,175,13,181,17,180,26,174,36,163,44,151,49,142,48,136,42'], 'sub_photo_id': 0, 'rles': [(-1, 170, 12, 1), (-1, 164, 13, 14), (-1, 161, 14, 17), (-1, 160, 15, 19), (-1, 156, 16, 25), (-1, 155, 17, 27), (-1, 153, 18, 30), (-1, 151, 19, 32), (-1, 150, 20, 32), (-1, 149, 21, 33), (-1, 147, 22, 35), (-1, 146, 23, 36), (-1, 144, 24, 38), (-1, 144, 25, 38), (-1, 143, 26, 39), (-1, 142, 27, 39), (-1, 142, 28, 39), (-1, 142, 29, 38), (-1, 140, 30, 40), (-1, 140, 31, 39), (-1, 140, 32, 38), (-1, 139, 33, 39), (-1, 138, 34, 39), (-1, 138, 35, 39), (-1, 138, 36, 38), (-1, 137, 37, 38), (-1, 137, 38, 36), (-1, 137, 39, 36), (-1, 138, 40, 33), (-1, 137, 41, 32), (-1, 137, 42, 31), (-1, 137, 43, 30), (-1, 137, 44, 29), (-1, 139, 45, 25), (-1, 139, 46, 22), (-1, 141, 47, 18), (-1, 142, 48, 14), (-1, 143, 49, 10)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 270 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 270 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-1.8369702e-16 -1.0000000e+00] [ 1.0000000e+00 -1.8369702e-16]] 270 [[-1.8369702e-16 -1.0000000e+00] [ 1.0000000e+00 -1.8369702e-16]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00040149688720703125 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0017833709716796875 .time for calcul the mask position with numpy : 0.0004591941833496094 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0015835762023925781 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 2 list_crops_rotate : : {'photo_id': -19, 'hashtag_id': 2087736828, 'type': 529, 'x0': 1, 'x1': 52, 'y0': 26, 'y1': 62, 'score': 1.0, 'id': None, 'points': ['41,32,50,40,52,50,49,57,39,61,26,60,13,54,4,46,1,37,5,29,15,25,28,26'], 'sub_photo_id': 0, 'rles': [(-1, 14, 26, 8), (-1, 12, 27, 18), (-1, 9, 28, 23), (-1, 7, 29, 27), (-1, 5, 30, 31), (-1, 5, 31, 33), (-1, 4, 32, 36), (-1, 4, 33, 38), (-1, 3, 34, 40), (-1, 3, 35, 41), (-1, 2, 36, 43), (-1, 2, 37, 45), (-1, 1, 38, 47), (-1, 1, 39, 48), (-1, 2, 40, 48), (-1, 2, 41, 49), (-1, 2, 42, 49), (-1, 3, 43, 48), (-1, 3, 44, 49), (-1, 3, 45, 49), (-1, 4, 46, 48), (-1, 4, 47, 48), (-1, 5, 48, 47), (-1, 6, 49, 47), (-1, 7, 50, 46), (-1, 8, 51, 45), (-1, 10, 52, 43), (-1, 11, 53, 41), (-1, 12, 54, 40), (-1, 13, 55, 38), (-1, 15, 56, 36), (-1, 17, 57, 33), (-1, 19, 58, 31), (-1, 21, 59, 27), (-1, 23, 60, 23), (-1, 25, 61, 18), (-1, 33, 62, 8)], 'hashtag': '', 'sum_segment': 0},: {'photo_id': -19, 'hashtag_id': 2087736828, 'type': 529, 'x0': 107, 'x1': 146, 'y0': 14, 'y1': 57, 'score': 1.0, 'id': None, 'points': ['107,44,110,32,119,22,129,15,138,13,144,16,145,25,143,36,134,47,124,54,115,56,108,52'], 'sub_photo_id': 0, 'rles': [(-1, 136, 14, 4), (-1, 132, 15, 10), (-1, 129, 16, 15), (-1, 127, 17, 19), (-1, 126, 18, 20), (-1, 124, 19, 22), (-1, 123, 20, 23), (-1, 122, 21, 24), (-1, 120, 22, 27), (-1, 119, 23, 28), (-1, 118, 24, 29), (-1, 117, 25, 30), (-1, 116, 26, 31), (-1, 115, 27, 32), (-1, 115, 28, 31), (-1, 114, 29, 32), (-1, 113, 30, 33), (-1, 112, 31, 34), (-1, 111, 32, 34), (-1, 110, 33, 35), (-1, 110, 34, 35), (-1, 110, 35, 35), (-1, 109, 36, 35), (-1, 109, 37, 35), (-1, 109, 38, 34), (-1, 109, 39, 33), (-1, 108, 40, 34), (-1, 108, 41, 33), (-1, 108, 42, 32), (-1, 108, 43, 31), (-1, 107, 44, 31), (-1, 107, 45, 30), (-1, 107, 46, 30), (-1, 107, 47, 29), (-1, 107, 48, 28), (-1, 107, 49, 27), (-1, 108, 50, 24), (-1, 108, 51, 23), (-1, 108, 52, 21), (-1, 108, 53, 20), (-1, 109, 54, 18), (-1, 111, 55, 14), (-1, 113, 56, 9), (-1, 115, 57, 3)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 285 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 285 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.25881905 -0.96592583] [ 0.96592583 0.25881905]] 285 [[ 0.25881905 -0.96592583] [ 0.96592583 0.25881905]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.000396728515625 nb_pixel_total : 727 time to create 1 rle with old method : 0.0012164115905761719 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00042176246643066406 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0019354820251464844 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -20, 'hashtag_id': 2087736828, 'type': 529, 'x0': 78, 'x1': 111, 'y0': 24, 'y1': 72, 'score': 1.0, 'id': None, 'points': ['78,62,77,50,83,38,91,28,99,24,107,25,110,34,110,45,104,58,97,67,88,72,81,70'], 'sub_photo_id': 0, 'rles': [(-1, 101, 24, 1), (-1, 98, 25, 8), (-1, 97, 26, 12), (-1, 95, 27, 14), (-1, 94, 28, 15), (-1, 92, 29, 17), (-1, 90, 30, 21), (-1, 90, 31, 21), (-1, 89, 32, 22), (-1, 88, 33, 24), (-1, 87, 34, 25), (-1, 87, 35, 25), (-1, 86, 36, 25), (-1, 85, 37, 27), (-1, 84, 38, 28), (-1, 84, 39, 28), (-1, 83, 40, 29), (-1, 83, 41, 29), (-1, 82, 42, 30), (-1, 81, 43, 31), (-1, 81, 44, 31), (-1, 81, 45, 31), (-1, 80, 46, 31), (-1, 80, 47, 31), (-1, 79, 48, 31), (-1, 79, 49, 32), (-1, 78, 50, 32), (-1, 78, 51, 31), (-1, 78, 52, 31), (-1, 78, 53, 30), (-1, 78, 54, 30), (-1, 78, 55, 29), (-1, 78, 56, 29), (-1, 78, 57, 29), (-1, 78, 58, 28), (-1, 78, 59, 28), (-1, 78, 60, 27), (-1, 78, 61, 26), (-1, 78, 62, 25), (-1, 78, 63, 24), (-1, 78, 64, 23), (-1, 79, 65, 21), (-1, 79, 66, 21), (-1, 80, 67, 19), (-1, 81, 68, 17), (-1, 81, 69, 15), (-1, 81, 70, 14), (-1, 82, 71, 10), (-1, 87, 72, 1), (-1, 89, 72, 3)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 300 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 300 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.5 -0.8660254] [ 0.8660254 0.5 ]] 300 [[ 0.5 -0.8660254] [ 0.8660254 0.5 ]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003952980041503906 nb_pixel_total : 250 time to create 1 rle with old method : 0.0003819465637207031 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004143714904785156 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0014328956604003906 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -21, 'hashtag_id': 2087736828, 'type': 529, 'x0': 52, 'x1': 82, 'y0': 44, 'y1': 93, 'score': 1.0, 'id': None, 'points': ['55,86,51,74,54,61,59,50,66,44,74,43,79,50,82,61,80,75,74,87,68,93,60,93'], 'sub_photo_id': 0, 'rles': [(-1, 67, 44, 8), (-1, 65, 45, 11), (-1, 65, 46, 12), (-1, 64, 47, 13), (-1, 62, 48, 17), (-1, 62, 49, 18), (-1, 61, 50, 19), (-1, 60, 51, 21), (-1, 59, 52, 22), (-1, 59, 53, 22), (-1, 58, 54, 23), (-1, 58, 55, 24), (-1, 57, 56, 25), (-1, 57, 57, 25), (-1, 57, 58, 25), (-1, 56, 59, 27), (-1, 55, 60, 28), (-1, 55, 61, 28), (-1, 55, 62, 28), (-1, 54, 63, 29), (-1, 54, 64, 29), (-1, 54, 65, 29), (-1, 54, 66, 29), (-1, 53, 67, 30), (-1, 53, 68, 29), (-1, 54, 69, 28), (-1, 53, 70, 29), (-1, 53, 71, 29), (-1, 53, 72, 28), (-1, 53, 73, 29), (-1, 52, 74, 29), (-1, 52, 75, 29), (-1, 52, 76, 29), (-1, 53, 77, 28), (-1, 53, 78, 27), (-1, 53, 79, 26), (-1, 53, 80, 26), (-1, 54, 81, 25), (-1, 54, 82, 24), (-1, 54, 83, 24), (-1, 55, 84, 22), (-1, 55, 85, 22), (-1, 55, 86, 21), (-1, 55, 87, 21), (-1, 56, 88, 19), (-1, 56, 89, 18), (-1, 57, 90, 16), (-1, 59, 91, 13), (-1, 59, 92, 12), (-1, 60, 93, 10)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 315 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 315 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.70710678 -0.70710678] [ 0.70710678 0.70710678]] 315 [[ 0.70710678 -0.70710678] [ 0.70710678 0.70710678]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003886222839355469 nb_pixel_total : 169 time to create 1 rle with old method : 0.0003085136413574219 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00039005279541015625 nb_pixel_total : 1161 time to create 1 rle with old method : 0.001468658447265625 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -22, 'hashtag_id': 2087736828, 'type': 529, 'x0': 34, 'x1': 62, 'y0': 69, 'y1': 121, 'score': 1.0, 'id': None, 'points': ['40,115,34,105,33,91,35,79,40,72,47,69,54,74,60,84,61,98,59,110,54,118,46,120'], 'sub_photo_id': 0, 'rles': [(-1, 48, 69, 1), (-1, 45, 70, 5), (-1, 41, 71, 1), (-1, 43, 71, 8), (-1, 40, 72, 13), (-1, 39, 73, 15), (-1, 39, 74, 16), (-1, 39, 75, 17), (-1, 38, 76, 18), (-1, 37, 77, 20), (-1, 37, 78, 21), (-1, 36, 79, 22), (-1, 36, 80, 22), (-1, 36, 81, 23), (-1, 35, 82, 25), (-1, 35, 83, 25), (-1, 35, 84, 26), (-1, 34, 85, 27), (-1, 34, 86, 27), (-1, 34, 87, 27), (-1, 34, 88, 28), (-1, 34, 89, 28), (-1, 34, 90, 28), (-1, 34, 91, 28), (-1, 34, 92, 28), (-1, 34, 93, 28), (-1, 34, 94, 28), (-1, 34, 95, 28), (-1, 34, 96, 29), (-1, 34, 97, 29), (-1, 34, 98, 29), (-1, 34, 99, 29), (-1, 34, 100, 29), (-1, 34, 101, 28), (-1, 34, 102, 28), (-1, 34, 103, 28), (-1, 34, 104, 28), (-1, 34, 105, 27), (-1, 35, 106, 26), (-1, 36, 107, 25), (-1, 36, 108, 25), (-1, 36, 109, 25), (-1, 37, 110, 23), (-1, 38, 111, 23), (-1, 38, 112, 22), (-1, 39, 113, 20), (-1, 40, 114, 19), (-1, 40, 115, 18), (-1, 40, 116, 17), (-1, 41, 117, 16), (-1, 42, 118, 15), (-1, 43, 119, 1), (-1, 45, 119, 11), (-1, 46, 120, 6), (-1, 47, 121, 2)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 330 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 330 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.8660254 -0.5 ] [ 0.5 0.8660254]] 330 [[ 0.8660254 -0.5 ] [ 0.5 0.8660254]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00036454200744628906 nb_pixel_total : 450 time to create 1 rle with old method : 0.0007612705230712891 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003685951232910156 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0014767646789550781 . crop are not in the shrunk photo ! On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -23, 'hashtag_id': 2087736828, 'type': 529, 'x0': 20, 'x1': 51, 'y0': 102, 'y1': 152, 'score': 1.0, 'id': None, 'points': ['33,148,24,140,20,127,19,115,22,106,28,101,36,105,44,113,49,126,51,139,48,147,41,151'], 'sub_photo_id': 0, 'rles': [(-1, 28, 102, 2), (-1, 27, 103, 5), (-1, 25, 104, 11), (-1, 24, 105, 14), (-1, 23, 106, 16), (-1, 22, 107, 17), (-1, 22, 108, 19), (-1, 22, 109, 20), (-1, 22, 110, 20), (-1, 21, 111, 23), (-1, 20, 112, 25), (-1, 21, 113, 25), (-1, 20, 114, 26), (-1, 20, 115, 26), (-1, 20, 116, 27), (-1, 20, 117, 27), (-1, 20, 118, 28), (-1, 20, 119, 28), (-1, 20, 120, 28), (-1, 20, 121, 29), (-1, 20, 122, 29), (-1, 21, 123, 28), (-1, 21, 124, 29), (-1, 21, 125, 29), (-1, 21, 126, 30), (-1, 21, 127, 30), (-1, 21, 128, 30), (-1, 21, 129, 30), (-1, 22, 130, 29), (-1, 22, 131, 29), (-1, 22, 132, 30), (-1, 22, 133, 29), (-1, 24, 134, 27), (-1, 24, 135, 28), (-1, 24, 136, 28), (-1, 24, 137, 28), (-1, 25, 138, 27), (-1, 25, 139, 27), (-1, 25, 140, 27), (-1, 26, 141, 25), (-1, 27, 142, 24), (-1, 27, 143, 24), (-1, 29, 144, 21), (-1, 30, 145, 20), (-1, 31, 146, 19), (-1, 32, 147, 18), (-1, 33, 148, 16), (-1, 34, 149, 14), (-1, 36, 150, 10), (-1, 37, 151, 1), (-1, 39, 151, 5), (-1, 41, 152, 2)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1356481593 of 345 degree temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 345 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.96592583 -0.25881905] [ 0.25881905 0.96592583]] 345 [[ 0.96592583 -0.25881905] [ 0.25881905 0.96592583]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004248619079589844 nb_pixel_total : 1237 time to create 1 rle with old method : 0.0014958381652832031 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003724098205566406 nb_pixel_total : 1157 time to create 1 rle with old method : 0.02710413932800293 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00034332275390625 nb_pixel_total : 234 time to create 1 rle with old method : 0.0004391670227050781 On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -24, 'hashtag_id': 2087736828, 'type': 529, 'x0': 12, 'x1': 49, 'y0': 138, 'y1': 183, 'score': 1.0, 'id': None, 'points': ['34,182,24,176,16,164,12,153,13,144,17,138,26,139,36,145,44,156,49,168,48,177,42,183'], 'sub_photo_id': 0, 'rles': [(-1, 18, 138, 2), (-1, 17, 139, 10), (-1, 16, 140, 13), (-1, 16, 141, 15), (-1, 15, 142, 17), (-1, 13, 143, 21), (-1, 13, 144, 23), (-1, 13, 145, 24), (-1, 13, 146, 25), (-1, 13, 147, 25), (-1, 13, 148, 27), (-1, 13, 149, 27), (-1, 12, 150, 29), (-1, 13, 151, 28), (-1, 13, 152, 29), (-1, 13, 153, 30), (-1, 13, 154, 31), (-1, 13, 155, 32), (-1, 13, 156, 32), (-1, 14, 157, 32), (-1, 14, 158, 32), (-1, 14, 159, 32), (-1, 15, 160, 32), (-1, 16, 161, 31), (-1, 16, 162, 32), (-1, 16, 163, 32), (-1, 16, 164, 32), (-1, 17, 165, 32), (-1, 18, 166, 31), (-1, 18, 167, 31), (-1, 19, 168, 31), (-1, 19, 169, 31), (-1, 20, 170, 30), (-1, 21, 171, 29), (-1, 22, 172, 28), (-1, 22, 173, 28), (-1, 23, 174, 27), (-1, 24, 175, 26), (-1, 24, 176, 26), (-1, 26, 177, 24), (-1, 27, 178, 22), (-1, 30, 179, 18), (-1, 30, 180, 17), (-1, 33, 181, 14), (-1, 34, 182, 11), (-1, 37, 183, 3), (-1, 41, 183, 3)], 'hashtag': '', 'sum_segment': 0} About to upload 24 photos upload in portfolio : 22735843 init cache_photo without model_param we have 24 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1746564010_2124625 we have uploaded 24 photos in the portfolio 22735843 time of upload the photos Elapsed time : 5.744412899017334 map_filename_photo_id : 24 map_filename_photo_id : {'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg': 1356481625, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg': 1356481626, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg': 1356481627, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg': 1356481628, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg': 1356481629, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg': 1356481630, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg': 1356481631, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg': 1356481632, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg': 1356481633, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg': 1356481634, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg': 1356481635, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg': 1356481636, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg': 1356481637, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg': 1356481638, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg': 1356481639, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg': 1356481640, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg': 1356481641, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg': 1356481642, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg': 1356481643, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg': 1356481644, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg': 1356481645, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg': 1356481647, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg': 1356481648, 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg': 1356481649} Len new_chis : 24 Len list_new_chi_with_photo_id : 28 of type : 529 list_new_chi_with_photo_id : [, , , , , , , , , , , , , , , , , , , , , , , , , , , ] batch 1 Loaded 28 chid ids of type : 529 Number RLEs to save : 1197 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! After datou_step_exec type output : time spend for datou_step_exec : 9.333532333374023 time spend to save output : 9.417533874511719e-05 total time spend for step 3 : 9.333626508712769 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [937852786, 937852786, '1356481593'] map_info['map_portfolio_photo'] : {} final : True mtd_id 243 list_pids : [937852786, 937852786, '1356481593'] Looping around the photos to save general results len do output : 24 /1356481625Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481626Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481627Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481628Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481629Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481630Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481631Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481632Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481633Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481634Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481635Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481636Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481637Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481638Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481639Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481640Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481641Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481642Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481643Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481644Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481645Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481647Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481648Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481649Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('243', None, None, None, None, None, None, None, None) ('243', None, '937852786', None, None, None, None, None, None) ('243', None, None, None, None, None, None, None, None) ('243', None, '937852786', None, None, None, None, None, None) ('243', None, None, None, None, None, None, None, None) ('243', None, '1356481593', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 75 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('243', None, '1356481625', 'None', None, None, None, None, None), ('243', None, '1356481626', 'None', None, None, None, None, None), ('243', None, '1356481627', 'None', None, None, None, None, None), ('243', None, '1356481628', 'None', None, None, None, None, None), ('243', None, '1356481629', 'None', None, None, None, None, None), ('243', None, '1356481630', 'None', None, None, None, None, None), ('243', None, '1356481631', 'None', None, None, None, None, None), ('243', None, '1356481632', 'None', None, None, None, None, None), ('243', None, '1356481633', 'None', None, None, None, None, None), ('243', None, '1356481634', 'None', None, None, None, None, None), ('243', None, '1356481635', 'None', None, None, None, None, None), ('243', None, '1356481636', 'None', None, None, None, None, None), ('243', None, '1356481637', 'None', None, None, None, None, None), ('243', None, '1356481638', 'None', None, None, None, None, None), ('243', None, '1356481639', 'None', None, None, None, None, None), ('243', None, '1356481640', 'None', None, None, None, None, None), ('243', None, '1356481641', 'None', None, None, None, None, None), ('243', None, '1356481642', 'None', None, None, None, None, None), ('243', None, '1356481643', 'None', None, None, None, None, None), ('243', None, '1356481644', 'None', None, None, None, None, None), ('243', None, '1356481645', 'None', None, None, None, None, None), ('243', None, '1356481647', 'None', None, None, None, None, None), ('243', None, '1356481648', 'None', None, None, None, None, None), ('243', None, '1356481649', 'None', None, None, None, None, None), ('243', None, '937852786', None, None, None, None, None, None), ('243', None, '1356481593', None, None, None, None, None, None)] time used for this insertion : 0.022853612899780273 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1356481625: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', [, ]], 1356481626: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', []], 1356481627: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', []], 1356481628: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', []], 1356481629: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', []], 1356481630: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', []], 1356481631: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', [, ]], 1356481632: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', []], 1356481633: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', []], 1356481634: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', []], 1356481635: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', []], 1356481636: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', []], 1356481637: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', [, ]], 1356481638: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', []], 1356481639: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', []], 1356481640: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', []], 1356481641: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', []], 1356481642: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', []], 1356481643: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', [, ]], 1356481644: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', []], 1356481645: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', []], 1356481647: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', []], 1356481648: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', []], 1356481649: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg', []]} ret_da : {1356481625: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', [, ]], 1356481626: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', []], 1356481627: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', []], 1356481628: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', []], 1356481629: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', []], 1356481630: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', []], 1356481631: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', [, ]], 1356481632: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', []], 1356481633: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', []], 1356481634: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', []], 1356481635: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', []], 1356481636: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', []], 1356481637: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', [, ]], 1356481638: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', []], 1356481639: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', []], 1356481640: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', []], 1356481641: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', []], 1356481642: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', []], 1356481643: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', [, ]], 1356481644: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', []], 1356481645: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', []], 1356481647: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', []], 1356481648: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', []], 1356481649: ['937852786', 'temp/1746563975_2124625_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg', []]} list chi : [[, ], [], [], [], [], [], [, ], [], [], [], [], [], [, ], [], [], [], [], [], [, ], [], [], [], [], []] ############################### TEST flip ################################ t Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=571 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=571 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 571 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=571 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : flip list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (911785586) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 911785586 download finish for photo 911785586 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.13599872589111328 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:flip Tue May 6 22:40:19 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746564019_2124625_911785586_d8582feabcd359151ff718b5832248c7-big.jpg': 911785586} map_photo_id_path_extension : {911785586: {'path': 'temp/1746564019_2124625_911785586_d8582feabcd359151ff718b5832248c7-big.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step_flip ! We are in a linear step without datou_depend ! batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 911785586) and `type` in (741) Loaded 6 chid ids of type : 741 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (18344206,18344211,18344210,18344209,18344208,18344207) +++++WARNING : Unexpected points, we should remove this data for chi_id : 18344210, for now we just ignore these empty polygon points +SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (18344206,18344211,18344210,18344209,18344208,18344207) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (18344206,18344211,18344210,18344209,18344208,18344207) map_chi_objs : {911785586: [, , , , , ]} photo_id in download_rotate_and_save : 911785586 list_chi_loc : 6 Vertical flip of photo 911785586 version de PIL : 9.5.0 vertically flipped image is saved in temp/1746564019_2124625_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg Horizontal flip of photo 911785586 version de PIL : 9.5.0 horizontally flipped image is saved in temp/1746564019_2124625_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg About to upload 2 photos upload in portfolio : 1090565 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1746564019_2124625 we have uploaded 2 photos in the portfolio 1090565 time of upload the photos Elapsed time : 0.7867836952209473 map_filename_photo_id : 2 map_filename_photo_id : {'temp/1746564019_2124625_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg': 1356481652, 'temp/1746564019_2124625_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg': 1356481653} Len new_chis : 12 Len list_new_chi_with_photo_id : 12 of type : 741 list_new_chi_with_photo_id : [, , , , , , , , , , , ] insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) batch 1 Loaded 12 chid ids of type : 741 INSERT IGNORE INTO MTRPhoto.crop_polygon_points (`crop_hashtag_id`, `points`) VALUES (%s, %s) Number RLEs to save : 0 INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) TO DO : save crop sub photo not yet done ! After datou_step_exec type output : time spend for datou_step_exec : 0.8951923847198486 time spend to save output : 7.62939453125e-05 total time spend for step 1 : 0.8952686786651611 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : flip we use saveGeneral [911785586] map_info['map_portfolio_photo'] : {} final : True mtd_id 571 list_pids : [911785586] Looping around the photos to save general results len do output : 2 /1356481652 /1356481653 before output type Managing all output in save final without adding information in the mtr_datou_result ('571', None, None, None, None, None, None, None, None) ('571', None, '911785586', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('571', None, '911785586', None, None, None, None, None, None)] time used for this insertion : 0.015349864959716797 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1356481652': ['911785586', 'temp/1746564019_2124625_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg', [, , , , , ]], '1356481653': ['911785586', 'temp/1746564019_2124625_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg', [, , , , , ]]} ############################### TEST crop_rles ################################ # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! Unexpected type seems boolean for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : TEST CROP RLES Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=686 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=686 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 686 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=686 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : crop list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (950103132) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 950103132 download finish for photo 950103132 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.12105870246887207 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:crop Tue May 6 22:40:20 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00.jpg': 950103132} map_photo_id_path_extension : {950103132: {'path': 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Crop ! param_json : {'photo_hashtag_type': 755, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'feed_id_new_photos': 0, 'host': 'www.fotonower.com', 'crop_type': 'rle', 'margin_relative': 0.1, 'min_score': 0.3, 'upload,type': 'python'} margin_type : margin_relative margin_value : [0.1, 0.1, 0.1, 0.1] Loading chi in step crop with photo_hashtag_type : 755 Loading chi in step crop for list_pids : 1 ! batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 950103132) and `type` in (755) and score>0.3 Loaded 8 chid ids of type : 755 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947670931,1947670932,1947670933,1947670934,1947670935,1947670936,1947670937,1947670938) ++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947670931,1947670932,1947670933,1947670934,1947670935,1947670936,1947670937,1947670938) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947670931,1947670932,1947670933,1947670934,1947670935,1947670936,1947670937,1947670938) select photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type, id from MTRPhoto.photo_sub_photos where photo_id in ( 950103132) WARNING : margin is only used for type bib ! type of cropped photo chosen : rle we resize croppped photo by 1 on x axis and by 1 on y axis we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! map_result returned by crop_photo_return_map_crop : length : 8 map_result after crop : {1947670931: {'crop': 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670931_0.jpg', 'coordonates': (183, 199, 15, 41), 'sub_photo_id': -1, 'same_chi': False}, 1947670932: {'crop': 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670932_0.jpg', 'coordonates': (38, 85, 113, 140), 'sub_photo_id': -1, 'same_chi': False}, 1947670933: {'crop': 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670933_0.jpg', 'coordonates': (168, 194, 141, 151), 'sub_photo_id': -1, 'same_chi': False}, 1947670934: {'crop': 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670934_0.jpg', 'coordonates': (47, 101, 16, 110), 'sub_photo_id': -1, 'same_chi': False}, 1947670935: {'crop': 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670935_0.jpg', 'coordonates': (175, 199, 104, 111), 'sub_photo_id': -1, 'same_chi': False}, 1947670936: {'crop': 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670936_0.jpg', 'coordonates': (86, 130, 184, 196), 'sub_photo_id': -1, 'same_chi': False}, 1947670937: {'crop': 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670937_0.jpg', 'coordonates': (79, 195, 0, 61), 'sub_photo_id': -1, 'same_chi': False}, 1947670938: {'crop': 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670938_0.jpg', 'coordonates': (131, 155, 181, 195), 'sub_photo_id': -1, 'same_chi': False}} Here we crop with rles About to insert : list_path_to_insert length 8 new photo from crops ! About to upload 8 photos https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc upload in portfolio : 22735887 in upload media Upload medias : ['temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg'] : url : https://marlene.fotonower.com/api/v1/secured/photo/upload?token=78d09a0790ec6ecbf119343125a81fdc&datou=0 temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg after data_to_send, before sending request after request b'{"photo_ids":["1356481728","1356481716","1356481730","1356481733","1356481687","1356481669","1356481657","1356481726"],"photo_ids_order":["1356481657","1356481669","1356481687","1356481716","1356481726","1356481728","1356481730","1356481733"],"photo_detail":[{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/5/6/eb530d997902c092001f9c52b0fb9bb2.jpg","text":"TemporaryFile(/tmp/multipartBody6329021816617262978asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1746564022099,"filename":"1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/5/6/398ca35c885888aeb2bb45e5809abe82.jpg","text":"TemporaryFile(/tmp/multipartBody575224222565137834asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1746564022099,"filename":"1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/5/6/4f1f3b7b2a64024e492ea2f7290644f0.jpg","text":"TemporaryFile(/tmp/multipartBody7968851811428343897asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1746564022099,"filename":"1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/5/6/7ab5f9f18c9cb73cad81e262b7d04077.jpg","text":"TemporaryFile(/tmp/multipartBody6120985227137037713asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1746564022099,"filename":"1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/5/6/2fe410f437df7e7abe0f260c3256216f.jpg","text":"TemporaryFile(/tmp/multipartBody8365174998735311639asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1746564022099,"filename":"1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/5/6/400e39e3b1b1d5b167527c8f56ab6253.jpg","text":"TemporaryFile(/tmp/multipartBody8062841078739519108asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1746564022099,"filename":"1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/5/6/32e6c913b454579c79324b3ab0842d05.jpg","text":"TemporaryFile(/tmp/multipartBody3510881357696313647asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1746564022099,"filename":"1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/5/6/3d9911af1dff515876effac316485d48.jpg","text":"TemporaryFile(/tmp/multipartBody3040196378192289975asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1746564022099,"filename":"1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg","height":0,"width":0}],"map_files_photo_id":{"file2":"1356481687","file6":"1356481730","file1":"1356481669","file7":"1356481733","file0":"1356481657","file4":"1356481726","file5":"1356481728","file3":"1356481716"},"map_files_photo_id_array":[{"photo_id":"1356481728","filename":"file5"},{"photo_id":"1356481687","filename":"file2"},{"photo_id":"1356481726","filename":"file4"},{"photo_id":"1356481733","filename":"file7"},{"photo_id":"1356481669","filename":"file1"},{"photo_id":"1356481657","filename":"file0"},{"photo_id":"1356481716","filename":"file3"},{"photo_id":"1356481730","filename":"file6"}],"portfolio_id":22735887,"hashtag_by_photo_ids":[{"1356481728":["hashtag1","hashtag2"]},{"1356481716":["hashtag1","hashtag2"]},{"1356481730":["hashtag1","hashtag2"]},{"1356481733":["hashtag1","hashtag2"]},{"1356481687":["hashtag1","hashtag2"]},{"1356481669":["hashtag1","hashtag2"]},{"1356481657":["hashtag1","hashtag2"]},{"1356481726":["hashtag1","hashtag2"]}],"comms":"Portfolio 22735887 used, photo_id : ArrayBuffer(1356481728, 1356481716, 1356481730, 1356481733, 1356481687, 1356481669, 1356481657, 1356481726)","result":[],"list_datou_current":[]}' Result OK ! uploaded one batch 0 Elapsed time : 23.75112819671631 map_result_insert : {'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg': 1356481687, 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg': 1356481730, 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg': 1356481669, 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg': 1356481733, 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg': 1356481657, 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg': 1356481726, 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg': 1356481728, 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg': 1356481716} Now we prepare data that will be used for ellipse search ! chi_id found to be used 1947670931 path of cropped varroa found to be used to match on an ellipse temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg sub_photo_id found to be used 1356481657 chi_id found to be used 1947670932 path of cropped varroa found to be used to match on an ellipse temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg sub_photo_id found to be used 1356481669 chi_id found to be used 1947670933 path of cropped varroa found to be used to match on an ellipse temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg sub_photo_id found to be used 1356481687 chi_id found to be used 1947670934 path of cropped varroa found to be used to match on an ellipse temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg sub_photo_id found to be used 1356481716 chi_id found to be used 1947670935 path of cropped varroa found to be used to match on an ellipse temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg sub_photo_id found to be used 1356481726 chi_id found to be used 1947670936 path of cropped varroa found to be used to match on an ellipse temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg sub_photo_id found to be used 1356481728 chi_id found to be used 1947670937 path of cropped varroa found to be used to match on an ellipse temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg sub_photo_id found to be used 1356481730 chi_id found to be used 1947670938 path of cropped varroa found to be used to match on an ellipse temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg sub_photo_id found to be used 1356481733 insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(1947670931, '1356481657', 31), (1947670932, '1356481669', 31), (1947670933, '1356481687', 31), (1947670934, '1356481716', 31), (1947670935, '1356481726', 31), (1947670936, '1356481728', 31), (1947670937, '1356481730', 31), (1947670938, '1356481733', 31)] map of cropped photos with some data : {'1356481657': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1356481669': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1356481687': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1356481716': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1356481726': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1356481728': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1356481730': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1356481733': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} After datou_step_exec type output : time spend for datou_step_exec : 23.814067602157593 time spend to save output : 7.319450378417969e-05 total time spend for step 1 : 23.814140796661377 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : crop we use saveGeneral [950103132] map_info['map_portfolio_photo'] : {} final : True mtd_id 686 list_pids : [950103132] Looping around the photos to save general results len do output : 8 /1356481657Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481669Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481687Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481716Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481726Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481728Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481730Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1356481733Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('686', None, None, None, None, None, None, None, None) ('686', None, '950103132', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 25 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('686', None, '1356481657', 'None', None, None, None, None, None), ('686', None, '1356481669', 'None', None, None, None, None, None), ('686', None, '1356481687', 'None', None, None, None, None, None), ('686', None, '1356481716', 'None', None, None, None, None, None), ('686', None, '1356481726', 'None', None, None, None, None, None), ('686', None, '1356481728', 'None', None, None, None, None, None), ('686', None, '1356481730', 'None', None, None, None, None, None), ('686', None, '1356481733', 'None', None, None, None, None, None), ('686', None, '950103132', None, None, None, None, None, None)] time used for this insertion : 0.01570749282836914 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1356481657': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1356481669': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1356481687': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1356481716': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1356481726': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1356481728': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1356481730': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1356481733': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} ret_da : {'1356481657': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1356481669': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1356481687': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1356481716': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1356481726': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1356481728': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1356481730': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1356481733': ['950103132', 'temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} 8 Found filename_to_hash : temp/1746564020_2124625_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg ############################### TEST angular_coeff ################################ t Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=852 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=852 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 852 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=852 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : angular_coeff list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (932296368) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 932296368 download finish for photo 932296368 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.14528679847717285 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:angular_coeff Tue May 6 22:40:44 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746564044_2124625_932296368_97c5e7b0f2830e550e2d6eeb248d8006.jpg': 932296368} map_photo_id_path_extension : {932296368: {'path': 'temp/1746564044_2124625_932296368_97c5e7b0f2830e550e2d6eeb248d8006.