python /home/admin/mtr/script_for_cron.py -j python_test3 -m 12 -a ' --short_python3 -v ' -s python_test3 -M 0 -S 0 -U 100,100,120 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 : 6579 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 : 0.11068606376647949 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 Mon May 26 18:35:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 6579 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 2025-05-26 18:35:33.124040: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-05-26 18:35:33.151154: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-05-26 18:35:33.153341: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f12d0000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-05-26 18:35:33.153421: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-05-26 18:35:33.158165: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-05-26 18:35:33.388992: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x177b9e80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-05-26 18:35:33.389046: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-05-26 18:35:33.390069: 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 2025-05-26 18:35:33.390516: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-26 18:35:33.392925: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-26 18:35:33.395391: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-26 18:35:33.395748: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-26 18:35:33.398559: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-26 18:35:33.400005: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-26 18:35:33.405132: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-26 18:35:33.406469: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-26 18:35:33.406553: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-26 18:35:33.407277: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-26 18:35:33.407295: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-26 18:35:33.407305: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-26 18:35:33.408481: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6042 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 2025-05-26 18:35:33.985585: 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 2025-05-26 18:35:33.985744: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-26 18:35:33.985773: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-26 18:35:33.985799: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-26 18:35:33.985826: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-26 18:35:33.985851: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-26 18:35:33.985893: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-26 18:35:33.985920: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-26 18:35:33.987323: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-26 18:35:33.988937: 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 2025-05-26 18:35:33.988992: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-26 18:35:33.989021: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-26 18:35:33.989048: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-26 18:35:33.989076: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-26 18:35:33.989103: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-26 18:35:33.989129: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-26 18:35:33.989155: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-26 18:35:33.990555: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-26 18:35:33.990613: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-26 18:35:33.990628: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-26 18:35:33.990640: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-26 18:35:33.992155: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6042 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 : [] 2025-05-26 18:35:41.993874: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-26 18:35:42.206072: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-26 18:35:44.316580: E tensorflow/stream_executor/cuda/cuda_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 : 3216 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 : 0.2823047637939453 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 Mon May 26 19:35:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3216 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-05-26 19:35:37.504123: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-05-26 19:35:37.539335: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-05-26 19:35:37.541349: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f12d0000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-05-26 19:35:37.541384: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-05-26 19:35:37.545733: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-05-26 19:35:37.686700: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1755d070 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-05-26 19:35:37.686760: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-05-26 19:35:37.687781: 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 2025-05-26 19:35:37.689165: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-26 19:35:37.695763: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-26 19:35:37.700361: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-26 19:35:37.702526: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-26 19:35:37.714702: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-26 19:35:37.718051: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-26 19:35:37.743814: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-26 19:35:37.745533: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-26 19:35:37.745641: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-26 19:35:37.746782: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-26 19:35:37.746811: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-26 19:35:37.746826: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-26 19:35:37.748693: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2764 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 2025-05-26 19:35:38.491463: 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 2025-05-26 19:35:38.491554: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-26 19:35:38.491573: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-26 19:35:38.491589: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-26 19:35:38.491609: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-26 19:35:38.491624: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-26 19:35:38.491638: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-26 19:35:38.491654: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-26 19:35:38.492527: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-26 19:35:38.493540: 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 2025-05-26 19:35:38.493612: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-26 19:35:38.493630: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-26 19:35:38.493645: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-26 19:35:38.493659: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-26 19:35:38.493673: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-26 19:35:38.493688: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-26 19:35:38.493703: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-26 19:35:38.494553: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-26 19:35:38.494585: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-26 19:35:38.494594: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-26 19:35:38.494602: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-26 19:35:38.495587: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2764 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 : [] 2025-05-26 19:35:46.893040: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-26 19:35:47.085196: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-26 19:35:48.682018: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.733741: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-26 19:35:48.734886: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.734939: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-26 19:35:48.743336: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.743376: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-26 19:35:48.744370: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.744390: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-26 19:35:48.751199: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.751227: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-26 19:35:48.751945: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.751964: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-26 19:35:48.782862: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.782893: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-26 19:35:48.783496: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.783512: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-26 19:35:48.789347: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.789368: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-26 19:35:48.789930: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.789947: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-26 19:35:48.823677: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.824275: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.826094: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.826642: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.870503: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.871136: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.873370: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.873958: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.881529: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.882082: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.886339: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.886933: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.898740: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.899421: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.901007: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.901594: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.907211: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.907804: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.909437: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.909982: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.916263: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.916852: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.918402: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.919022: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.945597: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.946204: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.946790: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.947452: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.951061: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.951655: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.967091: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.967690: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.968293: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.968878: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.981206: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.981799: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.982355: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.982940: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.987307: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.987917: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.992642: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:48.993246: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.005718: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.006301: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.010492: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.011102: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.011695: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.012260: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.033552: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.034155: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.034770: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.035390: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.035985: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.036582: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.051162: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.051767: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.084040: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.084107: W tensorflow/core/kernels/gpu_utils.cc:49] Failed to allocate memory for convolution redzone checking; skipping this check. This is benign and only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once. 2025-05-26 19:35:49.085190: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.086427: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.093590: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.094209: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.102517: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.103225: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.125695: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.126528: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.141962: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.142919: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.147739: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.148658: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.149521: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.150330: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.152792: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.161810: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.162415: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.173133: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.173754: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.174356: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.174953: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.175552: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:35:49.176164: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 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 1238386 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 2023 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 : 3216 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 : 0.0004134178161621094 nb_pixel_total : 16902 time to create 1 rle with old method : 0.019771575927734375 length of segment : 107 time for calcul the mask position with numpy : 0.01693415641784668 nb_pixel_total : 480743 time to create 1 rle with new method : 0.031473636627197266 length of segment : 632 time for calcul the mask position with numpy : 0.0004343986511230469 nb_pixel_total : 36642 time to create 1 rle with old method : 0.04262375831604004 length of segment : 133 time for calcul the mask position with numpy : 0.0001461505889892578 nb_pixel_total : 4793 time to create 1 rle with old method : 0.005795001983642578 length of segment : 51 time spent for convertir_results : 1.1085073947906494 time spend for datou_step_exec : 20.61392569541931 time spend to save output : 6.508827209472656e-05 total time spend for step 1 : 20.613990783691406 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 428 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 : 0.012527942657470703 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, 0.9988366, [(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, 0.99774796, [(711, 22, 21), (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), (545, 33, 502), (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), (489, 47, 589), (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), (450, 76, 640), (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), (148, 175, 940), (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), 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492601069, 445, 390, 550, 0, 54, 0.9391326, [(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), (420, 25, 99), (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)], ['449,46,443,46,442,45,426,45,424,41,424,37,423,36,422,31,420,28,420,25,419,24,419,21,418,20,418,17,417,15,409,6,402,3,402,1,413,1,414,0,420,0,421,1,440,1,441,0,500,0,501,1,507,1,508,0,535,0,536,1,543,1,546,2,546,4,542,8,530,18,527,19,525,21,522,22,520,24,512,28,508,33,505,34,502,37,494,41,492,41,490,43,488,43,484,45,473,45,472,46'])], 'temp/1748280932_935833_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 : 0.13913774490356445 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 Mon May 26 19:35:54 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 3216 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-05-26 19:35:57.578816: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-05-26 19:35:57.607270: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-05-26 19:35:57.609676: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f12dc000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-05-26 19:35:57.609720: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-05-26 19:35:57.614417: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-05-26 19:35:57.759866: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x18220d90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-05-26 19:35:57.759920: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-05-26 19:35:57.760497: 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 2025-05-26 19:35:57.760912: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-26 19:35:57.763502: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-26 19:35:57.766202: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-26 19:35:57.766591: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-26 19:35:57.770639: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-26 19:35:57.772422: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-26 19:35:57.781816: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-26 19:35:57.783746: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-26 19:35:57.783885: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-26 19:35:57.784600: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-26 19:35:57.784621: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-26 19:35:57.784633: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-26 19:35:57.785818: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2764 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. 2025-05-26 19:35:58.036053: 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 2025-05-26 19:35:58.036193: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-26 19:35:58.036224: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-26 19:35:58.036266: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-26 19:35:58.036287: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-26 19:35:58.036308: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-26 19:35:58.036336: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-26 19:35:58.036364: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-26 19:35:58.037489: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-26 19:35:58.038849: 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 2025-05-26 19:35:58.038989: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-26 19:35:58.039025: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-26 19:35:58.039052: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-26 19:35:58.039079: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-26 19:35:58.039113: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-26 19:35:58.039141: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-26 19:35:58.039169: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-26 19:35:58.040333: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-26 19:35:58.040377: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-26 19:35:58.040390: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-26 19:35:58.040403: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-26 19:35:58.041626: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2764 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 : [] 2025-05-26 19:36:07.142627: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-26 19:36:07.396756: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-26 19:36:09.125134: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.125237: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-26 19:36:09.125826: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.125846: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-26 19:36:09.137426: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.137475: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-26 19:36:09.138036: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.138052: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-26 19:36:09.145423: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.145483: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-26 19:36:09.146048: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.146064: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-26 19:36:09.187873: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.187921: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-26 19:36:09.188480: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.188496: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-26 19:36:09.195504: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.195563: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-26 19:36:09.196138: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.196155: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-26 19:36:09.244514: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.245230: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.247643: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.248308: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.301656: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.302271: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.304644: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.305225: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.315435: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.316052: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.323542: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.324139: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.337229: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.337882: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.339983: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.340660: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.346739: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.347405: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.349141: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.349727: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.355951: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.356556: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.358816: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.359647: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.391444: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.392223: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.392887: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.393558: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.398095: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.398822: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.417910: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.418843: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.419598: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.420399: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.434239: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.434994: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.435968: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.436632: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.441921: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.442678: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.448463: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.449232: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.462735: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.463629: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.468372: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.469127: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.469776: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.470435: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.493067: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.493814: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.494516: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.495278: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.495948: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.496609: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.513550: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.514293: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.546738: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.546944: W tensorflow/core/kernels/gpu_utils.cc:49] Failed to allocate memory for convolution redzone checking; skipping this check. This is benign and only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once. 2025-05-26 19:36:09.548117: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.549333: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.556994: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.557593: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.566320: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.566957: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.586747: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.587931: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.588856: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.589782: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.595335: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.596434: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.597554: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.598722: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.602082: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.615312: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.616432: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.628676: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.629824: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.630931: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.631979: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.633013: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-26 19:36:09.634034: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2363359232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 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 1239755 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1224 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 : 2417 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 : 0.41295862197875977 nb_pixel_total : 3693267 time to create 1 rle with new method : 1.1977818012237549 length of segment : 2042 time spent for convertir_results : 2.6599745750427246 time spend for datou_step_exec : 21.638600826263428 time spend to save output : 9.5367431640625e-05 total time spend for step 1 : 21.63869619369507 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 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++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+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++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 : 0.011937141418457031 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, 118, 2241, 0.9850117, [(675, 120, 112), (520, 121, 481), (1051, 121, 380), (502, 122, 948), (486, 123, 981), (470, 124, 1015), (455, 125, 1046), (442, 126, 1092), (429, 127, 1136), (417, 128, 1168), (405, 129, 1187), (394, 130, 1205), (383, 131, 1223), (373, 132, 1239), (368, 133, 1250), (366, 134, 1258), (363, 135, 1267), (361, 136, 1274), (359, 137, 1281), (357, 138, 1288), (355, 139, 1295), (353, 140, 1302), (351, 141, 1309), (349, 142, 1315), (347, 143, 1320), (345, 144, 1326), (343, 145, 1331), (342, 146, 1335), (340, 147, 1340), (338, 148, 1345), (337, 149, 1349), (335, 150, 1354), (334, 151, 1358), (332, 152, 1363), (331, 153, 1366), (330, 154, 1370), (328, 155, 1374), (327, 156, 1378), (326, 157, 1381), (325, 158, 1385), (323, 159, 1389), (322, 160, 1393), (321, 161, 1397), (319, 162, 1402), (318, 163, 1406), (317, 164, 1410), (315, 165, 1415), (314, 166, 1419), (312, 167, 1424), (310, 168, 1429), (309, 169, 1434), (307, 170, 1439), (305, 171, 1444), (304, 172, 1448), (302, 173, 1453), (300, 174, 1458), (298, 175, 1463), (296, 176, 1469), (294, 177, 1474), (292, 178, 1480), (289, 179, 1487), (286, 180, 1493), (283, 181, 1500), (280, 182, 1508), (278, 183, 1514), (275, 184, 1521), (272, 185, 1529), (269, 186, 1536), (266, 187, 1544), (263, 188, 1552), (260, 189, 1561), (257, 190, 1569), (254, 191, 1579), (251, 192, 1588), (248, 193, 1597), (245, 194, 1606), (242, 195, 1615), (239, 196, 1624), (237, 197, 1631), (234, 198, 1640), (231, 199, 1648), (228, 200, 1657), (225, 201, 1665), (222, 202, 1673), (219, 203, 1682), (216, 204, 1689), (213, 205, 1694), (210, 206, 1699), (208, 207, 1702), (206, 208, 1706), (204, 209, 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['1001,2150,936,2144,771,2092,694,2075,610,2037,371,1987,215,1963,128,1971,103,1936,54,1825,39,1677,39,1454,30,1312,27,757,21,695,27,543,39,458,93,308,116,278,210,206,291,179,363,135,520,121,1430,121,1584,128,1663,142,1768,178,1904,204,2021,306,2076,379,2148,535,2168,613,2165,833,2128,914,2112,994,2081,1068,2031,1132,1958,1273,1926,1378,1879,1444,1846,1670,1782,1863,1719,1973,1662,2015,1581,2015,1496,2039,1420,2046,1339,2070,1177,2101,1093,2142'])], 'temp/1748280954_935833_917877156_a9c2d4b99270c9302def4ed40606e685.jpg']} nb pixel non reg : 3692295 nb pixel common : 3691852 proportion of common points : 0.9998800204209035 [('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_5b33f60c5f6905fea072b7d2a40445920ab89df0 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_5b33f60c5f6905fea072b7d2a40445920ab89df0','{"mask_detection": "fail"}','0','http://marlene.fotonower-preprod.com/job/2025/May/26052025/python_test3//data_2/data_log/job/2025/May/26052025/python_test3/log-python3----short_python3--v--marlene-18:35:01.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 : 0.22022414207458496 #### 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 Mon May 26 19:36:22 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/1748280982_935833_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png': 1189321094} map_photo_id_path_extension : {1189321094: {'path': 'temp/1748280982_935833_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png', 'extension': 'png'}} map_subphoto_mainphoto : {} Beginning of datou step sam ! pht : 4677 Inside sam : nb paths : 1 ERROR in datou_step_exec, will save and exit ! CUDA error: out of memory CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1. 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 2430, in datou_step_exec return lib_process.datou_step_sam(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 367, in datou_step_sam sam.to(device=device) File "/home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 927, in to return self._apply(convert) File "/home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 579, in _apply module._apply(fn) File "/home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 579, in _apply module._apply(fn) File "/home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 579, in _apply module._apply(fn) File "/home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 602, in _apply param_applied = fn(param) File "/home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 925, in convert return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking) [1189321094] map_info['map_portfolio_photo'] : {} final : True mtd_id 4573 list_pids : [1189321094] 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 : [('4573', None, '1189321094', "[>, , , , , 'CUDA error: out of memory\\nCUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.\\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.']", '-1', '-1.0', '501120777', '1.0', None)] time used for this insertion : 0.026317358016967773 save_final ERROR in last step sam, CUDA error: out of memory CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1. time spend for datou_step_exec : 9.652262210845947 time spend to save output : 0.15929222106933594 total time spend for step 0 : 9.811554431915283 need to delete datou_research and reload, so keep current state 1 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) : 1 output : None ERROR nb objects espect : 98 nb_objects detect : 0 ERROR sam FAILED ############################### 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 : 0.1187582015991211 #### 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 Mon May 26 19:36:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748280992_935833_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg': 917754606} map_photo_id_path_extension : {917754606: {'path': 'temp/1748280992_935833_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/1748280992_935833_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 0.093s for 300 object proposals c : plaque list_crops.shape (72, 5) proba : 0.06384064 (374.12692, 293.91928, 430.81015, 317.80862) proba : 0.05221771 (382.17764, 297.1887, 552.3577, 344.658) proba : 0.012271247 (345.35672, 272.4298, 468.8577, 320.7243) We are managing local photo_id len de result frcnn : 1 After datou_step_exec type output : time spend for datou_step_exec : 3.685218095779419 time spend to save output : 0.00015735626220703125 total time spend for step 1 : 3.685375452041626 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, 0.06384064, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.05221771, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012271247, 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 : 0.013272523880004883 [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 : 0.011860847473144531 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, 0.06384064, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.05221771, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012271247, None)], 'temp/1748280992_935833_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 : 0.13808989524841309 #### 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 Mon May 26 19:36:36 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/1748280996_935833_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064} map_photo_id_path_extension : {916235064: {'path': 'temp/1748280996_935833_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 : 0.010944604873657227 time to convert the images to numpy array : 0.0016198158264160156 total time to convert the images to numpy array : 0.013153314590454102 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': 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'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 : 2583 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 : 2955 max_wait_temp : 1 max_wait : 0 dict_keys(['pool5', 'prob']) time used to do the prepocess of the images : 0.022271394729614258 time used to do the prediction : 0.11349916458129883 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 : 0.05435633659362793 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 : 0.8065204620361328 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', 0.00188152, 332, '355'), ('916235064', 'mokka_1027_gao__port_506374', 0.0011634092, 332, '355'), ('916235064', 'captur_1027_gao__port_506399', 0.0008156501, 332, '355'), ('916235064', 'sorento_1027_gao__port_506192', 0.0011772435, 332, '355'), ('916235064', 'navara_1027_gao__port_506205', 0.0025853622, 332, '355'), ('916235064', 'xc90_1027_gao__port_506350', 0.0041704224, 332, '355'), ('916235064', 'saxo_1027_gao__port_506052', 0.003480498, 332, '355'), ('916235064', 'trafic_1027_gao__port_506295', 0.007367961, 332, '355'), ('916235064', 'punto_evo_1027_gao__port_506066', 0.0021884155, 332, '355'), ('916235064', '5_1027_gao__port_506117', 0.00057981623, 332, '355'), ('916235064', '250_1027_gao__port_506065', 0.0045907344, 332, '355'), ('916235064', 'd_max_1027_gao__port_506125', 0.0031586993, 332, '355'), ('916235064', 'panamera_1027_gao__port_506387', 0.0022506083, 332, '355'), ('916235064', 'alhambra_1027_gao__port_506381', 0.0053208745, 332, '355'), ('916235064', 'x6_1027_gao__port_506349', 0.0010998772, 332, '355'), ('916235064', 'vitara_1027_gao__port_506328', 0.0054023624, 332, '355'), ('916235064', 'fiesta_1027_gao__port_506377', 0.003918656, 332, '355'), ('916235064', 'qashqai_1027_gao__port_506286', 0.0014786444, 332, '355'), ('916235064', '147_1027_gao__port_506124', 0.0019777801, 332, '355'), ('916235064', 'c5_1027_gao__port_506172', 0.0012441095, 332, '355'), ('916235064', 'q5_1027_gao__port_506206', 0.001504847, 332, '355'), ('916235064', 'giulia_1027_gao__port_506178', 0.0021690659, 332, '355'), ('916235064', 'karl_1027_gao__port_506371', 0.002708085, 332, '355'), ('916235064', 'mehari_1027_gao__port_506076', 0.004703425, 332, '355'), ('916235064', '911_1027_gao__port_506114', 0.0019417392, 332, '355'), ('916235064', '508_1027_gao__port_506329', 0.0009584842, 332, '355'), ('916235064', 'idea_1027_gao__port_506122', 0.00077005813, 332, '355'), ('916235064', 'megane_1027_gao__port_506220', 0.0019466513, 332, '355'), ('916235064', 'ghibli_1027_gao__port_506174', 0.0013725242, 332, '355'), ('916235064', 'touareg_1027_gao__port_506224', 0.001620057, 332, '355'), ('916235064', 'i10_1027_gao__port_506232', 0.0013925241, 332, '355'), ('916235064', 'jumper_1027_gao__port_506234', 0.010046818, 332, '355'), ('916235064', 'classe_clk_1027_gao__port_506173', 0.0010791648, 332, '355'), ('916235064', 'kuga_1027_gao__port_506181', 0.00084465265, 332, '355'), ('916235064', 'ct_1027_gao__port_506323', 0.0012519727, 332, '355'), ('916235064', 'leon_1027_gao__port_506326', 0.0025841966, 332, '355'), ('916235064', 'ds5_1027_gao__port_506376', 0.0012428002, 332, '355'), ('916235064', 'cordoba_1027_gao__port_506048', 0.0028646775, 332, '355'), ('916235064', 'classe_cla_1027_gao__port_506400', 0.0012947452, 332, '355'), ('916235064', 'jumpy_1027_gao__port_506179', 0.010341165, 332, '355'), ('916235064', 'avensis_1027_gao__port_506311', 0.0018763907, 332, '355'), ('916235064', 'juke_1027_gao__port_506325', 0.0011342816, 332, '355'), ('916235064', '4008_1027_gao__port_506402', 0.0015756425, 332, '355'), ('916235064', '190_series_1027_gao__port_506051', 0.003980201, 332, '355'), ('916235064', 'serie_3_1027_gao__port_506294', 0.002873905, 332, '355'), ('916235064', 'q7_1027_gao__port_506318', 0.0023354092, 332, '355'), ('916235064', 'glc_1027_gao__port_506303', 0.0012106318, 332, '355'), ('916235064', 'grand_vitara_1027_gao__port_506175', 0.0011447158, 332, '355'), ('916235064', 's40_1027_gao__port_506099', 0.0022334016, 332, '355'), ('916235064', 'toledo_1027_gao__port_506061', 0.0017464287, 332, '355'), ('916235064', '5008_1027_gao__port_506337', 0.004699551, 332, '355'), ('916235064', 'continental_1027_gao__port_506250', 0.0021912085, 332, '355'), ('916235064', 'coupe_1027_gao__port_506082', 0.0022626885, 332, '355'), ('916235064', 'iq_1027_gao__port_506166', 0.0018173772, 332, '355'), ('916235064', '407_1027_gao__port_506133', 0.0009056001, 332, '355'), ('916235064', 'touran_1027_gao__port_506308', 0.002040303, 332, '355'), ('916235064', '300c_1027_gao__port_506078', 0.0025336286, 332, '355'), ('916235064', 'classe_gl_1027_gao__port_506340', 0.0044887443, 332, '355'), ('916235064', 'vivaro_1027_gao__port_506310', 0.0034254647, 332, '355'), ('916235064', 'sl_1027_gao__port_506100', 0.0031350285, 332, '355'), ('916235064', 'elise_1027_gao__port_506121', 0.0010254505, 332, '355'), ('916235064', '1007_1027_gao__port_506070', 0.0015355974, 332, '355'), ('916235064', 'i40_1027_gao__port_506218', 0.0005914173, 332, '355'), ('916235064', 'bipper_tepee_1027_gao__port_506227', 0.0040301606, 332, '355'), ('916235064', 'focus_1027_gao__port_506272', 0.00115844, 332, '355'), ('916235064', 'primera_1027_gao__port_506147', 0.0012156105, 332, '355'), ('916235064', 'r4_1027_gao__port_506160', 0.014969154, 332, '355'), ('916235064', 'a8_1027_gao__port_506265', 0.0011319476, 332, '355'), ('916235064', 'boxer_1027_gao__port_506202', 0.010546923, 332, '355'), ('916235064', 's5_1027_gao__port_506222', 0.0011984039, 332, '355'), ('916235064', 'r21_1027_gao__port_506093', 0.00418499, 332, '355'), ('916235064', 'c3_1027_gao__port_506257', 0.002363386, 332, '355'), ('916235064', 'santa_fe_1027_gao__port_506208', 0.0016322954, 332, '355'), ('916235064', 'm4_1027_gao__port_506344', 0.0015566926, 332, '355'), ('916235064', 'safrane_1027_gao__port_506077', 0.0013957814, 332, '355'), ('916235064', 'classe_gle_1027_gao__port_506395', 0.0021975895, 332, '355'), ('916235064', '0_1027_gao__port_506094', 0.008828806, 332, '355'), ('916235064', 'ix35_1027_gao__port_506219', 0.001461585, 332, '355'), ('916235064', 'carens_1027_gao__port_506298', 0.0008824892, 332, '355'), ('916235064', 'classe_a_1027_gao__port_506339', 0.0024711592, 332, '355'), ('916235064', 'ix20_1027_gao__port_506343', 0.0010092928, 332, '355'), ('916235064', 'note_1027_gao__port_506365', 0.0015962509, 332, '355'), ('916235064', 'a5_1027_gao__port_506200', 0.0015328963, 332, '355'), ('916235064', 'sx4_1027_gao__port_506348', 0.0014916293, 332, '355'), ('916235064', 'sandero_1027_gao__port_506198', 0.0014585054, 332, '355'), ('916235064', '3008_1027_gao__port_506385', 0.00564491, 332, '355'), ('916235064', 'q50_1027_gao__port_506239', 0.0011164144, 332, '355'), 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'viano_1027_gao__port_506211', 0.0026946645, 332, '355'), ('916235064', 'pro_cee_d_1027_gao__port_506274', 0.00083180517, 332, '355'), ('916235064', 'a3_1027_gao__port_506321', 0.0037375381, 332, '355'), ('916235064', 'v50_1027_gao__port_506150', 0.00079192664, 332, '355'), ('916235064', 'voyager_1027_gao__port_506169', 0.0030529192, 332, '355'), ('916235064', 'corvette_1027_gao__port_506049', 0.0037227042, 332, '355'), ('916235064', 'rio_1027_gao__port_506379', 0.0017737758, 332, '355'), ('916235064', 'jazz_1027_gao__port_506252', 0.0015305397, 332, '355'), ('916235064', '200_1027_gao__port_506112', 0.004086333, 332, '355'), ('916235064', 'tts_1027_gao__port_506199', 0.0011861712, 332, '355'), ('916235064', 'zafira_1027_gao__port_506287', 0.0026952534, 332, '355'), ('916235064', 'asx_1027_gao__port_506266', 0.0011406499, 332, '355'), ('916235064', '607_1027_gao__port_506118', 0.0012527299, 332, '355'), ('916235064', '207_1027_gao__port_506103', 0.0015147955, 332, '355'), ('916235064', 'classe_s_1027_gao__port_506301', 0.0031652767, 332, '355'), ('916235064', 'c6_1027_gao__port_506105', 0.0017347066, 332, '355'), ('916235064', 'express_1027_gao__port_506137', 0.016726239, 332, '355'), ('916235064', 'classe_gla_1027_gao__port_506352', 0.0018253381, 332, '355'), ('916235064', 'v60_1027_gao__port_506333', 0.0021456915, 332, '355'), ('916235064', 'ka_1027_gao__port_506180', 0.0014150616, 332, '355'), ('916235064', 'range_rover_1027_gao__port_506254', 0.0020551214, 332, '355'), ('916235064', 'discovery_1027_gao__port_506375', 0.0022967577, 332, '355'), ('916235064', 'classe_r_1027_gao__port_506270', 0.0013942865, 332, '355'), ('916235064', 'transporter_1027_gao__port_506319', 0.011971689, 332, '355'), ('916235064', 'cee_d_1027_gao__port_506288', 0.0010547711, 332, '355'), ('916235064', 'zoe_1027_gao__port_506244', 0.0020712314, 332, '355'), ('916235064', 'i20_1027_gao__port_506284', 0.0017867731, 332, '355'), ('916235064', 'gtv_1027_gao__port_506059', 0.005721874, 332, '355'), ('916235064', 's4_avant_1027_gao__port_506261', 0.0027662492, 332, '355'), ('916235064', 'x1_1027_gao__port_506372', 0.0017144072, 332, '355'), ('916235064', 'autres_1027_gao__port_506127', 0.004825705, 332, '355'), ('916235064', '208_1027_gao__port_506359', 0.0018684791, 332, '355'), ('916235064', 'c8_1027_gao__port_506135', 0.0012581925, 332, '355'), ('916235064', 'astra_1027_gao__port_506215', 0.001262447, 332, '355'), ('916235064', '2_1027_gao__port_506151', 0.0009243544, 332, '355'), ('916235064', 'doblo_1027_gao__port_506251', 0.00746745, 332, '355'), ('916235064', '807_1027_gao__port_506152', 0.0007290561, 332, '355'), ('916235064', '206_1027_gao__port_506126', 0.0010384801, 332, '355'), ('916235064', 'a7_1027_gao__port_506373', 0.00069107494, 332, '355'), ('916235064', 'renegade_1027_gao__port_506346', 0.0021420268, 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 : 1.9073486328125e-05 save missing photos in datou_result : time spend for datou_step_exec : 7.699369192123413 time spend to save output : 1.7503421306610107 total time spend for step 1 : 9.449711322784424 step2:argmax Mon May 26 19:36:45 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/1748280996_935833_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064} map_photo_id_path_extension : {916235064: {'path': 'temp/1748280996_935833_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', 0.017714243, 332, '355'), 'temp/1748280996_935833_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 : 0.014520883560180664 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 : 0.015280723571777344 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', '0.017714243', None)] time used for this insertion : 0.013666391372680664 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 : 7.152557373046875e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.0002486705780029297 time spend to save output : 0.04417777061462402 total time spend for step 2 : 0.04442644119262695 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', 0.017714243, 332, '355'), 'temp/1748280996_935833_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 1171252764 download finish for photo 1171252784 download finish for photo 1171252487 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 : 0.3035919666290283 #### 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 Mon May 26 19:36:46 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/1748281006_935833_1171252764_29d5179a892cc50aadc9d67245534b59.jpg': 1171252764, 'temp/1748281006_935833_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg': 1171252784, 'temp/1748281006_935833_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg': 1171252487} map_photo_id_path_extension : {1171252764: {'path': 'temp/1748281006_935833_1171252764_29d5179a892cc50aadc9d67245534b59.jpg', 'extension': 'jpg'}, 1171252784: {'path': 'temp/1748281006_935833_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'extension': 'jpg'}, 1171252487: {'path': 'temp/1748281006_935833_1171252487_5ebdd6b0a6bb39942a3808ed114806de.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 2025-05-26 19:36:50.269693: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-05-26 19:36:50.270521: 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 2025-05-26 19:36:50.270644: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-26 19:36:50.270701: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-26 19:36:50.274056: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-26 19:36:50.274416: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-26 19:36:50.279567: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-26 19:36:50.281305: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-26 19:36:50.289596: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-26 19:36:50.291327: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-26 19:36:50.292080: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-05-26 19:36:50.327349: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-05-26 19:36:50.329046: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f1124000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-05-26 19:36:50.329105: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-05-26 19:36:50.332333: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1c13f4a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-05-26 19:36:50.332358: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-05-26 19:36:50.333325: 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 2025-05-26 19:36:50.333534: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-26 19:36:50.333558: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-26 19:36:50.333689: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-26 19:36:50.333715: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-26 19:36:50.333747: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-26 19:36:50.333780: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-26 19:36:50.333813: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-26 19:36:50.334825: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-26 19:36:50.334883: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-26 19:36:50.334951: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-26 19:36:50.334964: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-26 19:36:50.334972: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-26 19:36:50.336043: 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 : 6360 max_wait_temp : 1 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 : [] /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 : 10.54709506034851 time used to load_weights : 0.16009879112243652 0it [00:00, ?it/s] 3it [00:00, 796.59it/s]2025-05-26 19:37:04.169970: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 temp/1748281006_935833_1171252764_29d5179a892cc50aadc9d67245534b59.jpg temp/1748281006_935833_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg temp/1748281006_935833_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg Found 3 images belonging to 1 classes. begin to do the prediction : time used to do the prediction : 3.921224355697632 ['temp/image000000000_1748281006_935833_1171252764_29d5179a892cc50aadc9d67245534b59.jpg', 'temp/image000000001_1748281006_935833_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'temp/image000000002_1748281006_935833_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg'] (3,) (3, 5) (3, 1280) shape of features : (3, 1280) shape of new features : (1, 3, 1280) save descriptor for thcl : 3609 (3, 1280) Got the blobs of the net to insert : [0, 6, 0, 1, 0, 0, 0, 1, 0, 0] code_as_byte_string:b'0006000100'| Got the blobs of the net to insert : [0, 6, 0, 0, 1, 0, 0, 1, 0, 0] code_as_byte_string:b'0006000001'| Got the blobs of the net to insert : [0, 9, 0, 0, 0, 0, 1, 0, 0, 0] code_as_byte_string:b'0009000000'| time to traite the descriptors : 0.04632878303527832 Testing : ['1171252764', '1171252784', '1171252487'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (1171252764,1171252784,1171252487) result : {1171252487: {'photo_id': 1171252487, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/22/5ebdd6b0a6bb39942a3808ed114806de.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_21_55_35_005998m0.jpg 0.4259977941513062 for time 6.000020980834961, id_amount 3 this amount prod time diff : 0.006000020980834961'}, 1171252764: {'photo_id': 1171252764, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/22/29d5179a892cc50aadc9d67245534b59.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_21_55_41_005998m0.jpg 0.4319977941513062 for time 6.0, id_amount 3 this amount prod time diff : 0.006'}, 1171252784: {'photo_id': 1171252784, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/22/5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_21_55_47_006033m0.jpg 0.4379978291988373 for time 6.000035047531128, id_amount 4 this amount prod time diff : 0.006000035047531128'}} list_photo_exists : [1171252487, 1171252764, 1171252784] storage_type for insertDescriptorsMulti : 3 To insert : 1171252764 To insert : 1171252784 To insert : 1171252487 time to insert the descriptors : 0.9230039119720459 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 [1171252764, 1171252784, 1171252487] map_info['map_portfolio_photo'] : {} final : False mtd_id 4567 list_pids : [1171252764, 1171252784, 1171252487] Looping around the photos to save general results len do output : 3 /1171252764Didn't retrieve data . /1171252784Didn't retrieve data . /1171252487Didn'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 ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252764', None, None, None, None, None, None) ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252784', None, None, None, None, None, None) ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252487', 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 : [('4567', None, '1171252764', 'None', None, None, None, None, None), ('4567', None, '1171252784', 'None', None, None, None, None, None), ('4567', None, '1171252487', 'None', None, None, None, None, None)] time used for this insertion : 0.013512372970581055 save_final save missing photos in datou_result : time spend for datou_step_exec : 23.051575899124146 time spend to save output : 0.01390528678894043 total time spend for step 1 : 23.065481185913086 step2:argmax Mon May 26 19:37:09 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281006_935833_1171252764_29d5179a892cc50aadc9d67245534b59.jpg': 1171252764, 'temp/1748281006_935833_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg': 1171252784, 'temp/1748281006_935833_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg': 1171252487} map_photo_id_path_extension : {1171252764: {'path': 'temp/1748281006_935833_1171252764_29d5179a892cc50aadc9d67245534b59.jpg', 'extension': 'jpg'}, 1171252784: {'path': 'temp/1748281006_935833_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'extension': 'jpg'}, 1171252487: {'path': 'temp/1748281006_935833_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 3609 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : True verbose : True photo_id : 1171252764 output[photo_id] : [(1171252764, 'jrm', 0.9853596, 4674, '3609'), 'temp/1748281006_935833_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'] photo_id : 1171252784 output[photo_id] : [(1171252784, 'jrm', 0.9677421, 4674, '3609'), 'temp/1748281006_935833_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'] photo_id : 1171252487 output[photo_id] : [(1171252487, 'jrm', 0.9262282, 4674, '3609'), 'temp/1748281006_935833_1171252487_5ebdd6b0a6bb39942a3808ed114806de.