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} beginning of step detection filter param_json : {'input_type': 846, 'output_type': -1, 'orientation_type': 872, 'ref_crop_type': 846, 'condition_crop': 'car', 'criteria_crop': 'center_rect', 'crops_coeffs': {'CAR_EXTERIEUR_angle_avant_droit.*': {'aile-avant': [[15, 0.0], [240, 0.0], [285, 1.0], [345, 1.0]], 'capot': [[45, 1.0], [60, 0.5], [270, 0.0], [315, 1.0], [360, 1.0]]}}} angular_coefficients_to_crops batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 932296368) and `type` in (846) Loaded 19 chid ids of type : 846 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (769189713,769189714,769189715,769189716,769189717,769189718,769189721,769189723,769189724,769189725,769189727,769189729,769189730,769189732,769189733,769189734,769189737,769189738,769189739) SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (769189713,769189714,769189715,769189716,769189717,769189718,769189721,769189723,769189724,769189725,769189727,769189729,769189730,769189732,769189733,769189734,769189737,769189738,769189739) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (769189713,769189714,769189715,769189716,769189717,769189718,769189721,769189723,769189724,769189725,769189727,769189729,769189730,769189732,769189733,769189734,769189737,769189738,769189739) select distinct hashtag_id from MTRBack.photo_hashtag_ids where photo_id in (932296368) and type=872 treating photo 932296368 select distinct hashtag_id from MTRBack.photo_hashtag_ids where photo_id in (932296368) and type=872 After datou_step_exec type output : time spend for datou_step_exec : 0.08583235740661621 time spend to save output : 4.601478576660156e-05 total time spend for step 1 : 0.08587837219238281 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {932296368: ([(932296368, 2106233860, 846, 1066, 1277, 93, 340, 0.31964028378983567, 0, []), (932296368, 2106233860, 846, 434, 690, 218, 498, 0.7170410105787726, 0, []), (932296368, 503548896, 846, 902, 1111, 466, 576, 0.31724966, 769189715, []), (932296368, 599722655, 846, 523, 1100, 152, 337, 0.98039776, 0, []), (932296368, 492601069, 846, 143, 1190, 90, 695, 0.9696157, 769189717, []), (932296368, 492601069, 846, 0, 408, 246, 719, 0.9431181, 769189718, []), (932296368, 2096875722, 846, 567, 964, 162, 215, 0.55490255, 769189721, []), (932296368, 2096875709, 846, 437, 939, 24, 198, 0.9983077, 769189723, []), (932296368, 2096875709, 846, 1004, 1263, 28, 144, 0.9485744, 769189724, []), (932296368, 624624117, 846, 595, 1122, 331, 640, 0.99100167, 769189725, []), (932296368, 492624020, 846, 585, 874, 308, 393, 0.78697366, 769189727, []), (932296368, 2096875719, 846, 943, 1100, 428, 547, 0.96733797, 769189729, []), (932296368, 492654799, 846, 253, 467, 35, 441, 0.99621326, 769189730, []), (932296368, 492689227, 846, 1118, 1264, 270, 438, 0.9901647, 769189732, []), (932296368, 492689227, 846, 486, 671, 378, 690, 0.98789483, 769189733, []), (932296368, 492689227, 846, 161, 255, 229, 409, 0.70801014, 769189734, []), (932296368, 492925064, 846, 261, 421, 27, 193, 0.92215157, 769189737, []), (932296368, 492925064, 846, 873, 1045, 46, 156, 0.7535122, 769189738, []), (932296368, 492925064, 846, 1090, 1279, 20, 107, 0.45259848, 769189739, [])],)} test angular coeff is a success ! ############################### TEST detection_filter_by_crop ################################ t Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=708 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=708 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 708 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=708 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : detection_filter_by_crop list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (946711423) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 946711423 download finish for photo 946711423 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.2146892547607422 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:detection_filter_by_crop Tue May 6 22:40:44 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746564044_2124625_946711423_b4bef6b5c6c4b6ffae23f8718c42183c.jpg': 946711423} map_photo_id_path_extension : {946711423: {'path': 'temp/1746564044_2124625_946711423_b4bef6b5c6c4b6ffae23f8718c42183c.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} beginning of step detection filter param_json : {'input_type': 631, 'output_type': -1, 'condition_type': 445, 'condition_crop': 'car', 'criteria_crop': 'center_rect', 'min_surface_ratio': 0.7} conditional_crop_copy batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 946711423) and `type` in (445) Loaded 3 chid ids of type : 445 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947734477,18345275,18345276) +++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947734477,18345275,18345276) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947734477,18345275,18345276) batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 946711423) and `type` in (631) Loaded 35 chid ids of type : 631 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 946711423) and `type` in (445) Loaded 3 chid ids of type : 445 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947734477,18345275,18345276) +++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947734477,18345275,18345276) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947734477,18345275,18345276) treating photo 946711423 SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 624624117, 'type': 631, 'x0': 226, 'x1': 569, 'y0': 252, 'y1': 425, 'score': 0.99812776, 'id': 1947740368, 'points': ['395,419,341,419,340,418,316,418,315,417,306,417, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 162, 'x1': 245, 'y0': 233, 'y1': 396, 'score': 0.99702626, 'id': 1947740369, 'points': ['215,393,206,393,202,390,200,390,192,383,191,380, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 96, 'x1': 172, 'y0': 39, 'y1': 261, 'score': 0.9928518, 'id': 1947740370, 'points': ['143,252,143,249,141,246,140,246,138,248,138,251,137 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 545, 'x1': 612, 'y0': 186, 'y1': 276, 'score': 0.9876676, 'id': 1947740371, 'points': ['584,267,583,266,578,266,574,262,574,259,573,258,5 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875719, 'type': 631, 'x0': 468, 'x1': 555, 'y0': 292, 'y1': 365, 'score': 0.9830025, 'id': 1947740372, 'points': ['491,350,489,350,488,349,487,350,483,350,480,348, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 599722655, 'type': 631, 'x0': 176, 'x1': 535, 'y0': 138, 'y1': 264, 'score': 0.9818268, 'id': 1947740373, 'points': ['453,253,413,253,412,252,387,252,386,250,386,248,3 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 53, 'x1': 87, 'y0': 127, 'y1': 212, 'score': 0.9786105, 'id': 1947740374, 'points': ['74,201,69,201,67,199,66,199,65,198,62,192,62,190,61 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492844413, 'type': 631, 'x0': 89, 'x1': 163, 'y0': 93, 'y1': 144, 'score': 0.9772748, 'id': 1947740375, 'points': ['159,142,153,141,151,139,148,138,145,135,141,133,139 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 418, 'x1': 522, 'y0': 69, 'y1': 136, 'score': 0.97407305, 'id': 1947740376, 'points': ['510,121,507,121,505,119,501,120,500,119,500,113,4 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875709, 'type': 631, 'x0': 185, 'x1': 431, 'y0': 39, 'y1': 136, 'score': 0.97171515, 'id': 1947740377, 'points': ['331,134,287,134,286,133,284,133,283,134,272,134, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875722, 'type': 631, 'x0': 198, 'x1': 395, 'y0': 118, 'y1': 142, 'score': 0.9699756, 'id': 1947740378, 'points': ['328,137,251,137,250,136,249,137,241,137,240,136, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 499500794, 'type': 631, 'x0': 93, 'x1': 107, 'y0': 127, 'y1': 146, 'score': 0.9574813, 'id': 1947740379, 'points': ['101,143,98,143,95,139,95,131,97,129,100,129,101,13 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 71, 'x1': 125, 'y0': 36, 'y1': 95, 'score': 0.95296955, 'id': 1947740380, 'points': ['104,92,96,92,93,90,91,90,86,86,83,85,83,84,81,82,80 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 101, 'x1': 167, 'y0': 38, 'y1': 127, 'score': 0.9508439, 'id': 1947740381, 'points': ['154,117,152,115,152,112,150,110,148,106,148,104,14 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492624020, 'type': 631, 'x0': 249, 'x1': 400, 'y0': 219, 'y1': 316, 'score': 0.8792459, 'id': 1947740382, 'points': ['395,313,390,313,386,311,384,312,381,312,376,309,3 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 540, 'x1': 625, 'y0': 78, 'y1': 221, 'score': 0.87864035, 'id': 1947740383, 'points': ['567,127,566,127,565,126,564,109,562,106,560,106,5 surface aoutside conditional crop crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 547, 'x1': 640, 'y0': 79, 'y1': 129, 'score': 0.8165246, 'id': 1947740384, 'points': ['630,96,627,96,627,94,628,92,629,92,631,94,631,95', surface aoutside conditional crop crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 360, 'x1': 434, 'y0': 62, 'y1': 116, 'score': 0.74684095, 'id': 1947740385, 'points': ['415,103,413,103,411,101,408,101,405,99,403,99,401 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 503548896, 'type': 631, 'x0': 302, 'x1': 540, 'y0': 339, 'y1': 403, 'score': 0.7406652, 'id': 1947740386, 'points': ['442,401,372,401,372,397,370,395,369,392,366,390,3 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 53, 'x1': 85, 'y0': 75, 'y1': 182, 'score': 0.73015845, 'id': 1947740387, 'points': ['70,147,68,145,65,139,65,137,62,132,61,128,57,126,5 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875717, 'type': 631, 'x0': 477, 'x1': 510, 'y0': 220, 'y1': 243, 'score': 0.69028217, 'id': 1947740388, 'points': ['501,241,493,241,489,239,488,237,487,237,480,232 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 61, 'x1': 115, 'y0': 42, 'y1': 188, 'score': 0.6900027, 'id': 1947740389, 'points': ['92,47,91,45,92,44,96,45,94,45', '73,141,73,136,72,1 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875712, 'type': 631, 'x0': 309, 'x1': 326, 'y0': 382, 'y1': 404, 'score': 0.6633776, 'id': 1947740390, 'points': ['309,383,309,382,311,382', '325,385,324,383,319,3 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875719, 'type': 631, 'x0': 427, 'x1': 553, 'y0': 258, 'y1': 315, 'score': 0.6446218, 'id': 1947740391, 'points': ['531,284,526,284,525,283,525,281,523,279,522,280, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233861, 'type': 631, 'x0': 144, 'x1': 267, 'y0': 181, 'y1': 307, 'score': 0.63958377, 'id': 1947740392, 'points': ['212,251,209,251,208,250,203,251,201,250,201,249 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875712, 'type': 631, 'x0': 285, 'x1': 433, 'y0': 343, 'y1': 377, 'score': 0.61493844, 'id': 1947740393, 'points': ['431,376,286,376,285,375,285,368,286,367,286,362 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 146, 'x1': 287, 'y0': 140, 'y1': 311, 'score': 0.54784286, 'id': 1947740394, 'points': ['234,254,227,254,221,251,219,248,215,253,212,253 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 496, 'x1': 624, 'y0': 141, 'y1': 245, 'score': 0.46262404, 'id': 1947740395, 'points': ['603,176,599,173,595,176,593,176,590,174,589,174 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 495920967, 'type': 631, 'x0': 202, 'x1': 524, 'y0': 112, 'y1': 333, 'score': 0.45109355, 'id': 1947740396, 'points': ['483,289,483,286,482,285,482,283,480,279,480,274, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875722, 'type': 631, 'x0': 433, 'x1': 558, 'y0': 248, 'y1': 286, 'score': 0.44133398, 'id': 1947740397, 'points': ['492,272,474,272,473,271,468,271,465,269,460,269 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233861, 'type': 631, 'x0': 535, 'x1': 630, 'y0': 138, 'y1': 231, 'score': 0.42747068, 'id': 1947740398, 'points': ['590,171,589,170,585,170,584,171,581,169,579,169 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 399, 'x1': 569, 'y0': 68, 'y1': 251, 'score': 0.41876298, 'id': 1947740399, 'points': [], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', ' crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492624020, 'type': 631, 'x0': 420, 'x1': 552, 'y0': 244, 'y1': 293, 'score': 0.35962066, 'id': 1947740400, 'points': ['474,289,453,289,452,288,439,288,437,286,431,286, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 503548896, 'type': 631, 'x0': 301, 'x1': 540, 'y0': 339, 'y1': 403, 'score': 0.740756, 'id': 3140491551, 'points': ['442,401,371,401,371,397,366,390,365,386,356,386,35 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 496, 'x1': 624, 'y0': 140, 'y1': 245, 'score': 0.4627206, 'id': 3140491552, 'points': ['595,176,593,176,589,173,586,176,583,176,580,174, surface aoutside conditional crop After datou_step_exec type output : time spend for datou_step_exec : 0.12356185913085938 time spend to save output : 5.793571472167969e-05 total time spend for step 1 : 0.12361979484558105 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {946711423: ([(946711423, 624624117, 631, 226, 569, 252, 425, 0.99812776, 1947740368, ['395,419,341,419,340,418,316,418,315,417,306,417,305,416,293,415,290,413,284,412,283,411,280,411,272,407,264,405,258,400,254,398,250,394,244,391,242,389,242,386,239,380,240,368,239,367,239,347,238,346,238,331,237,330,237,327,238,326,237,314,239,311,239,308,237,304,238,302,243,298,244,296,244,292,246,291,250,291,251,290,259,290,260,289,264,289,265,288,269,288,271,290,273,294,278,299,280,300,285,300,286,301,293,301,294,302,302,304,305,307,309,308,312,310,314,310,317,312,335,312,336,313,343,313,344,314,370,314,371,315,381,315,382,314,389,313,393,311,405,309,406,308,408,308,412,306,414,304,417,304,421,307,426,308,427,309,433,309,434,310,464,309,467,306,471,304,476,304,477,303,489,303,490,302,494,302,495,301,500,301,501,300,515,300,516,299,519,298,522,292,525,290,533,290,534,291,540,291,541,290,543,290,547,288,550,285,550,285,552,289,552,291,553,292,553,313,552,314,552,324,550,328,550,333,549,334,549,336,544,346,543,353,539,361,532,368,531,368,527,372,519,374,509,379,503,384,499,385,498,386,496,386,492,388,490,390,486,392,484,392,479,396,475,397,474,398,472,398,471,399,469,399,462,403,460,403,459,404,457,404,456,405,454,405,450,407,448,407,443,410,425,413,424,414,422,414,416,417,404,417,403,418,396,418']), (946711423, 492689227, 631, 162, 245, 233, 396, 0.99702626, 1947740369, ['215,393,206,393,202,390,200,390,192,383,191,380,187,375,184,369,184,367,180,360,180,358,179,357,177,349,175,347,174,339,172,336,171,330,170,329,169,324,168,323,168,313,167,312,167,304,166,303,166,298,165,297,165,288,164,287,165,286,165,272,166,271,166,268,167,267,167,263,168,262,169,254,173,249,177,247,178,247,181,251,184,251,184,252,187,255,189,255,193,259,193,261,195,263,195,264,201,270,203,278,207,282,208,289,211,293,211,296,213,299,214,304,215,305,216,312,219,316,219,319,220,320,220,325,222,329,222,335,223,336,223,338,225,342,225,349,226,350,226,359,227,360,227,366,228,367,228,371,231,375,231,382,227,385,226,388,225,389,223,388,219,392,216,392']), (946711423, 492654799, 631, 96, 172, 39, 261, 0.9928518, 1947740370, ['143,252,143,249,141,246,140,246,138,248,138,251,137,250,137,248,135,246,134,246,132,248,127,244,124,244,122,241,122,236,121,235,121,232,118,229,117,225,116,224,116,212,113,209,115,207,116,201,111,194,110,184,106,178,107,154,108,152,112,148,113,144,112,143,112,138,110,136,108,136,107,135,103,128,103,124,102,123,102,121,103,120,103,118,106,115,106,106,107,105,110,104,113,101,117,93,117,71,114,65,116,61,116,59,117,58,117,55,118,54,119,49,122,45,122,44,124,42,150,42,151,43,153,43,153,47,152,48,152,50,154,52,155,56,156,57,156,85,155,86,155,95,154,96,154,98,155,99,155,105,156,106,155,107,155,116,157,120,159,121,159,123,156,127,156,134,157,135,157,138,156,139,156,141,154,145,152,147,150,151,149,159,148,160,148,164,149,165,149,174,148,175,148,197,149,198,149,215,150,216,150,241,149,242,149,245,148,247,146,245,144,247', '122,147,121,138,120,141,119,142,119,144,118,145,121,148']), (946711423, 2096875719, 631, 468, 555, 292, 365, 0.9830025, 1947740372, ['491,350,489,350,488,349,487,350,483,350,480,348,480,341,482,339,482,337,485,334,487,334,491,330,494,330,495,328,498,326,501,326,503,324,507,325,509,323,514,321,516,319,518,321,520,321,521,319,522,319,524,321,527,321,530,317,530,315,531,314,535,313,540,309,543,310,544,311,542,313,542,314,544,316,541,318,541,322,536,322,535,323,533,323,532,322,528,322,527,321,524,321,522,323,518,322,516,324,517,327,516,328,512,327,510,329,512,332,513,332,515,330,516,331,516,333,514,332,511,333,511,336,514,337,516,336,516,339,515,339,513,338,511,340,512,341,512,342,510,343,507,343,502,347,500,347,497,349,492,349', '514,325,515,324,513,322,512,322,511,325,512,326', '522,327,521,327,521,326,522,325']), (946711423, 599722655, 631, 176, 535, 138, 264, 0.9818268, 1947740373, ['453,253,413,253,412,252,387,252,386,250,386,248,383,246,379,245,376,243,361,243,361,240,362,239,359,238,358,237,356,237,355,236,352,236,351,235,333,235,332,234,329,234,329,233,331,231,331,229,329,228,328,224,330,222,330,221,324,218,308,219,307,218,302,218,298,216,288,217,287,218,285,218,283,220,283,221,287,224,295,225,295,225,294,226,289,226,288,227,283,227,282,228,273,228,272,229,271,228,259,228,258,227,254,227,253,226,247,225,247,225,251,221,248,218,243,216,247,213,248,213,249,212,248,211,246,211,245,210,241,210,240,209,237,209,236,208,231,207,230,206,228,202,224,201,223,200,221,200,220,199,214,198,213,195,211,193,208,193,203,189,203,184,201,181,201,176,198,171,199,170,199,158,203,154,205,153,205,151,206,149,209,149,210,148,225,148,226,147,283,147,284,148,287,148,288,147,305,147,306,148,312,148,313,147,354,147,355,146,428,146,429,147,433,147,434,148,437,148,438,149,451,149,457,156,459,162,462,165,464,166,471,166,472,165,477,165,480,167,480,171,486,175,488,175,489,176,502,176,503,178,503,180,509,185,509,189,512,193,512,199,513,200,513,203,514,204,514,210,513,211,514,217,512,221,513,222,513,225,510,229,510,235,507,237,504,238,502,243,490,243,489,244,485,244,484,245,480,245,479,246,463,246,462,247,460,247,458,249,457,252,454,252', '528,212,528,207,526,206,524,203,526,203,527,202,528,202', '299,215,302,212,299,211,298,210,291,210,290,211,281,212,286,215,290,215,291,216', '375,242,376,240,375,238,363,239,368,242,371,242,372,243']), (946711423, 492844413, 631, 89, 163, 93, 144, 0.9772748, 1947740375, ['159,142,153,141,151,139,148,138,145,135,141,133,139,133,138,132,131,132,130,131,125,131,124,130,121,130,120,129,116,129,115,128,112,128,108,126,106,126,100,123,98,121,94,113,94,104,97,101,103,98,105,98,106,97,110,97,111,96,116,96,117,95,132,95,133,96,139,97,141,99,144,100,149,105,150,107,154,108,155,113,157,115,158,115,160,118,160,120,161,121,161,133,160,134,160,140']), (946711423, 2096875709, 631, 185, 431, 39, 136, 0.97171515, 1947740377, ['331,134,287,134,286,133,284,133,283,134,272,134,271,133,264,133,263,134,258,134,257,133,254,133,253,132,236,132,235,131,225,131,224,132,223,131,213,131,212,130,208,130,207,129,204,129,203,128,199,127,193,121,192,117,189,113,189,110,188,109,187,93,186,92,187,91,187,89,186,88,186,65,185,64,186,63,186,61,185,60,185,48,186,47,186,42,187,40,232,40,233,41,248,41,249,42,281,43,282,44,290,44,291,45,300,45,301,46,308,46,309,47,314,47,315,48,322,49,328,53,334,54,336,56,339,57,344,62,349,64,351,66,353,67,356,67,358,69,359,72,363,76,367,78,369,80,379,91,380,93,383,94,390,100,393,101,395,103,396,106,399,109,402,110,406,115,408,115,410,117,410,120,412,123,411,127,409,129,399,129,398,130,395,130,394,131,378,131,377,132,368,132,367,131,346,131,345,132,342,132,341,133,332,133']), (946711423, 2096875722, 631, 198, 395, 118, 142, 0.9699756, 1947740378, ['328,137,251,137,250,136,249,137,241,137,240,136,219,136,218,135,213,135,212,134,206,133,205,132,201,131,200,130,200,122,201,121,205,121,206,122,222,122,226,124,239,124,240,125,369,125,370,124,371,125,389,125,391,127,391,133,390,134,386,134,385,135,380,135,379,134,375,134,374,135,341,135,340,136,329,136']), (946711423, 499500794, 631, 93, 107, 127, 146, 0.9574813, 1947740379, ['101,143,98,143,95,139,95,131,97,129,100,129,101,133,102,134,102,136,103,137,103,140']), (946711423, 492925064, 631, 71, 125, 36, 95, 0.95296955, 1947740380, ['104,92,96,92,93,90,91,90,86,86,83,85,83,84,81,82,80,82,75,77,75,75,74,74,74,66,75,65,75,62,77,60,77,58,80,55,80,54,83,51,83,50,88,45,94,44,95,43,99,43,100,42,113,42,117,45,117,47,116,48,116,51,115,52,114,59,113,60,112,65,111,66,111,69,110,70,110,75,109,76,109,83,108,84,108,86,109,87,108,89']), (946711423, 492925064, 631, 101, 167, 38, 127, 0.9508439, 1947740381, ['154,117,152,115,152,112,150,110,148,106,148,104,145,101,143,100,138,100,137,99,135,99,133,95,131,95,126,93,126,91,128,88,128,83,129,82,129,70,127,68,127,66,128,65,125,61,127,59,127,56,129,52,129,49,130,47,135,42,144,42,148,45,151,49,151,60,152,61,152,75,153,76,153,80,155,83,155,87,156,88,156,105,155,106,156,107,156,110,154,112,156,116', '109,100,108,100,107,99,109,97']), (946711423, 492624020, 631, 249, 400, 219, 316, 0.8792459, 1947740382, ['395,313,390,313,386,311,384,312,381,312,376,309,358,308,357,307,354,307,353,306,350,306,349,307,345,305,343,303,341,304,337,304,334,302,325,302,324,301,315,300,313,298,313,297,310,295,304,295,300,293,295,293,291,288,289,287,283,287,281,285,281,283,278,280,274,280,272,279,272,276,270,273,270,270,269,268,266,265,265,265,264,264,264,262,261,260,260,258,260,256,261,255,259,252,259,248,258,246,255,244,256,241,252,239,251,238,251,226,265,226,266,227,268,227,272,232,276,232,277,233,279,233,281,234,283,237,285,238,290,239,293,241,296,241,304,246,312,247,316,251,318,252,320,252,326,255,328,255,337,260,342,260,343,261,345,261,349,264,351,264,355,266,357,268,364,271,366,273,370,275,374,275,376,276,377,279,379,281,383,282,384,283,386,283,387,286,390,289,390,290,394,294,396,294,398,296,398,308,397,309,397,311']), (946711423, 503548896, 631, 302, 540, 339, 403, 0.7406652, 1947740386, ['442,401,372,401,372,397,370,395,369,392,366,390,367,389,366,386,357,386,354,384,350,384,349,383,320,383,319,382,320,378,318,376,318,374,314,370,309,370,308,369,306,369,305,363,305,357,306,356,306,353,307,353,308,354,313,354,314,355,315,354,320,354,321,353,331,353,332,354,335,354,336,355,339,355,340,356,379,356,380,357,406,357,407,356,409,356,410,357,474,357,475,356,482,356,484,357,485,356,488,356,493,353,501,354,502,353,506,353,507,352,517,352,518,351,522,351,525,347,527,346,530,347,530,349,533,351,530,355,528,355,527,356,515,356,509,359,508,361,505,362,503,365,497,368,494,372,490,373,489,374,492,376,495,376,493,377,488,378,490,380,495,380,497,381,497,381,487,382,485,385,476,387,469,392,466,392,465,393,460,393,456,396,453,397,451,399,443,400', '519,353,518,352,517,353,518,354']), (946711423, 2106233860, 631, 53, 85, 75, 182, 0.73015845, 1947740387, ['70,147,68,145,65,139,65,137,62,132,61,128,57,126,56,124,56,121,54,119,54,110,56,108,56,103,59,100,60,101,61,100,61,96,63,93,63,89,66,84,65,83,65,80,66,78,68,78,68,79,70,80,70,83,74,87,74,90,75,91,75,100,77,102,77,105,75,106,75,125,76,126,77,125,77,125,77,128,76,129,77,131,77,136,75,139,75,143,78,145,76,146,71,146', '61,107,60,106,59,107,60,108', '77,134,76,131,75,134,76,135']), (946711423, 2096875717, 631, 477, 510, 220, 243, 0.69028217, 1947740388, ['501,241,493,241,489,239,488,237,487,237,480,232,479,230,479,226,484,222,487,222,488,223,492,224,496,228,497,228,497,229,502,234,502,235,504,236,504,240,502,240']), (946711423, 2096875712, 631, 309, 326, 382, 404, 0.6633776, 1947740390, ['309,383,309,382,311,382', '325,385,324,383,319,383,318,382,325,382', '320,400,311,400,309,398,309,385,310,386,311,385,315,385,316,384,318,384,319,385,322,385,325,387,325,398,323,398']), (946711423, 2096875719, 631, 427, 553, 258, 315, 0.6446218, 1947740391, ['531,284,526,284,525,283,525,281,523,279,522,280,519,281,521,282,521,283,519,284,518,283,519,281,515,279,516,277,515,276,513,276,512,277,513,279,511,280,507,279,505,276,504,279,497,279,496,278,495,279,485,279,484,280,480,280,479,281,481,283,482,283,482,283,469,283,468,284,438,284,436,283,440,279,446,279,447,278,456,278,458,276,457,275,457,275,467,275,468,274,490,274,491,273,494,273,496,271,496,269,499,268,501,266,503,265,506,265,510,263,514,263,516,261,520,259,544,259,544,259,543,260,545,262,548,262,550,263,550,276,549,277,547,277,540,282,538,282,537,283,532,283']), (946711423, 2106233861, 631, 144, 267, 181, 307, 0.63958377, 1947740392, ['212,251,209,251,208,250,203,251,201,250,201,249,195,243,189,242,188,241,185,241,184,240,182,236,180,236,179,235,173,235,172,234,170,235,164,235,163,234,163,232,162,231,163,217,166,217,168,218,170,215,171,210,172,209,173,209,176,212,178,210,178,208,181,203,186,203,188,201,193,201,194,202,195,201,201,201,202,202,204,202,205,201,209,201,210,202,212,202,215,200,217,200,220,202,221,201,227,201,231,205,231,206,234,209,235,209,235,210,238,213,238,224,234,228,235,232,234,233,228,234,225,237,224,241,222,242,216,242,209,246,211,248,212,248,213,250', '221,228,220,227,219,228,220,229', '224,238,224,237,221,235,217,237,219,239']), (946711423, 2096875712, 631, 285, 433, 343, 377, 0.61493844, 1947740393, ['431,376,286,376,285,375,285,368,286,367,286,362,287,361,287,359,291,359,297,362,306,363,307,364,312,364,313,365,322,366,323,367,331,366,332,368,334,368,335,369,338,368,337,366,336,366,337,365,336,364,327,364,325,361,319,361,317,357,317,356,318,355,325,355,326,353,331,353,333,351,332,350,330,350,328,348,326,348,325,347,319,347,315,345,306,345,305,344,297,344,299,344,300,343,431,343,432,344,432,353,431,354,431,358,430,359,430,365,427,365,425,363,424,363,422,364,421,366,418,366,413,369,404,370,409,371,409,371,399,371,398,372,395,373,419,374,420,373,426,372,428,370,428,367,429,367,430,368,429,369,430,370,430,373,431,374', '381,373,378,372,377,371,356,371,356,371,359,370,347,369,345,367,343,367,342,368,343,369,341,370,354,371,354,371,352,372,353,373,359,373,360,374']), (946711423, 2106233860, 631, 146, 287, 140, 311, 0.54784286, 1947740394, ['234,254,227,254,221,251,219,248,215,253,212,253,210,252,206,247,203,247,198,243,197,243,194,239,189,238,186,236,182,236,181,235,167,235,164,233,164,228,159,227,158,226,158,219,159,218,159,213,162,207,162,205,169,192,169,186,170,185,172,185,177,179,175,175,173,173,177,171,181,171,182,170,184,170,187,167,187,164,188,163,188,161,199,161,202,164,205,165,207,167,209,167,212,165,215,165,216,168,218,170,219,170,221,168,221,164,220,163,220,161,222,161,223,160,230,160,231,159,242,159,244,158,247,161,248,161,247,162,246,168,248,172,248,174,253,176,254,180,253,182,249,182,247,185,249,188,253,188,254,189,254,194,249,194,247,196,247,198,249,200,252,200,253,199,255,202,255,205,254,206,254,208,250,207,249,206,246,209,246,210,249,214,252,212,254,212,254,214,255,215,255,217,254,218,254,221,252,221,249,219,247,221,247,225,249,228,250,228,252,226,253,224,253,224,253,229,252,229,251,228,249,228,247,230,247,233,246,234,246,237,245,238,245,240,243,244,243,247,239,251,237,251', '230,167,229,166,227,167,228,168']), (946711423, 495920967, 631, 202, 524, 112, 333, 0.45109355, 1947740396, ['483,289,483,286,482,285,482,283,480,279,480,274,476,270,472,268,465,268,464,269,459,269,458,268,454,268,453,267,437,267,436,268,428,268,427,269,418,269,417,270,414,270,410,266,410,265,416,262,418,262,421,260,423,260,425,259,426,257,424,255,422,255,419,253,417,253,416,252,412,251,410,250,410,249,412,249,413,248,415,248,416,247,422,246,428,243,429,242,428,241,424,240,423,239,420,239,419,238,390,238,389,237,386,237,385,236,369,236,368,235,363,234,363,233,364,232,364,230,366,226,365,225,357,220,344,220,341,218,339,218,339,218,342,212,342,210,336,207,327,207,326,206,319,206,318,205,314,205,313,204,297,204,291,207,288,210,288,212,291,217,290,220,288,222,284,224,282,224,278,227,273,228,271,230,270,235,265,239,262,236,261,232,263,228,266,226,261,224,256,219,256,210,249,206,242,205,237,202,234,195,226,186,227,184,227,180,228,179,225,175,225,174,222,171,225,165,227,163,229,158,230,157,232,156,235,156,236,155,239,155,240,154,245,154,246,155,254,155,255,156,258,156,259,157,268,157,269,156,272,156,273,155,280,155,281,156,298,156,300,155,301,156,307,156,308,157,311,157,318,152,322,151,323,150,333,150,338,146,339,146,342,143,343,143,346,140,357,140,362,136,366,134,368,134,369,133,373,133,374,132,377,132,378,131,388,131,389,130,410,130,411,131,417,131,428,140,432,142,434,142,435,143,443,145,446,147,448,147,451,154,453,156,457,158,462,159,463,160,466,160,467,161,472,162,474,163,474,164,481,171,489,175,491,175,492,176,494,176,495,177,499,178,500,179,502,184,507,189,517,194,518,195,514,201,514,203,518,207,519,209,518,214,515,218,517,227,515,229,515,231,514,232,515,236,518,239,518,252,519,253,519,263,518,264,518,267,517,269,514,272,512,273,512,274,506,280,500,277,498,273,496,272,493,272,491,274,491,278,490,279,490,281', '312,179,311,178,308,179,309,180', '268,269,264,269,259,266,259,262,261,258,261,250,265,245,269,250,270,257,274,260,278,265,275,267,269,268', '414,281,401,281,414,281']), (946711423, 2096875722, 631, 433, 558, 248, 286, 0.44133398, 1947740397, ['492,272,474,272,473,271,468,271,465,269,460,269,460,268,465,266,467,266,468,265,470,265,471,264,475,264,476,263,479,263,480,262,486,262,487,261,491,261,492,260,495,260,496,259,502,259,506,257,510,257,514,255,517,255,518,254,530,253,531,252,535,252,536,251,538,251,539,252,543,252,544,253,547,253,549,251,553,251,555,253,555,267,552,270,550,270,550,269,548,267,547,267,547,267,548,266,547,265,545,266,540,266,539,264,530,264,529,263,524,263,519,266,513,266,510,268,507,268,506,269,499,270,498,271,493,271', '438,279,435,279,435,273,436,272,448,271,449,272,448,274,443,274,440,277,440,278']), (946711423, 492654799, 631, 399, 569, 68, 251, 0.41876298, 1947740399, []), (946711423, 492624020, 631, 420, 552, 244, 293, 0.35962066, 1947740400, ['474,289,453,289,452,288,439,288,437,286,431,286,427,284,423,284,422,283,422,275,427,275,428,273,430,272,435,272,436,271,438,271,442,269,447,269,450,267,454,267,460,264,464,264,467,262,483,261,484,260,488,260,489,259,494,259,495,258,502,258,503,257,505,257,509,255,512,255,516,252,520,252,521,251,526,250,530,248,534,248,535,247,546,247,547,248,549,248,549,250,550,251,550,266,551,267,551,275,550,276,550,278,549,279,549,281,537,282,535,284,528,284,527,285,504,285,503,286,495,286,492,288,488,287,487,288,475,288']), (946711423, 503548896, 631, 301, 540, 339, 403, 0.740756, 3140491551, ['442,401,371,401,371,397,366,390,365,386,356,386,353,384,348,383,319,383,319,378,314,370,310,370,305,368,304,357,305,353,330,353,339,356,378,356,379,357,474,357,475,356,488,356,493,353,501,354,507,352,517,352,522,351,527,346,530,347,533,351,530,355,527,356,515,356,505,362,503,365,497,368,494,372,489,374,492,376,488,378,490,380,495,380,487,382,485,385,476,387,469,392,461,393,456,395,451,399,447,399', '519,353,518,352,517,353,518,354'])],)} test detection filter by crop is a success ! ############################### TEST detection_filter_by_classif ################################ t SELECT id FROM MTRPhoto.crop_hashtag_ids WHERE photo_id=946711423 AND `type`=816 DELETE FROM MTRPhoto.crop_hashtag_ids WHERE id IN (3786285462,3786285461,3786285460,3786285469,3786285468,3786285467,3786285466,3786285465,3786285474,3786285477,3786285463,3786285464,3786285473,3786285472,3786285478,3786285479,3786285480,3786285482,3786285470,3786285476,3786285475,3786285481,3786285471) Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=672 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=672 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 672 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=672 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : detection_filter_by_classif list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (946711423) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos ##### After load_data_input time to download the photos : 0.004216432571411133 #### fin chargement data Blocking on flush ? No conitnuing About to test input to load Calling datou_exec Inside datou_exec : verbose : True number of steps : 1 step1:detection_filter_by_classif Tue May 6 22:40:45 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 After prepare type args : Here we display some param of map_info ! map_filenames : {} map_photo_id_path_extension : {} map_subphoto_mainphoto : {} beginning of step detection filter with classification results param_json : {'input_type': 631, 'output_type': 816, 'condition_type': 872, 'crops_ok': {'CAR_DOCUMENT.*': {}, 'CAR_INTERIEUR.*': {}, 'CAR_EXTERIEUR_angle_avant_droit.*': {'Retroviseur': 2, 'Roue': 2, 'Capot': 1, 'Pare-brise': 1, 'vitre': 10, 'phare': 2, 'Feu-antibrouillard': 2, 'poignee': 2, 'porte': 2, 'calandre': 1, 'logo-marque': 1, 'Plaque-immatriculation': 1, 'Essuie-glace': 1, 'pare-choc': 1, 'toit': 1, 'logo-roue': 1, 'aile-avant': 1}}, 'separation': {'CAR_EXTERIEUR_avant.*': {'pare-choc': ['pare-chocs-avant'], 'phare': ['phare-gauche', 'a-droite-de', 'phare-droit']}, 'CAR_EXTERIEUR_angle_avant_droit.*': {'pare-choc': ['pare-chocs-avant'], 'phare': ['phare-droite', 'a-gauche-de', 'phare-gauche'], 'porte': ['porte-avant', 'a-droite-de', 'porte-arriere']}}} conditional_crop_by_classif_copy select distinct hashtag_id from MTRBack.photo_hashtag_ids where photo_id in (946711423) and type=872 batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 946711423) and `type` in (631) Loaded 35 chid ids of type : 631 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) treating photo 946711423 select distinct hashtag_id from MTRBack.photo_hashtag_ids where photo_id in (946711423) and type=872 list of crops kept {'retroviseur': 2, 'roue': 2, 'capot': 1, 'pare-brise': 1, 'vitre': 10, 'phare': 2, 'feu-antibrouillard': 2, 'poignee': 2, 'porte': 2, 'calandre': 1, 'logo-marque': 1, 'plaque-immatriculation': 1, 'essuie-glace': 1, 'pare-choc': 1, 'toit': 1, 'logo-roue': 1, 'aile-avant': 1} for hahstag car_exterieur_angle_avant_droit_merge__port_551052 crop not duplicated for hashtag aile-arriere : : {'photo_id': 946711423, 'hashtag_id': 2106233861, 'type': 631, 'x0': 144, 'x1': 267, 'y0': 181, 'y1': 307, 'score': 0.63958377, 'id': 1947740392, 'points': ['212,251,209,251,208,250,203,251,201,250,201,249 crop not duplicated for hashtag coffre : : {'photo_id': 946711423, 'hashtag_id': 495920967, 'type': 631, 'x0': 202, 'x1': 524, 'y0': 112, 'y1': 333, 'score': 0.45109355, 'id': 1947740396, 'points': ['483,289,483,286,482,285,482,283,480,279,480,274, crop not duplicated for hashtag aile-arriere : : {'photo_id': 946711423, 'hashtag_id': 2106233861, 'type': 631, 'x0': 535, 'x1': 630, 'y0': 138, 'y1': 231, 'score': 0.42747068, 'id': 1947740398, 'points': ['590,171,589,170,585,170,584,171,581,169,579,169 crop duplicated for hashtag retroviseur : : {'photo_id': 946711423, 'hashtag_id': 492844413, 'type': 631, 'x0': 89, 'x1': 163, 'y0': 93, 'y1': 144, 'score': 0.9772748, 'id': 1947740375, 'points': ['159,142,153,141,151,139,148,138,145,135,141,133,139 crop duplicated for hashtag roue : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 162, 'x1': 245, 'y0': 233, 'y1': 396, 'score': 0.99702626, 'id': 1947740369, 'points': ['215,393,206,393,202,390,200,390,192,383,191,380, crop duplicated for hashtag roue : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 545, 'x1': 612, 'y0': 186, 'y1': 276, 'score': 0.9876676, 'id': 1947740371, 'points': ['584,267,583,266,578,266,574,262,574,259,573,258,5 crop not duplicated for hashtag roue : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 53, 'x1': 87, 'y0': 127, 'y1': 212, 'score': 0.9786105, 'id': 1947740374, 'points': ['74,201,69,201,67,199,66,199,65,198,62,192,62,190,61 crop duplicated for hashtag capot : : {'photo_id': 946711423, 'hashtag_id': 599722655, 'type': 631, 'x0': 176, 'x1': 535, 'y0': 138, 'y1': 264, 'score': 0.9818268, 'id': 1947740373, 'points': ['453,253,413,253,412,252,387,252,386,250,386,248,3 crop duplicated for hashtag pare-brise : : {'photo_id': 946711423, 'hashtag_id': 2096875709, 'type': 631, 'x0': 185, 'x1': 431, 'y0': 39, 'y1': 136, 'score': 0.97171515, 'id': 1947740377, 'points': ['331,134,287,134,286,133,284,133,283,134,272,134, crop duplicated for hashtag vitre : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 418, 'x1': 522, 'y0': 69, 'y1': 136, 'score': 0.97407305, 'id': 1947740376, 'points': ['510,121,507,121,505,119,501,120,500,119,500,113,4 crop duplicated for hashtag vitre : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 71, 'x1': 125, 'y0': 36, 'y1': 95, 'score': 0.95296955, 'id': 1947740380, 'points': ['104,92,96,92,93,90,91,90,86,86,83,85,83,84,81,82,80 crop duplicated for hashtag vitre : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 101, 'x1': 167, 'y0': 38, 'y1': 127, 'score': 0.9508439, 'id': 1947740381, 'points': ['154,117,152,115,152,112,150,110,148,106,148,104,14 crop duplicated for hashtag vitre : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 547, 'x1': 640, 'y0': 79, 'y1': 129, 'score': 0.8165246, 'id': 1947740384, 'points': ['630,96,627,96,627,94,628,92,629,92,631,94,631,95', crop duplicated for hashtag vitre : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 360, 'x1': 434, 'y0': 62, 'y1': 116, 'score': 0.74684095, 'id': 1947740385, 'points': ['415,103,413,103,411,101,408,101,405,99,403,99,401 crop duplicated for hashtag phare : : {'photo_id': 946711423, 'hashtag_id': 492624020, 'type': 631, 'x0': 249, 'x1': 400, 'y0': 219, 'y1': 316, 'score': 0.8792459, 'id': 1947740382, 'points': ['395,313,390,313,386,311,384,312,381,312,376,309,3 crop duplicated for hashtag phare : : {'photo_id': 946711423, 'hashtag_id': 492624020, 'type': 631, 'x0': 420, 'x1': 552, 'y0': 244, 'y1': 293, 'score': 0.35962066, 'id': 1947740400, 'points': ['474,289,453,289,452,288,439,288,437,286,431,286, crop duplicated for hashtag feu-antibrouillard : : {'photo_id': 946711423, 'hashtag_id': 2096875712, 'type': 631, 'x0': 309, 'x1': 326, 'y0': 382, 'y1': 404, 'score': 0.6633776, 'id': 1947740390, 'points': ['309,383,309,382,311,382', '325,385,324,383,319,3 crop duplicated for hashtag feu-antibrouillard : : {'photo_id': 946711423, 'hashtag_id': 2096875712, 'type': 631, 'x0': 285, 'x1': 433, 'y0': 343, 'y1': 377, 'score': 0.61493844, 'id': 1947740393, 'points': ['431,376,286,376,285,375,285,368,286,367,286,362 crop duplicated for hashtag poignee : : {'photo_id': 946711423, 'hashtag_id': 499500794, 'type': 631, 'x0': 93, 'x1': 107, 'y0': 127, 'y1': 146, 'score': 0.9574813, 'id': 1947740379, 'points': ['101,143,98,143,95,139,95,131,97,129,100,129,101,13 crop duplicated for hashtag porte : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 96, 'x1': 172, 'y0': 39, 'y1': 261, 'score': 0.9928518, 'id': 1947740370, 'points': ['143,252,143,249,141,246,140,246,138,248,138,251,137 crop duplicated for hashtag porte : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 540, 'x1': 625, 'y0': 78, 'y1': 221, 'score': 0.87864035, 'id': 1947740383, 'points': ['567,127,566,127,565,126,564,109,562,106,560,106,5 crop not duplicated for hashtag porte : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 61, 'x1': 115, 'y0': 42, 'y1': 188, 'score': 0.6900027, 'id': 1947740389, 'points': ['92,47,91,45,92,44,96,45,94,45', '73,141,73,136,72,1 crop not duplicated for hashtag porte : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 399, 'x1': 569, 'y0': 68, 'y1': 251, 'score': 0.41876298, 'id': 1947740399, 'points': [], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', ' crop duplicated for hashtag calandre : : {'photo_id': 946711423, 'hashtag_id': 503548896, 'type': 631, 'x0': 301, 'x1': 540, 'y0': 339, 'y1': 403, 'score': 0.740756, 'id': 3140491551, 'points': ['442,401,371,401,371,397,366,390,365,386,356,386,35 crop not duplicated for hashtag calandre : : {'photo_id': 946711423, 'hashtag_id': 503548896, 'type': 631, 'x0': 302, 'x1': 540, 'y0': 339, 'y1': 403, 'score': 0.7406652, 'id': 1947740386, 'points': ['442,401,372,401,372,397,370,395,369,392,366,390,3 crop duplicated for hashtag logo-marque : : {'photo_id': 946711423, 'hashtag_id': 2096875717, 'type': 631, 'x0': 477, 'x1': 510, 'y0': 220, 'y1': 243, 'score': 0.69028217, 'id': 1947740388, 'points': ['501,241,493,241,489,239,488,237,487,237,480,232 crop duplicated for hashtag plaque-immatriculation : : {'photo_id': 946711423, 'hashtag_id': 2096875719, 'type': 631, 'x0': 468, 'x1': 555, 'y0': 292, 'y1': 365, 'score': 0.9830025, 'id': 1947740372, 'points': ['491,350,489,350,488,349,487,350,483,350,480,348, crop not duplicated for hashtag plaque-immatriculation : : {'photo_id': 946711423, 'hashtag_id': 2096875719, 'type': 631, 'x0': 427, 'x1': 553, 'y0': 258, 'y1': 315, 'score': 0.6446218, 'id': 1947740391, 'points': ['531,284,526,284,525,283,525,281,523,279,522,280, crop duplicated for hashtag essuie-glace : : {'photo_id': 946711423, 'hashtag_id': 2096875722, 'type': 631, 'x0': 198, 'x1': 395, 'y0': 118, 'y1': 142, 'score': 0.9699756, 'id': 1947740378, 'points': ['328,137,251,137,250,136,249,137,241,137,240,136, crop not duplicated for hashtag essuie-glace : : {'photo_id': 946711423, 'hashtag_id': 2096875722, 'type': 631, 'x0': 433, 'x1': 558, 'y0': 248, 'y1': 286, 'score': 0.44133398, 'id': 1947740397, 'points': ['492,272,474,272,473,271,468,271,465,269,460,269 crop duplicated for hashtag pare-choc : : {'photo_id': 946711423, 'hashtag_id': 624624117, 'type': 631, 'x0': 226, 'x1': 569, 'y0': 252, 'y1': 425, 'score': 0.99812776, 'id': 1947740368, 'points': ['395,419,341,419,340,418,316,418,315,417,306,417, crop duplicated for hashtag aile-avant : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 53, 'x1': 85, 'y0': 75, 'y1': 182, 'score': 0.73015845, 'id': 1947740387, 'points': ['70,147,68,145,65,139,65,137,62,132,61,128,57,126,5 crop not duplicated for hashtag aile-avant : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 146, 'x1': 287, 'y0': 140, 'y1': 311, 'score': 0.54784286, 'id': 1947740394, 'points': ['234,254,227,254,221,251,219,248,215,253,212,253 crop not duplicated for hashtag aile-avant : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 496, 'x1': 624, 'y0': 140, 'y1': 245, 'score': 0.4627206, 'id': 3140491552, 'points': ['595,176,593,176,589,173,586,176,583,176,580,174, crop not duplicated for hashtag aile-avant : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 496, 'x1': 624, 'y0': 141, 'y1': 245, 'score': 0.46262404, 'id': 1947740395, 'points': ['603,176,599,173,595,176,593,176,590,174,589,174 list of crops kept {'pare-choc': ('pare-chocs-avant',), 'phare': ('phare-droite', 'a-gauche-de', 'phare-gauche'), 'porte': ('porte-avant', 'a-droite-de', 'porte-arriere')} for hahstag car_exterieur_angle_avant_droit_merge__port_551052 batch 1 Loaded 0 chid ids of type : 0 insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) batch 1 Loaded 23 chid ids of type : 816 INSERT IGNORE INTO MTRPhoto.crop_polygon_points (`crop_hashtag_id`, `points`) VALUES (%s, %s) Number RLEs to save : 1600 INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) first line : ('3787227840', '117', '95', '16') ... last line : ('3787227862', '70', '147', '1') INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) TO DO : save crop sub photo not yet done ! After datou_step_exec type output : time spend for datou_step_exec : 0.31444406509399414 time spend to save output : 0.00018334388732910156 total time spend for step 1 : 0.31462740898132324 caffe_path_current : About to save ! 