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 : ('1171252764', '495916461', '4674') ... last line : ('1171252487', '495916461', '4674') time used for this insertion : 0.010719776153564453 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 : 0.013157129287719727 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 : [('4567', None, '1171252764', 'jrm', None, None, '495916461', '0.9853596', None), ('4567', None, '1171252784', 'jrm', None, None, '495916461', '0.9677421', None), ('4567', None, '1171252487', 'jrm', None, None, '495916461', '0.9262282', None)] time used for this insertion : 0.012837886810302734 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 : 5.0067901611328125e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.0002148151397705078 time spend to save output : 0.04161405563354492 total time spend for step 2 : 0.04182887077331543 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171252764': [(1171252764, 'jrm', 0.9853596, 4674, '3609'), 'temp/1748281006_935833_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'], '1171252784': [(1171252784, 'jrm', 0.9677421, 4674, '3609'), 'temp/1748281006_935833_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'], '1171252487': [(1171252487, 'jrm', 0.9262282, 4674, '3609'), 'temp/1748281006_935833_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg']} --------------------- test with use_multi_inputs=0 is succeded ------------------- ######################## 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 : 0.20490360260009766 #### 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 Mon May 26 19:37:09 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281029_935833_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg': 1171291875, 'temp/1748281029_935833_1171275372_76d81364ff7df843bff095f45c07ba35.jpg': 1171275372, 'temp/1748281029_935833_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg': 1171275314} map_photo_id_path_extension : {1171291875: {'path': 'temp/1748281029_935833_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'extension': 'jpg'}, 1171275372: {'path': 'temp/1748281029_935833_1171275372_76d81364ff7df843bff095f45c07ba35.jpg', 'extension': 'jpg'}, 1171275314: {'path': 'temp/1748281029_935833_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 inside check gpu memory inside check gpu memory inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 2485 wait 20 seconds inside check gpu memory inside check gpu memory l 3637 free memory gpu now : 2589 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" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_1 (InputLayer) [(None, 224, 224, 3) 0 __________________________________________________________________________________________________ input_2 (InputLayer) [(None, 1)] 0 __________________________________________________________________________________________________ module (KerasLayer) (None, 1280) 4049564 input_1[0][0] __________________________________________________________________________________________________ concatenate (Concatenate) (None, 1281) 0 input_2[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 : 9.338450193405151 time used to load_weights : 0.1501328945159912 found 3 data found 0 labels begin to do the prediction : time used to do the prediction : 1.2790470123291016 ['temp/1748281029_935833_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'temp/1748281029_935833_1171275372_76d81364ff7df843bff095f45c07ba35.jpg', 'temp/1748281029_935833_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 : 0.06702756881713867 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 : 1.128990650177002 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 : 0.01383066177368164 save_final save missing photos in datou_result : time spend for datou_step_exec : 41.96320843696594 time spend to save output : 0.014254331588745117 total time spend for step 1 : 41.97746276855469 step2:argmax Mon May 26 19:37:51 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/1748281029_935833_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg': 1171291875, 'temp/1748281029_935833_1171275372_76d81364ff7df843bff095f45c07ba35.jpg': 1171275372, 'temp/1748281029_935833_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg': 1171275314} map_photo_id_path_extension : {1171291875: {'path': 'temp/1748281029_935833_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'extension': 'jpg'}, 1171275372: {'path': 'temp/1748281029_935833_1171275372_76d81364ff7df843bff095f45c07ba35.jpg', 'extension': 'jpg'}, 1171275314: {'path': 'temp/1748281029_935833_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', 0.97061586, 4723, '3655'), 'temp/1748281029_935833_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'] photo_id : 1171275372 output[photo_id] : [(1171275372, 'tapis_vide', 0.9674336, 4723, '3655'), 'temp/1748281029_935833_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'] photo_id : 1171275314 output[photo_id] : [(1171275314, 'tapis_vide', 0.9651685, 4723, '3655'), 'temp/1748281029_935833_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 : 0.014820098876953125 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 : 0.015030622482299805 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', '0.97061586', None), ('4621', None, '1171275372', 'tapis_vide', None, None, '2107748999', '0.9674336', None), ('4621', None, '1171275314', 'tapis_vide', None, None, '2107748999', '0.9651685', None)] time used for this insertion : 0.012315034866333008 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 : 4.291534423828125e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.0001888275146484375 time spend to save output : 0.04751706123352051 total time spend for step 2 : 0.047705888748168945 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', 0.97061586, 4723, '3655'), 'temp/1748281029_935833_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'], '1171275372': [(1171275372, 'tapis_vide', 0.9674336, 4723, '3655'), 'temp/1748281029_935833_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'], '1171275314': [(1171275314, 'tapis_vide', 0.9651685, 4723, '3655'), 'temp/1748281029_935833_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg']} --------------------- test with use_multi_inputs=1 is succeded ------------------- ############################### 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 : 0.16463971138000488 #### 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 Mon May 26 19:37:53 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281073_935833_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1748281073_935833_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/1748281073_935833_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/1748281073_935833_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1748281073_935833_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg image_rotate.mode : RGB Rotation of photo 917849322 of 180 degree temp/1748281073_935833_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/1748281073_935833_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1748281073_935833_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg image_rotate.mode : RGB Rotation of photo 917849322 of 270 degree temp/1748281073_935833_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/1748281073_935833_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1748281073_935833_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/1748281074_935833 we have uploaded 3 photos in the portfolio 551782 time of upload the photos Elapsed time : 1.062758445739746 map_filename_photo_id : 3 map_filename_photo_id : {'temp/1748281073_935833_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg': 1361142795, 'temp/1748281073_935833_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg': 1361142797, 'temp/1748281073_935833_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg': 1361142798} 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 : 1.344043493270874 time spend to save output : 6.389617919921875e-05 total time spend for step 1 : 1.3441073894500732 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 /1361142795Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142797Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142798Didn'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, '1361142795', 'None', None, None, None, None, None), ('230', None, '1361142797', 'None', None, None, None, None, None), ('230', None, '1361142798', 'None', None, None, None, None, None), ('230', None, '917849322', None, None, None, None, None, None)] time used for this insertion : 0.014459609985351562 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1361142795: ['917849322', 'temp/1748281073_935833_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', []], 1361142797: ['917849322', 'temp/1748281073_935833_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', []], 1361142798: ['917849322', 'temp/1748281073_935833_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 : 0.1544504165649414 #### 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 Mon May 26 19:37:55 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281075_935833_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1748281075_935833_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 : 0.00021982192993164062 time to convert the images to numpy array : 1.588594913482666 total time to convert the images to numpy array : 1.589158296585083 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 : 2589 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 : 2589 max_wait_temp : 1 max_wait : 0 dict_keys(['pool5', 'prob']) time used to do the prepocess of the images : 2.321836233139038 time used to do the prediction : 0.13857746124267578 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 : 0.055588483810424805 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 : 0.5725798606872559 After datou_step_exec type output : time spend for datou_step_exec : 10.00849461555481 time spend to save output : 0.00010228157043457031 total time spend for step 1 : 10.008596897125244 step2:argmax Mon May 26 19:38:05 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 : {'917849322': [[('917849322', 'carteGrisesVerticales__port_549774', 0.9976497, 507, '500'), ('917849322', 'cartegrise_90deg__port_550987', 0.00050355284, 507, '500'), ('917849322', 'cartesGrisesEnvers__port_549765', 0.0003663025, 507, '500'), ('917849322', 'portfolio_270deg__port_550988', 0.0014804377, 507, '500')]]} input_args_next_step : {'917849322': ()} output_args : {'917849322': [[('917849322', 'carteGrisesVerticales__port_549774', 0.9976497, 507, '500'), ('917849322', 'cartegrise_90deg__port_550987', 0.00050355284, 507, '500'), ('917849322', 'cartesGrisesEnvers__port_549765', 0.0003663025, 507, '500'), ('917849322', 'portfolio_270deg__port_550988', 0.0014804377, 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', 0.9976497, 507, '500'), ('917849322', 'cartegrise_90deg__port_550987', 0.00050355284, 507, '500'), ('917849322', 'cartesGrisesEnvers__port_549765', 0.0003663025, 507, '500'), ('917849322', 'portfolio_270deg__port_550988', 0.0014804377, 507, '500')],) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281075_935833_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1748281075_935833_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 : 0.0002689361572265625 time spend to save output : 4.291534423828125e-05 total time spend for step 2 : 0.00031185150146484375 step3:rotate Mon May 26 19:38:05 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.9976497, 507, '500'), 'temp/1748281075_935833_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} input_args_next_step : {'917849322': ()} output_args : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.9976497, 507, '500'), 'temp/1748281075_935833_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} args : 917849322 depend.output_id : 1 complete output_args for input 1 : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.9976497, 507, '500'), 'temp/1748281075_935833_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} input_args_next_step : {'917849322': ('temp/1748281075_935833_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg',)} output_args : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.9976497, 507, '500'), 'temp/1748281075_935833_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/1748281075_935833_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', ('917849322', 'carteGrisesVerticales__port_549774', 0.9976497, 507, '500')) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281075_935833_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1748281075_935833_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/1748281075_935833_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/1748281075_935833_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1748281075_935833_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/1748281086_935833 we have uploaded 1 photos in the portfolio 551782 time of upload the photos Elapsed time : 0.6620328426361084 map_filename_photo_id : 1 map_filename_photo_id : {'temp/1748281075_935833_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg': 1361142809} 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 : 0.7720203399658203 time spend to save output : 6.318092346191406e-05 total time spend for step 3 : 0.7720835208892822 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 /1361142809Didn'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, '1361142809', 'None', None, None, None, None, None), ('233', None, '917849322', None, None, None, None, None, None)] time used for this insertion : 0.012570619583129883 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1361142809: ['917849322', 'temp/1748281075_935833_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 (3813166974,3813166975,3813166989,3813166990) # 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 : 0.1274557113647461 #### 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 Mon May 26 19:38:06 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/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg': 937852786} map_photo_id_path_extension : {937852786: {'path': 'temp/1748281086_935833_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/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg new_file_path_bib_crop : temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg new_file_path_bib_crop : temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg new_file_path_bib_crop : temp/1748281086_935833_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/1748281086_935833_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/1748281086_935833_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/1748281086_935833_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/1748281086_935833_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 : 23354281 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1748281088_935833 INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `mtr_user_id`, `created_at`) VALUES (23354281, 1361142813, 0, NOW()),(23354281, 1361142814, 0, NOW()),(23354281, 1361142815, 0, NOW()),(23354281, 1361142816, 0, NOW()) 4 we have uploaded 4 photos in the portfolio 23354281 time of upload the photos Elapsed time : 3.028769016265869 {'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1361142813, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1361142814, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1361142815, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1361142816} list_errors : [] map_result_insert : {'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1361142813, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1361142814, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1361142815, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1361142816} 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/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg sub_photo_id found to be used 1361142813 chi_id found to be used 8165076 path of cropped varroa found to be used to match on an ellipse temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg sub_photo_id found to be used 1361142814 chi_id found to be used 8165077 path of cropped varroa found to be used to match on an ellipse temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg sub_photo_id found to be used 1361142815 chi_id found to be used 8165078 path of cropped varroa found to be used to match on an ellipse temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg sub_photo_id found to be used 1361142816 insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(8165075, '1361142813', 31), (8165076, '1361142814', 31), (8165077, '1361142815', 31), (8165078, '1361142816', 31)] map of cropped photos with some data : {'1361142813': ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg', (426, 467, 312, 347)], '1361142814': ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg', (411, 445, 443, 480)], '1361142815': ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg', (103, 138, 358, 396)], '1361142816': ['937852786', 'temp/1748281086_935833_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/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_ellipsebest.jpg', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_varroa_with_ellipsebest.jpg', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_ellipsebest.jpg', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_varroa_with_ellipsebest.jpg', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_ellipsebest.jpg', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_varroa_with_ellipsebest.jpg', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_ellipsebest.jpg', 'temp/1748281086_935833_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 : 23354283 Result OK ! uploaded one batch 0 Elapsed time : 19.978639125823975 After datou_step_exec type output : time spend for datou_step_exec : 24.50997519493103 time spend to save output : 4.220008850097656e-05 total time spend for step 1 : 24.51001739501953 step2:tile Mon May 26 19:38:31 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 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/1748281086_935833_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/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg',)] After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg': 937852786, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1361142813, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1361142814, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1361142815, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1361142816} map_photo_id_path_extension : {937852786: {'path': 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg', 'extension': 'jpg'}, 1361142813: {'path': 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg'}, 1361142814: {'path': 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg'}, 1361142815: {'path': 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg'}, 1361142816: {'path': 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg'}} map_subphoto_mainphoto : {1361142813: 937852786, 1361142814: 937852786, 1361142815: 937852786, 1361142816: 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/1748281086_935833_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 (3813395328,3813395329,3813395330,3813395331) ++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (3813395328,3813395329,3813395330,3813395331) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (3813395328,3813395329,3813395330,3813395331) https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=tile_taggage_varroa&access_token=78d09a0790ec6ecbf119343125a81fdc created feed_id_new_photos : 23354290 with name tile_taggage_varroa feed_id_new_photos : 23354290 filename : temp/1748281086_935833_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/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg , 0 before upload mediasElapsed time : 0.010693788528442383 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/1748281118_935833 INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `mtr_user_id`, `created_at`) VALUES (23354290, 1361142853, 0, NOW()) 1 we have uploaded 1 photos in the portfolio 23354290 Importing ! upload mediasElapsed time : 0.6693487167358398 , 0insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(8165084, 1361142853, 0)] Saving 4 CHIs. list_chi_tile : [": {'photo_id': 1361142853, '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': 1361142853, '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': 1361142853, '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': 1361142853, '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 : 0.7407855987548828 map_pid_results : {'1361142853': ['temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg']} After datou_step_exec type output : time spend for datou_step_exec : 7.561677694320679 time spend to save output : 7.104873657226562e-05 total time spend for step 2 : 7.561748743057251 step3:rotate Mon May 26 19:38:38 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 : {'1361142853': ['temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg']} input_args_next_step : {'1361142853': ()} output_args : {'1361142853': ['temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg']} args : 1361142853 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/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg': 937852786, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1361142813, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1361142814, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1361142815, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1361142816, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg': 1361142853} map_photo_id_path_extension : {937852786: {'path': 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg', 'extension': 'jpg'}, 1361142813: {'path': 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg'}, 1361142814: {'path': 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg'}, 1361142815: {'path': 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg'}, 1361142816: {'path': 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg'}, 1361142853: {'path': 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg'}} map_subphoto_mainphoto : {1361142813: 937852786, 1361142814: 937852786, 1361142815: 937852786, 1361142816: 937852786, 1361142853: 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 ( 1361142853) 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 (3813395455,3813395456,3813395454,3813395453) ++WARNING : duplicated polygon, we should remove this data for chi_id : 3813395453. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3813395454. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3813395455. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3813395456. Ignored now SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (3813395455,3813395456,3813395454,3813395453) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (3813395455,3813395456,3813395454,3813395453) map_chi : {1361142853: [, , , ]} https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=rotate_data_augmentation_varroa_480_ellipse_320&access_token=78d09a0790ec6ecbf119343125a81fdc feed_id_new_photos : 23354295 photo_id in download_rotate_and_save : 1361142853 list_chi_loc : 4 Use all angle ! Rotation of photo 1361142853 of 0 degree temp/1748281086_935833_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 : 0.0003871917724609375 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0015521049499511719 .time for calcul the mask position with numpy : 0.0006148815155029297 nb_pixel_total : 1157 time to create 1 rle with old method : 0.002318859100341797 . 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 1361142853 of 15 degree temp/1748281086_935833_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 : 0.0004203319549560547 nb_pixel_total : 694 time to create 1 rle with old method : 0.001009225845336914 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004544258117675781 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0016858577728271484 . 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 1361142853 of 30 degree temp/1748281086_935833_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 : 0.00035762786865234375 nb_pixel_total : 221 time to create 1 rle with old method : 0.0002732276916503906 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003376007080078125 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0013399124145507812 . 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 1361142853 of 45 degree temp/1748281086_935833_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 : 0.00034165382385253906 nb_pixel_total : 143 time to create 1 rle with old method : 0.0001823902130126953 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003521442413330078 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0013217926025390625 . 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 1361142853 of 60 degree temp/1748281086_935833_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 : 0.00040721893310546875 nb_pixel_total : 414 time to create 1 rle with old method : 0.0004858970642089844 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003428459167480469 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0013370513916015625 . 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 1361142853 of 75 degree temp/1748281086_935833_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 : 0.0004324913024902344 nb_pixel_total : 1204 time to create 1 rle with old method : 0.0019783973693847656 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035500526428222656 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0019180774688720703 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00035381317138671875 nb_pixel_total : 264 time to create 1 rle with old method : 0.000476837158203125 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 1361142853 of 90 degree temp/1748281086_935833_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 : 0.00042700767517089844 nb_pixel_total : 1389 time to create 1 rle with old method : 0.001767873764038086 .time for calcul the mask position with numpy : 0.0004296302795410156 nb_pixel_total : 1157 time to create 1 rle with old method : 0.001857757568359375 . 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 1361142853 of 105 degree temp/1748281086_935833_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 : 0.0003800392150878906 nb_pixel_total : 694 time to create 1 rle with old method : 0.0011470317840576172 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004076957702636719 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0019373893737792969 . 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 1361142853 of 120 degree temp/1748281086_935833_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 : 0.0005156993865966797 nb_pixel_total : 221 time to create 1 rle with old method : 0.0004088878631591797 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00046753883361816406 nb_pixel_total : 1155 time to create 1 rle with old method : 0.002048492431640625 . 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 1361142853 of 135 degree temp/1748281086_935833_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 : 0.0004229545593261719 nb_pixel_total : 143 time to create 1 rle with old method : 0.0003719329833984375 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004482269287109375 nb_pixel_total : 1160 time to create 1 rle with old method : 0.0020918846130371094 . 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 1361142853 of 150 degree temp/1748281086_935833_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 : 0.0004551410675048828 nb_pixel_total : 414 time to create 1 rle with old method : 0.0008103847503662109 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004818439483642578 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0021500587463378906 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0009794235229492188 nb_pixel_total : 1 time to create 1 rle with old method : 5.340576171875e-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 1361142853 of 165 degree temp/1748281086_935833_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 : 0.0005273818969726562 nb_pixel_total : 1204 time to create 1 rle with old method : 0.003113985061645508 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0007076263427734375 nb_pixel_total : 1158 time to create 1 rle with old method : 0.0019073486328125 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00045800209045410156 nb_pixel_total : 264 time to create 1 rle with old method : 0.0005292892456054688 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 1361142853 of 180 degree temp/1748281086_935833_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 : 0.0004341602325439453 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0016634464263916016 .time for calcul the mask position with numpy : 0.0003829002380371094 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0014455318450927734 . 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 1361142853 of 195 degree temp/1748281086_935833_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 : 0.00039696693420410156 nb_pixel_total : 727 time to create 1 rle with old method : 0.0009348392486572266 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00036835670471191406 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0014259815216064453 . 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 1361142853 of 210 degree temp/1748281086_935833_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 : 0.0004048347473144531 nb_pixel_total : 250 time to create 1 rle with old method : 0.00039768218994140625 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00036597251892089844 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0014581680297851562 . 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 1361142853 of 225 degree temp/1748281086_935833_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 : 0.00039124488830566406 nb_pixel_total : 169 time to create 1 rle with old method : 0.00031185150146484375 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035858154296875 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0014638900756835938 . 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 1361142853 of 240 degree temp/1748281086_935833_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 : 0.0003750324249267578 nb_pixel_total : 450 time to create 1 rle with old method : 0.0006325244903564453 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003676414489746094 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0014395713806152344 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003464221954345703 nb_pixel_total : 1 time to create 1 rle with old method : 2.4557113647460938e-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 1361142853 of 255 degree temp/1748281086_935833_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 : 0.0004324913024902344 nb_pixel_total : 1237 time to create 1 rle with old method : 0.001544952392578125 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00037169456481933594 nb_pixel_total : 1158 time to create 1 rle with old method : 0.0014297962188720703 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003833770751953125 nb_pixel_total : 234 time to create 1 rle with old method : 0.0003902912139892578 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 1361142853 of 270 degree temp/1748281086_935833_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 : 0.0004143714904785156 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0017201900482177734 .time for calcul the mask position with numpy : 0.00041365623474121094 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0014357566833496094 . 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 1361142853 of 285 degree temp/1748281086_935833_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 : 0.0004932880401611328 nb_pixel_total : 727 time to create 1 rle with old method : 0.0008988380432128906 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00039267539978027344 nb_pixel_total : 1162 time to create 1 rle with old method : 0.001495361328125 . 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 1361142853 of 300 degree temp/1748281086_935833_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 : 0.00043702125549316406 nb_pixel_total : 250 time to create 1 rle with old method : 0.0003790855407714844 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00039386749267578125 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0014719963073730469 . 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 1361142853 of 315 degree temp/1748281086_935833_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 : 0.0004062652587890625 nb_pixel_total : 169 time to create 1 rle with old method : 0.00025177001953125 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003771781921386719 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0013604164123535156 . 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 1361142853 of 330 degree temp/1748281086_935833_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 : 0.0003440380096435547 nb_pixel_total : 450 time to create 1 rle with old method : 0.0005452632904052734 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003368854522705078 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0013408660888671875 . 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 1361142853 of 345 degree temp/1748281086_935833_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 : 0.00042247772216796875 nb_pixel_total : 1237 time to create 1 rle with old method : 0.001500844955444336 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003986358642578125 nb_pixel_total : 1157 time to create 1 rle with old method : 0.025669574737548828 . 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 : 0.0003864765167236328 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 : 23354295 init cache_photo without model_param we have 24 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1748281121_935833 we have uploaded 24 photos in the portfolio 23354295 time of upload the photos Elapsed time : 6.452816486358643 map_filename_photo_id : 24 map_filename_photo_id : {'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg': 1361142856, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg': 1361142857, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg': 1361142858, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg': 1361142859, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg': 1361142860, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg': 1361142861, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg': 1361142862, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg': 1361142863, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg': 1361142864, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg': 1361142865, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg': 1361142866, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg': 1361142867, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg': 1361142868, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg': 1361142869, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg': 1361142871, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg': 1361142873, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg': 1361142874, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg': 1361142875, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg': 1361142876, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg': 1361142877, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg': 1361142878, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg': 1361142879, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg': 1361142880, 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg': 1361142881} 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 : 10.091052293777466 time spend to save output : 0.00010132789611816406 total time spend for step 3 : 10.091153621673584 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, '1361142853'] map_info['map_portfolio_photo'] : {} final : True mtd_id 243 list_pids : [937852786, 937852786, '1361142853'] Looping around the photos to save general results len do output : 24 /1361142856Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142857Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142858Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142859Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142860Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142861Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142862Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142863Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142864Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142865Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142866Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142867Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142868Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142869Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142871Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142873Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142874Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142875Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142876Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142877Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142878Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142879Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142880Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142881Didn'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, '1361142853', 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, '1361142856', 'None', None, None, None, None, None), ('243', None, '1361142857', 'None', None, None, None, None, None), ('243', None, '1361142858', 'None', None, None, None, None, None), ('243', None, '1361142859', 'None', None, None, None, None, None), ('243', None, '1361142860', 'None', None, None, None, None, None), ('243', None, '1361142861', 'None', None, None, None, None, None), ('243', None, '1361142862', 'None', None, None, None, None, None), ('243', None, '1361142863', 'None', None, None, None, None, None), ('243', None, '1361142864', 'None', None, None, None, None, None), ('243', None, '1361142865', 'None', None, None, None, None, None), ('243', None, '1361142866', 'None', None, None, None, None, None), ('243', None, '1361142867', 'None', None, None, None, None, None), ('243', None, '1361142868', 'None', None, None, None, None, None), ('243', None, '1361142869', 'None', None, None, None, None, None), ('243', None, '1361142871', 'None', None, None, None, None, None), ('243', None, '1361142873', 'None', None, None, None, None, None), ('243', None, '1361142874', 'None', None, None, None, None, None), ('243', None, '1361142875', 'None', None, None, None, None, None), ('243', None, '1361142876', 'None', None, None, None, None, None), ('243', None, '1361142877', 'None', None, None, None, None, None), ('243', None, '1361142878', 'None', None, None, None, None, None), ('243', None, '1361142879', 'None', None, None, None, None, None), ('243', None, '1361142880', 'None', None, None, None, None, None), ('243', None, '1361142881', 'None', None, None, None, None, None), ('243', None, '937852786', None, None, None, None, None, None), ('243', None, '1361142853', None, None, None, None, None, None)] time used for this insertion : 0.017946958541870117 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1361142856: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', [, ]], 1361142857: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', []], 1361142858: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', []], 1361142859: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', []], 1361142860: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', []], 1361142861: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', []], 1361142862: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', [, ]], 1361142863: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', []], 1361142864: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', []], 1361142865: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', []], 1361142866: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', []], 1361142867: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', []], 1361142868: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', [, ]], 1361142869: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', []], 1361142871: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', []], 1361142873: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', []], 1361142874: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', []], 1361142875: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', []], 1361142876: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', [, ]], 1361142877: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', []], 1361142878: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', []], 1361142879: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', []], 1361142880: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', []], 1361142881: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg', []]} ret_da : {1361142856: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', [, ]], 1361142857: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', []], 1361142858: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', []], 1361142859: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', []], 1361142860: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', []], 1361142861: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', []], 1361142862: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', [, ]], 1361142863: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', []], 1361142864: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', []], 1361142865: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', []], 1361142866: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', []], 1361142867: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', []], 1361142868: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', [, ]], 1361142869: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', []], 1361142871: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', []], 1361142873: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', []], 1361142874: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', []], 1361142875: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', []], 1361142876: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', [, ]], 1361142877: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', []], 1361142878: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', []], 1361142879: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', []], 1361142880: ['937852786', 'temp/1748281086_935833_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', []], 1361142881: ['937852786', 'temp/1748281086_935833_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 : 0.11778950691223145 #### 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 Mon May 26 19:38:49 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/1748281129_935833_911785586_d8582feabcd359151ff718b5832248c7-big.jpg': 911785586} map_photo_id_path_extension : {911785586: {'path': 'temp/1748281129_935833_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/1748281129_935833_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg Horizontal flip of photo 911785586 version de PIL : 9.5.0 horizontally flipped image is saved in temp/1748281129_935833_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/1748281130_935833 we have uploaded 2 photos in the portfolio 1090565 time of upload the photos Elapsed time : 0.8445916175842285 map_filename_photo_id : 2 map_filename_photo_id : {'temp/1748281129_935833_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg': 1361142883, 'temp/1748281129_935833_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg': 1361142884} 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 : 0.9443705081939697 time spend to save output : 8.0108642578125e-05 total time spend for step 1 : 0.9444506168365479 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 /1361142883 /1361142884 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 : 0.015027999877929688 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1361142883': ['911785586', 'temp/1748281129_935833_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg', [, , , , , ]], '1361142884': ['911785586', 'temp/1748281129_935833_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 : 0.21899724006652832 #### 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 Mon May 26 19:38:50 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00.jpg': 950103132} map_photo_id_path_extension : {950103132: {'path': 'temp/1748281130_935833_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/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670931_0.jpg', 'coordonates': (183, 199, 15, 41), 'sub_photo_id': -1, 'same_chi': False}, 1947670932: {'crop': 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670932_0.jpg', 'coordonates': (38, 85, 113, 140), 'sub_photo_id': -1, 'same_chi': False}, 1947670933: {'crop': 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670933_0.jpg', 'coordonates': (168, 194, 141, 151), 'sub_photo_id': -1, 'same_chi': False}, 1947670934: {'crop': 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670934_0.jpg', 'coordonates': (47, 101, 16, 110), 'sub_photo_id': -1, 'same_chi': False}, 1947670935: {'crop': 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670935_0.jpg', 'coordonates': (175, 199, 104, 111), 'sub_photo_id': -1, 'same_chi': False}, 1947670936: {'crop': 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670936_0.jpg', 'coordonates': (86, 130, 184, 196), 'sub_photo_id': -1, 'same_chi': False}, 1947670937: {'crop': 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670937_0.jpg', 'coordonates': (79, 195, 0, 61), 'sub_photo_id': -1, 'same_chi': False}, 1947670938: {'crop': 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1748281130_935833_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 : 23354302 in upload media Upload medias : ['temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg'] : url : https://marlene.fotonower.com/api/v1/secured/photo/upload?token=78d09a0790ec6ecbf119343125a81fdc&datou=0 temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg after data_to_send, before sending request after request b'{"photo_ids":["1361142911","1361142908","1361142889","1361142901","1361142904","1361142909","1361142890","1361142887"],"photo_ids_order":["1361142887","1361142889","1361142890","1361142901","1361142904","1361142908","1361142909","1361142911"],"photo_detail":[{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/5/26/cdcbaaa1f909b2a867445567a73cd31d.jpg","text":"TemporaryFile(/tmp/multipartBody4085829578722669771asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1748281132584,"filename":"1748281130_935833_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/2025/5/26/a45dc4f8eb672be6911ca75fe6425570.jpg","text":"TemporaryFile(/tmp/multipartBody4295580787327355338asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1748281132584,"filename":"1748281130_935833_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/2025/5/26/a8c5117997c8bff01ef4c5bdd5969271.jpg","text":"TemporaryFile(/tmp/multipartBody5120670210461896911asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1748281132584,"filename":"1748281130_935833_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/2025/5/26/0ff0489529d3731b411ac54dcf97faf3.jpg","text":"TemporaryFile(/tmp/multipartBody5031364744549188107asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1748281132584,"filename":"1748281130_935833_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/2025/5/26/46c62f6adfb7286511e4a377610ed24f.jpg","text":"TemporaryFile(/tmp/multipartBody4527488864231445510asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1748281132584,"filename":"1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/5/26/fd2dc17ba2325b04da7d2bb95fb5f0ca.jpg","text":"TemporaryFile(/tmp/multipartBody861493763537342687asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1748281132584,"filename":"1748281130_935833_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/2025/5/26/7a00c5b950fd50054b1b4974f76963fd.jpg","text":"TemporaryFile(/tmp/multipartBody1354044550619865178asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1748281132584,"filename":"1748281130_935833_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/2025/5/26/61ed6d0a7b7729e4851442fd9e6679b3.jpg","text":"TemporaryFile(/tmp/multipartBody1818539262600828797asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1748281132584,"filename":"1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg","height":0,"width":0}],"map_files_photo_id":{"file2":"1361142890","file6":"1361142909","file1":"1361142889","file7":"1361142911","file0":"1361142887","file4":"1361142904","file5":"1361142908","file3":"1361142901"},"map_files_photo_id_array":[{"photo_id":"1361142908","filename":"file5"},{"photo_id":"1361142890","filename":"file2"},{"photo_id":"1361142904","filename":"file4"},{"photo_id":"1361142911","filename":"file7"},{"photo_id":"1361142889","filename":"file1"},{"photo_id":"1361142887","filename":"file0"},{"photo_id":"1361142901","filename":"file3"},{"photo_id":"1361142909","filename":"file6"}],"portfolio_id":23354302,"hashtag_by_photo_ids":[{"1361142911":["hashtag1","hashtag2"]},{"1361142908":["hashtag1","hashtag2"]},{"1361142889":["hashtag1","hashtag2"]},{"1361142901":["hashtag1","hashtag2"]},{"1361142904":["hashtag1","hashtag2"]},{"1361142909":["hashtag1","hashtag2"]},{"1361142890":["hashtag1","hashtag2"]},{"1361142887":["hashtag1","hashtag2"]}],"comms":"Portfolio 23354302 used, photo_id : ArrayBuffer(1361142911, 1361142908, 1361142889, 1361142901, 1361142904, 1361142909, 1361142890, 1361142887)","result":[],"list_datou_current":[]}' Result OK ! uploaded one batch 0 Elapsed time : 20.46944284439087 map_result_insert : {'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg': 1361142890, 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg': 1361142909, 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg': 1361142889, 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg': 1361142911, 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg': 1361142887, 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg': 1361142904, 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg': 1361142908, 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg': 1361142901} 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/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg sub_photo_id found to be used 1361142887 chi_id found to be used 1947670932 path of cropped varroa found to be used to match on an ellipse temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg sub_photo_id found to be used 1361142889 chi_id found to be used 1947670933 path of cropped varroa found to be used to match on an ellipse temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg sub_photo_id found to be used 1361142890 chi_id found to be used 1947670934 path of cropped varroa found to be used to match on an ellipse temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg sub_photo_id found to be used 1361142901 chi_id found to be used 1947670935 path of cropped varroa found to be used to match on an ellipse temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg sub_photo_id found to be used 1361142904 chi_id found to be used 1947670936 path of cropped varroa found to be used to match on an ellipse temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg sub_photo_id found to be used 1361142908 chi_id found to be used 1947670937 path of cropped varroa found to be used to match on an ellipse temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg sub_photo_id found to be used 1361142909 chi_id found to be used 1947670938 path of cropped varroa found to be used to match on an ellipse temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg sub_photo_id found to be used 1361142911 insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(1947670931, '1361142887', 31), (1947670932, '1361142889', 31), (1947670933, '1361142890', 31), (1947670934, '1361142901', 31), (1947670935, '1361142904', 31), (1947670936, '1361142908', 31), (1947670937, '1361142909', 31), (1947670938, '1361142911', 31)] map of cropped photos with some data : {'1361142887': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1361142889': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1361142890': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1361142901': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1361142904': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1361142908': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1361142909': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1361142911': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} After datou_step_exec type output : time spend for datou_step_exec : 20.527974367141724 time spend to save output : 4.1961669921875e-05 total time spend for step 1 : 20.528016328811646 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 /1361142887Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142889Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142890Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142901Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142904Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142908Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142909Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361142911Didn'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, '1361142887', 'None', None, None, None, None, None), ('686', None, '1361142889', 'None', None, None, None, None, None), ('686', None, '1361142890', 'None', None, None, None, None, None), ('686', None, '1361142901', 'None', None, None, None, None, None), ('686', None, '1361142904', 'None', None, None, None, None, None), ('686', None, '1361142908', 'None', None, None, None, None, None), ('686', None, '1361142909', 'None', None, None, None, None, None), ('686', None, '1361142911', 'None', None, None, None, None, None), ('686', None, '950103132', None, None, None, None, None, None)] time used for this insertion : 0.014560222625732422 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1361142887': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1361142889': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1361142890': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1361142901': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1361142904': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1361142908': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1361142909': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1361142911': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} ret_da : {'1361142887': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1361142889': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1361142890': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1361142901': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1361142904': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1361142908': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1361142909': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1361142911': ['950103132', 'temp/1748281130_935833_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} 8 Found filename_to_hash : temp/1748281130_935833_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 : 0.16803860664367676 #### 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 Mon May 26 19:39:11 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/1748281151_935833_932296368_97c5e7b0f2830e550e2d6eeb248d8006.jpg': 932296368} map_photo_id_path_extension : {932296368: {'path': 'temp/1748281151_935833_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 : 0.09192180633544922 time spend to save output : 0.0009598731994628906 total time spend for step 1 : 0.09288167953491211 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 : 0.12319564819335938 #### 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 Mon May 26 19:39:11 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/1748281151_935833_946711423_b4bef6b5c6c4b6ffae23f8718c42183c.jpg': 946711423} map_photo_id_path_extension : {946711423: {'path': 'temp/1748281151_935833_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 : 0.1194298267364502 time spend to save output : 2.5272369384765625e-05 total time spend for step 1 : 0.11945509910583496 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', 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'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 (3813169021,3813169020,3813169019,3813169028,3813169027,3813169026,3813169025,3813169024,3813169033,3813169036,3813169022,3813169023,3813169032,3813169031,3813169037,3813169038,3813169039,3813169041,3813169029,3813169035,3813169034,3813169040,3813169030) 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 : 0.0046443939208984375 #### 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 Mon May 26 19:39:12 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : ('3813396456', '117', '95', '16') ... last line : ('3813396478', '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 : 0.29587841033935547 time spend to save output : 0.00012922286987304688 total time spend for step 1 : 0.2960076332092285 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 : 0.11383199691772461 #### 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 Mon May 26 19:39:12 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281152_935833_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg': 930729675} map_photo_id_path_extension : {930729675: {'path': 'temp/1748281152_935833_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} inside step blur_detection methode: ratio et variance treat image : temp/1748281152_935833_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 : 0.20108318328857422 time spend to save output : 4.9114227294921875e-05 total time spend for step 1 : 0.20113229751586914 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 987515239 begin to download photo : 987515240 download finish for photo 987515188 begin to download photo : 987515189 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 987515240 begin to download photo : 987515241 download finish for photo 987515189 begin to download photo : 987515190 download finish for photo 987515226 begin to download photo : 987515227 download finish for photo 987515208 begin to download photo : 987515209 download finish for photo 987515176 begin to download photo : 987515177 download finish for photo 987515241 begin to download photo : 987515242 download finish for photo 987515190 begin to download photo : 987515192 download finish for photo 987515227 begin to download photo : 987515228 download finish for photo 987515209 begin to download photo : 987515211 download finish for photo 987515177 begin to download photo : 987515178 download finish for photo 987515242 begin to download photo : 987515243 download finish for photo 987515192 begin to download photo : 987515193 download finish for photo 987515228 begin to download photo : 987515230 download finish for photo 987515211 begin to download photo : 987515212 download finish for photo 987515243 begin to download photo : 987515244 download finish for photo 987515193 begin to download photo : 987515195 download finish for photo 987515178 begin to download photo : 987515179 download finish for photo 987515212 begin to download photo : 987515213 download finish for photo 987515230 begin to download photo : 987515231 download finish for photo 987515195 begin to download photo : 987515196 download finish for photo 987515244 begin to download photo : 987515245 download finish for photo 987515213 begin to download photo : 987515215 download finish for photo 987515196 begin to download photo : 987515198 download finish for photo 987515179 begin to download photo : 987515180 download finish for photo 987515231 begin to download photo : 987515232 download finish for photo 987515215 begin to download photo : 987515216 download finish for photo 987515198 begin to download photo : 987515200 download finish for photo 987515245 begin to download photo : 987515246 download finish for photo 987515232 begin to download photo : 987515233 download finish for photo 987515180 begin to download photo : 987515181 download finish for photo 987515216 begin to download photo : 987515217 download finish for photo 987515200 begin to download photo : 987515201 download finish for photo 987515233 begin to download photo : 987515234 download finish for photo 987515246 begin to download photo : 987515247 download finish for photo 987515181 begin to download photo : 987515182 download finish for photo 987515201 begin to download photo : 987515202 download finish for photo 987515234 begin to download photo : 987515235 download finish for photo 987515247 download finish for photo 987515182 begin to download photo : 987515248 begin to download photo : 987515183 download finish for photo 987515202 begin to download photo : 987515204 download finish for photo 987515248 begin to download photo : 987515249 download finish for photo 987515183 begin to download photo : 987515184 download finish for photo 987515235 begin to download photo : 987515236 download finish for photo 987515249 begin to download photo : 987515250 download finish for photo 987515204 begin to download photo : 987515205 download finish for photo 987515184 begin to download photo : 987515185 download finish for photo 987515236 begin to download photo : 987515237 download finish for photo 987515185 begin to download photo : 987515186 download finish for photo 987515205 download finish for photo 987515237 begin to download photo : 987515238 download finish for photo 987515250 download finish for photo 987515186 begin to download photo : 987515187 download finish for photo 987515238 download finish for photo 987515187 download finish for photo 987515217 begin to download photo : 987515219 download finish for photo 987515219 begin to download photo : 987515220 download finish for photo 987515220 begin to download photo : 987515222 download finish for photo 987515222 begin to download photo : 987515223 download finish for photo 987515223 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 : 2.175487756729126 #### 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 Mon May 26 19:39:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281152_935833_987515188_4116f9906657a69bb76c2fda982037b9.jpg': 987515188, 'temp/1748281152_935833_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg': 987515189, 'temp/1748281152_935833_987515190_d56932bfc6ba2a8c974c691108755017.jpg': 987515190, 'temp/1748281152_935833_987515192_b661073b218f5f056833d6af1c617153.jpg': 987515192, 'temp/1748281152_935833_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg': 987515193, 'temp/1748281152_935833_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg': 987515195, 'temp/1748281152_935833_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg': 987515196, 'temp/1748281152_935833_987515198_599e80f444c876f407e94b533c89360b.jpg': 987515198, 'temp/1748281152_935833_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg': 987515200, 'temp/1748281152_935833_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg': 987515201, 'temp/1748281152_935833_987515202_3314bd90d1404f31b827d8925abf2d62.jpg': 987515202, 'temp/1748281152_935833_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg': 987515204, 'temp/1748281152_935833_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg': 987515205, 'temp/1748281152_935833_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg': 987515239, 'temp/1748281152_935833_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg': 987515240, 