0 After save, about to update current ! test detection filter by classif is a success ! ############################### TEST blur_detection ################################ t Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1243 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=1243 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 1243 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=1243 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : blur_detection list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (930729675) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 930729675 download finish for photo 930729675 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.118896484375 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:blur_detection Tue May 6 22:40:45 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746564045_2124625_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg': 930729675} map_photo_id_path_extension : {930729675: {'path': 'temp/1746564045_2124625_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} inside step blur_detection methode: ratio et variance treat image : temp/1746564045_2124625_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg resize: (600, 800) 930729675 12.961859636534896 score_blur_detection : {930729675: [(930729675, 12.961859636534896, 492688767)]} After datou_step_exec type output : time spend for datou_step_exec : 0.2121434211730957 time spend to save output : 5.7697296142578125e-05 total time spend for step 1 : 0.21220111846923828 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {930729675: [(930729675, 12.961859636534896, 492688767)]} {930729675: [(930729675, 12.961859636534896, 492688767)]} ############################### TEST detect_point_224x224 ################################ test_detect_point_224x224 Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1908 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=1908 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 1908 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=1908 # 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) 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 : step 4589 thcl is not linked in the step_by_step architecture ! WARNING : step 4590 argmax is not linked in the step_by_step architecture ! 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 ! DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : thcl, argmax list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (987515175,987515176,987515177,987515178,987515179,987515180,987515181,987515182,987515183,987515184,987515185,987515186,987515187,987515188,987515189,987515190,987515192,987515193,987515195,987515196,987515198,987515200,987515201,987515202,987515204,987515205,987515207,987515208,987515209,987515211,987515212,987515213,987515215,987515216,987515217,987515219,987515220,987515222,987515223,987515224,987515226,987515227,987515228,987515230,987515231,987515232,987515233,987515234,987515235,987515236,987515237,987515238,987515239,987515240,987515241,987515242,987515243,987515244,987515245,987515246,987515247,987515248,987515249,987515250) Found this number of photos: 64 ##### Call download_photos : nb_thread : 5 begin to download photo : 987515175 begin to download photo : 987515188 begin to download photo : 987515207 begin to download photo : 987515224 begin to download photo : 987515239 download finish for photo 987515224 begin to download photo : 987515226 download finish for photo 987515207 begin to download photo : 987515208 download finish for photo 987515175 begin to download photo : 987515176 download finish for photo 987515239 begin to download photo : 987515240 download finish for photo 987515188 begin to download photo : 987515189 download finish for photo 987515226 begin to download photo : 987515227 download finish for photo 987515176 begin to download photo : 987515177 download finish for photo 987515208 begin to download photo : 987515209 download finish for photo 987515240 begin to download photo : 987515241 download finish for photo 987515189 begin to download photo : 987515190 download finish for photo 987515227 begin to download photo : 987515228 download finish for photo 987515177 begin to download photo : 987515178 download finish for photo 987515209 begin to download photo : 987515211 download finish for photo 987515190 begin to download photo : 987515192 download finish for photo 987515178 begin to download photo : 987515179 download finish for photo 987515228 begin to download photo : 987515230 download finish for photo 987515241 begin to download photo : 987515242 download finish for photo 987515192 begin to download photo : 987515193 download finish for photo 987515179 begin to download photo : 987515180 download finish for photo 987515211 begin to download photo : 987515212 download finish for photo 987515242 begin to download photo : 987515243 download finish for photo 987515180 begin to download photo : 987515181 download finish for photo 987515230 begin to download photo : 987515231 download finish for photo 987515193 begin to download photo : 987515195 download finish for photo 987515212 begin to download photo : 987515213 download finish for photo 987515195 begin to download photo : 987515196 download finish for photo 987515243 begin to download photo : 987515244 download finish for photo 987515181 begin to download photo : 987515182 download finish for photo 987515213 begin to download photo : 987515215 download finish for photo 987515231 begin to download photo : 987515232 download finish for photo 987515244 begin to download photo : 987515245 download finish for photo 987515182 begin to download photo : 987515183 download finish for photo 987515196 begin to download photo : 987515198 download finish for photo 987515232 begin to download photo : 987515233 download finish for photo 987515215 begin to download photo : 987515216 download finish for photo 987515198 begin to download photo : 987515200 download finish for photo 987515183 begin to download photo : 987515184 download finish for photo 987515233 begin to download photo : 987515234 download finish for photo 987515216 begin to download photo : 987515217 download finish for photo 987515200 begin to download photo : 987515201 download finish for photo 987515245 begin to download photo : 987515246 download finish for photo 987515217 begin to download photo : 987515219 download finish for photo 987515184 begin to download photo : 987515185 download finish for photo 987515234 begin to download photo : 987515235 download finish for photo 987515246 begin to download photo : 987515247 download finish for photo 987515201 begin to download photo : 987515202 download finish for photo 987515219 begin to download photo : 987515220 download finish for photo 987515185 begin to download photo : 987515186 download finish for photo 987515235 begin to download photo : 987515236 download finish for photo 987515247 begin to download photo : 987515248 download finish for photo 987515202 begin to download photo : 987515204 download finish for photo 987515186 begin to download photo : 987515187 download finish for photo 987515236 begin to download photo : 987515237 download finish for photo 987515220 begin to download photo : 987515222 download finish for photo 987515187 download finish for photo 987515248 begin to download photo : 987515249 download finish for photo 987515204 begin to download photo : 987515205 download finish for photo 987515237 begin to download photo : 987515238 download finish for photo 987515222 begin to download photo : 987515223 download finish for photo 987515249 begin to download photo : 987515250 download finish for photo 987515223 download finish for photo 987515238 download finish for photo 987515205 download finish for photo 987515250 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 64 ; length of list_pids : 64 ; length of list_args : 64 ##### After load_data_input time to download the photos : 1.5065360069274902 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 2 step1:thcl Tue May 6 22:40:47 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746564045_2124625_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg': 987515175, 'temp/1746564045_2124625_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg': 987515176, 'temp/1746564045_2124625_987515177_4a54e9967227806219ddf45d256539d8.jpg': 987515177, 'temp/1746564045_2124625_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg': 987515178, 'temp/1746564045_2124625_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg': 987515179, 'temp/1746564045_2124625_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg': 987515180, 'temp/1746564045_2124625_987515181_1738c2798fb31152809ecb443ac286d6.jpg': 987515181, 'temp/1746564045_2124625_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg': 987515182, 'temp/1746564045_2124625_987515183_6aab9ca0421398b4899892c10c2594c6.jpg': 987515183, 'temp/1746564045_2124625_987515184_19c8c2177209a285df6014d95fe53f2c.jpg': 987515184, 'temp/1746564045_2124625_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg': 987515185, 'temp/1746564045_2124625_987515186_797def426440b544aa80dbd63a19234a.jpg': 987515186, 'temp/1746564045_2124625_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg': 987515187, 'temp/1746564045_2124625_987515207_de216ddb041e249524b0fb2b949064a5.jpg': 987515207, 'temp/1746564045_2124625_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg': 987515208, 'temp/1746564045_2124625_987515209_02dfe1ae39f51994652f4a8538844aea.jpg': 987515209, 'temp/1746564045_2124625_987515211_72cc7664d45bd40477351b9b764f1500.jpg': 987515211, 'temp/1746564045_2124625_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg': 987515212, 'temp/1746564045_2124625_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg': 987515213, 'temp/1746564045_2124625_987515215_902ef348a7eebb9a8b87f42927347936.jpg': 987515215, 'temp/1746564045_2124625_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg': 987515216, 'temp/1746564045_2124625_987515217_78877bb2c5760be28518d17f77d1c609.jpg': 987515217, 'temp/1746564045_2124625_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg': 987515219, 'temp/1746564045_2124625_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg': 987515220, 'temp/1746564045_2124625_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg': 987515222, 'temp/1746564045_2124625_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg': 987515223, 'temp/1746564045_2124625_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg': 987515224, 'temp/1746564045_2124625_987515226_a18048dca1a77ae086b62cf07759f704.jpg': 987515226, 'temp/1746564045_2124625_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg': 987515227, 'temp/1746564045_2124625_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg': 987515228, 'temp/1746564045_2124625_987515230_846ad925884264181565c81d152a2e94.jpg': 987515230, 'temp/1746564045_2124625_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg': 987515231, 'temp/1746564045_2124625_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg': 987515232, 'temp/1746564045_2124625_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg': 987515233, 'temp/1746564045_2124625_987515234_2eca3480aed0f8b876242675ad99b666.jpg': 987515234, 'temp/1746564045_2124625_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg': 987515235, 'temp/1746564045_2124625_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg': 987515236, 'temp/1746564045_2124625_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg': 987515237, 'temp/1746564045_2124625_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg': 987515238, 'temp/1746564045_2124625_987515188_4116f9906657a69bb76c2fda982037b9.jpg': 987515188, 'temp/1746564045_2124625_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg': 987515189, 'temp/1746564045_2124625_987515190_d56932bfc6ba2a8c974c691108755017.jpg': 987515190, 'temp/1746564045_2124625_987515192_b661073b218f5f056833d6af1c617153.jpg': 987515192, 'temp/1746564045_2124625_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg': 987515193, 'temp/1746564045_2124625_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg': 987515195, 'temp/1746564045_2124625_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg': 987515196, 'temp/1746564045_2124625_987515198_599e80f444c876f407e94b533c89360b.jpg': 987515198, 'temp/1746564045_2124625_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg': 987515200, 'temp/1746564045_2124625_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg': 987515201, 'temp/1746564045_2124625_987515202_3314bd90d1404f31b827d8925abf2d62.jpg': 987515202, 'temp/1746564045_2124625_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg': 987515204, 'temp/1746564045_2124625_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg': 987515205, 'temp/1746564045_2124625_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg': 987515239, 'temp/1746564045_2124625_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg': 987515240, 'temp/1746564045_2124625_987515241_073420d938f5f010ffd5b4353c064e09.jpg': 987515241, 'temp/1746564045_2124625_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg': 987515242, 'temp/1746564045_2124625_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg': 987515243, 'temp/1746564045_2124625_987515244_419530eaef5ef868f75c758b94eea4b4.jpg': 987515244, 'temp/1746564045_2124625_987515245_757d9d208d5bd4375c5f21f68b699148.jpg': 987515245, 'temp/1746564045_2124625_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg': 987515246, 'temp/1746564045_2124625_987515247_e47b65403df916ba909bc9c439b0af73.jpg': 987515247, 'temp/1746564045_2124625_987515248_a70ad88462a22fb62a120721a42b2d42.jpg': 987515248, 'temp/1746564045_2124625_987515249_a70ad88462a22fb62a120721a42b2d42.jpg': 987515249, 'temp/1746564045_2124625_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg': 987515250} map_photo_id_path_extension : {987515175: {'path': 'temp/1746564045_2124625_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg', 'extension': 'jpg'}, 987515176: {'path': 'temp/1746564045_2124625_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg', 'extension': 'jpg'}, 987515177: {'path': 'temp/1746564045_2124625_987515177_4a54e9967227806219ddf45d256539d8.jpg', 'extension': 'jpg'}, 987515178: {'path': 'temp/1746564045_2124625_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg', 'extension': 'jpg'}, 987515179: {'path': 'temp/1746564045_2124625_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg', 'extension': 'jpg'}, 987515180: {'path': 'temp/1746564045_2124625_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg', 'extension': 'jpg'}, 987515181: {'path': 'temp/1746564045_2124625_987515181_1738c2798fb31152809ecb443ac286d6.jpg', 'extension': 'jpg'}, 987515182: {'path': 'temp/1746564045_2124625_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg', 'extension': 'jpg'}, 987515183: {'path': 'temp/1746564045_2124625_987515183_6aab9ca0421398b4899892c10c2594c6.jpg', 'extension': 'jpg'}, 987515184: {'path': 'temp/1746564045_2124625_987515184_19c8c2177209a285df6014d95fe53f2c.jpg', 'extension': 'jpg'}, 987515185: {'path': 'temp/1746564045_2124625_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg', 'extension': 'jpg'}, 987515186: {'path': 'temp/1746564045_2124625_987515186_797def426440b544aa80dbd63a19234a.jpg', 'extension': 'jpg'}, 987515187: {'path': 'temp/1746564045_2124625_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg', 'extension': 'jpg'}, 987515207: {'path': 'temp/1746564045_2124625_987515207_de216ddb041e249524b0fb2b949064a5.jpg', 'extension': 'jpg'}, 987515208: {'path': 'temp/1746564045_2124625_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg', 'extension': 'jpg'}, 987515209: {'path': 'temp/1746564045_2124625_987515209_02dfe1ae39f51994652f4a8538844aea.jpg', 'extension': 'jpg'}, 987515211: {'path': 'temp/1746564045_2124625_987515211_72cc7664d45bd40477351b9b764f1500.jpg', 'extension': 'jpg'}, 987515212: {'path': 'temp/1746564045_2124625_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg', 'extension': 'jpg'}, 987515213: {'path': 'temp/1746564045_2124625_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg', 'extension': 'jpg'}, 987515215: {'path': 'temp/1746564045_2124625_987515215_902ef348a7eebb9a8b87f42927347936.jpg', 'extension': 'jpg'}, 987515216: {'path': 'temp/1746564045_2124625_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg', 'extension': 'jpg'}, 987515217: {'path': 'temp/1746564045_2124625_987515217_78877bb2c5760be28518d17f77d1c609.jpg', 'extension': 'jpg'}, 987515219: {'path': 'temp/1746564045_2124625_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg', 'extension': 'jpg'}, 987515220: {'path': 'temp/1746564045_2124625_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg', 'extension': 'jpg'}, 987515222: {'path': 'temp/1746564045_2124625_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg', 'extension': 'jpg'}, 987515223: {'path': 'temp/1746564045_2124625_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg', 'extension': 'jpg'}, 987515224: {'path': 'temp/1746564045_2124625_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg', 'extension': 'jpg'}, 987515226: {'path': 'temp/1746564045_2124625_987515226_a18048dca1a77ae086b62cf07759f704.jpg', 'extension': 'jpg'}, 987515227: {'path': 'temp/1746564045_2124625_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg', 'extension': 'jpg'}, 987515228: {'path': 'temp/1746564045_2124625_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg', 'extension': 'jpg'}, 987515230: {'path': 'temp/1746564045_2124625_987515230_846ad925884264181565c81d152a2e94.jpg', 'extension': 'jpg'}, 987515231: {'path': 'temp/1746564045_2124625_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg', 'extension': 'jpg'}, 987515232: {'path': 'temp/1746564045_2124625_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg', 'extension': 'jpg'}, 987515233: {'path': 'temp/1746564045_2124625_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg', 'extension': 'jpg'}, 987515234: {'path': 'temp/1746564045_2124625_987515234_2eca3480aed0f8b876242675ad99b666.jpg', 'extension': 'jpg'}, 987515235: {'path': 'temp/1746564045_2124625_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg', 'extension': 'jpg'}, 987515236: {'path': 'temp/1746564045_2124625_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg', 'extension': 'jpg'}, 987515237: {'path': 'temp/1746564045_2124625_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg', 'extension': 'jpg'}, 987515238: {'path': 'temp/1746564045_2124625_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg', 'extension': 'jpg'}, 987515188: {'path': 'temp/1746564045_2124625_987515188_4116f9906657a69bb76c2fda982037b9.jpg', 'extension': 'jpg'}, 987515189: {'path': 'temp/1746564045_2124625_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg', 'extension': 'jpg'}, 987515190: {'path': 'temp/1746564045_2124625_987515190_d56932bfc6ba2a8c974c691108755017.jpg', 'extension': 'jpg'}, 987515192: {'path': 'temp/1746564045_2124625_987515192_b661073b218f5f056833d6af1c617153.jpg', 'extension': 'jpg'}, 987515193: {'path': 'temp/1746564045_2124625_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg', 'extension': 'jpg'}, 987515195: {'path': 'temp/1746564045_2124625_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg', 'extension': 'jpg'}, 987515196: {'path': 'temp/1746564045_2124625_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg', 'extension': 'jpg'}, 987515198: {'path': 'temp/1746564045_2124625_987515198_599e80f444c876f407e94b533c89360b.jpg', 'extension': 'jpg'}, 987515200: {'path': 'temp/1746564045_2124625_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg', 'extension': 'jpg'}, 987515201: {'path': 'temp/1746564045_2124625_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg', 'extension': 'jpg'}, 987515202: {'path': 'temp/1746564045_2124625_987515202_3314bd90d1404f31b827d8925abf2d62.jpg', 'extension': 'jpg'}, 987515204: {'path': 'temp/1746564045_2124625_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg', 'extension': 'jpg'}, 987515205: {'path': 'temp/1746564045_2124625_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg', 'extension': 'jpg'}, 987515239: {'path': 'temp/1746564045_2124625_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg', 'extension': 'jpg'}, 987515240: {'path': 'temp/1746564045_2124625_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg', 'extension': 'jpg'}, 987515241: {'path': 'temp/1746564045_2124625_987515241_073420d938f5f010ffd5b4353c064e09.jpg', 'extension': 'jpg'}, 987515242: {'path': 'temp/1746564045_2124625_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg', 'extension': 'jpg'}, 987515243: {'path': 'temp/1746564045_2124625_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg', 'extension': 'jpg'}, 987515244: {'path': 'temp/1746564045_2124625_987515244_419530eaef5ef868f75c758b94eea4b4.jpg', 'extension': 'jpg'}, 987515245: {'path': 'temp/1746564045_2124625_987515245_757d9d208d5bd4375c5f21f68b699148.jpg', 'extension': 'jpg'}, 987515246: {'path': 'temp/1746564045_2124625_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg', 'extension': 'jpg'}, 987515247: {'path': 'temp/1746564045_2124625_987515247_e47b65403df916ba909bc9c439b0af73.jpg', 'extension': 'jpg'}, 987515248: {'path': 'temp/1746564045_2124625_987515248_a70ad88462a22fb62a120721a42b2d42.jpg', 'extension': 'jpg'}, 987515249: {'path': 'temp/1746564045_2124625_987515249_a70ad88462a22fb62a120721a42b2d42.jpg', 'extension': 'jpg'}, 987515250: {'path': 'temp/1746564045_2124625_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Thcl ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'1528': 1} we are using the classfication for only one thcl 1528 In convert_file_to_np l 337 : 7 l343 7 In convert_file_to_np l 337 : 1 l343 1 In convert_file_to_np l 337 : 7 l343 7 In convert_file_to_np l 337 : 7 l343 7 In convert_file_to_np l 337 : 7 l343 7 In convert_file_to_np l 337 : 7 l343 7 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.0024378299713134766 time to convert the images to numpy array : 0.007024288177490234 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! In convert_file_to_np l 337 : 7 l343 7 In convert_file_to_np l 337 : 7 l343 7 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! In convert_file_to_np l 337 : 7 l343 7 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! In convert_file_to_np l 337 : 7 l343 7 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.009042024612426758 time to convert the images to numpy array : 0.04661107063293457 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.008862972259521484 time to convert the images to numpy array : 0.047412872314453125 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.011376619338989258 time to convert the images to numpy array : 0.04699850082397461 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.0088958740234375 time to convert the images to numpy array : 0.055843353271484375 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.014626502990722656 time to convert the images to numpy array : 0.04842948913574219 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.013049602508544922 time to convert the images to numpy array : 0.05130314826965332 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.015198469161987305 time to convert the images to numpy array : 0.05247831344604492 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.017364501953125 time to convert the images to numpy array : 0.04549694061279297 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.024089574813842773 time to convert the images to numpy array : 0.044316768646240234 total time to convert the images to numpy array : 0.07097148895263672 list photo_ids error: [] list photo_ids correct : [987515250, 987515175, 987515176, 987515177, 987515178, 987515179, 987515180, 987515181, 987515208, 987515209, 987515211, 987515212, 987515213, 987515215, 987515216, 987515182, 987515183, 987515184, 987515185, 987515186, 987515187, 987515207, 987515217, 987515219, 987515220, 987515222, 987515223, 987515224, 987515226, 987515202, 987515204, 987515205, 987515239, 987515240, 987515241, 987515242, 987515192, 987515193, 987515195, 987515196, 987515198, 987515200, 987515201, 987515227, 987515228, 987515230, 987515231, 987515232, 987515233, 987515234, 987515243, 987515244, 987515245, 987515246, 987515247, 987515248, 987515249, 987515235, 987515236, 987515237, 987515238, 987515188, 987515189, 987515190] number of photos to traite : 64 try to delete the photos incorrect in DB tagging for thcl : 1528 To do loadFromThcl(), then load ParamDescType : thcl1528 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (1528) thcls : [{'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'}] thcl {'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'} Update svm_hashtag_type_desc : 4421 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (4421) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4421, 'learn_refus_upm_blanches_1924', 16384, 25088, 'learn_refus_upm_blanches_1924', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2019, 10, 22, 17, 39, 25), datetime.datetime(2019, 10, 22, 17, 39, 25)) To loadFromThcl() : net_4421 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 2838 max_wait_temp : 1 max_wait : 0 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (4421) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4421, 'learn_refus_upm_blanches_1924', 16384, 25088, 'learn_refus_upm_blanches_1924', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2019, 10, 22, 17, 39, 25), datetime.datetime(2019, 10, 22, 17, 39, 25)) param : , param.caffemodel : learn_refus_upm_blanches_1924 None mean_file_type : mean_file_path : prototxt_file_path : model : learn_refus_upm_blanches_1924 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : learn_refus_upm_blanches_1924 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /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/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt caffemodel_filename : /data/models_weight/learn_refus_upm_blanches_1924/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3693 max_wait_temp : 1 max_wait : 0 dict_keys(['res5b', 'prob']) time used to do the prepocess of the images : 0.06683564186096191 time used to do the prediction : 0.26563525199890137 save descriptor for thcl : 1528 (64, 512, 7, 7) Got the blobs of the net to insert : [3, 3, 1, 4, 7, 7, 9, 6, 2, 1] code_as_byte_string:b'0303010407'| Got the blobs of the net to insert : [13, 9, 9, 8, 11, 13, 6, 14, 9, 18] code_as_byte_string:b'0d0909080b'| Got the blobs of the net to insert : [13, 9, 9, 8, 11, 13, 6, 14, 9, 18] code_as_byte_string:b'0d0909080b'| Got the blobs of the net to insert : [2, 2, 6, 7, 8, 6, 4, 3, 1, 8] code_as_byte_string:b'0202060708'| Got the blobs of the net to insert : [1, 2, 2, 1, 0, 1, 1, 2, 1, 1] code_as_byte_string:b'0102020100'| Got the blobs of the net to insert : [2, 2, 1, 3, 2, 3, 2, 0, 0, 1] code_as_byte_string:b'0202010302'| Got the blobs of the net to insert : [1, 0, 1, 2, 1, 0, 3, 3, 3, 8] code_as_byte_string:b'0100010201'| Got the blobs of the net to insert : [5, 5, 6, 4, 3, 6, 9, 7, 7, 7] code_as_byte_string:b'0505060403'| Got the blobs of the net to insert : [0, 0, 0, 1, 2, 2, 1, 1, 0, 0] code_as_byte_string:b'0000000102'| Got the blobs of the net to insert : [1, 0, 0, 1, 1, 1, 0, 1, 1, 3] code_as_byte_string:b'0100000101'| Got the blobs of the net to insert : [1, 1, 2, 3, 4, 4, 1, 2, 8, 7] code_as_byte_string:b'0101020304'| Got the blobs of the net to insert : [6, 8, 5, 7, 7, 8, 10, 12, 12, 6] code_as_byte_string:b'0608050707'| Got the blobs of the net to insert : [6, 8, 5, 7, 7, 8, 10, 12, 12, 6] code_as_byte_string:b'0608050707'| Got the blobs of the net to insert : [5, 5, 4, 3, 5, 6, 3, 4, 3, 3] code_as_byte_string:b'0505040305'| Got the blobs of the net to insert : [5, 3, 3, 1, 1, 1, 2, 2, 2, 4] code_as_byte_string:b'0503030101'| Got the blobs of the net to insert : [4, 6, 6, 4, 7, 8, 8, 8, 12, 8] code_as_byte_string:b'0406060407'| Got the blobs of the net to insert : [11, 8, 5, 9, 12, 14, 13, 14, 12, 6] code_as_byte_string:b'0b0805090c'| Got the blobs of the net to insert : [8, 7, 6, 4, 2, 1, 2, 4, 4, 4] code_as_byte_string:b'0807060402'| Got the blobs of the net to insert : [2, 3, 5, 5, 2, 2, 3, 0, 1, 4] code_as_byte_string:b'0203050502'| Got the blobs of the net to insert : [0, 0, 0, 1, 1, 1, 3, 3, 0, 0] code_as_byte_string:b'0000000101'| Got the blobs of the net to insert : [3, 1, 1, 1, 1, 1, 2, 1, 1, 2] code_as_byte_string:b'0301010101'| Got the blobs of the net to insert : [4, 2, 1, 2, 3, 1, 0, 1, 0, 1] code_as_byte_string:b'0402010203'| Got the blobs of the net to insert : [2, 1, 2, 2, 2, 0, 0, 1, 0, 3] code_as_byte_string:b'0201020202'| Got the blobs of the net to insert : [0, 0, 2, 3, 3, 1, 0, 0, 0, 0] code_as_byte_string:b'0000020303'| Got the blobs of the net to insert : [1, 0, 0, 1, 0, 0, 0, 3, 4, 5] code_as_byte_string:b'0100000100'| Got the blobs of the net to insert : [0, 1, 1, 4, 2, 1, 3, 7, 9, 9] code_as_byte_string:b'0001010402'| Got the blobs of the net to insert : [7, 10, 10, 2, 4, 7, 8, 5, 5, 2] code_as_byte_string:b'070a0a0204'| Got the blobs of the net to insert : [7, 7, 3, 8, 9, 6, 7, 10, 11, 6] code_as_byte_string:b'0707030809'| Got the blobs of the net to insert : [3, 3, 3, 1, 6, 7, 10, 5, 3, 5] code_as_byte_string:b'0303030106'| Got the blobs of the net to insert : [2, 5, 4, 6, 9, 9, 10, 9, 11, 5] code_as_byte_string:b'0205040609'| Got the blobs of the net to insert : [5, 2, 3, 7, 5, 7, 6, 2, 4, 3] code_as_byte_string:b'0502030705'| Got the blobs of the net to insert : [5, 7, 3, 2, 1, 2, 2, 2, 0, 0] code_as_byte_string:b'0507030201'| Got the blobs of the net to insert : [1, 1, 0, 0, 0, 0, 0, 3, 5, 1] code_as_byte_string:b'0101000000'| Got the blobs of the net to insert : [0, 3, 0, 1, 1, 6, 7, 5, 5, 3] code_as_byte_string:b'0003000101'| Got the blobs of the net to insert : [6, 6, 3, 3, 8, 8, 6, 6, 2, 0] code_as_byte_string:b'0606030308'| Got the blobs of the net to insert : [5, 5, 2, 4, 6, 5, 9, 9, 4, 2] code_as_byte_string:b'0505020406'| Got the blobs of the net to insert : [5, 2, 3, 6, 8, 12, 9, 9, 3, 5] code_as_byte_string:b'0502030608'| Got the blobs of the net to insert : [6, 3, 3, 0, 1, 2, 2, 5, 2, 4] code_as_byte_string:b'0603030001'| Got the blobs of the net to insert : [0, 0, 0, 0, 0, 2, 5, 1, 0, 0] code_as_byte_string:b'0000000000'| Got the blobs of the net to insert : [0, 0, 0, 0, 0, 2, 5, 1, 0, 0] code_as_byte_string:b'0000000000'| Got the blobs of the net to insert : [1, 1, 0, 0, 1, 2, 0, 0, 0, 1] code_as_byte_string:b'0101000001'| Got the blobs of the net to insert : [0, 1, 0, 1, 3, 4, 2, 1, 3, 5] code_as_byte_string:b'0001000103'| Got the blobs of the net to insert : [3, 3, 3, 8, 7, 5, 5, 4, 5, 3] code_as_byte_string:b'0303030807'| Got the blobs of the net to insert : [3, 2, 2, 1, 1, 2, 2, 3, 2, 5] code_as_byte_string:b'0302020101'| Got the blobs of the net to insert : [1, 0, 3, 1, 0, 0, 0, 0, 0, 5] code_as_byte_string:b'0100030100'| Got the blobs of the net to insert : [0, 0, 2, 2, 0, 0, 0, 0, 0, 3] code_as_byte_string:b'0000020200'| Got the blobs of the net to insert : [1, 1, 2, 1, 0, 0, 0, 3, 6, 7] code_as_byte_string:b'0101020100'| Got the blobs of the net to insert : [1, 3, 3, 3, 0, 1, 5, 6, 7, 4] code_as_byte_string:b'0103030300'| Got the blobs of the net to insert : [5, 7, 3, 2, 5, 9, 10, 3, 5, 0] code_as_byte_string:b'0507030205'| Got the blobs of the net to insert : [6, 8, 3, 6, 6, 6, 5, 10, 10, 3] code_as_byte_string:b'0608030606'| Got the blobs of the net to insert : [2, 1, 3, 5, 7, 5, 3, 4, 1, 3] code_as_byte_string:b'0201030507'| Got the blobs of the net to insert : [0, 0, 0, 1, 1, 3, 2, 1, 0, 2] code_as_byte_string:b'0000000101'| Got the blobs of the net to insert : [0, 0, 0, 0, 0, 0, 1, 3, 1, 0] code_as_byte_string:b'0000000000'| Got the blobs of the net to insert : [0, 0, 0, 1, 0, 0, 0, 0, 1, 3] code_as_byte_string:b'0000000100'| Got the blobs of the net to insert : [0, 0, 0, 1, 2, 0, 1, 4, 0, 2] code_as_byte_string:b'0000000102'| Got the blobs of the net to insert : [4, 1, 3, 4, 7, 3, 4, 2, 1, 0] code_as_byte_string:b'0401030407'| Got the blobs of the net to insert : [4, 1, 3, 4, 7, 3, 4, 2, 1, 0] code_as_byte_string:b'0401030407'| Got the blobs of the net to insert : [1, 2, 1, 5, 7, 10, 8, 2, 2, 1] code_as_byte_string:b'0102010507'| Got the blobs of the net to insert : [0, 0, 0, 1, 5, 4, 6, 3, 1, 3] code_as_byte_string:b'0000000105'| Got the blobs of the net to insert : [0, 2, 1, 0, 0, 0, 0, 0, 4, 2] code_as_byte_string:b'0002010000'| Got the blobs of the net to insert : [0, 2, 0, 1, 1, 0, 0, 0, 1, 3] code_as_byte_string:b'0002000101'| Got the blobs of the net to insert : [1, 1, 1, 2, 0, 3, 4, 3, 4, 5] code_as_byte_string:b'0101010200'| Got the blobs of the net to insert : [3, 2, 1, 4, 5, 7, 6, 5, 6, 5] code_as_byte_string:b'0302010405'| Got the blobs of the net to insert : [3, 4, 5, 5, 9, 9, 9, 9, 11, 7] code_as_byte_string:b'0304050509'| time to traite the descriptors : 3.1119487285614014 Testing : ['987515250', '987515175', '987515176', '987515177', '987515178', '987515179', '987515180', '987515181', '987515208', '987515209', '987515211', '987515212', '987515213', '987515215', '987515216', '987515182', '987515183', '987515184', '987515185', '987515186', '987515187', '987515207', '987515217', '987515219', '987515220', '987515222', '987515223', '987515224', '987515226', '987515202', '987515204', '987515205', '987515239', '987515240', '987515241', '987515242', '987515192', '987515193', '987515195', '987515196', '987515198', '987515200', '987515201', '987515227', '987515228', '987515230', '987515231', '987515232', '987515233', '987515234', '987515243', '987515244', '987515245', '987515246', '987515247', '987515248', '987515249', '987515235', '987515236', '987515237', '987515238', '987515188', '987515189', '987515190'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (987515250,987515175,987515176,987515177,987515178,987515179,987515180,987515181,987515208,987515209,987515211,987515212,987515213,987515215,987515216,987515182,987515183,987515184,987515185,987515186,987515187,987515207,987515217,987515219,987515220,987515222,987515223,987515224,987515226,987515202,987515204,987515205,987515239,987515240,987515241,987515242,987515192,987515193,987515195,987515196,987515198,987515200,987515201,987515227,987515228,987515230,987515231,987515232,987515233,987515234,987515243,987515244,987515245,987515246,987515247,987515248,987515249,987515235,987515236,987515237,987515238,987515188,987515189,987515190) result : {987515175: {'photo_id': 987515175, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/8b398cba2f448622cd9657f5eb3f9796.