'temp/1748281152_935833_987515241_073420d938f5f010ffd5b4353c064e09.jpg': 987515241, 'temp/1748281152_935833_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg': 987515242, 'temp/1748281152_935833_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg': 987515243, 'temp/1748281152_935833_987515244_419530eaef5ef868f75c758b94eea4b4.jpg': 987515244, 'temp/1748281152_935833_987515245_757d9d208d5bd4375c5f21f68b699148.jpg': 987515245, 'temp/1748281152_935833_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg': 987515246, 'temp/1748281152_935833_987515247_e47b65403df916ba909bc9c439b0af73.jpg': 987515247, 'temp/1748281152_935833_987515248_a70ad88462a22fb62a120721a42b2d42.jpg': 987515248, 'temp/1748281152_935833_987515249_a70ad88462a22fb62a120721a42b2d42.jpg': 987515249, 'temp/1748281152_935833_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg': 987515250, 'temp/1748281152_935833_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg': 987515224, 'temp/1748281152_935833_987515226_a18048dca1a77ae086b62cf07759f704.jpg': 987515226, 'temp/1748281152_935833_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg': 987515227, 'temp/1748281152_935833_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg': 987515228, 'temp/1748281152_935833_987515230_846ad925884264181565c81d152a2e94.jpg': 987515230, 'temp/1748281152_935833_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg': 987515231, 'temp/1748281152_935833_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg': 987515232, 'temp/1748281152_935833_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg': 987515233, 'temp/1748281152_935833_987515234_2eca3480aed0f8b876242675ad99b666.jpg': 987515234, 'temp/1748281152_935833_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg': 987515235, 'temp/1748281152_935833_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg': 987515236, 'temp/1748281152_935833_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg': 987515237, 'temp/1748281152_935833_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg': 987515238, 'temp/1748281152_935833_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg': 987515175, 'temp/1748281152_935833_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg': 987515176, 'temp/1748281152_935833_987515177_4a54e9967227806219ddf45d256539d8.jpg': 987515177, 'temp/1748281152_935833_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg': 987515178, 'temp/1748281152_935833_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg': 987515179, 'temp/1748281152_935833_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg': 987515180, 'temp/1748281152_935833_987515181_1738c2798fb31152809ecb443ac286d6.jpg': 987515181, 'temp/1748281152_935833_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg': 987515182, 'temp/1748281152_935833_987515183_6aab9ca0421398b4899892c10c2594c6.jpg': 987515183, 'temp/1748281152_935833_987515184_19c8c2177209a285df6014d95fe53f2c.jpg': 987515184, 'temp/1748281152_935833_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg': 987515185, 'temp/1748281152_935833_987515186_797def426440b544aa80dbd63a19234a.jpg': 987515186, 'temp/1748281152_935833_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg': 987515187, 'temp/1748281152_935833_987515207_de216ddb041e249524b0fb2b949064a5.jpg': 987515207, 'temp/1748281152_935833_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg': 987515208, 'temp/1748281152_935833_987515209_02dfe1ae39f51994652f4a8538844aea.jpg': 987515209, 'temp/1748281152_935833_987515211_72cc7664d45bd40477351b9b764f1500.jpg': 987515211, 'temp/1748281152_935833_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg': 987515212, 'temp/1748281152_935833_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg': 987515213, 'temp/1748281152_935833_987515215_902ef348a7eebb9a8b87f42927347936.jpg': 987515215, 'temp/1748281152_935833_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg': 987515216, 'temp/1748281152_935833_987515217_78877bb2c5760be28518d17f77d1c609.jpg': 987515217, 'temp/1748281152_935833_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg': 987515219, 'temp/1748281152_935833_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg': 987515220, 'temp/1748281152_935833_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg': 987515222, 'temp/1748281152_935833_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg': 987515223} map_photo_id_path_extension : {987515188: {'path': 'temp/1748281152_935833_987515188_4116f9906657a69bb76c2fda982037b9.jpg', 'extension': 'jpg'}, 987515189: {'path': 'temp/1748281152_935833_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg', 'extension': 'jpg'}, 987515190: {'path': 'temp/1748281152_935833_987515190_d56932bfc6ba2a8c974c691108755017.jpg', 'extension': 'jpg'}, 987515192: {'path': 'temp/1748281152_935833_987515192_b661073b218f5f056833d6af1c617153.jpg', 'extension': 'jpg'}, 987515193: {'path': 'temp/1748281152_935833_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg', 'extension': 'jpg'}, 987515195: {'path': 'temp/1748281152_935833_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg', 'extension': 'jpg'}, 987515196: {'path': 'temp/1748281152_935833_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg', 'extension': 'jpg'}, 987515198: {'path': 'temp/1748281152_935833_987515198_599e80f444c876f407e94b533c89360b.jpg', 'extension': 'jpg'}, 987515200: {'path': 'temp/1748281152_935833_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg', 'extension': 'jpg'}, 987515201: {'path': 'temp/1748281152_935833_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg', 'extension': 'jpg'}, 987515202: {'path': 'temp/1748281152_935833_987515202_3314bd90d1404f31b827d8925abf2d62.jpg', 'extension': 'jpg'}, 987515204: {'path': 'temp/1748281152_935833_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg', 'extension': 'jpg'}, 987515205: {'path': 'temp/1748281152_935833_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg', 'extension': 'jpg'}, 987515239: {'path': 'temp/1748281152_935833_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg', 'extension': 'jpg'}, 987515240: {'path': 'temp/1748281152_935833_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg', 'extension': 'jpg'}, 987515241: {'path': 'temp/1748281152_935833_987515241_073420d938f5f010ffd5b4353c064e09.jpg', 'extension': 'jpg'}, 987515242: {'path': 'temp/1748281152_935833_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg', 'extension': 'jpg'}, 987515243: {'path': 'temp/1748281152_935833_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg', 'extension': 'jpg'}, 987515244: {'path': 'temp/1748281152_935833_987515244_419530eaef5ef868f75c758b94eea4b4.jpg', 'extension': 'jpg'}, 987515245: {'path': 'temp/1748281152_935833_987515245_757d9d208d5bd4375c5f21f68b699148.jpg', 'extension': 'jpg'}, 987515246: {'path': 'temp/1748281152_935833_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg', 'extension': 'jpg'}, 987515247: {'path': 'temp/1748281152_935833_987515247_e47b65403df916ba909bc9c439b0af73.jpg', 'extension': 'jpg'}, 987515248: {'path': 'temp/1748281152_935833_987515248_a70ad88462a22fb62a120721a42b2d42.jpg', 'extension': 'jpg'}, 987515249: {'path': 'temp/1748281152_935833_987515249_a70ad88462a22fb62a120721a42b2d42.jpg', 'extension': 'jpg'}, 987515250: {'path': 'temp/1748281152_935833_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg', 'extension': 'jpg'}, 987515224: {'path': 'temp/1748281152_935833_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg', 'extension': 'jpg'}, 987515226: {'path': 'temp/1748281152_935833_987515226_a18048dca1a77ae086b62cf07759f704.jpg', 'extension': 'jpg'}, 987515227: {'path': 'temp/1748281152_935833_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg', 'extension': 'jpg'}, 987515228: {'path': 'temp/1748281152_935833_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg', 'extension': 'jpg'}, 987515230: {'path': 'temp/1748281152_935833_987515230_846ad925884264181565c81d152a2e94.jpg', 'extension': 'jpg'}, 987515231: {'path': 'temp/1748281152_935833_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg', 'extension': 'jpg'}, 987515232: {'path': 'temp/1748281152_935833_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg', 'extension': 'jpg'}, 987515233: {'path': 'temp/1748281152_935833_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg', 'extension': 'jpg'}, 987515234: {'path': 'temp/1748281152_935833_987515234_2eca3480aed0f8b876242675ad99b666.jpg', 'extension': 'jpg'}, 987515235: {'path': 'temp/1748281152_935833_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg', 'extension': 'jpg'}, 987515236: {'path': 'temp/1748281152_935833_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg', 'extension': 'jpg'}, 987515237: {'path': 'temp/1748281152_935833_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg', 'extension': 'jpg'}, 987515238: {'path': 'temp/1748281152_935833_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg', 'extension': 'jpg'}, 987515175: {'path': 'temp/1748281152_935833_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg', 'extension': 'jpg'}, 987515176: {'path': 'temp/1748281152_935833_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg', 'extension': 'jpg'}, 987515177: {'path': 'temp/1748281152_935833_987515177_4a54e9967227806219ddf45d256539d8.jpg', 'extension': 'jpg'}, 987515178: {'path': 'temp/1748281152_935833_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg', 'extension': 'jpg'}, 987515179: {'path': 'temp/1748281152_935833_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg', 'extension': 'jpg'}, 987515180: {'path': 'temp/1748281152_935833_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg', 'extension': 'jpg'}, 987515181: {'path': 'temp/1748281152_935833_987515181_1738c2798fb31152809ecb443ac286d6.jpg', 'extension': 'jpg'}, 987515182: {'path': 'temp/1748281152_935833_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg', 'extension': 'jpg'}, 987515183: {'path': 'temp/1748281152_935833_987515183_6aab9ca0421398b4899892c10c2594c6.jpg', 'extension': 'jpg'}, 987515184: {'path': 'temp/1748281152_935833_987515184_19c8c2177209a285df6014d95fe53f2c.jpg', 'extension': 'jpg'}, 987515185: {'path': 'temp/1748281152_935833_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg', 'extension': 'jpg'}, 987515186: {'path': 'temp/1748281152_935833_987515186_797def426440b544aa80dbd63a19234a.jpg', 'extension': 'jpg'}, 987515187: {'path': 'temp/1748281152_935833_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg', 'extension': 'jpg'}, 987515207: {'path': 'temp/1748281152_935833_987515207_de216ddb041e249524b0fb2b949064a5.jpg', 'extension': 'jpg'}, 987515208: {'path': 'temp/1748281152_935833_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg', 'extension': 'jpg'}, 987515209: {'path': 'temp/1748281152_935833_987515209_02dfe1ae39f51994652f4a8538844aea.jpg', 'extension': 'jpg'}, 987515211: {'path': 'temp/1748281152_935833_987515211_72cc7664d45bd40477351b9b764f1500.jpg', 'extension': 'jpg'}, 987515212: {'path': 'temp/1748281152_935833_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg', 'extension': 'jpg'}, 987515213: {'path': 'temp/1748281152_935833_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg', 'extension': 'jpg'}, 987515215: {'path': 'temp/1748281152_935833_987515215_902ef348a7eebb9a8b87f42927347936.jpg', 'extension': 'jpg'}, 987515216: {'path': 'temp/1748281152_935833_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg', 'extension': 'jpg'}, 987515217: {'path': 'temp/1748281152_935833_987515217_78877bb2c5760be28518d17f77d1c609.jpg', 'extension': 'jpg'}, 987515219: {'path': 'temp/1748281152_935833_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg', 'extension': 'jpg'}, 987515220: {'path': 'temp/1748281152_935833_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg', 'extension': 'jpg'}, 987515222: {'path': 'temp/1748281152_935833_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg', 'extension': 'jpg'}, 987515223: {'path': 'temp/1748281152_935833_987515223_ebb57f09941cd11d7ee45a9368a883c1.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 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 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 : 0.0005161762237548828 time to convert the images to numpy array : 0.006988048553466797 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 ! 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 : 0.00454258918762207 time to convert the images to numpy array : 0.037673234939575195 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 : 0.006471157073974609 time to convert the images to numpy array : 0.04053974151611328 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 : 0.007012128829956055 time to convert the images to numpy array : 0.039875030517578125 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 : 0.0026960372924804688 time to convert the images to numpy array : 0.045908451080322266 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 : 0.011305093765258789 time to convert the images to numpy array : 0.0397648811340332 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 : 0.009720563888549805 time to convert the images to numpy array : 0.03764629364013672 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 : 0.009415864944458008 time to convert the images to numpy array : 0.039459228515625 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 : 0.011009454727172852 time to convert the images to numpy array : 0.04124903678894043 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 : 0.015903234481811523 time to convert the images to numpy array : 0.034027099609375 total time to convert the images to numpy array : 0.05337238311767578 list photo_ids error: [] list photo_ids correct : [987515223, 987515198, 987515200, 987515201, 987515202, 987515204, 987515205, 987515239, 987515228, 987515230, 987515231, 987515232, 987515233, 987515234, 987515235, 987515236, 987515237, 987515238, 987515175, 987515176, 987515177, 987515178, 987515247, 987515248, 987515249, 987515250, 987515224, 987515226, 987515227, 987515188, 987515189, 987515190, 987515192, 987515193, 987515195, 987515196, 987515213, 987515215, 987515216, 987515217, 987515219, 987515220, 987515222, 987515179, 987515180, 987515181, 987515182, 987515183, 987515184, 987515185, 987515240, 987515241, 987515242, 987515243, 987515244, 987515245, 987515246, 987515186, 987515187, 987515207, 987515208, 987515209, 987515211, 987515212] 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 : 6589 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 : 6589 max_wait_temp : 1 max_wait : 0 dict_keys(['res5b', 'prob']) time used to do the prepocess of the images : 0.07729101181030273 time used to do the prediction : 0.26601171493530273 save descriptor for thcl : 1528 (64, 512, 7, 7) 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 : [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 : [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 : [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 : [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 : [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 : [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 : [3, 3, 1, 4, 7, 7, 9, 6, 2, 1] code_as_byte_string:b'0303010407'| 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 : [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, 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'| Got the blobs of the net to insert : [5, 3, 3, 6, 8, 12, 9, 9, 3, 5] code_as_byte_string:b'0503030608'| 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 : [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 : [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 : [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 : [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, 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 : [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, 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 : [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'| time to traite the descriptors : 3.2591869831085205 Testing : ['987515223', '987515198', '987515200', '987515201', '987515202', '987515204', '987515205', '987515239', '987515228', '987515230', '987515231', '987515232', '987515233', '987515234', '987515235', '987515236', '987515237', '987515238', '987515175', '987515176', '987515177', '987515178', '987515247', '987515248', '987515249', '987515250', '987515224', '987515226', '987515227', '987515188', '987515189', '987515190', '987515192', '987515193', '987515195', '987515196', '987515213', '987515215', '987515216', '987515217', '987515219', '987515220', '987515222', '987515179', '987515180', '987515181', '987515182', '987515183', '987515184', '987515185', '987515240', '987515241', '987515242', '987515243', '987515244', '987515245', '987515246', '987515186', '987515187', '987515207', '987515208', '987515209', '987515211', '987515212'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (987515223,987515198,987515200,987515201,987515202,987515204,987515205,987515239,987515228,987515230,987515231,987515232,987515233,987515234,987515235,987515236,987515237,987515238,987515175,987515176,987515177,987515178,987515247,987515248,987515249,987515250,987515224,987515226,987515227,987515188,987515189,987515190,987515192,987515193,987515195,987515196,987515213,987515215,987515216,987515217,987515219,987515220,987515222,987515179,987515180,987515181,987515182,987515183,987515184,987515185,987515240,987515241,987515242,987515243,987515244,987515245,987515246,987515186,987515187,987515207,987515208,987515209,987515211,987515212) 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': 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'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 : 987515223 To insert : 987515198 To insert : 987515200 To insert : 987515201 To insert : 987515202 To insert : 987515204 To insert : 987515205 To insert : 987515239 To insert : 987515228 To insert : 987515230 To insert : 987515231 To insert : 987515232 To insert : 987515233 To insert : 987515234 To insert : 987515235 To insert : 987515236 To insert : 987515237 To insert : 987515238 To insert : 987515175 To insert : 987515176 To insert : 987515177 To insert : 987515178 To insert : 987515247 To insert : 987515248 To insert : 987515249 To insert : 987515250 To insert : 987515224 To insert : 987515226 To insert : 987515227 To insert : 987515188 To insert : 987515189 To insert : 987515190 To insert : 987515192 To insert : 987515193 To insert : 987515195 To insert : 987515196 To insert : 987515213 To insert : 987515215 To insert : 987515216 To insert : 987515217 To insert : 987515219 To insert : 987515220 To insert : 987515222 To insert : 987515179 To insert : 987515180 To insert : 987515181 To insert : 987515182 To insert : 987515183 To insert : 987515184 To insert : 987515185 To insert : 987515240 To insert : 987515241 To insert : 987515242 To insert : 987515243 To insert : 987515244 To insert : 987515245 To insert : 987515246 To insert : 987515186 To insert : 987515187 To insert : 987515207 To insert : 987515208 To insert : 987515209 To insert : 987515211 To insert : 987515212 time to insert the descriptors : 13.328672647476196 After datou_step_exec type output : time spend for datou_step_exec : 20.567112922668457 time spend to save output : 8.440017700195312e-05 total time spend for step 1 : 20.56719732284546 step2:argmax Mon May 26 19:39:35 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/1748281152_935833_987515188_4116f9906657a69bb76c2fda982037b9.jpg': 987515188, 'temp/1748281152_935833_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg': 987515189, 'temp/1748281152_935833_987515190_d56932bfc6ba2a8c974c691108755017.jpg': 987515190, 'temp/1748281152_935833_987515192_b661073b218f5f056833d6af1c617153.jpg': 987515192, 'temp/1748281152_935833_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg': 987515193, 'temp/1748281152_935833_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg': 987515195, 'temp/1748281152_935833_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg': 987515196, 'temp/1748281152_935833_987515198_599e80f444c876f407e94b533c89360b.jpg': 987515198, 'temp/1748281152_935833_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg': 987515200, 'temp/1748281152_935833_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg': 987515201, 'temp/1748281152_935833_987515202_3314bd90d1404f31b827d8925abf2d62.jpg': 987515202, 'temp/1748281152_935833_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg': 987515204, 'temp/1748281152_935833_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg': 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'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 : 0.0008137226104736328 time spend to save output : 7.343292236328125e-05 total time spend for step 2 : 0.0008871555328369141 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'987515223': [('987515223', 'Carton', 0.9921027, 1927, '1528'), 'temp/1748281152_935833_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg'], '987515198': [('987515198', 'Carton', 0.96630985, 1927, '1528'), 'temp/1748281152_935833_987515198_599e80f444c876f407e94b533c89360b.jpg'], '987515200': [('987515200', 'Carton', 0.985992, 1927, '1528'), 'temp/1748281152_935833_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg'], '987515201': [('987515201', 'Carton', 0.99546534, 1927, '1528'), 'temp/1748281152_935833_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg'], '987515202': [('987515202', 'Carton', 0.99111736, 1927, '1528'), 'temp/1748281152_935833_987515202_3314bd90d1404f31b827d8925abf2d62.jpg'], '987515204': [('987515204', 'Papier_Magazine', 0.99508345, 1927, '1528'), 'temp/1748281152_935833_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg'], '987515205': [('987515205', 'Papier_Magazine', 0.9908875, 1927, '1528'), 'temp/1748281152_935833_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg'], '987515239': [('987515239', 'Carton', 0.99978346, 1927, '1528'), 'temp/1748281152_935833_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg'], '987515228': [('987515228', 'Papier_Magazine', 0.5214702, 1927, '1528'), 'temp/1748281152_935833_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg'], '987515230': [('987515230', 'Carton', 0.9994061, 1927, '1528'), 'temp/1748281152_935833_987515230_846ad925884264181565c81d152a2e94.jpg'], '987515231': [('987515231', 'Carton', 0.9994209, 1927, '1528'), 'temp/1748281152_935833_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg'], '987515232': [('987515232', 'Carton', 0.99924374, 1927, '1528'), 'temp/1748281152_935833_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg'], '987515233': [('987515233', 'Carton', 0.983352, 1927, '1528'), 'temp/1748281152_935833_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg'], '987515234': [('987515234', 'Carton', 0.94478124, 1927, '1528'), 'temp/1748281152_935833_987515234_2eca3480aed0f8b876242675ad99b666.jpg'], '987515235': [('987515235', 'Papier_Magazine', 0.8918476, 1927, '1528'), 'temp/1748281152_935833_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg'], '987515236': [('987515236', 'Papier_Magazine', 0.5370823, 1927, '1528'), 'temp/1748281152_935833_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg'], '987515237': [('987515237', 'Carton', 0.7700954, 1927, '1528'), 'temp/1748281152_935833_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg'], '987515238': [('987515238', 'Carton', 0.99957496, 1927, '1528'), 'temp/1748281152_935833_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg'], '987515175': [('987515175', 'Papier_Magazine', 0.9998128, 1927, '1528'), 'temp/1748281152_935833_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515176': [('987515176', 'Papier_Magazine', 0.9998142, 1927, '1528'), 'temp/1748281152_935833_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515177': [('987515177', 'Papier_Magazine', 0.97717094, 1927, '1528'), 'temp/1748281152_935833_987515177_4a54e9967227806219ddf45d256539d8.jpg'], '987515178': [('987515178', 'Carton', 0.8571688, 1927, '1528'), 'temp/1748281152_935833_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg'], '987515247': [('987515247', 'Carton', 0.9996685, 1927, '1528'), 'temp/1748281152_935833_987515247_e47b65403df916ba909bc9c439b0af73.jpg'], '987515248': [('987515248', 'Carton', 0.9813719, 1927, '1528'), 'temp/1748281152_935833_987515248_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515249': [('987515249', 'Carton', 0.9813122, 1927, '1528'), 'temp/1748281152_935833_987515249_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515250': [('987515250', 'Carton', 0.9808008, 1927, '1528'), 'temp/1748281152_935833_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg'], '987515224': [('987515224', 'Carton', 0.9084086, 1927, '1528'), 'temp/1748281152_935833_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg'], '987515226': [('987515226', 'Papier_Magazine', 0.98696923, 1927, '1528'), 'temp/1748281152_935833_987515226_a18048dca1a77ae086b62cf07759f704.jpg'], '987515227': [('987515227', 'Papier_Magazine', 0.9000315, 1927, '1528'), 'temp/1748281152_935833_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg'], '987515188': [('987515188', 'Carton', 0.9956311, 1927, '1528'), 'temp/1748281152_935833_987515188_4116f9906657a69bb76c2fda982037b9.jpg'], '987515189': [('987515189', 'Carton', 0.9977888, 1927, '1528'), 'temp/1748281152_935833_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg'], '987515190': [('987515190', 'Carton', 0.97630787, 1927, '1528'), 'temp/1748281152_935833_987515190_d56932bfc6ba2a8c974c691108755017.jpg'], '987515192': [('987515192', 'Papier_Magazine', 0.9999113, 1927, '1528'), 'temp/1748281152_935833_987515192_b661073b218f5f056833d6af1c617153.jpg'], '987515193': [('987515193', 'Papier_Magazine', 0.99939644, 1927, '1528'), 'temp/1748281152_935833_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg'], '987515195': [('987515195', 'Carton', 0.9846464, 1927, '1528'), 'temp/1748281152_935833_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515196': [('987515196', 'Carton', 0.98463976, 1927, '1528'), 'temp/1748281152_935833_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515213': [('987515213', 'Carton', 0.9869257, 1927, '1528'), 'temp/1748281152_935833_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515215': [('987515215', 'Papier_Magazine', 0.99391115, 1927, '1528'), 'temp/1748281152_935833_987515215_902ef348a7eebb9a8b87f42927347936.jpg'], '987515216': [('987515216', 'Papier_Magazine', 0.97740084, 1927, '1528'), 'temp/1748281152_935833_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg'], '987515217': [('987515217', 'Carton', 0.52912277, 1927, '1528'), 'temp/1748281152_935833_987515217_78877bb2c5760be28518d17f77d1c609.jpg'], '987515219': [('987515219', 'Carton', 0.9993699, 1927, '1528'), 'temp/1748281152_935833_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg'], '987515220': [('987515220', 'Carton', 0.9963785, 1927, '1528'), 'temp/1748281152_935833_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg'], '987515222': [('987515222', 'Carton', 0.99747014, 1927, '1528'), 'temp/1748281152_935833_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg'], '987515179': [('987515179', 'Carton', 0.9269919, 1927, '1528'), 'temp/1748281152_935833_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg'], '987515180': [('987515180', 'Carton', 0.98998004, 1927, '1528'), 'temp/1748281152_935833_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg'], '987515181': [('987515181', 'Carton', 0.99778, 1927, '1528'), 'temp/1748281152_935833_987515181_1738c2798fb31152809ecb443ac286d6.jpg'], '987515182': [('987515182', 'Carton', 0.99242216, 1927, '1528'), 'temp/1748281152_935833_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg'], '987515183': [('987515183', 'Papier_Magazine', 0.99999225, 1927, '1528'), 'temp/1748281152_935833_987515183_6aab9ca0421398b4899892c10c2594c6.jpg'], '987515184': [('987515184', 'Papier_Magazine', 0.99973243, 1927, '1528'), 'temp/1748281152_935833_987515184_19c8c2177209a285df6014d95fe53f2c.jpg'], '987515185': [('987515185', 'Papier_Magazine', 0.7979338, 1927, '1528'), 'temp/1748281152_935833_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg'], '987515240': [('987515240', 'Carton', 0.99952054, 1927, '1528'), 'temp/1748281152_935833_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg'], '987515241': [('987515241', 'Carton', 0.98216665, 1927, '1528'), 'temp/1748281152_935833_987515241_073420d938f5f010ffd5b4353c064e09.jpg'], '987515242': [('987515242', 'Carton', 0.9357916, 1927, '1528'), 'temp/1748281152_935833_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg'], '987515243': [('987515243', 'Papier_Magazine', 0.87418824, 1927, '1528'), 'temp/1748281152_935833_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg'], '987515244': [('987515244', 'Papier_Magazine', 0.81749, 1927, '1528'), 'temp/1748281152_935833_987515244_419530eaef5ef868f75c758b94eea4b4.jpg'], '987515245': [('987515245', 'Carton', 0.86590785, 1927, '1528'), 'temp/1748281152_935833_987515245_757d9d208d5bd4375c5f21f68b699148.jpg'], '987515246': [('987515246', 'Carton', 0.99923277, 1927, '1528'), 'temp/1748281152_935833_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg'], '987515186': [('987515186', 'Carton', 0.9847348, 1927, '1528'), 'temp/1748281152_935833_987515186_797def426440b544aa80dbd63a19234a.jpg'], '987515187': [('987515187', 'Carton', 0.98108, 1927, '1528'), 'temp/1748281152_935833_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg'], '987515207': [('987515207', 'Papier_Magazine', 0.8739247, 1927, '1528'), 'temp/1748281152_935833_987515207_de216ddb041e249524b0fb2b949064a5.jpg'], '987515208': [('987515208', 'Carton', 0.99171805, 1927, '1528'), 'temp/1748281152_935833_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg'], '987515209': [('987515209', 'Carton', 0.9677494, 1927, '1528'), 'temp/1748281152_935833_987515209_02dfe1ae39f51994652f4a8538844aea.jpg'], '987515211': [('987515211', 'Carton', 0.9733993, 1927, '1528'), 'temp/1748281152_935833_987515211_72cc7664d45bd40477351b9b764f1500.jpg'], '987515212': [('987515212', 'Carton', 0.9869192, 1927, '1528'), 'temp/1748281152_935833_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.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 : 0.12400174140930176 #### 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 Mon May 26 19:39:35 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/1748281175_935833_987515173_91fa471b1a04f95b356afdbaf021f623.jpg': 987515173} map_photo_id_path_extension : {987515173: {'path': 'temp/1748281175_935833_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/1748281175_935833_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 : 0.048601388931274414 time to do a prediction : 0.3628065586090088 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 : 1.7474167346954346 time spend to save output : 3.337860107421875e-05 total time spend for step 1 : 1.7474501132965088 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, 6.283178838328851e-12), (987515173, 1982, 'Autre_Environement', 144, -1, 112, -1, 2.4503879827997288e-11), (987515173, 1982, 'Autre_Environement', 176, -1, 112, -1, 1.0680001771845582e-08), (987515173, 1982, 'Autre_Environement', 208, -1, 112, -1, 4.443886041372025e-07), (987515173, 1982, 'Autre_Environement', 240, -1, 112, -1, 1.9239421362726716e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 112, -1, 3.7821046134922653e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 112, -1, 0.00012273724132683128), (987515173, 1982, 'Autre_Environement', 336, -1, 112, -1, 2.9429922506096773e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 144, -1, 2.371046292637402e-08), (987515173, 1982, 'Autre_Environement', 144, -1, 144, -1, 2.2147851552745124e-08), (987515173, 1982, 'Autre_Environement', 176, -1, 144, -1, 1.3817584942898975e-07), (987515173, 1982, 'Autre_Environement', 208, -1, 144, -1, 1.4752808965567965e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 144, -1, 1.128052463172935e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 144, -1, 0.0001579524832777679), (987515173, 1982, 'Autre_Environement', 304, -1, 144, -1, 0.0004443585348781198), (987515173, 1982, 'Autre_Environement', 336, -1, 144, -1, 6.547853990923613e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 176, -1, 1.3311810107552446e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 176, -1, 1.6260810298263095e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 176, -1, 2.511302682250971e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 176, -1, 1.6165585066119093e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 176, -1, 6.267458957154304e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 176, -1, 8.652028191136196e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 176, -1, 0.0003255842311773449), (987515173, 1982, 'Autre_Environement', 336, -1, 176, -1, 0.0003049863735213876), (987515173, 1982, 'Autre_Environement', 112, -1, 208, -1, 1.8557004295871593e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 208, -1, 7.920424650365021e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 208, -1, 2.7021578716812655e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 208, -1, 1.804476596589666e-05), (987515173, 1982, 'Autre_Environement', 240, -1, 208, -1, 2.3460499505745247e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 208, -1, 1.6999001672957093e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 208, -1, 4.548016022454249e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 208, -1, 8.790422725724056e-06), (987515173, 1982, 'Autre_Environement', 112, -1, 240, -1, 6.104938620410394e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 240, -1, 1.6477334838782554e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 240, -1, 1.963057911780197e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 240, -1, 1.4351990103023127e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 240, -1, 7.85563315730542e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 240, -1, 1.2838494512834586e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 240, -1, 9.302925718657207e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 240, -1, 2.1677929908037186e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 272, -1, 3.833776190731442e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 272, -1, 2.547225903981598e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 272, -1, 2.9626946798089193e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 272, -1, 2.7565131404116983e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 272, -1, 4.322756467445288e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 272, -1, 8.183614227164071e-06), (987515173, 1982, 'Autre_Environement', 304, -1, 272, -1, 1.146720751421526e-05), (987515173, 1982, 'Autre_Environement', 336, -1, 272, -1, 3.931914034183137e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 304, -1, 1.2076164239260834e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 304, -1, 1.570803033246193e-05), (987515173, 1982, 'Autre_Environement', 176, -1, 304, -1, 3.3492375223431736e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 304, -1, 0.00015480617003049701), (987515173, 1982, 'Autre_Environement', 240, -1, 304, -1, 0.0002595597761683166), (987515173, 1982, 'Autre_Environement', 272, -1, 304, -1, 0.00018736239871941507), (987515173, 1982, 'Autre_Environement', 304, -1, 304, -1, 0.00021324967383407056), (987515173, 1982, 'Autre_Environement', 336, -1, 304, -1, 0.00016466400120407343), (987515173, 1982, 'Autre_Environement', 112, -1, 336, -1, 4.5506521928473376e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 336, -1, 1.7350272173644044e-05), (987515173, 1982, 'Autre_Environement', 176, -1, 336, -1, 4.9341364501742646e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 336, -1, 0.00012121324834879488), (987515173, 1982, 'Autre_Environement', 240, -1, 336, -1, 0.00019575671467464417), (987515173, 1982, 'Autre_Environement', 272, -1, 336, -1, 0.00018785451538860798), (987515173, 1982, 'Autre_Environement', 304, -1, 336, -1, 0.0001235568051924929), (987515173, 1982, 'Autre_Environement', 336, -1, 336, -1, 0.0002715320442803204), (987515173, 1982, 'Carton', 112, -1, 112, -1, 1.5895723493031255e-07), (987515173, 1982, 'Carton', 144, -1, 112, -1, 4.055109457112849e-06), (987515173, 1982, 'Carton', 176, -1, 112, -1, 7.033162546576932e-06), (987515173, 1982, 'Carton', 208, -1, 112, -1, 0.0008720498299226165), (987515173, 1982, 'Carton', 240, -1, 112, -1, 0.0026458273641765118), (987515173, 1982, 'Carton', 272, -1, 112, -1, 0.003381428774446249), (987515173, 1982, 'Carton', 304, -1, 112, -1, 0.031302616000175476), (987515173, 1982, 'Carton', 336, -1, 112, -1, 0.055746231228113174), (987515173, 1982, 'Carton', 112, -1, 144, -1, 0.00012427028559613973), (987515173, 1982, 'Carton', 144, -1, 144, -1, 0.00020961769041605294), (987515173, 1982, 'Carton', 176, -1, 144, -1, 0.0003685763222165406), (987515173, 1982, 'Carton', 208, -1, 144, -1, 0.006837004795670509), (987515173, 1982, 'Carton', 240, -1, 144, -1, 0.015870487317442894), (987515173, 1982, 'Carton', 272, -1, 144, -1, 0.009420922957360744), (987515173, 1982, 'Carton', 304, -1, 144, -1, 0.009781007654964924), (987515173, 1982, 'Carton', 336, -1, 144, -1, 0.022149020805954933), (987515173, 1982, 'Carton', 112, -1, 176, -1, 0.021912917494773865), (987515173, 1982, 'Carton', 144, -1, 176, -1, 0.19369027018547058), (987515173, 1982, 'Carton', 176, -1, 176, -1, 0.09597273170948029), (987515173, 1982, 'Carton', 208, -1, 176, -1, 0.12363496422767639), (987515173, 1982, 'Carton', 240, -1, 176, -1, 0.5308095216751099), (987515173, 1982, 'Carton', 272, -1, 176, -1, 0.4603290557861328), (987515173, 1982, 'Carton', 304, -1, 176, -1, 0.7716986536979675), (987515173, 1982, 'Carton', 336, -1, 176, -1, 0.8664692044258118), (987515173, 1982, 'Carton', 112, -1, 208, -1, 0.8501710295677185), (987515173, 1982, 'Carton', 144, -1, 208, -1, 0.9843193888664246), (987515173, 1982, 'Carton', 176, -1, 208, -1, 0.9847456216812134), (987515173, 1982, 'Carton', 208, -1, 208, -1, 0.9919455647468567), (987515173, 1982, 'Carton', 240, -1, 208, -1, 0.9993784427642822), (987515173, 1982, 'Carton', 272, -1, 208, -1, 0.999411940574646), (987515173, 1982, 'Carton', 304, -1, 208, -1, 0.9995880722999573), (987515173, 1982, 'Carton', 336, -1, 208, -1, 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-1, 0.9452948570251465), (987515173, 1982, 'Carton', 304, -1, 336, -1, 0.936585545539856), (987515173, 1982, 'Carton', 336, -1, 336, -1, 0.9807819128036499), (987515173, 1982, 'Kraft', 112, -1, 112, -1, 1.9590300492922097e-09), (987515173, 1982, 'Kraft', 144, -1, 112, -1, 1.7083729986211438e-08), (987515173, 1982, 'Kraft', 176, -1, 112, -1, 9.686848443379859e-07), (987515173, 1982, 'Kraft', 208, -1, 112, -1, 3.131615449092351e-05), (987515173, 1982, 'Kraft', 240, -1, 112, -1, 4.4387699745129794e-05), (987515173, 1982, 'Kraft', 272, -1, 112, -1, 0.00020690899691544473), (987515173, 1982, 'Kraft', 304, -1, 112, -1, 0.001079976442269981), (987515173, 1982, 'Kraft', 336, -1, 112, -1, 0.0008292217971757054), (987515173, 1982, 'Kraft', 112, -1, 144, -1, 2.628824040584732e-05), (987515173, 1982, 'Kraft', 144, -1, 144, -1, 7.013579761405708e-06), (987515173, 1982, 'Kraft', 176, -1, 144, -1, 3.628523472798406e-06), (987515173, 1982, 'Kraft', 208, -1, 144, -1, 3.566916711861268e-05), (987515173, 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-1, 2.5917668608599342e-05), (987515173, 1982, 'Kraft', 208, -1, 208, -1, 3.545867366483435e-05), (987515173, 1982, 'Kraft', 240, -1, 208, -1, 3.74384289898444e-05), (987515173, 1982, 'Kraft', 272, -1, 208, -1, 8.660206367494538e-05), (987515173, 1982, 'Kraft', 304, -1, 208, -1, 0.00012370425974950194), (987515173, 1982, 'Kraft', 336, -1, 208, -1, 0.0003905435441993177), (987515173, 1982, 'Kraft', 112, -1, 240, -1, 0.000308099202811718), (987515173, 1982, 'Kraft', 144, -1, 240, -1, 4.169322710367851e-05), (987515173, 1982, 'Kraft', 176, -1, 240, -1, 1.2238004273967817e-05), (987515173, 1982, 'Kraft', 208, -1, 240, -1, 7.35052799427649e-06), (987515173, 1982, 'Kraft', 240, -1, 240, -1, 2.2959880880080163e-05), (987515173, 1982, 'Kraft', 272, -1, 240, -1, 5.801603037980385e-05), (987515173, 1982, 'Kraft', 304, -1, 240, -1, 6.56902338960208e-05), (987515173, 1982, 'Kraft', 336, -1, 240, -1, 0.00018677131447475404), (987515173, 1982, 'Kraft', 112, -1, 272, -1, 0.001463874476030469), (987515173, 1982, 'Kraft', 144, -1, 272, -1, 0.0006903908797539771), (987515173, 1982, 'Kraft', 176, -1, 272, -1, 0.00027381430845707655), (987515173, 1982, 'Kraft', 208, -1, 272, -1, 4.3601943616522476e-05), (987515173, 1982, 'Kraft', 240, -1, 272, -1, 3.3449468901380897e-05), (987515173, 1982, 'Kraft', 272, -1, 272, -1, 8.359091589227319e-05), (987515173, 1982, 'Kraft', 304, -1, 272, -1, 0.00011153064406244084), (987515173, 1982, 'Kraft', 336, -1, 272, -1, 0.000424038473283872), (987515173, 1982, 'Kraft', 112, -1, 304, -1, 0.0009911458473652601), (987515173, 1982, 'Kraft', 144, -1, 304, -1, 0.0009041787125170231), (987515173, 1982, 'Kraft', 176, -1, 304, -1, 0.0006196689791977406), (987515173, 1982, 'Kraft', 208, -1, 304, -1, 0.0010829080129042268), (987515173, 1982, 'Kraft', 240, -1, 304, -1, 0.0017855274491012096), (987515173, 1982, 'Kraft', 272, -1, 304, -1, 0.004672854673117399), (987515173, 1982, 'Kraft', 304, -1, 304, -1, 0.004675318021327257), (987515173, 1982, 'Kraft', 336, -1, 304, -1, 0.012518779374659061), (987515173, 1982, 'Kraft', 112, -1, 336, -1, 0.0021789371967315674), (987515173, 1982, 'Kraft', 144, -1, 336, -1, 0.0057129827328026295), (987515173, 1982, 'Kraft', 176, -1, 336, -1, 0.0008299081237055361), (987515173, 1982, 'Kraft', 208, -1, 336, -1, 0.0012639096239581704), (987515173, 1982, 'Kraft', 240, -1, 336, -1, 0.00779244489967823), (987515173, 1982, 'Kraft', 272, -1, 336, -1, 0.012522094883024693), (987515173, 1982, 'Kraft', 304, -1, 336, -1, 0.01793050207197666), (987515173, 1982, 'Kraft', 336, -1, 336, -1, 0.00776381092146039), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 112, -1, 1.4979083251542846e-10), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 112, -1, 8.23131607319283e-09), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 112, -1, 5.52368135231518e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 112, -1, 5.502619842445711e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 112, -1, 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-1, 0.00035803794162347913), (987515173, 1982, 'autre_refus', 240, -1, 208, -1, 0.000199084184714593), (987515173, 1982, 'autre_refus', 272, -1, 208, -1, 0.00028707797173410654), (987515173, 1982, 'autre_refus', 304, -1, 208, -1, 0.0002023832785198465), (987515173, 1982, 'autre_refus', 336, -1, 208, -1, 0.00024359964299947023), (987515173, 1982, 'autre_refus', 112, -1, 240, -1, 0.0002340533392271027), (987515173, 1982, 'autre_refus', 144, -1, 240, -1, 0.00010872808343265206), (987515173, 1982, 'autre_refus', 176, -1, 240, -1, 6.492434476967901e-05), (987515173, 1982, 'autre_refus', 208, -1, 240, -1, 2.5306087991339155e-05), (987515173, 1982, 'autre_refus', 240, -1, 240, -1, 7.277572149178013e-05), (987515173, 1982, 'autre_refus', 272, -1, 240, -1, 0.00013965132529847324), (987515173, 1982, 'autre_refus', 304, -1, 240, -1, 8.942804561229423e-05), (987515173, 1982, 'autre_refus', 336, -1, 240, -1, 8.17268155515194e-05), (987515173, 1982, 'autre_refus', 112, -1, 272, -1, 0.0002688820823095739), (987515173, 1982, 'autre_refus', 144, -1, 272, -1, 0.00011161774455104023), (987515173, 1982, 'autre_refus', 176, -1, 272, -1, 0.0001247210893779993), (987515173, 1982, 'autre_refus', 208, -1, 272, -1, 5.116685133543797e-05), (987515173, 1982, 'autre_refus', 240, -1, 272, -1, 2.917258461820893e-05), (987515173, 1982, 'autre_refus', 272, -1, 272, -1, 4.2777613998623565e-05), (987515173, 1982, 'autre_refus', 304, -1, 272, -1, 6.815540109528229e-05), (987515173, 1982, 'autre_refus', 336, -1, 272, -1, 0.000142482400406152), (987515173, 1982, 'autre_refus', 112, -1, 304, -1, 0.00011518536484800279), (987515173, 1982, 'autre_refus', 144, -1, 304, -1, 0.00021755001216661185), (987515173, 1982, 'autre_refus', 176, -1, 304, -1, 0.0004267749609425664), (987515173, 1982, 'autre_refus', 208, -1, 304, -1, 0.00042715808376669884), (987515173, 1982, 'autre_refus', 240, -1, 304, -1, 6.591665442101657e-05), (987515173, 1982, 'autre_refus', 272, -1, 304, -1, 3.177001417498104e-05), (987515173, 1982, 'autre_refus', 304, -1, 304, -1, 1.1655051821435336e-05), (987515173, 1982, 'autre_refus', 336, -1, 304, -1, 1.8820173863787204e-05), (987515173, 1982, 'autre_refus', 112, -1, 336, -1, 0.0002474258071742952), (987515173, 1982, 'autre_refus', 144, -1, 336, -1, 0.0004698036063928157), (987515173, 1982, 'autre_refus', 176, -1, 336, -1, 0.0003347955644130707), (987515173, 1982, 'autre_refus', 208, -1, 336, -1, 0.00023731451074127108), (987515173, 1982, 'autre_refus', 240, -1, 336, -1, 0.00010596985521260649), (987515173, 1982, 'autre_refus', 272, -1, 336, -1, 9.547825902700424e-05), (987515173, 1982, 'autre_refus', 304, -1, 336, -1, 0.0001311860396526754), (987515173, 1982, 'autre_refus', 336, -1, 336, -1, 0.0007285072933882475)]} result thcl : {'987515223': [('987515223', 'Carton', 0.9921027, 1927, '1528'), 'temp/1748281152_935833_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg'], '987515198': [('987515198', 'Carton', 0.96630985, 1927, '1528'), 'temp/1748281152_935833_987515198_599e80f444c876f407e94b533c89360b.jpg'], '987515200': [('987515200', 'Carton', 0.985992, 1927, '1528'), 'temp/1748281152_935833_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg'], '987515201': [('987515201', 'Carton', 0.99546534, 1927, '1528'), 'temp/1748281152_935833_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg'], '987515202': [('987515202', 'Carton', 0.99111736, 1927, '1528'), 'temp/1748281152_935833_987515202_3314bd90d1404f31b827d8925abf2d62.jpg'], '987515204': [('987515204', 'Papier_Magazine', 0.99508345, 1927, '1528'), 'temp/1748281152_935833_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg'], '987515205': [('987515205', 'Papier_Magazine', 0.9908875, 1927, '1528'), 'temp/1748281152_935833_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg'], '987515239': [('987515239', 'Carton', 0.99978346, 1927, '1528'), 'temp/1748281152_935833_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg'], '987515228': [('987515228', 'Papier_Magazine', 0.5214702, 1927, '1528'), 'temp/1748281152_935833_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg'], '987515230': [('987515230', 'Carton', 0.9994061, 1927, '1528'), 'temp/1748281152_935833_987515230_846ad925884264181565c81d152a2e94.jpg'], '987515231': [('987515231', 'Carton', 0.9994209, 1927, '1528'), 'temp/1748281152_935833_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg'], '987515232': [('987515232', 'Carton', 0.99924374, 1927, '1528'), 'temp/1748281152_935833_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg'], '987515233': [('987515233', 'Carton', 0.983352, 1927, '1528'), 'temp/1748281152_935833_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg'], '987515234': [('987515234', 'Carton', 0.94478124, 1927, '1528'), 'temp/1748281152_935833_987515234_2eca3480aed0f8b876242675ad99b666.jpg'], '987515235': [('987515235', 'Papier_Magazine', 0.8918476, 1927, '1528'), 'temp/1748281152_935833_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg'], '987515236': [('987515236', 'Papier_Magazine', 0.5370823, 1927, '1528'), 'temp/1748281152_935833_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg'], '987515237': [('987515237', 'Carton', 0.7700954, 1927, '1528'), 'temp/1748281152_935833_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg'], '987515238': [('987515238', 'Carton', 0.99957496, 1927, '1528'), 'temp/1748281152_935833_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg'], '987515175': [('987515175', 'Papier_Magazine', 0.9998128, 1927, '1528'), 'temp/1748281152_935833_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515176': [('987515176', 'Papier_Magazine', 0.9998142, 1927, '1528'), 'temp/1748281152_935833_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515177': [('987515177', 'Papier_Magazine', 0.97717094, 1927, '1528'), 'temp/1748281152_935833_987515177_4a54e9967227806219ddf45d256539d8.jpg'], '987515178': [('987515178', 'Carton', 0.8571688, 1927, '1528'), 'temp/1748281152_935833_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg'], '987515247': [('987515247', 'Carton', 0.9996685, 1927, '1528'), 'temp/1748281152_935833_987515247_e47b65403df916ba909bc9c439b0af73.jpg'], '987515248': [('987515248', 'Carton', 0.9813719, 1927, '1528'), 'temp/1748281152_935833_987515248_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515249': [('987515249', 'Carton', 0.9813122, 1927, '1528'), 'temp/1748281152_935833_987515249_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515250': [('987515250', 'Carton', 0.9808008, 1927, '1528'), 'temp/1748281152_935833_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg'], '987515224': [('987515224', 'Carton', 0.9084086, 1927, '1528'), 'temp/1748281152_935833_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg'], '987515226': [('987515226', 'Papier_Magazine', 0.98696923, 1927, '1528'), 'temp/1748281152_935833_987515226_a18048dca1a77ae086b62cf07759f704.jpg'], '987515227': [('987515227', 'Papier_Magazine', 0.9000315, 1927, '1528'), 'temp/1748281152_935833_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg'], '987515188': [('987515188', 'Carton', 0.9956311, 1927, '1528'), 'temp/1748281152_935833_987515188_4116f9906657a69bb76c2fda982037b9.jpg'], '987515189': [('987515189', 'Carton', 0.9977888, 1927, '1528'), 'temp/1748281152_935833_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg'], '987515190': [('987515190', 'Carton', 0.97630787, 1927, '1528'), 'temp/1748281152_935833_987515190_d56932bfc6ba2a8c974c691108755017.jpg'], '987515192': [('987515192', 'Papier_Magazine', 0.9999113, 1927, '1528'), 'temp/1748281152_935833_987515192_b661073b218f5f056833d6af1c617153.jpg'], '987515193': [('987515193', 'Papier_Magazine', 0.99939644, 1927, '1528'), 'temp/1748281152_935833_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg'], '987515195': [('987515195', 'Carton', 0.9846464, 1927, '1528'), 'temp/1748281152_935833_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515196': [('987515196', 'Carton', 0.98463976, 1927, '1528'), 'temp/1748281152_935833_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515213': [('987515213', 'Carton', 0.9869257, 1927, '1528'), 'temp/1748281152_935833_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515215': [('987515215', 'Papier_Magazine', 0.99391115, 1927, '1528'), 'temp/1748281152_935833_987515215_902ef348a7eebb9a8b87f42927347936.jpg'], '987515216': [('987515216', 'Papier_Magazine', 0.97740084, 1927, '1528'), 'temp/1748281152_935833_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg'], '987515217': [('987515217', 'Carton', 0.52912277, 1927, '1528'), 'temp/1748281152_935833_987515217_78877bb2c5760be28518d17f77d1c609.jpg'], '987515219': [('987515219', 'Carton', 0.9993699, 1927, '1528'), 'temp/1748281152_935833_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg'], '987515220': [('987515220', 'Carton', 0.9963785, 1927, '1528'), 'temp/1748281152_935833_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg'], '987515222': [('987515222', 'Carton', 0.99747014, 1927, '1528'), 'temp/1748281152_935833_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg'], '987515179': [('987515179', 'Carton', 0.9269919, 1927, '1528'), 'temp/1748281152_935833_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg'], '987515180': [('987515180', 'Carton', 0.98998004, 1927, '1528'), 'temp/1748281152_935833_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg'], '987515181': [('987515181', 'Carton', 0.99778, 1927, '1528'), 'temp/1748281152_935833_987515181_1738c2798fb31152809ecb443ac286d6.jpg'], '987515182': [('987515182', 'Carton', 0.99242216, 1927, '1528'), 'temp/1748281152_935833_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg'], '987515183': [('987515183', 'Papier_Magazine', 0.99999225, 1927, '1528'), 'temp/1748281152_935833_987515183_6aab9ca0421398b4899892c10c2594c6.jpg'], '987515184': [('987515184', 'Papier_Magazine', 0.99973243, 1927, '1528'), 'temp/1748281152_935833_987515184_19c8c2177209a285df6014d95fe53f2c.jpg'], '987515185': [('987515185', 'Papier_Magazine', 0.7979338, 1927, '1528'), 'temp/1748281152_935833_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg'], '987515240': [('987515240', 'Carton', 0.99952054, 1927, '1528'), 'temp/1748281152_935833_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg'], '987515241': [('987515241', 'Carton', 0.98216665, 1927, '1528'), 'temp/1748281152_935833_987515241_073420d938f5f010ffd5b4353c064e09.jpg'], '987515242': [('987515242', 'Carton', 0.9357916, 1927, '1528'), 'temp/1748281152_935833_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg'], '987515243': [('987515243', 'Papier_Magazine', 0.87418824, 1927, '1528'), 'temp/1748281152_935833_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg'], '987515244': [('987515244', 'Papier_Magazine', 0.81749, 1927, '1528'), 'temp/1748281152_935833_987515244_419530eaef5ef868f75c758b94eea4b4.jpg'], '987515245': [('987515245', 'Carton', 0.86590785, 1927, '1528'), 'temp/1748281152_935833_987515245_757d9d208d5bd4375c5f21f68b699148.jpg'], '987515246': [('987515246', 'Carton', 0.99923277, 1927, '1528'), 'temp/1748281152_935833_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg'], '987515186': [('987515186', 'Carton', 0.9847348, 1927, '1528'), 'temp/1748281152_935833_987515186_797def426440b544aa80dbd63a19234a.jpg'], '987515187': [('987515187', 'Carton', 0.98108, 1927, '1528'), 'temp/1748281152_935833_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg'], '987515207': [('987515207', 'Papier_Magazine', 0.8739247, 1927, '1528'), 'temp/1748281152_935833_987515207_de216ddb041e249524b0fb2b949064a5.jpg'], '987515208': [('987515208', 'Carton', 0.99171805, 1927, '1528'), 'temp/1748281152_935833_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg'], '987515209': [('987515209', 'Carton', 0.9677494, 1927, '1528'), 'temp/1748281152_935833_987515209_02dfe1ae39f51994652f4a8538844aea.jpg'], '987515211': [('987515211', 'Carton', 0.9733993, 1927, '1528'), 'temp/1748281152_935833_987515211_72cc7664d45bd40477351b9b764f1500.jpg'], '987515212': [('987515212', 'Carton', 0.9869192, 1927, '1528'), 'temp/1748281152_935833_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg']} result detect_point : {987515173: [(987515173, 1982, 'Autre_Environement', 112, -1, 112, 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(987515173, 1982, 'autre_refus', 176, -1, 240, -1, 6.492434476967901e-05), (987515173, 1982, 'autre_refus', 208, -1, 240, -1, 2.5306087991339155e-05), (987515173, 1982, 'autre_refus', 240, -1, 240, -1, 7.277572149178013e-05), (987515173, 1982, 'autre_refus', 272, -1, 240, -1, 0.00013965132529847324), (987515173, 1982, 'autre_refus', 304, -1, 240, -1, 8.942804561229423e-05), (987515173, 1982, 'autre_refus', 336, -1, 240, -1, 8.17268155515194e-05), (987515173, 1982, 'autre_refus', 112, -1, 272, -1, 0.0002688820823095739), (987515173, 1982, 'autre_refus', 144, -1, 272, -1, 0.00011161774455104023), (987515173, 1982, 'autre_refus', 176, -1, 272, -1, 0.0001247210893779993), (987515173, 1982, 'autre_refus', 208, -1, 272, -1, 5.116685133543797e-05), (987515173, 1982, 'autre_refus', 240, -1, 272, -1, 2.917258461820893e-05), (987515173, 1982, 'autre_refus', 272, -1, 272, -1, 4.2777613998623565e-05), (987515173, 1982, 'autre_refus', 304, -1, 272, -1, 6.815540109528229e-05), (987515173, 1982, 'autre_refus', 336, -1, 272, -1, 0.000142482400406152), (987515173, 1982, 'autre_refus', 112, -1, 304, -1, 0.00011518536484800279), (987515173, 1982, 'autre_refus', 144, -1, 304, -1, 0.00021755001216661185), (987515173, 1982, 'autre_refus', 176, -1, 304, -1, 0.0004267749609425664), (987515173, 1982, 'autre_refus', 208, -1, 304, -1, 0.00042715808376669884), (987515173, 1982, 'autre_refus', 240, -1, 304, -1, 6.591665442101657e-05), (987515173, 1982, 'autre_refus', 272, -1, 304, -1, 3.177001417498104e-05), (987515173, 1982, 'autre_refus', 304, -1, 304, -1, 1.1655051821435336e-05), (987515173, 1982, 'autre_refus', 336, -1, 304, -1, 1.8820173863787204e-05), (987515173, 1982, 'autre_refus', 112, -1, 336, -1, 0.0002474258071742952), (987515173, 1982, 'autre_refus', 144, -1, 336, -1, 0.0004698036063928157), (987515173, 1982, 'autre_refus', 176, -1, 336, -1, 0.0003347955644130707), (987515173, 1982, 'autre_refus', 208, -1, 336, -1, 0.00023731451074127108), (987515173, 1982, 'autre_refus', 240, -1, 336, -1, 0.00010596985521260649), (987515173, 1982, 'autre_refus', 272, -1, 336, -1, 9.547825902700424e-05), (987515173, 1982, 'autre_refus', 304, -1, 336, -1, 0.0001311860396526754), (987515173, 1982, 'autre_refus', 336, -1, 336, -1, 0.0007285072933882475)]} ############################### 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 : 0.20458173751831055 #### 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 Mon May 26 19:39:37 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/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136} map_photo_id_path_extension : {987321136: {'path': 'temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} debut step init detect dechets input : temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg ON MODIFIE NB AVEC LE INPUT map photo id path extension : temp/1748281177_935833_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 : 0.014760494232177734 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.00014281272888183594 time spend to save output : 0.014989376068115234 total time spend for step 1 : 0.01513218879699707 step2:tile Mon May 26 19:39:37 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/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': ()} output_args : {'987321136': ['temp/1748281177_935833_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/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136} map_photo_id_path_extension : {987321136: {'path': 'temp/1748281177_935833_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/1748281177_935833_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 : 23354344 with name tile_correct_upm feed_id_new_photos : 23354344 filename : temp/1748281177_935833_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/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg , 0 before upload mediasElapsed time : 0.008990287780761719 About to upload 1 photos upload in portfolio : 23354344 Result OK ! uploaded one batch 0 Elapsed time : 5.081896066665649 upload mediasElapsed time : 5.090951204299927 , 0insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(1608847328, 1361142935, 0)] Saving 0 CHIs. list_chi_tile : [] end of tileElapsed time : 5.1034934520721436 map_pid_results : {'1361142935': ['temp/1748281177_935833_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, '1361142935'] map_info['map_portfolio_photo'] : {1902940: [987321136]} final : False mtd_id 1848 list_pids : [987321136, 987321136, '1361142935'] Looping around the photos to save general results len do output : 1 /1361142935Didn'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, '1361142935', 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, '1361142935', 'None', None, None, None, None, None), ('1848', '1902940', '987321136', None, None, None, None, None, None)] time used for this insertion : 0.01482248306274414 save_final save missing photos in datou_result : time spend for datou_step_exec : 11.889419317245483 time spend to save output : 0.01505136489868164 total time spend for step 2 : 11.904470682144165 step3:detect_points Mon May 26 19:39:49 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 : {'1361142935': ['temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} input_args_next_step : {'1361142935': ()} output_args : {'1361142935': ['temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} args : 1361142935 depend.output_id : 0 complete output_args for input 1 : {'987321136': ['temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'1361142935': ('temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',), '987321136': ()} output_args : {'987321136': ['temp/1748281177_935833_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/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1361142935} map_photo_id_path_extension : {987321136: {'path': 'temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1361142935: {'path': 'temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1361142935: 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/1748281177_935833_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 : 0.05301547050476074 time to do a prediction : 15.304449319839478 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 : {1361142935: [(1361142935, 1945, 'Autre_Environement', 185, -1, 118, -1, 1.8337410438107327e-05), (1361142935, 1945, 'Autre_Environement', 320, -1, 118, -1, 0.0001291327498620376), (1361142935, 1945, 'Autre_Environement', 388, -1, 151, -1, 1.3445685908664018e-05), (1361142935, 1945, 'Autre_Environement', 286, -1, 185, -1, 0.00013466527161654085), (1361142935, 1945, 'Autre_Environement', 118, -1, 219, -1, 9.86390750767896e-06), (1361142935, 1945, 'Autre_Environement', 219, -1, 219, -1, 0.00038494516047649086), (1361142935, 1945, 'Autre_Environement', 354, -1, 219, -1, 0.0003431888180784881), (1361142935, 1945, 'Autre_Environement', 421, -1, 253, -1, 1.494663450785083e-07), (1361142935, 1945, 'Autre_Environement', 185, -1, 286, -1, 1.814520658172114e-07), (1361142935, 1945, 'Autre_Environement', 320, -1, 286, -1, 4.115241154067917e-06), (1361142935, 1945, 'Autre_Environement', 118, -1, 320, -1, 2.5274063397695556e-10), (1361142935, 1945, 'Autre_Environement', 253, -1, 320, -1, 1.0447603199237321e-10), (1361142935, 1945, 'Autre_Environement', 388, -1, 320, -1, 4.5192118136583304e-07), (1361142935, 1945, 'Carton', 151, -1, 118, -1, 0.9897055625915527), (1361142935, 1945, 'Carton', 286, -1, 118, -1, 0.8210961222648621), (1361142935, 1945, 'Carton', 421, -1, 118, -1, 0.4851985573768616), (1361142935, 1945, 'Carton', 219, -1, 151, -1, 0.9841372966766357), (1361142935, 1945, 'Carton', 118, -1, 185, -1, 0.9978604912757874), (1361142935, 1945, 'Carton', 185, -1, 219, -1, 0.7996194958686829), (1361142935, 1945, 'Carton', 354, -1, 219, -1, 0.13047637045383453), (1361142935, 1945, 'Carton', 286, -1, 253, -1, 0.05070793256163597), (1361142935, 1945, 'Carton', 118, -1, 286, -1, 0.31989040970802307), (1361142935, 1945, 'Carton', 219, -1, 286, -1, 0.0034983144141733646), (1361142935, 1945, 'Carton', 421, -1, 286, -1, 0.01660206913948059), (1361142935, 1945, 'Carton', 354, -1, 320, -1, 0.0016258988762274384), (1361142935, 1945, 'Kraft', 185, -1, 118, -1, 0.004793279338628054), (1361142935, 1945, 'Kraft', 286, -1, 118, -1, 0.0023909551091492176), (1361142935, 1945, 'Kraft', 421, -1, 118, -1, 0.002102428814396262), (1361142935, 1945, 'Kraft', 354, -1, 151, -1, 0.05212335288524628), (1361142935, 1945, 'Kraft', 118, -1, 185, -1, 0.0017166697653010488), (1361142935, 1945, 'Kraft', 253, -1, 185, -1, 0.04599893465638161), (1361142935, 1945, 'Kraft', 185, -1, 219, -1, 0.021099306643009186), (1361142935, 1945, 'Kraft', 388, -1, 219, -1, 6.061792737455107e-05), (1361142935, 1945, 'Kraft', 286, -1, 253, -1, 0.0037522290367633104), (1361142935, 1945, 'Kraft', 118, -1, 286, -1, 0.010319208726286888), (1361142935, 1945, 'Kraft', 219, -1, 286, -1, 5.351284926291555e-05), (1361142935, 1945, 'Kraft', 421, -1, 286, -1, 9.467339623370208e-06), (1361142935, 1945, 'Kraft', 320, -1, 320, -1, 0.00017124204896390438), (1361142935, 1945, 'Lointain_Papier_Magazine', 185, -1, 118, -1, 2.352082447032444e-06), (1361142935, 1945, 'Lointain_Papier_Magazine', 320, -1, 118, -1, 1.991412864299491e-05), (1361142935, 1945, 'Lointain_Papier_Magazine', 253, -1, 151, -1, 1.1851832823595032e-05), (1361142935, 1945, 'Lointain_Papier_Magazine', 354, -1, 185, -1, 8.877575601218268e-05), (1361142935, 1945, 'Lointain_Papier_Magazine', 118, -1, 219, -1, 2.1015976017224602e-06), (1361142935, 1945, 'Lointain_Papier_Magazine', 219, -1, 219, -1, 7.86672972026281e-05), (1361142935, 1945, 'Lointain_Papier_Magazine', 421, -1, 219, -1, 5.019426794206083e-07), (1361142935, 1945, 'Lointain_Papier_Magazine', 320, -1, 253, -1, 8.220934978453442e-05), (1361142935, 1945, 'Lointain_Papier_Magazine', 185, -1, 286, -1, 3.6812457437918056e-07), (1361142935, 1945, 'Lointain_Papier_Magazine', 388, -1, 286, -1, 3.3782250739022857e-06), (1361142935, 1945, 'Lointain_Papier_Magazine', 118, -1, 320, -1, 3.5824958555252806e-09), (1361142935, 1945, 'Lointain_Papier_Magazine', 286, -1, 320, -1, 1.2866975396264024e-07), (1361142935, 1945, 'Metal', 185, -1, 118, -1, 7.473808364011347e-05), (1361142935, 1945, 'Metal', 286, -1, 118, -1, 3.432366065680981e-05), (1361142935, 1945, 'Metal', 118, -1, 151, -1, 1.1519473446242046e-06), (1361142935, 1945, 'Metal', 354, -1, 151, -1, 0.001217490527778864), (1361142935, 1945, 'Metal', 253, -1, 185, -1, 0.001836584648117423), (1361142935, 1945, 'Metal', 421, -1, 185, -1, 3.084278432652354e-05), (1361142935, 1945, 'Metal', 185, -1, 219, -1, 0.0011127882171422243), (1361142935, 1945, 'Metal', 118, -1, 253, -1, 2.7215228328714147e-05), (1361142935, 1945, 'Metal', 354, -1, 253, -1, 0.0005037166411057115), (1361142935, 1945, 'Metal', 219, -1, 286, -1, 1.657771281315945e-05), (1361142935, 1945, 'Metal', 421, -1, 286, -1, 2.2439740860136226e-05), (1361142935, 1945, 'Metal', 151, -1, 320, -1, 1.393576087860282e-10), (1361142935, 1945, 'Metal', 320, -1, 320, -1, 3.1560623028781265e-05), (1361142935, 1945, 'Papier_Magazine', 118, -1, 118, -1, 0.001665871823206544), (1361142935, 1945, 'Papier_Magazine', 253, -1, 118, -1, 0.28050497174263), (1361142935, 1945, 'Papier_Magazine', 185, -1, 151, -1, 0.003942570183426142), (1361142935, 1945, 'Papier_Magazine', 421, -1, 151, -1, 0.9828073978424072), (1361142935, 1945, 'Papier_Magazine', 354, -1, 185, -1, 0.7690255045890808), (1361142935, 1945, 'Papier_Magazine', 286, -1, 219, -1, 0.9288092851638794), (1361142935, 1945, 'Papier_Magazine', 118, -1, 253, -1, 0.04313919320702553), (1361142935, 1945, 'Papier_Magazine', 219, -1, 253, -1, 0.8978631496429443), (1361142935, 1945, 'Papier_Magazine', 388, -1, 253, -1, 0.9886879324913025), (1361142935, 1945, 'Papier_Magazine', 151, -1, 320, -1, 0.9999996423721313), (1361142935, 1945, 'Papier_Magazine', 253, -1, 320, -1, 0.9999960660934448), (1361142935, 1945, 'Papier_Magazine', 354, -1, 320, -1, 0.9942159056663513), (1361142935, 1945, 'Plastique', 118, -1, 118, -1, 1.236031312146224e-05), (1361142935, 1945, 'Plastique', 219, -1, 118, -1, 0.00030184732167981565), (1361142935, 1945, 'Plastique', 320, -1, 118, -1, 0.0002493929350748658), (1361142935, 1945, 'Plastique', 253, -1, 185, -1, 0.007031592074781656), (1361142935, 1945, 'Plastique', 354, -1, 185, -1, 0.032503753900527954), (1361142935, 1945, 'Plastique', 185, -1, 219, -1, 0.050375860184431076), (1361142935, 1945, 'Plastique', 421, -1, 219, -1, 0.00012226666149217635), (1361142935, 1945, 'Plastique', 118, -1, 253, -1, 0.003930176142603159), (1361142935, 1945, 'Plastique', 286, -1, 253, -1, 0.0025490771513432264), (1361142935, 1945, 'Plastique', 219, -1, 286, -1, 6.120907346485183e-05), (1361142935, 1945, 'Plastique', 354, -1, 286, -1, 0.00538312504068017), (1361142935, 1945, 'Plastique', 151, -1, 320, -1, 1.8926894773674263e-10), (1361142935, 1945, 'Plastique', 421, -1, 320, -1, 0.00020204381144139916), (1361142935, 1945, 'Sol_Environement', 185, -1, 118, -1, 9.372543900099117e-06), (1361142935, 1945, 'Sol_Environement', 320, -1, 118, -1, 2.7338848667568527e-05), (1361142935, 1945, 'Sol_Environement', 118, -1, 151, -1, 2.5881729470711434e-07), (1361142935, 1945, 'Sol_Environement', 253, -1, 185, -1, 0.00011454988998593763), (1361142935, 1945, 'Sol_Environement', 354, -1, 185, -1, 0.00020838991622440517), (1361142935, 1945, 'Sol_Environement', 185, -1, 219, -1, 9.349620813736692e-05), (1361142935, 1945, 'Sol_Environement', 421, -1, 219, -1, 1.1348908657282664e-07), (1361142935, 1945, 'Sol_Environement', 118, -1, 253, -1, 7.004572921687213e-07), (1361142935, 1945, 'Sol_Environement', 320, -1, 253, -1, 5.724640504922718e-05), (1361142935, 1945, 'Sol_Environement', 219, -1, 286, -1, 3.869550369017816e-08), (1361142935, 1945, 'Sol_Environement', 388, -1, 286, -1, 6.792529802623903e-06), (1361142935, 1945, 'Sol_Environement', 151, -1, 320, -1, 2.6225014184994705e-14), (1361142935, 1945, 'Sol_Environement', 286, -1, 320, -1, 1.5886031690115487e-07), (1361142935, 1945, 'Teint_Dans_La_Masse', 185, -1, 118, -1, 0.002272234996780753), (1361142935, 1945, 'Teint_Dans_La_Masse', 286, -1, 118, -1, 0.001813237671740353), (1361142935, 1945, 'Teint_Dans_La_Masse', 388, -1, 118, -1, 0.04538803920149803), (1361142935, 1945, 'Teint_Dans_La_Masse', 118, -1, 151, -1, 0.00010940170614048839), (1361142935, 1945, 'Teint_Dans_La_Masse', 253, -1, 185, -1, 0.0056123328395187855), (1361142935, 1945, 'Teint_Dans_La_Masse', 354, -1, 185, -1, 0.15166763961315155), (1361142935, 1945, 'Teint_Dans_La_Masse', 185, -1, 219, -1, 0.0013750138459727168), (1361142935, 1945, 'Teint_Dans_La_Masse', 118, -1, 253, -1, 6.252125604078174e-05), (1361142935, 1945, 'Teint_Dans_La_Masse', 286, -1, 253, -1, 0.0018639108166098595), (1361142935, 1945, 'Teint_Dans_La_Masse', 388, -1, 253, -1, 0.001438076258637011), (1361142935, 1945, 'Teint_Dans_La_Masse', 219, -1, 286, -1, 1.016586884361459e-05), (1361142935, 1945, 'Teint_Dans_La_Masse', 151, -1, 320, -1, 3.425693932967988e-07), (1361142935, 1945, 'Teint_Dans_La_Masse', 320, -1, 320, -1, 0.0020001486409455538), (1361142935, 1945, 'Teint_Dans_La_Masse', 421, -1, 320, -1, 8.311669807881117e-05), (1361142935, 1945, 'autre_refus', 185, -1, 118, -1, 0.028516046702861786), (1361142935, 1945, 'autre_refus', 354, -1, 118, -1, 0.0014720888575538993), (1361142935, 1945, 'autre_refus', 118, -1, 151, -1, 3.824138184427284e-05), (1361142935, 1945, 'autre_refus', 253, -1, 185, -1, 0.07516990602016449), (1361142935, 1945, 'autre_refus', 185, -1, 219, -1, 0.010234796442091465), (1361142935, 1945, 'autre_refus', 354, -1, 219, -1, 0.04460597038269043), (1361142935, 1945, 'autre_refus', 118, -1, 253, -1, 0.00015751634782645851), (1361142935, 1945, 'autre_refus', 286, -1, 253, -1, 0.01393966656178236), (1361142935, 1945, 'autre_refus', 219, -1, 286, -1, 4.3672833271557465e-05), (1361142935, 1945, 'autre_refus', 388, -1, 286, -1, 0.0030952864326536655), (1361142935, 1945, 'autre_refus', 151, -1, 320, -1, 1.5080686699420198e-10), (1361142935, 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) [('1361142935', '492774966', '1945', '151', '-1', '118', '-1', '0.9897055625915527'), ('1361142935', '492774966', '1945', '286', '-1', '118', '-1', '0.8210961222648621'), ('1361142935', '492774966', '1945', '421', '-1', '118', '-1', '0.4851985573768616'), ('1361142935', '492774966', '1945', '219', '-1', '151', '-1', '0.9841372966766357'), ('1361142935', '492774966', '1945', '118', '-1', '185', '-1', '0.9978604912757874'), ('1361142935', '492774966', '1945', '185', '-1', '219', '-1', '0.7996194958686829'), ('1361142935', '492774966', '1945', '354', '-1', '219', '-1', '0.13047637045383453'), ('1361142935', '492774966', '1945', '286', '-1', '253', '-1', '0.05070793256163597'), ('1361142935', '492774966', '1945', '118', '-1', '286', '-1', '0.31989040970802307'), ('1361142935', '493202403', '1945', '354', '-1', '151', '-1', '0.05212335288524628'), ('1361142935', '2107752386', '1945', '253', '-1', '118', '-1', '0.28050497174263'), ('1361142935', '2107752386', '1945', '421', '-1', '151', '-1', '0.9828073978424072'), ('1361142935', '2107752386', '1945', '354', '-1', '185', '-1', '0.7690255045890808'), ('1361142935', '2107752386', '1945', '286', '-1', '219', '-1', '0.9288092851638794'), ('1361142935', '2107752386', '1945', '219', '-1', '253', '-1', '0.8978631496429443'), ('1361142935', '2107752386', '1945', '388', '-1', '253', '-1', '0.9886879324913025'), ('1361142935', '2107752386', '1945', '151', '-1', '320', '-1', '0.9999996423721313'), ('1361142935', '2107752386', '1945', '253', '-1', '320', '-1', '0.9999960660934448'), ('1361142935', '2107752386', '1945', '354', '-1', '320', '-1', '0.9942159056663513'), ('1361142935', '492725882', '1945', '185', '-1', '219', '-1', '0.050375860184431076'), ('1361142935', '2107752385', '1945', '354', '-1', '185', '-1', '0.15166763961315155'), ('1361142935', '2107752406', '1945', '253', '-1', '185', '-1', '0.07516990602016449')] final : False save missing photos in datou_result : time spend for datou_step_exec : 16.389020681381226 time spend to save output : 0.0648350715637207 total time spend for step 3 : 16.453855752944946 step4:count_percent_refus Mon May 26 19:40:06 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/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': ()} output_args : {'987321136': ['temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} args : 987321136 depend.output_id : 1 complete output_args for input 1 : {'1361142935': ['temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} input_args_next_step : {'987321136': (987321136,), '1361142935': ()} output_args : {'1361142935': ['temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} args : 1361142935 depend.output_id : 0 complete output_args for input 2 : {'987321136': ['temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': (987321136,), '1361142935': ('temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',)} output_args : {'987321136': ['temp/1748281177_935833_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/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1361142935} map_photo_id_path_extension : {987321136: {'path': 'temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1361142935: {'path': 'temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1361142935: 987321136} debut step count percent refus args : {'987321136': (987321136, 0.9481481481481482), '1361142935': ('temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',)} (987321136, 0.9481481481481482) ('temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',) on trouve le portfolio_id = 1902940 list_photo : [987321136] list_photo_correc : [1361142935] 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}, [1361142935], {'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 : 0.056780338287353516 save missing photos in datou_result : time spend for datou_step_exec : 0.01930069923400879 time spend to save output : 0.05768418312072754 total time spend for step 4 : 0.07698488235473633 step5:brightness Mon May 26 19:40:06 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/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': ()} output_args : {'987321136': ['temp/1748281177_935833_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/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1361142935} map_photo_id_path_extension : {987321136: {'path': 'temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1361142935: {'path': 'temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1361142935: 987321136} inside step calcul brightness treat image : temp/1748281177_935833_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 : 0.008811235427856445 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 : 0.012063980102539062 save missing photos in datou_result : time spend for datou_step_exec : 0.05913996696472168 time spend to save output : 0.025939226150512695 total time spend for step 5 : 0.08507919311523438 step6:blur_detection Mon May 26 19:40:06 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/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': ()} output_args : {'987321136': ['temp/1748281177_935833_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/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1361142935} map_photo_id_path_extension : {987321136: {'path': 'temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1361142935: {'path': 'temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1361142935: 987321136} inside step blur_detection score_blur_detection : {} methode: ratio et variance treat image : temp/1748281177_935833_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 : 0.0077092647552490234 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 : 0.00896906852722168 save missing photos in datou_result : time spend for datou_step_exec : 0.10006165504455566 time spend to save output : 0.021226167678833008 total time spend for step 6 : 0.12128782272338867 step7:send_mail_dechet Mon May 26 19:40:06 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}, [1361142935], {'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}, [1361142935], {'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}, [1361142935], {'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/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1361142935} map_photo_id_path_extension : {987321136: {'path': 'temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1361142935: {'path': 'temp/1748281177_935833_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1361142935: 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, '1361142935'] map_info['map_portfolio_photo'] : {1902940: [987321136]} final : True mtd_id 1848 list_pids : [987321136, 987321136, '1361142935'] 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, '1361142935', 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, '1361142935', None, None, None, None, None, None)] time used for this insertion : 0.012323379516601562 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.40314698219299316 time spend to save output : 0.012712955474853516 total time spend for step 7 : 0.4158599376678467 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 : 0.13293099403381348 #### 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 Mon May 26 19:40:06 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/1748281206_935833_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg': 984484223} map_photo_id_path_extension : {984484223: {'path': 'temp/1748281206_935833_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} inside step blanche_jaune_detection treat image : temp/1748281206_935833_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg 984484223 1.004309911525615 After datou_step_exec type output : time spend for datou_step_exec : 0.15647101402282715 time spend to save output : 9.202957153320312e-05 total time spend for step 1 : 0.15656304359436035 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 : 0.019074201583862305 #### 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 Mon May 26 19:40:07 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 (23354376, 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 : 0.15716099739074707 time spend to save output : 0.0001671314239501953 total time spend for step 1 : 0.15732812881469727 caffe_path_current : About to save ! 0 After save, about to update current ! {15: [(23354376, 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 950003838 begin to download photo : 950003813 download finish for photo 950003812 begin to download photo : 950003696 download finish for photo 950003695 begin to download photo : 926687666 download finish for photo 950003813 download finish for photo 926687666 download finish for photo 950003696 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 : 0.3240058422088623 #### 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 Mon May 26 19:40:07 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281207_935833_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg': 950003838, 'temp/1748281207_935833_950003813_e28be02dfcce79cce594a390a9911a0a.jpg': 950003813, 'temp/1748281207_935833_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg': 950003695, 'temp/1748281207_935833_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg': 926687666, 'temp/1748281207_935833_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg': 950003812, 'temp/1748281207_935833_950003696_11e3a77b72af4b332d366d98984039c7.jpg': 950003696} map_photo_id_path_extension : {950003838: {'path': 'temp/1748281207_935833_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg', 'extension': 'jpg'}, 950003813: {'path': 'temp/1748281207_935833_950003813_e28be02dfcce79cce594a390a9911a0a.jpg', 'extension': 'jpg'}, 950003695: {'path': 'temp/1748281207_935833_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg', 'extension': 'jpg'}, 926687666: {'path': 'temp/1748281207_935833_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg', 'extension': 'jpg'}, 950003812: {'path': 'temp/1748281207_935833_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg', 'extension': 'jpg'}, 950003696: {'path': 'temp/1748281207_935833_950003696_11e3a77b72af4b332d366d98984039c7.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 : [] local folder : /data/models_weight/learn_piece_voiture_0808_v2 /data/models_weight/learn_piece_voiture_0808_v2/caffemodel size_local : 350215080 size in s3 : 350215080 create time local : 2021-08-09 05:30:22 create time in s3 : 2021-08-06 19:24:16 caffemodel already exist and didn't need to update /data/models_weight/learn_piece_voiture_0808_v2/test.prototxt size_local : 7166 size in s3 : 7166 create time local : 2021-08-09 05:30:22 create time in s3 : 2021-08-06 19:24:16 test.prototxt already exist and didn't need to update prototxt : /data/models_weight/learn_piece_voiture_0808_v2/test.prototxt caffemodel : /data/models_weight/learn_piece_voiture_0808_v2/caffemodel Loaded network /data/models_weight/learn_piece_voiture_0808_v2/caffemodel About to compute detect_faster_rcnn : len(args) : 6 Inside frcnn step exec : nb paths : 6 image_path : temp/1748281207_935833_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg image_size (294, 285, 3) [[[ 29 29 29] [ 29 29 29] [ 30 30 30] ... [182 172 165] [141 131 124] [103 94 90]] [[ 29 29 29] [ 29 29 29] [ 31 31 31] ... [231 220 212] [202 193 184] [164 154 147]] [[ 30 30 30] [ 27 27 27] [ 26 26 26] ... [223 211 199] [229 219 209] [228 217 209]] ... [[ 22 27 25] [ 16 21 19] [ 11 16 14] ... [166 145 123] [168 147 125] [170 149 127]] [[ 20 25 23] [ 17 22 20] [ 15 20 18] ... [163 142 120] [165 144 122] [166 145 123]] [[ 13 18 16] [ 17 22 20] [ 20 25 23] ... [162 141 119] [163 142 120] [163 142 121]]] Detection took 0.197s for 300 object proposals c : aile-arriere list_crops.shape (29, 5) proba : 0.052099347 (104.296394, 64.89211, 245.0188, 262.59406) proba : 0.01147626 (156.04819, 154.27693, 276.97562, 234.77818) proba : 0.010073668 (133.89398, 191.0312, 199.7927, 262.34637) c : aile-avant list_crops.shape (30, 5) proba : 0.16405638 (93.40259, 110.00486, 261.18332, 244.4559) proba : 0.011733912 (153.08725, 215.96095, 224.00317, 288.55383) c : autre list_crops.shape (31, 5) c : cache-reservoir list_crops.shape (30, 5) proba : 0.016790668 (12.162407, 54.527374, 94.0456, 89.234406) c : capot list_crops.shape (24, 5) proba : 0.10150701 (69.29463, 0.0, 280.0066, 54.911522) proba : 0.08461616 (113.76143, 84.13615, 262.94287, 231.19196) proba : 0.040670473 (6.741211, 36.86281, 96.11412, 92.56636) c : carrosserie-autre list_crops.shape (27, 5) c : coffre list_crops.shape (23, 5) proba : 0.047657095 (53.505203, 0.0, 284.0, 92.37486) proba : 0.032661073 (10.160156, 31.720852, 95.703835, 100.61211) proba : 0.012154212 (235.39508, 155.04231, 284.0, 216.00703) c : essuie-glace list_crops.shape (31, 5) proba : 0.8093907 (149.03806, 63.964066, 284.0, 87.61563) proba : 0.034236345 (13.640781, 56.070747, 92.06976, 89.41587) proba : 0.020682106 (234.30283, 175.7608, 284.0, 231.14984) c : feu-antibrouillard list_crops.shape (33, 5) proba : 0.05568403 (27.49685, 50.04132, 86.947174, 89.45697) proba : 0.01811527 (229.45944, 178.31311, 279.93936, 220.47192) proba : 0.010318356 (206.26474, 260.463, 253.11981, 293.0) c : feu-arriere list_crops.shape (34, 5) proba : 0.06829295 (27.727543, 49.239876, 86.89491, 88.09205) proba : 0.024009129 (231.78752, 177.13324, 279.96036, 219.16995) c : info-modele list_crops.shape (34, 5) proba : 0.015898507 (27.181103, 48.777905, 87.640976, 89.7207) c : logo-marque list_crops.shape (32, 5) proba : 0.010619727 (206.26936, 219.39398, 262.95163, 293.0) c : logo-roue list_crops.shape (30, 5) c : pare-brise list_crops.shape (24, 5) proba : 0.38038525 (84.4519, 0.0, 284.0, 80.628845) proba : 0.06983222 (21.856312, 45.929783, 86.83162, 90.58208) proba : 0.010923002 (162.29074, 212.50839, 227.27939, 293.0) proba : 0.010167411 (238.71925, 156.77785, 284.0, 216.58827) c : pare-choc list_crops.shape (20, 5) proba : 0.156586 (75.03962, 12.040421, 284.0, 83.469284) proba : 0.03918184 (173.62625, 206.8937, 284.0, 287.1315) proba : 0.029327326 (3.535038, 39.4454, 99.710724, 103.58836) proba : 0.021778308 (236.46074, 158.82768, 284.0, 219.9409) c : phare list_crops.shape (33, 5) proba : 0.29551038 (16.085556, 55.13051, 94.4929, 86.99963) proba : 0.01716382 (85.86616, 17.957005, 272.26593, 68.43976) proba : 0.013774219 (241.58226, 159.2386, 284.0, 213.74611) proba : 0.011597007 (207.11096, 255.55176, 262.2591, 293.0) c : plaque-immatriculation list_crops.shape (38, 5) proba : 0.1310519 (235.82117, 174.32208, 284.0, 225.21951) proba : 0.03964432 (30.735939, 49.492245, 81.55852, 87.70566) c : poignee list_crops.shape (27, 5) c : porte list_crops.shape (24, 5) proba : 0.5999852 (23.915024, 0.0, 103.21737, 219.54753) proba : 0.028164119 (2.5912113, 0.0, 48.603592, 196.39459) proba : 0.01211803 (103.53324, 0.103645325, 235.17029, 252.15573) c : pot-echappement list_crops.shape (30, 5) proba : 0.021319738 (228.94415, 176.45486, 280.0202, 221.25793) proba : 0.013257668 (208.91895, 255.16165, 263.81314, 293.0) proba : 0.013239963 (26.967411, 48.156235, 87.178406, 89.945694) c : radiateur list_crops.shape (30, 5) c : retroviseur list_crops.shape (32, 5) proba : 0.037686843 (14.681625, 54.76487, 94.67331, 89.5873) proba : 0.012229767 (210.45914, 255.86461, 263.11237, 293.0) proba : 0.010143521 (231.19218, 176.60648, 279.57343, 220.06183) c : roue list_crops.shape (34, 5) proba : 0.8120945 (146.79636, 218.47403, 218.13412, 292.024) proba : 0.27982935 (135.77396, 224.51108, 169.61398, 293.0) proba : 0.044856396 (29.87775, 37.665005, 91.516785, 83.93828) proba : 0.02531965 (7.3840523, 0.092681885, 44.25529, 34.973965) proba : 0.01880163 (234.26233, 172.02612, 277.35828, 219.31058) proba : 0.011281824 (206.8189, 251.68556, 258.87207, 293.0) c : toit list_crops.shape (29, 5) c : vitre list_crops.shape (32, 5) proba : 0.14832415 (152.6727, 220.60988, 208.9739, 293.0) proba : 0.034725573 (235.49042, 173.76303, 284.0, 228.08011) proba : 0.030397678 (138.02531, 226.14864, 164.9871, 293.0) proba : 0.0188492 (18.038994, 51.71556, 93.861115, 87.637085) We are managing local photo_id image_path : temp/1748281207_935833_950003813_e28be02dfcce79cce594a390a9911a0a.jpg image_size (254, 229, 3) [[[202 190 186] [205 193 189] [205 194 190] ... [ 81 70 56] [ 80 69 55] [ 78 67 53]] [[198 187 183] [200 189 185] [198 189 185] ... [ 50 41 28] [ 44 36 23] [ 45 36 23]] [[192 187 184] [191 186 183] [191 186 183] ... [ 36 30 23] [ 32 29 21] [ 33 27 20]] ... [[187 186 190] [186 185 189] [188 184 189] ... [ 43 38 35] [ 37 33 28] [ 33 28 25]] [[184 185 189] [183 184 188] [184 183 187] ... [ 28 23 22] [ 29 24 21] [ 33 28 27]] [[181 185 186] [180 184 185] [182 184 185] ... [ 23 15 16] [ 22 14 14] [ 24 16 17]]] Detection took 0.063s for 300 object proposals c : aile-arriere list_crops.shape (29, 5) proba : 0.06771055 (98.071365, 150.88261, 151.93869, 223.72304) proba : 0.040917892 (33.36013, 90.1349, 179.12991, 182.83849) proba : 0.016990822 (79.35235, 122.71878, 111.49549, 230.36551) c : aile-avant list_crops.shape (30, 5) proba : 0.048593655 (43.71639, 17.109932, 139.32442, 207.17996) proba : 0.010096299 (62.04154, 104.14584, 183.72293, 182.3082) c : autre list_crops.shape (32, 5) c : cache-reservoir list_crops.shape (33, 5) c : capot list_crops.shape (22, 5) proba : 0.09681845 (38.04149, 75.421104, 207.15202, 166.65808) c : carrosserie-autre list_crops.shape (24, 5) c : coffre list_crops.shape (21, 5) proba : 0.06443614 (165.47507, 115.30492, 226.3937, 165.47655) c : essuie-glace list_crops.shape (34, 5) proba : 0.38617417 (88.269196, 10.883799, 179.58469, 31.356676) proba : 0.042393245 (133.08565, 1.8785601, 227.74263, 26.528473) proba : 0.024031106 (169.25403, 117.38529, 218.9107, 165.43329) proba : 0.010853164 (61.551834, 1.9375887, 117.818405, 33.00387) c : feu-antibrouillard list_crops.shape (32, 5) proba : 0.019601727 (168.92393, 117.954666, 219.52592, 165.85603) c : feu-arriere list_crops.shape (30, 5) proba : 0.022242485 (171.07262, 119.67797, 221.00426, 164.20001) c : info-modele list_crops.shape (31, 5) proba : 0.010201109 (168.5215, 116.05337, 219.94884, 166.43362) c : logo-marque list_crops.shape (34, 5) proba : 0.0151499035 (154.4575, 207.10855, 180.12544, 244.77008) c : logo-roue list_crops.shape (30, 5) c : pare-brise list_crops.shape (27, 5) proba : 0.047095794 (168.76782, 117.314865, 222.8822, 165.84196) proba : 0.01360163 (53.461132, 0.0, 178.90869, 22.406097) proba : 0.012163818 (15.941405, 0.0, 44.896694, 48.295265) c : pare-choc list_crops.shape (23, 5) proba : 0.08983616 (166.0652, 118.16121, 226.87505, 168.71342) proba : 0.012228996 (108.19799, 130.85756, 228.0, 249.22716) c : phare list_crops.shape (37, 5) proba : 0.013693857 (172.01378, 118.397995, 223.3029, 162.23991) c : plaque-immatriculation list_crops.shape (38, 5) proba : 0.051214125 (173.59384, 120.67873, 218.52002, 161.78456) c : poignee list_crops.shape (31, 5) c : porte list_crops.shape (25, 5) proba : 0.03930021 (4.774891, 0.0, 38.134586, 169.70963) proba : 0.014839206 (170.32455, 112.399765, 220.70108, 170.46397) proba : 0.013463313 (19.337994, 0.0, 92.45218, 176.19649) c : pot-echappement list_crops.shape (31, 5) proba : 0.02178765 (168.5671, 115.56397, 219.41109, 166.486) proba : 0.01647243 (154.52905, 206.32008, 179.7135, 244.10432) c : radiateur list_crops.shape (30, 5) c : retroviseur list_crops.shape (34, 5) proba : 0.010121202 (169.5348, 116.20987, 219.59969, 166.28848) c : roue list_crops.shape (36, 5) proba : 0.10634057 (83.72043, 166.04865, 133.41412, 253.0) proba : 0.05047724 (73.957924, 164.66907, 104.24093, 236.14459) proba : 0.032670468 (18.137676, 0.0, 45.997715, 33.785934) c : toit list_crops.shape (33, 5) c : vitre list_crops.shape (34, 5) proba : 0.02829551 (20.59945, 0.0, 45.493385, 43.05551) proba : 0.01535077 (170.87396, 118.45989, 221.02893, 164.49493) We are managing local photo_id image_path : temp/1748281207_935833_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg image_size (2160, 3840, 3) [[[111 118 91] [113 120 93] [115 120 93] ... [ 23 40 37] [ 23 40 37] [ 24 41 38]] [[111 118 91] [112 119 92] [115 120 93] ... [ 23 40 37] [ 23 40 37] [ 23 40 37]] [[113 118 91] [114 119 92] [115 120 93] ... [ 22 39 36] [ 23 40 37] [ 23 40 37]] ... [[120 125 94] [119 124 93] [118 123 92] ... [ 22 36 34] [ 22 36 34] [ 23 37 35]] [[119 124 93] [119 124 93] [118 123 92] ... [ 22 36 34] [ 22 36 34] [ 22 36 34]] [[118 123 91] [117 122 90] [117 122 91] ... [ 22 36 34] [ 22 36 34] [ 22 36 34]]] Detection took 1.452s for 300 object proposals c : aile-arriere list_crops.shape (45, 5) proba : 0.016386092 (3266.7039, 1031.8993, 3839.0, 1714.3163) proba : 0.016077064 (16.385712, 491.10278, 406.7727, 799.8469) proba : 0.012598678 (1997.6519, 259.4789, 2506.4448, 848.9446) proba : 0.011021514 (51.39949, 1690.1299, 479.94452, 1966.6416) c : aile-avant list_crops.shape (41, 5) c : autre list_crops.shape (46, 5) c : cache-reservoir list_crops.shape (46, 5) c : capot list_crops.shape (38, 5) c : carrosserie-autre list_crops.shape (44, 5) c : coffre list_crops.shape (31, 5) c : essuie-glace list_crops.shape (49, 5) c : feu-antibrouillard list_crops.shape (44, 5) proba : 0.014232367 (3282.572, 1219.7751, 3793.5466, 1821.4829) proba : 0.012606563 (27.953613, 498.90277, 410.01135, 789.638) c : feu-arriere list_crops.shape (44, 5) proba : 0.07466735 (7.599655, 454.29858, 376.22174, 791.5845) proba : 0.025205154 (3284.6772, 1151.9546, 3782.7158, 1834.2437) proba : 0.011048986 (17.658463, 1586.6804, 282.00757, 1995.7551) c : info-modele list_crops.shape (44, 5) proba : 0.01871645 (35.904144, 482.72305, 414.19882, 791.46533) c : logo-marque list_crops.shape (46, 5) proba : 0.017159652 (41.257065, 486.37988, 409.73828, 792.7175) c : logo-roue list_crops.shape (45, 5) c : pare-brise list_crops.shape (38, 5) proba : 0.01659972 (24.736359, 0.0, 388.8584, 684.5054) c : pare-choc list_crops.shape (35, 5) c : phare list_crops.shape (48, 5) c : plaque-immatriculation list_crops.shape (47, 5) proba : 0.025558176 (26.292511, 479.65906, 404.91946, 771.0735) proba : 0.016557144 (28.251236, 1620.7332, 299.3698, 1974.6033) proba : 0.014697709 (18.401276, 3.5810318, 386.72675, 255.02255) c : poignee list_crops.shape (43, 5) c : porte list_crops.shape (40, 5) proba : 0.11930539 (1872.7805, 10.216522, 2442.9631, 862.2228) proba : 0.11467625 (2136.5068, 52.950653, 2855.983, 815.2799) proba : 0.024789525 (3234.829, 69.626495, 3823.1182, 847.85095) proba : 0.013809722 (109.56145, 1835.4781, 459.84198, 2159.0) proba : 0.011384305 (3315.014, 1115.606, 3779.0168, 1883.8972) proba : 0.01138364 (1348.9719, 1049.3442, 1927.9482, 1777.008) proba : 0.0107578095 (2525.6118, 168.82248, 3531.4917, 924.1282) c : pot-echappement list_crops.shape (43, 5) proba : 0.0334206 (5.11409, 1746.7177, 343.44177, 2019.285) proba : 0.010658525 (117.19641, 1862.9634, 459.03424, 2155.1729) c : radiateur list_crops.shape (43, 5) c : retroviseur list_crops.shape (47, 5) proba : 0.0180384 (3296.5876, 1200.0015, 3787.9998, 1822.6357) proba : 0.013664958 (13.039093, 1748.9751, 340.3817, 2015.7688) proba : 0.012989944 (124.445206, 1867.7468, 456.31842, 2150.5205) c : roue list_crops.shape (45, 5) proba : 0.58397543 (3132.7405, 1107.5474, 3839.0, 1925.3474) proba : 0.045879494 (3481.3076, 1409.8956, 3814.2769, 1997.3666) proba : 0.0372978 (38.598846, 1751.9752, 339.52985, 2013.6791) proba : 0.02917707 (3244.3765, 40.529846, 3721.5938, 739.19684) proba : 0.018186167 (3167.0613, 383.7099, 3484.2498, 1016.3538) proba : 0.012428115 (229.0726, 0.0, 685.80994, 557.645) proba : 0.010361151 (2689.1016, 225.03879, 3238.6724, 934.31775) c : toit list_crops.shape (48, 5) c : vitre list_crops.shape (44, 5) proba : 0.018341776 (23.033783, 0.0, 379.9094, 299.26526) proba : 0.014214581 (3321.231, 1194.3098, 3770.2056, 1805.9648) We are managing local photo_id image_path : temp/1748281207_935833_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg image_size (480, 640, 3) [[[36 41 44] [36 41 44] [35 40 43] ... [ 8 10 10] [ 8 10 10] [ 8 10 10]] [[37 42 45] [36 41 44] [35 40 43] ... [ 5 7 7] [ 5 7 7] [ 5 7 7]] [[37 42 45] [36 41 44] [35 40 43] ... [ 3 5 5] [ 4 6 6] [ 4 6 6]] ... [[42 47 50] [41 46 49] [40 45 48] ... [ 8 10 10] [ 8 10 10] [ 8 10 10]] [[41 46 49] [41 46 49] [40 45 48] ... [ 0 2 2] [10 12 12] [22 24 24]] [[40 45 48] [40 45 48] [40 45 48] ... [10 12 12] [17 19 19] [26 28 28]]] Detection took 0.041s for 300 object proposals c : aile-arriere list_crops.shape (32, 5) proba : 0.12728494 (160.85544, 172.85056, 306.19583, 321.3927) proba : 0.011539879 (538.53186, 190.83319, 613.9651, 298.88846) proba : 0.010252409 (197.33119, 160.36795, 490.47467, 311.27023) c : aile-avant list_crops.shape (40, 5) proba : 0.9481673 (161.78645, 149.53848, 330.65057, 343.41827) proba : 0.09023353 (20.77099, 105.89322, 54.780426, 195.62857) proba : 0.022176933 (152.80438, 143.27356, 217.17358, 306.2657) proba : 0.013756986 (320.7018, 330.90826, 439.32846, 414.44586) proba : 0.011395105 (557.7661, 199.67062, 608.666, 270.5084) c : autre list_crops.shape (36, 5) proba : 0.011426999 (458.26996, 15.29858, 521.14417, 98.992966) c : cache-reservoir list_crops.shape (37, 5) proba : 0.02975046 (470.56714, 21.58405, 531.4651, 89.20062) proba : 0.025542347 (368.27374, 265.85748, 446.9007, 331.26764) proba : 0.014927578 (353.33182, 360.48926, 412.4888, 423.1305) proba : 0.014706393 (451.1123, 57.61369, 523.1541, 125.9301) c : capot list_crops.shape (33, 5) proba : 0.9944273 (211.27667, 115.14333, 555.77673, 300.74332) proba : 0.23368256 (65.79134, 24.148853, 285.73325, 46.316715) proba : 0.03484949 (343.23712, 279.90933, 508.20905, 431.9211) proba : 0.017472778 (89.425446, 37.51777, 357.5151, 63.628494) proba : 0.010149982 (424.3767, 246.6672, 564.6207, 357.0054) c : carrosserie-autre list_crops.shape (35, 5) proba : 0.02854567 (454.54266, 25.088871, 523.51514, 121.407074) c : coffre list_crops.shape (31, 5) proba : 0.12960036 (446.4302, 19.310467, 528.3304, 117.90675) proba : 0.10255302 (139.89503, 45.982918, 419.52515, 192.66501) proba : 0.030859323 (269.29358, 162.00201, 553.94037, 348.9427) c : essuie-glace list_crops.shape (40, 5) proba : 0.78952104 (203.86537, 108.23131, 416.75665, 147.35794) proba : 0.14472218 (369.0622, 267.53116, 445.03558, 332.95914) proba : 0.09796253 (391.05823, 261.18723, 527.0141, 318.13065) proba : 0.07719912 (470.4785, 22.210827, 532.0739, 88.02254) proba : 0.035417784 (354.3855, 360.35925, 414.2046, 422.97693) proba : 0.014148991 (114.837074, 22.670801, 335.36917, 42.909843) proba : 0.012226143 (254.57935, 94.72243, 456.8186, 288.55975) proba : 0.01093047 (353.83862, 285.56592, 484.7901, 414.69598) c : feu-antibrouillard list_crops.shape (39, 5) proba : 0.21194471 (341.78415, 363.48273, 404.8503, 430.6582) proba : 0.055869132 (368.6624, 268.62845, 446.44495, 330.09274) proba : 0.025234457 (470.42926, 21.908142, 531.39526, 89.25692) proba : 0.023562519 (389.44684, 264.04617, 529.78937, 316.95987) proba : 0.016457234 (451.48865, 58.84283, 522.90686, 125.20607) proba : 0.010561554 (467.00592, 343.55017, 573.76373, 420.20203) c : feu-arriere list_crops.shape (39, 5) proba : 0.10164658 (329.67007, 259.25998, 456.71774, 315.55057) proba : 0.042237688 (454.30334, 28.767757, 522.906, 104.09572) proba : 0.025247013 (333.5109, 352.84576, 405.05966, 425.97504) proba : 0.010687803 (475.5653, 341.53986, 575.443, 420.15167) c : info-modele list_crops.shape (37, 5) proba : 0.070451684 (470.4897, 21.814522, 532.0424, 89.57253) proba : 0.038774226 (353.82898, 361.54095, 412.99603, 422.85272) proba : 0.033352003 (368.70792, 266.81427, 448.24356, 331.62988) proba : 0.023028461 (450.84253, 57.473732, 524.06647, 126.33897) proba : 0.015797792 (390.1408, 260.85043, 533.17053, 317.53458) c : logo-marque list_crops.shape (39, 5) proba : 0.11936614 (352.9693, 361.31396, 412.53143, 422.70062) proba : 0.03297161 (470.55695, 20.778976, 530.9592, 88.80183) proba : 0.027512379 (370.81384, 264.5849, 447.20825, 329.64172) proba : 0.025025712 (397.47125, 260.82495, 526.3095, 316.55157) proba : 0.01432876 (584.1553, 0.7486534, 639.0, 70.73653) c : logo-roue list_crops.shape (37, 5) proba : 0.014608949 (353.32275, 360.72885, 413.24738, 423.6064) proba : 0.013224577 (470.34485, 21.601006, 532.0651, 89.581154) c : pare-brise list_crops.shape (34, 5) proba : 0.9569804 (141.09827, 42.465805, 444.0923, 147.77292) proba : 0.10514284 (319.4551, 22.01419, 424.0322, 129.13058) proba : 0.06655302 (453.85526, 19.659801, 523.27, 91.54599) proba : 0.05099205 (287.21246, 164.06036, 547.321, 296.7368) proba : 0.023298472 (95.59767, 40.148212, 158.49359, 162.53964) c : pare-choc list_crops.shape (29, 5) proba : 0.9453801 (272.8993, 257.50323, 580.23535, 444.44446) proba : 0.21966855 (233.49011, 224.18677, 397.76483, 411.54724) proba : 0.028824631 (487.98495, 17.575188, 613.4226, 120.56991) proba : 0.02874259 (435.95914, 307.78607, 588.67725, 426.578) c : phare list_crops.shape (38, 5) proba : 0.7776455 (326.8396, 251.89093, 477.77557, 312.86353) proba : 0.076986045 (328.221, 359.6383, 410.2417, 426.9676) proba : 0.03883011 (292.80917, 225.1388, 466.74326, 392.34387) proba : 0.033107083 (538.23785, 197.21306, 600.2161, 303.44183) proba : 0.026038866 (466.65143, 20.839922, 531.7362, 84.11861) proba : 0.01885784 (305.60785, 209.4725, 597.7123, 307.11697) proba : 0.014258519 (478.92178, 344.12872, 578.6284, 416.75287) proba : 0.011289306 (96.670364, 66.98555, 163.91154, 147.69383) c : plaque-immatriculation list_crops.shape (38, 5) proba : 0.23007093 (518.5555, 294.16483, 582.3378, 390.75772) proba : 0.033767473 (438.44455, 271.76074, 568.42065, 386.56097) proba : 0.025869232 (347.18576, 259.77948, 452.87918, 315.94458) proba : 0.021142216 (470.38934, 23.621532, 531.61127, 85.37875) c : poignee list_crops.shape (35, 5) proba : 0.05437683 (583.35675, 0.030944824, 639.0, 70.98617) proba : 0.034033537 (470.45398, 21.598156, 531.6744, 89.77423) proba : 0.026380574 (353.1713, 360.95804, 412.89462, 423.59952) proba : 0.024486065 (368.05457, 266.1231, 448.05615, 332.26862) c : porte list_crops.shape (28, 5) proba : 0.9854106 (78.16554, 43.073242, 169.6544, 306.4445) proba : 0.9672492 (33.643482, 44.180687, 90.647446, 241.28842) proba : 0.09158108 (452.36993, 19.605862, 521.0893, 113.53558) proba : 0.034176376 (158.70958, 46.139404, 421.3371, 212.15448) proba : 0.0124274995 (436.9677, 223.37698, 575.0614, 403.33023) c : pot-echappement list_crops.shape (37, 5) proba : 0.055538286 (353.82162, 360.7021, 412.2314, 423.3385) proba : 0.018788762 (458.0549, 14.546082, 521.22046, 99.67038) proba : 0.01293839 (584.394, 0.0, 639.0, 71.10106) proba : 0.011320076 (466.1448, 340.3395, 575.4006, 420.87833) c : radiateur list_crops.shape (38, 5) c : retroviseur list_crops.shape (39, 5) proba : 0.23818137 (452.17767, 56.800816, 522.1632, 124.12401) proba : 0.20784362 (471.15372, 21.540134, 531.2207, 89.27219) proba : 0.1933564 (369.9025, 266.0212, 447.4471, 332.58774) proba : 0.04561908 (342.49457, 362.45737, 404.12854, 431.03354) proba : 0.02658106 (176.92017, 115.94905, 409.84937, 150.13274) proba : 0.021464339 (584.38226, 0.0, 639.0, 70.68973) proba : 0.01362427 (392.9411, 259.88425, 530.61, 318.64426) proba : 0.010496802 (101.94364, 73.78771, 164.67946, 158.12817) c : roue list_crops.shape (38, 5) proba : 0.94177973 (187.78558, 271.2435, 306.51013, 426.3298) proba : 0.13538393 (334.87332, 358.32144, 400.07767, 428.7977) proba : 0.049789123 (538.7118, 250.99724, 599.9934, 395.2788) proba : 0.03737304 (12.778793, 136.7522, 64.16691, 244.92178) proba : 0.034813307 (549.3056, 192.38788, 605.79486, 305.7144) proba : 0.015742403 (479.68723, 335.9157, 572.48145, 421.26562) c : toit list_crops.shape (34, 5) proba : 0.7919596 (83.632034, 31.277206, 341.39307, 55.134705) c : vitre list_crops.shape (37, 5) proba : 0.9804714 (95.83035, 50.274773, 162.35281, 142.97379) proba : 0.84251344 (42.275963, 43.051846, 91.28243, 114.96529) proba : 0.07667274 (277.41486, 28.772907, 404.65912, 139.55339) proba : 0.064491816 (455.88516, 20.45959, 518.84143, 90.14313) proba : 0.02751436 (148.30217, 51.204914, 356.0141, 112.508316) proba : 0.013177862 (336.1942, 352.38376, 408.22003, 424.23264) proba : 0.012460389 (364.9295, 257.4683, 445.5263, 324.04025) We are managing local photo_id image_path : temp/1748281207_935833_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg image_size (480, 614, 3) [[[ 44 44 44] [ 49 51 51] [ 42 44 44] ... [ 8 10 10] [ 8 10 10] [ 8 10 10]] [[ 43 43 43] [ 36 38 38] [ 39 41 41] ... [ 5 7 7] [ 5 7 7] [ 5 7 7]] [[ 70 70 70] [ 40 42 42] [ 41 43 43] ... [ 4 6 6] [ 4 6 6] [ 4 6 6]] ... [[103 101 101] [110 108 108] [ 61 59 59] ... [ 8 10 10] [ 8 10 10] [ 8 10 10]] [[ 98 96 96] [115 113 113] [ 73 71 71] ... [ 0 2 2] [ 11 13 13] [ 21 23 23]] [[ 92 90 90] [114 112 112] [ 87 82 83] ... [ 10 12 12] [ 18 20 20] [ 25 27 27]]] Detection took 0.064s for 300 object proposals c : aile-arriere list_crops.shape (30, 5) proba : 0.13240953 (133.29189, 172.38783, 280.826, 321.50037) proba : 0.013276823 (153.19504, 232.50832, 369.7628, 359.78308) c : aile-avant list_crops.shape (35, 5) proba : 0.950391 (133.97119, 146.1601, 305.7786, 344.49127) proba : 0.02074943 (128.1466, 143.00705, 188.90363, 305.039) proba : 0.014929656 (294.24445, 331.83783, 412.49805, 415.32953) proba : 0.01119993 (533.05725, 198.27632, 583.4973, 268.45685) proba : 0.010003625 (0.0, 114.21206, 47.591057, 234.00299) c : autre list_crops.shape (36, 5) c : cache-reservoir list_crops.shape (35, 5) proba : 0.025677871 (426.6999, 57.93489, 496.61926, 125.31201) proba : 0.025273968 (342.52167, 266.6155, 421.34314, 330.5552) proba : 0.017259173 (306.41116, 352.4296, 382.60562, 422.0015) c : capot list_crops.shape (32, 5) proba : 0.99047136 (196.74257, 110.37375, 532.5076, 291.96768) proba : 0.22532377 (40.24877, 25.965992, 253.16144, 48.717922) proba : 0.05844854 (318.8877, 286.61392, 481.59174, 427.89658) proba : 0.017970432 (64.4218, 38.410267, 331.2907, 64.35642) proba : 0.010638468 (116.14872, 36.75155, 368.6318, 155.92752) c : carrosserie-autre list_crops.shape (29, 5) proba : 0.030297695 (429.3437, 22.05832, 497.81628, 122.48096) c : coffre list_crops.shape (29, 5) proba : 0.12301221 (421.3337, 15.516251, 501.77365, 119.22212) proba : 0.10119871 (112.74213, 46.270157, 395.27182, 193.28516) proba : 0.06282824 (247.78423, 171.97957, 517.5145, 335.85602) c : essuie-glace list_crops.shape (37, 5) proba : 0.7717542 (178.34984, 109.068016, 389.55267, 147.69397) proba : 0.14845423 (343.2805, 268.29117, 419.46103, 332.3441) proba : 0.10080025 (364.45374, 261.52032, 501.2318, 317.86383) proba : 0.047932614 (446.58, 21.590199, 508.3315, 88.69951) proba : 0.03781669 (328.75983, 360.46194, 389.85632, 423.33444) proba : 0.014705765 (85.60287, 24.015587, 308.10596, 43.34745) proba : 0.01385311 (267.0376, 240.33777, 412.9605, 348.48663) proba : 0.012842637 (427.07498, 58.1542, 496.35287, 124.40303) c : feu-antibrouillard list_crops.shape (37, 5) proba : 0.21098754 (307.27994, 354.31342, 381.79538, 421.6104) proba : 0.05684828 (342.86584, 269.3656, 420.86035, 329.44177) proba : 0.028136741 (427.12283, 58.815784, 496.14957, 124.5508) proba : 0.023515377 (362.9286, 264.44678, 504.08954, 316.8404) c : feu-arriere list_crops.shape (34, 5) proba : 0.09635338 (303.1505, 259.6426, 431.29108, 316.44632) proba : 0.041329492 (428.94818, 25.616703, 497.4831, 104.48634) proba : 0.03447066 (305.47406, 351.55908, 379.5111, 423.9405) c : info-modele list_crops.shape (36, 5) proba : 0.042865776 (306.7388, 353.51428, 383.1755, 421.5606) proba : 0.03521785 (426.52325, 57.95116, 497.44006, 125.61244) proba : 0.03336316 (342.96075, 267.5599, 422.67865, 330.89584) proba : 0.016011745 (363.5852, 261.26355, 507.57068, 317.39307) proba : 0.010808125 (445.89893, 19.32589, 507.99823, 91.46554) c : logo-marque list_crops.shape (36, 5) proba : 0.10544186 (327.43314, 361.32748, 387.9545, 423.03415) proba : 0.028344158 (344.9644, 265.39203, 421.6276, 329.04633) proba : 0.025367195 (370.85962, 261.33923, 500.61487, 316.38684) proba : 0.017483983 (427.601, 57.64548, 496.789, 125.72362) c : logo-roue list_crops.shape (34, 5) proba : 0.014208335 (327.6623, 360.72037, 388.80023, 424.04056) c : pare-brise list_crops.shape (30, 5) proba : 0.9546369 (113.60828, 42.325954, 417.92328, 147.55597) proba : 0.1069802 (294.6801, 24.328224, 399.7052, 129.17944) proba : 0.054091077 (258.47858, 161.26228, 521.5881, 295.05402) proba : 0.027624419 (424.62256, 20.86147, 499.56976, 97.22682) proba : 0.017623788 (72.59046, 41.744843, 127.25228, 168.79414) c : pare-choc list_crops.shape (25, 5) proba : 0.9485635 (247.46754, 265.472, 552.9363, 445.27454) proba : 0.09526595 (194.32056, 225.2351, 348.43347, 419.2273) proba : 0.019611463 (422.4501, 329.6101, 549.5322, 424.81213) proba : 0.014918032 (455.54248, 26.131039, 592.8164, 120.21471) c : phare list_crops.shape (35, 5) proba : 0.67815685 (318.13934, 264.64734, 489.91852, 310.62476) proba : 0.40324858 (261.64246, 234.1177, 408.5135, 331.02457) proba : 0.09589574 (301.1042, 356.4168, 385.49786, 425.23474) proba : 0.014655772 (525.66693, 198.54504, 575.2177, 289.9494) proba : 0.013314211 (427.99374, 23.331085, 501.2996, 100.648254) proba : 0.011495445 (277.7351, 207.81831, 557.26514, 296.8568) c : plaque-immatriculation list_crops.shape (36, 5) proba : 0.19054858 (491.39825, 294.22528, 563.8239, 390.17786) proba : 0.057782095 (439.5147, 291.40817, 531.0498, 407.16696) proba : 0.022952404 (298.9808, 257.41556, 447.53647, 319.17233) proba : 0.011340452 (309.8405, 356.20645, 386.15668, 421.9346) c : poignee list_crops.shape (34, 5) proba : 0.025672102 (327.5085, 360.97168, 388.47128, 424.01862) proba : 0.024167404 (342.31458, 266.88998, 422.4934, 331.52768) proba : 0.021987144 (426.4256, 57.751568, 497.07947, 125.79605) proba : 0.013409202 (559.0071, 0.020671844, 613.0, 71.29181) c : porte list_crops.shape (27, 5) proba : 0.9922963 (51.415348, 40.437546, 152.79521, 306.38635) proba : 0.97203326 (4.567642, 51.356186, 72.87424, 242.46828) proba : 0.058677416 (425.31842, 19.071102, 501.12122, 130.88304) proba : 0.03051086 (132.0887, 46.462677, 397.1651, 213.58133) proba : 0.014542864 (360.3003, 244.28168, 551.61017, 395.622) c : pot-echappement list_crops.shape (36, 5) proba : 0.05058327 (328.16974, 360.69702, 387.76825, 423.7301) proba : 0.020144574 (427.1569, 57.46178, 496.2939, 125.84093) c : radiateur list_crops.shape (35, 5) c : retroviseur list_crops.shape (35, 5) proba : 0.43161792 (427.4084, 56.775253, 495.94495, 123.66815) proba : 0.1857399 (344.11105, 266.8148, 421.86307, 331.86948) proba : 0.11662473 (446.8653, 19.437584, 506.46545, 90.95233) proba : 0.048026722 (316.70694, 363.1923, 377.63937, 430.79214) proba : 0.027540136 (150.80542, 116.12033, 381.5962, 150.2211) proba : 0.0127773285 (366.3126, 260.3139, 504.92538, 318.49078) proba : 0.011194432 (77.32822, 81.65887, 130.17514, 174.47797) c : roue list_crops.shape (37, 5) proba : 0.9375625 (166.73532, 261.12933, 275.2205, 420.81006) proba : 0.14077185 (307.79614, 351.35663, 385.3504, 430.9638) proba : 0.114026934 (4.5557594, 161.28702, 50.799404, 253.06839) proba : 0.04501324 (513.9554, 252.556, 573.9212, 393.48044) proba : 0.0371112 (526.6664, 191.77579, 581.6011, 303.90457) c : toit list_crops.shape (33, 5) proba : 0.7964256 (58.201508, 31.454428, 316.00647, 55.599182) c : vitre list_crops.shape (33, 5) proba : 0.9803699 (69.39469, 49.352108, 135.83209, 142.42294) proba : 0.8937702 (14.668642, 41.219543, 66.17396, 115.17752) proba : 0.11751065 (205.4856, 48.102497, 379.27448, 127.90582) proba : 0.04375867 (428.12604, 21.894474, 499.2102, 97.1543) proba : 0.017622797 (308.70734, 352.47757, 385.68646, 423.2487) proba : 0.012129081 (338.94977, 258.53705, 419.88477, 323.7387) We are managing local photo_id image_path : temp/1748281207_935833_950003696_11e3a77b72af4b332d366d98984039c7.jpg image_size (2160, 3264, 3) [[[168 165 161] [168 165 161] [168 165 161] ... [ 47 59 63] [ 48 60 64] [ 48 60 64]] [[168 165 161] [168 165 161] [168 165 161] ... [ 47 59 63] [ 47 59 63] [ 48 60 64]] [[168 165 161] [168 165 161] [168 165 161] ... [ 47 59 63] [ 47 59 63] [ 47 59 63]] ... [[167 164 160] [167 164 160] [167 164 160] ... [ 44 59 61] [ 44 59 61] [ 44 59 61]] [[165 162 158] [165 162 158] [165 162 158] ... [ 45 60 62] [ 45 60 62] [ 45 60 62]] [[164 161 157] [164 161 157] [164 161 157] ... [ 45 60 62] [ 45 60 62] [ 45 60 62]]] Detection took 0.674s for 300 object proposals c : aile-arriere list_crops.shape (52, 5) proba : 0.020721387 (396.2275, 192.55852, 703.2374, 482.50873) proba : 0.013662438 (2606.4822, 1489.7378, 2954.3953, 2131.393) proba : 0.010280677 (2270.177, 1651.4274, 2706.3875, 2138.8582) proba : 0.010019045 (2922.7322, 683.27057, 3263.0, 1188.897) c : aile-avant list_crops.shape (55, 5) proba : 0.025829516 (2616.204, 1475.3538, 2938.8389, 2144.0322) c : autre list_crops.shape (53, 5) proba : 0.013096892 (405.69394, 192.22005, 689.0912, 476.50537) c : cache-reservoir list_crops.shape (50, 5) proba : 0.029478658 (2629.4731, 1461.0208, 2954.7158, 2148.8457) proba : 0.024541816 (299.21417, 89.51254, 716.66125, 498.37216) proba : 0.020119606 (2232.051, 676.77325, 2590.5178, 1171.2031) proba : 0.018791052 (2298.3872, 1682.8022, 2783.6611, 2156.6445) proba : 0.012400597 (2317.1353, 1436.0571, 2614.482, 1924.9583) proba : 0.01213523 (2925.148, 677.3058, 3258.894, 1209.0438) c : capot list_crops.shape (44, 5) proba : 0.018009905 (2646.0115, 973.0503, 3214.7283, 1642.1025) proba : 0.016421735 (77.74695, 795.9553, 693.9824, 1481.113) c : carrosserie-autre list_crops.shape (45, 5) proba : 0.0293149 (251.71701, 0.0, 712.34973, 634.99194) proba : 0.013688982 (2284.698, 1569.4863, 2687.3684, 2159.0) c : coffre list_crops.shape (42, 5) proba : 0.13925062 (283.04486, 0.0, 631.68286, 633.3987) proba : 0.013912303 (1720.587, 507.75992, 2227.8428, 1191.3582) c : essuie-glace list_crops.shape (56, 5) proba : 0.018926881 (320.8921, 110.23695, 601.00183, 462.48242) proba : 0.0138125755 (2187.6128, 749.48834, 2497.3213, 1214.4453) proba : 0.013557538 (2629.3281, 1462.9319, 2950.8657, 2139.646) proba : 0.01334316 (74.39137, 721.1924, 391.74957, 1112.9591) c : feu-antibrouillard list_crops.shape (52, 5) proba : 0.060857747 (372.10052, 242.48851, 759.43304, 515.9216) proba : 0.018309275 (2928.3794, 694.31146, 3257.6504, 1206.6523) proba : 0.018102575 (2299.543, 1693.8586, 2784.125, 2158.6943) proba : 0.015857762 (2230.3682, 698.55725, 2591.1724, 1175.293) proba : 0.014356218 (2630.748, 1480.992, 2954.4736, 2151.7498) proba : 0.011736633 (383.55692, 1437.1288, 937.1439, 1845.1278) c : feu-arriere list_crops.shape (48, 5) proba : 0.64668804 (354.85095, 213.37978, 730.1616, 532.05145) proba : 0.12032515 (2225.662, 661.21014, 2605.2207, 1235.8997) proba : 0.07940124 (256.18536, 24.241302, 540.246, 478.12408) proba : 0.060693003 (1807.4286, 656.7408, 2155.907, 1147.3539) proba : 0.03355938 (2308.1853, 1667.571, 2786.7566, 2159.0) proba : 0.03226127 (2930.442, 638.0884, 3237.7642, 1243.1958) proba : 0.018530268 (619.94763, 619.25244, 953.7433, 1223.5817) proba : 0.017301125 (2630.6829, 1496.9247, 2955.3792, 2159.0) proba : 0.016657772 (2317.2378, 1411.0992, 2618.2646, 1961.6993) proba : 0.0158185 (770.62616, 939.7168, 1053.5702, 1456.1438) proba : 0.010594566 (2489.4495, 424.49866, 2939.1877, 1234.4347) proba : 0.010264415 (828.05286, 1.927124, 1174.9663, 369.90942) proba : 0.010093711 (14.196671, 430.8955, 165.10507, 734.0321) c : info-modele list_crops.shape (49, 5) proba : 0.07010116 (403.72818, 194.21481, 693.0531, 475.90414) proba : 0.022200853 (2299.2551, 1684.2521, 2787.114, 2154.6646) proba : 0.019056309 (2232.6099, 682.55145, 2592.7407, 1170.8301) proba : 0.017020384 (2630.581, 1468.2198, 2959.7212, 2151.3442) proba : 0.010882027 (1.7524109, 575.7832, 261.89505, 885.70996) c : logo-marque list_crops.shape (51, 5) proba : 0.051886305 (408.49512, 200.25732, 688.2732, 481.55737) proba : 0.03407572 (2304.7786, 1690.4406, 2793.8875, 2159.0) proba : 0.018234616 (2631.4712, 1489.2529, 2967.995, 2159.0) proba : 0.013038501 (2236.6904, 683.91125, 2594.5972, 1180.4824) proba : 0.011358707 (2319.9639, 1440.4114, 2621.3862, 1933.8381) c : logo-roue list_crops.shape (52, 5) proba : 0.030219454 (402.7806, 191.06737, 692.7544, 478.266) proba : 0.020900488 (2230.9324, 677.7641, 2592.7224, 1173.9943) proba : 0.018412463 (2512.7656, 492.66626, 2833.5518, 843.9911) proba : 0.01807187 (2298.6846, 1681.3522, 2786.085, 2156.1255) proba : 0.013550618 (2610.384, 558.7978, 3007.5369, 1178.0142) proba : 0.013279149 (1762.7963, 675.09393, 2234.7063, 1109.9445) proba : 0.012411657 (2317.804, 1434.3568, 2615.2634, 1928.4269) proba : 0.011196427 (2783.5857, 1527.3981, 3036.1204, 2077.743) c : pare-brise list_crops.shape (46, 5) proba : 0.054989323 (2411.5857, 398.6755, 2957.3616, 1171.5435) proba : 0.045024626 (2240.3508, 634.92615, 2579.2654, 1217.4196) proba : 0.026940854 (1424.8815, 304.92905, 2074.783, 1161.585) proba : 0.025700318 (312.55493, 97.24339, 605.92456, 472.6009) proba : 0.024203148 (2954.8025, 604.7567, 3255.361, 1264.4867) proba : 0.021967646 (2056.8696, 93.771576, 2392.5186, 713.7234) proba : 0.020822385 (323.80988, 1090.6998, 787.1551, 1678.9489) proba : 0.018572705 (103.62311, 794.4201, 696.17804, 1524.2812) proba : 0.01708505 (2701.8164, 949.3742, 3158.2466, 1362.8494) proba : 0.011479365 (2240.9006, 3.359192, 2720.1995, 772.57874) proba : 0.010908696 (1694.8093, 659.7035, 2162.8647, 1116.5967) c : pare-choc list_crops.shape (38, 5) proba : 0.10619522 (347.37885, 1367.4418, 1073.0288, 1857.0494) proba : 0.016681252 (1310.4022, 1570.4393, 2576.6172, 2051.27) proba : 0.010981337 (0.0, 545.1217, 997.93866, 1254.1361) proba : 0.010150319 (2850.249, 1465.3951, 3233.626, 2099.7566) c : phare list_crops.shape (49, 5) proba : 0.06945008 (2930.691, 682.5459, 3257.667, 1220.2467) proba : 0.06410873 (310.4892, 118.32338, 715.9466, 508.5396) proba : 0.028811743 (324.5661, 1179.3387, 758.8072, 1634.1932) proba : 0.0130038755 (2226.5752, 695.2529, 2603.4375, 1195.8009) proba : 0.01120489 (83.40994, 743.9807, 419.87357, 1117.4823) proba : 0.010501361 (2318.0781, 1449.4464, 2631.6816, 1922.1366) c : plaque-immatriculation list_crops.shape (55, 5) proba : 0.057377867 (399.627, 207.96115, 679.60834, 493.9542) proba : 0.04576119 (1744.0808, 665.8685, 2221.1506, 1071.6486) proba : 0.023891395 (2294.9846, 1697.7517, 2792.1936, 2132.5798) proba : 0.023446303 (2538.795, 1521.5748, 2898.875, 2073.3613) proba : 0.014000355 (2783.46, 1564.8691, 3034.8926, 2056.804) proba : 0.013541807 (2245.3347, 695.27435, 2573.1648, 1168.425) proba : 0.011302234 (2945.942, 676.89685, 3251.017, 1190.9711) c : poignee list_crops.shape (48, 5) proba : 0.057074018 (2629.5083, 1457.6089, 2958.7676, 2152.9631) proba : 0.023785561 (2299.0876, 1682.4327, 2785.6086, 2157.393) proba : 0.018132295 (2232.171, 677.86346, 2592.099, 1173.5791) proba : 0.0144223645 (403.0234, 191.02986, 692.2671, 478.38232) c : porte list_crops.shape (42, 5) proba : 0.12676875 (2295.5745, 1531.3804, 2685.6677, 2159.0) proba : 0.044970296 (2857.5093, 1450.7476, 3263.0, 2033.0061) proba : 0.041268438 (2466.142, 1663.9407, 3030.1982, 2159.0) proba : 0.02829335 (390.1521, 1195.1892, 897.9584, 1796.6475) proba : 0.023546357 (1444.4941, 1494.0979, 2537.7566, 2103.8542) proba : 0.017251436 (311.3448, 15.945831, 609.7375, 659.7407) proba : 0.012646806 (2938.5781, 622.988, 3247.8052, 1300.4739) c : pot-echappement list_crops.shape (49, 5) proba : 0.033074025 (2632.8499, 1456.6232, 2954.7917, 2152.6843) proba : 0.030935058 (2302.4873, 1680.4291, 2781.5732, 2157.6838) proba : 0.015973857 (405.30908, 189.98407, 691.14404, 477.74835) proba : 0.011729564 (67.241516, 695.8175, 394.60718, 1122.9202) proba : 0.011400056 (1748.354, 537.04675, 2217.013, 1143.0496) proba : 0.011030981 (2926.5437, 674.97485, 3258.5608, 1211.8702) c : radiateur list_crops.shape (50, 5) c : retroviseur list_crops.shape (52, 5) proba : 0.08366503 (2235.2065, 682.12463, 2591.2075, 1175.3986) proba : 0.05949171 (2321.9429, 1443.8905, 2613.5679, 1922.5834) proba : 0.043869015 (2937.0742, 682.2776, 3252.6147, 1205.0361) proba : 0.042290337 (2632.6487, 1459.1227, 2953.6687, 2146.31) proba : 0.034093916 (71.96648, 699.8288, 393.3302, 1120.523) proba : 0.03188378 (308.78076, 91.36067, 716.16003, 497.187) proba : 0.025059842 (2306.4988, 1681.2102, 2777.2517, 2150.37) proba : 0.01979073 (2394.7385, 1122.3643, 2636.1614, 1534.456) proba : 0.018271392 (2293.512, 971.5107, 2576.617, 1378.5505) proba : 0.01642113 (2956.3386, 1594.2266, 3251.5354, 2041.8687) proba : 0.015730692 (10.902649, 573.522, 252.95364, 882.5071) proba : 0.015469301 (2710.9844, 884.6547, 3001.4312, 1337.1105) proba : 0.014101229 (2619.5112, 564.03186, 2997.375, 1174.4977) proba : 0.013078147 (162.06836, 998.622, 907.7977, 1740.7119) proba : 0.0128300665 (2117.2517, 1467.1458, 2431.4875, 2134.7546) proba : 0.012182772 (2070.6833, 153.22986, 2426.6262, 695.5816) c : roue list_crops.shape (48, 5) proba : 0.082092986 (2921.2815, 638.3773, 3254.5315, 1258.6937) proba : 0.058020208 (2507.543, 1513.9806, 2926.25, 2083.4546) proba : 0.05731251 (2746.1375, 1692.6079, 3205.8562, 2127.3523) proba : 0.038228124 (2267.4556, 1589.3503, 2719.938, 2144.4573) proba : 0.029194498 (2521.5908, 448.8144, 3069.2056, 1139.647) proba : 0.026401471 (416.58868, 1400.4749, 896.4648, 1839.0698) proba : 0.022059416 (258.3818, 30.396332, 777.4852, 579.228) proba : 0.018163558 (269.51514, 1082.4305, 809.2134, 1696.9344) proba : 0.016242238 (814.9376, 0.0, 1180.1412, 343.60016) proba : 0.014415094 (1708.7614, 636.11774, 2206.2217, 1093.4148) proba : 0.01140645 (2210.2764, 646.75415, 2617.3384, 1202.6604) c : toit list_crops.shape (51, 5) c : vitre list_crops.shape (47, 5) proba : 0.17966002 (2201.528, 706.08344, 2505.8025, 1225.604) proba : 0.13707863 (2332.1958, 1420.2557, 2614.7715, 1932.466) proba : 0.09208087 (2941.3254, 639.27295, 3238.2424, 1233.312) proba : 0.047084577 (321.5065, 101.23312, 604.4536, 480.10104) proba : 0.041153796 (2545.2397, 540.12604, 2972.1328, 1045.7517) proba : 0.039572414 (2362.6436, 1470.2212, 2901.4219, 2152.238) proba : 0.03048273 (2784.0947, 1531.0583, 3025.375, 2079.1086) proba : 0.017642766 (1748.7098, 547.4479, 2193.0378, 1162.6295) proba : 0.015795471 (2879.6345, 1427.3862, 3199.1853, 2141.8394) proba : 0.010809954 (2403.91, 706.2803, 2720.5085, 1277.5759) proba : 0.010704404 (2683.0703, 868.7751, 2997.1787, 1348.8604) We are managing local photo_id len de result frcnn : 6 After datou_step_exec type output : time spend for datou_step_exec : 5.137960433959961 time spend to save output : 0.0007894039154052734 total time spend for step 1 : 5.138749837875366 step2:crop_condition Mon May 26 19:40:12 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281207_935833_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg': 950003838, 'temp/1748281207_935833_950003813_e28be02dfcce79cce594a390a9911a0a.jpg': 950003813, 'temp/1748281207_935833_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg': 950003695, 'temp/1748281207_935833_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg': 926687666, 'temp/1748281207_935833_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg': 950003812, 'temp/1748281207_935833_950003696_11e3a77b72af4b332d366d98984039c7.jpg': 950003696} map_photo_id_path_extension : {950003838: {'path': 'temp/1748281207_935833_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg', 'extension': 'jpg'}, 950003813: {'path': 'temp/1748281207_935833_950003813_e28be02dfcce79cce594a390a9911a0a.jpg', 'extension': 'jpg'}, 950003695: {'path': 'temp/1748281207_935833_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg', 'extension': 'jpg'}, 926687666: {'path': 'temp/1748281207_935833_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg', 'extension': 'jpg'}, 950003812: {'path': 'temp/1748281207_935833_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg', 'extension': 'jpg'}, 950003696: {'path': 'temp/1748281207_935833_950003696_11e3a77b72af4b332d366d98984039c7.