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22062023_14_16_02_694514_0001.jpg'}, 987515176: {'photo_id': 987515176, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/8b398cba2f448622cd9657f5eb3f9796.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_144.jpg'}, 987515177: {'photo_id': 987515177, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/4a54e9967227806219ddf45d256539d8.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_176.jpg'}, 987515178: {'photo_id': 987515178, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/298b3d2bfe0fda6787b59a78e2e68867.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_208.jpg'}, 987515179: {'photo_id': 987515179, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/f7d4d1757a470f4c96dc3541eac88b9e.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_240.jpg'}, 987515180: {'photo_id': 987515180, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/776a5d7d8486ee2961bbe3a0d90f95b5.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_272.jpg'}, 987515181: {'photo_id': 987515181, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/1738c2798fb31152809ecb443ac286d6.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_304.jpg'}, 987515182: {'photo_id': 987515182, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/fe7f29bf6d13e08c3e985f91b5232178.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_336.jpg'}, 987515183: {'photo_id': 987515183, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/6aab9ca0421398b4899892c10c2594c6.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_112.jpg'}, 987515184: {'photo_id': 987515184, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/19c8c2177209a285df6014d95fe53f2c.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_144.jpg'}, 987515185: {'photo_id': 987515185, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e172d54457cabee9d7f02ee1300f3ae9.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_176.jpg'}, 987515186: {'photo_id': 987515186, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/797def426440b544aa80dbd63a19234a.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_208.jpg'}, 987515187: {'photo_id': 987515187, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/9f62f98efd3caca0b9c17d27f5c70440.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_240.jpg'}, 987515188: {'photo_id': 987515188, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/4116f9906657a69bb76c2fda982037b9.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_272.jpg'}, 987515189: {'photo_id': 987515189, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/8e8590a26f72249d4c2116dffd0cf668.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_304.jpg'}, 987515190: {'photo_id': 987515190, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/d56932bfc6ba2a8c974c691108755017.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_336.jpg'}, 987515192: {'photo_id': 987515192, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b661073b218f5f056833d6af1c617153.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_112.jpg'}, 987515193: {'photo_id': 987515193, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/1a97fceb4dcbf5821d783b2e00b52fe6.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_144.jpg'}, 987515195: {'photo_id': 987515195, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/30ccb89dfe410c445878a7f2819ddc36.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22062023_17_37_58_622227.jpg'}, 987515196: {'photo_id': 987515196, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/30ccb89dfe410c445878a7f2819ddc36.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_208.jpg'}, 987515198: {'photo_id': 987515198, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/599e80f444c876f407e94b533c89360b.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_240.jpg'}, 987515200: {'photo_id': 987515200, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/978964436b5d5fb0eeda17e3bfafe889.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_272.jpg'}, 987515201: {'photo_id': 987515201, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b224d2acdc7fa2bbb134c09db6bca7ce.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_304.jpg'}, 987515202: {'photo_id': 987515202, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/3314bd90d1404f31b827d8925abf2d62.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_336.jpg'}, 987515204: {'photo_id': 987515204, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/9779c4f9d44360a9c80499e3b01e8a09.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_112.jpg'}, 987515205: {'photo_id': 987515205, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/fd4b136d0b3a9a1a347942d7191f6fea.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_144.jpg'}, 987515207: {'photo_id': 987515207, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/de216ddb041e249524b0fb2b949064a5.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_176.jpg'}, 987515208: {'photo_id': 987515208, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/a2b90cb74908aa64bbc4aae58f0c5ae8.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_208.jpg'}, 987515209: {'photo_id': 987515209, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/02dfe1ae39f51994652f4a8538844aea.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_240.jpg'}, 987515211: {'photo_id': 987515211, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/72cc7664d45bd40477351b9b764f1500.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_272.jpg'}, 987515212: {'photo_id': 987515212, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22062023_09_32_14_525625.jpg'}, 987515213: {'photo_id': 987515213, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_336.jpg'}, 987515215: {'photo_id': 987515215, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/902ef348a7eebb9a8b87f42927347936.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_112.jpg'}, 987515216: {'photo_id': 987515216, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/4f7dc21f1d2cd3fcabadc4a6755921e1.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_144.jpg'}, 987515217: {'photo_id': 987515217, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/78877bb2c5760be28518d17f77d1c609.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_176.jpg'}, 987515219: {'photo_id': 987515219, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/c2d417a5ba6ccf7c84527636f8d5eef9.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_208.jpg'}, 987515220: {'photo_id': 987515220, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e729f316c4c3b32049adfbaaa336d95c.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_240.jpg'}, 987515222: {'photo_id': 987515222, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/067a027bc7402f969b6277d0dcb47eaa.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_272.jpg'}, 987515223: {'photo_id': 987515223, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/ebb57f09941cd11d7ee45a9368a883c1.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_304.jpg'}, 987515224: {'photo_id': 987515224, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e8747b400e713ecbd08d5b75db4d7568.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_336.jpg'}, 987515226: {'photo_id': 987515226, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/a18048dca1a77ae086b62cf07759f704.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_112.jpg'}, 987515227: {'photo_id': 987515227, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e9c45a0e576ec9e44c1379c3fc5fec7c.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_144.jpg'}, 987515228: {'photo_id': 987515228, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/9f1759f20c9e603bccb9f9879d2f0d54.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_176.jpg'}, 987515230: {'photo_id': 987515230, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/846ad925884264181565c81d152a2e94.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_208.jpg'}, 987515231: {'photo_id': 987515231, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/dbf4cafa71b6db4771c5c8f0c25e9cda.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_240.jpg'}, 987515232: {'photo_id': 987515232, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/38db7950cdb3c674ee0ad65915b021f3.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_272.jpg'}, 987515233: {'photo_id': 987515233, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/a92514bed0e8c5724f2d032d3ab1e2ad.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_304.jpg'}, 987515234: {'photo_id': 987515234, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/2eca3480aed0f8b876242675ad99b666.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_336.jpg'}, 987515235: {'photo_id': 987515235, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/87075955a2f76b3948b47ffe1825ecd9.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_112.jpg'}, 987515236: {'photo_id': 987515236, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/8b44a98b1aceadad73ed000d65836a9a.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_144.jpg'}, 987515237: {'photo_id': 987515237, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/1183dfa371a457f11ce2b622c7cf9467.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_176.jpg'}, 987515238: {'photo_id': 987515238, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e6292cb81e05894cfeb4b99f21a1d3f8.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_208.jpg'}, 987515239: {'photo_id': 987515239, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b3fa6f29636080b5138c8d8c33fea309.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_240.jpg'}, 987515240: {'photo_id': 987515240, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_272.jpg'}, 987515241: {'photo_id': 987515241, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/073420d938f5f010ffd5b4353c064e09.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_304.jpg'}, 987515242: {'photo_id': 987515242, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/327abb5215d6fd1f0aad51f53ed8c324.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_336.jpg'}, 987515243: {'photo_id': 987515243, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/4375283f3bc5cdaa431c2fc6f17f53a4.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_112.jpg'}, 987515244: {'photo_id': 987515244, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/419530eaef5ef868f75c758b94eea4b4.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_144.jpg'}, 987515245: {'photo_id': 987515245, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/757d9d208d5bd4375c5f21f68b699148.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_176.jpg'}, 987515246: {'photo_id': 987515246, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/671a708f67f2efa19004b8257fc7b9c8.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_208.jpg'}, 987515247: {'photo_id': 987515247, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e47b65403df916ba909bc9c439b0af73.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_240.jpg'}, 987515248: {'photo_id': 987515248, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/a70ad88462a22fb62a120721a42b2d42.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22062023_14_16_02_694514_0002.jpg'}, 987515249: {'photo_id': 987515249, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/a70ad88462a22fb62a120721a42b2d42.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_304.jpg'}, 987515250: {'photo_id': 987515250, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b2827c9639df69656f23abcc7f2f82d9.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_336.jpg'}} list_photo_exists : [987515175, 987515176, 987515177, 987515178, 987515179, 987515180, 987515181, 987515182, 987515183, 987515184, 987515185, 987515186, 987515187, 987515188, 987515189, 987515190, 987515192, 987515193, 987515195, 987515196, 987515198, 987515200, 987515201, 987515202, 987515204, 987515205, 987515207, 987515208, 987515209, 987515211, 987515212, 987515213, 987515215, 987515216, 987515217, 987515219, 987515220, 987515222, 987515223, 987515224, 987515226, 987515227, 987515228, 987515230, 987515231, 987515232, 987515233, 987515234, 987515235, 987515236, 987515237, 987515238, 987515239, 987515240, 987515241, 987515242, 987515243, 987515244, 987515245, 987515246, 987515247, 987515248, 987515249, 987515250] storage_type for insertDescriptorsMulti : 1 To insert : 987515250 To insert : 987515175 To insert : 987515176 To insert : 987515177 To insert : 987515178 To insert : 987515179 To insert : 987515180 To insert : 987515181 To insert : 987515208 To insert : 987515209 To insert : 987515211 To insert : 987515212 To insert : 987515213 To insert : 987515215 To insert : 987515216 To insert : 987515182 To insert : 987515183 To insert : 987515184 To insert : 987515185 To insert : 987515186 To insert : 987515187 To insert : 987515207 To insert : 987515217 To insert : 987515219 To insert : 987515220 To insert : 987515222 To insert : 987515223 To insert : 987515224 To insert : 987515226 To insert : 987515202 To insert : 987515204 To insert : 987515205 To insert : 987515239 To insert : 987515240 To insert : 987515241 To insert : 987515242 To insert : 987515192 To insert : 987515193 To insert : 987515195 To insert : 987515196 To insert : 987515198 To insert : 987515200 To insert : 987515201 To insert : 987515227 To insert : 987515228 To insert : 987515230 To insert : 987515231 To insert : 987515232 To insert : 987515233 To insert : 987515234 To insert : 987515243 To insert : 987515244 To insert : 987515245 To insert : 987515246 To insert : 987515247 To insert : 987515248 To insert : 987515249 To insert : 987515235 To insert : 987515236 To insert : 987515237 To insert : 987515238 To insert : 987515188 To insert : 987515189 To insert : 987515190 time to insert the descriptors : 32.60856342315674 After datou_step_exec type output : time spend for datou_step_exec : 39.89847707748413 time spend to save output : 0.08010506629943848 total time spend for step 1 : 39.97858214378357 step2:argmax Tue May 6 22:41:27 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746564045_2124625_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg': 987515175, 'temp/1746564045_2124625_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg': 987515176, 'temp/1746564045_2124625_987515177_4a54e9967227806219ddf45d256539d8.jpg': 987515177, 'temp/1746564045_2124625_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg': 987515178, 'temp/1746564045_2124625_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg': 987515179, 'temp/1746564045_2124625_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg': 987515180, 'temp/1746564045_2124625_987515181_1738c2798fb31152809ecb443ac286d6.jpg': 987515181, 'temp/1746564045_2124625_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg': 987515182, 'temp/1746564045_2124625_987515183_6aab9ca0421398b4899892c10c2594c6.jpg': 987515183, 'temp/1746564045_2124625_987515184_19c8c2177209a285df6014d95fe53f2c.jpg': 987515184, 'temp/1746564045_2124625_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg': 987515185, 'temp/1746564045_2124625_987515186_797def426440b544aa80dbd63a19234a.jpg': 987515186, 'temp/1746564045_2124625_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg': 987515187, 'temp/1746564045_2124625_987515207_de216ddb041e249524b0fb2b949064a5.jpg': 987515207, 'temp/1746564045_2124625_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg': 987515208, 'temp/1746564045_2124625_987515209_02dfe1ae39f51994652f4a8538844aea.jpg': 987515209, 'temp/1746564045_2124625_987515211_72cc7664d45bd40477351b9b764f1500.jpg': 987515211, 'temp/1746564045_2124625_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg': 987515212, 'temp/1746564045_2124625_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg': 987515213, 'temp/1746564045_2124625_987515215_902ef348a7eebb9a8b87f42927347936.jpg': 987515215, 'temp/1746564045_2124625_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg': 987515216, 'temp/1746564045_2124625_987515217_78877bb2c5760be28518d17f77d1c609.jpg': 987515217, 'temp/1746564045_2124625_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg': 987515219, 'temp/1746564045_2124625_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg': 987515220, 'temp/1746564045_2124625_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg': 987515222, 'temp/1746564045_2124625_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg': 987515223, 'temp/1746564045_2124625_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg': 987515224, 'temp/1746564045_2124625_987515226_a18048dca1a77ae086b62cf07759f704.jpg': 987515226, 'temp/1746564045_2124625_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg': 987515227, 'temp/1746564045_2124625_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg': 987515228, 'temp/1746564045_2124625_987515230_846ad925884264181565c81d152a2e94.jpg': 987515230, 'temp/1746564045_2124625_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg': 987515231, 'temp/1746564045_2124625_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg': 987515232, 'temp/1746564045_2124625_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg': 987515233, 'temp/1746564045_2124625_987515234_2eca3480aed0f8b876242675ad99b666.jpg': 987515234, 'temp/1746564045_2124625_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg': 987515235, 'temp/1746564045_2124625_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg': 987515236, 'temp/1746564045_2124625_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg': 987515237, 'temp/1746564045_2124625_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg': 987515238, 'temp/1746564045_2124625_987515188_4116f9906657a69bb76c2fda982037b9.jpg': 987515188, 'temp/1746564045_2124625_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg': 987515189, 'temp/1746564045_2124625_987515190_d56932bfc6ba2a8c974c691108755017.jpg': 987515190, 'temp/1746564045_2124625_987515192_b661073b218f5f056833d6af1c617153.jpg': 987515192, 'temp/1746564045_2124625_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg': 987515193, 'temp/1746564045_2124625_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg': 987515195, 'temp/1746564045_2124625_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg': 987515196, 'temp/1746564045_2124625_987515198_599e80f444c876f407e94b533c89360b.jpg': 987515198, 'temp/1746564045_2124625_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg': 987515200, 'temp/1746564045_2124625_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg': 987515201, 'temp/1746564045_2124625_987515202_3314bd90d1404f31b827d8925abf2d62.jpg': 987515202, 'temp/1746564045_2124625_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg': 987515204, 'temp/1746564045_2124625_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg': 987515205, 'temp/1746564045_2124625_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg': 987515239, 'temp/1746564045_2124625_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg': 987515240, 'temp/1746564045_2124625_987515241_073420d938f5f010ffd5b4353c064e09.jpg': 987515241, 'temp/1746564045_2124625_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg': 987515242, 'temp/1746564045_2124625_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg': 987515243, 'temp/1746564045_2124625_987515244_419530eaef5ef868f75c758b94eea4b4.jpg': 987515244, 'temp/1746564045_2124625_987515245_757d9d208d5bd4375c5f21f68b699148.jpg': 987515245, 'temp/1746564045_2124625_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg': 987515246, 'temp/1746564045_2124625_987515247_e47b65403df916ba909bc9c439b0af73.jpg': 987515247, 'temp/1746564045_2124625_987515248_a70ad88462a22fb62a120721a42b2d42.jpg': 987515248, 'temp/1746564045_2124625_987515249_a70ad88462a22fb62a120721a42b2d42.jpg': 987515249, 'temp/1746564045_2124625_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg': 987515250} map_photo_id_path_extension : {987515175: {'path': 'temp/1746564045_2124625_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg', 'extension': 'jpg'}, 987515176: {'path': 'temp/1746564045_2124625_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg', 'extension': 'jpg'}, 987515177: {'path': 'temp/1746564045_2124625_987515177_4a54e9967227806219ddf45d256539d8.jpg', 'extension': 'jpg'}, 987515178: {'path': 'temp/1746564045_2124625_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg', 'extension': 'jpg'}, 987515179: {'path': 'temp/1746564045_2124625_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg', 'extension': 'jpg'}, 987515180: {'path': 'temp/1746564045_2124625_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg', 'extension': 'jpg'}, 987515181: {'path': 'temp/1746564045_2124625_987515181_1738c2798fb31152809ecb443ac286d6.jpg', 'extension': 'jpg'}, 987515182: {'path': 'temp/1746564045_2124625_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg', 'extension': 'jpg'}, 987515183: {'path': 'temp/1746564045_2124625_987515183_6aab9ca0421398b4899892c10c2594c6.jpg', 'extension': 'jpg'}, 987515184: {'path': 'temp/1746564045_2124625_987515184_19c8c2177209a285df6014d95fe53f2c.jpg', 'extension': 'jpg'}, 987515185: {'path': 'temp/1746564045_2124625_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg', 'extension': 'jpg'}, 987515186: {'path': 'temp/1746564045_2124625_987515186_797def426440b544aa80dbd63a19234a.jpg', 'extension': 'jpg'}, 987515187: {'path': 'temp/1746564045_2124625_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg', 'extension': 'jpg'}, 987515207: {'path': 'temp/1746564045_2124625_987515207_de216ddb041e249524b0fb2b949064a5.jpg', 'extension': 'jpg'}, 987515208: {'path': 'temp/1746564045_2124625_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg', 'extension': 'jpg'}, 987515209: {'path': 'temp/1746564045_2124625_987515209_02dfe1ae39f51994652f4a8538844aea.jpg', 'extension': 'jpg'}, 987515211: {'path': 'temp/1746564045_2124625_987515211_72cc7664d45bd40477351b9b764f1500.jpg', 'extension': 'jpg'}, 987515212: {'path': 'temp/1746564045_2124625_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg', 'extension': 'jpg'}, 987515213: {'path': 'temp/1746564045_2124625_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg', 'extension': 'jpg'}, 987515215: {'path': 'temp/1746564045_2124625_987515215_902ef348a7eebb9a8b87f42927347936.jpg', 'extension': 'jpg'}, 987515216: {'path': 'temp/1746564045_2124625_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg', 'extension': 'jpg'}, 987515217: {'path': 'temp/1746564045_2124625_987515217_78877bb2c5760be28518d17f77d1c609.jpg', 'extension': 'jpg'}, 987515219: {'path': 'temp/1746564045_2124625_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg', 'extension': 'jpg'}, 987515220: {'path': 'temp/1746564045_2124625_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg', 'extension': 'jpg'}, 987515222: {'path': 'temp/1746564045_2124625_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg', 'extension': 'jpg'}, 987515223: {'path': 'temp/1746564045_2124625_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg', 'extension': 'jpg'}, 987515224: {'path': 'temp/1746564045_2124625_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg', 'extension': 'jpg'}, 987515226: {'path': 'temp/1746564045_2124625_987515226_a18048dca1a77ae086b62cf07759f704.jpg', 'extension': 'jpg'}, 987515227: {'path': 'temp/1746564045_2124625_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg', 'extension': 'jpg'}, 987515228: {'path': 'temp/1746564045_2124625_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg', 'extension': 'jpg'}, 987515230: {'path': 'temp/1746564045_2124625_987515230_846ad925884264181565c81d152a2e94.jpg', 'extension': 'jpg'}, 987515231: {'path': 'temp/1746564045_2124625_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg', 'extension': 'jpg'}, 987515232: {'path': 'temp/1746564045_2124625_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg', 'extension': 'jpg'}, 987515233: {'path': 'temp/1746564045_2124625_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg', 'extension': 'jpg'}, 987515234: {'path': 'temp/1746564045_2124625_987515234_2eca3480aed0f8b876242675ad99b666.jpg', 'extension': 'jpg'}, 987515235: {'path': 'temp/1746564045_2124625_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg', 'extension': 'jpg'}, 987515236: {'path': 'temp/1746564045_2124625_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg', 'extension': 'jpg'}, 987515237: {'path': 'temp/1746564045_2124625_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg', 'extension': 'jpg'}, 987515238: {'path': 'temp/1746564045_2124625_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg', 'extension': 'jpg'}, 987515188: {'path': 'temp/1746564045_2124625_987515188_4116f9906657a69bb76c2fda982037b9.jpg', 'extension': 'jpg'}, 987515189: {'path': 'temp/1746564045_2124625_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg', 'extension': 'jpg'}, 987515190: {'path': 'temp/1746564045_2124625_987515190_d56932bfc6ba2a8c974c691108755017.jpg', 'extension': 'jpg'}, 987515192: {'path': 'temp/1746564045_2124625_987515192_b661073b218f5f056833d6af1c617153.jpg', 'extension': 'jpg'}, 987515193: {'path': 'temp/1746564045_2124625_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg', 'extension': 'jpg'}, 987515195: {'path': 'temp/1746564045_2124625_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg', 'extension': 'jpg'}, 987515196: {'path': 'temp/1746564045_2124625_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg', 'extension': 'jpg'}, 987515198: {'path': 'temp/1746564045_2124625_987515198_599e80f444c876f407e94b533c89360b.jpg', 'extension': 'jpg'}, 987515200: {'path': 'temp/1746564045_2124625_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg', 'extension': 'jpg'}, 987515201: {'path': 'temp/1746564045_2124625_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg', 'extension': 'jpg'}, 987515202: {'path': 'temp/1746564045_2124625_987515202_3314bd90d1404f31b827d8925abf2d62.jpg', 'extension': 'jpg'}, 987515204: {'path': 'temp/1746564045_2124625_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg', 'extension': 'jpg'}, 987515205: {'path': 'temp/1746564045_2124625_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg', 'extension': 'jpg'}, 987515239: {'path': 'temp/1746564045_2124625_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg', 'extension': 'jpg'}, 987515240: {'path': 'temp/1746564045_2124625_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg', 'extension': 'jpg'}, 987515241: {'path': 'temp/1746564045_2124625_987515241_073420d938f5f010ffd5b4353c064e09.jpg', 'extension': 'jpg'}, 987515242: {'path': 'temp/1746564045_2124625_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg', 'extension': 'jpg'}, 987515243: {'path': 'temp/1746564045_2124625_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg', 'extension': 'jpg'}, 987515244: {'path': 'temp/1746564045_2124625_987515244_419530eaef5ef868f75c758b94eea4b4.jpg', 'extension': 'jpg'}, 987515245: {'path': 'temp/1746564045_2124625_987515245_757d9d208d5bd4375c5f21f68b699148.jpg', 'extension': 'jpg'}, 987515246: {'path': 'temp/1746564045_2124625_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg', 'extension': 'jpg'}, 987515247: {'path': 'temp/1746564045_2124625_987515247_e47b65403df916ba909bc9c439b0af73.jpg', 'extension': 'jpg'}, 987515248: {'path': 'temp/1746564045_2124625_987515248_a70ad88462a22fb62a120721a42b2d42.jpg', 'extension': 'jpg'}, 987515249: {'path': 'temp/1746564045_2124625_987515249_a70ad88462a22fb62a120721a42b2d42.jpg', 'extension': 'jpg'}, 987515250: {'path': 'temp/1746564045_2124625_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 1528 After datou_step_exec type output : time spend for datou_step_exec : 0.0013828277587890625 time spend to save output : 0.00018978118896484375 total time spend for step 2 : 0.0015726089477539062 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'987515250': [('987515250', 'Carton', 0.98080564, 1927, '1528'), 'temp/1746564045_2124625_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg'], '987515175': [('987515175', 'Papier_Magazine', 0.9998135, 1927, '1528'), 'temp/1746564045_2124625_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515176': [('987515176', 'Papier_Magazine', 0.999814, 1927, '1528'), 'temp/1746564045_2124625_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515177': [('987515177', 'Papier_Magazine', 0.97709346, 1927, '1528'), 'temp/1746564045_2124625_987515177_4a54e9967227806219ddf45d256539d8.jpg'], '987515178': [('987515178', 'Carton', 0.8577468, 1927, '1528'), 'temp/1746564045_2124625_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg'], '987515179': [('987515179', 'Carton', 0.92710525, 1927, '1528'), 'temp/1746564045_2124625_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg'], '987515180': [('987515180', 'Carton', 0.9899968, 1927, '1528'), 'temp/1746564045_2124625_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg'], '987515181': [('987515181', 'Carton', 0.9977804, 1927, '1528'), 'temp/1746564045_2124625_987515181_1738c2798fb31152809ecb443ac286d6.jpg'], '987515208': [('987515208', 'Carton', 0.9917305, 1927, '1528'), 'temp/1746564045_2124625_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg'], '987515209': [('987515209', 'Carton', 0.96781266, 1927, '1528'), 'temp/1746564045_2124625_987515209_02dfe1ae39f51994652f4a8538844aea.jpg'], '987515211': [('987515211', 'Carton', 0.97339123, 1927, '1528'), 'temp/1746564045_2124625_987515211_72cc7664d45bd40477351b9b764f1500.jpg'], '987515212': [('987515212', 'Carton', 0.9869255, 1927, '1528'), 'temp/1746564045_2124625_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515213': [('987515213', 'Carton', 0.9869081, 1927, '1528'), 'temp/1746564045_2124625_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515215': [('987515215', 'Papier_Magazine', 0.9939253, 1927, '1528'), 'temp/1746564045_2124625_987515215_902ef348a7eebb9a8b87f42927347936.jpg'], '987515216': [('987515216', 'Papier_Magazine', 0.97745866, 1927, '1528'), 'temp/1746564045_2124625_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg'], '987515182': [('987515182', 'Carton', 0.99241745, 1927, '1528'), 'temp/1746564045_2124625_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg'], '987515183': [('987515183', 'Papier_Magazine', 0.99999213, 1927, '1528'), 'temp/1746564045_2124625_987515183_6aab9ca0421398b4899892c10c2594c6.jpg'], '987515184': [('987515184', 'Papier_Magazine', 0.99973196, 1927, '1528'), 'temp/1746564045_2124625_987515184_19c8c2177209a285df6014d95fe53f2c.jpg'], '987515185': [('987515185', 'Papier_Magazine', 0.7976216, 1927, '1528'), 'temp/1746564045_2124625_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg'], '987515186': [('987515186', 'Carton', 0.984749, 1927, '1528'), 'temp/1746564045_2124625_987515186_797def426440b544aa80dbd63a19234a.jpg'], '987515187': [('987515187', 'Carton', 0.9809986, 1927, '1528'), 'temp/1746564045_2124625_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg'], '987515207': [('987515207', 'Papier_Magazine', 0.8741362, 1927, '1528'), 'temp/1746564045_2124625_987515207_de216ddb041e249524b0fb2b949064a5.jpg'], '987515217': [('987515217', 'Carton', 0.52918035, 1927, '1528'), 'temp/1746564045_2124625_987515217_78877bb2c5760be28518d17f77d1c609.jpg'], '987515219': [('987515219', 'Carton', 0.99936944, 1927, '1528'), 'temp/1746564045_2124625_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg'], '987515220': [('987515220', 'Carton', 0.996393, 1927, '1528'), 'temp/1746564045_2124625_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg'], '987515222': [('987515222', 'Carton', 0.9974712, 1927, '1528'), 'temp/1746564045_2124625_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg'], '987515223': [('987515223', 'Carton', 0.9920556, 1927, '1528'), 'temp/1746564045_2124625_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg'], '987515224': [('987515224', 'Carton', 0.90841335, 1927, '1528'), 'temp/1746564045_2124625_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg'], '987515226': [('987515226', 'Papier_Magazine', 0.9870517, 1927, '1528'), 'temp/1746564045_2124625_987515226_a18048dca1a77ae086b62cf07759f704.jpg'], '987515202': [('987515202', 'Carton', 0.9911305, 1927, '1528'), 'temp/1746564045_2124625_987515202_3314bd90d1404f31b827d8925abf2d62.jpg'], '987515204': [('987515204', 'Papier_Magazine', 0.99507034, 1927, '1528'), 'temp/1746564045_2124625_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg'], '987515205': [('987515205', 'Papier_Magazine', 0.9908608, 1927, '1528'), 'temp/1746564045_2124625_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg'], '987515239': [('987515239', 'Carton', 0.99978346, 1927, '1528'), 'temp/1746564045_2124625_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg'], '987515240': [('987515240', 'Carton', 0.9995203, 1927, '1528'), 'temp/1746564045_2124625_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg'], '987515241': [('987515241', 'Carton', 0.9820961, 1927, '1528'), 'temp/1746564045_2124625_987515241_073420d938f5f010ffd5b4353c064e09.jpg'], '987515242': [('987515242', 'Carton', 0.93594974, 1927, '1528'), 'temp/1746564045_2124625_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg'], '987515192': [('987515192', 'Papier_Magazine', 0.99991167, 1927, '1528'), 'temp/1746564045_2124625_987515192_b661073b218f5f056833d6af1c617153.jpg'], '987515193': [('987515193', 'Papier_Magazine', 0.9993957, 1927, '1528'), 'temp/1746564045_2124625_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg'], '987515195': [('987515195', 'Carton', 0.9846379, 1927, '1528'), 'temp/1746564045_2124625_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515196': [('987515196', 'Carton', 0.98466843, 1927, '1528'), 'temp/1746564045_2124625_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515198': [('987515198', 'Carton', 0.9661375, 1927, '1528'), 'temp/1746564045_2124625_987515198_599e80f444c876f407e94b533c89360b.jpg'], '987515200': [('987515200', 'Carton', 0.9859461, 1927, '1528'), 'temp/1746564045_2124625_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg'], '987515201': [('987515201', 'Carton', 0.9954619, 1927, '1528'), 'temp/1746564045_2124625_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg'], '987515227': [('987515227', 'Papier_Magazine', 0.900665, 1927, '1528'), 'temp/1746564045_2124625_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg'], '987515228': [('987515228', 'Papier_Magazine', 0.52149576, 1927, '1528'), 'temp/1746564045_2124625_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg'], '987515230': [('987515230', 'Carton', 0.99940526, 1927, '1528'), 'temp/1746564045_2124625_987515230_846ad925884264181565c81d152a2e94.jpg'], '987515231': [('987515231', 'Carton', 0.999421, 1927, '1528'), 'temp/1746564045_2124625_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg'], '987515232': [('987515232', 'Carton', 0.9992481, 1927, '1528'), 'temp/1746564045_2124625_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg'], '987515233': [('987515233', 'Carton', 0.98343444, 1927, '1528'), 'temp/1746564045_2124625_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg'], '987515234': [('987515234', 'Carton', 0.94490546, 1927, '1528'), 'temp/1746564045_2124625_987515234_2eca3480aed0f8b876242675ad99b666.jpg'], '987515243': [('987515243', 'Papier_Magazine', 0.874293, 1927, '1528'), 'temp/1746564045_2124625_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg'], '987515244': [('987515244', 'Papier_Magazine', 0.8177143, 1927, '1528'), 'temp/1746564045_2124625_987515244_419530eaef5ef868f75c758b94eea4b4.jpg'], '987515245': [('987515245', 'Carton', 0.8660789, 1927, '1528'), 'temp/1746564045_2124625_987515245_757d9d208d5bd4375c5f21f68b699148.jpg'], '987515246': [('987515246', 'Carton', 0.9992322, 1927, '1528'), 'temp/1746564045_2124625_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg'], '987515247': [('987515247', 'Carton', 0.9996691, 1927, '1528'), 'temp/1746564045_2124625_987515247_e47b65403df916ba909bc9c439b0af73.jpg'], '987515248': [('987515248', 'Carton', 0.9812789, 1927, '1528'), 'temp/1746564045_2124625_987515248_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515249': [('987515249', 'Carton', 0.9813571, 1927, '1528'), 'temp/1746564045_2124625_987515249_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515235': [('987515235', 'Papier_Magazine', 0.8919314, 1927, '1528'), 'temp/1746564045_2124625_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg'], '987515236': [('987515236', 'Papier_Magazine', 0.53671837, 1927, '1528'), 'temp/1746564045_2124625_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg'], '987515237': [('987515237', 'Carton', 0.76962644, 1927, '1528'), 'temp/1746564045_2124625_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg'], '987515238': [('987515238', 'Carton', 0.99957424, 1927, '1528'), 'temp/1746564045_2124625_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg'], '987515188': [('987515188', 'Carton', 0.9956547, 1927, '1528'), 'temp/1746564045_2124625_987515188_4116f9906657a69bb76c2fda982037b9.jpg'], '987515189': [('987515189', 'Carton', 0.9977881, 1927, '1528'), 'temp/1746564045_2124625_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg'], '987515190': [('987515190', 'Carton', 0.97631013, 1927, '1528'), 'temp/1746564045_2124625_987515190_d56932bfc6ba2a8c974c691108755017.jpg']} Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1879 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=1879 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 1879 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=1879 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : detect_points list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (987515173) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 987515173 download finish for photo 987515173 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.1190791130065918 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:detect_points Tue May 6 22:41:27 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746564087_2124625_987515173_91fa471b1a04f95b356afdbaf021f623.jpg': 987515173} map_photo_id_path_extension : {987515173: {'path': 'temp/1746564087_2124625_987515173_91fa471b1a04f95b356afdbaf021f623.