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} param_json : {'photo_hashtag_type': 757, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'feed_id_new_photos': 1981313, 'host': 'www.fotonower.com', 'filter': {'phare': {'margin_type': 'margin', 'margin_value': 300, 'feed_id_new_photos': 1097966}, 'aile-avant': {}}, 'crop_type': 'bib', 'margin_type': 'margin_relative', 'margin_value': [0.5, 0.1, 0.5, 0.1], 'min_score': 0.3} list_filenames in step crop : ['temp/1748281207_935833_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg', 'temp/1748281207_935833_950003813_e28be02dfcce79cce594a390a9911a0a.jpg', 'temp/1748281207_935833_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg', 'temp/1748281207_935833_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg', 'temp/1748281207_935833_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg', 'temp/1748281207_935833_950003696_11e3a77b72af4b332d366d98984039c7.jpg'] Loading chi in step crop with photo_hashtag_type : 757 Loading chi in step crop for subpids : 6 ! batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 950003838,950003813,950003695,926687666,950003812,950003696) and `type` in (757) Loaded 32 chid ids of type : 757 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1655713604,1655713607,1655713608,1655713612,1655713617,1655713619,1655713621,1655713623,1655713624,1655713628,1655713630,1655713631,1655713632,1655713634,1655713635,1655713639,1655713644,1655713646,1655713647,1655713648,1655713650,1655713651,1655713652,1655713655,1655713658,1655713659,1655713660,1655713662,1655713695,1655713696,1655713700,1655713701) SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1655713604,1655713607,1655713608,1655713612,1655713617,1655713619,1655713621,1655713623,1655713624,1655713628,1655713630,1655713631,1655713632,1655713634,1655713635,1655713639,1655713644,1655713646,1655713647,1655713648,1655713650,1655713651,1655713652,1655713655,1655713658,1655713659,1655713660,1655713662,1655713695,1655713696,1655713700,1655713701) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1655713604,1655713607,1655713608,1655713612,1655713617,1655713619,1655713621,1655713623,1655713624,1655713628,1655713630,1655713631,1655713632,1655713634,1655713635,1655713639,1655713644,1655713646,1655713647,1655713648,1655713650,1655713651,1655713652,1655713655,1655713658,1655713659,1655713660,1655713662,1655713695,1655713696,1655713700,1655713701) 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 ( 950003838,950003813,950003695,926687666,950003812,950003696) select sub_photo_id, crop_hashtag_id from MTRPhoto.crop_sub_photo_ids where sub_photo_id in (971020109,971021726,971022383,1071808962,1071808969,1071808957,1071808960,1071808966) map_pid_crop from SQL : ['[]', '[, , , , , , , , , , , ]', '[, , , , , , , , , , , , , ]', '[]', '[, , , ]'] begin to crop the class : phare param for this class : {'margin_type': 'margin', 'margin_value': 300, 'feed_id_new_photos': 1097966} filtre for class : phare hashtag_id of this class : 492624020 select * from (select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge, IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on chi.id =css.crop_hashtag_id where type = 757 and photo_id in (950003838,950003813,950003695,926687666,950003812,950003696) and hashtag_id = 492624020) as a where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 and coverage >= 0 and surface_rectangle >= 0; chi_id interessant : [1655713621] chi_id interessant : [1655713621, 1655713648] chi_id interessant : [1655713621, 1655713648, 1655713647] WARNING : margin is only used for type bib ! type of cropped photo chosen : bib we resize croppped photo by 1 on x axis and by 1 on y axis new_file_path_bib_crop : temp/1748281207_935833_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713621_0.jpg new_file_path_bib_crop : temp/1748281207_935833_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713647_0.jpg new_file_path_bib_crop : temp/1748281207_935833_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713648_0.jpg map_result returned by crop_photo_return_map_crop : length : 3 map_result after crop : {1655713621: {'crop': 'temp/1748281207_935833_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713621_0.jpg', 'photo_id': 926687666, 'sub_photo_id': 1071808962, 'coordonates': (326, 477, 251, 312), 'sub_photo_infos': (26, 640, 0, 480, 1, 1), 'same_chi': True}, 1655713647: {'crop': 'temp/1748281207_935833_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713647_0.jpg', 'photo_id': 950003812, 'sub_photo_id': 1071808957, 'coordonates': (318, 489, 264, 310), 'sub_photo_infos': (18, 614, 0, 480, 1, 1), 'same_chi': True}, 1655713648: {'crop': 'temp/1748281207_935833_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713648_0.jpg', 'photo_id': 950003812, 'sub_photo_id': 1071808960, 'coordonates': (261, 408, 234, 331), 'sub_photo_infos': (0, 614, 0, 480, 1, 1), 'same_chi': True}} About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! map_result_insert : {} insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [] insert ignore into MTRPhoto.photo_sub_photos (photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s) : [] map of cropped photos with some data : {1071808962: [926687666, 'temp/1748281207_935833_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713621_0.jpg', (326, 477, 251, 312)], 1071808957: [950003812, 'temp/1748281207_935833_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713647_0.jpg', (318, 489, 264, 310)], 1071808960: [950003812, 'temp/1748281207_935833_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713648_0.jpg', (261, 408, 234, 331)]} we have finished the crop for the class : phare begin to crop the class : aile-avant param for this class : {} filtre for class : aile-avant hashtag_id of this class : 2106233860 select * from (select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge, IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on chi.id =css.crop_hashtag_id where type = 757 and photo_id in (950003838,950003813,950003695,926687666,950003812,950003696) and hashtag_id = 2106233860) as a where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 and coverage >= 0 and surface_rectangle >= 0; chi_id interessant : [1655713607] chi_id interessant : [1655713607, 1655713634] WARNING : margin is only used for type bib ! type of cropped photo chosen : bib we resize croppped photo by 1 on x axis and by 1 on y axis new_file_path_bib_crop : temp/1748281207_935833_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713607_0.jpg now we use margin_relative for the photo_id : 926687666 new_file_path_bib_crop : temp/1748281207_935833_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713634_0.jpg now we use margin_relative for the photo_id : 950003812 map_result returned by crop_photo_return_map_crop : length : 2 map_result after crop : {1655713607: {'crop': 'temp/1748281207_935833_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713607_0.jpg', 'photo_id': 926687666, 'sub_photo_id': 1071808969, 'coordonates': (161, 330, 149, 343), 'sub_photo_infos': (64, 349, 65, 359, 1, 1), 'same_chi': True}, 1655713634: {'crop': 'temp/1748281207_935833_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713634_0.jpg', 'photo_id': 950003812, 'sub_photo_id': 1071808966, 'coordonates': (133, 305, 146, 344), 'sub_photo_infos': (34, 324, 60, 361, 1, 1), 'same_chi': True}} About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! map_result_insert : {} insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [] insert ignore into MTRPhoto.photo_sub_photos (photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s) : [] map of cropped photos with some data : {1071808969: [926687666, 'temp/1748281207_935833_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713607_0.jpg', (161, 330, 149, 343)], 1071808966: [950003812, 'temp/1748281207_935833_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713634_0.jpg', (133, 305, 146, 344)]} we have finished the crop for the class : aile-avant map of total cropped photos with some data : {1071808962: [926687666, 'temp/1748281207_935833_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713621_0.jpg', (326, 477, 251, 312)], 1071808957: [950003812, 'temp/1748281207_935833_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713647_0.jpg', (318, 489, 264, 310)], 1071808960: [950003812, 'temp/1748281207_935833_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713648_0.jpg', (261, 408, 234, 331)], 1071808969: [926687666, 'temp/1748281207_935833_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713607_0.jpg', (161, 330, 149, 343)], 1071808966: [950003812, 'temp/1748281207_935833_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713634_0.jpg', (133, 305, 146, 344)]} After datou_step_exec type output : time spend for datou_step_exec : 0.4295332431793213 time spend to save output : 0.000125885009765625 total time spend for step 2 : 0.4296591281890869 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {1071808962: [926687666, 'temp/1748281207_935833_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713621_0.jpg', (326, 477, 251, 312)], 1071808957: [950003812, 'temp/1748281207_935833_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713647_0.jpg', (318, 489, 264, 310)], 1071808960: [950003812, 'temp/1748281207_935833_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713648_0.jpg', (261, 408, 234, 331)], 1071808969: [926687666, 'temp/1748281207_935833_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713607_0.jpg', (161, 330, 149, 343)], 1071808966: [950003812, 'temp/1748281207_935833_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713634_0.jpg', (133, 305, 146, 344)]} ############################### TEST image_blanchir ################################ Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1818 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=1818 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= 1818 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=1818 # 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_blanchir 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 (990111206) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 990111206 download finish for photo 990111206 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 : 0.15365052223205566 #### 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:image_blanchir Mon May 26 19:40:13 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/1748281213_935833_990111206_7ca22c7e68dd0a10509c7987af0cf549.png': 990111206} map_photo_id_path_extension : {990111206: {'path': 'temp/1748281213_935833_990111206_7ca22c7e68dd0a10509c7987af0cf549.png', 'extension': 'png'}} map_subphoto_mainphoto : {} inside step blanchir_image https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc feed_id_new_photos:23354379 treat image : temp/1748281213_935833_990111206_7ca22c7e68dd0a10509c7987af0cf549.png blanchir func in upload media Upload medias : ['temp/1748281213_935833_990111206_7ca22c7e68dd0a10509c7987af0cf549.png'] : url : https://marlene.fotonower.com/api/v1/secured/photo/upload?token=78d09a0790ec6ecbf119343125a81fdc&datou=0 temp/1748281213_935833_990111206_7ca22c7e68dd0a10509c7987af0cf549.png after data_to_send, before sending request after request b'{"photo_ids":["1361142960"],"photo_id":"1361142960","photo_detail":[{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/5/26/407d42d47fc587f2e6bd4d157ad681a0.png","text":"TemporaryFile(/tmp/multipartBody1222208156604508576asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1748281215119,"filename":"1748281213_935833_990111206_7ca22c7e68dd0a10509c7987af0cf549.png","height":0,"width":0}],"map_files_photo_id":{"file0":"1361142960"},"map_files_photo_id_array":[{"photo_id":"file0","filename":"1361142960"}],"hashtags":[],"portfolio_id":"23354379","result":[],"list_datou_current":[]}' Result OK ! After datou_step_exec type output : time spend for datou_step_exec : 7.167932748794556 time spend to save output : 2.384185791015625e-05 total time spend for step 1 : 7.167956590652466 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True sauvegarde pour la step blanchir_image 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 : [[1818, 0, 990111206, 1, 1, 1, None, 1, None]] time used for this insertion : 0.01335597038269043 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : [(990111206, '1361142960', 0, 300, 0, 381, 1, 1, 'blanc')] [(990111206, '1361142960', 0, 300, 0, 381, 1, 1, 'blanc')] ############################### TEST darker_image ################################ Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2085 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=2085 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= 2085 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=2085 # 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 : darker_image 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 (2077915) 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 (2077915) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 989962950, 'portfolio_id': 2077915}] map_portfolio_id_photo_id: {2077915: [989962950]} ##### Call download_photos : nb_thread : 5 begin to download photo : 989962950 download finish for photo 989962950 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 : 0.19090652465820312 #### 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:darker_image Mon May 26 19:40:20 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/1748281220_935833_989962950_4d2e56be59e275c3d57b085a836be0ba.jpg': 989962950} map_photo_id_path_extension : {989962950: {'path': 'temp/1748281220_935833_989962950_4d2e56be59e275c3d57b085a836be0ba.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} dans la step darker batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 989962950) and `type` in (2228) Loaded 7 chid ids of type : 2228 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1753484977,1753484978,1753484979,1753484980,1753484981,1753484982,1753484983) +WARNING : Unexpected points, we should remove this data for chi_id : 1753484977, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484978, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484979, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484980, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484981, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484982, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484983, for now we just ignore these empty polygon points SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1753484977,1753484978,1753484979,1753484980,1753484981,1753484982,1753484983) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1753484977,1753484978,1753484979,1753484980,1753484981,1753484982,1753484983) treat image : temp/1748281220_935833_989962950_4d2e56be59e275c3d57b085a836be0ba.jpg in upload media Upload medias : ['temp/1748281220_935833_989962950_4d2e56be59e275c3d57b085a836be0badarker.jpg'] : url : https://marlene.fotonower.com/api/v1/secured/photo/upload?token=78d09a0790ec6ecbf119343125a81fdc&datou=0 temp/1748281220_935833_989962950_4d2e56be59e275c3d57b085a836be0badarker.jpg after data_to_send, before sending request after request b'{"photo_ids":["1361142965"],"photo_id":"1361142965","photo_detail":[{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/5/26/dfae277ba64445def619a47d367a37dd.jpg","text":"TemporaryFile(/tmp/multipartBody3555808013308890349asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1748281221781,"filename":"1748281220_935833_989962950_4d2e56be59e275c3d57b085a836be0badarker.jpg","height":0,"width":0}],"map_files_photo_id":{"file0":"1361142965"},"map_files_photo_id_array":[{"photo_id":"file0","filename":"1361142965"}],"hashtags":[],"portfolio_id":"2213400","result":[],"list_datou_current":[]}' Result OK ! list chi to save [, , , , , , ] 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 7 chid ids of type : 2228 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 ! crops sauvegardes After datou_step_exec type output : time spend for datou_step_exec : 9.459949970245361 time spend to save output : 4.506111145019531e-05 total time spend for step 1 : 9.459995031356812 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True sauvegarde pour la step blanchir_image 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 : [[2085, 0, 989962950, 1, 1, 1, None, 1, None]] time used for this insertion : 0.011761903762817383 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : [(989962950, '1361142965', 0, 897, 0, 1431, 1, 1, 'darker')] [(989962950, '1361142965', 0, 897, 0, 1431, 1, 1, 'darker')] batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 1361142965) and `type` in (2228) Loaded 7 chid ids of type : 2228 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (3813396650,3813396651,3813396649,3813396647,3813396648,3813396645,3813396646) SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (3813396650,3813396651,3813396649,3813396647,3813396648,3813396645,3813396646) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (3813396650,3813396651,3813396649,3813396647,3813396648,3813396645,3813396646) ############################### TEST img_aug ################################ Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2041 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=2041 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= 2041 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=2041 # 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 : data_aug 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 (2077915) 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 (2077915) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 989962950, 'portfolio_id': 2077915}] map_portfolio_id_photo_id: {2077915: [989962950]} ##### Call download_photos : nb_thread : 5 begin to download photo : 989962950 download finish for photo 989962950 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 : 0.15376901626586914 #### 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:data_aug Mon May 26 19:40:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281230_935833_989962950_4d2e56be59e275c3d57b085a836be0ba.jpg': 989962950} map_photo_id_path_extension : {989962950: {'path': 'temp/1748281230_935833_989962950_4d2e56be59e275c3d57b085a836be0ba.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} numpy.version est ancienne, on utilise l'ancien bit generator numpy.version est ancienne, on utilise l'ancien bit generator batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 989962950) and `type` in (2228) Loaded 7 chid ids of type : 2228 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1753484977,1753484978,1753484979,1753484980,1753484981,1753484982,1753484983) +WARNING : Unexpected points, we should remove this data for chi_id : 1753484977, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484978, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484979, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484980, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484981, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484982, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484983, for now we just ignore these empty polygon points SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1753484977,1753484978,1753484979,1753484980,1753484981,1753484982,1753484983) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1753484977,1753484978,1753484979,1753484980,1753484981,1753484982,1753484983) on traite des points in upload media Upload medias : ['temp/1748281230_935833_989962950_4d2e56be59e275c3d57b085a836be0ba_aug.jpg'] : url : https://marlene.fotonower.com/api/v1/secured/photo/upload?token=7ad776945df9e5335881f03fafdabb27&datou=0 temp/1748281230_935833_989962950_4d2e56be59e275c3d57b085a836be0ba_aug.jpg after data_to_send, before sending request after request b'{"photo_ids":["1361142974"],"photo_id":"1361142974","photo_detail":[{"mtr_user_id":0,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/5/26/f93b18da79dd3bc56e284f34404adbd1.jpg","text":"TemporaryFile(/tmp/multipartBody7339144856693753739asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1748281231296,"filename":"1748281230_935833_989962950_4d2e56be59e275c3d57b085a836be0ba_aug.jpg","height":0,"width":0}],"map_files_photo_id":{"file0":"1361142974"},"map_files_photo_id_array":[{"photo_id":"file0","filename":"1361142974"}],"hashtags":[],"portfolio_id":"2095671","result":[],"list_datou_current":[]}' Result OK ! 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 7 chid ids of type : 2260 ERROR missing MTRPhoto.crop_hashtag_ids : 492774966 on photo_id : 1361142974 ERROR missing MTRPhoto.crop_hashtag_ids : 492774966 on photo_id : 1361142974 ERROR missing MTRPhoto.crop_hashtag_ids : 492725882 on photo_id : 1361142974 ERROR missing MTRPhoto.crop_hashtag_ids : 492725882 on photo_id : 1361142974 ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1361142974 ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1361142974 ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1361142974 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! photo_uploade augmentation faite pour la photo : 989962950 After datou_step_exec type output : time spend for datou_step_exec : 7.147237539291382 time spend to save output : 5.221366882324219e-05 total time spend for step 1 : 7.147289752960205 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True sauvegarde pour la step blanchir_image 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 : [[2041, 0, 989962950, 1, 1, 1, None, 1, None]] time used for this insertion : 0.012530326843261719 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : [(989962950, 1361142974, 0, 1431, 0, 897, 1, 1, 'img_aug')] [(989962950, 1361142974, 0, 1431, 0, 897, 1, 1, 'img_aug')] batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 1361142974) and `type` in (2260) Loaded 7 chid ids of type : 2260 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (3813396889,3813396890,3813396888,3813396886,3813396887,3813396884,3813396885) SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (3813396889,3813396890,3813396888,3813396886,3813396887,3813396884,3813396885) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (3813396889,3813396890,3813396888,3813396886,3813396887,3813396884,3813396885) ############################### TEST rubbia ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=3789 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=3789 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= 3789 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=3789 # 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 (4599398) 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 (4599398) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 1049318362, 'portfolio_id': 4599398}, {'photo_id': 1049318360, 'portfolio_id': 4599398}, {'photo_id': 1049318358, 'portfolio_id': 4599398}, {'photo_id': 1049318356, 'portfolio_id': 4599398}, {'photo_id': 1049318342, 'portfolio_id': 4599398}, {'photo_id': 1049318339, 'portfolio_id': 4599398}, {'photo_id': 1049318337, 'portfolio_id': 4599398}, {'photo_id': 1049318311, 'portfolio_id': 4599398}, {'photo_id': 1049318310, 'portfolio_id': 4599398}, {'photo_id': 1049318309, 'portfolio_id': 4599398}, {'photo_id': 1049318294, 'portfolio_id': 4599398}, {'photo_id': 1049318293, 'portfolio_id': 4599398}, {'photo_id': 1049318291, 'portfolio_id': 4599398}, {'photo_id': 1049318289, 'portfolio_id': 4599398}, {'photo_id': 1049318288, 'portfolio_id': 4599398}, {'photo_id': 1049318287, 'portfolio_id': 4599398}, {'photo_id': 1049318279, 'portfolio_id': 4599398}, {'photo_id': 1049318276, 'portfolio_id': 4599398}, {'photo_id': 1049318273, 'portfolio_id': 4599398}, {'photo_id': 1049318271, 'portfolio_id': 4599398}, {'photo_id': 1049318268, 'portfolio_id': 4599398}, {'photo_id': 1049318265, 'portfolio_id': 4599398}, {'photo_id': 1049318260, 'portfolio_id': 4599398}, {'photo_id': 1049318257, 'portfolio_id': 4599398}, {'photo_id': 1049318253, 'portfolio_id': 4599398}, {'photo_id': 1049318250, 'portfolio_id': 4599398}, {'photo_id': 1049318247, 'portfolio_id': 4599398}, {'photo_id': 1049318246, 'portfolio_id': 4599398}, {'photo_id': 1049318222, 'portfolio_id': 4599398}, {'photo_id': 1049318219, 'portfolio_id': 4599398}, {'photo_id': 1049318216, 'portfolio_id': 4599398}, {'photo_id': 1049318214, 'portfolio_id': 4599398}, {'photo_id': 1049318213, 'portfolio_id': 4599398}, {'photo_id': 1049318212, 'portfolio_id': 4599398}, {'photo_id': 1049317554, 'portfolio_id': 4599398}, {'photo_id': 1049317551, 'portfolio_id': 4599398}, {'photo_id': 1049317549, 'portfolio_id': 4599398}, {'photo_id': 1049317546, 'portfolio_id': 4599398}, {'photo_id': 1049317542, 'portfolio_id': 4599398}, {'photo_id': 1049317536, 'portfolio_id': 4599398}, {'photo_id': 1049317526, 'portfolio_id': 4599398}, {'photo_id': 1049317525, 'portfolio_id': 4599398}, {'photo_id': 1049317524, 'portfolio_id': 4599398}, {'photo_id': 1049317522, 'portfolio_id': 4599398}, {'photo_id': 1049317520, 'portfolio_id': 4599398}, {'photo_id': 1049317517, 'portfolio_id': 4599398}, {'photo_id': 1049317497, 'portfolio_id': 4599398}, {'photo_id': 1049317493, 'portfolio_id': 4599398}, {'photo_id': 1049317491, 'portfolio_id': 4599398}, {'photo_id': 1049317489, 'portfolio_id': 4599398}, {'photo_id': 1049317487, 'portfolio_id': 4599398}, {'photo_id': 1049317485, 'portfolio_id': 4599398}, {'photo_id': 1049317468, 'portfolio_id': 4599398}, {'photo_id': 1049317461, 'portfolio_id': 4599398}, {'photo_id': 1049317457, 'portfolio_id': 4599398}, {'photo_id': 1049317453, 'portfolio_id': 4599398}, {'photo_id': 1049317444, 'portfolio_id': 4599398}, {'photo_id': 1049317440, 'portfolio_id': 4599398}, {'photo_id': 1049317359, 'portfolio_id': 4599398}, {'photo_id': 1049317333, 'portfolio_id': 4599398}, {'photo_id': 1049317282, 'portfolio_id': 4599398}, {'photo_id': 1049317225, 'portfolio_id': 4599398}, {'photo_id': 1049317210, 'portfolio_id': 4599398}, {'photo_id': 1049317197, 'portfolio_id': 4599398}, {'photo_id': 1049316790, 'portfolio_id': 4599398}, {'photo_id': 1049316785, 'portfolio_id': 4599398}, {'photo_id': 1049316782, 'portfolio_id': 4599398}, {'photo_id': 1049316778, 'portfolio_id': 4599398}, {'photo_id': 1049316752, 'portfolio_id': 4599398}, {'photo_id': 1049316749, 'portfolio_id': 4599398}, {'photo_id': 1049316610, 'portfolio_id': 4599398}, {'photo_id': 1049316600, 'portfolio_id': 4599398}, {'photo_id': 1049316597, 'portfolio_id': 4599398}, {'photo_id': 1049316594, 'portfolio_id': 4599398}, {'photo_id': 1049316588, 'portfolio_id': 4599398}, {'photo_id': 1049316582, 'portfolio_id': 4599398}, {'photo_id': 1049316545, 'portfolio_id': 4599398}, {'photo_id': 1049316543, 'portfolio_id': 4599398}, {'photo_id': 1049316540, 'portfolio_id': 4599398}, {'photo_id': 1049316537, 'portfolio_id': 4599398}, {'photo_id': 1049316534, 'portfolio_id': 4599398}, {'photo_id': 1049316520, 'portfolio_id': 4599398}, {'photo_id': 1049316338, 'portfolio_id': 4599398}, {'photo_id': 1049316336, 'portfolio_id': 4599398}, {'photo_id': 1049316332, 'portfolio_id': 4599398}, {'photo_id': 1049316331, 'portfolio_id': 4599398}, {'photo_id': 1049316257, 'portfolio_id': 4599398}, {'photo_id': 1049316255, 'portfolio_id': 4599398}, {'photo_id': 1049316222, 'portfolio_id': 4599398}, {'photo_id': 1049316216, 'portfolio_id': 4599398}, {'photo_id': 1049316214, 'portfolio_id': 4599398}, {'photo_id': 1049316212, 'portfolio_id': 4599398}, {'photo_id': 1049316210, 'portfolio_id': 4599398}, {'photo_id': 1049316209, 'portfolio_id': 4599398}, {'photo_id': 1049313025, 'portfolio_id': 4599398}, {'photo_id': 1049312984, 'portfolio_id': 4599398}, {'photo_id': 1049312803, 'portfolio_id': 4599398}, {'photo_id': 1049312588, 'portfolio_id': 4599398}, {'photo_id': 1049312585, 'portfolio_id': 4599398}, {'photo_id': 1049312583, 'portfolio_id': 4599398}, {'photo_id': 1049312579, 'portfolio_id': 4599398}, {'photo_id': 1049312574, 'portfolio_id': 4599398}, {'photo_id': 1049312573, 'portfolio_id': 4599398}, {'photo_id': 1049312571, 'portfolio_id': 4599398}, {'photo_id': 1049312568, 'portfolio_id': 4599398}, {'photo_id': 1049312566, 'portfolio_id': 4599398}, {'photo_id': 1049312562, 'portfolio_id': 4599398}, {'photo_id': 1049312556, 'portfolio_id': 4599398}, {'photo_id': 1049312508, 'portfolio_id': 4599398}, {'photo_id': 1049312489, 'portfolio_id': 4599398}, {'photo_id': 1049312488, 'portfolio_id': 4599398}, {'photo_id': 1049312487, 'portfolio_id': 4599398}, {'photo_id': 1049312485, 'portfolio_id': 4599398}, {'photo_id': 1049312484, 'portfolio_id': 4599398}, {'photo_id': 1049312464, 'portfolio_id': 4599398}, {'photo_id': 1049312463, 'portfolio_id': 4599398}, {'photo_id': 1049312462, 'portfolio_id': 4599398}, {'photo_id': 1049312461, 'portfolio_id': 4599398}, {'photo_id': 1049312460, 'portfolio_id': 4599398}, {'photo_id': 1049312449, 'portfolio_id': 4599398}, {'photo_id': 1049312445, 'portfolio_id': 4599398}, {'photo_id': 1049312444, 'portfolio_id': 4599398}, {'photo_id': 1049312442, 'portfolio_id': 4599398}, {'photo_id': 1049312440, 'portfolio_id': 4599398}, {'photo_id': 1049312438, 'portfolio_id': 4599398}, {'photo_id': 1049312429, 'portfolio_id': 4599398}, {'photo_id': 1049312426, 'portfolio_id': 4599398}, {'photo_id': 1049312424, 'portfolio_id': 4599398}, {'photo_id': 1049312422, 'portfolio_id': 4599398}, {'photo_id': 1049312420, 'portfolio_id': 4599398}, {'photo_id': 1049312409, 'portfolio_id': 4599398}, {'photo_id': 1049312406, 'portfolio_id': 4599398}, {'photo_id': 1049312404, 'portfolio_id': 4599398}, {'photo_id': 1049312363, 'portfolio_id': 4599398}, {'photo_id': 1049312208, 'portfolio_id': 4599398}, {'photo_id': 1049311964, 'portfolio_id': 4599398}, {'photo_id': 1049311963, 'portfolio_id': 4599398}, {'photo_id': 1049311962, 'portfolio_id': 4599398}, {'photo_id': 1049311961, 'portfolio_id': 4599398}, {'photo_id': 1049311960, 'portfolio_id': 4599398}, {'photo_id': 1049311943, 'portfolio_id': 4599398}, {'photo_id': 1049311938, 'portfolio_id': 4599398}, {'photo_id': 1049311937, 'portfolio_id': 4599398}, {'photo_id': 1049311935, 'portfolio_id': 4599398}, {'photo_id': 1049311934, 'portfolio_id': 4599398}, {'photo_id': 1049311932, 'portfolio_id': 4599398}, {'photo_id': 1049311795, 'portfolio_id': 4599398}, {'photo_id': 1049311793, 'portfolio_id': 4599398}, {'photo_id': 1049311791, 'portfolio_id': 4599398}, {'photo_id': 1049311771, 'portfolio_id': 4599398}, {'photo_id': 1049311767, 'portfolio_id': 4599398}, {'photo_id': 1049311267, 'portfolio_id': 4599398}, {'photo_id': 1049311266, 'portfolio_id': 4599398}, {'photo_id': 1049311263, 'portfolio_id': 4599398}, {'photo_id': 1049311252, 'portfolio_id': 4599398}, {'photo_id': 1049311199, 'portfolio_id': 4599398}, {'photo_id': 1049311136, 'portfolio_id': 4599398}, {'photo_id': 1049311073, 'portfolio_id': 4599398}, {'photo_id': 1049311009, 'portfolio_id': 4599398}, {'photo_id': 1049311006, 'portfolio_id': 4599398}, {'photo_id': 1049310994, 'portfolio_id': 4599398}, {'photo_id': 1049310992, 'portfolio_id': 4599398}, {'photo_id': 1049310991, 'portfolio_id': 4599398}, {'photo_id': 1049310984, 'portfolio_id': 4599398}, {'photo_id': 1049310982, 'portfolio_id': 4599398}, {'photo_id': 1049310981, 'portfolio_id': 4599398}, {'photo_id': 1049310919, 'portfolio_id': 4599398}, {'photo_id': 1049310914, 'portfolio_id': 4599398}, {'photo_id': 1049310911, 'portfolio_id': 4599398}, {'photo_id': 1049310909, 'portfolio_id': 4599398}, {'photo_id': 1049310907, 'portfolio_id': 4599398}, {'photo_id': 1049310905, 'portfolio_id': 4599398}, {'photo_id': 1049310165, 'portfolio_id': 4599398}, {'photo_id': 1049310162, 'portfolio_id': 4599398}, {'photo_id': 1049310159, 'portfolio_id': 4599398}, {'photo_id': 1049310145, 'portfolio_id': 4599398}, {'photo_id': 1049310141, 'portfolio_id': 4599398}, {'photo_id': 1049310139, 'portfolio_id': 4599398}, {'photo_id': 1049310138, 'portfolio_id': 4599398}, {'photo_id': 1049310134, 'portfolio_id': 4599398}, {'photo_id': 1049310132, 'portfolio_id': 4599398}, {'photo_id': 1049309737, 'portfolio_id': 4599398}, {'photo_id': 1049309734, 'portfolio_id': 4599398}, {'photo_id': 1049309732, 'portfolio_id': 4599398}, {'photo_id': 1049309706, 'portfolio_id': 4599398}, {'photo_id': 1049309703, 'portfolio_id': 4599398}, {'photo_id': 1049309701, 'portfolio_id': 4599398}, {'photo_id': 1049309686, 'portfolio_id': 4599398}, {'photo_id': 1049309681, 'portfolio_id': 4599398}, {'photo_id': 1049309677, 'portfolio_id': 4599398}, {'photo_id': 1049309675, 'portfolio_id': 4599398}, {'photo_id': 1049309672, 'portfolio_id': 4599398}, {'photo_id': 1049309670, 'portfolio_id': 4599398}, {'photo_id': 1049309658, 'portfolio_id': 4599398}, {'photo_id': 1049309657, 'portfolio_id': 4599398}, {'photo_id': 1049309656, 'portfolio_id': 4599398}, {'photo_id': 1049309655, 'portfolio_id': 4599398}, {'photo_id': 1049309653, 'portfolio_id': 4599398}, {'photo_id': 1049309651, 'portfolio_id': 4599398}, {'photo_id': 1049309605, 'portfolio_id': 4599398}, {'photo_id': 1049309603, 'portfolio_id': 4599398}, {'photo_id': 1049309599, 'portfolio_id': 4599398}, {'photo_id': 1049309597, 'portfolio_id': 4599398}, {'photo_id': 1049309595, 'portfolio_id': 4599398}, {'photo_id': 1049309592, 'portfolio_id': 4599398}, {'photo_id': 1049309385, 'portfolio_id': 4599398}, {'photo_id': 1049309383, 'portfolio_id': 4599398}, {'photo_id': 1049309382, 'portfolio_id': 4599398}, {'photo_id': 1049309381, 'portfolio_id': 4599398}, {'photo_id': 1049309380, 'portfolio_id': 4599398}, {'photo_id': 1049309379, 'portfolio_id': 4599398}, {'photo_id': 1049309345, 'portfolio_id': 4599398}, {'photo_id': 1049308384, 'portfolio_id': 4599398}, {'photo_id': 1049308381, 'portfolio_id': 4599398}, {'photo_id': 1049308376, 'portfolio_id': 4599398}, {'photo_id': 1049308280, 'portfolio_id': 4599398}, {'photo_id': 1049308276, 'portfolio_id': 4599398}, {'photo_id': 1049308275, 'portfolio_id': 4599398}, {'photo_id': 1049308235, 'portfolio_id': 4599398}, {'photo_id': 1049307693, 'portfolio_id': 4599398}, {'photo_id': 1049306823, 'portfolio_id': 4599398}, {'photo_id': 1049306804, 'portfolio_id': 4599398}, {'photo_id': 1049306792, 'portfolio_id': 4599398}, {'photo_id': 1049306791, 'portfolio_id': 4599398}, {'photo_id': 1049306635, 'portfolio_id': 4599398}, {'photo_id': 1049306205, 'portfolio_id': 4599398}, {'photo_id': 1049304810, 'portfolio_id': 4599398}, {'photo_id': 1049303925, 'portfolio_id': 4599398}, {'photo_id': 1049296996, 'portfolio_id': 4599398}, {'photo_id': 1049296121, 'portfolio_id': 4599398}, {'photo_id': 1049294990, 'portfolio_id': 4599398}, {'photo_id': 1049293230, 'portfolio_id': 4599398}] map_portfolio_id_photo_id: {4599398: [1049318362, 1049318360, 1049318358, 1049318356, 1049318342, 1049318339, 1049318337, 1049318311, 1049318310, 1049318309, 1049318294, 1049318293, 1049318291, 1049318289, 1049318288, 1049318287, 1049318279, 1049318276, 1049318273, 1049318271, 1049318268, 1049318265, 1049318260, 1049318257, 1049318253, 1049318250, 1049318247, 1049318246, 1049318222, 1049318219, 1049318216, 1049318214, 1049318213, 1049318212, 1049317554, 1049317551, 1049317549, 1049317546, 1049317542, 1049317536, 1049317526, 1049317525, 1049317524, 1049317522, 1049317520, 1049317517, 1049317497, 1049317493, 1049317491, 1049317489, 1049317487, 1049317485, 1049317468, 1049317461, 1049317457, 1049317453, 1049317444, 1049317440, 1049317359, 1049317333, 1049317282, 1049317225, 1049317210, 1049317197, 1049316790, 1049316785, 1049316782, 1049316778, 1049316752, 1049316749, 1049316610, 1049316600, 1049316597, 1049316594, 1049316588, 1049316582, 1049316545, 1049316543, 1049316540, 1049316537, 1049316534, 1049316520, 1049316338, 1049316336, 1049316332, 1049316331, 1049316257, 1049316255, 1049316222, 1049316216, 1049316214, 1049316212, 1049316210, 1049316209, 1049313025, 1049312984, 1049312803, 1049312588, 1049312585, 1049312583, 1049312579, 1049312574, 1049312573, 1049312571, 1049312568, 1049312566, 1049312562, 1049312556, 1049312508, 1049312489, 1049312488, 1049312487, 1049312485, 1049312484, 1049312464, 1049312463, 1049312462, 1049312461, 1049312460, 1049312449, 1049312445, 1049312444, 1049312442, 1049312440, 1049312438, 1049312429, 1049312426, 1049312424, 1049312422, 1049312420, 1049312409, 1049312406, 1049312404, 1049312363, 1049312208, 1049311964, 1049311963, 1049311962, 1049311961, 1049311960, 1049311943, 1049311938, 1049311937, 1049311935, 1049311934, 1049311932, 1049311795, 1049311793, 1049311791, 1049311771, 1049311767, 1049311267, 1049311266, 1049311263, 1049311252, 1049311199, 1049311136, 1049311073, 1049311009, 1049311006, 1049310994, 1049310992, 1049310991, 1049310984, 1049310982, 1049310981, 1049310919, 1049310914, 1049310911, 1049310909, 1049310907, 1049310905, 1049310165, 1049310162, 1049310159, 1049310145, 1049310141, 1049310139, 1049310138, 1049310134, 1049310132, 1049309737, 1049309734, 1049309732, 1049309706, 1049309703, 1049309701, 1049309686, 1049309681, 1049309677, 1049309675, 1049309672, 1049309670, 1049309658, 1049309657, 1049309656, 1049309655, 1049309653, 1049309651, 1049309605, 1049309603, 1049309599, 1049309597, 1049309595, 1049309592, 1049309385, 1049309383, 1049309382, 1049309381, 1049309380, 1049309379, 1049309345, 1049308384, 1049308381, 1049308376, 1049308280, 1049308276, 1049308275, 1049308235, 1049307693, 1049306823, 1049306804, 1049306792, 1049306791, 1049306635, 1049306205, 1049304810, 1049303925, 1049296996, 1049296121, 1049294990, 1049293230]} ##### 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 : 0.03434896469116211 #### fin chargement data Blocking on flush ? No conitnuing About to test input to load Calling datou_exec Inside datou_exec : verbose : True we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save number of steps : 1 step1:split_time_score Mon May 26 19:40:38 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 : {} begin split time score 2022-04-13 10:29:59 0 SELECT app_name, token FROM MTRUser.mtr_app_api_token WHERE mtr_user_id=739 AND app_name="token_split_time_score" AND expire_at > NOW() TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] select SUBSTRING(ph.text,16,2) as h, count(*) from MTRUser.mtr_portfolio_photos mpp inner join MTRBack.photos ph on mpp.mtr_photo_id = ph.photo_id where mpp.mtr_portfolio_id = 4599398 group by h (('05', 2), ('07', 25), ('06', 1), ('08', 96), ('09', 44), ('10', 64)) SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4599398 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 100000 SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1 where mpp1.mtr_photo_id=cps.photo_id AND h.hashtag_id=cps.hashtag_id AND mpp1.hide_status=0 AND mpp1.mtr_portfolio_id in (4599398) AND cps.thcl in (861) order by cps.score desc LIMIT 0, 100000 SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1 where mpp1.mtr_photo_id=cps.photo_id AND h.hashtag_id=cps.hashtag_id AND mpp1.hide_status=0 AND mpp1.mtr_portfolio_id in (4599398) AND cps.thcl in (758) order by cps.score desc LIMIT 0, 100000 ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {1: 188, 2: 36, 3: 8} 07092021 4599398 Nombre de photos uploadées : 232 / 23040 (1%) 07092021 4599398 Nombre de photos taguées (types de déchets): 232 / 232 (100%) 07092021 4599398 Nombre de photos taguées (volume) : 232 / 232 (100%) [{'photo_id': 1049293230, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/9/7/f23ba47cedee4657ed22c3d9f9535a34.jpg', 'username': None, 'uploaded_at': 1630984864, 'text': 'image_07092021_05_20_04_010050m0.jpg 0.001 for time 1, id_amount 3 this amount prod time diff : 0.001'}, {'photo_id': 1049296121, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/9/7/0093b445a3c4de9b92562659d544a352.jpg', 'username': None, 'uploaded_at': 1630986542, 'text': 'image_07092021_05_47_33_009984m0.jpg 0.002 for time 1, id_amount 3 this amount prod time diff : 0.001'}, {'photo_id': 1049296996, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/9/7/f6ab2ade6ce6c6a1cdc5e37444e94ea8.jpg', 'username': None, 'uploaded_at': 1630987563, 'text': 'image_07092021_06_05_54_010003m0.jpg 0.003 for time 1, id_amount 3 this amount prod time diff : 0.001'}, {'photo_id': 1049303925, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/9/7/225c2975cddc22def1487c51a2d2f466.jpg', 'username': None, 'uploaded_at': 1630990923, 'text': 'image_07092021_07_00_04_009910m0.jpg 0.004 for time 1, id_amount 3 this amount prod time diff : 0.001'}] 232 [{'photo_id': 1049318293, 'score': 0.9999683, 'hashtag': '05102018_Papier_non_papier_tres_dense', 'hashtag_id': 2107751016, 'label': 4599398}, {'photo_id': 1049312426, 'score': 0.9998722, 'hashtag': '05102018_Papier_non_papier_tres_dense', 'hashtag_id': 2107751016, 'label': 4599398}, {'photo_id': 1049318291, 'score': 0.9998473, 'hashtag': '05102018_Papier_non_papier_tres_dense', 'hashtag_id': 2107751016, 'label': 4599398}, {'photo_id': 1049318289, 'score': 0.99982566, 'hashtag': '05102018_Papier_non_papier_tres_dense', 'hashtag_id': 2107751016, 'label': 4599398}] elapsed_time : load_data_split_time_score 3.5762786865234375e-06 elapsed_time : order_list_meta_photo_and_scores 0.0001308917999267578 elapsed_time : fill_and_build_computed_from_old_data 0.012696981430053711 INSERT INTO `MTRPhoto`.`dashboard_entry_day` (`dashboard_place_id`, `mtr_portfolio_id`, `date`) VALUES ( 42, 4599398, '2021-09-07' ) ON DUPLICATE KEY UPDATE mtr_portfolio_id=VALUES(mtr_portfolio_id), updated_at=NOW(); INSERT INTO `MTRPhoto`.`dashboard_run_ids` (`dashboard_entry_day`, `mtr_user_id`, `misc_info`) VALUES (106260,739,"{}"); elapsed_time : insert_dashboard_record_day_entry 0.024590253829956055 Creating list_photo_total in select_descriptors : SELECT `photo_id`, `type_store` FROM MTRPhoto.photo_desc_search WHERE photo_id IN (1049293230,1049296121,1049296996,1049303925,1049304810,1049306205,1049306635,1049306791,1049294990,1049306792,1049306804,1049306823,1049307693,1049308235,1049308275,1049308276,1049308280,1049308376,1049308381,1049308384,1049309345,1049309379,1049309380,1049309381,1049309382,1049309383,1049309385,1049309592,1049309595,1049309597,1049309599,1049309603,1049309605,1049309651,1049309653,1049309655,1049309656,1049309657,1049309658,1049309670,1049309672,1049309675,1049309677,1049309681,1049309686,1049309701,1049309703,1049309706,1049309732,1049309734,1049309737,1049310132,1049310134,1049310138,1049310139,1049310141,1049310145,1049310159,1049310162,1049310165,1049310905,1049310907,1049310909,1049310911,1049310914,1049310919,1049310981,1049310982,1049310984,1049310991,1049310992,1049310994,1049311006,1049311009,1049311073,1049311136,1049311199,1049311252,1049311263,1049311266,1049311267,1049311767,1049311771,1049311791,1049311793,1049311795,1049311932,1049311934,1049311935,1049311937,1049311938,1049311943,1049311960,1049311961,1049311962,1049311963,1049311964,1049312208,1049312363,1049312404,1049312406,1049312409,1049312420,1049312422,1049312424,1049312426,1049312429,1049312438,1049312440,1049312442,1049312444,1049312445,1049312449,1049312460,1049312461,1049312462,1049312463,1049312464,1049312484,1049312485,1049312487,1049312488,1049312489,1049312508,1049312556,1049312562,1049312566,1049312568,1049312571,1049312573,1049312574,1049312579,1049312583,1049312585,1049312588,1049312803,1049312984,1049313025,1049316209,1049316210,1049316212,1049316214,1049316216,1049316222,1049316255,1049316257,1049316331,1049316332,1049316336,1049316338,1049316520,1049316534,1049316537,1049316540,1049316543,1049316545,1049316582,1049316588,1049316594,1049316597,1049316600,1049316610,1049316749,1049316752,1049316778,1049316782,1049316785,1049316790,1049317197,1049317210,1049317225,1049317282,1049317333,1049317359,1049317440,1049317444,1049317453,1049317457,1049317461,1049317468,1049317485,1049317487,1049317489,1049317491,1049317493,1049317497,1049317517,1049317520,1049317522,1049317524,1049317525,1049317526,1049317536,1049317542,1049317546,1049317549,1049317551,1049317554,1049318212,1049318213,1049318214,1049318216,1049318219,1049318222,1049318246,1049318247,1049318250,1049318253,1049318257,1049318260,1049318265,1049318268,1049318271,1049318273,1049318276,1049318279,1049318287,1049318288,1049318289,1049318291,1049318293,1049318294,1049318309,1049318310,1049318311,1049318337,1049318339,1049318342,1049318356,1049318358,1049318360,1049318362) AND `type`=3963 elapsed_time : select_descriptors 20.151505708694458 07092021 4599398 Nombre de photos avec descriptors (type 3963) : 232 / 232 (100%) ERROR : Hum hum, what can we do for different size of descriptors (ignore the difference ) : 0 vs 2048 photo_id : 1049293230 photo_id_prec : 0 0:00:00|ON:0:27:28.999934|OFF:1:46:59.999878|ON:0:00:20.000007|OFF:0:01:51.000162|ON:0:12:18.999909|OFF:0:01:01.000055|ON:0:08:50.000116|OFF:0:00:09.999867|ON:0:00:19.999899|OFF:0:00:09.000058|ON:0:00:29.999860|OFF:0:01:40.000249|ON:0:00:30.999931|OFF:0:07:40.000107|ON:0:00:28.999981|OFF:0:00:09.999968|ON:0:00:10.999986|OFF:0:08:09.999919|ON:0:00:40.000176|OFF:0:01:08.999784|ON:0:00:11.000245|OFF:0:00:39.999921|ON:0:00:19.000004|OFF:0:06:31.000039|ON:0:02:09.999929|OFF:0:01:40.000021|ON:0:00:39.000031|OFF:0:07:10.999966|ON:0:12:30.000101|OFF:0:00:18.999765|ON:0:00:39.999946|OFF:0:00:11.000212|ON:0:00:29.999851|OFF:0:00:20.000150|ON:0:00:30.000042|OFF:0:00:18.999771|ON:0:07:31.000243|OFF:0:00:09.999942|ON:0:00:08.999822|OFF:0:00:11.000172|ON:0:00:39.999914|OFF:0:00:20|ON:0:31:10.000147|OFF:0:12:18.999857|ON:0:01:39.999950|OFF:0:00:19.999947|ON:0:00:21.000213|OFF:0:00:28.999911|ON:0:00:21.000117|OFF:0:00:40.000020|ON:0:10:58.999762|OFF:0:00:41.000023|ON:0:00:09.000008|OFF:0:00:21.000234|ON:0:00:29.999765|OFF:0:00:28.999920|ON:0:00:21.000174|OFF:0:00:30.000078|ON:0:00:29.999938|OFF:0:00:29.999871|ON:0:00:08.999965|OFF:0:09:31.000234|ON:0:00:09.999916|OFF:0:00:20.000049|ON:0:04:09.999926|OFF:0:01:09.000014|ON:0:02:00.999957|OFF:0:00:08.999951|ON:0:00:21.000053|OFF:0:00:18.999927|ON:0:00:39.999997|OFF:0:00:30.000158|ON: 07092021 Removing 115 photos because of the 'same image' condition list_time_on : 36 first ten : [datetime.timedelta(seconds=1648, microseconds=999934), datetime.timedelta(seconds=20, microseconds=7), datetime.timedelta(seconds=738, microseconds=999909), datetime.timedelta(seconds=530, microseconds=116), datetime.timedelta(seconds=19, microseconds=999899), datetime.timedelta(seconds=29, microseconds=999860), datetime.timedelta(seconds=30, microseconds=999931), datetime.timedelta(seconds=28, microseconds=999981), datetime.timedelta(seconds=10, microseconds=999986), datetime.timedelta(seconds=40, microseconds=176)] list_time_off : 37 first ten : [datetime.timedelta(0), datetime.timedelta(seconds=6419, microseconds=999878), datetime.timedelta(seconds=111, microseconds=162), datetime.timedelta(seconds=61, microseconds=55), datetime.timedelta(seconds=9, microseconds=999867), datetime.timedelta(seconds=9, microseconds=58), datetime.timedelta(seconds=100, microseconds=249), datetime.timedelta(seconds=460, microseconds=107), datetime.timedelta(seconds=9, microseconds=999968), datetime.timedelta(seconds=489, microseconds=999919)] Total on : 7859.999814999999 list_time_on {'nb': 36, 'mean': 218.33332819444445, 'stddev': 429.7602163813713, 'min': 8.999822, 'max': 1870.000147, 'quantil_10': {'min': [9.999916], 'max': [738.999909]}, 'quantil_100': {'min': [8.999822], 'max': [1870.000147]}, 'quantil_1000': {'min': [8.999822], 'max': [1870.000147]}, 'quantil_5000': {'min': [8.999822], 'max': [1870.000147]}, 'quantil_10000': {'min': [8.999822], 'max': [1870.000147]}} Total off : 10509.0002 list_time_off {'nb': 37, 'mean': 284.02703243243246, 'stddev': 1039.1357821819204, 'min': 0.0, 'max': 6419.999878, 'quantil_10': {'min': [9.999867], 'max': [489.999919]}, 'quantil_100': {'min': [0.0], 'max': [6419.999878]}, 'quantil_1000': {'min': [0.0], 'max': [6419.999878]}, 'quantil_5000': {'min': [0.0], 'max': [6419.999878]}, 'quantil_10000': {'min': [0.0], 'max': [6419.999878]}} dist_desc begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 232 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id) first line : (1049293230, 2107752370, 1539) ... last line : (1049318362, 2107752370, 1539) time used for this insertion : 0.08811283111572266 photos_removed : len 115 elapsed_time : remove_photo_duplicate 0.15581178665161133 Creating list_photo_total XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX elapsed_time : count_sum_diff_and_build_graph 0.03345537185668945 Total photos : 232 ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ....can't find max_score_info .....can't find max_score_info .can't find max_score_info ...Change port : 10 hashtag : 2107750911 photo_id =1049308384 : rungis_plastique_fonce ..can't find max_score_info ...can't find max_score_info ....can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ....can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ....can't find max_score_info ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .....can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info .....Change port : 25 hashtag : 2107750908 photo_id =1049311795 : rungis_papier ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ...Change port : 4 hashtag : 2107750911 photo_id =1049311961 : rungis_plastique_fonce .can't find max_score_info .can't find max_score_info .can't find max_score_info .Change port : 1 hashtag : 2107750908 photo_id =1049312208 : rungis_papier .....Change port : 5 hashtag : 2107750911 photo_id =1049312420 : rungis_plastique_fonce .Change port : 1 hashtag : 2107750908 photo_id =1049312422 : rungis_papier ..can't find max_score_info .can't find max_score_info .Change port : 2 hashtag : 2107750911 photo_id =1049312438 : rungis_plastique_fonce ....can't find max_score_info ...can't find max_score_info .can't find max_score_info ....can't find max_score_info .can't find max_score_info ....Change port : 12 hashtag : 2107750908 photo_id =1049312556 : rungis_papier .can't find max_score_info ..can't find max_score_info .....can't find max_score_info .can't find max_score_info ...Change port : 8 hashtag : 2107750911 photo_id =1049312984 : rungis_plastique_fonce ...can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .........can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .Change port : 17 hashtag : 2107751280 photo_id =1049317359 : rungis_aluminium .can't find max_score_info .can't find max_score_info ....can't find max_score_info .can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info ...Change port : 8 hashtag : 2107750913 photo_id =1049317524 : rungis_tetrapak .can't find max_score_info .can't find max_score_info .can't find max_score_info ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .Change port : 2 hashtag : 2107750911 photo_id =1049318212 : rungis_plastique_fonce .can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ..........Change port : 12 hashtag : 2107750908 photo_id =1049318287 : rungis_papier ...can't find max_score_info ..Change port : 4 hashtag : 2107750911 photo_id =1049318294 : rungis_plastique_fonce .can't find max_score_info .can't find max_score_info .....can't find max_score_info .can't find max_score_info .can't find max_score_info . Total photos : 232 Number of lists : 15 counter photos in port : 117 hashtag : rungis_aluminium(2107751280) : 8 photos in 1 portfolios ! hashtag : rungis_carton(2107750907) : 0 photos in 0 portfolios ! hashtag : rungis_papier(2107750908) : 33 photos in 6 portfolios ! hashtag : rungis_plastique_clair(2107750909) : 0 photos in 0 portfolios ! hashtag : rungis_plastique_dur(2107750910) : 0 photos in 0 portfolios ! hashtag : rungis_plastique_fonce(2107750911) : 74 photos in 7 portfolios ! hashtag : rungis_tapis_vide(2107750912) : 0 photos in 0 portfolios ! hashtag : rungis_tetrapak(2107750913) : 2 photos in 1 portfolios ! elapsed_time : group_photo_by_moyenne_exp 0.007321834564208984 elapsed_time : compute_and_correct_tag_with_moyenne_mobile 4.76837158203125e-06 today str has not a value , we define it as the date of the first image todaystr_first : 07092021 attention , prev_timestamp is 0 , we do nothing *******o** BIG TIME 550.0000100135803 (11.000229120254517, 2, 0, 0, 0.979349, 0, 0, 0, 0, 0, 0.8199999980926513, 0.0012, 0.0008999778032302856, 2.9, 0.11100016188621521, -0.0) on 3 1049307693 2021-09-07 07:45:54.010041 id_data : 12 * BIG TIME 168.99982810020447 (11.000229120254517, 2, 0, 0, 0.979349, 0, 0, 0, 0, 0, 0.8199999980926513, 0.0012, 0.0008999778032302856, 2.9, 0.11100016188621521, -0.0) on 3 1049308235 2021-09-07 07:48:43.009869 id_data : 13 ** BIG TIME 499.9998118877411 (191.00026988983154, 2, 0, 0, 0.93498826, 0, 0, 0, 0, 0, 0.9018999995946884, 0.002, 0.002999984407424927, 2.9, 0.019000051021575926, -0.01666705330212911) on 7 1049309345 2021-09-07 07:58:43.009858 id_data : 20 * BIG TIME 371.0001440048218 (271.00081276893616, 5, 0.24365342, 0, 0, 0, 0, 0.38418204, 0, 0.34177557, 0.9720000085830689, 0.0041, 0.014999971222877502, 2.9, 0.061000473976135255, -0.2516670016447703) on 15 1049310132 2021-09-07 08:09:54.010082 id_data : 51 * BIG TIME 461.0001001358032 (301.0006546974182, 0, 0.5752453, 0, 0, 0, 0, 0, 0, 0, 1.0228999980926514, 0.0056, 0.02589997522830963, 2.9, 0.009999968051910401, -0.0) on 18 1049310905 2021-09-07 08:18:54.009936 id_data : 60 * BIG TIME 370.0001759529114 (359.0003435611725, 5, 0, 0, 0, 0, 0, 0.85983855, 0, 0, 1.0898999773979188, 0.0078, 0.04109999623298645, 2.9, 0.008999762058258056, -0.0) on 24 1049311767 2021-09-07 08:28:24.010105 id_data : 81 *** BIG TIME 411.0001149177551 (557.9999935626984, 2, 0, 0, 0.66983944, 0, 0, 0, 0, 0, 1.1558999848842622, 0.0094, 0.050199996829032895, 2.9, 0.00900000500679016, -0.0) on 32 1049312208 2021-09-07 08:40:04.010052 id_data : 97 * BIG TIME 549.9999330043793 (557.9999935626984, 2, 0, 0, 0.66983944, 0, 0, 0, 0, 0, 1.1558999848842622, 0.0094, 0.050199996829032895, 2.9, 0.00900000500679016, -0.0) on 32 1049312363 2021-09-07 08:49:14.009985 id_data : 98 ** BIG TIME 168.99987387657166 (867.0004575252533, 2, 0, 0, 0.5498895, 0, 0, 0, 0, 0.29987606, 1.293000004196167, 0.0123, 0.06009994525909424, 0.9, 0.011000201940536499, -0.0) on 49 1049312508 2021-09-07 08:58:23.009966 id_data : 123 * BIG TIME 259.99999809265137 (867.0004575252533, 2, 0, 0, 0.5498895, 0, 0, 0, 0, 0.29987606, 1.293000004196167, 0.0123, 0.06009994525909424, 0.9, 0.011000201940536499, -0.0) on 49 1049312556 2021-09-07 09:02:43.009964 id_data : 124 * BIG TIME 190.00016593933105 (929.000762462616, 5, 0, 0, 0.44379362, 0, 0, 0.54574114, 0, 0, 1.3459999933004378, 0.0135, 0.06409994735717774, 2.9, 0.009999895095825195, -0.0) on 55 1049312803 2021-09-07 09:07:34.010149 id_data : 135 * BIG TIME 180.0000081062317 (929.000762462616, 5, 0, 0, 0.44379362, 0, 0, 0.54574114, 0, 0, 1.3459999933004378, 0.0135, 0.06409994735717774, 2.9, 0.009999895095825195, -0.0) on 55 1049312984 2021-09-07 09:10:34.010157 id_data : 136 * BIG TIME 1480.0000269412994 (939.0006575584412, 5, 0, 0, 0.2945285, 0, 0, 0.48689777, 0, 0.20073189, 1.3838999820947646, 0.0138, 0.06409994735717774, 2.9, 0.018999608993530273, -0.6316664799054463) on 56 1049316209 2021-09-07 09:35:23.009898 id_data : 138 * BIG TIME 668.9998891353607 (939.0006575584412, 5, 0, 0, 0.2945285, 0, 0, 0.48689777, 0, 0.20073189, 1.3838999820947646, 0.0138, 0.06409994735717774, 2.9, 0.018999608993530273, -0.6316664799054463) on 56 1049316332 2021-09-07 09:47:53.009987 id_data : 147 * BIG TIME 649.9999890327454 (1086.000019311905, 5, 0, 0, 0, 0, 0, 0.69907516, 0, 0.23055789, 1.6300000094890594, 0.0168, 0.08039999685287476, 2.9, 0.01100021505355835, -0.0) on 68 1049317197 2021-09-07 10:02:34.010134 id_data : 168 * BIG TIME 540.0001981258392 (1189.0002081394196, 0, 0.8074409, 0, 0, 0, 0, 0, 0, 0, 1.7199999867916107, 0.0194, 0.09519999706745148, 2.9, 0.009999808073043823, -0.0) on 78 1049318212 2021-09-07 10:16:24.010117 id_data : 198 * BIG TIME 190.00007104873657 (1199.0000162124634, 5, 0, 0.22708784, 0, 0, 0, 0.7244179, 0, 0, 1.781000003194809, 0.0202, 0.10109996955394746, 2.9, 0.011000241041183472, -0.0) on 79 1049318219 2021-09-07 10:20:04.010153 id_data : 202 **Count Time bigger than 30s : 31 #Number Photos for regression : {'07092021': {2107751280: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 80.99965000152588, 2107751017: 0}, 2107750907: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 49.000049114227295, 2107751017: 0}, 2107750908: {2107751013: 0, 2107751014: 11.000201940536499, 2107751015: 0, 2107751016: 534.9997780323029, 2107751017: 0}, 2107750909: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 0, 2107751017: 0}, 2107750910: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 31.000057697296143, 2107751017: 0}, 2107750911: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 564.0013737678528, 2107751017: 19.999656200408936}, 2107750912: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 0, 2107751017: 0}, 2107750913: {2107751013: 0, 2107751014: 8.999944925308228, 2107751015: 0, 2107751016: 89.99986672401428, 2107751017: 9.999994993209839}}} 07092021|rungis_aluminium, 05102018_papier_non_papier_dense:0 07092021|rungis_aluminium, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_aluminium, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_aluminium, 05102018_papier_non_papier_tres_dense:80.99965000152588 07092021|rungis_aluminium, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_carton, 05102018_papier_non_papier_dense:0 07092021|rungis_carton, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_carton, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_carton, 05102018_papier_non_papier_tres_dense:49.000049114227295 07092021|rungis_carton, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_papier, 05102018_papier_non_papier_dense:0 07092021|rungis_papier, 05102018_papier_non_papier_peu_dense:11.000201940536499 07092021|rungis_papier, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_papier, 05102018_papier_non_papier_tres_dense:534.9997780323029 07092021|rungis_papier, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_plastique_clair, 05102018_papier_non_papier_dense:0 07092021|rungis_plastique_clair, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_plastique_clair, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_plastique_clair, 05102018_papier_non_papier_tres_dense:0 07092021|rungis_plastique_clair, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_plastique_dur, 05102018_papier_non_papier_dense:0 07092021|rungis_plastique_dur, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_plastique_dur, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_plastique_dur, 05102018_papier_non_papier_tres_dense:31.000057697296143 07092021|rungis_plastique_dur, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_plastique_fonce, 05102018_papier_non_papier_dense:0 07092021|rungis_plastique_fonce, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_plastique_fonce, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_plastique_fonce, 05102018_papier_non_papier_tres_dense:564.0013737678528 07092021|rungis_plastique_fonce, 05102018_papier_non_papier_tres_peu_dense:19.999656200408936 07092021|rungis_tapis_vide, 05102018_papier_non_papier_dense:0 07092021|rungis_tapis_vide, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_tapis_vide, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_tapis_vide, 05102018_papier_non_papier_tres_dense:0 07092021|rungis_tapis_vide, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_tetrapak, 05102018_papier_non_papier_dense:0 07092021|rungis_tetrapak, 05102018_papier_non_papier_peu_dense:8.999944925308228 07092021|rungis_tetrapak, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_tetrapak, 05102018_papier_non_papier_tres_dense:89.99986672401428 07092021|rungis_tetrapak, 05102018_papier_non_papier_tres_peu_dense:9.999994993209839 #Number Photos for regression amount gros magasin papier (time_diff then nb_photo) : We have not displayed the number of photos removed for one material since Rungis_Papier wasn't in the thcl used ! 