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step predict points ! Inside try reload ! classes : ['Autre_Environement', 'Carton', 'Kraft', 'Lointain_Papier_Magazine', 'Metal', 'Papier_Magazine', 'Plastique', 'Sol_Environement', 'Teint_Dans_La_Masse', 'autre_refus'] pht : 1927 model_name : learn_refus_upm_blanches_1924 {'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'} gpu_mode in detect_points : 1 To load net FromThcl() model_param file didn't exist model_name : learn_refus_upm_blanches_1924 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update reshape net's input to : (224, 224) origin shape : (10, 3, 224, 224) after reshape : (1, 3, 224, 224) [('data', (1, 3, 224, 224)), ('conv1', (1, 64, 112, 112)), ('pool1', (1, 64, 56, 56)), ('pool1_pool1_0_split_0', (1, 64, 56, 56)), ('pool1_pool1_0_split_1', (1, 64, 56, 56)), ('res2a_branch1', (1, 64, 56, 56)), ('res2a_branch2a', (1, 64, 56, 56)), ('res2a_branch2b', (1, 64, 56, 56)), ('res2a', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_0', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_1', (1, 64, 56, 56)), ('res2b_branch2a', (1, 64, 56, 56)), ('res2b_branch2b', (1, 64, 56, 56)), ('res2b', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_0', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_1', (1, 64, 56, 56)), ('res3a_branch1', (1, 128, 28, 28)), ('res3a_branch2a', (1, 128, 28, 28)), ('res3a_branch2b', (1, 128, 28, 28)), ('res3a', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_0', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_1', (1, 128, 28, 28)), ('res3b_branch2a', (1, 128, 28, 28)), ('res3b_branch2b', (1, 128, 28, 28)), ('res3b', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_0', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_1', (1, 128, 28, 28)), ('res4a_branch1', (1, 256, 14, 14)), ('res4a_branch2a', (1, 256, 14, 14)), ('res4a_branch2b', (1, 256, 14, 14)), ('res4a', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_0', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_1', (1, 256, 14, 14)), ('res4b_branch2a', (1, 256, 14, 14)), ('res4b_branch2b', (1, 256, 14, 14)), ('res4b', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_0', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_1', (1, 256, 14, 14)), ('res5a_branch1', (1, 512, 7, 7)), ('res5a_branch2a', (1, 512, 7, 7)), ('res5a_branch2b', (1, 512, 7, 7)), ('res5a', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_0', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_1', (1, 512, 7, 7)), ('res5b_branch2a', (1, 512, 7, 7)), ('res5b_branch2b', (1, 512, 7, 7)), ('res5b', (1, 512, 7, 7)), ('fc2019-10-22_15-02-46', (1, 10, 1, 1)), ('prob', (1, 10, 1, 1))] set image transformer : About to compute detect the points : len(args) : 1 Inside predict_points step exec : nb paths : 1 treate image : temp/1746564087_2124625_987515173_91fa471b1a04f95b356afdbaf021f623.jpg size of numpy array img : 2408584 scale method : caffe/skimage size of numpy array img_scale : 2408584 (448, 448, 3) nb_h 8 nb_w 8 size of sub images : (224, 224, 3) size of caffe_input : 38535320 (64, 3, 224, 224) time to do the preprocess : 0.053400278091430664 time to do a prediction : 0.33472371101379395 dict_keys(['prob']) shape of output (64, 10, 1, 1) shape of the out_put heatmap (10, 8, 8) number of sub_photos vertical and horizon 8 8 size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) After datou_step_exec type output : time spend for datou_step_exec : 1.8975272178649902 time spend to save output : 5.9604644775390625e-05 total time spend for step 1 : 1.8975868225097656 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {987515173: [(987515173, 1982, 'Autre_Environement', 112, -1, 112, -1, 6.299185999203427e-12), (987515173, 1982, 'Autre_Environement', 144, -1, 112, -1, 2.4354029209971984e-11), (987515173, 1982, 'Autre_Environement', 176, -1, 112, -1, 1.0608561140657002e-08), (987515173, 1982, 'Autre_Environement', 208, -1, 112, -1, 4.4438695567805553e-07), (987515173, 1982, 'Autre_Environement', 240, -1, 112, -1, 1.9181406969437376e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 112, -1, 3.789926995523274e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 112, -1, 0.00012294021144043654), (987515173, 1982, 'Autre_Environement', 336, -1, 112, -1, 2.9510207241401076e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 144, -1, 2.3534779458600497e-08), (987515173, 1982, 'Autre_Environement', 144, -1, 144, -1, 2.2150397072095984e-08), (987515173, 1982, 'Autre_Environement', 176, -1, 144, -1, 1.3809660970309778e-07), (987515173, 1982, 'Autre_Environement', 208, -1, 144, -1, 1.4747731711395318e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 144, -1, 1.1318640645185951e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 144, -1, 0.00015753194747958332), (987515173, 1982, 'Autre_Environement', 304, -1, 144, -1, 0.0004437107127159834), (987515173, 1982, 'Autre_Environement', 336, -1, 144, -1, 6.528615631395951e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 176, -1, 1.328779603682051e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 176, -1, 1.623386310711794e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 176, -1, 2.5332205950689968e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 176, -1, 1.613662789168302e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 176, -1, 6.274173756537493e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 176, -1, 8.604821050539613e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 176, -1, 0.0003268723958171904), (987515173, 1982, 'Autre_Environement', 336, -1, 176, -1, 0.00030706453253515065), (987515173, 1982, 'Autre_Environement', 112, -1, 208, -1, 1.8615861336002126e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 208, -1, 7.933252163638826e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 208, -1, 2.7021080313716084e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 208, -1, 1.7959946490009315e-05), (987515173, 1982, 'Autre_Environement', 240, -1, 208, -1, 2.3413718736264855e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 208, -1, 1.6980904547381215e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 208, -1, 4.545032425085083e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 208, -1, 8.81552477949299e-06), (987515173, 1982, 'Autre_Environement', 112, -1, 240, -1, 6.098573521740036e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 240, -1, 1.6448411770397797e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 240, -1, 1.96067662727728e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 240, -1, 1.4342890608531889e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 240, -1, 7.873609320085961e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 240, -1, 1.2828784747398458e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 240, -1, 9.311908797826618e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 240, -1, 2.1642506908392534e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 272, -1, 3.827708951575914e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 272, -1, 2.549672217355692e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 272, -1, 2.9625393835885916e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 272, -1, 2.7473795398691436e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 272, -1, 4.327345322963083e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 272, -1, 8.197881470550783e-06), (987515173, 1982, 'Autre_Environement', 304, -1, 272, -1, 1.1530723895702977e-05), (987515173, 1982, 'Autre_Environement', 336, -1, 272, -1, 3.9176582504296675e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 304, -1, 1.2082839020877145e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 304, -1, 1.5749565136502497e-05), (987515173, 1982, 'Autre_Environement', 176, -1, 304, -1, 3.331287734908983e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 304, -1, 0.0001544763072161004), (987515173, 1982, 'Autre_Environement', 240, -1, 304, -1, 0.0002582166052889079), (987515173, 1982, 'Autre_Environement', 272, -1, 304, -1, 0.0001879721530713141), (987515173, 1982, 'Autre_Environement', 304, -1, 304, -1, 0.00021336021018214524), (987515173, 1982, 'Autre_Environement', 336, -1, 304, -1, 0.00016406837676186115), (987515173, 1982, 'Autre_Environement', 112, -1, 336, -1, 4.545543106360128e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 336, -1, 1.7524675058666617e-05), (987515173, 1982, 'Autre_Environement', 176, -1, 336, -1, 4.930860814056359e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 336, -1, 0.00012107240763725713), (987515173, 1982, 'Autre_Environement', 240, -1, 336, -1, 0.00019737912225537002), (987515173, 1982, 'Autre_Environement', 272, -1, 336, -1, 0.00018724106485024095), (987515173, 1982, 'Autre_Environement', 304, -1, 336, -1, 0.00012335702194832265), (987515173, 1982, 'Autre_Environement', 336, -1, 336, -1, 0.0002721178170759231), (987515173, 1982, 'Carton', 112, -1, 112, -1, 1.5864678459820425e-07), (987515173, 1982, 'Carton', 144, -1, 112, -1, 4.03231797463377e-06), (987515173, 1982, 'Carton', 176, -1, 112, -1, 6.991095688135829e-06), (987515173, 1982, 'Carton', 208, -1, 112, -1, 0.0008727589156478643), (987515173, 1982, 'Carton', 240, -1, 112, -1, 0.002642342122271657), (987515173, 1982, 'Carton', 272, -1, 112, -1, 0.0033851603511720896), (987515173, 1982, 'Carton', 304, -1, 112, -1, 0.03135448694229126), (987515173, 1982, 'Carton', 336, -1, 112, -1, 0.055807650089263916), (987515173, 1982, 'Carton', 112, -1, 144, -1, 0.00012319513189140707), (987515173, 1982, 'Carton', 144, -1, 144, -1, 0.00020911803585477173), (987515173, 1982, 'Carton', 176, -1, 144, -1, 0.00036795344203710556), (987515173, 1982, 'Carton', 208, -1, 144, -1, 0.006849064026027918), (987515173, 1982, 'Carton', 240, -1, 144, -1, 0.015909679234027863), (987515173, 1982, 'Carton', 272, -1, 144, -1, 0.009407011792063713), (987515173, 1982, 'Carton', 304, -1, 144, -1, 0.009767088107764721), (987515173, 1982, 'Carton', 336, -1, 144, -1, 0.022109758108854294), (987515173, 1982, 'Carton', 112, -1, 176, -1, 0.021874763071537018), (987515173, 1982, 'Carton', 144, -1, 176, -1, 0.19390763342380524), (987515173, 1982, 'Carton', 176, -1, 176, -1, 0.09682448208332062), (987515173, 1982, 'Carton', 208, -1, 176, -1, 0.12305475026369095), (987515173, 1982, 'Carton', 240, -1, 176, -1, 0.5336940884590149), (987515173, 1982, 'Carton', 272, -1, 176, -1, 0.4603225290775299), (987515173, 1982, 'Carton', 304, -1, 176, -1, 0.7706670761108398), (987515173, 1982, 'Carton', 336, -1, 176, -1, 0.8661702871322632), (987515173, 1982, 'Carton', 112, -1, 208, -1, 0.8499331474304199), (987515173, 1982, 'Carton', 144, -1, 208, -1, 0.9843107461929321), (987515173, 1982, 'Carton', 176, -1, 208, -1, 0.9847518801689148), (987515173, 1982, 'Carton', 208, -1, 208, -1, 0.9919575452804565), (987515173, 1982, 'Carton', 240, -1, 208, -1, 0.9993775486946106), (987515173, 1982, 'Carton', 272, -1, 208, -1, 0.9994128942489624), (987515173, 1982, 'Carton', 304, -1, 208, -1, 0.9995879530906677), (987515173, 1982, 'Carton', 336, -1, 208, -1, 0.9992258548736572), (987515173, 1982, 'Carton', 112, -1, 240, -1, 0.9278057217597961), (987515173, 1982, 'Carton', 144, -1, 240, -1, 0.9810516834259033), (987515173, 1982, 'Carton', 176, -1, 240, -1, 0.9660813212394714), (987515173, 1982, 'Carton', 208, -1, 240, -1, 0.9678425788879395), (987515173, 1982, 'Carton', 240, -1, 240, -1, 0.9963894486427307), (987515173, 1982, 'Carton', 272, -1, 240, -1, 0.9994200468063354), (987515173, 1982, 'Carton', 304, -1, 240, -1, 0.9997860789299011), (987515173, 1982, 'Carton', 336, -1, 240, -1, 0.9996691942214966), (987515173, 1982, 'Carton', 112, -1, 272, -1, 0.9895121455192566), (987515173, 1982, 'Carton', 144, -1, 272, -1, 0.9954617619514465), (987515173, 1982, 'Carton', 176, -1, 272, -1, 0.9855201840400696), (987515173, 1982, 'Carton', 208, -1, 272, -1, 0.9734230041503906), (987515173, 1982, 'Carton', 240, -1, 272, -1, 0.997469425201416), (987515173, 1982, 'Carton', 272, -1, 272, -1, 0.9992039799690247), (987515173, 1982, 'Carton', 304, -1, 272, -1, 0.9995161294937134), (987515173, 1982, 'Carton', 336, -1, 272, -1, 0.9991306662559509), (987515173, 1982, 'Carton', 112, -1, 304, -1, 0.997774064540863), (987515173, 1982, 'Carton', 144, -1, 304, -1, 0.9977602958679199), (987515173, 1982, 'Carton', 176, -1, 304, -1, 0.995539128780365), (987515173, 1982, 'Carton', 208, -1, 304, -1, 0.9927338361740112), (987515173, 1982, 'Carton', 240, -1, 304, -1, 0.9920431971549988), (987515173, 1982, 'Carton', 272, -1, 304, -1, 0.9835704565048218), (987515173, 1982, 'Carton', 304, -1, 304, -1, 0.9820127487182617), (987515173, 1982, 'Carton', 336, -1, 304, -1, 0.9808406233787537), (987515173, 1982, 'Carton', 112, -1, 336, -1, 0.9924324750900269), (987515173, 1982, 'Carton', 144, -1, 336, -1, 0.9760972261428833), (987515173, 1982, 'Carton', 176, -1, 336, -1, 0.9911888837814331), (987515173, 1982, 'Carton', 208, -1, 336, -1, 0.9869945645332336), (987515173, 1982, 'Carton', 240, -1, 336, -1, 0.9080501198768616), (987515173, 1982, 'Carton', 272, -1, 336, -1, 0.9451318979263306), (987515173, 1982, 'Carton', 304, -1, 336, -1, 0.9367108345031738), (987515173, 1982, 'Carton', 336, -1, 336, -1, 0.980778694152832), (987515173, 1982, 'Kraft', 112, -1, 112, -1, 1.9715273857912052e-09), (987515173, 1982, 'Kraft', 144, -1, 112, -1, 1.700512086699746e-08), (987515173, 1982, 'Kraft', 176, -1, 112, -1, 9.62380568125809e-07), (987515173, 1982, 'Kraft', 208, -1, 112, -1, 3.144802030874416e-05), (987515173, 1982, 'Kraft', 240, -1, 112, -1, 4.438618998392485e-05), (987515173, 1982, 'Kraft', 272, -1, 112, -1, 0.0002064783766400069), (987515173, 1982, 'Kraft', 304, -1, 112, -1, 0.0010803906479850411), (987515173, 1982, 'Kraft', 336, -1, 112, -1, 0.0008296229061670601), (987515173, 1982, 'Kraft', 112, -1, 144, -1, 2.609303373901639e-05), (987515173, 1982, 'Kraft', 144, -1, 144, -1, 6.97314681019634e-06), (987515173, 1982, 'Kraft', 176, -1, 144, -1, 3.6288954561314313e-06), (987515173, 1982, 'Kraft', 208, -1, 144, -1, 3.559760079951957e-05), (987515173, 1982, 'Kraft', 240, -1, 144, -1, 6.71095767756924e-05), (987515173, 1982, 'Kraft', 272, -1, 144, -1, 8.686640649102628e-05), (987515173, 1982, 'Kraft', 304, -1, 144, -1, 0.00012190704001113772), (987515173, 1982, 'Kraft', 336, -1, 144, -1, 0.00011237272701691836), (987515173, 1982, 'Kraft', 112, -1, 176, -1, 0.0004974188050255179), (987515173, 1982, 'Kraft', 144, -1, 176, -1, 0.00012339913519099355), (987515173, 1982, 'Kraft', 176, -1, 176, -1, 9.101921750698239e-05), (987515173, 1982, 'Kraft', 208, -1, 176, -1, 5.181990854907781e-05), (987515173, 1982, 'Kraft', 240, -1, 176, -1, 0.00011571541108423844), (987515173, 1982, 'Kraft', 272, -1, 176, -1, 0.0004278551787137985), (987515173, 1982, 'Kraft', 304, -1, 176, -1, 0.0009287258726544678), (987515173, 1982, 'Kraft', 336, -1, 176, -1, 0.0014269377570599318), (987515173, 1982, 'Kraft', 112, -1, 208, -1, 6.879967986606061e-05), (987515173, 1982, 'Kraft', 144, -1, 208, -1, 1.848130159487482e-05), (987515173, 1982, 'Kraft', 176, -1, 208, -1, 2.5886238290695474e-05), (987515173, 1982, 'Kraft', 208, -1, 208, -1, 3.5376382584217936e-05), (987515173, 1982, 'Kraft', 240, -1, 208, -1, 3.7380203139036894e-05), (987515173, 1982, 'Kraft', 272, -1, 208, -1, 8.643531327834353e-05), (987515173, 1982, 'Kraft', 304, -1, 208, -1, 0.00012367642193567008), (987515173, 1982, 'Kraft', 336, -1, 208, -1, 0.0003913660766556859), (987515173, 1982, 'Kraft', 112, -1, 240, -1, 0.0003052453976124525), (987515173, 1982, 'Kraft', 144, -1, 240, -1, 4.160253592999652e-05), (987515173, 1982, 'Kraft', 176, -1, 240, -1, 1.2252347914909478e-05), (987515173, 1982, 'Kraft', 208, -1, 240, -1, 7.311899480555439e-06), (987515173, 1982, 'Kraft', 240, -1, 240, -1, 2.298239451192785e-05), (987515173, 1982, 'Kraft', 272, -1, 240, -1, 5.8625977544579655e-05), (987515173, 1982, 'Kraft', 304, -1, 240, -1, 6.562330236192793e-05), (987515173, 1982, 'Kraft', 336, -1, 240, -1, 0.0001867178943939507), (987515173, 1982, 'Kraft', 112, -1, 272, -1, 0.0014608963392674923), (987515173, 1982, 'Kraft', 144, -1, 272, -1, 0.0006900038570165634), (987515173, 1982, 'Kraft', 176, -1, 272, -1, 0.0002737260947469622), (987515173, 1982, 'Kraft', 208, -1, 272, -1, 4.3348398321541026e-05), (987515173, 1982, 'Kraft', 240, -1, 272, -1, 3.3511139918118715e-05), (987515173, 1982, 'Kraft', 272, -1, 272, -1, 8.371318835997954e-05), (987515173, 1982, 'Kraft', 304, -1, 272, -1, 0.00011209826334379613), (987515173, 1982, 'Kraft', 336, -1, 272, -1, 0.0004225780430715531), (987515173, 1982, 'Kraft', 112, -1, 304, -1, 0.0009938485454767942), (987515173, 1982, 'Kraft', 144, -1, 304, -1, 0.0009072886896319687), (987515173, 1982, 'Kraft', 176, -1, 304, -1, 0.0006177747854962945), (987515173, 1982, 'Kraft', 208, -1, 304, -1, 0.0010816584108397365), (987515173, 1982, 'Kraft', 240, -1, 304, -1, 0.0017824125243350863), (987515173, 1982, 'Kraft', 272, -1, 304, -1, 0.00467672199010849), (987515173, 1982, 'Kraft', 304, -1, 304, -1, 0.0046839932911098), (987515173, 1982, 'Kraft', 336, -1, 304, -1, 0.01251695491373539), (987515173, 1982, 'Kraft', 112, -1, 336, -1, 0.0021798976231366396), (987515173, 1982, 'Kraft', 144, -1, 336, -1, 0.005731274373829365), (987515173, 1982, 'Kraft', 176, -1, 336, -1, 0.0008301298366859555), (987515173, 1982, 'Kraft', 208, -1, 336, -1, 0.001254101749509573), (987515173, 1982, 'Kraft', 240, -1, 336, -1, 0.007878794334828854), (987515173, 1982, 'Kraft', 272, -1, 336, -1, 0.012601234018802643), (987515173, 1982, 'Kraft', 304, -1, 336, -1, 0.017904823645949364), (987515173, 1982, 'Kraft', 336, -1, 336, -1, 0.007784634362906218), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 112, -1, 1.5032806943704458e-10), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 112, -1, 8.189221745169561e-09), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 112, -1, 5.509673428605311e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 112, -1, 5.509515176527202e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 112, -1, 8.218115908675827e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 112, -1, 3.574374568415806e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 112, -1, 8.152747614076361e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 112, -1, 4.22919838456437e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 144, -1, 3.6241274870008056e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 144, -1, 1.3090914308122592e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 144, -1, 2.474627990523004e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 144, -1, 3.4591446365084266e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 144, -1, 5.722768491978059e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 144, -1, 2.05930664378684e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 144, -1, 3.9023296267259866e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 144, -1, 1.3651990229845978e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 176, -1, 3.8744440189475426e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 176, -1, 2.556116442065104e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 176, -1, 1.2038444765494205e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 176, -1, 7.82030929258326e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 176, -1, 6.355273853841936e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 176, -1, 2.631835923239123e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 176, -1, 3.930303864763118e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 176, -1, 1.852205787145067e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 208, -1, 1.803503323571931e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 208, -1, 5.701312488781696e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 208, -1, 2.1977652977511752e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 208, -1, 1.732847636048973e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 208, -1, 3.9939328644322813e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 208, -1, 2.0290343627493712e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 208, -1, 9.864437799933512e-08), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 208, -1, 8.686428998316842e-08), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 240, -1, 1.815862219700648e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 240, -1, 6.317463885352481e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 240, -1, 7.825294687791029e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 240, -1, 6.38922529105912e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 240, -1, 7.721331485299743e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 240, -1, 5.195895482756896e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 240, -1, 1.7717368905323383e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 240, -1, 1.4421127048080962e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 272, -1, 4.1844859310913307e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 272, -1, 3.171725211359444e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 272, -1, 5.088551802145957e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 272, -1, 7.322981900870218e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 272, -1, 4.029905653624155e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 272, -1, 3.2225253221440653e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 272, -1, 2.1043986464519548e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 272, -1, 3.4764514111884637e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 304, -1, 3.3003121302499494e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 304, -1, 3.2597429822089907e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 304, -1, 8.375151878681208e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 304, -1, 3.3004828310367884e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 304, -1, 4.407366759551223e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 304, -1, 5.030621196056018e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 304, -1, 8.192484528990462e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 304, -1, 5.571591373154661e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 336, -1, 4.191424238797481e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 336, -1, 8.29569671623176e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 336, -1, 7.135827218007762e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 336, -1, 1.9428632640483556e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 336, -1, 3.964289589930559e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 336, -1, 2.4446794668619987e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 336, -1, 3.4285678793821717e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 336, -1, 4.853383870795369e-06), (987515173, 1982, 'Metal', 112, -1, 112, -1, 7.447231720192349e-11), (987515173, 1982, 'Metal', 144, -1, 112, -1, 1.9201737977425637e-09), (987515173, 1982, 'Metal', 176, -1, 112, -1, 7.290755092981271e-07), (987515173, 1982, 'Metal', 208, -1, 112, -1, 9.451183359487914e-06), (987515173, 1982, 'Metal', 240, -1, 112, -1, 2.6513103875913657e-05), (987515173, 1982, 'Metal', 272, -1, 112, -1, 0.00016979477368295193), (987515173, 1982, 'Metal', 304, -1, 112, -1, 0.0007538229692727327), (987515173, 1982, 'Metal', 336, -1, 112, -1, 0.0003031627566087991), (987515173, 1982, 'Metal', 112, -1, 144, -1, 5.309876769388211e-07), (987515173, 1982, 'Metal', 144, -1, 144, -1, 4.3811866135001765e-07), (987515173, 1982, 'Metal', 176, -1, 144, -1, 3.188276195942308e-06), (987515173, 1982, 'Metal', 208, -1, 144, -1, 9.607507308828644e-06), (987515173, 1982, 'Metal', 240, -1, 144, -1, 1.660874295339454e-05), (987515173, 1982, 'Metal', 272, -1, 144, -1, 7.64819560572505e-05), (987515173, 1982, 'Metal', 304, -1, 144, -1, 0.00013110485451761633), (987515173, 1982, 'Metal', 336, -1, 144, -1, 4.660010745283216e-05), (987515173, 1982, 'Metal', 112, -1, 176, -1, 5.898625659028767e-06), (987515173, 1982, 'Metal', 144, -1, 176, -1, 4.025656380690634e-06), (987515173, 1982, 'Metal', 176, -1, 176, -1, 1.0127695531991776e-05), (987515173, 1982, 'Metal', 208, -1, 176, -1, 4.161669494351372e-06), (987515173, 1982, 'Metal', 240, -1, 176, -1, 6.661565294052707e-06), (987515173, 1982, 'Metal', 272, -1, 176, -1, 5.405334377428517e-05), (987515173, 1982, 'Metal', 304, -1, 176, -1, 0.0001295995753025636), (987515173, 1982, 'Metal', 336, -1, 176, -1, 6.567761010956019e-05), (987515173, 1982, 'Metal', 112, -1, 208, -1, 8.137811164488085e-06), (987515173, 1982, 'Metal', 144, -1, 208, -1, 2.255686922580935e-06), (987515173, 1982, 'Metal', 176, -1, 208, -1, 6.638259037572425e-06), (987515173, 1982, 'Metal', 208, -1, 208, -1, 4.590505795931676e-06), (987515173, 1982, 'Metal', 240, -1, 208, -1, 1.7152718783108867e-06), (987515173, 1982, 'Metal', 272, -1, 208, -1, 1.3711674000660423e-06), (987515173, 1982, 'Metal', 304, -1, 208, -1, 5.789502210973296e-07), (987515173, 1982, 'Metal', 336, -1, 208, -1, 8.343748731931555e-07), (987515173, 1982, 'Metal', 112, -1, 240, -1, 2.914566721301526e-06), (987515173, 1982, 'Metal', 144, -1, 240, -1, 7.384363698292873e-07), (987515173, 1982, 'Metal', 176, -1, 240, -1, 6.081010042180424e-07), (987515173, 1982, 'Metal', 208, -1, 240, -1, 6.211363370312029e-07), (987515173, 1982, 'Metal', 240, -1, 240, -1, 1.199231178361515e-06), (987515173, 1982, 'Metal', 272, -1, 240, -1, 6.998379831202328e-07), (987515173, 1982, 'Metal', 304, -1, 240, -1, 3.1470432304558926e-07), (987515173, 1982, 'Metal', 336, -1, 240, -1, 4.113363729629782e-07), (987515173, 1982, 'Metal', 112, -1, 272, -1, 2.6210993837594287e-06), (987515173, 1982, 'Metal', 144, -1, 272, -1, 8.052065822994336e-07), (987515173, 1982, 'Metal', 176, -1, 272, -1, 8.213787054955901e-07), (987515173, 1982, 'Metal', 208, -1, 272, -1, 3.99679009888132e-07), (987515173, 1982, 'Metal', 240, -1, 272, -1, 2.745636891177128e-07), (987515173, 1982, 'Metal', 272, -1, 272, -1, 3.313505771984637e-07), (987515173, 1982, 'Metal', 304, -1, 272, -1, 3.441670628490101e-07), (987515173, 1982, 'Metal', 336, -1, 272, -1, 1.4744520058229682e-06), (987515173, 1982, 'Metal', 112, -1, 304, -1, 2.3108293589757523e-06), (987515173, 1982, 'Metal', 144, -1, 304, -1, 1.955168954737019e-06), (987515173, 1982, 'Metal', 176, -1, 304, -1, 4.530034402705496e-06), (987515173, 1982, 'Metal', 208, -1, 304, -1, 1.3342222700885031e-05), (987515173, 1982, 'Metal', 240, -1, 304, -1, 5.6335661611228716e-06), (987515173, 1982, 'Metal', 272, -1, 304, -1, 5.686426447937265e-06), (987515173, 1982, 'Metal', 304, -1, 304, -1, 6.362740350596141e-06), (987515173, 1982, 'Metal', 336, -1, 304, -1, 7.369954346359009e-06), (987515173, 1982, 'Metal', 112, -1, 336, -1, 7.135930445656413e-06), (987515173, 1982, 'Metal', 144, -1, 336, -1, 3.3874537621159106e-05), (987515173, 1982, 'Metal', 176, -1, 336, -1, 4.00718672608491e-05), (987515173, 1982, 'Metal', 208, -1, 336, -1, 7.565772830275819e-05), (987515173, 1982, 'Metal', 240, -1, 336, -1, 0.0001240534766111523), (987515173, 1982, 'Metal', 272, -1, 336, -1, 3.705116250785068e-05), (987515173, 1982, 'Metal', 304, -1, 336, -1, 2.904446591855958e-05), (987515173, 1982, 'Metal', 336, -1, 336, -1, 2.811690683302004e-05), (987515173, 1982, 'Papier_Magazine', 112, -1, 112, -1, 0.9999953508377075), (987515173, 1982, 'Papier_Magazine', 144, -1, 112, -1, 0.9999926090240479), (987515173, 1982, 'Papier_Magazine', 176, -1, 112, -1, 0.9999086856842041), (987515173, 1982, 'Papier_Magazine', 208, -1, 112, -1, 0.9949654936790466), (987515173, 1982, 'Papier_Magazine', 240, -1, 112, -1, 0.9937736988067627), (987515173, 1982, 'Papier_Magazine', 272, -1, 112, -1, 0.9866220355033875), (987515173, 1982, 'Papier_Magazine', 304, -1, 112, -1, 0.8924120664596558), (987515173, 1982, 'Papier_Magazine', 336, -1, 112, -1, 0.8721243143081665), (987515173, 1982, 'Papier_Magazine', 112, -1, 144, -1, 0.9998154044151306), (987515173, 1982, 'Papier_Magazine', 144, -1, 144, -1, 0.9997422099113464), (987515173, 1982, 'Papier_Magazine', 176, -1, 144, -1, 0.999413251876831), (987515173, 1982, 'Papier_Magazine', 208, -1, 144, -1, 0.9911826848983765), (987515173, 1982, 'Papier_Magazine', 240, -1, 144, -1, 0.97808837890625), (987515173, 1982, 'Papier_Magazine', 272, -1, 144, -1, 0.9002286791801453), (987515173, 1982, 'Papier_Magazine', 304, -1, 144, -1, 0.5355162620544434), (987515173, 1982, 'Papier_Magazine', 336, -1, 144, -1, 0.8111801743507385), (987515173, 1982, 'Papier_Magazine', 112, -1, 176, -1, 0.9775855541229248), (987515173, 1982, 'Papier_Magazine', 144, -1, 176, -1, 0.8057297468185425), (987515173, 1982, 'Papier_Magazine', 176, -1, 176, -1, 0.9024547934532166), (987515173, 1982, 'Papier_Magazine', 208, -1, 176, -1, 0.8756533265113831), (987515173, 1982, 'Papier_Magazine', 240, -1, 176, -1, 0.46485498547554016), (987515173, 1982, 'Papier_Magazine', 272, -1, 176, -1, 0.5257745385169983), (987515173, 1982, 'Papier_Magazine', 304, -1, 176, -1, 0.16682389378547668), (987515173, 1982, 'Papier_Magazine', 336, -1, 176, -1, 0.11056400090456009), (987515173, 1982, 'Papier_Magazine', 112, -1, 208, -1, 0.14978572726249695), (987515173, 1982, 'Papier_Magazine', 144, -1, 208, -1, 0.01543077640235424), (987515173, 1982, 'Papier_Magazine', 176, -1, 208, -1, 0.01477794162929058), (987515173, 1982, 'Papier_Magazine', 208, -1, 208, -1, 0.007582188118249178), (987515173, 1982, 'Papier_Magazine', 240, -1, 208, -1, 0.00034808620694093406), (987515173, 1982, 'Papier_Magazine', 272, -1, 208, -1, 0.00017059440142475069), (987515173, 1982, 'Papier_Magazine', 304, -1, 208, -1, 5.384488758863881e-05), (987515173, 1982, 'Papier_Magazine', 336, -1, 208, -1, 6.26453838776797e-05), (987515173, 1982, 'Papier_Magazine', 112, -1, 240, -1, 0.07145499438047409), (987515173, 1982, 'Papier_Magazine', 144, -1, 240, -1, 0.0187542587518692), (987515173, 1982, 'Papier_Magazine', 176, -1, 240, -1, 0.03381296619772911), (987515173, 1982, 'Papier_Magazine', 208, -1, 240, -1, 0.032112207263708115), (987515173, 1982, 'Papier_Magazine', 240, -1, 240, -1, 0.0034934685099869967), (987515173, 1982, 'Papier_Magazine', 272, -1, 240, -1, 0.0003419028071220964), (987515173, 1982, 'Papier_Magazine', 304, -1, 240, -1, 2.9678369173780084e-05), (987515173, 1982, 'Papier_Magazine', 336, -1, 240, -1, 1.6375928680645302e-05), (987515173, 1982, 'Papier_Magazine', 112, -1, 272, -1, 0.008513962849974632), (987515173, 1982, 'Papier_Magazine', 144, -1, 272, -1, 0.0036537281703203917), (987515173, 1982, 'Papier_Magazine', 176, -1, 272, -1, 0.014019660651683807), (987515173, 1982, 'Papier_Magazine', 208, -1, 272, -1, 0.02645852230489254), (987515173, 1982, 'Papier_Magazine', 240, -1, 272, -1, 0.002454788889735937), (987515173, 1982, 'Papier_Magazine', 272, -1, 272, -1, 0.0006442199810408056), (987515173, 1982, 'Papier_Magazine', 304, -1, 272, -1, 0.0002749585546553135), (987515173, 1982, 'Papier_Magazine', 336, -1, 272, -1, 0.00015891017392277718), (987515173, 1982, 'Papier_Magazine', 112, -1, 304, -1, 0.0010025865631178021), (987515173, 1982, 'Papier_Magazine', 144, -1, 304, -1, 0.00098313856869936), (987515173, 1982, 'Papier_Magazine', 176, -1, 304, -1, 0.0032131473999470472), (987515173, 1982, 'Papier_Magazine', 208, -1, 304, -1, 0.005032587796449661), (987515173, 1982, 'Papier_Magazine', 240, -1, 304, -1, 0.0034951751586049795), (987515173, 1982, 'Papier_Magazine', 272, -1, 304, -1, 0.004066930152475834), (987515173, 1982, 'Papier_Magazine', 304, -1, 304, -1, 0.007215147837996483), (987515173, 1982, 'Papier_Magazine', 336, -1, 304, -1, 0.001959689659997821), (987515173, 1982, 'Papier_Magazine', 112, -1, 336, -1, 0.00487109087407589), (987515173, 1982, 'Papier_Magazine', 144, -1, 336, -1, 0.015574467368423939), (987515173, 1982, 'Papier_Magazine', 176, -1, 336, -1, 0.006772225257009268), (987515173, 1982, 'Papier_Magazine', 208, -1, 336, -1, 0.00795884057879448), (987515173, 1982, 'Papier_Magazine', 240, -1, 336, -1, 0.019054267555475235), (987515173, 1982, 'Papier_Magazine', 272, -1, 336, -1, 0.003666685428470373), (987515173, 1982, 'Papier_Magazine', 304, -1, 336, -1, 0.0062993667088449), (987515173, 1982, 'Papier_Magazine', 336, -1, 336, -1, 0.004449167288839817), (987515173, 1982, 'Plastique', 112, -1, 112, -1, 5.628618993114287e-08), (987515173, 1982, 'Plastique', 144, -1, 112, -1, 8.126639841066208e-07), (987515173, 1982, 'Plastique', 176, -1, 112, -1, 6.246673001442105e-05), (987515173, 1982, 'Plastique', 208, -1, 112, -1, 0.003538272110745311), (987515173, 1982, 'Plastique', 240, -1, 112, -1, 0.0031610489822924137), (987515173, 1982, 'Plastique', 272, -1, 112, -1, 0.007520067971199751), (987515173, 1982, 'Plastique', 304, -1, 112, -1, 0.054662466049194336), (987515173, 1982, 'Plastique', 336, -1, 112, -1, 0.05967717990279198), (987515173, 1982, 'Plastique', 112, -1, 144, -1, 2.7673156637320062e-06), (987515173, 1982, 'Plastique', 144, -1, 144, -1, 1.906651414174121e-05), (987515173, 1982, 'Plastique', 176, -1, 144, -1, 0.000176782050402835), (987515173, 1982, 'Plastique', 208, -1, 144, -1, 0.0015032748924568295), (987515173, 1982, 'Plastique', 240, -1, 144, -1, 0.005069505423307419), (987515173, 1982, 'Plastique', 272, -1, 144, -1, 0.08443636447191238), (987515173, 1982, 'Plastique', 304, -1, 144, -1, 0.410014271736145), (987515173, 1982, 'Plastique', 336, -1, 144, -1, 0.07098741829395294), (987515173, 1982, 'Plastique', 112, -1, 176, -1, 3.924418251699535e-06), (987515173, 1982, 'Plastique', 144, -1, 176, -1, 9.541850158711895e-05), (987515173, 1982, 'Plastique', 176, -1, 176, -1, 0.0003401074791327119), (987515173, 1982, 'Plastique', 208, -1, 176, -1, 0.0003897205169778317), (987515173, 1982, 'Plastique', 240, -1, 176, -1, 0.0006227337871678174), (987515173, 1982, 'Plastique', 272, -1, 176, -1, 0.00891969632357359), (987515173, 1982, 'Plastique', 304, -1, 176, -1, 0.03751831501722336), (987515173, 1982, 'Plastique', 336, -1, 176, -1, 0.002074483083561063), (987515173, 1982, 'Plastique', 112, -1, 208, -1, 7.618155359523371e-05), (987515173, 1982, 'Plastique', 144, -1, 208, -1, 4.133123729843646e-05), (987515173, 1982, 'Plastique', 176, -1, 208, -1, 8.240794704761356e-05), (987515173, 1982, 'Plastique', 208, -1, 208, -1, 3.827915861620568e-05), (987515173, 1982, 'Plastique', 240, -1, 208, -1, 8.361557775060646e-06), (987515173, 1982, 'Plastique', 272, -1, 208, -1, 1.1995794920949265e-05), (987515173, 1982, 'Plastique', 304, -1, 208, -1, 1.2109919225622434e-05), (987515173, 1982, 'Plastique', 336, -1, 208, -1, 4.108186658413615e-06), (987515173, 1982, 'Plastique', 112, -1, 240, -1, 0.00014421493688132614), (987515173, 1982, 'Plastique', 144, -1, 240, -1, 3.535229552653618e-05), (987515173, 1982, 'Plastique', 176, -1, 240, -1, 2.115689858328551e-05), (987515173, 1982, 'Plastique', 208, -1, 240, -1, 7.336885119002545e-06), (987515173, 1982, 'Plastique', 240, -1, 240, -1, 4.869380973104853e-06), (987515173, 1982, 'Plastique', 272, -1, 240, -1, 2.7787407361756777e-06), (987515173, 1982, 'Plastique', 304, -1, 240, -1, 7.971618742885767e-07), (987515173, 1982, 'Plastique', 336, -1, 240, -1, 3.532012726736866e-07), (987515173, 1982, 'Plastique', 112, -1, 272, -1, 3.369362457306124e-05), (987515173, 1982, 'Plastique', 144, -1, 272, -1, 1.1666946193145122e-05), (987515173, 1982, 'Plastique', 176, -1, 272, -1, 1.3395730093179736e-05), (987515173, 1982, 'Plastique', 208, -1, 272, -1, 5.415592113422463e-06), (987515173, 1982, 'Plastique', 240, -1, 272, -1, 9.571833743393654e-07), (987515173, 1982, 'Plastique', 272, -1, 272, -1, 6.870099582556577e-07), (987515173, 1982, 'Plastique', 304, -1, 272, -1, 5.699378107237862e-07), (987515173, 1982, 'Plastique', 336, -1, 272, -1, 7.442125706802472e-07), (987515173, 1982, 'Plastique', 112, -1, 304, -1, 3.35555819219735e-06), (987515173, 1982, 'Plastique', 144, -1, 304, -1, 3.3583323784114327e-06), (987515173, 1982, 'Plastique', 176, -1, 304, -1, 6.515527729789028e-06), (987515173, 1982, 'Plastique', 208, -1, 304, -1, 8.743736543692648e-06), (987515173, 1982, 'Plastique', 240, -1, 304, -1, 3.3802155030571157e-06), (987515173, 1982, 'Plastique', 272, -1, 304, -1, 4.75964543511509e-06), (987515173, 1982, 'Plastique', 304, -1, 304, -1, 4.507819994614692e-06), (987515173, 1982, 'Plastique', 336, -1, 304, -1, 2.417163841528236e-06), (987515173, 1982, 'Plastique', 112, -1, 336, -1, 1.5496783817070536e-05), (987515173, 1982, 'Plastique', 144, -1, 336, -1, 5.45319817319978e-05), (987515173, 1982, 'Plastique', 176, -1, 336, -1, 3.4191580198239535e-05), (987515173, 1982, 'Plastique', 208, -1, 336, -1, 2.3650032744626515e-05), (987515173, 1982, 'Plastique', 240, -1, 336, -1, 3.942390321753919e-05), (987515173, 1982, 'Plastique', 272, -1, 336, -1, 2.5726001695147716e-05), (987515173, 1982, 'Plastique', 304, -1, 336, -1, 3.318051676615141e-05), (987515173, 1982, 'Plastique', 336, -1, 336, -1, 4.1925599362002686e-05), (987515173, 1982, 'Sol_Environement', 112, -1, 112, -1, 2.940393371494987e-12), (987515173, 1982, 'Sol_Environement', 144, -1, 112, -1, 5.517383772080109e-10), (987515173, 1982, 'Sol_Environement', 176, -1, 112, -1, 4.918547915622185e-07), (987515173, 1982, 'Sol_Environement', 208, -1, 112, -1, 1.0143633517145645e-05), (987515173, 1982, 'Sol_Environement', 240, -1, 112, -1, 9.17372562980745e-06), (987515173, 1982, 'Sol_Environement', 272, -1, 112, -1, 5.6445569498464465e-05), (987515173, 1982, 'Sol_Environement', 304, -1, 112, -1, 0.0003785344597417861), (987515173, 1982, 'Sol_Environement', 336, -1, 112, -1, 0.00016536946350242943), (987515173, 1982, 'Sol_Environement', 112, -1, 144, -1, 1.021115423327501e-07), (987515173, 1982, 'Sol_Environement', 144, -1, 144, -1, 5.79917127652152e-07), (987515173, 1982, 'Sol_Environement', 176, -1, 144, -1, 1.4361562534759287e-06), (987515173, 1982, 'Sol_Environement', 208, -1, 144, -1, 5.8970563259208575e-06), (987515173, 1982, 'Sol_Environement', 240, -1, 144, -1, 1.8262093362864107e-05), (987515173, 1982, 'Sol_Environement', 272, -1, 144, -1, 0.00012591190170496702), (987515173, 1982, 'Sol_Environement', 304, -1, 144, -1, 0.0003239624493289739), (987515173, 1982, 'Sol_Environement', 336, -1, 144, -1, 9.627967665437609e-05), (987515173, 1982, 'Sol_Environement', 112, -1, 176, -1, 4.1706246634021227e-07), (987515173, 1982, 'Sol_Environement', 144, -1, 176, -1, 1.0199498774454696e-06), (987515173, 1982, 'Sol_Environement', 176, -1, 176, -1, 6.585862593055936e-06), (987515173, 1982, 'Sol_Environement', 208, -1, 176, -1, 1.7231742504009162e-06), (987515173, 1982, 'Sol_Environement', 240, -1, 176, -1, 3.7328638882172527e-06), (987515173, 1982, 'Sol_Environement', 272, -1, 176, -1, 2.785021388262976e-05), (987515173, 1982, 'Sol_Environement', 304, -1, 176, -1, 0.00015427170728798956), (987515173, 1982, 'Sol_Environement', 336, -1, 176, -1, 0.00024293918977491558), (987515173, 1982, 'Sol_Environement', 112, -1, 208, -1, 4.010400061815744e-06), (987515173, 1982, 'Sol_Environement', 144, -1, 208, -1, 7.507703116971243e-07), (987515173, 1982, 'Sol_Environement', 176, -1, 208, -1, 1.3331404034033767e-06), (987515173, 1982, 'Sol_Environement', 208, -1, 208, -1, 9.04301828086318e-07), (987515173, 1982, 'Sol_Environement', 240, -1, 208, -1, 6.944066512915015e-07), (987515173, 1982, 'Sol_Environement', 272, -1, 208, -1, 1.08171661850065e-06), (987515173, 1982, 'Sol_Environement', 304, -1, 208, -1, 1.4425490917346906e-06), (987515173, 1982, 'Sol_Environement', 336, -1, 208, -1, 2.323302624063217e-06), (987515173, 1982, 'Sol_Environement', 112, -1, 240, -1, 4.589903255691752e-06), (987515173, 1982, 'Sol_Environement', 144, -1, 240, -1, 3.767145813071693e-07), (987515173, 1982, 'Sol_Environement', 176, -1, 240, -1, 2.4853477498254506e-07), (987515173, 1982, 'Sol_Environement', 208, -1, 240, -1, 2.1582860654234537e-07), (987515173, 1982, 'Sol_Environement', 240, -1, 240, -1, 5.589519673776522e-07), (987515173, 1982, 'Sol_Environement', 272, -1, 240, -1, 8.665043651490123e-07), (987515173, 1982, 'Sol_Environement', 304, -1, 240, -1, 6.646989163527905e-07), (987515173, 1982, 'Sol_Environement', 336, -1, 240, -1, 7.816057632226148e-07), (987515173, 1982, 'Sol_Environement', 112, -1, 272, -1, 2.9952141176181613e-06), (987515173, 1982, 'Sol_Environement', 144, -1, 272, -1, 6.877136229377356e-07), (987515173, 1982, 'Sol_Environement', 176, -1, 272, -1, 5.020575031267072e-07), (987515173, 1982, 'Sol_Environement', 208, -1, 272, -1, 2.0009267132081732e-07), (987515173, 1982, 'Sol_Environement', 240, -1, 272, -1, 1.3200848059113923e-07), (987515173, 1982, 'Sol_Environement', 272, -1, 272, -1, 2.70324193252236e-07), (987515173, 1982, 'Sol_Environement', 304, -1, 272, -1, 1.613342988093791e-07), (987515173, 1982, 'Sol_Environement', 336, -1, 272, -1, 4.84771589981392e-07), (987515173, 1982, 'Sol_Environement', 112, -1, 304, -1, 6.98781434493867e-07), (987515173, 1982, 'Sol_Environement', 144, -1, 304, -1, 4.065262544372672e-07), (987515173, 1982, 'Sol_Environement', 176, -1, 304, -1, 8.900503303266305e-07), (987515173, 1982, 'Sol_Environement', 208, -1, 304, -1, 2.33267383009661e-06), (987515173, 1982, 'Sol_Environement', 240, -1, 304, -1, 3.049969109270023e-06), (987515173, 1982, 'Sol_Environement', 272, -1, 304, -1, 2.671959464350948e-06), (987515173, 1982, 'Sol_Environement', 304, -1, 304, -1, 1.4994966477388516e-06), (987515173, 1982, 'Sol_Environement', 336, -1, 304, -1, 1.610581989552884e-06), (987515173, 1982, 'Sol_Environement', 112, -1, 336, -1, 4.6315574309119256e-07), (987515173, 1982, 'Sol_Environement', 144, -1, 336, -1, 1.1445222298789304e-06), (987515173, 1982, 'Sol_Environement', 176, -1, 336, -1, 6.795256126679305e-07), (987515173, 1982, 'Sol_Environement', 208, -1, 336, -1, 1.429223402737989e-06), (987515173, 1982, 'Sol_Environement', 240, -1, 336, -1, 3.399001798243262e-06), (987515173, 1982, 'Sol_Environement', 272, -1, 336, -1, 2.1180719613766996e-06), (987515173, 1982, 'Sol_Environement', 304, -1, 336, -1, 2.29417651098629e-06), (987515173, 1982, 'Sol_Environement', 336, -1, 336, -1, 3.3905967029568274e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 112, -1, 4.575847469823202e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 112, -1, 2.460965561112971e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 112, -1, 1.7641963495407254e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 112, -1, 0.0004739058786071837), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 112, -1, 0.00011572476068977267), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 112, -1, 0.00020998391846660525), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 112, -1, 0.001441184664145112), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 112, -1, 0.002021940890699625), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 144, -1, 3.0665665690321475e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 144, -1, 1.723464265523944e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 144, -1, 2.078898251056671e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 144, -1, 0.00027382804546505213), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 144, -1, 0.00046515112626366317), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 144, -1, 0.0005284376675263047), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 144, -1, 0.00018965140043292195), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 144, -1, 0.0002052709460258484), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 176, -1, 1.499031532148365e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 176, -1, 6.768323601136217e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 176, -1, 2.1079100406495854e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 176, -1, 2.419175325485412e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 176, -1, 3.539620229275897e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 176, -1, 7.854169234633446e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 176, -1, 0.00010158951045013964), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 176, -1, 0.0003781390842050314), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 208, -1, 1.5082629033713602e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 208, -1, 1.464372417103732e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 208, -1, 4.7350013119284995e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 208, -1, 3.8161333577590995e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 208, -1, 3.6540925520966994e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 208, -1, 1.1669434570649173e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 208, -1, 1.3356401723285671e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 208, -1, 5.9734928072430193e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 240, -1, 4.209062899462879e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 240, -1, 5.31286423210986e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 240, -1, 3.9045626181177795e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 240, -1, 2.3957366011018166e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 240, -1, 5.768911250925157e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 240, -1, 2.104985287587624e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 240, -1, 1.7834596292232163e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 240, -1, 2.266034971398767e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 272, -1, 0.00020098219101782888), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 272, -1, 6.693278555758297e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 272, -1, 4.353698022896424e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 272, -1, 1.4473799637926277e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 272, -1, 7.0927553679212e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 272, -1, 1.550008346384857e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 272, -1, 1.540880293759983e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 272, -1, 0.00010354327969253063), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 304, -1, 9.513091936241835e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 304, -1, 0.00011014967458322644), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 304, -1, 0.00015781840193085372), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 304, -1, 0.000541884743142873), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 304, -1, 0.0023387991823256016), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 304, -1, 0.0074480012990534306), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 304, -1, 0.00584239698946476), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 304, -1, 0.004482793156057596), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 336, -1, 0.0002411372697679326), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 336, -1, 0.0020116048399358988), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 336, -1, 0.0007494039600715041), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 336, -1, 0.003332410706207156), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 336, -1, 0.0645388662815094), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 336, -1, 0.038250476121902466), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 336, -1, 0.03876246511936188), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 336, -1, 0.005903724115341902), (987515173, 1982, 'autre_refus', 112, -1, 112, -1, 5.851480966434508e-10), (987515173, 1982, 'autre_refus', 144, -1, 112, -1, 2.0594731253709142e-08), (987515173, 1982, 'autre_refus', 176, -1, 112, -1, 1.4454044503509067e-06), (987515173, 1982, 'autre_refus', 208, -1, 112, -1, 9.248015703633428e-05), (987515173, 1982, 'autre_refus', 240, -1, 112, -1, 0.00021706322149839252), (987515173, 1982, 'autre_refus', 272, -1, 112, -1, 0.001756428275257349), (987515173, 1982, 'autre_refus', 304, -1, 112, -1, 0.0177126657217741), (987515173, 1982, 'autre_refus', 336, -1, 112, -1, 0.008998946286737919), (987515173, 1982, 'autre_refus', 112, -1, 144, -1, 9.120078630076023e-07), (987515173, 1982, 'autre_refus', 144, -1, 144, -1, 2.9223053843452362e-06), (987515173, 1982, 'autre_refus', 176, -1, 144, -1, 1.033488388202386e-05), (987515173, 1982, 'autre_refus', 208, -1, 144, -1, 0.00013500035856850445), (987515173, 1982, 'autre_refus', 240, -1, 144, -1, 0.000348318659234792), (987515173, 1982, 'autre_refus', 272, -1, 144, -1, 0.004932259675115347), (987515173, 1982, 'autre_refus', 304, -1, 144, -1, 0.04345298558473587), (987515173, 1982, 'autre_refus', 336, -1, 144, -1, 0.09518309682607651), (987515173, 1982, 'autre_refus', 112, -1, 176, -1, 1.511305890744552e-05), (987515173, 1982, 'autre_refus', 144, -1, 176, -1, 0.00012784563296008855), (987515173, 1982, 'autre_refus', 176, -1, 176, -1, 0.00023730279644951224), (987515173, 1982, 'autre_refus', 208, -1, 176, -1, 0.000810895930044353), (987515173, 1982, 'autre_refus', 240, -1, 176, -1, 0.0006539719761349261), (987515173, 1982, 'autre_refus', 272, -1, 176, -1, 0.004282562527805567), (987515173, 1982, 'autre_refus', 304, -1, 176, -1, 0.02331022173166275), (987515173, 1982, 'autre_refus', 336, -1, 176, -1, 0.01875206269323826), (987515173, 1982, 'autre_refus', 112, -1, 208, -1, 8.849771984387189e-05), (987515173, 1982, 'autre_refus', 144, -1, 208, -1, 0.00018569415260571986), (987515173, 1982, 'autre_refus', 176, -1, 208, -1, 0.0003199160273652524), (987515173, 1982, 'autre_refus', 208, -1, 208, -1, 0.00035743703483603895), (987515173, 1982, 'autre_refus', 240, -1, 208, -1, 0.00019874803547281772), (987515173, 1982, 'autre_refus', 272, -1, 208, -1, 0.0002866908034775406), (987515173, 1982, 'autre_refus', 304, -1, 208, -1, 0.00020236412819940597), (987515173, 1982, 'autre_refus', 336, -1, 208, -1, 0.0002442058175802231), (987515173, 1982, 'autre_refus', 112, -1, 240, -1, 0.00023232278181239963), (987515173, 1982, 'autre_refus', 144, -1, 240, -1, 0.000108526655822061), (987515173, 1982, 'autre_refus', 176, -1, 240, -1, 6.488244980573654e-05), (987515173, 1982, 'autre_refus', 208, -1, 240, -1, 2.5217044822056778e-05), (987515173, 1982, 'autre_refus', 240, -1, 240, -1, 7.29677194613032e-05), (987515173, 1982, 'autre_refus', 272, -1, 240, -1, 0.00014070658653508872), (987515173, 1982, 'autre_refus', 304, -1, 240, -1, 8.935788355302066e-05), (987515173, 1982, 'autre_refus', 336, -1, 240, -1, 8.171387162292376e-05), (987515173, 1982, 'autre_refus', 112, -1, 272, -1, 0.00026850696303881705), (987515173, 1982, 'autre_refus', 144, -1, 272, -1, 0.00011160651774844155), (987515173, 1982, 'autre_refus', 176, -1, 272, -1, 0.00012472311209421605), (987515173, 1982, 'autre_refus', 208, -1, 272, -1, 5.106349635752849e-05), (987515173, 1982, 'autre_refus', 240, -1, 272, -1, 2.919041071436368e-05), (987515173, 1982, 'autre_refus', 272, -1, 272, -1, 4.2713716538855806e-05), (987515173, 1982, 'autre_refus', 304, -1, 272, -1, 6.84538172208704e-05), (987515173, 1982, 'autre_refus', 336, -1, 272, -1, 0.00014215277042239904), (987515173, 1982, 'autre_refus', 112, -1, 304, -1, 0.000115537790406961), (987515173, 1982, 'autre_refus', 144, -1, 304, -1, 0.00021747223217971623), (987515173, 1982, 'autre_refus', 176, -1, 304, -1, 0.00042600996675901115), (987515173, 1982, 'autre_refus', 208, -1, 304, -1, 0.00042784257675521076), (987515173, 1982, 'autre_refus', 240, -1, 304, -1, 6.567744276253507e-05), (987515173, 1982, 'autre_refus', 272, -1, 304, -1, 3.168384137097746e-05), (987515173, 1982, 'autre_refus', 304, -1, 304, -1, 1.1663217264867853e-05), (987515173, 1982, 'autre_refus', 336, -1, 304, -1, 1.8784747226163745e-05), (987515173, 1982, 'autre_refus', 112, -1, 336, -1, 0.0002473437343724072), (987515173, 1982, 'autre_refus', 144, -1, 336, -1, 0.0004773969412781298), (987515173, 1982, 'autre_refus', 176, -1, 336, -1, 0.0003343804564792663), (987515173, 1982, 'autre_refus', 208, -1, 336, -1, 0.0002362925879424438), (987515173, 1982, 'autre_refus', 240, -1, 336, -1, 0.00010974430915666744), (987515173, 1982, 'autre_refus', 272, -1, 336, -1, 9.505417256150395e-05), (987515173, 1982, 'autre_refus', 304, -1, 336, -1, 0.0001310940133407712), (987515173, 1982, 'autre_refus', 336, -1, 336, -1, 0.000733303721062839)]} result thcl : {'987515250': [('987515250', 'Carton', 0.98080564, 1927, '1528'), 'temp/1746564045_2124625_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg'], '987515175': [('987515175', 'Papier_Magazine', 0.9998135, 1927, '1528'), 'temp/1746564045_2124625_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515176': [('987515176', 'Papier_Magazine', 0.999814, 1927, '1528'), 'temp/1746564045_2124625_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515177': [('987515177', 'Papier_Magazine', 0.97709346, 1927, '1528'), 'temp/1746564045_2124625_987515177_4a54e9967227806219ddf45d256539d8.jpg'], '987515178': [('987515178', 'Carton', 0.8577468, 1927, '1528'), 'temp/1746564045_2124625_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg'], '987515179': [('987515179', 'Carton', 0.92710525, 1927, '1528'), 'temp/1746564045_2124625_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg'], '987515180': [('987515180', 'Carton', 0.9899968, 1927, '1528'), 'temp/1746564045_2124625_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg'], '987515181': [('987515181', 'Carton', 0.9977804, 1927, '1528'), 'temp/1746564045_2124625_987515181_1738c2798fb31152809ecb443ac286d6.jpg'], '987515208': [('987515208', 'Carton', 0.9917305, 1927, '1528'), 'temp/1746564045_2124625_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg'], '987515209': [('987515209', 'Carton', 0.96781266, 1927, '1528'), 'temp/1746564045_2124625_987515209_02dfe1ae39f51994652f4a8538844aea.jpg'], '987515211': [('987515211', 'Carton', 0.97339123, 1927, '1528'), 'temp/1746564045_2124625_987515211_72cc7664d45bd40477351b9b764f1500.jpg'], '987515212': [('987515212', 'Carton', 0.9869255, 1927, '1528'), 'temp/1746564045_2124625_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515213': [('987515213', 'Carton', 0.9869081, 1927, '1528'), 'temp/1746564045_2124625_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515215': [('987515215', 'Papier_Magazine', 0.9939253, 1927, '1528'), 'temp/1746564045_2124625_987515215_902ef348a7eebb9a8b87f42927347936.jpg'], '987515216': [('987515216', 'Papier_Magazine', 0.97745866, 1927, '1528'), 'temp/1746564045_2124625_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg'], '987515182': [('987515182', 'Carton', 0.99241745, 1927, '1528'), 'temp/1746564045_2124625_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg'], '987515183': [('987515183', 'Papier_Magazine', 0.99999213, 1927, '1528'), 'temp/1746564045_2124625_987515183_6aab9ca0421398b4899892c10c2594c6.jpg'], '987515184': [('987515184', 'Papier_Magazine', 0.99973196, 1927, '1528'), 'temp/1746564045_2124625_987515184_19c8c2177209a285df6014d95fe53f2c.jpg'], '987515185': [('987515185', 'Papier_Magazine', 0.7976216, 1927, '1528'), 'temp/1746564045_2124625_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg'], '987515186': [('987515186', 'Carton', 0.984749, 1927, '1528'), 'temp/1746564045_2124625_987515186_797def426440b544aa80dbd63a19234a.jpg'], '987515187': [('987515187', 'Carton', 0.9809986, 1927, '1528'), 'temp/1746564045_2124625_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg'], '987515207': [('987515207', 'Papier_Magazine', 0.8741362, 1927, '1528'), 'temp/1746564045_2124625_987515207_de216ddb041e249524b0fb2b949064a5.jpg'], '987515217': [('987515217', 'Carton', 0.52918035, 1927, '1528'), 'temp/1746564045_2124625_987515217_78877bb2c5760be28518d17f77d1c609.jpg'], '987515219': [('987515219', 'Carton', 0.99936944, 1927, '1528'), 'temp/1746564045_2124625_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg'], '987515220': [('987515220', 'Carton', 0.996393, 1927, '1528'), 'temp/1746564045_2124625_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg'], '987515222': [('987515222', 'Carton', 0.9974712, 1927, '1528'), 'temp/1746564045_2124625_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg'], '987515223': [('987515223', 'Carton', 0.9920556, 1927, '1528'), 'temp/1746564045_2124625_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg'], '987515224': [('987515224', 'Carton', 0.90841335, 1927, '1528'), 'temp/1746564045_2124625_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg'], '987515226': [('987515226', 'Papier_Magazine', 0.9870517, 1927, '1528'), 'temp/1746564045_2124625_987515226_a18048dca1a77ae086b62cf07759f704.jpg'], '987515202': [('987515202', 'Carton', 0.9911305, 1927, '1528'), 'temp/1746564045_2124625_987515202_3314bd90d1404f31b827d8925abf2d62.jpg'], '987515204': [('987515204', 'Papier_Magazine', 0.99507034, 1927, '1528'), 'temp/1746564045_2124625_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg'], '987515205': [('987515205', 'Papier_Magazine', 0.9908608, 1927, '1528'), 'temp/1746564045_2124625_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg'], '987515239': [('987515239', 'Carton', 0.99978346, 1927, '1528'), 'temp/1746564045_2124625_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg'], '987515240': [('987515240', 'Carton', 0.9995203, 1927, '1528'), 'temp/1746564045_2124625_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg'], '987515241': [('987515241', 'Carton', 0.9820961, 1927, '1528'), 'temp/1746564045_2124625_987515241_073420d938f5f010ffd5b4353c064e09.jpg'], '987515242': [('987515242', 'Carton', 0.93594974, 1927, '1528'), 'temp/1746564045_2124625_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg'], '987515192': [('987515192', 'Papier_Magazine', 0.99991167, 1927, '1528'), 'temp/1746564045_2124625_987515192_b661073b218f5f056833d6af1c617153.jpg'], '987515193': [('987515193', 'Papier_Magazine', 0.9993957, 1927, '1528'), 'temp/1746564045_2124625_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg'], '987515195': [('987515195', 'Carton', 0.9846379, 1927, '1528'), 'temp/1746564045_2124625_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515196': [('987515196', 'Carton', 0.98466843, 1927, '1528'), 'temp/1746564045_2124625_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515198': [('987515198', 'Carton', 0.9661375, 1927, '1528'), 'temp/1746564045_2124625_987515198_599e80f444c876f407e94b533c89360b.jpg'], '987515200': [('987515200', 'Carton', 0.9859461, 1927, '1528'), 'temp/1746564045_2124625_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg'], '987515201': [('987515201', 'Carton', 0.9954619, 1927, '1528'), 'temp/1746564045_2124625_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg'], '987515227': [('987515227', 'Papier_Magazine', 0.900665, 1927, '1528'), 'temp/1746564045_2124625_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg'], '987515228': [('987515228', 'Papier_Magazine', 0.52149576, 1927, '1528'), 'temp/1746564045_2124625_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg'], '987515230': [('987515230', 'Carton', 0.99940526, 1927, '1528'), 'temp/1746564045_2124625_987515230_846ad925884264181565c81d152a2e94.jpg'], '987515231': [('987515231', 'Carton', 0.999421, 1927, '1528'), 'temp/1746564045_2124625_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg'], '987515232': [('987515232', 'Carton', 0.9992481, 1927, '1528'), 'temp/1746564045_2124625_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg'], '987515233': [('987515233', 'Carton', 0.98343444, 1927, '1528'), 'temp/1746564045_2124625_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg'], '987515234': [('987515234', 'Carton', 0.94490546, 1927, '1528'), 'temp/1746564045_2124625_987515234_2eca3480aed0f8b876242675ad99b666.jpg'], '987515243': [('987515243', 'Papier_Magazine', 0.874293, 1927, '1528'), 'temp/1746564045_2124625_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg'], '987515244': [('987515244', 'Papier_Magazine', 0.8177143, 1927, '1528'), 'temp/1746564045_2124625_987515244_419530eaef5ef868f75c758b94eea4b4.jpg'], '987515245': [('987515245', 'Carton', 0.8660789, 1927, '1528'), 'temp/1746564045_2124625_987515245_757d9d208d5bd4375c5f21f68b699148.jpg'], '987515246': [('987515246', 'Carton', 0.9992322, 1927, '1528'), 'temp/1746564045_2124625_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg'], '987515247': [('987515247', 'Carton', 0.9996691, 1927, '1528'), 'temp/1746564045_2124625_987515247_e47b65403df916ba909bc9c439b0af73.jpg'], '987515248': [('987515248', 'Carton', 0.9812789, 1927, '1528'), 'temp/1746564045_2124625_987515248_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515249': [('987515249', 'Carton', 0.9813571, 1927, '1528'), 'temp/1746564045_2124625_987515249_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515235': [('987515235', 'Papier_Magazine', 0.8919314, 1927, '1528'), 'temp/1746564045_2124625_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg'], '987515236': [('987515236', 'Papier_Magazine', 0.53671837, 1927, '1528'), 'temp/1746564045_2124625_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg'], '987515237': [('987515237', 'Carton', 0.76962644, 1927, '1528'), 'temp/1746564045_2124625_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg'], '987515238': [('987515238', 'Carton', 0.99957424, 1927, '1528'), 'temp/1746564045_2124625_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg'], '987515188': [('987515188', 'Carton', 0.9956547, 1927, '1528'), 'temp/1746564045_2124625_987515188_4116f9906657a69bb76c2fda982037b9.jpg'], '987515189': [('987515189', 'Carton', 0.9977881, 1927, '1528'), 'temp/1746564045_2124625_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg'], '987515190': [('987515190', 'Carton', 0.97631013, 1927, '1528'), 'temp/1746564045_2124625_987515190_d56932bfc6ba2a8c974c691108755017.jpg']} result detect_point : {987515173: [(987515173, 1982, 'Autre_Environement', 112, -1, 112, -1, 6.299185999203427e-12), (987515173, 1982, 'Autre_Environement', 144, -1, 112, -1, 2.4354029209971984e-11), (987515173, 1982, 'Autre_Environement', 176, -1, 112, -1, 1.0608561140657002e-08), (987515173, 1982, 'Autre_Environement', 208, -1, 112, -1, 4.4438695567805553e-07), (987515173, 1982, 'Autre_Environement', 240, -1, 112, -1, 1.9181406969437376e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 112, -1, 3.789926995523274e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 112, -1, 0.00012294021144043654), (987515173, 1982, 'Autre_Environement', 336, -1, 112, -1, 2.9510207241401076e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 144, -1, 2.3534779458600497e-08), (987515173, 1982, 'Autre_Environement', 144, -1, 144, -1, 2.2150397072095984e-08), (987515173, 1982, 'Autre_Environement', 176, -1, 144, -1, 1.3809660970309778e-07), (987515173, 1982, 'Autre_Environement', 208, -1, 144, -1, 1.4747731711395318e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 144, -1, 1.1318640645185951e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 144, -1, 0.00015753194747958332), (987515173, 1982, 'Autre_Environement', 304, -1, 144, -1, 0.0004437107127159834), (987515173, 1982, 'Autre_Environement', 336, -1, 144, -1, 6.528615631395951e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 176, -1, 1.328779603682051e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 176, -1, 1.623386310711794e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 176, -1, 2.5332205950689968e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 176, -1, 1.613662789168302e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 176, -1, 6.274173756537493e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 176, -1, 8.604821050539613e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 176, -1, 0.0003268723958171904), (987515173, 1982, 'Autre_Environement', 336, -1, 176, -1, 0.00030706453253515065), (987515173, 1982, 'Autre_Environement', 112, -1, 208, -1, 1.8615861336002126e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 208, -1, 7.933252163638826e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 208, -1, 2.7021080313716084e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 208, -1, 1.7959946490009315e-05), (987515173, 1982, 'Autre_Environement', 240, -1, 208, -1, 2.3413718736264855e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 208, -1, 1.6980904547381215e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 208, -1, 4.545032425085083e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 208, -1, 8.81552477949299e-06), (987515173, 1982, 'Autre_Environement', 112, -1, 240, -1, 6.098573521740036e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 240, -1, 1.6448411770397797e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 240, -1, 1.96067662727728e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 240, -1, 1.4342890608531889e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 240, -1, 7.873609320085961e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 240, -1, 1.2828784747398458e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 240, -1, 9.311908797826618e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 240, -1, 2.1642506908392534e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 272, -1, 3.827708951575914e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 272, -1, 2.549672217355692e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 272, -1, 2.9625393835885916e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 272, -1, 2.7473795398691436e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 272, -1, 4.327345322963083e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 272, -1, 8.197881470550783e-06), (987515173, 1982, 'Autre_Environement', 304, -1, 272, -1, 1.1530723895702977e-05), (987515173, 1982, 'Autre_Environement', 336, -1, 272, -1, 3.9176582504296675e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 304, -1, 1.2082839020877145e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 304, -1, 1.5749565136502497e-05), (987515173, 1982, 'Autre_Environement', 176, -1, 304, -1, 3.331287734908983e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 304, -1, 0.0001544763072161004), (987515173, 1982, 'Autre_Environement', 240, -1, 304, -1, 0.0002582166052889079), (987515173, 1982, 'Autre_Environement', 272, -1, 304, -1, 0.0001879721530713141), (987515173, 1982, 'Autre_Environement', 304, -1, 304, -1, 0.00021336021018214524), (987515173, 1982, 'Autre_Environement', 336, -1, 304, -1, 0.00016406837676186115), (987515173, 1982, 'Autre_Environement', 112, -1, 336, -1, 4.545543106360128e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 336, -1, 1.7524675058666617e-05), (987515173, 1982, 'Autre_Environement', 176, -1, 336, -1, 4.930860814056359e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 336, -1, 0.00012107240763725713), (987515173, 1982, 'Autre_Environement', 240, -1, 336, -1, 0.00019737912225537002), (987515173, 1982, 'Autre_Environement', 272, -1, 336, -1, 0.00018724106485024095), (987515173, 1982, 'Autre_Environement', 304, -1, 336, -1, 0.00012335702194832265), (987515173, 1982, 'Autre_Environement', 336, -1, 336, -1, 0.0002721178170759231), (987515173, 1982, 'Carton', 112, -1, 112, -1, 1.5864678459820425e-07), (987515173, 1982, 'Carton', 144, -1, 112, -1, 4.03231797463377e-06), (987515173, 1982, 'Carton', 176, -1, 112, -1, 6.991095688135829e-06), (987515173, 1982, 'Carton', 208, -1, 112, -1, 0.0008727589156478643), (987515173, 1982, 'Carton', 240, -1, 112, -1, 0.002642342122271657), (987515173, 1982, 'Carton', 272, -1, 112, -1, 0.0033851603511720896), (987515173, 1982, 'Carton', 304, -1, 112, -1, 0.03135448694229126), (987515173, 1982, 'Carton', 336, -1, 112, -1, 0.055807650089263916), (987515173, 1982, 'Carton', 112, -1, 144, -1, 0.00012319513189140707), (987515173, 1982, 'Carton', 144, -1, 144, -1, 0.00020911803585477173), (987515173, 1982, 'Carton', 176, -1, 144, -1, 0.00036795344203710556), (987515173, 1982, 'Carton', 208, -1, 144, -1, 0.006849064026027918), (987515173, 1982, 'Carton', 240, -1, 144, -1, 0.015909679234027863), (987515173, 1982, 'Carton', 272, -1, 144, -1, 0.009407011792063713), (987515173, 1982, 'Carton', 304, -1, 144, -1, 0.009767088107764721), (987515173, 1982, 'Carton', 336, -1, 144, -1, 0.022109758108854294), (987515173, 1982, 'Carton', 112, -1, 176, -1, 0.021874763071537018), (987515173, 1982, 'Carton', 144, -1, 176, -1, 0.19390763342380524), (987515173, 1982, 'Carton', 176, -1, 176, -1, 0.09682448208332062), (987515173, 1982, 'Carton', 208, -1, 176, -1, 0.12305475026369095), (987515173, 1982, 'Carton', 240, -1, 176, -1, 0.5336940884590149), (987515173, 1982, 'Carton', 272, -1, 176, -1, 0.4603225290775299), (987515173, 1982, 'Carton', 304, -1, 176, -1, 0.7706670761108398), (987515173, 1982, 'Carton', 336, -1, 176, -1, 0.8661702871322632), (987515173, 1982, 'Carton', 112, -1, 208, -1, 0.8499331474304199), (987515173, 1982, 'Carton', 144, -1, 208, -1, 0.9843107461929321), (987515173, 1982, 'Carton', 176, -1, 208, -1, 0.9847518801689148), (987515173, 1982, 'Carton', 208, -1, 208, -1, 0.9919575452804565), (987515173, 1982, 'Carton', 240, -1, 208, -1, 0.9993775486946106), (987515173, 1982, 'Carton', 272, -1, 208, -1, 0.9994128942489624), (987515173, 1982, 'Carton', 304, -1, 208, -1, 0.9995879530906677), (987515173, 1982, 'Carton', 336, -1, 208, -1, 0.9992258548736572), (987515173, 1982, 'Carton', 112, -1, 240, -1, 0.9278057217597961), (987515173, 1982, 'Carton', 144, -1, 240, -1, 0.9810516834259033), (987515173, 1982, 'Carton', 176, -1, 240, -1, 0.9660813212394714), (987515173, 1982, 'Carton', 208, -1, 240, -1, 0.9678425788879395), (987515173, 1982, 'Carton', 240, -1, 240, -1, 0.9963894486427307), (987515173, 1982, 'Carton', 272, -1, 240, -1, 0.9994200468063354), (987515173, 1982, 'Carton', 304, -1, 240, -1, 0.9997860789299011), (987515173, 1982, 'Carton', 336, -1, 240, -1, 0.9996691942214966), (987515173, 1982, 'Carton', 112, -1, 272, -1, 0.9895121455192566), (987515173, 1982, 'Carton', 144, -1, 272, -1, 0.9954617619514465), (987515173, 1982, 'Carton', 176, -1, 272, -1, 0.9855201840400696), (987515173, 1982, 'Carton', 208, -1, 272, -1, 0.9734230041503906), (987515173, 1982, 'Carton', 240, -1, 272, -1, 0.997469425201416), (987515173, 1982, 'Carton', 272, -1, 272, -1, 0.9992039799690247), (987515173, 1982, 'Carton', 304, -1, 272, -1, 0.9995161294937134), (987515173, 1982, 'Carton', 336, -1, 272, -1, 0.9991306662559509), (987515173, 1982, 'Carton', 112, -1, 304, -1, 0.997774064540863), (987515173, 1982, 'Carton', 144, -1, 304, -1, 0.9977602958679199), (987515173, 1982, 'Carton', 176, -1, 304, -1, 0.995539128780365), (987515173, 1982, 'Carton', 208, -1, 304, -1, 0.9927338361740112), (987515173, 1982, 'Carton', 240, -1, 304, -1, 0.9920431971549988), (987515173, 1982, 'Carton', 272, -1, 304, -1, 0.9835704565048218), (987515173, 1982, 'Carton', 304, -1, 304, -1, 0.9820127487182617), (987515173, 1982, 'Carton', 336, -1, 304, -1, 0.9808406233787537), (987515173, 1982, 'Carton', 112, -1, 336, -1, 0.9924324750900269), (987515173, 1982, 'Carton', 144, -1, 336, -1, 0.9760972261428833), (987515173, 1982, 'Carton', 176, -1, 336, -1, 0.9911888837814331), (987515173, 1982, 'Carton', 208, -1, 336, -1, 0.9869945645332336), (987515173, 1982, 'Carton', 240, -1, 336, -1, 0.9080501198768616), (987515173, 1982, 'Carton', 272, -1, 336, -1, 0.9451318979263306), (987515173, 1982, 'Carton', 304, -1, 336, -1, 0.9367108345031738), (987515173, 1982, 'Carton', 336, -1, 336, -1, 0.980778694152832), (987515173, 1982, 'Kraft', 112, -1, 112, -1, 1.9715273857912052e-09), (987515173, 1982, 'Kraft', 144, -1, 112, -1, 1.700512086699746e-08), (987515173, 1982, 'Kraft', 176, -1, 112, -1, 9.62380568125809e-07), (987515173, 1982, 'Kraft', 208, -1, 112, -1, 3.144802030874416e-05), (987515173, 1982, 'Kraft', 240, -1, 112, -1, 4.438618998392485e-05), (987515173, 1982, 'Kraft', 272, -1, 112, -1, 0.0002064783766400069), (987515173, 1982, 'Kraft', 304, -1, 112, -1, 0.0010803906479850411), (987515173, 1982, 'Kraft', 336, -1, 112, -1, 0.0008296229061670601), (987515173, 1982, 'Kraft', 112, -1, 144, -1, 2.609303373901639e-05), (987515173, 1982, 'Kraft', 144, -1, 144, -1, 6.97314681019634e-06), (987515173, 1982, 'Kraft', 176, -1, 144, -1, 3.6288954561314313e-06), (987515173, 1982, 'Kraft', 208, -1, 144, -1, 3.559760079951957e-05), (987515173, 1982, 'Kraft', 240, -1, 144, -1, 6.71095767756924e-05), (987515173, 1982, 'Kraft', 272, -1, 144, -1, 8.686640649102628e-05), (987515173, 1982, 'Kraft', 304, -1, 144, -1, 0.00012190704001113772), (987515173, 1982, 'Kraft', 336, -1, 144, -1, 0.00011237272701691836), (987515173, 1982, 'Kraft', 112, -1, 176, -1, 0.0004974188050255179), (987515173, 1982, 'Kraft', 144, -1, 176, -1, 0.00012339913519099355), (987515173, 1982, 'Kraft', 176, -1, 176, -1, 9.101921750698239e-05), (987515173, 1982, 'Kraft', 208, -1, 176, -1, 5.181990854907781e-05), (987515173, 1982, 'Kraft', 240, -1, 176, -1, 0.00011571541108423844), (987515173, 1982, 'Kraft', 272, -1, 176, -1, 0.0004278551787137985), (987515173, 1982, 'Kraft', 304, -1, 176, -1, 0.0009287258726544678), (987515173, 1982, 'Kraft', 336, -1, 176, -1, 0.0014269377570599318), (987515173, 1982, 'Kraft', 112, -1, 208, -1, 6.879967986606061e-05), (987515173, 1982, 'Kraft', 144, -1, 208, -1, 1.848130159487482e-05), (987515173, 1982, 'Kraft', 176, -1, 208, -1, 2.5886238290695474e-05), (987515173, 1982, 'Kraft', 208, -1, 208, -1, 3.5376382584217936e-05), (987515173, 1982, 'Kraft', 240, -1, 208, -1, 3.7380203139036894e-05), (987515173, 1982, 'Kraft', 272, -1, 208, -1, 8.643531327834353e-05), (987515173, 1982, 'Kraft', 304, -1, 208, -1, 0.00012367642193567008), (987515173, 1982, 'Kraft', 336, -1, 208, -1, 0.0003913660766556859), (987515173, 1982, 'Kraft', 112, -1, 240, -1, 0.0003052453976124525), (987515173, 1982, 'Kraft', 144, -1, 240, -1, 4.160253592999652e-05), (987515173, 1982, 'Kraft', 176, -1, 240, -1, 1.2252347914909478e-05), (987515173, 1982, 'Kraft', 208, -1, 240, -1, 7.311899480555439e-06), (987515173, 1982, 'Kraft', 240, -1, 240, -1, 2.298239451192785e-05), (987515173, 1982, 'Kraft', 272, -1, 240, -1, 5.8625977544579655e-05), (987515173, 1982, 'Kraft', 304, -1, 240, -1, 6.562330236192793e-05), (987515173, 1982, 'Kraft', 336, -1, 240, -1, 0.0001867178943939507), (987515173, 1982, 'Kraft', 112, -1, 272, -1, 0.0014608963392674923), (987515173, 1982, 'Kraft', 144, -1, 272, -1, 0.0006900038570165634), (987515173, 1982, 'Kraft', 176, -1, 272, -1, 0.0002737260947469622), (987515173, 1982, 'Kraft', 208, -1, 272, -1, 4.3348398321541026e-05), (987515173, 1982, 'Kraft', 240, -1, 272, -1, 3.3511139918118715e-05), (987515173, 1982, 'Kraft', 272, -1, 272, -1, 8.371318835997954e-05), (987515173, 1982, 'Kraft', 304, -1, 272, -1, 0.00011209826334379613), (987515173, 1982, 'Kraft', 336, -1, 272, -1, 0.0004225780430715531), (987515173, 1982, 'Kraft', 112, -1, 304, -1, 0.0009938485454767942), (987515173, 1982, 'Kraft', 144, -1, 304, -1, 0.0009072886896319687), (987515173, 1982, 'Kraft', 176, -1, 304, -1, 0.0006177747854962945), (987515173, 1982, 'Kraft', 208, -1, 304, -1, 0.0010816584108397365), (987515173, 1982, 'Kraft', 240, -1, 304, -1, 0.0017824125243350863), (987515173, 1982, 'Kraft', 272, -1, 304, -1, 0.00467672199010849), (987515173, 1982, 'Kraft', 304, -1, 304, -1, 0.0046839932911098), (987515173, 1982, 'Kraft', 336, -1, 304, -1, 0.01251695491373539), (987515173, 1982, 'Kraft', 112, -1, 336, -1, 0.0021798976231366396), (987515173, 1982, 'Kraft', 144, -1, 336, -1, 0.005731274373829365), (987515173, 1982, 'Kraft', 176, -1, 336, -1, 0.0008301298366859555), (987515173, 1982, 'Kraft', 208, -1, 336, -1, 0.001254101749509573), (987515173, 1982, 'Kraft', 240, -1, 336, -1, 0.007878794334828854), (987515173, 1982, 'Kraft', 272, -1, 336, -1, 0.012601234018802643), (987515173, 1982, 'Kraft', 304, -1, 336, -1, 0.017904823645949364), (987515173, 1982, 'Kraft', 336, -1, 336, -1, 0.007784634362906218), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 112, -1, 1.5032806943704458e-10), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 112, -1, 8.189221745169561e-09), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 112, -1, 5.509673428605311e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 112, -1, 5.509515176527202e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 112, -1, 8.218115908675827e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 112, -1, 3.574374568415806e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 112, -1, 8.152747614076361e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 112, -1, 4.22919838456437e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 144, -1, 3.6241274870008056e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 144, -1, 1.3090914308122592e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 144, -1, 2.474627990523004e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 144, -1, 3.4591446365084266e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 144, -1, 5.722768491978059e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 144, -1, 2.05930664378684e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 144, -1, 3.9023296267259866e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 144, -1, 1.3651990229845978e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 176, -1, 3.8744440189475426e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 176, -1, 2.556116442065104e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 176, -1, 1.2038444765494205e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 176, -1, 7.82030929258326e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 176, -1, 6.355273853841936e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 176, -1, 2.631835923239123e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 176, -1, 3.930303864763118e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 176, -1, 1.852205787145067e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 208, -1, 1.803503323571931e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 208, -1, 5.701312488781696e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 208, -1, 2.1977652977511752e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 208, -1, 1.732847636048973e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 208, -1, 3.9939328644322813e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 208, -1, 2.0290343627493712e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 208, -1, 9.864437799933512e-08), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 208, -1, 8.686428998316842e-08), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 240, -1, 1.815862219700648e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 240, -1, 6.317463885352481e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 240, -1, 7.825294687791029e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 240, -1, 6.38922529105912e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 240, -1, 7.721331485299743e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 240, -1, 5.195895482756896e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 240, -1, 1.7717368905323383e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 240, -1, 1.4421127048080962e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 272, -1, 4.1844859310913307e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 272, -1, 3.171725211359444e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 272, -1, 5.088551802145957e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 272, -1, 7.322981900870218e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 272, -1, 4.029905653624155e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 272, -1, 3.2225253221440653e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 272, -1, 2.1043986464519548e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 272, -1, 3.4764514111884637e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 304, -1, 3.3003121302499494e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 304, -1, 3.2597429822089907e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 304, -1, 8.375151878681208e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 304, -1, 3.3004828310367884e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 304, -1, 4.407366759551223e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 304, -1, 5.030621196056018e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 304, -1, 8.192484528990462e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 304, -1, 5.571591373154661e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 336, -1, 4.191424238797481e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 336, -1, 8.29569671623176e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 336, -1, 7.135827218007762e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 336, -1, 1.9428632640483556e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 336, -1, 3.964289589930559e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 336, -1, 2.4446794668619987e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 336, -1, 3.4285678793821717e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 336, -1, 4.853383870795369e-06), (987515173, 1982, 'Metal', 112, -1, 112, -1, 7.447231720192349e-11), (987515173, 1982, 'Metal', 144, -1, 112, -1, 1.9201737977425637e-09), (987515173, 1982, 'Metal', 176, -1, 112, -1, 7.290755092981271e-07), (987515173, 1982, 'Metal', 208, -1, 112, -1, 9.451183359487914e-06), (987515173, 1982, 'Metal', 240, -1, 112, -1, 2.6513103875913657e-05), (987515173, 1982, 'Metal', 272, -1, 112, -1, 0.00016979477368295193), (987515173, 1982, 'Metal', 304, -1, 112, -1, 0.0007538229692727327), (987515173, 1982, 'Metal', 336, -1, 112, -1, 0.0003031627566087991), (987515173, 1982, 'Metal', 112, -1, 144, -1, 