07092021_time_diff_distrib Number amount portfolio for this type of dechet : aluminium 8 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_aluminium_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 23354398 with name like 07092021_aluminium_05102018_papier_non_papier_tres_dense Number amount portfolio for this type of dechet : carton 6 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_carton_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 23354401 with name like 07092021_carton_05102018_papier_non_papier_tres_dense Number amount portfolio for this type of dechet : papier 29 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_papier_05102018_papier_non_papier_peu_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 23354404 with name like 07092021_papier_05102018_papier_non_papier_peu_dense https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_papier_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 23354406 with name like 07092021_papier_05102018_papier_non_papier_tres_dense Number amount portfolio for this type of dechet : plastique_clair 0 Number amount portfolio for this type of dechet : plastique_dur 2 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_plastique_dur_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 23354408 with name like 07092021_plastique_dur_05102018_papier_non_papier_tres_dense Number amount portfolio for this type of dechet : plastique_fonce 42 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_plastique_fonce_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 23354411 with name like 07092021_plastique_fonce_05102018_papier_non_papier_tres_dense https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_plastique_fonce_05102018_papier_non_papier_tres_peu_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 23354413 with name like 07092021_plastique_fonce_05102018_papier_non_papier_tres_peu_dense Number amount portfolio for this type of dechet : tapis_vide 0 Number amount portfolio for this type of dechet : tetrapak 11 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_tetrapak_05102018_papier_non_papier_peu_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 23354415 with name like 07092021_tetrapak_05102018_papier_non_papier_peu_dense https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_tetrapak_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 23354417 with name like 07092021_tetrapak_05102018_papier_non_papier_tres_dense https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_tetrapak_05102018_papier_non_papier_tres_peu_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 23354419 with name like 07092021_tetrapak_05102018_papier_non_papier_tres_peu_dense NUMBER BATCH : 15 list_ponderation used : [0.001, 0.001, 0.001, 0.001, 0.001] , list_hashtag_class_create_as_list : ['pcnc', 'pcm', 'jrm', 'flux_dev', 'pehd_pp', 'papier', 'carton', 'plastique_dur', 'plastique_clair', 'pet_clair', 'plastique_fonce', 'tetrapak', 'aluminium', 'carton_emr', 'grands_cartons', 'gros_de_magasin', 'tapis_vide'] We filter photos on hashtag condition ! We filter photos on hashtag condition ! Listed one port to create portfolio : papier_diff_batch__07092021_05_20_04_010050 Nombres de balles papier_diff_batch__07092021_05_20_04_010050 : 0.19500026988983155 duration : 9008.999763965607 update_text_in_photos list_photo_id_text len : 10 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 10, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 191.00026988983154, 3: 0, 4: 0}, 'duration': 9008.999763965607, 'nb_balles_papier': 0.19500026988983155, 'begin_time_port': 'image_07092021_05_20_04_010050m0.jpg 0.001 for time 1, id_amount 3 this amount prod time diff : 0.001'} Production hashtag (incorrect ponderation at 20-10-18) : 0.19500026988983155 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! Listed one port to create portfolio : plastique_fonce_diff_batch__07092021_07_50_23_010046 Nombres de balles plastique_fonce_diff_batch__07092021_07_50_23_010046 : 0.2299996557235718 duration : 2329.999878883362 update_text_in_photos list_photo_id_text len : 25 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 25, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 225.99965572357178, 3: 0, 4: 0}, 'duration': 2329.999878883362, 'nb_balles_papier': 0.2299996557235718, 'begin_time_port': 'image_07092021_07_50_23_010046m0.jpg 0.010000231981277466 for time 10.000231981277466, id_amount 3 this amount prod time diff : 0.010000231981277466'} Production hashtag (incorrect ponderation at 20-10-18) : 0.2299996557235718 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! Listed one port to create portfolio : papier_diff_batch__07092021_08_29_24_010122 Nombres de balles papier_diff_batch__07092021_08_29_24_010122 : 0.14100006794929504 duration : 189.9997718334198 update_text_in_photos list_photo_id_text len : 4 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 4, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 141.00006794929504, 3: 0, 4: 0}, 'duration': 189.9997718334198, 'nb_balles_papier': 0.14100006794929504, 'begin_time_port': 'image_07092021_08_29_24_010122m0.jpg 0.011000197172164917 for time 11.000197172164917, id_amount 3 this amount prod time diff : 0.011000197172164917'} Production hashtag (incorrect ponderation at 20-10-18) : 0.14100006794929504 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! Listed one port to create portfolio : plastique_fonce_diff_batch__07092021_08_32_43_009899 Nombres de balles plastique_fonce_diff_batch__07092021_08_32_43_009899 : 0.00900000500679016 duration : 0 update_text_in_photos list_photo_id_text len : 1 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 1, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 9.000005006790161, 3: 0, 4: 0}, 'duration': 0, 'nb_balles_papier': 0.00900000500679016, 'begin_time_port': 'image'} Production hashtag (incorrect ponderation at 20-10-18) : 0.00900000500679016 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! Listed one port to create portfolio : papier_diff_batch__07092021_08_40_04_010052 Nombres de balles papier_diff_batch__07092021_08_40_04_010052 : 0.15099991703033447 duration : 698.9998500347137 update_text_in_photos list_photo_id_text len : 5 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 5, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 148.99991703033447, 3: 0, 4: 0}, 'duration': 698.9998500347137, 'nb_balles_papier': 0.15099991703033447, 'begin_time_port': 'image_07092021_08_40_04_010052m0.jpg 0.001 for time 1, id_amount 3 this amount prod time diff : 0.001'} Production hashtag (incorrect ponderation at 20-10-18) : 0.15099991703033447 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! Listed one port to create portfolio : plastique_fonce_diff_batch__07092021_08_51_54_010119 Nombres de balles plastique_fonce_diff_batch__07092021_08_51_54_010119 : 0.011000216960906983 duration : 0 update_text_in_photos list_photo_id_text len : 1 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 1, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 11.000216960906982, 3: 0, 4: 0}, 'duration': 0, 'nb_balles_papier': 0.011000216960906983, 'begin_time_port': 'image'} Production hashtag (incorrect ponderation at 20-10-18) : 0.011000216960906983 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! Listed one port to create portfolio : papier_diff_batch__07092021_08_52_13_009932 Nombres de balles papier_diff_batch__07092021_08_52_13_009932 : 0.0289998140335083 duration : 0 update_text_in_photos list_photo_id_text len : 2 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 2, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 28.9998140335083, 3: 0, 4: 0}, 'duration': 0, 'nb_balles_papier': 0.0289998140335083, 'begin_time_port': 'image'} Production hashtag (incorrect ponderation at 20-10-18) : 0.0289998140335083 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! Listed one port to create portfolio : plastique_fonce_diff_batch__07092021_08_52_53_009918 Nombres de balles plastique_fonce_diff_batch__07092021_08_52_53_009918 : 0.12300071287155151 duration : 330.0000479221344 update_text_in_photos list_photo_id_text len : 12 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 1, 1: 0, 2: 11, 3: 0, 4: 0}, 'map_time_amount': {0: 11.000201940536499, 1: 0, 2: 111.00051093101501, 3: 0, 4: 0}, 'duration': 330.0000479221344, 'nb_balles_papier': 0.12300071287155151, 'begin_time_port': 'image_07092021_08_52_53_009918m0.jpg 0.01 for time 10.0, id_amount 3 this amount prod time diff : 0.01'} Production hashtag (incorrect ponderation at 20-10-18) : 0.12300071287155151 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! Listed one port to create portfolio : papier_diff_batch__07092021_09_02_43_009964 Nombres de balles papier_diff_batch__07092021_09_02_43_009964 : 0.06299999809265137 duration : 291.0001850128174 update_text_in_photos list_photo_id_text len : 8 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 8, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 60.99999809265137, 3: 0, 4: 0}, 'duration': 291.0001850128174, 'nb_balles_papier': 0.06299999809265137, 'begin_time_port': 'image_07092021_09_02_43_009964m0.jpg 0.001 for time 1, id_amount 3 this amount prod time diff : 0.001'} Production hashtag (incorrect ponderation at 20-10-18) : 0.06299999809265137 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! Listed one port to create portfolio : plastique_fonce_diff_batch__07092021_09_10_34_010157 Nombres de balles plastique_fonce_diff_batch__07092021_09_10_34_010157 : 0.16199957680702212 duration : 3119.999976873398 update_text_in_photos list_photo_id_text len : 17 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 1, 1: 0, 2: 16, 3: 0, 4: 0}, 'map_time_amount': {0: 8.999944925308228, 1: 0, 2: 148.99963188171387, 3: 0, 4: 0}, 'duration': 3119.999976873398, 'nb_balles_papier': 0.16199957680702212, 'begin_time_port': 'image_07092021_09_10_34_010157m0.jpg 0.001 for time 1, id_amount 3 this amount prod time diff : 0.001'} Production hashtag (incorrect ponderation at 20-10-18) : 0.16199957680702212 We filter photos on hashtag condition ! Listed one port to create portfolio : aluminium_diff_batch__07092021_10_03_24_009930 Nombres de balles aluminium_diff_batch__07092021_10_03_24_009930 : 0.08200012373924254 duration : 150.00017404556274 update_text_in_photos list_photo_id_text len : 8 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_aluminium:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 7, 3: 1, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 72.00012874603271, 3: 9.999994993209839, 4: 0}, 'duration': 150.00017404556274, 'nb_balles_papier': 0.08200012373924254, 'begin_time_port': 'image_07092021_10_03_24_009930m0.jpg 0.009999775886535644 for time 9.999775886535645, id_amount 3 this amount prod time diff : 0.009999775886535644'} Production hashtag (incorrect ponderation at 20-10-18) : 0.08200012373924254 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! Listed one port to create portfolio : tetrapak_diff_batch__07092021_10_06_04_009954 Nombres de balles tetrapak_diff_batch__07092021_10_06_04_009954 : 0.01999965810775757 duration : 0 update_text_in_photos list_photo_id_text len : 2 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_tetrapak:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 2, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 19.99965810775757, 3: 0, 4: 0}, 'duration': 0, 'nb_balles_papier': 0.01999965810775757, 'begin_time_port': 'image'} Production hashtag (incorrect ponderation at 20-10-18) : 0.01999965810775757 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! Listed one port to create portfolio : plastique_fonce_diff_batch__07092021_10_16_24_010117 Nombres de balles plastique_fonce_diff_batch__07092021_10_16_24_010117 : 0.10300015306472779 duration : 428.99973487854004 update_text_in_photos list_photo_id_text len : 12 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 10, 3: 2, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 81.00049686431885, 3: 19.999656200408936, 4: 0}, 'duration': 428.99973487854004, 'nb_balles_papier': 0.10300015306472779, 'begin_time_port': 'image_07092021_10_16_24_010117m0.jpg 0.001 for time 1, id_amount 3 this amount prod time diff : 0.001'} Production hashtag (incorrect ponderation at 20-10-18) : 0.10300015306472779 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! Listed one port to create portfolio : papier_diff_batch__07092021_10_23_44_010139 Nombres de balles papier_diff_batch__07092021_10_23_44_010139 : 0.04000009298324585 duration : 38.99979090690613 update_text_in_photos list_photo_id_text len : 4 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 4, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 40.00009298324585, 3: 0, 4: 0}, 'duration': 38.99979090690613, 'nb_balles_papier': 0.04000009298324585, 'begin_time_port': 'image_07092021_10_23_44_010139m0.jpg 0.011000287055969239 for time 11.000287055969238, id_amount 3 this amount prod time diff : 0.011000287055969239'} Production hashtag (incorrect ponderation at 20-10-18) : 0.04000009298324585 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! Listed one port to create portfolio : plastique_fonce_diff_batch__07092021_10_24_34_010156 Nombres de balles plastique_fonce_diff_batch__07092021_10_24_34_010156 : 0.060000311136245724 duration : 98.99990916252136 update_text_in_photos list_photo_id_text len : 6 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 6, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 60.00031113624573, 3: 0, 4: 0}, 'duration': 98.99990916252136, 'nb_balles_papier': 0.060000311136245724, 'begin_time_port': 'image_07092021_10_24_34_010156m0.jpg 0.011000226020812989 for time 11.000226020812988, id_amount 3 this amount prod time diff : 0.011000226020812989'} Production hashtag (incorrect ponderation at 20-10-18) : 0.060000311136245724 We filter photos on hashtag condition ! We have rejected 0 photos because of the batch_size condition ! NUMBER BATCH list_of_portfolios_to_create : 15 list_same_port_ids : [13545772] find same portfolio which already exist 13545772 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 13545772 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=13545772 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 list_same_port_ids : [13545774] find same portfolio which already exist 13545774 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 13545774 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=13545774 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 list_same_port_ids : [13545777] find same portfolio which already exist 13545777 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 13545777 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=13545777 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 list_same_port_ids : [5570414] find same portfolio which already exist 5570414 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 5570414 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=5570414 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 list_same_port_ids : [13545779] find same portfolio which already exist 13545779 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 13545779 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=13545779 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 list_same_port_ids : [13545780] find same portfolio which already exist 13545780 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 13545780 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=13545780 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 list_same_port_ids : [13545783] find same portfolio which already exist 13545783 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 13545783 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=13545783 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 list_same_port_ids : [13545785] find same portfolio which already exist 13545785 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 13545785 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=13545785 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 list_same_port_ids : [13545787] find same portfolio which already exist 13545787 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 13545787 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=13545787 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 list_same_port_ids : [13545788] find same portfolio which already exist 13545788 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 13545788 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=13545788 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 list_same_port_ids : [13543473] find same portfolio which already exist 13543473 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 13543473 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=13543473 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 list_same_port_ids : [13543474] find same portfolio which already exist 13543474 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 13543474 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=13543474 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 list_same_port_ids : [13543475] find same portfolio which already exist 13543475 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 13543475 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=13543475 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 list_same_port_ids : [13543476] find same portfolio which already exist 13543476 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 13543476 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=13543476 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 list_same_port_ids : [13543477] find same portfolio which already exist 13543477 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 13543477 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=13543477 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 13545772 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=370 ORDER BY id desc LIMIT 1 SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 13545772 AND result is not null and result<>"None" and result=0.0 AND mtd_id=370 ORDER BY created_at desc LIMIT 1 list_result_datou : [] INSERT into MTRPhoto.mtr_datou_current (mtd_id, mtr_portfolio_id, mtr_user_id) VALUES (370,13545772,739) ON DUPLICATE KEY UPDATE backup = backup + 1 select url from MTRUser.mtr_files where mtd_id = 370 and mtr_portfolio_id = 13545772 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545772 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844242, '_______papier', '2021-09-07 05:20:04.010050', '2021-09-07 07:50:13.009814', 10, 13545772, -1, -1, '-1', "{'max_time_prod_two_photos': 111.00016188621521}" ); SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 13545774 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=3979 ORDER BY id desc LIMIT 1 list_result_datou : [{'mtr_datou_id': 3979, 'mtr_datou_current_id': 2711361, 'mtr_portfolio_id': 13545774, 'created_at': datetime.datetime(2025, 4, 1, 4, 43, 50), 'result': '0.005823220486111111', 'result_long': None, 'result_double': None}] Qualite : 0.005823220486111111 select url from MTRUser.mtr_files where mtd_id = 3979 and mtr_portfolio_id = 13545774 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545774 AND mptpi.`type`=4199 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844242, '_______plastique_fonce', '2021-09-07 07:50:23.010046', '2021-09-07 08:29:13.009925', 25, 13545774, 0.005823220486111111, 2711361, '-1', "{'max_time_prod_two_photos': 29.999945163726807, 'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P13545774_21-11-2024_04_22_16.pdf', 'environnement': {'hashtag': 'environnement', 'sub_port_id': 13644096, 'pht': 4199}, 'pet_clair': {'hashtag': 'pet_clair', 'sub_port_id': 13644097, 'pht': 4199}, 'etiquette': {'hashtag': 'etiquette', 'sub_port_id': 13644098, 'pht': 4199}, 'pet_opaque': {'hashtag': 'pet_opaque', 'sub_port_id': 13644099, 'pht': 4199}, 'barquette_opaque': {'hashtag': 'barquette_opaque', 'sub_port_id': 13644100, 'pht': 4199}, 'pet_fonce': {'hashtag': 'pet_fonce', 'sub_port_id': 13644101, 'pht': 4199}, 'pehd': {'hashtag': 'pehd', 'sub_port_id': 13644102, 'pht': 4199}, 'film_plastique': {'hashtag': 'film_plastique', 'sub_port_id': 13644103, 'pht': 4199}, 'ela': {'hashtag': 'ela', 'sub_port_id': 13644104, 'pht': 4199}, 'metal': {'hashtag': 'metal', 'sub_port_id': 13644105, 'pht': 4199}, 'papier': {'hashtag': 'papier', 'sub_port_id': 13644106, 'pht': 4199}, 'textiles_sanitaires': {'hashtag': 'textiles_sanitaires', 'sub_port_id': 13644107, 'pht': 4199}, 'kraft': {'hashtag': 'kraft', 'sub_port_id': 13644108, 'pht': 4199}, 'carton': {'hashtag': 'carton', 'sub_port_id': 13644109, 'pht': 4199}}" ); SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 13545777 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=370 ORDER BY id desc LIMIT 1 list_result_datou : [{'mtr_datou_id': 370, 'mtr_datou_current_id': 2710694, 'mtr_portfolio_id': 13545777, 'created_at': datetime.datetime(2025, 3, 31, 23, 49, 49), 'result': '0.1888521086140681', 'result_long': None, 'result_double': None}] Qualite : 0.1888521086140681 select url from MTRUser.mtr_files where mtd_id = 370 and mtr_portfolio_id = 13545777 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545777 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844242, '_______papier', '2021-09-07 08:29:24.010122', '2021-09-07 08:32:34.009894', 4, 13545777, 0.1888521086140681, 2710694, '-1', "{'max_time_prod_two_photos': 61.00024700164795}" ); SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 5570414 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=3979 ORDER BY id desc LIMIT 1 list_result_datou : [{'mtr_datou_id': 3979, 'mtr_datou_current_id': 2711362, 'mtr_portfolio_id': 5570414, 'created_at': datetime.datetime(2025, 4, 1, 3, 59, 59), 'result': '0.007415846836419753', 'result_long': None, 'result_double': None}] Qualite : 0.007415846836419753 select url from MTRUser.mtr_files where mtd_id = 3979 and mtr_portfolio_id = 5570414 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=5570414 AND mptpi.`type`=4199 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844242, '_______plastique_fonce', '2021-09-07 08:32:43.009899', '2021-09-07 08:32:43.009899', 1, 5570414, 0.007415846836419753, 2711362, '-1', "{'max_time_prod_two_photos': 9.000005006790161, 'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P5570414_21-11-2024_00_01_20.pdf', 'pet_clair': {'hashtag': 'pet_clair', 'sub_port_id': 5570430, 'pht': 4199}, 'kraft': {'hashtag': 'kraft', 'sub_port_id': 5570431, 'pht': 4199}, 'pet_opaque': {'hashtag': 'pet_opaque', 'sub_port_id': 5570432, 'pht': 4199}, 'papier': {'hashtag': 'papier', 'sub_port_id': 5570433, 'pht': 4199}, 'textiles_sanitaires': {'hashtag': 'textiles_sanitaires', 'sub_port_id': 5570434, 'pht': 4199}, 'ela': {'hashtag': 'ela', 'sub_port_id': 5570435, 'pht': 4199}, 'etiquette': {'hashtag': 'etiquette', 'sub_port_id': 5570436, 'pht': 4199}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 5570437, 'pht': 4199}, 'carton': {'hashtag': 'carton', 'sub_port_id': 5570438, 'pht': 4199}, 'pehd': {'hashtag': 'pehd', 'sub_port_id': 5570439, 'pht': 4199}, 'film_plastique': {'hashtag': 'film_plastique', 'sub_port_id': 5570440, 'pht': 4199}, 'pet_fonce': {'hashtag': 'pet_fonce', 'sub_port_id': 5570441, 'pht': 4199}, 'barquette_opaque': {'hashtag': 'barquette_opaque', 'sub_port_id': 5570442, 'pht': 4199}, 'metal': {'hashtag': 'metal', 'sub_port_id': 5570443, 'pht': 4199}}" ); SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 13545779 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=370 ORDER BY id desc LIMIT 1 SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 13545779 AND result is not null and result<>"None" and result=0.0 AND mtd_id=370 ORDER BY created_at desc LIMIT 1 select value from MTRUser.portfolio_carac_ratio where portfolio_id = 13545779 and created_at >= ( SELECT min(created_at) FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 13545779 AND result is not null and result<>"None" and result=0.0 AND mtd_id=370 )and created_at < ( SELECT addtime(max(created_at),100) FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 13545779 AND result is not null and result<>"None" and result=0.0 AND mtd_id=370 ) order by value desc list_result_datou : [] INSERT into MTRPhoto.mtr_datou_current (mtd_id, mtr_portfolio_id, mtr_user_id) VALUES (370,13545779,739) ON DUPLICATE KEY UPDATE backup = backup + 1 select url from MTRUser.mtr_files where mtd_id = 370 and mtr_portfolio_id = 13545779 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545779 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844242, '_______papier', '2021-09-07 08:40:04.010052', '2021-09-07 08:51:43.009902', 5, 13545779, -1, -1, '-1', "{'max_time_prod_two_photos': 119.00011491775513}" ); SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 13545780 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=3979 ORDER BY id desc LIMIT 1 list_result_datou : [{'mtr_datou_id': 3979, 'mtr_datou_current_id': 2591870, 'mtr_portfolio_id': 13545780, 'created_at': datetime.datetime(2025, 2, 16, 9, 56, 31), 'result': '0.004572120949074074', 'result_long': None, 'result_double': None}] Qualite : 0.004572120949074074 select url from MTRUser.mtr_files where mtd_id = 3979 and mtr_portfolio_id = 13545780 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545780 AND mptpi.`type`=4199 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844242, '_______plastique_fonce', '2021-09-07 08:51:54.010119', '2021-09-07 08:51:54.010119', 1, 13545780, 0.004572120949074074, 2591870, '-1', "{'max_time_prod_two_photos': 11.000216960906982, 'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P13545780_23-01-2025_12_31_57.pdf', 'ela': {'hashtag': 'ela', 'sub_port_id': 13644047, 'pht': 4199}, 'barquette_opaque': {'hashtag': 'barquette_opaque', 'sub_port_id': 13644048, 'pht': 4199}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 13644049, 'pht': 4199}, 'kraft': {'hashtag': 'kraft', 'sub_port_id': 13644050, 'pht': 4199}, 'metal': {'hashtag': 'metal', 'sub_port_id': 13644051, 'pht': 4199}, 'textiles_sanitaires': {'hashtag': 'textiles_sanitaires', 'sub_port_id': 13644052, 'pht': 4199}, 'pet_opaque': {'hashtag': 'pet_opaque', 'sub_port_id': 13644053, 'pht': 4199}, 'film_plastique': {'hashtag': 'film_plastique', 'sub_port_id': 13644054, 'pht': 4199}, 'papier': {'hashtag': 'papier', 'sub_port_id': 13644055, 'pht': 4199}, 'carton': {'hashtag': 'carton', 'sub_port_id': 13644056, 'pht': 4199}, 'etiquette': {'hashtag': 'etiquette', 'sub_port_id': 13644057, 'pht': 4199}, 'pehd': {'hashtag': 'pehd', 'sub_port_id': 13644058, 'pht': 4199}, 'pet_fonce': {'hashtag': 'pet_fonce', 'sub_port_id': 13644059, 'pht': 4199}, 'pet_clair': {'hashtag': 'pet_clair', 'sub_port_id': 13644060, 'pht': 4199}}" ); SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 13545783 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=370 ORDER BY id desc LIMIT 1 SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 13545783 AND result is not null and result<>"None" and result=0.0 AND mtd_id=370 ORDER BY created_at desc LIMIT 1 list_result_datou : [] select url from MTRUser.mtr_files where mtd_id = 370 and mtr_portfolio_id = 13545783 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545783 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844242, '_______papier', '2021-09-07 08:52:13.009932', '2021-09-07 08:52:23.009933', 2, 13545783, -1, -1, '-1', "{'max_time_prod_two_photos': 18.999813079833984}" ); SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 13545785 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=3979 ORDER BY id desc LIMIT 1 list_result_datou : [{'mtr_datou_id': 3979, 'mtr_datou_current_id': 2711365, 'mtr_portfolio_id': 13545785, 'created_at': datetime.datetime(2025, 4, 1, 3, 58, 52), 'result': '0.00907640496399177', 'result_long': None, 'result_double': None}] Qualite : 0.00907640496399177 select url from MTRUser.mtr_files where mtd_id = 3979 and mtr_portfolio_id = 13545785 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545785 AND mptpi.`type`=4199 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844242, '_______plastique_fonce', '2021-09-07 08:52:53.009918', '2021-09-07 08:58:23.009966', 12, 13545785, 0.00907640496399177, 2711365, '-1', "{'max_time_prod_two_photos': 20.00016689300537, 'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P13545785_21-11-2024_00_23_50.pdf', 'ela': {'hashtag': 'ela', 'sub_port_id': 13644079, 'pht': 4199}, 'textiles_sanitaires': {'hashtag': 'textiles_sanitaires', 'sub_port_id': 13644080, 'pht': 4199}, 'pet_opaque': {'hashtag': 'pet_opaque', 'sub_port_id': 13644081, 'pht': 4199}, 'metal': {'hashtag': 'metal', 'sub_port_id': 13644082, 'pht': 4199}, 'pehd': {'hashtag': 'pehd', 'sub_port_id': 13644083, 'pht': 4199}, 'pet_fonce': {'hashtag': 'pet_fonce', 'sub_port_id': 13644084, 'pht': 4199}, 'kraft': {'hashtag': 'kraft', 'sub_port_id': 13644085, 'pht': 4199}, 'carton': {'hashtag': 'carton', 'sub_port_id': 13644086, 'pht': 4199}, 'pet_clair': {'hashtag': 'pet_clair', 'sub_port_id': 13644087, 'pht': 4199}, 'etiquette': {'hashtag': 'etiquette', 'sub_port_id': 13644088, 'pht': 4199}, 'barquette_opaque': {'hashtag': 'barquette_opaque', 'sub_port_id': 13644089, 'pht': 4199}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 13644090, 'pht': 4199}, 'film_plastique': {'hashtag': 'film_plastique', 'sub_port_id': 13644091, 'pht': 4199}, 'papier': {'hashtag': 'papier', 'sub_port_id': 13644092, 'pht': 4199}}" ); SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 13545787 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=370 ORDER BY id desc LIMIT 1 SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 13545787 AND result is not null and result<>"None" and result=0.0 AND mtd_id=370 ORDER BY created_at desc LIMIT 1 select value from MTRUser.portfolio_carac_ratio where portfolio_id = 13545787 and created_at >= ( SELECT min(created_at) FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 13545787 AND result is not null and result<>"None" and result=0.0 AND mtd_id=370 )and created_at < ( SELECT addtime(max(created_at),100) FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 13545787 AND result is not null and result<>"None" and result=0.0 AND mtd_id=370 ) order by value desc list_result_datou : [] select url from MTRUser.mtr_files where mtd_id = 370 and mtr_portfolio_id = 13545787 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545787 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844242, '_______papier', '2021-09-07 09:02:43.009964', '2021-09-07 09:07:34.010149', 8, 13545787, -1, -1, '-1', "{'max_time_prod_two_photos': 11.000171899795532}" ); SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 13545788 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=3979 ORDER BY id desc LIMIT 1 list_result_datou : [{'mtr_datou_id': 3979, 'mtr_datou_current_id': 2711367, 'mtr_portfolio_id': 13545788, 'created_at': datetime.datetime(2025, 4, 1, 3, 51, 30), 'result': '0.01485129824918373', 'result_long': None, 'result_double': None}] Qualite : 0.01485129824918373 select url from MTRUser.mtr_files where mtd_id = 3979 and mtr_portfolio_id = 13545788 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545788 AND mptpi.`type`=4199 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844242, '_______plastique_fonce', '2021-09-07 09:10:34.010157', '2021-09-07 10:02:34.010134', 17, 13545788, 0.01485129824918373, 2711367, '-1', "{'max_time_prod_two_photos': 21.00007677078247, 'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P13545788_21-11-2024_00_29_38.pdf', 'ela': {'hashtag': 'ela', 'sub_port_id': 13644062, 'pht': 4199}, 'papier': {'hashtag': 'papier', 'sub_port_id': 13644063, 'pht': 4199}, 'pet_fonce': {'hashtag': 'pet_fonce', 'sub_port_id': 13644064, 'pht': 4199}, 'film_plastique': {'hashtag': 'film_plastique', 'sub_port_id': 13644065, 'pht': 4199}, 'pet_opaque': {'hashtag': 'pet_opaque', 'sub_port_id': 13644066, 'pht': 4199}, 'textiles_sanitaires': {'hashtag': 'textiles_sanitaires', 'sub_port_id': 13644067, 'pht': 4199}, 'etiquette': {'hashtag': 'etiquette', 'sub_port_id': 13644068, 'pht': 4199}, 'metal': {'hashtag': 'metal', 'sub_port_id': 13644069, 'pht': 4199}, 'carton': {'hashtag': 'carton', 'sub_port_id': 13644070, 'pht': 4199}, 'kraft': {'hashtag': 'kraft', 'sub_port_id': 13644071, 'pht': 4199}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 13644072, 'pht': 4199}, 'pet_clair': {'hashtag': 'pet_clair', 'sub_port_id': 13644073, 'pht': 4199}, 'barquette_opaque': {'hashtag': 'barquette_opaque', 'sub_port_id': 13644074, 'pht': 4199}, 'pehd': {'hashtag': 'pehd', 'sub_port_id': 13644075, 'pht': 4199}}" ); select url from MTRUser.mtr_files where mtd_id = 0 and mtr_portfolio_id = 13543473 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) 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 0 init_dummy_multi_datou is not linked in the step_by_step architecture ! WARNING : step 1294 init_dummy_multi_datou 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 ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13543473 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844242, '_______aluminium', '2021-09-07 10:03:24.009930', '2021-09-07 10:05:54.010104', 8, 13543473, -1, -1, '-1', "{'max_time_prod_two_photos': 11.00031304359436}" ); select url from MTRUser.mtr_files where mtd_id = 0 and mtr_portfolio_id = 13543474 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) 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 0 init_dummy_multi_datou is not linked in the step_by_step architecture ! WARNING : step 1294 init_dummy_multi_datou 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 ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13543474 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844242, '_______tetrapak', '2021-09-07 10:06:04.009954', '2021-09-07 10:06:44.009918', 2, 13543474, -1, -1, '-1', "{'max_time_prod_two_photos': 9.999850034713745}" ); SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 13543475 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=3979 ORDER BY id desc LIMIT 1 list_result_datou : [{'mtr_datou_id': 3979, 'mtr_datou_current_id': 2711368, 'mtr_portfolio_id': 13543475, 'created_at': datetime.datetime(2025, 4, 1, 3, 46, 54), 'result': '0.003848153410463827', 'result_long': None, 'result_double': None}] Qualite : 0.003848153410463827 select url from MTRUser.mtr_files where mtd_id = 3979 and mtr_portfolio_id = 13543475 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13543475 AND mptpi.`type`=4199 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844242, '_______plastique_fonce', '2021-09-07 10:16:24.010117', '2021-09-07 10:23:33.009852', 12, 13543475, 0.003848153410463827, 2711368, '-1', "{'max_time_prod_two_photos': 11.000241041183472, 'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P13543475_21-11-2024_00_33_36.pdf', 'pet_opaque': {'hashtag': 'pet_opaque', 'sub_port_id': 13648411, 'pht': 4199}, 'ela': {'hashtag': 'ela', 'sub_port_id': 13648412, 'pht': 4199}, 'pet_clair': {'hashtag': 'pet_clair', 'sub_port_id': 13648413, 'pht': 4199}, 'pehd': {'hashtag': 'pehd', 'sub_port_id': 13648414, 'pht': 4199}, 'etiquette': {'hashtag': 'etiquette', 'sub_port_id': 13648415, 'pht': 4199}, 'carton': {'hashtag': 'carton', 'sub_port_id': 13648416, 'pht': 4199}, 'papier': {'hashtag': 'papier', 'sub_port_id': 13648417, 'pht': 4199}, 'metal': {'hashtag': 'metal', 'sub_port_id': 13648418, 'pht': 4199}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 13648419, 'pht': 4199}, 'pet_fonce': {'hashtag': 'pet_fonce', 'sub_port_id': 13648420, 'pht': 4199}, 'kraft': {'hashtag': 'kraft', 'sub_port_id': 13648421, 'pht': 4199}, 'barquette_opaque': {'hashtag': 'barquette_opaque', 'sub_port_id': 13648422, 'pht': 4199}, 'textiles_sanitaires': {'hashtag': 'textiles_sanitaires', 'sub_port_id': 13648423, 'pht': 4199}, 'film_plastique': {'hashtag': 'film_plastique', 'sub_port_id': 13648424, 'pht': 4199}}" ); SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 13543476 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=370 ORDER BY id desc LIMIT 1 list_result_datou : [{'mtr_datou_id': 370, 'mtr_datou_current_id': 2710698, 'mtr_portfolio_id': 13543476, 'created_at': datetime.datetime(2025, 3, 31, 21, 49, 54), 'result': '0.11478003563407302', 'result_long': None, 'result_double': None}] Qualite : 0.11478003563407302 select url from MTRUser.mtr_files where mtd_id = 370 and mtr_portfolio_id = 13543476 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13543476 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844242, '_______papier', '2021-09-07 10:23:44.010139', '2021-09-07 10:24:23.009930', 4, 13543476, 0.11478003563407302, 2710698, '-1', "{'max_time_prod_two_photos': 11.000287055969238}" ); SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 13543477 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=3979 ORDER BY id desc LIMIT 1 list_result_datou : [{'mtr_datou_id': 3979, 'mtr_datou_current_id': 2711369, 'mtr_portfolio_id': 13543477, 'created_at': datetime.datetime(2025, 4, 1, 3, 48, 33), 'result': '0.019897576026366652', 'result_long': None, 'result_double': None}] Qualite : 0.019897576026366652 select url from MTRUser.mtr_files where mtd_id = 3979 and mtr_portfolio_id = 13543477 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13543477 AND mptpi.`type`=4199 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844242, '_______plastique_fonce', '2021-09-07 10:24:34.010156', '2021-09-07 10:26:13.010065', 6, 13543477, 0.019897576026366652, 2711369, '-1', "{'max_time_prod_two_photos': 11.000226020812988, 'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P13543477_21-11-2024_00_52_17.pdf', 'pet_opaque': {'hashtag': 'pet_opaque', 'sub_port_id': 13648396, 'pht': 4199}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 13648397, 'pht': 4199}, 'pet_clair': {'hashtag': 'pet_clair', 'sub_port_id': 13648398, 'pht': 4199}, 'pehd': {'hashtag': 'pehd', 'sub_port_id': 13648399, 'pht': 4199}, 'ela': {'hashtag': 'ela', 'sub_port_id': 13648400, 'pht': 4199}, 'papier': {'hashtag': 'papier', 'sub_port_id': 13648401, 'pht': 4199}, 'metal': {'hashtag': 'metal', 'sub_port_id': 13648402, 'pht': 4199}, 'pet_fonce': {'hashtag': 'pet_fonce', 'sub_port_id': 13648403, 'pht': 4199}, 'textiles_sanitaires': {'hashtag': 'textiles_sanitaires', 'sub_port_id': 13648404, 'pht': 4199}, 'etiquette': {'hashtag': 'etiquette', 'sub_port_id': 13648405, 'pht': 4199}, 'carton': {'hashtag': 'carton', 'sub_port_id': 13648406, 'pht': 4199}, 'film_plastique': {'hashtag': 'film_plastique', 'sub_port_id': 13648407, 'pht': 4199}, 'barquette_opaque': {'hashtag': 'barquette_opaque', 'sub_port_id': 13648408, 'pht': 4199}, 'kraft': {'hashtag': 'kraft', 'sub_port_id': 13648409, 'pht': 4199}}" ); elapsed_time : count_nb_balles_and_create_portfolio 37.89009404182434 # DISPLAY ALL COLLECTED DATA : {'07092021': {'nb_upload': 232, 'nb_taggue_class': 232, 'nb_taggue_densite': 232, 'nb_descriptors': 232, 'number_port': 15, 'count_photo_in_port': 117, 'nb_port_per_class': {'rungis_aluminium': {'nb_photos': 8, 'nb_portfolios': 1}, 'rungis_carton': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_papier': {'nb_photos': 33, 'nb_portfolios': 6}, 'rungis_plastique_clair': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_dur': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_fonce': {'nb_photos': 74, 'nb_portfolios': 7}, 'rungis_tapis_vide': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_tetrapak': {'nb_photos': 2, 'nb_portfolios': 1}}}} After datou_step_exec type output : time spend for datou_step_exec : 58.39506649971008 time spend to save output : 0.00012826919555664062 total time spend for step 1 : 58.39519476890564 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1049318362, 1049318360, 1049318358, 1049318356, 1049318342, 1049318339, 1049318337, 1049318311, 1049318310, 1049318309, 1049318294, 1049318293, 1049318291, 1049318289, 1049318288, 1049318287, 1049318279, 1049318276, 1049318273, 1049318271, 1049318268, 1049318265, 1049318260, 1049318257, 1049318253, 1049318250, 1049318247, 1049318246, 1049318222, 1049318219, 1049318216, 1049318214, 1049318213, 1049318212, 1049317554, 1049317551, 1049317549, 1049317546, 1049317542, 1049317536, 1049317526, 1049317525, 1049317524, 1049317522, 1049317520, 1049317517, 1049317497, 1049317493, 1049317491, 1049317489, 1049317487, 1049317485, 1049317468, 1049317461, 1049317457, 1049317453, 1049317444, 1049317440, 1049317359, 1049317333, 1049317282, 1049317225, 1049317210, 1049317197, 1049316790, 1049316785, 1049316782, 1049316778, 1049316752, 1049316749, 1049316610, 1049316600, 1049316597, 1049316594, 1049316588, 1049316582, 1049316545, 1049316543, 1049316540, 1049316537, 1049316534, 1049316520, 1049316338, 1049316336, 1049316332, 1049316331, 1049316257, 1049316255, 1049316222, 1049316216, 1049316214, 1049316212, 1049316210, 1049316209, 1049313025, 1049312984, 1049312803, 1049312588, 1049312585, 1049312583, 1049312579, 1049312574, 1049312573, 1049312571, 1049312568, 1049312566, 1049312562, 1049312556, 1049312508, 1049312489, 1049312488, 1049312487, 1049312485, 1049312484, 1049312464, 1049312463, 1049312462, 1049312461, 1049312460, 1049312449, 1049312445, 1049312444, 1049312442, 1049312440, 1049312438, 1049312429, 1049312426, 1049312424, 1049312422, 1049312420, 1049312409, 1049312406, 1049312404, 1049312363, 1049312208, 1049311964, 1049311963, 1049311962, 1049311961, 1049311960, 1049311943, 1049311938, 1049311937, 1049311935, 1049311934, 1049311932, 1049311795, 1049311793, 1049311791, 1049311771, 1049311767, 1049311267, 1049311266, 1049311263, 1049311252, 1049311199, 1049311136, 1049311073, 1049311009, 1049311006, 1049310994, 1049310992, 1049310991, 1049310984, 1049310982, 1049310981, 1049310919, 1049310914, 1049310911, 1049310909, 1049310907, 1049310905, 1049310165, 1049310162, 1049310159, 1049310145, 1049310141, 1049310139, 1049310138, 1049310134, 1049310132, 1049309737, 1049309734, 1049309732, 1049309706, 1049309703, 1049309701, 1049309686, 1049309681, 1049309677, 1049309675, 1049309672, 1049309670, 1049309658, 1049309657, 1049309656, 1049309655, 1049309653, 1049309651, 1049309605, 1049309603, 1049309599, 1049309597, 1049309595, 1049309592, 1049309385, 1049309383, 1049309382, 1049309381, 1049309380, 1049309379, 1049309345, 1049308384, 1049308381, 1049308376, 1049308280, 1049308276, 1049308275, 1049308235, 1049307693, 1049306823, 1049306804, 1049306792, 1049306791, 1049306635, 1049306205, 1049304810, 1049303925, 1049296996, 1049296121, 1049294990, 1049293230] map_info['map_portfolio_photo'] : {4599398: [1049318362, 1049318360, 1049318358, 1049318356, 1049318342, 1049318339, 1049318337, 1049318311, 1049318310, 1049318309, 1049318294, 1049318293, 1049318291, 1049318289, 1049318288, 1049318287, 1049318279, 1049318276, 1049318273, 1049318271, 1049318268, 1049318265, 1049318260, 1049318257, 1049318253, 1049318250, 1049318247, 1049318246, 1049318222, 1049318219, 1049318216, 1049318214, 1049318213, 1049318212, 1049317554, 1049317551, 1049317549, 1049317546, 1049317542, 1049317536, 1049317526, 1049317525, 1049317524, 1049317522, 1049317520, 1049317517, 1049317497, 1049317493, 1049317491, 1049317489, 1049317487, 1049317485, 1049317468, 1049317461, 1049317457, 1049317453, 1049317444, 1049317440, 1049317359, 1049317333, 1049317282, 1049317225, 1049317210, 1049317197, 1049316790, 1049316785, 1049316782, 1049316778, 1049316752, 1049316749, 1049316610, 1049316600, 1049316597, 1049316594, 1049316588, 1049316582, 1049316545, 1049316543, 1049316540, 1049316537, 1049316534, 1049316520, 1049316338, 1049316336, 1049316332, 1049316331, 1049316257, 1049316255, 1049316222, 1049316216, 1049316214, 1049316212, 1049316210, 1049316209, 1049313025, 1049312984, 1049312803, 1049312588, 1049312585, 1049312583, 1049312579, 1049312574, 1049312573, 1049312571, 1049312568, 1049312566, 1049312562, 1049312556, 1049312508, 1049312489, 1049312488, 1049312487, 1049312485, 1049312484, 1049312464, 1049312463, 1049312462, 1049312461, 1049312460, 1049312449, 1049312445, 1049312444, 1049312442, 1049312440, 1049312438, 1049312429, 1049312426, 1049312424, 1049312422, 1049312420, 1049312409, 1049312406, 1049312404, 1049312363, 1049312208, 1049311964, 1049311963, 1049311962, 1049311961, 1049311960, 1049311943, 1049311938, 1049311937, 1049311935, 1049311934, 1049311932, 1049311795, 1049311793, 1049311791, 1049311771, 1049311767, 1049311267, 1049311266, 1049311263, 1049311252, 1049311199, 1049311136, 1049311073, 1049311009, 1049311006, 1049310994, 1049310992, 1049310991, 1049310984, 1049310982, 1049310981, 1049310919, 1049310914, 1049310911, 1049310909, 1049310907, 1049310905, 1049310165, 1049310162, 1049310159, 1049310145, 1049310141, 1049310139, 1049310138, 1049310134, 1049310132, 1049309737, 1049309734, 1049309732, 1049309706, 1049309703, 1049309701, 1049309686, 1049309681, 1049309677, 1049309675, 1049309672, 1049309670, 1049309658, 1049309657, 1049309656, 1049309655, 1049309653, 1049309651, 1049309605, 1049309603, 1049309599, 1049309597, 1049309595, 1049309592, 1049309385, 1049309383, 1049309382, 1049309381, 1049309380, 1049309379, 1049309345, 1049308384, 1049308381, 1049308376, 1049308280, 1049308276, 1049308275, 1049308235, 1049307693, 1049306823, 1049306804, 1049306792, 1049306791, 1049306635, 1049306205, 1049304810, 1049303925, 1049296996, 1049296121, 1049294990, 1049293230]} final : True mtd_id 3789 list_pids : [1049318362, 1049318360, 1049318358, 1049318356, 1049318342, 1049318339, 1049318337, 1049318311, 1049318310, 1049318309, 1049318294, 1049318293, 1049318291, 1049318289, 1049318288, 1049318287, 1049318279, 1049318276, 1049318273, 1049318271, 1049318268, 1049318265, 1049318260, 1049318257, 1049318253, 1049318250, 1049318247, 1049318246, 1049318222, 1049318219, 1049318216, 1049318214, 1049318213, 1049318212, 1049317554, 1049317551, 1049317549, 1049317546, 1049317542, 1049317536, 1049317526, 1049317525, 1049317524, 1049317522, 1049317520, 1049317517, 1049317497, 1049317493, 1049317491, 1049317489, 1049317487, 1049317485, 1049317468, 1049317461, 1049317457, 1049317453, 1049317444, 1049317440, 1049317359, 1049317333, 1049317282, 1049317225, 1049317210, 1049317197, 1049316790, 1049316785, 1049316782, 1049316778, 1049316752, 1049316749, 1049316610, 1049316600, 1049316597, 1049316594, 1049316588, 1049316582, 1049316545, 1049316543, 1049316540, 1049316537, 1049316534, 1049316520, 1049316338, 1049316336, 1049316332, 1049316331, 1049316257, 1049316255, 1049316222, 1049316216, 1049316214, 1049316212, 1049316210, 1049316209, 1049313025, 1049312984, 1049312803, 1049312588, 1049312585, 1049312583, 1049312579, 1049312574, 1049312573, 1049312571, 1049312568, 1049312566, 1049312562, 1049312556, 1049312508, 1049312489, 1049312488, 1049312487, 1049312485, 1049312484, 1049312464, 1049312463, 1049312462, 1049312461, 1049312460, 1049312449, 1049312445, 1049312444, 1049312442, 1049312440, 1049312438, 1049312429, 1049312426, 1049312424, 1049312422, 1049312420, 1049312409, 1049312406, 1049312404, 1049312363, 1049312208, 1049311964, 1049311963, 1049311962, 1049311961, 1049311960, 1049311943, 1049311938, 1049311937, 1049311935, 1049311934, 1049311932, 1049311795, 1049311793, 1049311791, 1049311771, 1049311767, 1049311267, 1049311266, 1049311263, 1049311252, 1049311199, 1049311136, 1049311073, 1049311009, 1049311006, 1049310994, 1049310992, 1049310991, 1049310984, 1049310982, 1049310981, 1049310919, 1049310914, 1049310911, 1049310909, 1049310907, 1049310905, 1049310165, 1049310162, 1049310159, 1049310145, 1049310141, 1049310139, 1049310138, 1049310134, 1049310132, 1049309737, 1049309734, 1049309732, 1049309706, 1049309703, 1049309701, 1049309686, 1049309681, 1049309677, 1049309675, 1049309672, 1049309670, 1049309658, 1049309657, 1049309656, 1049309655, 1049309653, 1049309651, 1049309605, 1049309603, 1049309599, 1049309597, 1049309595, 1049309592, 1049309385, 1049309383, 1049309382, 1049309381, 1049309380, 1049309379, 1049309345, 1049308384, 1049308381, 1049308376, 1049308280, 1049308276, 1049308275, 1049308235, 1049307693, 1049306823, 1049306804, 1049306792, 1049306791, 1049306635, 1049306205, 1049304810, 1049303925, 1049296996, 1049296121, 1049294990, 1049293230] Looping around the photos to save general results len do output : 1 /4599398Didn'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 ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', 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('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309653', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309651', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309605', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309603', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309599', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309597', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309595', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309592', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309385', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309383', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309382', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309381', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309380', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309379', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309345', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308384', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308381', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308376', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308280', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308276', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308275', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308235', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049307693', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306823', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306804', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306792', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306791', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306635', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306205', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049304810', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049303925', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049296996', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049296121', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049294990', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049293230', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 233 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 : [('3789', None, '4599398', 'None', None, None, None, None, None), ('3789', '4599398', '1049318362', None, None, None, None, None, None), ('3789', '4599398', '1049318360', None, None, None, None, None, None), ('3789', '4599398', '1049318358', None, None, None, None, None, None), ('3789', '4599398', '1049318356', None, None, None, None, None, None), ('3789', '4599398', '1049318342', None, None, None, None, None, None), ('3789', '4599398', '1049318339', None, None, None, None, None, None), ('3789', '4599398', '1049318337', None, None, None, None, None, None), ('3789', '4599398', '1049318311', None, None, None, None, None, None), ('3789', '4599398', '1049318310', None, None, None, None, None, None), ('3789', '4599398', '1049318309', None, None, None, None, None, None), ('3789', '4599398', '1049318294', None, None, None, None, None, None), ('3789', '4599398', '1049318293', None, None, None, None, None, None), ('3789', '4599398', '1049318291', None, None, None, None, None, None), ('3789', '4599398', '1049318289', None, None, None, None, None, None), ('3789', '4599398', '1049318288', None, None, None, None, None, None), ('3789', '4599398', '1049318287', None, None, None, None, None, None), ('3789', '4599398', '1049318279', None, None, None, None, None, None), ('3789', '4599398', '1049318276', None, None, None, None, None, None), ('3789', '4599398', '1049318273', None, None, None, None, None, None), ('3789', '4599398', '1049318271', None, None, None, None, None, None), ('3789', '4599398', '1049318268', None, None, None, None, None, None), ('3789', '4599398', '1049318265', None, None, None, None, None, None), ('3789', '4599398', '1049318260', None, None, None, None, None, None), ('3789', '4599398', '1049318257', None, None, None, None, None, None), ('3789', '4599398', '1049318253', None, None, None, None, None, None), ('3789', '4599398', '1049318250', None, None, None, None, None, None), ('3789', '4599398', '1049318247', None, None, None, None, None, None), ('3789', '4599398', '1049318246', None, None, None, None, None, None), ('3789', '4599398', '1049318222', None, None, None, None, None, None), ('3789', '4599398', '1049318219', None, None, None, None, None, None), ('3789', '4599398', '1049318216', None, None, None, None, None, None), ('3789', '4599398', '1049318214', None, None, None, None, None, None), ('3789', '4599398', '1049318213', None, None, None, None, None, None), ('3789', '4599398', '1049318212', None, None, None, None, None, None), ('3789', '4599398', '1049317554', None, None, None, None, None, None), ('3789', '4599398', '1049317551', None, None, None, None, None, None), ('3789', '4599398', '1049317549', None, None, None, None, None, None), ('3789', '4599398', '1049317546', None, None, None, None, None, None), ('3789', '4599398', '1049317542', None, None, None, None, None, None), ('3789', '4599398', '1049317536', None, None, None, None, None, None), ('3789', '4599398', '1049317526', None, None, None, None, None, None), ('3789', '4599398', '1049317525', None, None, None, None, None, None), ('3789', '4599398', '1049317524', None, None, None, None, None, None), ('3789', '4599398', '1049317522', None, None, None, None, None, None), ('3789', '4599398', '1049317520', None, None, None, None, None, None), ('3789', '4599398', '1049317517', None, None, None, None, None, None), ('3789', '4599398', '1049317497', None, None, None, None, None, None), ('3789', '4599398', '1049317493', None, None, None, None, None, None), ('3789', '4599398', '1049317491', None, None, None, None, None, None), ('3789', '4599398', '1049317489', None, None, None, None, None, None), ('3789', '4599398', '1049317487', None, None, None, None, None, None), ('3789', '4599398', '1049317485', None, None, None, None, None, None), ('3789', '4599398', '1049317468', None, None, None, None, None, None), ('3789', '4599398', '1049317461', None, None, None, None, None, None), ('3789', '4599398', '1049317457', None, None, None, None, None, None), ('3789', '4599398', '1049317453', None, None, None, None, None, None), ('3789', '4599398', '1049317444', None, None, None, None, None, None), ('3789', '4599398', '1049317440', None, None, None, None, None, None), ('3789', '4599398', '1049317359', None, None, None, None, None, None), ('3789', '4599398', '1049317333', None, None, None, None, None, None), ('3789', '4599398', '1049317282', None, None, None, None, None, None), ('3789', '4599398', '1049317225', None, None, None, None, None, None), ('3789', '4599398', '1049317210', None, None, None, None, None, None), ('3789', '4599398', '1049317197', None, None, None, None, None, None), ('3789', '4599398', '1049316790', None, None, None, None, None, None), ('3789', '4599398', '1049316785', None, None, None, None, None, None), ('3789', '4599398', '1049316782', None, None, None, None, None, None), ('3789', '4599398', '1049316778', None, None, None, None, None, None), ('3789', '4599398', '1049316752', None, None, None, None, None, None), ('3789', '4599398', '1049316749', None, None, None, None, None, None), ('3789', '4599398', '1049316610', None, None, None, None, None, None), ('3789', '4599398', '1049316600', None, None, None, None, None, None), ('3789', '4599398', '1049316597', None, None, None, None, None, None), ('3789', '4599398', '1049316594', None, None, None, None, None, None), ('3789', '4599398', '1049316588', None, None, None, None, None, None), ('3789', '4599398', '1049316582', None, None, None, None, None, None), ('3789', '4599398', '1049316545', None, None, None, None, None, None), ('3789', '4599398', '1049316543', None, None, None, None, None, None), ('3789', '4599398', '1049316540', None, None, None, None, None, None), ('3789', '4599398', '1049316537', None, None, None, None, None, None), ('3789', '4599398', '1049316534', None, None, None, None, None, None), ('3789', '4599398', '1049316520', None, None, None, None, None, None), ('3789', '4599398', '1049316338', None, None, None, None, None, None), ('3789', '4599398', '1049316336', None, None, None, None, None, None), ('3789', '4599398', '1049316332', None, None, None, None, None, None), ('3789', '4599398', '1049316331', None, None, None, None, None, None), ('3789', '4599398', '1049316257', None, None, None, None, None, None), ('3789', '4599398', '1049316255', None, None, None, None, None, None), ('3789', '4599398', '1049316222', None, None, None, None, None, None), ('3789', '4599398', '1049316216', None, None, None, None, None, None), ('3789', '4599398', '1049316214', None, None, None, None, None, None), ('3789', '4599398', '1049316212', None, None, None, None, None, None), ('3789', '4599398', '1049316210', None, None, None, None, None, None), ('3789', '4599398', '1049316209', None, None, None, None, None, None), ('3789', '4599398', '1049313025', None, None, None, None, None, None), ('3789', '4599398', '1049312984', None, None, None, None, None, None), ('3789', '4599398', '1049312803', None, None, None, None, None, None), ('3789', '4599398', '1049312588', None, None, None, None, None, None), ('3789', '4599398', '1049312585', None, None, None, None, None, None), ('3789', '4599398', '1049312583', None, None, None, None, None, None), ('3789', '4599398', '1049312579', None, None, None, None, None, None), ('3789', '4599398', '1049312574', None, None, None, None, None, None), ('3789', '4599398', '1049312573', None, None, None, None, None, None), ('3789', '4599398', '1049312571', None, None, None, None, None, None), ('3789', '4599398', '1049312568', None, None, None, None, None, None), ('3789', '4599398', '1049312566', None, None, None, None, None, None), ('3789', '4599398', '1049312562', None, None, None, None, None, None), ('3789', '4599398', '1049312556', None, None, None, None, None, None), ('3789', '4599398', '1049312508', None, None, None, None, None, None), ('3789', '4599398', '1049312489', None, None, None, None, None, None), ('3789', '4599398', '1049312488', None, None, None, None, None, None), ('3789', '4599398', '1049312487', None, None, None, None, None, None), ('3789', '4599398', '1049312485', None, None, None, None, None, None), ('3789', '4599398', '1049312484', None, None, None, None, None, None), ('3789', '4599398', '1049312464', None, None, None, None, None, None), ('3789', '4599398', '1049312463', None, None, None, None, None, None), ('3789', '4599398', '1049312462', None, None, None, None, None, None), ('3789', '4599398', '1049312461', None, None, None, None, None, None), ('3789', '4599398', '1049312460', None, None, None, None, None, None), ('3789', '4599398', '1049312449', None, None, None, None, None, None), ('3789', '4599398', '1049312445', None, None, None, None, None, None), ('3789', '4599398', '1049312444', None, None, None, None, None, None), ('3789', '4599398', '1049312442', None, None, None, None, None, None), ('3789', '4599398', '1049312440', None, None, None, None, None, None), ('3789', '4599398', '1049312438', None, None, None, None, None, None), ('3789', '4599398', '1049312429', None, None, None, None, None, None), ('3789', '4599398', '1049312426', None, None, None, None, None, None), ('3789', '4599398', '1049312424', None, None, None, None, None, None), ('3789', '4599398', '1049312422', None, None, None, None, None, None), ('3789', '4599398', '1049312420', None, None, None, None, None, None), ('3789', '4599398', '1049312409', None, None, None, None, None, None), ('3789', '4599398', '1049312406', None, None, None, None, None, None), ('3789', '4599398', '1049312404', None, None, None, None, None, None), ('3789', '4599398', '1049312363', None, None, None, None, None, None), ('3789', '4599398', '1049312208', None, None, None, None, None, None), ('3789', '4599398', '1049311964', None, None, None, None, None, None), ('3789', '4599398', '1049311963', None, None, None, None, None, None), ('3789', '4599398', '1049311962', None, None, None, None, None, None), ('3789', '4599398', '1049311961', None, None, None, None, None, None), ('3789', '4599398', '1049311960', None, None, None, None, None, None), ('3789', '4599398', '1049311943', None, None, None, None, None, None), ('3789', '4599398', '1049311938', None, None, None, None, None, None), ('3789', '4599398', '1049311937', None, None, None, None, None, None), ('3789', '4599398', '1049311935', None, None, None, None, None, None), ('3789', '4599398', '1049311934', None, None, None, None, None, None), ('3789', '4599398', '1049311932', None, None, None, None, None, None), ('3789', '4599398', '1049311795', None, None, None, None, None, None), ('3789', '4599398', '1049311793', None, None, None, None, None, None), ('3789', '4599398', '1049311791', None, None, None, None, None, None), ('3789', '4599398', '1049311771', None, None, None, None, None, None), ('3789', '4599398', '1049311767', None, None, None, None, None, None), ('3789', '4599398', '1049311267', None, None, None, None, None, None), ('3789', '4599398', '1049311266', None, None, None, None, None, None), ('3789', '4599398', '1049311263', None, None, None, None, None, None), ('3789', '4599398', '1049311252', None, None, None, None, None, None), ('3789', '4599398', '1049311199', None, None, None, None, None, None), ('3789', '4599398', '1049311136', None, None, None, None, None, None), ('3789', '4599398', '1049311073', None, None, None, None, None, None), ('3789', '4599398', '1049311009', None, None, None, None, None, None), ('3789', '4599398', '1049311006', None, None, None, None, None, None), ('3789', '4599398', '1049310994', None, None, None, None, None, None), ('3789', '4599398', '1049310992', None, None, None, None, None, None), ('3789', '4599398', '1049310991', None, None, None, None, None, None), ('3789', '4599398', '1049310984', None, None, None, None, None, None), ('3789', '4599398', '1049310982', None, None, None, None, None, None), ('3789', '4599398', '1049310981', None, None, None, None, None, None), ('3789', '4599398', '1049310919', None, None, None, None, None, None), ('3789', '4599398', '1049310914', None, None, None, None, None, None), ('3789', '4599398', '1049310911', None, None, None, None, None, None), ('3789', '4599398', '1049310909', None, None, None, None, None, None), ('3789', '4599398', '1049310907', None, None, None, None, None, None), ('3789', '4599398', '1049310905', None, None, None, None, None, None), ('3789', '4599398', '1049310165', None, None, None, None, None, None), ('3789', '4599398', '1049310162', None, None, None, None, None, None), ('3789', '4599398', '1049310159', None, None, None, None, None, None), ('3789', '4599398', '1049310145', None, None, None, None, None, None), ('3789', '4599398', '1049310141', None, None, None, None, None, None), ('3789', '4599398', '1049310139', None, None, None, None, None, None), ('3789', '4599398', '1049310138', None, None, None, None, None, None), ('3789', '4599398', '1049310134', None, None, None, None, None, None), ('3789', '4599398', '1049310132', None, None, None, None, None, None), ('3789', '4599398', '1049309737', None, None, None, None, None, None), ('3789', '4599398', '1049309734', None, None, None, None, None, None), ('3789', '4599398', '1049309732', None, None, None, None, None, None), ('3789', '4599398', '1049309706', None, None, None, None, None, None), ('3789', '4599398', '1049309703', None, None, None, None, None, None), ('3789', '4599398', '1049309701', None, None, None, None, None, None), ('3789', '4599398', '1049309686', None, None, None, None, None, None), ('3789', '4599398', '1049309681', None, None, None, None, None, None), ('3789', '4599398', '1049309677', None, None, None, None, None, None), ('3789', '4599398', '1049309675', None, None, None, None, None, None), ('3789', '4599398', '1049309672', None, None, None, None, None, None), ('3789', '4599398', '1049309670', None, None, None, None, None, None), ('3789', '4599398', '1049309658', None, None, None, None, None, None), ('3789', '4599398', '1049309657', None, None, None, None, None, None), ('3789', '4599398', '1049309656', None, None, None, None, None, None), ('3789', '4599398', '1049309655', None, None, None, None, None, None), ('3789', '4599398', '1049309653', None, None, None, None, None, None), ('3789', '4599398', '1049309651', None, None, None, None, None, None), ('3789', '4599398', '1049309605', None, None, None, None, None, None), ('3789', '4599398', '1049309603', None, None, None, None, None, None), ('3789', '4599398', '1049309599', None, None, None, None, None, None), ('3789', '4599398', '1049309597', None, None, None, None, None, None), ('3789', '4599398', '1049309595', None, None, None, None, None, None), ('3789', '4599398', '1049309592', None, None, None, None, None, None), ('3789', '4599398', '1049309385', None, None, None, None, None, None), ('3789', '4599398', '1049309383', None, None, None, None, None, None), ('3789', '4599398', '1049309382', None, None, None, None, None, None), ('3789', '4599398', '1049309381', None, None, None, None, None, None), ('3789', '4599398', '1049309380', None, None, None, None, None, None), ('3789', '4599398', '1049309379', None, None, None, None, None, None), ('3789', '4599398', '1049309345', None, None, None, None, None, None), ('3789', '4599398', '1049308384', None, None, None, None, None, None), ('3789', '4599398', '1049308381', None, None, None, None, None, None), ('3789', '4599398', '1049308376', None, None, None, None, None, None), ('3789', '4599398', '1049308280', None, None, None, None, None, None), ('3789', '4599398', '1049308276', None, None, None, None, None, None), ('3789', '4599398', '1049308275', None, None, None, None, None, None), ('3789', '4599398', '1049308235', None, None, None, None, None, None), ('3789', '4599398', '1049307693', None, None, None, None, None, None), ('3789', '4599398', '1049306823', None, None, None, None, None, None), ('3789', '4599398', '1049306804', None, None, None, None, None, None), ('3789', '4599398', '1049306792', None, None, None, None, None, None), ('3789', '4599398', '1049306791', None, None, None, None, None, None), ('3789', '4599398', '1049306635', None, None, None, None, None, None), ('3789', '4599398', '1049306205', None, None, None, None, None, None), ('3789', '4599398', '1049304810', None, None, None, None, None, None), ('3789', '4599398', '1049303925', None, None, None, None, None, None), ('3789', '4599398', '1049296996', None, None, None, None, None, None), ('3789', '4599398', '1049296121', None, None, None, None, None, None), ('3789', '4599398', '1049294990', None, None, None, None, None, None), ('3789', '4599398', '1049293230', None, None, None, None, None, None)] time used for this insertion : 0.07924842834472656 save_final save missing photos in datou_result : After save, about to update current ! Result test rubbia : {'4599398': ([[0, 7, 8, 9, 11, 12, 13, 14, 17, 18], [19, 20, 22, 23, 25, 26, 27, 38, 39, 40, 51, 52, 53, 55, 60, 61, 62, 63, 71, 76, 77, 81, 82, 83, 84], [85, 86, 91, 92], [93], [97, 98, 99, 100, 101], [102], [103, 104], [107, 108, 109, 110, 112, 113, 116, 117, 118, 121, 122, 123], [124, 126, 128, 129, 130, 131, 134, 135], [136, 137, 138, 147, 148, 149, 150, 151, 152, 153, 154, 157, 158, 161, 162, 167, 168], [173, 176, 177, 178, 182, 183, 187, 188], [189, 193], [198, 201, 202, 207, 208, 209, 210, 211, 212, 213, 214, 215], [216, 217, 218, 220], [221, 224, 225, 226, 227, 231]], {'rungis_aluminium': [(10, 11)], 'rungis_carton': [], 'rungis_papier': [(0, 1), (2, 3), (4, 5), (6, 7), (8, 9), (13, 14)], 'rungis_plastique_clair': [], 'rungis_plastique_dur': [], 'rungis_plastique_fonce': [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (12, 13), (14, 15)], 'rungis_tapis_vide': [], 'rungis_tetrapak': [(11, 12)]}, {13545772: {'list_of_photos': [1049293230, 1049306791, 1049294990, 1049306792, 1049306823, 1049307693, 1049308235, 1049308275, 1049308376, 1049308381], 'hashtag': 'papier'}, 13545774: {'list_of_photos': [1049308384, 1049309345, 1049309380, 1049309381, 1049309383, 1049309385, 1049309592, 1049309658, 1049309670, 1049309672, 1049310132, 1049310134, 1049310138, 1049310141, 1049310905, 1049310907, 1049310909, 1049310911, 1049310994, 1049311199, 1049311252, 1049311767, 1049311771, 1049311791, 1049311793], 'hashtag': 'plastique_fonce'}, 13545777: {'list_of_photos': [1049311795, 1049311932, 1049311943, 1049311960], 'hashtag': 'papier'}, 5570414: {'list_of_photos': [1049311961], 'hashtag': 'plastique_fonce'}, 13545779: {'list_of_photos': [1049312208, 1049312363, 1049312404, 1049312406, 1049312409], 'hashtag': 'papier'}, 13545780: {'list_of_photos': [1049312420], 'hashtag': 'plastique_fonce'}, 13545783: {'list_of_photos': [1049312422, 1049312424], 'hashtag': 'papier'}, 13545785: {'list_of_photos': [1049312438, 1049312440, 1049312442, 1049312444, 1049312449, 1049312460, 1049312463, 1049312464, 1049312484, 1049312488, 1049312489, 1049312508], 'hashtag': 'plastique_fonce'}, 13545787: {'list_of_photos': [1049312556, 1049312566, 1049312571, 1049312573, 1049312574, 1049312579, 1049312588, 1049312803], 'hashtag': 'papier'}, 13545788: {'list_of_photos': [1049312984, 1049313025, 1049316209, 1049316332, 1049316336, 1049316338, 1049316520, 1049316534, 1049316537, 1049316540, 1049316543, 1049316588, 1049316594, 1049316610, 1049316749, 1049316790, 1049317197], 'hashtag': 'plastique_fonce'}, 13543473: {'list_of_photos': [1049317359, 1049317453, 1049317457, 1049317461, 1049317489, 1049317491, 1049317520, 1049317522], 'hashtag': 'aluminium'}, 13543474: {'list_of_photos': [1049317524, 1049317542], 'hashtag': 'tetrapak'}, 13543475: {'list_of_photos': [1049318212, 1049318216, 1049318219, 1049318253, 1049318257, 1049318260, 1049318265, 1049318268, 1049318271, 1049318273, 1049318276, 1049318279], 'hashtag': 'plastique_fonce'}, 13543476: {'list_of_photos': [1049318287, 1049318288, 1049318289, 1049318293], 'hashtag': 'papier'}, 13543477: {'list_of_photos': [1049318294, 1049318311, 1049318337, 1049318339, 1049318342, 1049318362], 'hashtag': 'plastique_fonce'}}, {2107751280: 8, 2107750907: 0, 2107750908: 33, 2107750909: 0, 2107750910: 0, 2107750911: 74, 2107750912: 0, 2107750913: 2}, {'amount_uploaded_and_tagged': {'07092021': {'nb_upload': 232, 'nb_taggue_class': 232, 'nb_taggue_densite': 232, 'nb_descriptors': 232, 'number_port': 15, 'count_photo_in_port': 117, 'nb_port_per_class': {'rungis_aluminium': {'nb_photos': 8, 'nb_portfolios': 1}, 'rungis_carton': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_papier': {'nb_photos': 33, 'nb_portfolios': 6}, 'rungis_plastique_clair': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_dur': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_fonce': {'nb_photos': 74, 'nb_portfolios': 7}, 'rungis_tapis_vide': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_tetrapak': {'nb_photos': 2, 'nb_portfolios': 1}}}}, 'map_all_result_after_group_moy_exp': {'number_port': 15, 'count_photo_in_port': 117, 'nb_port_per_class': {'rungis_aluminium': {'nb_photos': 8, 'nb_portfolios': 1}, 'rungis_carton': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_papier': {'nb_photos': 33, 'nb_portfolios': 6}, 'rungis_plastique_clair': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_dur': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_fonce': {'nb_photos': 74, 'nb_portfolios': 7}, 'rungis_tapis_vide': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_tetrapak': {'nb_photos': 2, 'nb_portfolios': 1}}}, 'map_info_after_moyenne_mobile': {'07092021': {'distrib_time_diff': {'nb': 207, 'mean': 12.512076641626404, 'stddev': 12.296218880583977, 'min': 0.0, 'max': 119.00011491775513, 'quantil_10': {'min': [8.999778032302856], 'max': [11.000291109085083]}, 'quantil_100': {'min': [8.999709129333496], 'max': [61.00024700164795]}, 'quantil_1000': {'min': [0.0], 'max': [119.00011491775513]}, 'quantil_5000': {'min': [0.0], 'max': [119.00011491775513]}, 'quantil_10000': {'min': [0.0], 'max': [119.00011491775513]}}, 'time_diff': {'rungis_aluminium': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 80.99965000152588, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_carton': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 49.000049114227295, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_papier': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 11.000201940536499, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 534.9997780323029, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_clair': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_dur': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 31.000057697296143, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_fonce': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 564.0013737678528, '05102018_papier_non_papier_tres_peu_dense': 19.999656200408936}, 'rungis_tapis_vide': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_tetrapak': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 8.999944925308228, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 89.99986672401428, '05102018_papier_non_papier_tres_peu_dense': 9.999994993209839}}, 'time_diff_removed': {'rungis_aluminium': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 11.000208854675293, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 158.99993062019348, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_carton': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 101.00013995170593, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_papier': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 18.999826192855835, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 168.99926328659058, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_clair': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_dur': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_fonce': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 21.00012707710266, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 489.9993727207184, '05102018_papier_non_papier_tres_peu_dense': 11.000169038772583}, 'rungis_tapis_vide': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_tetrapak': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 38.99988508224487, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 170.0003685951233, '05102018_papier_non_papier_tres_peu_dense': 0}}, 'nb_photos': {'rungis_aluminium': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 8, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_carton': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 6, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_papier': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 1, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 28, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_clair': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_dur': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 2, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_fonce': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 40, '05102018_papier_non_papier_tres_peu_dense': 2}, 'rungis_tapis_vide': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_tetrapak': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 1, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 9, '05102018_papier_non_papier_tres_peu_dense': 1}}, 'nb_photos_removed': {'rungis_aluminium': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 1, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 16, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_carton': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 5, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_papier': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 2, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 16, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_clair': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_dur': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_fonce': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 1, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 46, '05102018_papier_non_papier_tres_peu_dense': 1}, 'rungis_tapis_vide': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_tetrapak': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 4, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 17, '05102018_papier_non_papier_tres_peu_dense': 0}}}}, 'map_amount_per_hashtag': {'rungis_aluminium': [(10, 11)], 'rungis_carton': [], 'rungis_papier': [(0, 1), (2, 3), (4, 5), (6, 7), (8, 9), (13, 14)], 'rungis_plastique_clair': [], 'rungis_plastique_dur': [], 'rungis_plastique_fonce': [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (12, 13), (14, 15)], 'rungis_tapis_vide': [], 'rungis_tetrapak': [(11, 12)]}, 'count': {'rungis_aluminium': [(10, 11)], 'rungis_carton': [], 'rungis_papier': [(0, 1), (2, 3), (4, 5), (6, 7), (8, 9), (13, 14)], 'rungis_plastique_clair': [], 'rungis_plastique_dur': [], 'rungis_plastique_fonce': [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (12, 13), (14, 15)], 'rungis_tapis_vide': [], 'rungis_tetrapak': [(11, 12)]}})}| Result context_with_local_rubbia.cache_model_config.map_io test rubbia : {'input': {}, 'output': {}}| ############################### TEST rubbia_split_dark ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=3787 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=3787 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= 3787 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=3787 # 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_with_photo 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 (4608689) 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 (4608689) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 1055013727, 'portfolio_id': 4608689}, {'photo_id': 1055013724, 'portfolio_id': 4608689}, {'photo_id': 1055013693, 'portfolio_id': 4608689}, {'photo_id': 1055012727, 'portfolio_id': 4608689}, {'photo_id': 1055012722, 'portfolio_id': 4608689}, {'photo_id': 1055012686, 'portfolio_id': 4608689}, {'photo_id': 1055012684, 'portfolio_id': 4608689}, {'photo_id': 1055011740, 'portfolio_id': 4608689}, {'photo_id': 1055011733, 'portfolio_id': 4608689}, {'photo_id': 1055011726, 'portfolio_id': 4608689}, {'photo_id': 1055011459, 'portfolio_id': 4608689}, {'photo_id': 1055011454, 'portfolio_id': 4608689}, {'photo_id': 1055011441, 'portfolio_id': 4608689}, {'photo_id': 1055011086, 'portfolio_id': 4608689}, {'photo_id': 1055011076, 'portfolio_id': 4608689}, {'photo_id': 1055011074, 'portfolio_id': 4608689}, {'photo_id': 1055011072, 'portfolio_id': 4608689}, {'photo_id': 1055010743, 'portfolio_id': 4608689}, {'photo_id': 1055010739, 'portfolio_id': 4608689}, {'photo_id': 1055010737, 'portfolio_id': 4608689}, {'photo_id': 1055010730, 'portfolio_id': 4608689}, {'photo_id': 1055010725, 'portfolio_id': 4608689}, {'photo_id': 1055010723, 'portfolio_id': 4608689}, {'photo_id': 1055010143, 'portfolio_id': 4608689}, {'photo_id': 1055008638, 'portfolio_id': 4608689}, {'photo_id': 1055008599, 'portfolio_id': 4608689}, {'photo_id': 1055008597, 'portfolio_id': 4608689}, {'photo_id': 1055008184, 'portfolio_id': 4608689}, {'photo_id': 1055008181, 'portfolio_id': 4608689}, {'photo_id': 1055007992, 'portfolio_id': 4608689}, {'photo_id': 1055007953, 'portfolio_id': 4608689}, {'photo_id': 1055007950, 'portfolio_id': 4608689}, {'photo_id': 1055004798, 'portfolio_id': 4608689}, {'photo_id': 1055004627, 'portfolio_id': 4608689}, {'photo_id': 1055004608, 'portfolio_id': 4608689}, {'photo_id': 1055004600, 'portfolio_id': 4608689}, {'photo_id': 1055004278, 'portfolio_id': 4608689}, {'photo_id': 1055004217, 'portfolio_id': 4608689}, {'photo_id': 1055003679, 'portfolio_id': 4608689}, {'photo_id': 1055003357, 'portfolio_id': 4608689}, {'photo_id': 1055003348, 'portfolio_id': 4608689}, {'photo_id': 1055003292, 'portfolio_id': 4608689}, {'photo_id': 1055003278, 'portfolio_id': 4608689}, {'photo_id': 1055003266, 'portfolio_id': 4608689}, {'photo_id': 1055003261, 'portfolio_id': 4608689}, {'photo_id': 1055003259, 'portfolio_id': 4608689}, {'photo_id': 1055003249, 'portfolio_id': 4608689}, {'photo_id': 1055003202, 'portfolio_id': 4608689}, {'photo_id': 1055003198, 'portfolio_id': 4608689}, {'photo_id': 1055003197, 'portfolio_id': 4608689}, {'photo_id': 1055003185, 'portfolio_id': 4608689}, {'photo_id': 1055003134, 'portfolio_id': 4608689}, {'photo_id': 1055003131, 'portfolio_id': 4608689}, {'photo_id': 1055002045, 'portfolio_id': 4608689}, {'photo_id': 1055001545, 'portfolio_id': 4608689}, {'photo_id': 1055001542, 'portfolio_id': 4608689}, {'photo_id': 1055001092, 'portfolio_id': 4608689}, {'photo_id': 1055001085, 'portfolio_id': 4608689}, {'photo_id': 1055000228, 'portfolio_id': 4608689}, {'photo_id': 1055000070, 'portfolio_id': 4608689}, {'photo_id': 1055000068, 'portfolio_id': 4608689}, {'photo_id': 1055000063, 'portfolio_id': 4608689}, {'photo_id': 1055000059, 'portfolio_id': 4608689}, {'photo_id': 1055000055, 'portfolio_id': 4608689}] map_portfolio_id_photo_id: {4608689: [1055013727, 1055013724, 1055013693, 1055012727, 1055012722, 1055012686, 1055012684, 1055011740, 1055011733, 1055011726, 1055011459, 1055011454, 1055011441, 1055011086, 1055011076, 1055011074, 1055011072, 1055010743, 1055010739, 1055010737, 1055010730, 1055010725, 1055010723, 1055010143, 1055008638, 1055008599, 1055008597, 1055008184, 1055008181, 1055007992, 1055007953, 1055007950, 1055004798, 1055004627, 1055004608, 1055004600, 1055004278, 1055004217, 1055003679, 1055003357, 1055003348, 1055003292, 1055003278, 1055003266, 1055003261, 1055003259, 1055003249, 1055003202, 1055003198, 1055003197, 1055003185, 1055003134, 1055003131, 1055002045, 1055001545, 1055001542, 1055001092, 1055001085, 1055000228, 1055000070, 1055000068, 1055000063, 1055000059, 1055000055]} ##### Call download_photos : nb_thread : 5 begin to download photo : 1055013727 begin to download photo : 1055011086 begin to download photo : 1055008597 begin to download photo : 1055003357 begin to download photo : 1055003131 download finish for photo 1055011086 begin to download photo : 1055011076 download finish for photo 1055003357 begin to download photo : 1055003348 download finish for photo 1055013727 begin to download photo : 1055013724 download finish for photo 1055008597 begin to download photo : 1055008184 download finish for photo 1055003131 begin to download photo : 1055002045 download finish for photo 1055011076 begin to download photo : 1055011074 download finish for photo 1055008184 begin to download photo : 1055008181 download finish for photo 1055013724 begin to download photo : 1055013693 download finish for photo 1055003348 begin to download photo : 1055003292 download finish for photo 1055002045 begin to download photo : 1055001545 download finish for photo 1055011074 begin to download photo : 1055011072 download finish for photo 1055013693 begin to download photo : 1055012727 download finish for photo 1055008181 begin to download photo : 1055007992 download finish for photo 1055003292 begin to download photo : 1055003278 download finish for photo 1055001545 begin to download photo : 1055001542 download finish for photo 1055012727 begin to download photo : 1055012722 download finish for photo 1055011072 begin to download photo : 1055010743 download finish for photo 1055007992 begin to download photo : 1055007953 download finish for photo 1055003278 begin to download photo : 1055003266 download finish for photo 1055001542 begin to download photo : 1055001092 download finish for photo 1055010743 begin to download photo : 1055010739 download finish for photo 1055012722 begin to download photo : 1055012686 download finish for photo 1055003266 begin to download photo : 1055003261 download finish for photo 1055007953 begin to download photo : 1055007950 download finish for photo 1055001092 begin to download photo : 1055001085 download finish for photo 1055012686 begin to download photo : 1055012684 download finish for photo 1055003261 begin to download photo : 1055003259 download finish for photo 1055001085 begin to download photo : 1055000228 download finish for photo 1055010739 begin to download photo : 1055010737 download finish for photo 1055007950 begin to download photo : 1055004798 download finish for photo 1055012684 begin to download photo : 1055011740 download finish for photo 1055000228 begin to download photo : 1055000070 download finish for photo 1055003259 begin to download photo : 1055003249 download finish for photo 1055010737 begin to download photo : 1055010730 download finish for photo 1055004798 begin to download photo : 1055004627 download finish for photo 1055011740 begin to download photo : 1055011733 download finish for photo 1055003249 begin to download photo : 1055003202 download finish for photo 1055000070 begin to download photo : 1055000068 download finish for photo 1055011733 begin to download photo : 1055011726 download finish for photo 1055004627 begin to download photo : 1055004608 download finish for photo 1055010730 begin to download photo : 1055010725 download finish for photo 1055000068 begin to download photo : 1055000063 download finish for photo 1055003202 begin to download photo : 1055003198 download finish for photo 1055011726 begin to download photo : 1055011459 download finish for photo 1055010725 begin to download photo : 1055010723 download finish for photo 1055004608 begin to download photo : 1055004600 download finish for photo 1055011459 begin to download photo : 1055011454 download finish for photo 1055000063 begin to download photo : 1055000059 download finish for photo 1055010723 begin to download photo : 1055010143 download finish for photo 1055003198 begin to download photo : 1055003197 download finish for photo 1055004600 begin to download photo : 1055004278 download finish for photo 1055011454 begin to download photo : 1055011441 download finish for photo 1055000059 begin to download photo : 1055000055 download finish for photo 1055003197 begin to download photo : 1055003185 download finish for photo 1055010143 begin to download photo : 1055008638 download finish for photo 1055011441 download finish for photo 1055000055 download finish for photo 1055004278 begin to download photo : 1055004217 download finish for photo 1055008638 begin to download photo : 1055008599 download finish for photo 1055003185 begin to download photo : 1055003134 download finish for photo 1055004217 begin to download photo : 1055003679 download finish for photo 1055008599 download finish for photo 1055003134 download finish for photo 1055003679 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 : 4.438260316848755 #### 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 we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save number of steps : 1 step1:split_time_score_with_photo Mon May 26 19:41:41 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281297_935833_1055013727_f34e29870c3ec81fcb476bf0068450cf.jpg': 1055013727, 'temp/1748281297_935833_1055013724_8e582d8384c64704f84356f873bb03d8.jpg': 1055013724, 'temp/1748281297_935833_1055013693_876ad4d5805887cd941b46d5ca7d5b1b.jpg': 1055013693, 'temp/1748281297_935833_1055012727_3a1bdb1d67309822657123609c77e797.jpg': 1055012727, 'temp/1748281297_935833_1055012722_f9babb3fe56ced25ffede8bbef5069fb.jpg': 1055012722, 'temp/1748281297_935833_1055012686_a45d0e00cad891daaf8f041ee26a78d1.jpg': 1055012686, 'temp/1748281297_935833_1055012684_ba679a0e20ea6f16bfd9069fc90f689d.jpg': 1055012684, 'temp/1748281297_935833_1055011740_0b33951d4709683b89a108912f3c0dd3.jpg': 1055011740, 'temp/1748281297_935833_1055011733_801c47d539f95680cda20cf369779093.jpg': 1055011733, 'temp/1748281297_935833_1055011726_707dc466731322dc036267ca30d7e700.jpg': 1055011726, 'temp/1748281297_935833_1055011459_76e96710774ab5e4a8e5f23142b2b45e.jpg': 1055011459, 'temp/1748281297_935833_1055011454_46ec921f09112d298dcc47353d779d55.jpg': 1055011454, 'temp/1748281297_935833_1055011441_d825f9623da221614af7ce7a13591d1a.jpg': 1055011441, 'temp/1748281297_935833_1055003131_12bcf047c16351b2a2b41b2ba70db7b7.jpg': 1055003131, 'temp/1748281297_935833_1055002045_390fce166d8a4e346d18cfdb695bd3f8.jpg': 1055002045, 'temp/1748281297_935833_1055001545_292c889b214383c9eff957d779217aeb.jpg': 1055001545, 'temp/1748281297_935833_1055001542_5e1a9c0f7788cfc726846cda743dd454.jpg': 1055001542, 'temp/1748281297_935833_1055001092_fd1729b4879040e36f41b3af545fd28c.jpg': 1055001092, 'temp/1748281297_935833_1055001085_586137b302133d7442a9621d01dfd9ee.jpg': 1055001085, 'temp/1748281297_935833_1055000228_6046bacdc732ec23020791db686fd31b.jpg': 1055000228, 'temp/1748281297_935833_1055000070_639c1516bf67f61fa16e61a34313e98b.jpg': 1055000070, 'temp/1748281297_935833_1055000068_fa025777da94026b7a3688fce1c4c657.jpg': 1055000068, 'temp/1748281297_935833_1055000063_44f2dd3c2dff30aa3bc9da4b89a2daf0.jpg': 1055000063, 'temp/1748281297_935833_1055000059_4b09d365e4e3dcefaeb8b421405350f9.jpg': 1055000059, 'temp/1748281297_935833_1055000055_c9e450bd4b6e2cb9cfa8e540e915987c.jpg': 1055000055, 'temp/1748281297_935833_1055011086_bdfadbfd9b854d0e57bff554442c9bcd.jpg': 1055011086, 'temp/1748281297_935833_1055011076_8f47b07eac25de1c6c004fe4d3d508be.jpg': 1055011076, 'temp/1748281297_935833_1055011074_af75a035dcd1829a43a1e1de711c1c5c.jpg': 1055011074, 'temp/1748281297_935833_1055011072_37a292bbe61b6a71ca20b3b88d1105ca.jpg': 1055011072, 'temp/1748281297_935833_1055010743_8e677a02379626daa3d4ee7356ad894e.jpg': 1055010743, 'temp/1748281297_935833_1055010739_e4eec41f6a47dc3e97c189a1e5caa46f.jpg': 1055010739, 'temp/1748281297_935833_1055010737_350522f4866a7dcde0258de8aa920041.jpg': 1055010737, 'temp/1748281297_935833_1055010730_1cfbca5001cab6e5d12df6b4b06e86c5.jpg': 1055010730, 'temp/1748281297_935833_1055010725_915c21ba8a484205e02874d398e0faef.jpg': 1055010725, 'temp/1748281297_935833_1055010723_2601cc78c82bdb85cb92f991b4df99cb.jpg': 1055010723, 'temp/1748281297_935833_1055010143_9d8ce5a6bcd913fe183d34bf4af34991.jpg': 1055010143, 'temp/1748281297_935833_1055008638_3468359b1556541fe6baa375af06bd9a.jpg': 1055008638, 'temp/1748281297_935833_1055008599_4ab44774eb0f6cda7b9ec80578561b31.jpg': 1055008599, 'temp/1748281297_935833_1055003357_724bbfa472d57dcea0975679f4a393ff.jpg': 1055003357, 'temp/1748281297_935833_1055003348_588ecea5e15961fe1292f72d4cf33b3c.jpg': 1055003348, 'temp/1748281297_935833_1055003292_4ad5363c1776217234fa6acc8a9fbd85.jpg': 1055003292, 'temp/1748281297_935833_1055003278_1e53f6abe2076619fbf0cb6255fca71b.jpg': 1055003278, 'temp/1748281297_935833_1055003266_eae639e1ccc20bd1a6a2f69e66c41382.jpg': 1055003266, 'temp/1748281297_935833_1055003261_fb67a67f6cc7a87b0af2a03906a24b7d.jpg': 1055003261, 'temp/1748281297_935833_1055003259_cc27886462be43921344678712ff777f.jpg': 1055003259, 'temp/1748281297_935833_1055003249_a3800e1944260107f11d988a743bd54f.jpg': 1055003249, 'temp/1748281297_935833_1055003202_39321c32c32d09c1ffaa48a867a690fe.jpg': 1055003202, 'temp/1748281297_935833_1055003198_3f7cf97aea8a045db187250a09903f94.jpg': 1055003198, 'temp/1748281297_935833_1055003197_8735b1a92ec92b085fcf6d4191102ae4.jpg': 1055003197, 'temp/1748281297_935833_1055003185_f94dcbfb2802c930ee56eb9d1c9e9420.jpg': 1055003185, 'temp/1748281297_935833_1055003134_b26c74c56f9d8d7a5c9ccd672aa5722e.jpg': 1055003134, 'temp/1748281297_935833_1055008597_33977b4f5c3fdcfef21a921815b91908.jpg': 1055008597, 'temp/1748281297_935833_1055008184_23ee09498d38b063b7df290f72693b5b.jpg': 1055008184, 'temp/1748281297_935833_1055008181_872ed5f6160fc9985f7c144e533a552d.jpg': 1055008181, 'temp/1748281297_935833_1055007992_3f0247dd9a329bb2634a6df866c7993b.jpg': 1055007992, 'temp/1748281297_935833_1055007953_831e4c41962162209ffbd3492d14ca01.jpg': 1055007953, 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'temp/1748281297_935833_1055004627_9afc9aa5c93533104d86b96e0b5de9d0.jpg', 'extension': 'jpg'}, 1055004608: {'path': 'temp/1748281297_935833_1055004608_8f10a6ef80cc1cb72e52bb22cdd7bac9.jpg', 'extension': 'jpg'}, 1055004600: {'path': 'temp/1748281297_935833_1055004600_d545fe61ea780a6598c9d5febba7d576.jpg', 'extension': 'jpg'}, 1055004278: {'path': 'temp/1748281297_935833_1055004278_7d9a1423053759339afb51f45a1b5d84.jpg', 'extension': 'jpg'}, 1055004217: {'path': 'temp/1748281297_935833_1055004217_63353acd8a59167cd1364f16c9c06813.jpg', 'extension': 'jpg'}, 1055003679: {'path': 'temp/1748281297_935833_1055003679_9231db8e9237505d07f9358467266703.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} ----- Debut du copier-coller des param necessaire pour fonction main de STS ----- SELECT app_name, token FROM MTRUser.mtr_app_api_token WHERE mtr_user_id=739 AND app_name="token_split_time_score" AND expire_at > NOW() TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] select SUBSTRING(ph.text,16,2) as h, count(*) from MTRUser.mtr_portfolio_photos mpp inner join MTRBack.photos ph on mpp.mtr_photo_id = ph.photo_id where mpp.mtr_portfolio_id = 4608689 group by h (('18', 4), ('19', 5), ('20', 5), ('24', 8), ('26', 6), ('17', 1), ('27', 9), ('51', 7), ('28', 2), ('21', 4), ('52', 2), ('25', 6), ('50', 3), ('22', 2)) SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4608689 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 100000 SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1 where mpp1.mtr_photo_id=cps.photo_id AND h.hashtag_id=cps.hashtag_id AND mpp1.hide_status=0 AND mpp1.mtr_portfolio_id in (4608689) AND cps.thcl in (861) order by cps.score desc LIMIT 0, 100000 SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1 where mpp1.mtr_photo_id=cps.photo_id AND h.hashtag_id=cps.hashtag_id AND mpp1.hide_status=0 AND mpp1.mtr_portfolio_id in (4608689) AND cps.thcl in (758) order by cps.score desc LIMIT 0, 100000 ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 06102021 4608689 Nombre de photos uploadées : 64 / 23040 (0%) 06102021 4608689 Nombre de photos taguées (types de déchets): 0 / 64 (0%) 06102021 4608689 Nombre de photos taguées (volume) : 0 / 64 (0%) [{'photo_id': 1055000228, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/10/7/6046bacdc732ec23020791db686fd31b.jpg', 'username': None, 'uploaded_at': 1633601256, 'text': 'IMG_20211006_101733.jpg', 'path': 'temp/1748281297_935833_1055000228_6046bacdc732ec23020791db686fd31b.jpg', 'black': False}, {'photo_id': 1055007950, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/10/7/3aea8b8cb1f8c6e4b87714e0d17e12c5.jpg', 'username': None, 'uploaded_at': 1633603536, 'text': 'IMG_20211006_101843.jpg', 'path': 'temp/1748281297_935833_1055007950_3aea8b8cb1f8c6e4b87714e0d17e12c5.jpg', 'black': False}, {'photo_id': 1055003348, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/10/7/588ecea5e15961fe1292f72d4cf33b3c.jpg', 'username': None, 'uploaded_at': 1633602087, 'text': 'IMG_20211006_101853.jpg', 'path': 'temp/1748281297_935833_1055003348_588ecea5e15961fe1292f72d4cf33b3c.jpg', 'black': False}, {'photo_id': 1055000055, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/10/7/c9e450bd4b6e2cb9cfa8e540e915987c.jpg', 'username': None, 'uploaded_at': 1633601187, 'text': 'IMG_20211006_101804.jpg', 'path': 'temp/1748281297_935833_1055000055_c9e450bd4b6e2cb9cfa8e540e915987c.jpg', 'black': False}] 0 [] elapsed_time : load_data_split_time_score 1.71661376953125e-05 elapsed_time : order_list_meta_photo_and_scores 1.6450881958007812e-05 ???????????????????????????????????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.003365755081176758 INSERT INTO `MTRPhoto`.`dashboard_entry_day` (`dashboard_place_id`, `mtr_portfolio_id`, `date`) VALUES ( 41, 4608689, '2021-10-06' ) ON DUPLICATE KEY UPDATE mtr_portfolio_id=VALUES(mtr_portfolio_id), updated_at=NOW(); INSERT INTO `MTRPhoto`.`dashboard_run_ids` (`dashboard_entry_day`, `mtr_user_id`, `misc_info`) VALUES (106232,739,"{}"); elapsed_time : insert_dashboard_record_day_entry 0.02260446548461914 ***** BEGIN SPLIT BY DARK ***** To DO 08/10/21 Let s Iterate on Ds Object and set black attribute == > 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) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id) first line : (1055001085, 492584841, 4330) ... last line : (1055001085, 492584841, 4330) time used for this insertion : 0.018414735794067383 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 2 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id) first line : (1055001085, 492584841, 4330) ... last line : (1055008638, 492584841, 4330) time used for this insertion : 0.00889277458190918 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) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id) first line : (1055001085, 492584841, 4330) ... last line : (1055010730, 492584841, 4330) time used for this insertion : 0.010100126266479492 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 4 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id) first line : (1055001085, 492584841, 4330) ... last line : (1055011086, 492584841, 4330) time used for this insertion : 0.008885383605957031 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 5 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id) first line : (1055001085, 492584841, 4330) ... last line : (1055012686, 492584841, 4330) time used for this insertion : 0.009808540344238281 elapsed_time : SPLIT_BY_DARK 0.06648015975952148 ***** END SPLIT BY DARK ***** ((1055001085,), (1055008638,), (1055010730,), (1055011086,), (1055012686,)) ***** BEGIN SPLIT TIME ***** [12, 20, 29, 38, 51] ````````````````````````````````````````````````````````````````list printed: [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]] forced_hashtag: jrm force hashtag to jrm elapsed_time : SPLIT_TIME 0.0063512325286865234 ***** END SPLIT TIME ***** NUMBER BATCH : 7 list_ponderation used : [0.001, 0.001, 0.001, 0.001, 0.001] , list_hashtag_class_create_as_list : ['refus', 'jrm'] Listed one port to create portfolio : jrm_diff_batch__06102021_10_17_33_000000 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info Nombres de balles jrm_diff_batch__06102021_10_17_33_000000 : 0 duration : 172.0 update_text_in_photos list_photo_id_text len : 0 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 172.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_101733.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 Listed one port to create portfolio : jrm_diff_batch__06102021_10_20_49_000000 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info Nombres de balles jrm_diff_batch__06102021_10_20_49_000000 : 0 duration : 72.0 update_text_in_photos list_photo_id_text len : 0 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 72.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_102049.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 Listed one port to create portfolio : jrm_diff_batch__06102021_10_24_04_000000 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info Nombres de balles jrm_diff_batch__06102021_10_24_04_000000 : 0 duration : 52.0 update_text_in_photos list_photo_id_text len : 0 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 52.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_102404.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 Listed one port to create portfolio : jrm_diff_batch__06102021_10_25_25_000000 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info Nombres de balles jrm_diff_batch__06102021_10_25_25_000000 : 0 duration : 53.0 update_text_in_photos list_photo_id_text len : 0 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 53.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_102525.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 Listed one port to create portfolio : jrm_diff_batch__06102021_10_26_45_000000 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info Nombres de balles jrm_diff_batch__06102021_10_26_45_000000 : 0 duration : 81.0 update_text_in_photos list_photo_id_text len : 0 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 81.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_102645.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 Empty batch, bug or could have been filtered ! Listed one port to create portfolio : jrm_diff_batch__06102021_14_50_39_000000 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info Nombres de balles jrm_diff_batch__06102021_14_50_39_000000 : 0 duration : 102.0 update_text_in_photos list_photo_id_text len : 0 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 102.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_145039.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 We have rejected 0 photos because of the batch_size condition ! NUMBER BATCH list_of_portfolios_to_create : 6 list_same_port_ids : [4938484] find same portfolio which already exist 4938484 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 4938484 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4938484 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 list_same_port_ids : [4938485] find same portfolio which already exist 4938485 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 4938485 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4938485 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 list_same_port_ids : [4938486] find same portfolio which already exist 4938486 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 4938486 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4938486 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 list_same_port_ids : [4938487] find same portfolio which already exist 4938487 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 4938487 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4938487 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 list_same_port_ids : [4938488] find same portfolio which already exist 4938488 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 4938488 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4938488 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 list_same_port_ids : [4756245] find same portfolio which already exist 4756245 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 4756245 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4756245 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4938484 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=3543 ORDER BY id desc LIMIT 1 SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4938484 AND result is not null and result<>"None" and result=0.0 AND mtd_id=3543 ORDER BY created_at desc LIMIT 1 list_result_datou : [] select url from MTRUser.mtr_files where mtd_id = 3543 and mtr_portfolio_id = 4938484 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4938484 AND mptpi.`type`=4038 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844243, '_______jrm', '2021-10-06 10:17:33', '2021-10-06 10:20:25', 12, 4938484, -1, -1, '-1', "{'max_time_prod_two_photos': 0, 'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P4938484_21-11-2024_00_58_17.pdf', 'cartonnette': {'hashtag': 'cartonnette', 'sub_port_id': 4938531, 'pht': 4038}, 'Carton_brun': {'hashtag': 'Carton_brun', 'sub_port_id': 4938532, 'pht': 4038}, 'Carton_gris': {'hashtag': 'Carton_gris', 'sub_port_id': 4938533, 'pht': 4038}, 'Teint_Dans_La_Masse': {'hashtag': 'Teint_Dans_La_Masse', 'sub_port_id': 4938534, 'pht': 4038}, 'metal': {'hashtag': 'metal', 'sub_port_id': 4938535, 'pht': 4038}, 'plastique': {'hashtag': 'plastique', 'sub_port_id': 4938536, 'pht': 4038}, 'autre_refus': {'hashtag': 'autre_refus', 'sub_port_id': 4938537, 'pht': 4038}, 'papier': {'hashtag': 'papier', 'sub_port_id': 4938538, 'pht': 4038}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 4938539, 'pht': 4038}, 'kraft': {'hashtag': 'kraft', 'sub_port_id': 4938540, 'pht': 4038}}" ); SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4938485 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=3543 ORDER BY id desc LIMIT 1 SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4938485 AND result is not null and result<>"None" and result=0.0 AND mtd_id=3543 ORDER BY created_at desc LIMIT 1 list_result_datou : [] select url from MTRUser.mtr_files where mtd_id = 3543 and mtr_portfolio_id = 4938485 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4938485 AND mptpi.`type`=4038 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844243, '_______jrm', '2021-10-06 10:20:49', '2021-10-06 10:22:01', 7, 4938485, -1, -1, '-1', "{'max_time_prod_two_photos': 0, 'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P4938485_21-11-2024_01_05_11.pdf', 'autre_refus': {'hashtag': 'autre_refus', 'sub_port_id': 4938520, 'pht': 4038}, 'Carton_gris': {'hashtag': 'Carton_gris', 'sub_port_id': 4938521, 'pht': 4038}, 'Carton_brun': {'hashtag': 'Carton_brun', 'sub_port_id': 4938522, 'pht': 4038}, 'metal': {'hashtag': 'metal', 'sub_port_id': 4938523, 'pht': 4038}, 'kraft': {'hashtag': 'kraft', 'sub_port_id': 4938524, 'pht': 4038}, 'Teint_Dans_La_Masse': {'hashtag': 'Teint_Dans_La_Masse', 'sub_port_id': 4938525, 'pht': 4038}, 'plastique': {'hashtag': 'plastique', 'sub_port_id': 4938526, 'pht': 4038}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 4938527, 'pht': 4038}, 'papier': {'hashtag': 'papier', 'sub_port_id': 4938528, 'pht': 4038}, 'cartonnette': {'hashtag': 'cartonnette', 'sub_port_id': 4938529, 'pht': 4038}}" ); SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4938486 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=3543 ORDER BY id desc LIMIT 1 SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4938486 AND result is not null and result<>"None" and result=0.0 AND mtd_id=3543 ORDER BY created_at desc LIMIT 1 list_result_datou : [] select url from MTRUser.mtr_files where mtd_id = 3543 and mtr_portfolio_id = 4938486 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4938486 AND mptpi.`type`=4038 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844243, '_______jrm', '2021-10-06 10:24:04', '2021-10-06 10:24:56', 8, 4938486, -1, -1, '-1', "{'max_time_prod_two_photos': 0, 'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P4938486_21-11-2024_01_17_15.pdf', 'Carton_gris': {'hashtag': 'Carton_gris', 'sub_port_id': 4938542, 'pht': 4038}, 'plastique': {'hashtag': 'plastique', 'sub_port_id': 4938543, 'pht': 4038}, 'autre_refus': {'hashtag': 'autre_refus', 'sub_port_id': 4938544, 'pht': 4038}, 'Teint_Dans_La_Masse': {'hashtag': 'Teint_Dans_La_Masse', 'sub_port_id': 4938545, 'pht': 4038}, 'metal': {'hashtag': 'metal', 'sub_port_id': 4938546, 'pht': 4038}, 'Carton_brun': {'hashtag': 'Carton_brun', 'sub_port_id': 4938547, 'pht': 4038}, 'cartonnette': {'hashtag': 'cartonnette', 'sub_port_id': 4938548, 'pht': 4038}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 4938549, 'pht': 4038}, 'papier': {'hashtag': 'papier', 'sub_port_id': 4938550, 'pht': 4038}, 'kraft': {'hashtag': 'kraft', 'sub_port_id': 4938551, 'pht': 4038}}" ); SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4938487 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=3543 ORDER BY id desc LIMIT 1 SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4938487 AND result is not null and result<>"None" and result=0.0 AND mtd_id=3543 ORDER BY created_at desc LIMIT 1 list_result_datou : [] select url from MTRUser.mtr_files where mtd_id = 3543 and mtr_portfolio_id = 4938487 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4938487 AND mptpi.`type`=4038 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844243, '_______jrm', '2021-10-06 10:25:25', '2021-10-06 10:26:18', 8, 4938487, -1, -1, '-1', "{'max_time_prod_two_photos': 0, 'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P4938487_21-11-2024_01_27_52.pdf', 'Carton_brun': {'hashtag': 'Carton_brun', 'sub_port_id': 4938552, 'pht': 4038}, 'autre_refus': {'hashtag': 'autre_refus', 'sub_port_id': 4938553, 'pht': 4038}, 'Carton_gris': {'hashtag': 'Carton_gris', 'sub_port_id': 4938554, 'pht': 4038}, 'plastique': {'hashtag': 'plastique', 'sub_port_id': 4938555, 'pht': 4038}, 'papier': {'hashtag': 'papier', 'sub_port_id': 4938556, 'pht': 4038}, 'metal': {'hashtag': 'metal', 'sub_port_id': 4938557, 'pht': 4038}, 'Teint_Dans_La_Masse': {'hashtag': 'Teint_Dans_La_Masse', 'sub_port_id': 4938558, 'pht': 4038}, 'cartonnette': {'hashtag': 'cartonnette', 'sub_port_id': 4938559, 'pht': 4038}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 4938560, 'pht': 4038}, 'kraft': {'hashtag': 'kraft', 'sub_port_id': 4938561, 'pht': 4038}}" ); SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4938488 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=3543 ORDER BY id desc LIMIT 1 SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4938488 AND result is not null and result<>"None" and result=0.0 AND mtd_id=3543 ORDER BY created_at desc LIMIT 1 list_result_datou : [] select url from MTRUser.mtr_files where mtd_id = 3543 and mtr_portfolio_id = 4938488 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4938488 AND mptpi.`type`=4038 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844243, '_______jrm', '2021-10-06 10:26:45', '2021-10-06 10:28:06', 12, 4938488, -1, -1, '-1', "{'max_time_prod_two_photos': 0, 'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P4938488_21-11-2024_01_43_46.pdf', 'Carton_gris': {'hashtag': 'Carton_gris', 'sub_port_id': 4938797, 'pht': 4038}, 'papier': {'hashtag': 'papier', 'sub_port_id': 4938798, 'pht': 4038}, 'cartonnette': {'hashtag': 'cartonnette', 'sub_port_id': 4938799, 'pht': 4038}, 'plastique': {'hashtag': 'plastique', 'sub_port_id': 4938800, 'pht': 4038}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 4938801, 'pht': 4038}, 'Carton_brun': {'hashtag': 'Carton_brun', 'sub_port_id': 4938802, 'pht': 4038}, 'metal': {'hashtag': 'metal', 'sub_port_id': 4938803, 'pht': 4038}, 'Teint_Dans_La_Masse': {'hashtag': 'Teint_Dans_La_Masse', 'sub_port_id': 4938804, 'pht': 4038}, 'autre_refus': {'hashtag': 'autre_refus', 'sub_port_id': 4938805, 'pht': 4038}, 'kraft': {'hashtag': 'kraft', 'sub_port_id': 4938806, 'pht': 4038}}" ); SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4756245 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=3543 ORDER BY id desc LIMIT 1 SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4756245 AND result is not null and result<>"None" and result=0.0 AND mtd_id=3543 ORDER BY created_at desc LIMIT 1 list_result_datou : [] select url from MTRUser.mtr_files where mtd_id = 3543 and mtr_portfolio_id = 4756245 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4756245 AND mptpi.