5.309876769388211e-07), (987515173, 1982, 'Metal', 144, -1, 144, -1, 4.3811866135001765e-07), (987515173, 1982, 'Metal', 176, -1, 144, -1, 3.188276195942308e-06), (987515173, 1982, 'Metal', 208, -1, 144, -1, 9.607507308828644e-06), (987515173, 1982, 'Metal', 240, -1, 144, -1, 1.660874295339454e-05), (987515173, 1982, 'Metal', 272, -1, 144, -1, 7.64819560572505e-05), (987515173, 1982, 'Metal', 304, -1, 144, -1, 0.00013110485451761633), (987515173, 1982, 'Metal', 336, -1, 144, -1, 4.660010745283216e-05), (987515173, 1982, 'Metal', 112, -1, 176, -1, 5.898625659028767e-06), (987515173, 1982, 'Metal', 144, -1, 176, -1, 4.025656380690634e-06), (987515173, 1982, 'Metal', 176, -1, 176, -1, 1.0127695531991776e-05), (987515173, 1982, 'Metal', 208, -1, 176, -1, 4.161669494351372e-06), (987515173, 1982, 'Metal', 240, -1, 176, -1, 6.661565294052707e-06), (987515173, 1982, 'Metal', 272, -1, 176, -1, 5.405334377428517e-05), (987515173, 1982, 'Metal', 304, -1, 176, -1, 0.0001295995753025636), (987515173, 1982, 'Metal', 336, -1, 176, -1, 6.567761010956019e-05), (987515173, 1982, 'Metal', 112, -1, 208, -1, 8.137811164488085e-06), (987515173, 1982, 'Metal', 144, -1, 208, -1, 2.255686922580935e-06), (987515173, 1982, 'Metal', 176, -1, 208, -1, 6.638259037572425e-06), (987515173, 1982, 'Metal', 208, -1, 208, -1, 4.590505795931676e-06), (987515173, 1982, 'Metal', 240, -1, 208, -1, 1.7152718783108867e-06), (987515173, 1982, 'Metal', 272, -1, 208, -1, 1.3711674000660423e-06), (987515173, 1982, 'Metal', 304, -1, 208, -1, 5.789502210973296e-07), (987515173, 1982, 'Metal', 336, -1, 208, -1, 8.343748731931555e-07), (987515173, 1982, 'Metal', 112, -1, 240, -1, 2.914566721301526e-06), (987515173, 1982, 'Metal', 144, -1, 240, -1, 7.384363698292873e-07), (987515173, 1982, 'Metal', 176, -1, 240, -1, 6.081010042180424e-07), (987515173, 1982, 'Metal', 208, -1, 240, -1, 6.211363370312029e-07), (987515173, 1982, 'Metal', 240, -1, 240, -1, 1.199231178361515e-06), (987515173, 1982, 'Metal', 272, -1, 240, -1, 6.998379831202328e-07), (987515173, 1982, 'Metal', 304, -1, 240, -1, 3.1470432304558926e-07), (987515173, 1982, 'Metal', 336, -1, 240, -1, 4.113363729629782e-07), (987515173, 1982, 'Metal', 112, -1, 272, -1, 2.6210993837594287e-06), (987515173, 1982, 'Metal', 144, -1, 272, -1, 8.052065822994336e-07), (987515173, 1982, 'Metal', 176, -1, 272, -1, 8.213787054955901e-07), (987515173, 1982, 'Metal', 208, -1, 272, -1, 3.99679009888132e-07), (987515173, 1982, 'Metal', 240, -1, 272, -1, 2.745636891177128e-07), (987515173, 1982, 'Metal', 272, -1, 272, -1, 3.313505771984637e-07), (987515173, 1982, 'Metal', 304, -1, 272, -1, 3.441670628490101e-07), (987515173, 1982, 'Metal', 336, -1, 272, -1, 1.4744520058229682e-06), (987515173, 1982, 'Metal', 112, -1, 304, -1, 2.3108293589757523e-06), (987515173, 1982, 'Metal', 144, -1, 304, -1, 1.955168954737019e-06), (987515173, 1982, 'Metal', 176, -1, 304, -1, 4.530034402705496e-06), (987515173, 1982, 'Metal', 208, -1, 304, -1, 1.3342222700885031e-05), (987515173, 1982, 'Metal', 240, -1, 304, -1, 5.6335661611228716e-06), (987515173, 1982, 'Metal', 272, -1, 304, -1, 5.686426447937265e-06), (987515173, 1982, 'Metal', 304, -1, 304, -1, 6.362740350596141e-06), (987515173, 1982, 'Metal', 336, -1, 304, -1, 7.369954346359009e-06), (987515173, 1982, 'Metal', 112, -1, 336, -1, 7.135930445656413e-06), (987515173, 1982, 'Metal', 144, -1, 336, -1, 3.3874537621159106e-05), (987515173, 1982, 'Metal', 176, -1, 336, -1, 4.00718672608491e-05), (987515173, 1982, 'Metal', 208, -1, 336, -1, 7.565772830275819e-05), (987515173, 1982, 'Metal', 240, -1, 336, -1, 0.0001240534766111523), (987515173, 1982, 'Metal', 272, -1, 336, -1, 3.705116250785068e-05), (987515173, 1982, 'Metal', 304, -1, 336, -1, 2.904446591855958e-05), (987515173, 1982, 'Metal', 336, -1, 336, -1, 2.811690683302004e-05), (987515173, 1982, 'Papier_Magazine', 112, -1, 112, -1, 0.9999953508377075), (987515173, 1982, 'Papier_Magazine', 144, -1, 112, -1, 0.9999926090240479), (987515173, 1982, 'Papier_Magazine', 176, -1, 112, -1, 0.9999086856842041), (987515173, 1982, 'Papier_Magazine', 208, -1, 112, -1, 0.9949654936790466), (987515173, 1982, 'Papier_Magazine', 240, -1, 112, -1, 0.9937736988067627), (987515173, 1982, 'Papier_Magazine', 272, -1, 112, -1, 0.9866220355033875), (987515173, 1982, 'Papier_Magazine', 304, -1, 112, -1, 0.8924120664596558), (987515173, 1982, 'Papier_Magazine', 336, -1, 112, -1, 0.8721243143081665), (987515173, 1982, 'Papier_Magazine', 112, -1, 144, -1, 0.9998154044151306), (987515173, 1982, 'Papier_Magazine', 144, -1, 144, -1, 0.9997422099113464), (987515173, 1982, 'Papier_Magazine', 176, -1, 144, -1, 0.999413251876831), (987515173, 1982, 'Papier_Magazine', 208, -1, 144, -1, 0.9911826848983765), (987515173, 1982, 'Papier_Magazine', 240, -1, 144, -1, 0.97808837890625), (987515173, 1982, 'Papier_Magazine', 272, -1, 144, -1, 0.9002286791801453), (987515173, 1982, 'Papier_Magazine', 304, -1, 144, -1, 0.5355162620544434), (987515173, 1982, 'Papier_Magazine', 336, -1, 144, -1, 0.8111801743507385), (987515173, 1982, 'Papier_Magazine', 112, -1, 176, -1, 0.9775855541229248), (987515173, 1982, 'Papier_Magazine', 144, -1, 176, -1, 0.8057297468185425), (987515173, 1982, 'Papier_Magazine', 176, -1, 176, -1, 0.9024547934532166), (987515173, 1982, 'Papier_Magazine', 208, -1, 176, -1, 0.8756533265113831), (987515173, 1982, 'Papier_Magazine', 240, -1, 176, -1, 0.46485498547554016), (987515173, 1982, 'Papier_Magazine', 272, -1, 176, -1, 0.5257745385169983), (987515173, 1982, 'Papier_Magazine', 304, -1, 176, -1, 0.16682389378547668), (987515173, 1982, 'Papier_Magazine', 336, -1, 176, -1, 0.11056400090456009), (987515173, 1982, 'Papier_Magazine', 112, -1, 208, -1, 0.14978572726249695), (987515173, 1982, 'Papier_Magazine', 144, -1, 208, -1, 0.01543077640235424), (987515173, 1982, 'Papier_Magazine', 176, -1, 208, -1, 0.01477794162929058), (987515173, 1982, 'Papier_Magazine', 208, -1, 208, -1, 0.007582188118249178), (987515173, 1982, 'Papier_Magazine', 240, -1, 208, -1, 0.00034808620694093406), (987515173, 1982, 'Papier_Magazine', 272, -1, 208, -1, 0.00017059440142475069), (987515173, 1982, 'Papier_Magazine', 304, -1, 208, -1, 5.384488758863881e-05), (987515173, 1982, 'Papier_Magazine', 336, -1, 208, -1, 6.26453838776797e-05), (987515173, 1982, 'Papier_Magazine', 112, -1, 240, -1, 0.07145499438047409), (987515173, 1982, 'Papier_Magazine', 144, -1, 240, -1, 0.0187542587518692), (987515173, 1982, 'Papier_Magazine', 176, -1, 240, -1, 0.03381296619772911), (987515173, 1982, 'Papier_Magazine', 208, -1, 240, -1, 0.032112207263708115), (987515173, 1982, 'Papier_Magazine', 240, -1, 240, -1, 0.0034934685099869967), (987515173, 1982, 'Papier_Magazine', 272, -1, 240, -1, 0.0003419028071220964), (987515173, 1982, 'Papier_Magazine', 304, -1, 240, -1, 2.9678369173780084e-05), (987515173, 1982, 'Papier_Magazine', 336, -1, 240, -1, 1.6375928680645302e-05), (987515173, 1982, 'Papier_Magazine', 112, -1, 272, -1, 0.008513962849974632), (987515173, 1982, 'Papier_Magazine', 144, -1, 272, -1, 0.0036537281703203917), (987515173, 1982, 'Papier_Magazine', 176, -1, 272, -1, 0.014019660651683807), (987515173, 1982, 'Papier_Magazine', 208, -1, 272, -1, 0.02645852230489254), (987515173, 1982, 'Papier_Magazine', 240, -1, 272, -1, 0.002454788889735937), (987515173, 1982, 'Papier_Magazine', 272, -1, 272, -1, 0.0006442199810408056), (987515173, 1982, 'Papier_Magazine', 304, -1, 272, -1, 0.0002749585546553135), (987515173, 1982, 'Papier_Magazine', 336, -1, 272, -1, 0.00015891017392277718), (987515173, 1982, 'Papier_Magazine', 112, -1, 304, -1, 0.0010025865631178021), (987515173, 1982, 'Papier_Magazine', 144, -1, 304, -1, 0.00098313856869936), (987515173, 1982, 'Papier_Magazine', 176, -1, 304, -1, 0.0032131473999470472), (987515173, 1982, 'Papier_Magazine', 208, -1, 304, -1, 0.005032587796449661), (987515173, 1982, 'Papier_Magazine', 240, -1, 304, -1, 0.0034951751586049795), (987515173, 1982, 'Papier_Magazine', 272, -1, 304, -1, 0.004066930152475834), (987515173, 1982, 'Papier_Magazine', 304, -1, 304, -1, 0.007215147837996483), (987515173, 1982, 'Papier_Magazine', 336, -1, 304, -1, 0.001959689659997821), (987515173, 1982, 'Papier_Magazine', 112, -1, 336, -1, 0.00487109087407589), (987515173, 1982, 'Papier_Magazine', 144, -1, 336, -1, 0.015574467368423939), (987515173, 1982, 'Papier_Magazine', 176, -1, 336, -1, 0.006772225257009268), (987515173, 1982, 'Papier_Magazine', 208, -1, 336, -1, 0.00795884057879448), (987515173, 1982, 'Papier_Magazine', 240, -1, 336, -1, 0.019054267555475235), (987515173, 1982, 'Papier_Magazine', 272, -1, 336, -1, 0.003666685428470373), (987515173, 1982, 'Papier_Magazine', 304, -1, 336, -1, 0.0062993667088449), (987515173, 1982, 'Papier_Magazine', 336, -1, 336, -1, 0.004449167288839817), (987515173, 1982, 'Plastique', 112, -1, 112, -1, 5.628618993114287e-08), (987515173, 1982, 'Plastique', 144, -1, 112, -1, 8.126639841066208e-07), (987515173, 1982, 'Plastique', 176, -1, 112, -1, 6.246673001442105e-05), (987515173, 1982, 'Plastique', 208, -1, 112, -1, 0.003538272110745311), (987515173, 1982, 'Plastique', 240, -1, 112, -1, 0.0031610489822924137), (987515173, 1982, 'Plastique', 272, -1, 112, -1, 0.007520067971199751), (987515173, 1982, 'Plastique', 304, -1, 112, -1, 0.054662466049194336), (987515173, 1982, 'Plastique', 336, -1, 112, -1, 0.05967717990279198), (987515173, 1982, 'Plastique', 112, -1, 144, -1, 2.7673156637320062e-06), (987515173, 1982, 'Plastique', 144, -1, 144, -1, 1.906651414174121e-05), (987515173, 1982, 'Plastique', 176, -1, 144, -1, 0.000176782050402835), (987515173, 1982, 'Plastique', 208, -1, 144, -1, 0.0015032748924568295), (987515173, 1982, 'Plastique', 240, -1, 144, -1, 0.005069505423307419), (987515173, 1982, 'Plastique', 272, -1, 144, -1, 0.08443636447191238), (987515173, 1982, 'Plastique', 304, -1, 144, -1, 0.410014271736145), (987515173, 1982, 'Plastique', 336, -1, 144, -1, 0.07098741829395294), (987515173, 1982, 'Plastique', 112, -1, 176, -1, 3.924418251699535e-06), (987515173, 1982, 'Plastique', 144, -1, 176, -1, 9.541850158711895e-05), (987515173, 1982, 'Plastique', 176, -1, 176, -1, 0.0003401074791327119), (987515173, 1982, 'Plastique', 208, -1, 176, -1, 0.0003897205169778317), (987515173, 1982, 'Plastique', 240, -1, 176, -1, 0.0006227337871678174), (987515173, 1982, 'Plastique', 272, -1, 176, -1, 0.00891969632357359), (987515173, 1982, 'Plastique', 304, -1, 176, -1, 0.03751831501722336), (987515173, 1982, 'Plastique', 336, -1, 176, -1, 0.002074483083561063), (987515173, 1982, 'Plastique', 112, -1, 208, -1, 7.618155359523371e-05), (987515173, 1982, 'Plastique', 144, -1, 208, -1, 4.133123729843646e-05), (987515173, 1982, 'Plastique', 176, -1, 208, -1, 8.240794704761356e-05), (987515173, 1982, 'Plastique', 208, -1, 208, -1, 3.827915861620568e-05), (987515173, 1982, 'Plastique', 240, -1, 208, -1, 8.361557775060646e-06), (987515173, 1982, 'Plastique', 272, -1, 208, -1, 1.1995794920949265e-05), (987515173, 1982, 'Plastique', 304, -1, 208, -1, 1.2109919225622434e-05), (987515173, 1982, 'Plastique', 336, -1, 208, -1, 4.108186658413615e-06), (987515173, 1982, 'Plastique', 112, -1, 240, -1, 0.00014421493688132614), (987515173, 1982, 'Plastique', 144, -1, 240, -1, 3.535229552653618e-05), (987515173, 1982, 'Plastique', 176, -1, 240, -1, 2.115689858328551e-05), (987515173, 1982, 'Plastique', 208, -1, 240, -1, 7.336885119002545e-06), (987515173, 1982, 'Plastique', 240, -1, 240, -1, 4.869380973104853e-06), (987515173, 1982, 'Plastique', 272, -1, 240, -1, 2.7787407361756777e-06), (987515173, 1982, 'Plastique', 304, -1, 240, -1, 7.971618742885767e-07), (987515173, 1982, 'Plastique', 336, -1, 240, -1, 3.532012726736866e-07), (987515173, 1982, 'Plastique', 112, -1, 272, -1, 3.369362457306124e-05), (987515173, 1982, 'Plastique', 144, -1, 272, -1, 1.1666946193145122e-05), (987515173, 1982, 'Plastique', 176, -1, 272, -1, 1.3395730093179736e-05), (987515173, 1982, 'Plastique', 208, -1, 272, -1, 5.415592113422463e-06), (987515173, 1982, 'Plastique', 240, -1, 272, -1, 9.571833743393654e-07), (987515173, 1982, 'Plastique', 272, -1, 272, -1, 6.870099582556577e-07), (987515173, 1982, 'Plastique', 304, -1, 272, -1, 5.699378107237862e-07), (987515173, 1982, 'Plastique', 336, -1, 272, -1, 7.442125706802472e-07), (987515173, 1982, 'Plastique', 112, -1, 304, -1, 3.35555819219735e-06), (987515173, 1982, 'Plastique', 144, -1, 304, -1, 3.3583323784114327e-06), (987515173, 1982, 'Plastique', 176, -1, 304, -1, 6.515527729789028e-06), (987515173, 1982, 'Plastique', 208, -1, 304, -1, 8.743736543692648e-06), (987515173, 1982, 'Plastique', 240, -1, 304, -1, 3.3802155030571157e-06), (987515173, 1982, 'Plastique', 272, -1, 304, -1, 4.75964543511509e-06), (987515173, 1982, 'Plastique', 304, -1, 304, -1, 4.507819994614692e-06), (987515173, 1982, 'Plastique', 336, -1, 304, -1, 2.417163841528236e-06), (987515173, 1982, 'Plastique', 112, -1, 336, -1, 1.5496783817070536e-05), (987515173, 1982, 'Plastique', 144, -1, 336, -1, 5.45319817319978e-05), (987515173, 1982, 'Plastique', 176, -1, 336, -1, 3.4191580198239535e-05), (987515173, 1982, 'Plastique', 208, -1, 336, -1, 2.3650032744626515e-05), (987515173, 1982, 'Plastique', 240, -1, 336, -1, 3.942390321753919e-05), (987515173, 1982, 'Plastique', 272, -1, 336, -1, 2.5726001695147716e-05), (987515173, 1982, 'Plastique', 304, -1, 336, -1, 3.318051676615141e-05), (987515173, 1982, 'Plastique', 336, -1, 336, -1, 4.1925599362002686e-05), (987515173, 1982, 'Sol_Environement', 112, -1, 112, -1, 2.940393371494987e-12), (987515173, 1982, 'Sol_Environement', 144, -1, 112, -1, 5.517383772080109e-10), (987515173, 1982, 'Sol_Environement', 176, -1, 112, -1, 4.918547915622185e-07), (987515173, 1982, 'Sol_Environement', 208, -1, 112, -1, 1.0143633517145645e-05), (987515173, 1982, 'Sol_Environement', 240, -1, 112, -1, 9.17372562980745e-06), (987515173, 1982, 'Sol_Environement', 272, -1, 112, -1, 5.6445569498464465e-05), (987515173, 1982, 'Sol_Environement', 304, -1, 112, -1, 0.0003785344597417861), (987515173, 1982, 'Sol_Environement', 336, -1, 112, -1, 0.00016536946350242943), (987515173, 1982, 'Sol_Environement', 112, -1, 144, -1, 1.021115423327501e-07), (987515173, 1982, 'Sol_Environement', 144, -1, 144, -1, 5.79917127652152e-07), (987515173, 1982, 'Sol_Environement', 176, -1, 144, -1, 1.4361562534759287e-06), (987515173, 1982, 'Sol_Environement', 208, -1, 144, -1, 5.8970563259208575e-06), (987515173, 1982, 'Sol_Environement', 240, -1, 144, -1, 1.8262093362864107e-05), (987515173, 1982, 'Sol_Environement', 272, -1, 144, -1, 0.00012591190170496702), (987515173, 1982, 'Sol_Environement', 304, -1, 144, -1, 0.0003239624493289739), (987515173, 1982, 'Sol_Environement', 336, -1, 144, -1, 9.627967665437609e-05), (987515173, 1982, 'Sol_Environement', 112, -1, 176, -1, 4.1706246634021227e-07), (987515173, 1982, 'Sol_Environement', 144, -1, 176, -1, 1.0199498774454696e-06), (987515173, 1982, 'Sol_Environement', 176, -1, 176, -1, 6.585862593055936e-06), (987515173, 1982, 'Sol_Environement', 208, -1, 176, -1, 1.7231742504009162e-06), (987515173, 1982, 'Sol_Environement', 240, -1, 176, -1, 3.7328638882172527e-06), (987515173, 1982, 'Sol_Environement', 272, -1, 176, -1, 2.785021388262976e-05), (987515173, 1982, 'Sol_Environement', 304, -1, 176, -1, 0.00015427170728798956), (987515173, 1982, 'Sol_Environement', 336, -1, 176, -1, 0.00024293918977491558), (987515173, 1982, 'Sol_Environement', 112, -1, 208, -1, 4.010400061815744e-06), (987515173, 1982, 'Sol_Environement', 144, -1, 208, -1, 7.507703116971243e-07), (987515173, 1982, 'Sol_Environement', 176, -1, 208, -1, 1.3331404034033767e-06), (987515173, 1982, 'Sol_Environement', 208, -1, 208, -1, 9.04301828086318e-07), (987515173, 1982, 'Sol_Environement', 240, -1, 208, -1, 6.944066512915015e-07), (987515173, 1982, 'Sol_Environement', 272, -1, 208, -1, 1.08171661850065e-06), (987515173, 1982, 'Sol_Environement', 304, -1, 208, -1, 1.4425490917346906e-06), (987515173, 1982, 'Sol_Environement', 336, -1, 208, -1, 2.323302624063217e-06), (987515173, 1982, 'Sol_Environement', 112, -1, 240, -1, 4.589903255691752e-06), (987515173, 1982, 'Sol_Environement', 144, -1, 240, -1, 3.767145813071693e-07), (987515173, 1982, 'Sol_Environement', 176, -1, 240, -1, 2.4853477498254506e-07), (987515173, 1982, 'Sol_Environement', 208, -1, 240, -1, 2.1582860654234537e-07), (987515173, 1982, 'Sol_Environement', 240, -1, 240, -1, 5.589519673776522e-07), (987515173, 1982, 'Sol_Environement', 272, -1, 240, -1, 8.665043651490123e-07), (987515173, 1982, 'Sol_Environement', 304, -1, 240, -1, 6.646989163527905e-07), (987515173, 1982, 'Sol_Environement', 336, -1, 240, -1, 7.816057632226148e-07), (987515173, 1982, 'Sol_Environement', 112, -1, 272, -1, 2.9952141176181613e-06), (987515173, 1982, 'Sol_Environement', 144, -1, 272, -1, 6.877136229377356e-07), (987515173, 1982, 'Sol_Environement', 176, -1, 272, -1, 5.020575031267072e-07), (987515173, 1982, 'Sol_Environement', 208, -1, 272, -1, 2.0009267132081732e-07), (987515173, 1982, 'Sol_Environement', 240, -1, 272, -1, 1.3200848059113923e-07), (987515173, 1982, 'Sol_Environement', 272, -1, 272, -1, 2.70324193252236e-07), (987515173, 1982, 'Sol_Environement', 304, -1, 272, -1, 1.613342988093791e-07), (987515173, 1982, 'Sol_Environement', 336, -1, 272, -1, 4.84771589981392e-07), (987515173, 1982, 'Sol_Environement', 112, -1, 304, -1, 6.98781434493867e-07), (987515173, 1982, 'Sol_Environement', 144, -1, 304, -1, 4.065262544372672e-07), (987515173, 1982, 'Sol_Environement', 176, -1, 304, -1, 8.900503303266305e-07), (987515173, 1982, 'Sol_Environement', 208, -1, 304, -1, 2.33267383009661e-06), (987515173, 1982, 'Sol_Environement', 240, -1, 304, -1, 3.049969109270023e-06), (987515173, 1982, 'Sol_Environement', 272, -1, 304, -1, 2.671959464350948e-06), (987515173, 1982, 'Sol_Environement', 304, -1, 304, -1, 1.4994966477388516e-06), (987515173, 1982, 'Sol_Environement', 336, -1, 304, -1, 1.610581989552884e-06), (987515173, 1982, 'Sol_Environement', 112, -1, 336, -1, 4.6315574309119256e-07), (987515173, 1982, 'Sol_Environement', 144, -1, 336, -1, 1.1445222298789304e-06), (987515173, 1982, 'Sol_Environement', 176, -1, 336, -1, 6.795256126679305e-07), (987515173, 1982, 'Sol_Environement', 208, -1, 336, -1, 1.429223402737989e-06), (987515173, 1982, 'Sol_Environement', 240, -1, 336, -1, 3.399001798243262e-06), (987515173, 1982, 'Sol_Environement', 272, -1, 336, -1, 2.1180719613766996e-06), (987515173, 1982, 'Sol_Environement', 304, -1, 336, -1, 2.29417651098629e-06), (987515173, 1982, 'Sol_Environement', 336, -1, 336, -1, 3.3905967029568274e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 112, -1, 4.575847469823202e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 112, -1, 2.460965561112971e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 112, -1, 1.7641963495407254e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 112, -1, 0.0004739058786071837), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 112, -1, 0.00011572476068977267), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 112, -1, 0.00020998391846660525), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 112, -1, 0.001441184664145112), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 112, -1, 0.002021940890699625), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 144, -1, 3.0665665690321475e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 144, -1, 1.723464265523944e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 144, -1, 2.078898251056671e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 144, -1, 0.00027382804546505213), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 144, -1, 0.00046515112626366317), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 144, -1, 0.0005284376675263047), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 144, -1, 0.00018965140043292195), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 144, -1, 0.0002052709460258484), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 176, -1, 1.499031532148365e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 176, -1, 6.768323601136217e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 176, -1, 2.1079100406495854e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 176, -1, 2.419175325485412e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 176, -1, 3.539620229275897e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 176, -1, 7.854169234633446e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 176, -1, 0.00010158951045013964), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 176, -1, 0.0003781390842050314), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 208, -1, 1.5082629033713602e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 208, -1, 1.464372417103732e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 208, -1, 4.7350013119284995e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 208, -1, 3.8161333577590995e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 208, -1, 3.6540925520966994e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 208, -1, 1.1669434570649173e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 208, -1, 1.3356401723285671e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 208, -1, 5.9734928072430193e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 240, -1, 4.209062899462879e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 240, -1, 5.31286423210986e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 240, -1, 3.9045626181177795e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 240, -1, 2.3957366011018166e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 240, -1, 5.768911250925157e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 240, -1, 2.104985287587624e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 240, -1, 1.7834596292232163e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 240, -1, 2.266034971398767e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 272, -1, 0.00020098219101782888), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 272, -1, 6.693278555758297e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 272, -1, 4.353698022896424e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 272, -1, 1.4473799637926277e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 272, -1, 7.0927553679212e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 272, -1, 1.550008346384857e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 272, -1, 1.540880293759983e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 272, -1, 0.00010354327969253063), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 304, -1, 9.513091936241835e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 304, -1, 0.00011014967458322644), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 304, -1, 0.00015781840193085372), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 304, -1, 0.000541884743142873), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 304, -1, 0.0023387991823256016), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 304, -1, 0.0074480012990534306), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 304, -1, 0.00584239698946476), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 304, -1, 0.004482793156057596), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 336, -1, 0.0002411372697679326), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 336, -1, 0.0020116048399358988), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 336, -1, 0.0007494039600715041), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 336, -1, 0.003332410706207156), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 336, -1, 0.0645388662815094), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 336, -1, 0.038250476121902466), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 336, -1, 0.03876246511936188), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 336, -1, 0.005903724115341902), (987515173, 1982, 'autre_refus', 112, -1, 112, -1, 5.851480966434508e-10), (987515173, 1982, 'autre_refus', 144, -1, 112, -1, 2.0594731253709142e-08), (987515173, 1982, 'autre_refus', 176, -1, 112, -1, 1.4454044503509067e-06), (987515173, 1982, 'autre_refus', 208, -1, 112, -1, 9.248015703633428e-05), (987515173, 1982, 'autre_refus', 240, -1, 112, -1, 0.00021706322149839252), (987515173, 1982, 'autre_refus', 272, -1, 112, -1, 0.001756428275257349), (987515173, 1982, 'autre_refus', 304, -1, 112, -1, 0.0177126657217741), (987515173, 1982, 'autre_refus', 336, -1, 112, -1, 0.008998946286737919), (987515173, 1982, 'autre_refus', 112, -1, 144, -1, 9.120078630076023e-07), (987515173, 1982, 'autre_refus', 144, -1, 144, -1, 2.9223053843452362e-06), (987515173, 1982, 'autre_refus', 176, -1, 144, -1, 1.033488388202386e-05), (987515173, 1982, 'autre_refus', 208, -1, 144, -1, 0.00013500035856850445), (987515173, 1982, 'autre_refus', 240, -1, 144, -1, 0.000348318659234792), (987515173, 1982, 'autre_refus', 272, -1, 144, -1, 0.004932259675115347), (987515173, 1982, 'autre_refus', 304, -1, 144, -1, 0.04345298558473587), (987515173, 1982, 'autre_refus', 336, -1, 144, -1, 0.09518309682607651), (987515173, 1982, 'autre_refus', 112, -1, 176, -1, 1.511305890744552e-05), (987515173, 1982, 'autre_refus', 144, -1, 176, -1, 0.00012784563296008855), (987515173, 1982, 'autre_refus', 176, -1, 176, -1, 0.00023730279644951224), (987515173, 1982, 'autre_refus', 208, -1, 176, -1, 0.000810895930044353), (987515173, 1982, 'autre_refus', 240, -1, 176, -1, 0.0006539719761349261), (987515173, 1982, 'autre_refus', 272, -1, 176, -1, 0.004282562527805567), (987515173, 1982, 'autre_refus', 304, -1, 176, -1, 0.02331022173166275), (987515173, 1982, 'autre_refus', 336, -1, 176, -1, 0.01875206269323826), (987515173, 1982, 'autre_refus', 112, -1, 208, -1, 8.849771984387189e-05), (987515173, 1982, 'autre_refus', 144, -1, 208, -1, 0.00018569415260571986), (987515173, 1982, 'autre_refus', 176, -1, 208, -1, 0.0003199160273652524), (987515173, 1982, 'autre_refus', 208, -1, 208, -1, 0.00035743703483603895), (987515173, 1982, 'autre_refus', 240, -1, 208, -1, 0.00019874803547281772), (987515173, 1982, 'autre_refus', 272, -1, 208, -1, 0.0002866908034775406), (987515173, 1982, 'autre_refus', 304, -1, 208, -1, 0.00020236412819940597), (987515173, 1982, 'autre_refus', 336, -1, 208, -1, 0.0002442058175802231), (987515173, 1982, 'autre_refus', 112, -1, 240, -1, 0.00023232278181239963), (987515173, 1982, 'autre_refus', 144, -1, 240, -1, 0.000108526655822061), (987515173, 1982, 'autre_refus', 176, -1, 240, -1, 6.488244980573654e-05), (987515173, 1982, 'autre_refus', 208, -1, 240, -1, 2.5217044822056778e-05), (987515173, 1982, 'autre_refus', 240, -1, 240, -1, 7.29677194613032e-05), (987515173, 1982, 'autre_refus', 272, -1, 240, -1, 0.00014070658653508872), (987515173, 1982, 'autre_refus', 304, -1, 240, -1, 8.935788355302066e-05), (987515173, 1982, 'autre_refus', 336, -1, 240, -1, 8.171387162292376e-05), (987515173, 1982, 'autre_refus', 112, -1, 272, -1, 0.00026850696303881705), (987515173, 1982, 'autre_refus', 144, -1, 272, -1, 0.00011160651774844155), (987515173, 1982, 'autre_refus', 176, -1, 272, -1, 0.00012472311209421605), (987515173, 1982, 'autre_refus', 208, -1, 272, -1, 5.106349635752849e-05), (987515173, 1982, 'autre_refus', 240, -1, 272, -1, 2.919041071436368e-05), (987515173, 1982, 'autre_refus', 272, -1, 272, -1, 4.2713716538855806e-05), (987515173, 1982, 'autre_refus', 304, -1, 272, -1, 6.84538172208704e-05), (987515173, 1982, 'autre_refus', 336, -1, 272, -1, 0.00014215277042239904), (987515173, 1982, 'autre_refus', 112, -1, 304, -1, 0.000115537790406961), (987515173, 1982, 'autre_refus', 144, -1, 304, -1, 0.00021747223217971623), (987515173, 1982, 'autre_refus', 176, -1, 304, -1, 0.00042600996675901115), (987515173, 1982, 'autre_refus', 208, -1, 304, -1, 0.00042784257675521076), (987515173, 1982, 'autre_refus', 240, -1, 304, -1, 6.567744276253507e-05), (987515173, 1982, 'autre_refus', 272, -1, 304, -1, 3.168384137097746e-05), (987515173, 1982, 'autre_refus', 304, -1, 304, -1, 1.1663217264867853e-05), (987515173, 1982, 'autre_refus', 336, -1, 304, -1, 1.8784747226163745e-05), (987515173, 1982, 'autre_refus', 112, -1, 336, -1, 0.0002473437343724072), (987515173, 1982, 'autre_refus', 144, -1, 336, -1, 0.0004773969412781298), (987515173, 1982, 'autre_refus', 176, -1, 336, -1, 0.0003343804564792663), (987515173, 1982, 'autre_refus', 208, -1, 336, -1, 0.0002362925879424438), (987515173, 1982, 'autre_refus', 240, -1, 336, -1, 0.00010974430915666744), (987515173, 1982, 'autre_refus', 272, -1, 336, -1, 9.505417256150395e-05), (987515173, 1982, 'autre_refus', 304, -1, 336, -1, 0.0001310940133407712), (987515173, 1982, 'autre_refus', 336, -1, 336, -1, 0.000733303721062839)]} ############################### TEST certificat_qualite_papier ################################ TEST certificat qualite papier Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1848 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=1848 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 1848 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=1848 # 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 ! 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 ! Step 4442 tile have less inputs used (1) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 4441 detect_points is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 4443 count_percent_refus is not consistent : 4 used against 3 in the step definition ! Step 4444 send_mail_dechet 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 : output 1 of step 4440 have datatype=1 whereas input 0 of step 4443 have datatype=2 WARNING : type of output 1 of step 4441 doesn't seem to be define in the database( WARNING : type of input 4 of step 4443 doesn't seem to be define in the database( DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : init_dechet, tile, detect_points, count_percent_refus, brightness, blur_detection, send_mail_dechet list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT ph.photo_id, ph.url FROM MTRBack.photos ph WHERE ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (1902940) and hide_status = 0 ) ORDER BY ph.photo_id DESC LIMIT 0, 10000 Catched exception ! Connect or reconnect ! We have 1 , {} SELECT mtr_photo_id, mtr_portfolio_id FROM MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (1902940) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 987321136, 'portfolio_id': 1902940}] map_portfolio_id_photo_id: {1902940: [987321136]} ##### Call download_photos : nb_thread : 5 begin to download photo : 987321136 download finish for photo 987321136 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.20325398445129395 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 7 step1:init_dechet Tue May 6 22:41:29 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136} map_photo_id_path_extension : {987321136: {'path': 'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} debut step init detect dechets input : temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg ON MODIFIE NB AVEC LE INPUT map photo id path extension : temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg scale : 0.9481481481481482 FIN step init dechet After datou_step_exec type output : map_portfolio_photo : len 1 keys : dict_keys([1902940]) Inside saveOutput : final : False verbose : True saveOutput not yet implemented for datou_step.type : init_dechet we use saveGeneral [987321136] map_info['map_portfolio_photo'] : {1902940: [987321136]} final : False mtd_id 1848 list_pids : [987321136] Looping around the photos to save general results len do output : 1 /987321136Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('1848', '1902940', '987321136', 'None', None, None, None, None, None)] time used for this insertion : 0.021646499633789062 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.00017499923706054688 time spend to save output : 0.02193593978881836 total time spend for step 1 : 0.022110939025878906 step2:tile Tue May 6 22:41:29 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 : {'987321136': ['temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': ()} output_args : {'987321136': ['temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} args : 987321136 depend.output_id : 0 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 input_args_next_step, len :1, first value : ('temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136} map_photo_id_path_extension : {987321136: {'path': 'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {0: 987321136} verbose : True param_json : {'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'tile_correct_upm', 'ETA': 86400, 'new_width': 1500, 'new_height': 20000, 'host': 'www.fotonower.com', 'protocol': 'https', 'photo_tile_type': 1522, 'option_bande': 'True'} type(crop_hashtag_type) : type(crop_hashtag_type_tiled) : We consider crop_hashtag_type is an integer ! map_chi_type_to_chi_type_cropped : {406: 410} map_filenames : {987321136: 'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg'} list_pids : 1 list_pids : 2 list_subpids to replace list_pids : 1 batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 987321136,987321136,987321136) and `type` in (406) Loaded 0 chid ids of type : 0 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in () https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=tile_correct_upm&access_token=78d09a0790ec6ecbf119343125a81fdc created feed_id_new_photos : 22735909 with name tile_correct_upm feed_id_new_photos : 22735909 filename : temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg photo_id : 987321136 height_image_input : 439 width_image_input : 562 new_width : 1500 new_height : 20000 stride : 0 stride_relative : 0.1 chi to copy from the main photo to the tiled photo input_chi_for_this_image_as_chi : 0 list_bib_to_crops : 1 [(0, 562, 0, 439, 0)] calcul des nouveaux crops pour le tile x0:0,x1:562,y0:0,y1:439 chi selectionnes : [] new_crops_tiles : 1 crop_transformed : 0 insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) [(987321136, 2090988864, 1522, 0, 562, 0, 439, 1.0)] list_photo_ids_cropped : [987321136] batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 987321136) and `type` in (1522) Loaded 1 chid ids of type : 1522 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1608847328) SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1608847328) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1608847328) treat the image : temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg , 0 before upload mediasElapsed time : 0.010893583297729492 About to upload 1 photos upload in portfolio : 22735909 Result OK ! uploaded one batch 0 Elapsed time : 5.410055637359619 upload mediasElapsed time : 5.421013593673706 , 0insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(1608847328, 1356482040, 0)] Saving 0 CHIs. list_chi_tile : [] end of tileElapsed time : 5.4351818561553955 map_pid_results : {'1356482040': ['temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} After datou_step_exec type output : map_portfolio_photo : len 1 keys : dict_keys([1902940]) Inside saveOutput : final : False verbose : True saveOutput not yet implemented for datou_step.type : tile we use saveGeneral [987321136, 987321136, '1356482040'] map_info['map_portfolio_photo'] : {1902940: [987321136]} final : False mtd_id 1848 list_pids : [987321136, 987321136, '1356482040'] Looping around the photos to save general results len do output : 1 /1356482040Didn'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 ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', None, '1356482040', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('1848', None, '1356482040', 'None', None, None, None, None, None), ('1848', '1902940', '987321136', None, None, None, None, None, None)] time used for this insertion : 0.018874406814575195 save_final save missing photos in datou_result : time spend for datou_step_exec : 12.17043399810791 time spend to save output : 0.019033193588256836 total time spend for step 2 : 12.189467191696167 step3:detect_points Tue May 6 22:41:41 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 : {'1356482040': ['temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} input_args_next_step : {'1356482040': ()} output_args : {'1356482040': ['temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} args : 1356482040 depend.output_id : 0 complete output_args for input 1 : {'987321136': ['temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'1356482040': ('temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',), '987321136': ()} output_args : {'987321136': ['temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} args : 987321136 depend.output_id : 2 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :2, first value : ('temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1356482040} map_photo_id_path_extension : {987321136: {'path': 'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1356482040: {'path': 'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1356482040: 987321136} Beginning of datou step predict points ! Inside try reload ! classes : ['Autre_Environement', 'Carton', 'Kraft', 'Lointain_Papier_Magazine', 'Metal', 'Papier_Magazine', 'Plastique', 'Sol_Environement', 'Teint_Dans_La_Masse', 'autre_refus'] pht : 1927 model_name : learn_refus_upm_blanches_1924 {'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'} gpu_mode in detect_points : False To load net FromThcl() model_param file didn't exist model_name : learn_refus_upm_blanches_1924 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update reshape net's input to : (224, 224) origin shape : (10, 3, 224, 224) after reshape : (1, 3, 224, 224) [('data', (1, 3, 224, 224)), ('conv1', (1, 64, 112, 112)), ('pool1', (1, 64, 56, 56)), ('pool1_pool1_0_split_0', (1, 64, 56, 56)), ('pool1_pool1_0_split_1', (1, 64, 56, 56)), ('res2a_branch1', (1, 64, 56, 56)), ('res2a_branch2a', (1, 64, 56, 56)), ('res2a_branch2b', (1, 64, 56, 56)), ('res2a', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_0', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_1', (1, 64, 56, 56)), ('res2b_branch2a', (1, 64, 56, 56)), ('res2b_branch2b', (1, 64, 56, 56)), ('res2b', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_0', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_1', (1, 64, 56, 56)), ('res3a_branch1', (1, 128, 28, 28)), ('res3a_branch2a', (1, 128, 28, 28)), ('res3a_branch2b', (1, 128, 28, 28)), ('res3a', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_0', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_1', (1, 128, 28, 28)), ('res3b_branch2a', (1, 128, 28, 28)), ('res3b_branch2b', (1, 128, 28, 28)), ('res3b', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_0', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_1', (1, 128, 28, 28)), ('res4a_branch1', (1, 256, 14, 14)), ('res4a_branch2a', (1, 256, 14, 14)), ('res4a_branch2b', (1, 256, 14, 14)), ('res4a', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_0', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_1', (1, 256, 14, 14)), ('res4b_branch2a', (1, 256, 14, 14)), ('res4b_branch2b', (1, 256, 14, 14)), ('res4b', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_0', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_1', (1, 256, 14, 14)), ('res5a_branch1', (1, 512, 7, 7)), ('res5a_branch2a', (1, 512, 7, 7)), ('res5a_branch2b', (1, 512, 7, 7)), ('res5a', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_0', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_1', (1, 512, 7, 7)), ('res5b_branch2a', (1, 512, 7, 7)), ('res5b_branch2b', (1, 512, 7, 7)), ('res5b', (1, 512, 7, 7)), ('fc2019-10-22_15-02-46', (1, 10, 1, 1)), ('prob', (1, 10, 1, 1))] set image transformer : About to compute detect the points : len(args) : 2 Inside predict_points step exec : nb paths : 1 treate image : temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea_0.jpg size of numpy array img : 2960752 scale method : caffe/skimage size of numpy array img_scale : 2655880 (416, 532, 3) nb_h 7 nb_w 11 size of sub images : (224, 224, 3) size of caffe_input : 46362776 (77, 3, 224, 224) time to do the preprocess : 0.04733848571777344 time to do a prediction : 15.74735713005066 dict_keys(['prob']) shape of output (77, 10, 1, 1) shape of the out_put heatmap (10, 7, 11) number of sub_photos vertical and horizon 7 11 size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) After datou_step_exec type output : map_portfolio_photo : len 1 keys : dict_keys([1902940]) Inside saveOutput : final : False verbose : True Inside savePoints : final : False verbose : True threshold to save the result : 0.05 maximun points to save in the table mtr_datou_result for each class : 100 output flattener 5 example : {1356482040: [(1356482040, 1945, 'Autre_Environement', 185, -1, 118, -1, 1.8337410438107327e-05), (1356482040, 1945, 'Autre_Environement', 320, -1, 118, -1, 0.0001291327498620376), (1356482040, 1945, 'Autre_Environement', 388, -1, 151, -1, 1.3445685908664018e-05), (1356482040, 1945, 'Autre_Environement', 286, -1, 185, -1, 0.00013466527161654085), (1356482040, 1945, 'Autre_Environement', 118, -1, 219, -1, 9.86390750767896e-06), (1356482040, 1945, 'Autre_Environement', 219, -1, 219, -1, 0.00038494516047649086), (1356482040, 1945, 'Autre_Environement', 354, -1, 219, -1, 0.0003431888180784881), (1356482040, 1945, 'Autre_Environement', 421, -1, 253, -1, 1.494663450785083e-07), (1356482040, 1945, 'Autre_Environement', 185, -1, 286, -1, 1.814520658172114e-07), (1356482040, 1945, 'Autre_Environement', 320, -1, 286, -1, 4.115241154067917e-06), (1356482040, 1945, 'Autre_Environement', 118, -1, 320, -1, 2.5274063397695556e-10), (1356482040, 1945, 'Autre_Environement', 253, -1, 320, -1, 1.0447603199237321e-10), (1356482040, 1945, 'Autre_Environement', 388, -1, 320, -1, 4.5192118136583304e-07), (1356482040, 1945, 'Carton', 151, -1, 118, -1, 0.9897055625915527), (1356482040, 1945, 'Carton', 286, -1, 118, -1, 0.8210961222648621), (1356482040, 1945, 'Carton', 421, -1, 118, -1, 0.4851985573768616), (1356482040, 1945, 'Carton', 219, -1, 151, -1, 0.9841372966766357), (1356482040, 1945, 'Carton', 118, -1, 185, -1, 0.9978604912757874), (1356482040, 1945, 'Carton', 185, -1, 219, -1, 0.7996194958686829), (1356482040, 1945, 'Carton', 354, -1, 219, -1, 0.13047637045383453), (1356482040, 1945, 'Carton', 286, -1, 253, -1, 0.05070793256163597), (1356482040, 1945, 'Carton', 118, -1, 286, -1, 0.31989040970802307), (1356482040, 1945, 'Carton', 219, -1, 286, -1, 0.0034983144141733646), (1356482040, 1945, 'Carton', 421, -1, 286, -1, 0.01660206913948059), (1356482040, 1945, 'Carton', 354, -1, 320, -1, 0.0016258988762274384), (1356482040, 1945, 'Kraft', 185, -1, 118, -1, 0.004793279338628054), (1356482040, 1945, 'Kraft', 286, -1, 118, -1, 0.0023909551091492176), (1356482040, 1945, 'Kraft', 421, -1, 118, -1, 0.002102428814396262), (1356482040, 1945, 'Kraft', 354, -1, 151, -1, 0.05212335288524628), (1356482040, 1945, 'Kraft', 118, -1, 185, -1, 0.0017166697653010488), (1356482040, 1945, 'Kraft', 253, -1, 185, -1, 0.04599893465638161), (1356482040, 1945, 'Kraft', 185, -1, 219, -1, 0.021099306643009186), (1356482040, 1945, 'Kraft', 388, -1, 219, -1, 6.061792737455107e-05), (1356482040, 1945, 'Kraft', 286, -1, 253, -1, 0.0037522290367633104), (1356482040, 1945, 'Kraft', 118, -1, 286, -1, 0.010319208726286888), (1356482040, 1945, 'Kraft', 219, -1, 286, -1, 5.351284926291555e-05), (1356482040, 1945, 'Kraft', 421, -1, 286, -1, 9.467339623370208e-06), (1356482040, 1945, 'Kraft', 320, -1, 320, -1, 0.00017124204896390438), (1356482040, 1945, 'Lointain_Papier_Magazine', 185, -1, 118, -1, 2.352082447032444e-06), (1356482040, 1945, 'Lointain_Papier_Magazine', 320, -1, 118, -1, 1.991412864299491e-05), (1356482040, 1945, 'Lointain_Papier_Magazine', 253, -1, 151, -1, 1.1851832823595032e-05), (1356482040, 1945, 'Lointain_Papier_Magazine', 354, -1, 185, -1, 8.877575601218268e-05), (1356482040, 1945, 'Lointain_Papier_Magazine', 118, -1, 219, -1, 2.1015976017224602e-06), (1356482040, 1945, 'Lointain_Papier_Magazine', 219, -1, 219, -1, 7.86672972026281e-05), (1356482040, 1945, 'Lointain_Papier_Magazine', 421, -1, 219, -1, 5.019426794206083e-07), (1356482040, 1945, 'Lointain_Papier_Magazine', 320, -1, 253, -1, 8.220934978453442e-05), (1356482040, 1945, 'Lointain_Papier_Magazine', 185, -1, 286, -1, 3.6812457437918056e-07), (1356482040, 1945, 'Lointain_Papier_Magazine', 388, -1, 286, -1, 3.3782250739022857e-06), (1356482040, 1945, 'Lointain_Papier_Magazine', 118, -1, 320, -1, 3.5824958555252806e-09), (1356482040, 1945, 'Lointain_Papier_Magazine', 286, -1, 320, -1, 1.2866975396264024e-07), (1356482040, 1945, 'Metal', 185, -1, 118, -1, 7.473808364011347e-05), (1356482040, 1945, 'Metal', 286, -1, 118, -1, 3.432366065680981e-05), (1356482040, 1945, 'Metal', 118, -1, 151, -1, 1.1519473446242046e-06), (1356482040, 1945, 'Metal', 354, -1, 151, -1, 0.001217490527778864), (1356482040, 1945, 'Metal', 253, -1, 185, -1, 0.001836584648117423), (1356482040, 1945, 'Metal', 421, -1, 185, -1, 3.084278432652354e-05), (1356482040, 1945, 'Metal', 185, -1, 219, -1, 0.0011127882171422243), (1356482040, 1945, 'Metal', 118, -1, 253, -1, 2.7215228328714147e-05), (1356482040, 1945, 'Metal', 354, -1, 253, -1, 0.0005037166411057115), (1356482040, 1945, 'Metal', 219, -1, 286, -1, 1.657771281315945e-05), (1356482040, 1945, 'Metal', 421, -1, 286, -1, 2.2439740860136226e-05), (1356482040, 1945, 'Metal', 151, -1, 320, -1, 1.393576087860282e-10), (1356482040, 1945, 'Metal', 320, -1, 320, -1, 3.1560623028781265e-05), (1356482040, 1945, 'Papier_Magazine', 118, -1, 118, -1, 0.001665871823206544), (1356482040, 1945, 'Papier_Magazine', 253, -1, 118, -1, 0.28050497174263), (1356482040, 1945, 'Papier_Magazine', 185, -1, 151, -1, 0.003942570183426142), (1356482040, 1945, 'Papier_Magazine', 421, -1, 151, -1, 0.9828073978424072), (1356482040, 1945, 'Papier_Magazine', 354, -1, 185, -1, 0.7690255045890808), (1356482040, 1945, 'Papier_Magazine', 286, -1, 219, -1, 0.9288092851638794), (1356482040, 1945, 'Papier_Magazine', 118, -1, 253, -1, 0.04313919320702553), (1356482040, 1945, 'Papier_Magazine', 219, -1, 253, -1, 0.8978631496429443), (1356482040, 1945, 'Papier_Magazine', 388, -1, 253, -1, 0.9886879324913025), (1356482040, 1945, 'Papier_Magazine', 151, -1, 320, -1, 0.9999996423721313), (1356482040, 1945, 'Papier_Magazine', 253, -1, 320, -1, 0.9999960660934448), (1356482040, 1945, 'Papier_Magazine', 354, -1, 320, -1, 0.9942159056663513), (1356482040, 1945, 'Plastique', 118, -1, 118, -1, 1.236031312146224e-05), (1356482040, 1945, 'Plastique', 219, -1, 118, -1, 0.00030184732167981565), (1356482040, 1945, 'Plastique', 320, -1, 118, -1, 0.0002493929350748658), (1356482040, 1945, 'Plastique', 253, -1, 185, -1, 0.007031592074781656), (1356482040, 1945, 'Plastique', 354, -1, 185, -1, 0.032503753900527954), (1356482040, 1945, 'Plastique', 185, -1, 219, -1, 0.050375860184431076), (1356482040, 1945, 'Plastique', 421, -1, 219, -1, 0.00012226666149217635), (1356482040, 1945, 'Plastique', 118, -1, 253, -1, 0.003930176142603159), (1356482040, 1945, 'Plastique', 286, -1, 253, -1, 0.0025490771513432264), (1356482040, 1945, 'Plastique', 219, -1, 286, -1, 6.120907346485183e-05), (1356482040, 1945, 'Plastique', 354, -1, 286, -1, 0.00538312504068017), (1356482040, 1945, 'Plastique', 151, -1, 320, -1, 1.8926894773674263e-10), (1356482040, 1945, 'Plastique', 421, -1, 320, -1, 0.00020204381144139916), (1356482040, 1945, 'Sol_Environement', 185, -1, 118, -1, 9.372543900099117e-06), (1356482040, 1945, 'Sol_Environement', 320, -1, 118, -1, 2.7338848667568527e-05), (1356482040, 1945, 'Sol_Environement', 118, -1, 151, -1, 2.5881729470711434e-07), (1356482040, 1945, 'Sol_Environement', 253, -1, 185, -1, 0.00011454988998593763), (1356482040, 1945, 'Sol_Environement', 354, -1, 185, -1, 0.00020838991622440517), (1356482040, 1945, 'Sol_Environement', 185, -1, 219, -1, 9.349620813736692e-05), (1356482040, 1945, 'Sol_Environement', 421, -1, 219, -1, 1.1348908657282664e-07), (1356482040, 1945, 'Sol_Environement', 118, -1, 253, -1, 7.004572921687213e-07), (1356482040, 1945, 'Sol_Environement', 320, -1, 253, -1, 5.724640504922718e-05), (1356482040, 1945, 'Sol_Environement', 219, -1, 286, -1, 3.869550369017816e-08), (1356482040, 1945, 'Sol_Environement', 388, -1, 286, -1, 6.792529802623903e-06), (1356482040, 1945, 'Sol_Environement', 151, -1, 320, -1, 2.6225014184994705e-14), (1356482040, 1945, 'Sol_Environement', 286, -1, 320, -1, 1.5886031690115487e-07), (1356482040, 1945, 'Teint_Dans_La_Masse', 185, -1, 118, -1, 0.002272234996780753), (1356482040, 1945, 'Teint_Dans_La_Masse', 286, -1, 118, -1, 0.001813237671740353), (1356482040, 1945, 'Teint_Dans_La_Masse', 388, -1, 118, -1, 0.04538803920149803), (1356482040, 1945, 'Teint_Dans_La_Masse', 118, -1, 151, -1, 0.00010940170614048839), (1356482040, 1945, 'Teint_Dans_La_Masse', 253, -1, 185, -1, 0.0056123328395187855), (1356482040, 1945, 'Teint_Dans_La_Masse', 354, -1, 185, -1, 0.15166763961315155), (1356482040, 1945, 'Teint_Dans_La_Masse', 185, -1, 219, -1, 0.0013750138459727168), (1356482040, 1945, 'Teint_Dans_La_Masse', 118, -1, 253, -1, 6.252125604078174e-05), (1356482040, 1945, 'Teint_Dans_La_Masse', 286, -1, 253, -1, 0.0018639108166098595), (1356482040, 1945, 'Teint_Dans_La_Masse', 388, -1, 253, -1, 0.001438076258637011), (1356482040, 1945, 'Teint_Dans_La_Masse', 219, -1, 286, -1, 1.016586884361459e-05), (1356482040, 1945, 'Teint_Dans_La_Masse', 151, -1, 320, -1, 3.425693932967988e-07), (1356482040, 1945, 'Teint_Dans_La_Masse', 320, -1, 320, -1, 0.0020001486409455538), (1356482040, 1945, 'Teint_Dans_La_Masse', 421, -1, 320, -1, 8.311669807881117e-05), (1356482040, 1945, 'autre_refus', 185, -1, 118, -1, 0.028516046702861786), (1356482040, 1945, 'autre_refus', 354, -1, 118, -1, 0.0014720888575538993), (1356482040, 1945, 'autre_refus', 118, -1, 151, -1, 3.824138184427284e-05), (1356482040, 1945, 'autre_refus', 253, -1, 185, -1, 0.07516990602016449), (1356482040, 1945, 'autre_refus', 185, -1, 219, -1, 0.010234796442091465), (1356482040, 1945, 'autre_refus', 354, -1, 219, -1, 0.04460597038269043), (1356482040, 1945, 'autre_refus', 118, -1, 253, -1, 0.00015751634782645851), (1356482040, 1945, 'autre_refus', 286, -1, 253, -1, 0.01393966656178236), (1356482040, 1945, 'autre_refus', 219, -1, 286, -1, 4.3672833271557465e-05), (1356482040, 1945, 'autre_refus', 388, -1, 286, -1, 0.0030952864326536655), (1356482040, 1945, 'autre_refus', 151, -1, 320, -1, 1.5080686699420198e-10), (1356482040, 1945, 'autre_refus', 320, -1, 320, -1, 0.0030849112663418055)]} hashtag or score ? = 0.9897055625915527 hashtag or score ? = 0.8210961222648621 hashtag or score ? = 0.4851985573768616 hashtag or score ? = 0.9841372966766357 hashtag or score ? = 0.9978604912757874 hashtag or score ? = 0.7996194958686829 hashtag or score ? = 0.13047637045383453 hashtag or score ? = 0.05070793256163597 hashtag or score ? = 0.31989040970802307 hashtag or score ? = 0.05212335288524628 hashtag or score ? = 0.28050497174263 hashtag or score ? = 0.9828073978424072 hashtag or score ? = 0.7690255045890808 hashtag or score ? = 0.9288092851638794 hashtag or score ? = 0.8978631496429443 hashtag or score ? = 0.9886879324913025 hashtag or score ? = 0.9999996423721313 hashtag or score ? = 0.9999960660934448 hashtag or score ? = 0.9942159056663513 hashtag or score ? = 0.050375860184431076 hashtag or score ? = 0.15166763961315155 hashtag or score ? = 0.07516990602016449 insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) [('1356482040', '492774966', '1945', '151', '-1', '118', '-1', '0.9897055625915527'), ('1356482040', '492774966', '1945', '286', '-1', '118', '-1', '0.8210961222648621'), ('1356482040', '492774966', '1945', '421', '-1', '118', '-1', '0.4851985573768616'), ('1356482040', '492774966', '1945', '219', '-1', '151', '-1', '0.9841372966766357'), ('1356482040', '492774966', '1945', '118', '-1', '185', '-1', '0.9978604912757874'), ('1356482040', '492774966', '1945', '185', '-1', '219', '-1', '0.7996194958686829'), ('1356482040', '492774966', '1945', '354', '-1', '219', '-1', '0.13047637045383453'), ('1356482040', '492774966', '1945', '286', '-1', '253', '-1', '0.05070793256163597'), ('1356482040', '492774966', '1945', '118', '-1', '286', '-1', '0.31989040970802307'), ('1356482040', '493202403', '1945', '354', '-1', '151', '-1', '0.05212335288524628'), ('1356482040', '2107752386', '1945', '253', '-1', '118', '-1', '0.28050497174263'), ('1356482040', '2107752386', '1945', '421', '-1', '151', '-1', '0.9828073978424072'), ('1356482040', '2107752386', '1945', '354', '-1', '185', '-1', '0.7690255045890808'), ('1356482040', '2107752386', '1945', '286', '-1', '219', '-1', '0.9288092851638794'), ('1356482040', '2107752386', '1945', '219', '-1', '253', '-1', '0.8978631496429443'), ('1356482040', '2107752386', '1945', '388', '-1', '253', '-1', '0.9886879324913025'), ('1356482040', '2107752386', '1945', '151', '-1', '320', '-1', '0.9999996423721313'), ('1356482040', '2107752386', '1945', '253', '-1', '320', '-1', '0.9999960660934448'), ('1356482040', '2107752386', '1945', '354', '-1', '320', '-1', '0.9942159056663513'), ('1356482040', '492725882', '1945', '185', '-1', '219', '-1', '0.050375860184431076'), ('1356482040', '2107752385', '1945', '354', '-1', '185', '-1', '0.15166763961315155'), ('1356482040', '2107752406', '1945', '253', '-1', '185', '-1', '0.07516990602016449')] final : False save missing photos in datou_result : time spend for datou_step_exec : 17.041988849639893 time spend to save output : 0.06163334846496582 total time spend for step 3 : 17.10362219810486 step4:count_percent_refus Tue May 6 22:41:58 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 complete output_args for input 0 : {'987321136': ['temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': ()} output_args : {'987321136': ['temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} args : 987321136 depend.output_id : 1 complete output_args for input 1 : {'1356482040': ['temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} input_args_next_step : {'987321136': (987321136,), '1356482040': ()} output_args : {'1356482040': ['temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} args : 1356482040 depend.output_id : 0 complete output_args for input 2 : {'987321136': ['temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': (987321136,), '1356482040': ('temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',)} output_args : {'987321136': ['temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} args : 987321136 depend.output_id : 2 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :2, first value : (987321136, 0.9481481481481482) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1356482040} map_photo_id_path_extension : {987321136: {'path': 'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1356482040: {'path': 'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1356482040: 987321136} debut step count percent refus args : {'987321136': (987321136, 0.9481481481481482), '1356482040': ('temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',)} (987321136, 0.9481481481481482) ('temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',) on trouve le portfolio_id = 1902940 list_photo : [987321136] list_photo_correc : [1356482040] debut step count percent refus Treating photo_id : 987321136 Calcul du count_res count res : ((492774966, 3), (2107752386, 7)) Hashtag_id : 492774966 Hashtag_id : 2107752386 We have 2 classes in this image After datou_step_exec type output : map_portfolio_photo : len 1 keys : dict_keys([1902940]) Inside saveOutput : final : False verbose : True map_info[mapportfolio_photo] : {1902940: [987321136]} dans le for photo id : 987321136 output[photo_id] : [({'carton': 3, 'Papier_Magazine': 7}, [1356482040], {'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}, {'refus_total': 61.64383561643836, 'carton': 61.64383561643836, 'Papier_Magazine': 38.35616438356164}, 1902940)] begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 insert into MTRLabel.upm_carac (ol,type_carac,portfolio_id,value,material,hashtag_type) values (0,'qualipapia_surface',1902940,30.0,'refus_total',1945) on duplicate key update value= 30.0 list_values : [['0', 'qualipapia_surface', 1902940, 30.0, 'refus_total', 1945]] insert into MTRLabel.upm_carac (ol,type_carac,portfolio_id,value,material,hashtag_type) values (0,'qualipapia_gravi',1902940,61.64383561643836,'refus_total',1945) on duplicate key update value= 61.64383561643836 list_values : [['0', 'qualipapia_surface', 1902940, 30.0, 'refus_total', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'refus_total', 1945]] insert into MTRLabel.upm_carac (ol,type_carac,portfolio_id,value,material,hashtag_type) values (0,'qualipapia_surface',1902940,30.0,'carton',1945) on duplicate key update value= 30.0 list_values : [['0', 'qualipapia_surface', 1902940, 30.0, 'refus_total', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'refus_total', 1945], ['0', 'qualipapia_surface', 1902940, 30.0, 'carton', 1945]] insert into MTRLabel.upm_carac (ol,type_carac,portfolio_id,value,material,hashtag_type) values (0,'qualipapia_gravi',1902940,61.64383561643836,'carton',1945) on duplicate key update value= 61.64383561643836 list_values : [['0', 'qualipapia_surface', 1902940, 30.0, 'refus_total', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'refus_total', 1945], ['0', 'qualipapia_surface', 1902940, 30.0, 'carton', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'carton', 1945]] insert into MTRLabel.upm_carac (ol,type_carac,portfolio_id,value,material,hashtag_type) values (0,'qualipapia_surface',1902940,70.0,'Papier_Magazine',1945) on duplicate key update value= 70.0 list_values : [['0', 'qualipapia_surface', 1902940, 30.0, 'refus_total', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'refus_total', 1945], ['0', 'qualipapia_surface', 1902940, 30.0, 'carton', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'carton', 1945], ['0', 'qualipapia_surface', 1902940, 70.0, 'Papier_Magazine', 1945]] insert into MTRLabel.upm_carac (ol,type_carac,portfolio_id,value,material,hashtag_type) values (0,'qualipapia_gravi',1902940,38.35616438356164,'Papier_Magazine',1945) on duplicate key update value= 38.35616438356164 list_values : [['0', 'qualipapia_surface', 1902940, 30.0, 'refus_total', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'refus_total', 1945], ['0', 'qualipapia_surface', 1902940, 30.0, 'carton', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'carton', 1945], ['0', 'qualipapia_surface', 1902940, 70.0, 'Papier_Magazine', 1945], ['0', 'qualipapia_gravi', 1902940, 38.35616438356164, 'Papier_Magazine', 1945]] time used for this insertion : 0.05786275863647461 save missing photos in datou_result : time spend for datou_step_exec : 0.018816232681274414 time spend to save output : 0.05819272994995117 total time spend for step 4 : 0.07700896263122559 step5:brightness Tue May 6 22:41:58 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 complete output_args for input 0 : {'987321136': ['temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': ()} output_args : {'987321136': ['temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} args : 987321136 depend.output_id : 0 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :1, first value : ('temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1356482040} map_photo_id_path_extension : {987321136: {'path': 'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1356482040: {'path': 'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1356482040: 987321136} inside step calcul brightness treat image : temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg pour la photo_id : -0.39870825574700136, le score de luminosite est de 987321136 brightness_score : {987321136: [(987321136, -0.39870825574700136, 496442774)]} After datou_step_exec type output : map_portfolio_photo : len 1 keys : dict_keys([1902940]) Inside saveOutput : final : False verbose : True select photo_hashtag_type from MTRDatou.classification_theme where id = 1154 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 0.008431434631347656 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) first line : ('987321136', '496442774', '1426') ... last line : ('987321136', '496442774', '1426') time used for this insertion : 0.014020204544067383 save missing photos in datou_result : time spend for datou_step_exec : 0.06872820854187012 time spend to save output : 0.02905869483947754 total time spend for step 5 : 0.09778690338134766 step6:blur_detection Tue May 6 22:41:59 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 : {'987321136': ['temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': ()} output_args : {'987321136': ['temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} args : 987321136 depend.output_id : 0 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :1, first value : ('temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1356482040} map_photo_id_path_extension : {987321136: {'path': 'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1356482040: {'path': 'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1356482040: 987321136} inside step blur_detection score_blur_detection : {} methode: ratio et variance treat image : temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg resize: (439, 562) 987321136 -5.392404060312662 score_blur_detection : {987321136: [(987321136, -5.392404060312662, 492609224)]} After datou_step_exec type output : map_portfolio_photo : len 1 keys : dict_keys([1902940]) Inside saveOutput : final : False verbose : True select photo_hashtag_type from MTRDatou.classification_theme where id = 1055 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 0.02073836326599121 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) first line : ('987321136', '492609224', '1294') ... last line : ('987321136', '492609224', '1294') time used for this insertion : 0.013974666595458984 save missing photos in datou_result : time spend for datou_step_exec : 0.10589289665222168 time spend to save output : 0.038974761962890625 total time spend for step 6 : 0.1448676586151123 step7:send_mail_dechet Tue May 6 22:41:59 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 : {987321136: [(987321136, -5.392404060312662, 492609224)]} input_args_next_step : {987321136: ()} output_args : {987321136: [(987321136, -5.392404060312662, 492609224)]} args : 987321136 depend.output_id : 0 complete output_args for input 1 : {987321136: [(987321136, -0.39870825574700136, 496442774)]} input_args_next_step : {987321136: ((987321136, -5.392404060312662, 492609224),)} output_args : {987321136: [(987321136, -0.39870825574700136, 496442774)]} args : 987321136 depend.output_id : 0 complete output_args for input 2 : {987321136: [({'carton': 3, 'Papier_Magazine': 7}, [1356482040], {'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}, {'refus_total': 61.64383561643836, 'carton': 61.64383561643836, 'Papier_Magazine': 38.35616438356164}, 1902940)]} input_args_next_step : {987321136: ((987321136, -5.392404060312662, 492609224), (987321136, -0.39870825574700136, 496442774))} output_args : {987321136: [({'carton': 3, 'Papier_Magazine': 7}, [1356482040], {'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}, {'refus_total': 61.64383561643836, 'carton': 61.64383561643836, 'Papier_Magazine': 38.35616438356164}, 1902940)]} args : 987321136 depend.output_id : 0 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 input_args_next_step, len :1, first value : ((987321136, -5.392404060312662, 492609224), (987321136, -0.39870825574700136, 496442774), ({'carton': 3, 'Papier_Magazine': 7}, [1356482040], {'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}, {'refus_total': 61.64383561643836, 'carton': 61.64383561643836, 'Papier_Magazine': 38.35616438356164}, 1902940)) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1356482040} map_photo_id_path_extension : {987321136: {'path': 'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1356482040: {'path': 'temp/1746564089_2124625_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1356482040: 987321136} dans la step send mail dechet list_name : ['one', 'sample', 'debug', 'board', 'détect', 'port'] corps du mail : La photo est trop sombre et nette, merci de reprendre la photo
Lien affichage photo


Dans ces conditions de prise de photo, les résultats sur le tas sont les suivants :
Le pourcentage de matière impropre est de 61.64 %.

Pour plus de détails:

Teint Dans La Masse: 0%.

carton: 61.64%.

metal: 0%.

plastique: 0%.

senders@fotonower.com retour de l'envoi du mail : None After datou_step_exec type output : map_portfolio_photo : len 1 keys : dict_keys([1902940]) Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : send_mail_dechet we use saveGeneral [987321136, 987321136, '1356482040'] map_info['map_portfolio_photo'] : {1902940: [987321136]} final : True mtd_id 1848 list_pids : [987321136, 987321136, '1356482040'] Looping around the photos to save general results len do output : 1 /987321136. 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 ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', None, '1356482040', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('1848', '1902940', '987321136', "{'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}", None, None, None, None, None), ('1848', None, '1356482040', None, None, None, None, None, None)] time used for this insertion : 0.012816667556762695 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.40642857551574707 time spend to save output : 0.013127565383911133 total time spend for step 7 : 0.4195561408996582 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 7 output : {987321136: (-110, -0.39870825574700136, -5.392404060312662, 30.0, 61.64383561643836, {'carton': 3, 'Papier_Magazine': 7}, {'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}, {'refus_total': 61.64383561643836, 'carton': 61.64383561643836, 'Papier_Magazine': 38.35616438356164}, 0.6164383561643836)} ############################### TEST image_temperature_detection ################################ t Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1807 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=1807 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 1807 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=1807 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : image_temperature_detection list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (984484223) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 984484223 download finish for photo 984484223 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.11490273475646973 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:image_temperature_detection Tue May 6 22:41:59 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746564119_2124625_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg': 984484223} map_photo_id_path_extension : {984484223: {'path': 'temp/1746564119_2124625_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} inside step blanche_jaune_detection treat image : temp/1746564119_2124625_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg 984484223 1.004309911525615 After datou_step_exec type output : time spend for datou_step_exec : 0.18688178062438965 time spend to save output : 0.00011539459228515625 total time spend for step 1 : 0.1869971752166748 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {984484223: [(984484223, 1.004309911525615, 492630606)]} {984484223: [(984484223, 1.004309911525615, 492630606)]} ############################### TEST broca ################################ t Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4041 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4041 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4041 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4041 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : split_time_score list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT ph.photo_id, ph.url FROM MTRBack.photos ph WHERE ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (5205529) and hide_status = 0 ) ORDER BY ph.photo_id DESC LIMIT 0, 10000 We have 1 , {} SELECT mtr_photo_id, mtr_portfolio_id FROM MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (5205529) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 1064921404, 'portfolio_id': 5205529}, {'photo_id': 1064921402, 'portfolio_id': 5205529}, {'photo_id': 1064921401, 'portfolio_id': 5205529}, {'photo_id': 1064921201, 'portfolio_id': 5205529}, {'photo_id': 1064921196, 'portfolio_id': 5205529}, {'photo_id': 1064919876, 'portfolio_id': 5205529}, {'photo_id': 1064919873, 'portfolio_id': 5205529}, {'photo_id': 1064919869, 'portfolio_id': 5205529}, {'photo_id': 1064919862, 'portfolio_id': 5205529}, {'photo_id': 1064919858, 'portfolio_id': 5205529}, {'photo_id': 1064919856, 'portfolio_id': 5205529}, {'photo_id': 1064919752, 'portfolio_id': 5205529}, {'photo_id': 1064919748, 'portfolio_id': 5205529}, {'photo_id': 1064919745, 'portfolio_id': 5205529}, {'photo_id': 1064919741, 'portfolio_id': 5205529}, {'photo_id': 1064919737, 'portfolio_id': 5205529}, {'photo_id': 1064919730, 'portfolio_id': 5205529}, {'photo_id': 1064919660, 'portfolio_id': 5205529}] map_portfolio_id_photo_id: {5205529: [1064921404, 1064921402, 1064921401, 1064921201, 1064921196, 1064919876, 1064919873, 1064919869, 1064919862, 1064919858, 1064919856, 1064919752, 1064919748, 1064919745, 1064919741, 1064919737, 1064919730, 1064919660]} ##### Call download_photos : nb_thread : 5 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos ##### After load_data_input time to download the photos : 0.02457451820373535 #### fin chargement data Blocking on flush ? No conitnuing About to test input to load Calling datou_exec Inside datou_exec : verbose : True number of steps : 1 step1:split_time_score Tue May 6 22:42:00 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 After prepare type args : Here we display some param of map_info ! map_filenames : {} map_photo_id_path_extension : {} map_subphoto_mainphoto : {} split portfolio by speed calcul order for each photo with time calcul time for a portfolio query : SELECT photo_id, text FROM MTRBack.photos where photo_id in (SELECT mtr_photo_id FROM MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 5205529); result : ((1064919660, 'image_01122021_10_11_30_014389.jpg'), (1064919730, 'image_01122021_10_12_17_665202.jpg'), (1064919737, 'image_01122021_10_11_40_031052.jpg'), (1064919741, 'image_01122021_10_11_34_021658.jpg'), (1064919745, 'image_01122021_10_11_32_018001.jpg'), (1064919748, 'image_01122021_10_12_27_027057.jpg'), (1064919752, 'image_01122021_10_12_24_005017.jpg'), (1064919856, 'image_01122021_10_13_13_399843.jpg'), (1064919858, 'image_01122021_10_13_04_729164.jpg'), (1064919862, 'image_01122021_10_12_56_581019.jpg'), (1064919869, 'image_01122021_10_12_29_030603.jpg'), (1064919873, 'image_01122021_10_13_30_005720.jpg'), (1064919876, 'image_01122021_10_13_22_147712.jpg'), (1064921196, 'image_01122021_10_16_18_114975.jpg'), (1064921201, 'image_01122021_10_16_14_925132.jpg'), (1064921401, 'image_01122021_10_16_57_981306.jpg'), (1064921402, 'image_01122021_10_16_53_913663.jpg'), (1064921404, 'image_01122021_10_16_47_889875.jpg')) INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) VALUES (%s, %s, %s) on duplicate key update `order`=VALUES(`order`); first line : (5205529, 1064919660, 1098136690) ... last line : (5205529, 1064921404, 1098137007) 2021-12-01 10:11:30 2021-12-01 10:11:32 2021-12-01 10:11:30 2021-12-01 10:11:34 2021-12-01 10:11:32 2021-12-01 10:11:40 2021-12-01 10:11:34 2021-12-01 10:12:17 2021-12-01 10:11:40 2021-12-01 10:12:24 2021-12-01 10:12:17 2021-12-01 10:12:27 2021-12-01 10:12:24 2021-12-01 10:12:29 2021-12-01 10:12:27 2021-12-01 10:12:56 2021-12-01 10:12:29 2021-12-01 10:13:04 2021-12-01 10:12:56 2021-12-01 10:13:13 2021-12-01 10:13:04 2021-12-01 10:13:04 distance 1.4513659170185111 2021-12-01 10:13:13 2021-12-01 10:13:22 2021-12-01 10:13:13 2021-12-01 10:13:30 2021-12-01 10:13:22 2021-12-01 10:16:14 2021-12-01 10:13:30 2021-12-01 10:13:30 distance 8.382409567451603 2021-12-01 10:16:14 2021-12-01 10:16:18 2021-12-01 10:16:14 2021-12-01 10:16:47 2021-12-01 10:16:18 2021-12-01 10:16:53 2021-12-01 10:16:47 2021-12-01 10:16:47 distance 8.03396608896571 2021-12-01 10:16:53 2021-12-01 10:16:57 2021-12-01 10:16:53 dict_time_useful: {0: [1098136690, 1098136784, 48.864288393888884, 2.19199505125, [datetime.datetime(2021, 12, 1, 10, 11, 30), datetime.datetime(2021, 12, 1, 10, 13, 4), 94]], 1: [1098136974, 1098137007, 48.86291258986111, 2.19361357125, [datetime.datetime(2021, 12, 1, 10, 16, 14), datetime.datetime(2021, 12, 1, 10, 16, 47), 33]]} len of dic_time_useful : 2 get gps info of PAV SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV; get gps info of PAV SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV WHERE type_pav = "CS"; get gps info of PAV SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV WHERE type_pav = "OM"; select cs_nb_photo / nb_photo, om_nb_photo / nb_photo from (select sum(1) as nb_photo,sum(if (tags= "[CS]",1,0)) as cs_nb_photo, sum(if (tags= "[OM]",1,0)) as om_nb_photo from MTRBack.photos where photo_id in ()) t1; select cs_nb_photo / nb_photo, om_nb_photo / nb_photo from (select sum(1) as nb_photo,sum(if (tags= "[CS]",1,0)) as cs_nb_photo, sum(if (tags= "[OM]",1,0)) as om_nb_photo from MTRBack.photos where photo_id in (1064919660, 1064919745, 1064919741, 1064919737, 1064919730, 1064919752, 1064919748, 1064919869, 1064919862, 1064919858)) t1; distance: RUEIL14CS [48.864288393888884, 2.19199505125] 16.57008455321128 (22735948, 48.864288393888884, 2.19199505125, 10, 1064919752, [datetime.datetime(2021, 12, 1, 10, 11, 30), datetime.datetime(2021, 12, 1, 10, 13, 4), 94.0], 5205529) After datou_step_exec type output : time spend for datou_step_exec : 0.1686849594116211 time spend to save output : 0.00011444091796875 total time spend for step 1 : 0.16879940032958984 caffe_path_current : About to save ! 0 After save, about to update current ! {15: [(22735948, 48.864288393888884, 2.19199505125, 10, 1064919752, [datetime.datetime(2021, 12, 1, 10, 11, 30), datetime.datetime(2021, 12, 1, 10, 13, 4), 94.0], 5205529)]} résultat du premier test BROCA : True True ############################### TEST crop_conditional ################################ t Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=719 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=719 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 719 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=719 # 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) 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 : step 1335 frcnn is not linked in the step_by_step architecture ! WARNING : step 1336 crop_condition is not linked in the step_by_step architecture ! 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 ! DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : frcnn, crop_condition list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT ph.photo_id, ph.url FROM MTRBack.photos ph WHERE ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (1981316) and hide_status = 0 ) ORDER BY ph.photo_id DESC LIMIT 0, 10000 We have 1 , {} SELECT mtr_photo_id, mtr_portfolio_id FROM MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (1981316) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 950003838, 'portfolio_id': 1981316}, {'photo_id': 950003813, 'portfolio_id': 1981316}, {'photo_id': 950003812, 'portfolio_id': 1981316}, {'photo_id': 950003696, 'portfolio_id': 1981316}, {'photo_id': 950003695, 'portfolio_id': 1981316}, {'photo_id': 926687666, 'portfolio_id': 1981316}] map_portfolio_id_photo_id: {1981316: [950003838, 950003813, 950003812, 950003696, 950003695, 926687666]} ##### Call download_photos : nb_thread : 5 begin to download photo : 950003838 begin to download photo : 950003812 begin to download photo : 950003695 download finish for photo 950003812 begin to download photo : 950003696 download finish for photo 950003695 begin to download photo : 926687666 download finish for photo 950003838 begin to download photo : 950003813 download finish for photo 926687666 download finish for photo 950003696 download finish for photo 950003813 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 6 ; length of list_pids : 6 ; length of list_args : 6 ##### After load_data_input time to download the photos : 0.397491455078125 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 2 step1:frcnn Tue May 6 22:42:00 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1746564120_2124625_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg': 950003695, 'temp/1746564120_2124625_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg': 926687666, 'temp/1746564120_2124625_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg': 950003812, 'temp/1746564120_2124625_950003696_11e3a77b72af4b332d366d98984039c7.jpg': 950003696, 'temp/1746564120_2124625_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg': 950003838, 'temp/1746564120_2124625_950003813_e28be02dfcce79cce594a390a9911a0a.jpg': 950003813} map_photo_id_path_extension : {950003695: {'path': 'temp/1746564120_2124625_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg', 'extension': 'jpg'}, 926687666: {'path': 'temp/1746564120_2124625_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg', 'extension': 'jpg'}, 950003812: {'path': 'temp/1746564120_2124625_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg', 'extension': 'jpg'}, 950003696: {'path': 'temp/1746564120_2124625_950003696_11e3a77b72af4b332d366d98984039c7.jpg', 'extension': 'jpg'}, 950003838: {'path': 'temp/1746564120_2124625_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg', 'extension': 'jpg'}, 950003813: {'path': 'temp/1746564120_2124625_950003813_e28be02dfcce79cce594a390a9911a0a.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Faster rcnn ! Inside try reload ! classes : ['background', 'retroviseur', 'roue', 'capot', 'pare-brise', 'vitre', 'phare', 'feu-antibrouillard', 'feu-arriere', 'poignee', 'porte', 'radiateur', 'logo-marque', 'cache-reservoir', 'plaque-immatriculation', 'pot-echappement', 'info-modele', 'essuie-glace', 'pare-choc', 'coffre', 'carrosserie-autre', 'toit', 'logo-roue', 'aile-avant', 'aile-arriere', 'autre'] pht : 757 caffemodel_name (should be vgg16_immat_307 but not used because net loaded outside in the fonction) : {'id': 685, 'mtr_user_id': 31, 'name': 'learn_piece_voiture_0808_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,retroviseur,roue,capot,pare-brise,vitre,phare,feu-antibrouillard,feu-arriere,poignee,porte,radiateur,logo-marque,cache-reservoir,plaque-immatriculation,pot-echappement,info-modele,essuie-glace,pare-choc,coffre,carrosserie-autre,toit,logo-roue,aile-avant,aile-arriere,autre', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 757, 'photo_desc_type': 3800, 'type_classification': 'caffe_faster_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} To loadFromThcl() model_param file didn't exist model_name : learn_piece_voiture_0808_v2 model_type : caffe_faster_rcnn list file need : ['caffemodel', 'test.prototxt'] file exist in s3 : ['caffemodel', 'test.prototxt'] file manque in s3 : [] WARNING: Logging before InitGoogleLogging() is written to STDERR F0506 22:42:03.185434 2124625 syncedmem.cpp:71] Check failed: error == cudaSuccess (2 vs. 0) out of memory *** Check failure stack trace: *** Command terminated by signal 6 94.85user 60.16system 1:06:39elapsed 3%CPU (0avgtext+0avgdata 6609248maxresident)k 9329928inputs+44024outputs (30903major+6680554minor)pagefaults 0swaps