`type`=4038 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844243, '_______jrm', '2021-10-06 14:50:39', '2021-10-06 14:52:21', 12, 4756245, -1, -1, '-1', "{'max_time_prod_two_photos': 0, 'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P4756245_21-11-2024_01_56_10.pdf', 'kraft': {'hashtag': 'kraft', 'sub_port_id': 4756351, 'pht': 4038}, 'metal': {'hashtag': 'metal', 'sub_port_id': 4756352, 'pht': 4038}, 'Teint_Dans_La_Masse': {'hashtag': 'Teint_Dans_La_Masse', 'sub_port_id': 4756353, 'pht': 4038}, 'Carton_gris': {'hashtag': 'Carton_gris', 'sub_port_id': 4756354, 'pht': 4038}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 4756355, 'pht': 4038}, 'plastique': {'hashtag': 'plastique', 'sub_port_id': 4756356, 'pht': 4038}, 'cartonnette': {'hashtag': 'cartonnette', 'sub_port_id': 4756357, 'pht': 4038}, 'Carton_brun': {'hashtag': 'Carton_brun', 'sub_port_id': 4756358, 'pht': 4038}, 'papier': {'hashtag': 'papier', 'sub_port_id': 4756359, 'pht': 4038}, 'autre_refus': {'hashtag': 'autre_refus', 'sub_port_id': 4756360, 'pht': 4038}}" ); elapsed_time : count_nb_balles_and_create_portfolio 1.09169340133667 # DISPLAY ALL COLLECTED DATA : {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} ------ Fin du Copier-Coller ------ ---------- ONE RESULT --------- ([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]], {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, {4938484: {'list_of_photos': [1055000228, 1055000055, 1055003357, 1055007950, 1055003348, 1055007953, 1055000059, 1055007992, 1055008181, 1055003197, 1055003198, 1055008184], 'hashtag': 'jrm'}, 4938485: {'list_of_photos': [1055000063, 1055004600, 1055008597, 1055003134, 1055008599, 1055003679, 1055004627], 'hashtag': 'jrm'}, 4938486: {'list_of_photos': [1055004217, 1055010143, 1055004278, 1055010723, 1055003131, 1055003202, 1055010725, 1055000068], 'hashtag': 'jrm'}, 4938487: {'list_of_photos': [1055010737, 1055010739, 1055003278, 1055010743, 1055011072, 1055011074, 1055011076, 1055000070], 'hashtag': 'jrm'}, 4938488: {'list_of_photos': [1055011441, 1055011454, 1055003185, 1055011459, 1055001092, 1055001542, 1055003292, 1055011726, 1055011733, 1055011740, 1055012684, 1055002045], 'hashtag': 'jrm'}, 4756245: {'list_of_photos': [1055012722, 1055004798, 1055004608, 1055012727, 1055013693, 1055013724, 1055003249, 1055001545, 1055003259, 1055013727, 1055003266, 1055003261], 'hashtag': 'jrm'}}, {2107757407: 59}, {'amount_uploaded_and_tagged': {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}}) ---------- END de ONE RESULT ---------- Suppression des photos Telecharges After datou_step_exec type output : time spend for datou_step_exec : 6.548841714859009 time spend to save output : 0.00015544891357421875 total time spend for step 1 : 6.548997163772583 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : split_time_score_with_photo we use saveGeneral [1055013727, 1055013724, 1055013693, 1055012727, 1055012722, 1055012686, 1055012684, 1055011740, 1055011733, 1055011726, 1055011459, 1055011454, 1055011441, 1055003131, 1055002045, 1055001545, 1055001542, 1055001092, 1055001085, 1055000228, 1055000070, 1055000068, 1055000063, 1055000059, 1055000055, 1055011086, 1055011076, 1055011074, 1055011072, 1055010743, 1055010739, 1055010737, 1055010730, 1055010725, 1055010723, 1055010143, 1055008638, 1055008599, 1055003357, 1055003348, 1055003292, 1055003278, 1055003266, 1055003261, 1055003259, 1055003249, 1055003202, 1055003198, 1055003197, 1055003185, 1055003134, 1055008597, 1055008184, 1055008181, 1055007992, 1055007953, 1055007950, 1055004798, 1055004627, 1055004608, 1055004600, 1055004278, 1055004217, 1055003679] map_info['map_portfolio_photo'] : {4608689: [1055013727, 1055013724, 1055013693, 1055012727, 1055012722, 1055012686, 1055012684, 1055011740, 1055011733, 1055011726, 1055011459, 1055011454, 1055011441, 1055011086, 1055011076, 1055011074, 1055011072, 1055010743, 1055010739, 1055010737, 1055010730, 1055010725, 1055010723, 1055010143, 1055008638, 1055008599, 1055008597, 1055008184, 1055008181, 1055007992, 1055007953, 1055007950, 1055004798, 1055004627, 1055004608, 1055004600, 1055004278, 1055004217, 1055003679, 1055003357, 1055003348, 1055003292, 1055003278, 1055003266, 1055003261, 1055003259, 1055003249, 1055003202, 1055003198, 1055003197, 1055003185, 1055003134, 1055003131, 1055002045, 1055001545, 1055001542, 1055001092, 1055001085, 1055000228, 1055000070, 1055000068, 1055000063, 1055000059, 1055000055]} final : True mtd_id 3787 list_pids : [1055013727, 1055013724, 1055013693, 1055012727, 1055012722, 1055012686, 1055012684, 1055011740, 1055011733, 1055011726, 1055011459, 1055011454, 1055011441, 1055003131, 1055002045, 1055001545, 1055001542, 1055001092, 1055001085, 1055000228, 1055000070, 1055000068, 1055000063, 1055000059, 1055000055, 1055011086, 1055011076, 1055011074, 1055011072, 1055010743, 1055010739, 1055010737, 1055010730, 1055010725, 1055010723, 1055010143, 1055008638, 1055008599, 1055003357, 1055003348, 1055003292, 1055003278, 1055003266, 1055003261, 1055003259, 1055003249, 1055003202, 1055003198, 1055003197, 1055003185, 1055003134, 1055008597, 1055008184, 1055008181, 1055007992, 1055007953, 1055007950, 1055004798, 1055004627, 1055004608, 1055004600, 1055004278, 1055004217, 1055003679] Looping around the photos to save general results len do output : 5 /[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]] /{'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]} /{4938484: {'list_of_photos': [1055000228, 1055000055, 1055003357, 1055007950, 1055003348, 1055007953, 1055000059, 1055007992, 1055008181, 1055003197, 1055003198, 1055008184], 'hashtag': 'jrm'}, 4938485: {'list_of_photos': [1055000063, 1055004600, 1055008597, 1055003134, 1055008599, 1055003679, 1055004627], 'hashtag': 'jrm'}, 4938486: {'list_of_photos': [1055004217, 1055010143, 1055004278, 1055010723, 1055003131, 1055003202, 1055010725, 1055000068], 'hashtag': 'jrm'}, 4938487: {'list_of_photos': [1055010737, 1055010739, 1055003278, 1055010743, 1055011072, 1055011074, 1055011076, 1055000070], 'hashtag': 'jrm'}, 4938488: {'list_of_photos': [1055011441, 1055011454, 1055003185, 1055011459, 1055001092, 1055001542, 1055003292, 1055011726, 1055011733, 1055011740, 1055012684, 1055002045], 'hashtag': 'jrm'}, 4756245: {'list_of_photos': [1055012722, 1055004798, 1055004608, 1055012727, 1055013693, 1055013724, 1055003249, 1055001545, 1055003259, 1055013727, 1055003266, 1055003261], 'hashtag': 'jrm'}} /{2107757407: 59} /{'amount_uploaded_and_tagged': {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}} before output type Managing all output in save final without adding information in the mtr_datou_result ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055013727', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055013724', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055013693', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055012727', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055012722', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055012686', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055012684', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011740', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011733', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011726', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011459', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011454', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011441', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003131', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055002045', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055001545', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055001542', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055001092', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055001085', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000228', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000070', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000068', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000063', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000059', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000055', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011086', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011076', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011074', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011072', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010743', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010739', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010737', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010730', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010725', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010723', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010143', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055008638', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055008599', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003357', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003348', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003292', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003278', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003266', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003261', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003259', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003249', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003202', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003198', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003197', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003185', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003134', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055008597', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055008184', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055008181', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055007992', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055007953', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055007950', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004798', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004627', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004608', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004600', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004278', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004217', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003679', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 64 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 : [('3787', '4608689', '1055013727', None, None, None, None, None, None), ('3787', '4608689', '1055013724', None, None, None, None, None, None), ('3787', '4608689', '1055013693', None, None, None, None, None, None), ('3787', '4608689', '1055012727', None, None, None, None, None, None), ('3787', '4608689', '1055012722', None, None, None, None, None, None), ('3787', '4608689', '1055012686', None, None, None, None, None, None), ('3787', '4608689', '1055012684', None, None, None, None, None, None), ('3787', '4608689', '1055011740', None, None, None, None, None, None), ('3787', '4608689', '1055011733', None, None, None, None, None, None), ('3787', '4608689', '1055011726', None, None, None, None, None, None), ('3787', '4608689', '1055011459', None, None, None, None, None, None), ('3787', '4608689', '1055011454', None, None, None, None, None, None), ('3787', '4608689', '1055011441', None, None, None, None, None, None), ('3787', '4608689', '1055003131', None, None, None, None, None, None), ('3787', '4608689', '1055002045', None, None, None, None, None, None), ('3787', '4608689', '1055001545', None, None, None, None, None, None), ('3787', '4608689', '1055001542', None, None, None, None, None, None), ('3787', '4608689', '1055001092', None, None, None, None, None, None), ('3787', '4608689', '1055001085', None, None, None, None, None, None), ('3787', '4608689', '1055000228', None, None, None, None, None, None), ('3787', '4608689', '1055000070', None, None, None, None, None, None), ('3787', '4608689', '1055000068', None, None, None, None, None, None), ('3787', '4608689', '1055000063', None, None, None, None, None, None), ('3787', '4608689', '1055000059', None, None, None, None, None, None), ('3787', '4608689', '1055000055', None, None, None, None, None, None), ('3787', '4608689', '1055011086', None, None, None, None, None, None), ('3787', '4608689', '1055011076', None, None, None, None, None, None), ('3787', '4608689', '1055011074', None, None, None, None, None, None), ('3787', '4608689', '1055011072', None, None, None, None, None, None), ('3787', '4608689', '1055010743', None, None, None, None, None, None), ('3787', '4608689', '1055010739', None, None, None, None, None, None), ('3787', '4608689', '1055010737', None, None, None, None, None, None), ('3787', '4608689', '1055010730', None, None, None, None, None, None), ('3787', '4608689', '1055010725', None, None, None, None, None, None), ('3787', '4608689', '1055010723', None, None, None, None, None, None), ('3787', '4608689', '1055010143', None, None, None, None, None, None), ('3787', '4608689', '1055008638', None, None, None, None, None, None), ('3787', '4608689', '1055008599', None, None, None, None, None, None), ('3787', '4608689', '1055003357', None, None, None, None, None, None), ('3787', '4608689', '1055003348', None, None, None, None, None, None), ('3787', '4608689', '1055003292', None, None, None, None, None, None), ('3787', '4608689', '1055003278', None, None, None, None, None, None), ('3787', '4608689', '1055003266', None, None, None, None, None, None), ('3787', '4608689', '1055003261', None, None, None, None, None, None), ('3787', '4608689', '1055003259', None, None, None, None, None, None), ('3787', '4608689', '1055003249', None, None, None, None, None, None), ('3787', '4608689', '1055003202', None, None, None, None, None, None), ('3787', '4608689', '1055003198', None, None, None, None, None, None), ('3787', '4608689', '1055003197', None, None, None, None, None, None), ('3787', '4608689', '1055003185', None, None, None, None, None, None), ('3787', '4608689', '1055003134', None, None, None, None, None, None), ('3787', '4608689', '1055008597', None, None, None, None, None, None), ('3787', '4608689', '1055008184', None, None, None, None, None, None), ('3787', '4608689', '1055008181', None, None, None, None, None, None), ('3787', '4608689', '1055007992', None, None, None, None, None, None), ('3787', '4608689', '1055007953', None, None, None, None, None, None), ('3787', '4608689', '1055007950', None, None, None, None, None, None), ('3787', '4608689', '1055004798', None, None, None, None, None, None), ('3787', '4608689', '1055004627', None, None, None, None, None, None), ('3787', '4608689', '1055004608', None, None, None, None, None, None), ('3787', '4608689', '1055004600', None, None, None, None, None, None), ('3787', '4608689', '1055004278', None, None, None, None, None, None), ('3787', '4608689', '1055004217', None, None, None, None, None, None), ('3787', '4608689', '1055003679', None, None, None, None, None, None)] time used for this insertion : 0.032355546951293945 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : ([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]], {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, {4938484: {'list_of_photos': [1055000228, 1055000055, 1055003357, 1055007950, 1055003348, 1055007953, 1055000059, 1055007992, 1055008181, 1055003197, 1055003198, 1055008184], 'hashtag': 'jrm'}, 4938485: {'list_of_photos': [1055000063, 1055004600, 1055008597, 1055003134, 1055008599, 1055003679, 1055004627], 'hashtag': 'jrm'}, 4938486: {'list_of_photos': [1055004217, 1055010143, 1055004278, 1055010723, 1055003131, 1055003202, 1055010725, 1055000068], 'hashtag': 'jrm'}, 4938487: {'list_of_photos': [1055010737, 1055010739, 1055003278, 1055010743, 1055011072, 1055011074, 1055011076, 1055000070], 'hashtag': 'jrm'}, 4938488: {'list_of_photos': [1055011441, 1055011454, 1055003185, 1055011459, 1055001092, 1055001542, 1055003292, 1055011726, 1055011733, 1055011740, 1055012684, 1055002045], 'hashtag': 'jrm'}, 4756245: {'list_of_photos': [1055012722, 1055004798, 1055004608, 1055012727, 1055013693, 1055013724, 1055003249, 1055001545, 1055003259, 1055013727, 1055003266, 1055003261], 'hashtag': 'jrm'}}, {2107757407: 59}, {'amount_uploaded_and_tagged': {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}}) Result test split dark : ([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]], {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, {4938484: {'list_of_photos': [1055000228, 1055000055, 1055003357, 1055007950, 1055003348, 1055007953, 1055000059, 1055007992, 1055008181, 1055003197, 1055003198, 1055008184], 'hashtag': 'jrm'}, 4938485: {'list_of_photos': [1055000063, 1055004600, 1055008597, 1055003134, 1055008599, 1055003679, 1055004627], 'hashtag': 'jrm'}, 4938486: {'list_of_photos': [1055004217, 1055010143, 1055004278, 1055010723, 1055003131, 1055003202, 1055010725, 1055000068], 'hashtag': 'jrm'}, 4938487: {'list_of_photos': [1055010737, 1055010739, 1055003278, 1055010743, 1055011072, 1055011074, 1055011076, 1055000070], 'hashtag': 'jrm'}, 4938488: {'list_of_photos': [1055011441, 1055011454, 1055003185, 1055011459, 1055001092, 1055001542, 1055003292, 1055011726, 1055011733, 1055011740, 1055012684, 1055002045], 'hashtag': 'jrm'}, 4756245: {'list_of_photos': [1055012722, 1055004798, 1055004608, 1055012727, 1055013693, 1055013724, 1055003249, 1055001545, 1055003259, 1055013727, 1055003266, 1055003261], 'hashtag': 'jrm'}}, {2107757407: 59}, {'amount_uploaded_and_tagged': {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}})| ############################### TEST rubbia_append ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=3856 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=3856 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= 3856 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=3856 # 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 (4599006) and hide_status = 0 ) ORDER BY ph.photo_id DESC LIMIT 0, 10000 We have 1 , 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 (4505992) 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 (4599006,4505992) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 1054572537, 'portfolio_id': 4599006}, {'photo_id': 1054572534, 'portfolio_id': 4599006}, {'photo_id': 1054572532, 'portfolio_id': 4599006}, {'photo_id': 1051605195, 'portfolio_id': 4505992}] map_portfolio_id_photo_id: {4599006: [1054572537, 1054572534, 1054572532], 4505992: [1051605195]} ##### 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 : 0.024631023406982422 #### fin chargement data Blocking on flush ? No conitnuing About to test input to load Calling datou_exec Inside datou_exec : verbose : True we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save number of steps : 1 step1:split_time_score Mon May 26 19:41:48 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 : {} map_photo_id_path_extension : {} map_subphoto_mainphoto : {} SELECT app_name, token FROM MTRUser.mtr_app_api_token WHERE mtr_user_id=739 AND app_name="token_split_time_score" AND expire_at > NOW() TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] select SUBSTRING(ph.text,16,2) as h, count(*) from MTRUser.mtr_portfolio_photos mpp inner join MTRBack.photos ph on mpp.mtr_photo_id = ph.photo_id where mpp.mtr_portfolio_id = 4599006 group by h (('30', 1), ('36', 1), ('43', 1)) SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4599006 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 100000 SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1 where mpp1.mtr_photo_id=cps.photo_id AND h.hashtag_id=cps.hashtag_id AND mpp1.hide_status=0 AND mpp1.mtr_portfolio_id in (4599006) AND cps.thcl in (861) order by cps.score desc LIMIT 0, 100000 SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1 where mpp1.mtr_photo_id=cps.photo_id AND h.hashtag_id=cps.hashtag_id AND mpp1.hide_status=0 AND mpp1.mtr_portfolio_id in (4599006) AND cps.thcl in (758) order by cps.score desc LIMIT 0, 100000 ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 21092021 4599006 Nombre de photos uploadées : 3 / 23040 (0%) 21092021 4599006 Nombre de photos taguées (types de déchets): 0 / 3 (0%) 21092021 4599006 Nombre de photos taguées (volume) : 0 / 3 (0%) [{'photo_id': 1054572532, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/10/5/4298b86687a1b085f5190a510e3d99e9.jpg', 'username': None, 'uploaded_at': 1633441829, 'text': 'IMG_20210921_163030.jpg'}, {'photo_id': 1054572534, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/10/5/c726e7bbaa3020412b11d5d8e4d3e9aa.jpg', 'username': None, 'uploaded_at': 1633441829, 'text': 'IMG_20210921_163648.jpg'}, {'photo_id': 1054572537, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/10/5/e7bb2e829c5bfe305d429b7dfb818d83.jpg', 'username': None, 'uploaded_at': 1633441829, 'text': 'IMG_20210921_164310.jpg'}] 0 [] elapsed_time : load_data_split_time_score 8.58306884765625e-06 elapsed_time : order_list_meta_photo_and_scores 6.9141387939453125e-06 ??? elapsed_time : fill_and_build_computed_from_old_data 0.00021457672119140625 INSERT INTO `MTRPhoto`.`dashboard_entry_day` (`dashboard_place_id`, `mtr_portfolio_id`, `date`) VALUES ( 13, 4599006, '2021-09-21' ) ON DUPLICATE KEY UPDATE updated_at=NOW(); INSERT INTO `MTRPhoto`.`dashboard_run_ids` (`dashboard_entry_day`, `mtr_user_id`, `misc_info`) VALUES (145759,739,"{}"); elapsed_time : insert_dashboard_record_day_entry 0.02291131019592285 ---------- APPEND TASK BEGIN ---------- INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (152846, '_______Plastique_fonce', '2021-09-21 16:30:30', '2021-09-21 16:43:10', 0, 4599006, 0, 0, '0', "{}" ); ---------- APPEND TASK END ---------- We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps SELECT dri.dashboard_entry_day, dr.dashboard_run_id, dr.mtr_portfolio_id, dr.id, dr.hashtag, dr.completion_json FROM MTRPhoto.dashboard_results dr, MTRPhoto.dashboard_run_ids dri, MTRPhoto.dashboard_entry_day ded WHERE ded.id= 145759 AND ded.last_run_id=dri.id AND dri.dashboard_entry_day=ded.id AND dri.id=dr.dashboard_run_id [{'dashboat_entry_day': 145759, 'dashboard_run_id': 152846, 'mtr_portfolio_id': 4599006, 'dashboard_result_id': 10912769, 'hashtag': '_______Plastique_fonce', 'completion_json': '{}'}] SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4599006 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=3759 ORDER BY id desc LIMIT 1 SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4599006 AND result is not null and result<>"None" and result=0.0 AND mtd_id=3759 ORDER BY created_at desc LIMIT 1 list_result_datou : [] select url from MTRUser.mtr_files where mtd_id = 3759 and mtr_portfolio_id = 4599006 order by file_id desc limit 1 select url from MTRUser.mtr_files where text like '%4599006%' order by file_id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 4599006 order by id desc limit 1 update MTRPhoto.dashboard_results set completion_json = "{'url_report': ''}" where mtr_portfolio_id = 4599006 and dashboard_run_id = 152846 SELECT mtr_photo_id FROM MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id IN (4599006) result : ((1054572532,), (1054572534,), (1054572537,)) NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'21092021': {'nb_upload': 3, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] select SUBSTRING(ph.text,16,2) as h, count(*) from MTRUser.mtr_portfolio_photos mpp inner join MTRBack.photos ph on mpp.mtr_photo_id = ph.photo_id where mpp.mtr_portfolio_id = 4505992 group by h (('12', 1), ('-0', 3)) SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4505992 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 100000 SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1 where mpp1.mtr_photo_id=cps.photo_id AND h.hashtag_id=cps.hashtag_id AND mpp1.hide_status=0 AND mpp1.mtr_portfolio_id in (4505992) AND cps.thcl in (861) order by cps.score desc LIMIT 0, 100000 SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1 where mpp1.mtr_photo_id=cps.photo_id AND h.hashtag_id=cps.hashtag_id AND mpp1.hide_status=0 AND mpp1.mtr_portfolio_id in (4505992) AND cps.thcl in (758) order by cps.score desc LIMIT 0, 100000 ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 21092021 4505992 Nombre de photos uploadées : 1 / 23040 (0%) 21092021 4505992 Nombre de photos taguées (types de déchets): 0 / 1 (0%) 21092021 4505992 Nombre de photos taguées (volume) : 0 / 1 (0%) [{'photo_id': 1051605195, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/9/21/4cd66aff3fb05d55424e3583f716f45c.jpg', 'username': None, 'uploaded_at': 1632215726, 'text': 'IMG_20210921_111217.jpg'}] 0 [] elapsed_time : load_data_split_time_score 3.5762786865234375e-06 elapsed_time : order_list_meta_photo_and_scores 1.33514404296875e-05 ? elapsed_time : fill_and_build_computed_from_old_data 0.00024199485778808594 INSERT INTO `MTRPhoto`.`dashboard_entry_day` (`dashboard_place_id`, `mtr_portfolio_id`, `date`) VALUES ( 13, 4505992, '2021-09-21' ) ON DUPLICATE KEY UPDATE updated_at=NOW(); INSERT INTO `MTRPhoto`.`dashboard_run_ids` (`dashboard_entry_day`, `mtr_user_id`, `misc_info`) VALUES (145759,739,"{}"); elapsed_time : insert_dashboard_record_day_entry 0.022384166717529297 ---------- APPEND TASK BEGIN ---------- INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (152846, '_______Plastique_fonce', '2021-09-21 11:12:17', '2021-09-21 11:12:17', 0, 4505992, 0, 0, '0', "{}" ); ---------- APPEND TASK END ---------- We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps SELECT dri.dashboard_entry_day, dr.dashboard_run_id, dr.mtr_portfolio_id, dr.id, dr.hashtag, dr.completion_json FROM MTRPhoto.dashboard_results dr, MTRPhoto.dashboard_run_ids dri, MTRPhoto.dashboard_entry_day ded WHERE ded.id= 145759 AND ded.last_run_id=dri.id AND dri.dashboard_entry_day=ded.id AND dri.id=dr.dashboard_run_id [{'dashboat_entry_day': 145759, 'dashboard_run_id': 152846, 'mtr_portfolio_id': 4599006, 'dashboard_result_id': 10912769, 'hashtag': '_______Plastique_fonce', 'completion_json': "{'url_report': ''}"}, {'dashboat_entry_day': 145759, 'dashboard_run_id': 152846, 'mtr_portfolio_id': 4505992, 'dashboard_result_id': 10912772, 'hashtag': '_______Plastique_fonce', 'completion_json': '{}'}] SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4599006 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=3759 ORDER BY id desc LIMIT 1 SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4599006 AND result is not null and result<>"None" and result=0.0 AND mtd_id=3759 ORDER BY created_at desc LIMIT 1 list_result_datou : [] select url from MTRUser.mtr_files where mtd_id = 3759 and mtr_portfolio_id = 4599006 order by file_id desc limit 1 select url from MTRUser.mtr_files where text like '%4599006%' order by file_id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 4599006 order by id desc limit 1 update MTRPhoto.dashboard_results set completion_json = "{'url_report': ''}" where mtr_portfolio_id = 4599006 and dashboard_run_id = 152846 SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4505992 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=3759 ORDER BY id desc LIMIT 1 SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4505992 AND result is not null and result<>"None" and result=0.0 AND mtd_id=3759 ORDER BY created_at desc LIMIT 1 list_result_datou : [] select url from MTRUser.mtr_files where mtd_id = 3759 and mtr_portfolio_id = 4505992 order by file_id desc limit 1 select url from MTRUser.mtr_files where text like '%4505992%' order by file_id desc limit 1 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_COD_P4505992_21-09-2021.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 4505992 order by id desc limit 1 update MTRPhoto.dashboard_results set completion_json = "{'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_COD_P4505992_21-09-2021.pdf'}" where mtr_portfolio_id = 4505992 and dashboard_run_id = 152846 SELECT mtr_photo_id FROM MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id IN (4599006,4505992) result : ((1051605195,), (1051615607,), (1051615609,), (1051615610,), (1054572532,), (1054572534,), (1054572537,)) NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'21092021': {'nb_upload': 1, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} After datou_step_exec type output : time spend for datou_step_exec : 2.730048179626465 time spend to save output : 0.00010347366333007812 total time spend for step 1 : 2.730151653289795 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1054572537, 1054572534, 1054572532, 1051605195] map_info['map_portfolio_photo'] : {4599006: [1054572537, 1054572534, 1054572532], 4505992: [1051605195]} final : True mtd_id 3856 list_pids : [1054572537, 1054572534, 1054572532, 1051605195] Looping around the photos to save general results len do output : 2 /4599006Didn't retrieve data . /4505992Didn'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 ('3856', None, None, None, None, None, None, None, None) ('3856', '4599006', '1054572537', None, None, None, None, None, None) ('3856', None, None, None, None, None, None, None, None) ('3856', '4599006', '1054572534', None, None, None, None, None, None) ('3856', None, None, None, None, None, None, None, None) ('3856', '4599006', '1054572532', None, None, None, None, None, None) ('3856', None, None, None, None, None, None, None, None) ('3856', '4505992', '1051605195', 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 : [('3856', None, '4599006', 'None', None, None, None, None, None), ('3856', None, '4505992', 'None', None, None, None, None, None), ('3856', '4599006', '1054572537', None, None, None, None, None, None), ('3856', '4599006', '1054572534', None, None, None, None, None, None), ('3856', '4599006', '1054572532', None, None, None, None, None, None), ('3856', '4505992', '1051605195', None, None, None, None, None, None)] time used for this insertion : 0.015647172927856445 save_final save missing photos in datou_result : After save, about to update current ! SELECT dashboard_run_id FROM MTRPhoto.dashboard_results where mtr_portfolio_id = 4599006 ############################### TEST rubbia_horaire ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=3181 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=3181 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= 3181 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=3181 # 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 (3609515) 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 (3609515) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 1014106095, 'portfolio_id': 3609515}, {'photo_id': 1014106094, 'portfolio_id': 3609515}, {'photo_id': 1014106093, 'portfolio_id': 3609515}, {'photo_id': 1014105800, 'portfolio_id': 3609515}, {'photo_id': 1014105799, 'portfolio_id': 3609515}, {'photo_id': 1014105798, 'portfolio_id': 3609515}, {'photo_id': 1014105797, 'portfolio_id': 3609515}, {'photo_id': 1014105796, 'portfolio_id': 3609515}, {'photo_id': 1014105795, 'portfolio_id': 3609515}, {'photo_id': 1014105791, 'portfolio_id': 3609515}, {'photo_id': 1014105790, 'portfolio_id': 3609515}, {'photo_id': 1014105786, 'portfolio_id': 3609515}, {'photo_id': 1014105785, 'portfolio_id': 3609515}, {'photo_id': 1014105784, 'portfolio_id': 3609515}, {'photo_id': 1014105783, 'portfolio_id': 3609515}, {'photo_id': 1014105782, 'portfolio_id': 3609515}, {'photo_id': 1014105781, 'portfolio_id': 3609515}, {'photo_id': 1014105778, 'portfolio_id': 3609515}, {'photo_id': 1014105777, 'portfolio_id': 3609515}, {'photo_id': 1014099035, 'portfolio_id': 3609515}, {'photo_id': 1014098602, 'portfolio_id': 3609515}, {'photo_id': 1014098236, 'portfolio_id': 3609515}, {'photo_id': 1014097924, 'portfolio_id': 3609515}, {'photo_id': 1014097580, 'portfolio_id': 3609515}, {'photo_id': 1014097499, 'portfolio_id': 3609515}, {'photo_id': 1014097497, 'portfolio_id': 3609515}, {'photo_id': 1014097492, 'portfolio_id': 3609515}, {'photo_id': 1014054235, 'portfolio_id': 3609515}, {'photo_id': 1014054234, 'portfolio_id': 3609515}, {'photo_id': 1014054233, 'portfolio_id': 3609515}, {'photo_id': 1014054232, 'portfolio_id': 3609515}, {'photo_id': 1014054231, 'portfolio_id': 3609515}, {'photo_id': 1014054230, 'portfolio_id': 3609515}] map_portfolio_id_photo_id: {3609515: [1014106095, 1014106094, 1014106093, 1014105800, 1014105799, 1014105798, 1014105797, 1014105796, 1014105795, 1014105791, 1014105790, 1014105786, 1014105785, 1014105784, 1014105783, 1014105782, 1014105781, 1014105778, 1014105777, 1014099035, 1014098602, 1014098236, 1014097924, 1014097580, 1014097499, 1014097497, 1014097492, 1014054235, 1014054234, 1014054233, 1014054232, 1014054231, 1014054230]} ##### 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 : 0.02337193489074707 #### fin chargement data Blocking on flush ? No conitnuing About to test input to load Calling datou_exec Inside datou_exec : verbose : True we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save number of steps : 1 step1:split_time_score Mon May 26 19:41:51 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 : {} SELECT app_name, token FROM MTRUser.mtr_app_api_token WHERE mtr_user_id=739 AND app_name="token_split_time_score" AND expire_at > NOW() TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] select SUBSTRING(ph.text,16,2) as h, count(*) from MTRUser.mtr_portfolio_photos mpp inner join MTRBack.photos ph on mpp.mtr_photo_id = ph.photo_id where mpp.mtr_portfolio_id = 3609515 group by h (('02', 6), ('05', 8), ('06', 19)) SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=3609515 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 100000 SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1 where mpp1.mtr_photo_id=cps.photo_id AND h.hashtag_id=cps.hashtag_id AND mpp1.hide_status=0 AND mpp1.mtr_portfolio_id in (3609515) AND cps.thcl in (861) order by cps.score desc LIMIT 0, 100000 SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1 where mpp1.mtr_photo_id=cps.photo_id AND h.hashtag_id=cps.hashtag_id AND mpp1.hide_status=0 AND mpp1.mtr_portfolio_id in (3609515) AND cps.thcl in (758) order by cps.score desc LIMIT 0, 100000 ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 08032021 3609515 Nombre de photos uploadées : 33 / 23040 (0%) 08032021 3609515 Nombre de photos taguées (types de déchets): 0 / 33 (0%) 08032021 3609515 Nombre de photos taguées (volume) : 0 / 33 (0%) [{'photo_id': 1014054233, 'url': 'https://s3-eu-west-1.amazonaws.com/photo.fotonower.com/2021/3/8/7b7625f3e3d6b1c188adbbc3cb328bdf.jpg', 'username': None, 'uploaded_at': 1615177397, 'text': 'image_08032021_02_08_09_034455m0.jpg 1e-05 for time 1, id_amount 3 this amount prod time diff : 1e-05'}, {'photo_id': 1014054232, 'url': 'https://s3-eu-west-1.amazonaws.com/photo.fotonower.com/2021/3/8/34274adf797a96624e0c26e2d60facd7.jpg', 'username': None, 'uploaded_at': 1615177397, 'text': 'image_08032021_02_08_13_039073m0.jpg 2e-05 for time 1, id_amount 3 this amount prod time diff : 1e-05'}, {'photo_id': 1014054231, 'url': 'https://s3-eu-west-1.amazonaws.com/photo.fotonower.com/2021/3/8/4e0df418bccd7a484aadeef86e02b28d.jpg', 'username': None, 'uploaded_at': 1615177397, 'text': 'image_08032021_02_08_17_045370m0.jpg 3.0000000000000004e-05 for time 1, id_amount 3 this amount prod time diff : 1e-05'}, {'photo_id': 1014054230, 'url': 'https://s3-eu-west-1.amazonaws.com/photo.fotonower.com/2021/3/8/4d30f9d1ccb7fa68b9b9678acfcef613.jpg', 'username': None, 'uploaded_at': 1615177397, 'text': 'image_08032021_02_08_21_051026m0.jpg 4e-05 for time 1, id_amount 3 this amount prod time diff : 1e-05'}] 0 [] elapsed_time : load_data_split_time_score 4.0531158447265625e-06 elapsed_time : order_list_meta_photo_and_scores 1.1920928955078125e-05 ????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0015025138854980469 INSERT INTO `MTRPhoto`.`dashboard_entry_day` (`dashboard_place_id`, `mtr_portfolio_id`, `date`) VALUES ( 10, 3609515, '2021-03-08' ) ON DUPLICATE KEY UPDATE mtr_portfolio_id=VALUES(mtr_portfolio_id), updated_at=NOW(); INSERT INTO `MTRPhoto`.`dashboard_run_ids` (`dashboard_entry_day`, `mtr_user_id`, `misc_info`) VALUES (34064,739,"{}"); elapsed_time : insert_dashboard_record_day_entry 0.023294448852539062 Creating list_photo_total in select_descriptors : SELECT `photo_id`, `type_store` FROM MTRPhoto.photo_desc_search WHERE photo_id IN (1014054233,1014054232,1014054231,1014054230,1014054235,1014054234,1014097492,1014097499,1014097497,1014097580,1014097924,1014098236,1014098602,1014099035,1014105778,1014105777,1014105784,1014105783,1014105782,1014105781,1014105786,1014105785,1014105791,1014105790,1014105798,1014105797,1014105796,1014105795,1014105800,1014105799,1014106095,1014106094,1014106093) AND `type`=3963 elapsed_time : select_descriptors 0.010409832000732422 08032021 3609515 Nombre de photos avec descriptors (type 3963) : 0 / 33 (0%) Missing descriptors for photos 0 and 1014054233 0:00:00|ON:Missing descriptors for photos 1014054233 and 1014054232 Missing descriptors for photos 1014054232 and 1014054231 Missing descriptors for photos 1014054231 and 1014054230 Missing descriptors for photos 1014054230 and 1014054235 Missing descriptors for photos 1014054235 and 1014054234 Missing descriptors for photos 1014054234 and 1014097492 Missing descriptors for photos 1014097492 and 1014097499 Missing descriptors for photos 1014097499 and 1014097497 Missing descriptors for photos 1014097497 and 1014097580 Missing descriptors for photos 1014097580 and 1014097924 Missing descriptors for photos 1014097924 and 1014098236 Missing descriptors for photos 1014098236 and 1014098602 Missing descriptors for photos 1014098602 and 1014099035 Missing descriptors for photos 1014099035 and 1014105778 Missing descriptors for photos 1014105778 and 1014105777 Missing descriptors for photos 1014105777 and 1014105784 Missing descriptors for photos 1014105784 and 1014105783 Missing descriptors for photos 1014105783 and 1014105782 Missing descriptors for photos 1014105782 and 1014105781 Missing descriptors for photos 1014105781 and 1014105786 Missing descriptors for photos 1014105786 and 1014105785 Missing descriptors for photos 1014105785 and 1014105791 Missing descriptors for photos 1014105791 and 1014105790 Missing descriptors for photos 1014105790 and 1014105798 Missing descriptors for photos 1014105798 and 1014105797 Missing descriptors for photos 1014105797 and 1014105796 Missing descriptors for photos 1014105796 and 1014105795 Missing descriptors for photos 1014105795 and 1014105800 Missing descriptors for photos 1014105800 and 1014105799 Missing descriptors for photos 1014105799 and 1014106095 Missing descriptors for photos 1014106095 and 1014106094 Missing descriptors for photos 1014106094 and 1014106093 08032021 Removing 0 photos because of the 'same image' condition list_time_on : 0 first ten : [] list_time_off : 1 first ten : [datetime.timedelta(0)] Total on : 0 Total off : 0.0 list_time_off {'nb': 1, 'mean': 0.0, 'stddev': 0, 'min': 0.0, 'max': 0.0, 'quantil_10': {'min': [0.0], 'max': [0.0]}, 'quantil_100': {'min': [0.0], 'max': [0.0]}, 'quantil_1000': {'min': [0.0], 'max': [0.0]}, 'quantil_5000': {'min': [0.0], 'max': [0.0]}, 'quantil_10000': {'min': [0.0], 'max': [0.0]}} Warning in study_and_display_distrib_list : min=max : 0.0 0.0 dist_desc Warning in study_and_display_distrib_list : min=max : -1 -1 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 33 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id) first line : (1014054233, 2107752370, 1539) ... last line : (1014106093, 2107752371, 1539) time used for this insertion : 0.023738622665405273 photos_removed : len 0 elapsed_time : remove_photo_duplicate 0.04735708236694336 To do, maybe not at the correct place ! .................................force hashtag to JRM elapsed_time : CREATE_PORT_BATCH_BY_HOUR 0.005656242370605469 NUMBER BATCH : 3 list_ponderation used : [1e-05, 1e-05, 1e-05, 1e-05, 1e-05] , list_hashtag_class_create_as_list : ['jrm'] We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We have rejected 0 photos because of the batch_size condition ! NUMBER BATCH list_of_portfolios_to_create : 0 elapsed_time : count_nb_balles_and_create_portfolio 0.02504897117614746 # DISPLAY ALL COLLECTED DATA : {'08032021': {'nb_upload': 33, 'nb_taggue_class': 0, 'nb_taggue_densite': 0, 'nb_descriptors': 0}} After datou_step_exec type output : time spend for datou_step_exec : 0.17593097686767578 time spend to save output : 3.6716461181640625e-05 total time spend for step 1 : 0.17596769332885742 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1014106095, 1014106094, 1014106093, 1014105800, 1014105799, 1014105798, 1014105797, 1014105796, 1014105795, 1014105791, 1014105790, 1014105786, 1014105785, 1014105784, 1014105783, 1014105782, 1014105781, 1014105778, 1014105777, 1014099035, 1014098602, 1014098236, 1014097924, 1014097580, 1014097499, 1014097497, 1014097492, 1014054235, 1014054234, 1014054233, 1014054232, 1014054231, 1014054230] map_info['map_portfolio_photo'] : {3609515: [1014106095, 1014106094, 1014106093, 1014105800, 1014105799, 1014105798, 1014105797, 1014105796, 1014105795, 1014105791, 1014105790, 1014105786, 1014105785, 1014105784, 1014105783, 1014105782, 1014105781, 1014105778, 1014105777, 1014099035, 1014098602, 1014098236, 1014097924, 1014097580, 1014097499, 1014097497, 1014097492, 1014054235, 1014054234, 1014054233, 1014054232, 1014054231, 1014054230]} final : True mtd_id 3181 list_pids : [1014106095, 1014106094, 1014106093, 1014105800, 1014105799, 1014105798, 1014105797, 1014105796, 1014105795, 1014105791, 1014105790, 1014105786, 1014105785, 1014105784, 1014105783, 1014105782, 1014105781, 1014105778, 1014105777, 1014099035, 1014098602, 1014098236, 1014097924, 1014097580, 1014097499, 1014097497, 1014097492, 1014054235, 1014054234, 1014054233, 1014054232, 1014054231, 1014054230] Looping around the photos to save general results len do output : 1 /3609515Didn'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 ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014106095', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014106094', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014106093', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105800', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105799', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105798', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105797', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105796', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105795', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105791', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105790', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105786', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105785', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105784', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105783', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105782', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105781', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105778', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105777', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014099035', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014098602', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014098236', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014097924', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014097580', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014097499', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014097497', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014097492', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054235', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054234', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054233', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054232', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054231', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054230', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 34 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 : [('3181', None, '3609515', 'None', None, None, None, None, None), ('3181', '3609515', '1014106095', None, None, None, None, None, None), ('3181', '3609515', '1014106094', None, None, None, None, None, None), ('3181', '3609515', '1014106093', None, None, None, None, None, None), ('3181', '3609515', '1014105800', None, None, None, None, None, None), ('3181', '3609515', '1014105799', None, None, None, None, None, None), ('3181', '3609515', '1014105798', None, None, None, None, None, None), ('3181', '3609515', '1014105797', None, None, None, None, None, None), ('3181', '3609515', '1014105796', None, None, None, None, None, None), ('3181', '3609515', '1014105795', None, None, None, None, None, None), ('3181', '3609515', '1014105791', None, None, None, None, None, None), ('3181', '3609515', '1014105790', None, None, None, None, None, None), ('3181', '3609515', '1014105786', None, None, None, None, None, None), ('3181', '3609515', '1014105785', None, None, None, None, None, None), ('3181', '3609515', '1014105784', None, None, None, None, None, None), ('3181', '3609515', '1014105783', None, None, None, None, None, None), ('3181', '3609515', '1014105782', None, None, None, None, None, None), ('3181', '3609515', '1014105781', None, None, None, None, None, None), ('3181', '3609515', '1014105778', None, None, None, None, None, None), ('3181', '3609515', '1014105777', None, None, None, None, None, None), ('3181', '3609515', '1014099035', None, None, None, None, None, None), ('3181', '3609515', '1014098602', None, None, None, None, None, None), ('3181', '3609515', '1014098236', None, None, None, None, None, None), ('3181', '3609515', '1014097924', None, None, None, None, None, None), ('3181', '3609515', '1014097580', None, None, None, None, None, None), ('3181', '3609515', '1014097499', None, None, None, None, None, None), ('3181', '3609515', '1014097497', None, None, None, None, None, None), ('3181', '3609515', '1014097492', None, None, None, None, None, None), ('3181', '3609515', '1014054235', None, None, None, None, None, None), ('3181', '3609515', '1014054234', None, None, None, None, None, None), ('3181', '3609515', '1014054233', None, None, None, None, None, None), ('3181', '3609515', '1014054232', None, None, None, None, None, None), ('3181', '3609515', '1014054231', None, None, None, None, None, None), ('3181', '3609515', '1014054230', None, None, None, None, None, None)] time used for this insertion : 0.023354530334472656 save_final save missing photos in datou_result : After save, about to update current ! {'3609515': ([[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11, 12, 13], [14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32]], {'Rungis_JRM': []}, {}, {2107757407: 0}, {'amount_uploaded_and_tagged': {'08032021': {'nb_upload': 33, 'nb_taggue_class': 0, 'nb_taggue_densite': 0, 'nb_descriptors': 0}}, 'map_amount_per_hashtag': {'Rungis_JRM': []}, 'count': {'Rungis_JRM': []}})} got : {'Rungis_JRM': []} expected : {'Rungis_JRM': [(0, 1), (1, 2), (2, 3)]} ERROR rubbia_horaire FAILED ############################### TEST rle_unique_nms_with_priority ################################ t SELECT id FROM MTRPhoto.crop_hashtag_ids WHERE photo_id=998957128 AND `type`=2805 DELETE FROM MTRPhoto.crop_hashtag_ids WHERE id IN (3813175999,3813176000,3813176001,3813176002,3813176003,3813176004,3813176005,3813176006,3813176007,3813176008) SELECT id FROM MTRPhoto.crop_hashtag_ids WHERE photo_id=996751167 AND `type`=2805 DELETE FROM MTRPhoto.crop_hashtag_ids WHERE id IN (3813176221,3813176194,3813176222,3813176201,3813176184,3813176198,3813176181,3813176212,3813176200,3813176191,3813176219,3813176203,3813176208,3813176209,3813176214,3813176211,3813176187,3813176216,3813176192,3813176195,3813176210,3813176218,3813176197,3813176202,3813176193,3813176183,3813176207,3813176213,3813176185,3813176180,3813176189,3813176182,3813176199,3813176217,3813176204,3813176190,3813176206,3813176205,3813176188,3813176215,3813176220,3813176186,3813176196,3813176179) DELETE FROM MTRPhoto.photo_carac_ratio WHERE hashtag_type=2805; Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2548 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=2548 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= 2548 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=2548 # 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 : rle_unique_nms_with_priority 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 (998957128) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 998957128 download finish for photo 998957128 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 : 0.20306754112243652 #### 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:rle_unique_nms_with_priority Mon May 26 19:41:51 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/1748281311_935833_998957128_f45017ffedbd1f7cb33fed47ac33648c.jpg': 998957128} map_photo_id_path_extension : {998957128: {'path': 'temp/1748281311_935833_998957128_f45017ffedbd1f7cb33fed47ac33648c.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Begin step rle-unique-nms on traite la photo : temp/1748281311_935833_998957128_f45017ffedbd1f7cb33fed47ac33648c.jpg batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 998957128) and `type` in (2804) and hashtag_id in (2107754127,2107755994) Loaded 10 chid ids of type : 2804 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1872612725,1872612726,1872612727,1872612728,1872612729,1872612730,1872612731,1872612732,1872612733,1872612734) ++WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now +WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1872612725,1872612726,1872612727,1872612728,1872612729,1872612730,1872612731,1872612732,1872612733,1872612734) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1872612725,1872612726,1872612727,1872612728,1872612729,1872612730,1872612731,1872612732,1872612733,1872612734) nb_obj : 10 nb_hashtags : 2 time to prepare the origin masks : 0.48052334785461426 time for calcul the mask position with numpy : 0.0051479339599609375 nb_pixel_total : 217207 time to create 1 rle with new method : 0.04154324531555176 time for calcul the mask position with numpy : 0.0027925968170166016 nb_pixel_total : 1008 time to create 1 rle with old method : 0.0011284351348876953 time for calcul the mask position with numpy : 0.002740621566772461 nb_pixel_total : 751 time to create 1 rle with old method : 0.0008511543273925781 time for calcul the mask position with numpy : 0.0028076171875 nb_pixel_total : 722 time to create 1 rle with old method : 0.0008349418640136719 time for calcul the mask position with numpy : 0.0028841495513916016 nb_pixel_total : 2949 time to create 1 rle with old method : 0.003319263458251953 time for calcul the mask position with numpy : 0.0029609203338623047 nb_pixel_total : 497 time to create 1 rle with old method : 0.0005805492401123047 time for calcul the mask position with numpy : 0.002671957015991211 nb_pixel_total : 1086 time to create 1 rle with old method : 0.012505769729614258 time for calcul the mask position with numpy : 0.004822254180908203 nb_pixel_total : 1924 time to create 1 rle with old method : 0.002076864242553711 time for calcul the mask position with numpy : 0.0027801990509033203 nb_pixel_total : 413 time to create 1 rle with old method : 0.0004703998565673828 time for calcul the mask position with numpy : 0.0026428699493408203 nb_pixel_total : 526 time to create 1 rle with old method : 0.0006177425384521484 create new chi : 0.09649443626403809 proportion hashtag : balle_pet_clair 0.23568467881944444 proportion hashtag : contaminant_du_pet_clair 0.010716145833333333 time to delete rle : 0.015977144241333008 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 10 chid ids of type : 2805 Number RLEs to save : 1674 INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) first line : ('3813397118', '538', '174', '1') ... last line : ('3813397127', '691', '285', '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 ! save time : 0.14534902572631836 INSERT IGNORE INTO MTRPhoto.photo_carac_ratio (`photo_id`, `hashtag_type`, `hashtag_id`, `ratio`) VALUES (%s, %s, %s, %s) on duplicate key update `ratio` = VALUES(`ratio`) map_output_result : {998957128: (0.0, 'Should be the crop_list due to order', 0)} End step rle-unique-nms After datou_step_exec type output : time spend for datou_step_exec : 0.8772032260894775 time spend to save output : 6.413459777832031e-05 total time spend for step 1 : 0.8772673606872559 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {998957128: (0.0, 'Should be the crop_list due to order', 0)} Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2573 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=2573 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= 2573 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=2573 # 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 : rle_unique_nms_with_priority 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 (1066511071) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 1066511071 download finish for photo 1066511071 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 : 0.2683897018432617 #### 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:rle_unique_nms_with_priority Mon May 26 19:41:53 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281312_935833_1066511071_cca6b29b0253f105b76231d9c28fabe3.jpg': 1066511071} map_photo_id_path_extension : {1066511071: {'path': 'temp/1748281312_935833_1066511071_cca6b29b0253f105b76231d9c28fabe3.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Begin step rle-unique-nms on traite la photo : temp/1748281312_935833_1066511071_cca6b29b0253f105b76231d9c28fabe3.jpg batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 1066511071) and `type` in (4169) and hashtag_id in (492774966,492668766,538914404) Loaded 10 chid ids of type : 4169 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (2256126232,2256126224,2256126221,2256126220,2256126223,2256126225,2256126229,2256126228,2256126222,2256126218) SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (2256126232,2256126224,2256126221,2256126220,2256126223,2256126225,2256126229,2256126228,2256126222,2256126218) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (2256126232,2256126224,2256126221,2256126220,2256126223,2256126225,2256126229,2256126228,2256126222,2256126218) seulement à utiliser dans la step consolidation 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 10 chid ids of type : 2805 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.030062198638916016 INSERT IGNORE INTO MTRPhoto.photo_carac_ratio (`photo_id`, `hashtag_type`, `hashtag_id`, `ratio`) VALUES (%s, %s, %s, %s) on duplicate key update `ratio` = VALUES(`ratio`) map_output_result : {1066511071: (0.0, 'Should be the crop_list due to order', 0)} End step rle-unique-nms After datou_step_exec type output : time spend for datou_step_exec : 0.44537353515625 time spend to save output : 6.222724914550781e-05 total time spend for step 1 : 0.4454357624053955 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1066511071: (0.0, 'Should be the crop_list due to order', 0)} Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2574 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=2574 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= 2574 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=2574 # 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 : rle_unique_nms_with_priority 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 (996751167) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 996751167 download finish for photo 996751167 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 : 0.24332785606384277 #### 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:rle_unique_nms_with_priority Mon May 26 19:41:53 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281313_935833_996751167_a4a67aed9b2473876b59443347f3912e.jpg': 996751167} map_photo_id_path_extension : {996751167: {'path': 'temp/1748281313_935833_996751167_a4a67aed9b2473876b59443347f3912e.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Begin step rle-unique-nms on traite la photo : temp/1748281313_935833_996751167_a4a67aed9b2473876b59443347f3912e.jpg batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 996751167) and `type` in (2596) and score>0.5and hashtag_id in (492622729,501120777) Loaded 91 chid ids of type : 2596 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1855152024,1947270239,1947270255,1947270247,1855152035,1947270267,1947270236,1855152019,1855152044,1947270245,1855152040,1855152025,1947270251,1855152027,1947270250,1855152020,1947270237,1855152029,1947270241,1855152058,1947270274,1855152062,1855152061,1947270275,1947270277,1855152055,1855152052,1947270261,1855152046,1947270265,1855152021,1947270238,1947270269,1855152045,1947270276,1947270257,1855152054,1855152031,1947270263,1855152023,1855156849,1947270240,1855152042,1947270249,1855152047,1947270273,1947270258,1855152050,1947270270,1855152034,1855152039,1947270259,1855152063,1947270272,1855152038,1947270252,1855152026,1947270242,1855152049,1947270256,1855152048,1947270262,1855152056,1947270266,1947270271,1855152037,1855156850,1947270253,1855152033,1947270246,1855152030,1855152041,1947270260,1855152057,1855152036,1947270268,1855152043,1855152065,1855152064,1947270254,1855152060,1855152032,1947270243,1855152059,1855152051,1855152053,1947270264,1855152022,1947270248,1947270244,1855152028) +++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1855152024,1947270239,1947270255,1947270247,1855152035,1947270267,1947270236,1855152019,1855152044,1947270245,1855152040,1855152025,1947270251,1855152027,1947270250,1855152020,1947270237,1855152029,1947270241,1855152058,1947270274,1855152062,1855152061,1947270275,1947270277,1855152055,1855152052,1947270261,1855152046,1947270265,1855152021,1947270238,1947270269,1855152045,1947270276,1947270257,1855152054,1855152031,1947270263,1855152023,1855156849,1947270240,1855152042,1947270249,1855152047,1947270273,1947270258,1855152050,1947270270,1855152034,1855152039,1947270259,1855152063,1947270272,1855152038,1947270252,1855152026,1947270242,1855152049,1947270256,1855152048,1947270262,1855152056,1947270266,1947270271,1855152037,1855156850,1947270253,1855152033,1947270246,1855152030,1855152041,1947270260,1855152057,1855152036,1947270268,1855152043,1855152065,1855152064,1947270254,1855152060,1855152032,1947270243,1855152059,1855152051,1855152053,1947270264,1855152022,1947270248,1947270244,1855152028) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1855152024,1947270239,1947270255,1947270247,1855152035,1947270267,1947270236,1855152019,1855152044,1947270245,1855152040,1855152025,1947270251,1855152027,1947270250,1855152020,1947270237,1855152029,1947270241,1855152058,1947270274,1855152062,1855152061,1947270275,1947270277,1855152055,1855152052,1947270261,1855152046,1947270265,1855152021,1947270238,1947270269,1855152045,1947270276,1947270257,1855152054,1855152031,1947270263,1855152023,1855156849,1947270240,1855152042,1947270249,1855152047,1947270273,1947270258,1855152050,1947270270,1855152034,1855152039,1947270259,1855152063,1947270272,1855152038,1947270252,1855152026,1947270242,1855152049,1947270256,1855152048,1947270262,1855152056,1947270266,1947270271,1855152037,1855156850,1947270253,1855152033,1947270246,1855152030,1855152041,1947270260,1855152057,1855152036,1947270268,1855152043,1855152065,1855152064,1947270254,1855152060,1855152032,1947270243,1855152059,1855152051,1855152053,1947270264,1855152022,1947270248,1947270244,1855152028) nb_obj : 43 nb_hashtags : 2 time to prepare the origin masks : 14.331036806106567 time for calcul the mask position with numpy : 0.3670012950897217 nb_pixel_total : 5233657 time to create 1 rle with new method : 0.3899827003479004 time for calcul the mask position with numpy : 0.032526493072509766 nb_pixel_total : 11972 time to create 1 rle with old method : 0.013048648834228516 time for calcul the mask position with numpy : 0.03251957893371582 nb_pixel_total : 15054 time to create 1 rle with old method : 0.017121076583862305 time for calcul the mask position with numpy : 0.03232932090759277 nb_pixel_total : 13954 time to create 1 rle with old method : 0.014889717102050781 time for calcul the mask position with numpy : 0.032030344009399414 nb_pixel_total : 4888 time to create 1 rle with old method : 0.005695819854736328 time for calcul the mask position with numpy : 0.03865957260131836 nb_pixel_total : 1188492 time to create 1 rle with new method : 0.4685349464416504 time for calcul the mask position with numpy : 0.03488564491271973 nb_pixel_total : 184585 time to create 1 rle with new method : 0.44965672492980957 time for calcul the mask position with numpy : 0.03766584396362305 nb_pixel_total : 18620 time to create 1 rle with old method : 0.023752689361572266 time for calcul the mask position with numpy : 0.03618574142456055 nb_pixel_total : 62945 time to create 1 rle with old method : 0.07462716102600098 time for calcul the mask position with numpy : 0.034844398498535156 nb_pixel_total : 9427 time to create 1 rle with old method : 0.010603666305541992 time for calcul the mask position with numpy : 0.032936811447143555 nb_pixel_total : 9081 time to create 1 rle with old method : 0.010093212127685547 time for calcul the mask position with numpy : 0.032881975173950195 nb_pixel_total : 15987 time to create 1 rle with old method : 0.017443418502807617 time for calcul the mask position with numpy : 0.033559322357177734 nb_pixel_total : 33276 time to create 1 rle with old method : 0.0399937629699707 time for calcul the mask position with numpy : 0.032811880111694336 nb_pixel_total : 17533 time to create 1 rle with old method : 0.019076824188232422 time for calcul the mask position with numpy : 0.03325152397155762 nb_pixel_total : 4876 time to create 1 rle with old method : 0.005498409271240234 time for calcul the mask position with numpy : 0.033272504806518555 nb_pixel_total : 25226 time to create 1 rle with old method : 0.028032541275024414 time for calcul the mask position with numpy : 0.03264784812927246 nb_pixel_total : 30773 time to create 1 rle with old method : 0.033502817153930664 time for calcul the mask position with numpy : 0.03354144096374512 nb_pixel_total : 65671 time to create 1 rle with old method : 0.07095098495483398 time for calcul the mask position with numpy : 0.03273177146911621 nb_pixel_total : 12230 time to create 1 rle with old method : 0.013474225997924805 time for calcul the mask position with numpy : 0.032819509506225586 nb_pixel_total : 29560 time to create 1 rle with old method : 0.03239011764526367 time for calcul the mask position with numpy : 0.03187680244445801 nb_pixel_total : 14310 time to create 1 rle with old method : 0.015438318252563477 time for calcul the mask position with numpy : 0.032077789306640625 nb_pixel_total : 15117 time to create 1 rle with old method : 0.016492128372192383 time for calcul the mask position with numpy : 0.03447461128234863 nb_pixel_total : 301487 time to create 1 rle with new method : 0.3828885555267334 time for calcul the mask position with numpy : 0.03341960906982422 nb_pixel_total : 29821 time to create 1 rle with old method : 0.03266191482543945 time for calcul the mask position with numpy : 0.032912254333496094 nb_pixel_total : 40299 time to create 1 rle with old method : 0.04433083534240723 time for calcul the mask position with numpy : 0.033234596252441406 nb_pixel_total : 12680 time to create 1 rle with old method : 0.01636052131652832 time for calcul the mask position with numpy : 0.03310060501098633 nb_pixel_total : 9449 time to create 1 rle with old method : 0.01038670539855957 time for calcul the mask position with numpy : 0.03309202194213867 nb_pixel_total : 15168 time to create 1 rle with old method : 0.01685333251953125 time for calcul the mask position with numpy : 0.03368878364562988 nb_pixel_total : 11140 time to create 1 rle with old method : 0.012393712997436523 time for calcul the mask position with numpy : 0.03268790245056152 nb_pixel_total : 29065 time to create 1 rle with old method : 0.0319514274597168 time for calcul the mask position with numpy : 0.03290128707885742 nb_pixel_total : 22774 time to create 1 rle with old method : 0.024682044982910156 time for calcul the mask position with numpy : 0.03380990028381348 nb_pixel_total : 13880 time to create 1 rle with old method : 0.01525425910949707 time for calcul the mask position with numpy : 0.03525567054748535 nb_pixel_total : 155366 time to create 1 rle with new method : 0.5676145553588867 time for calcul the mask position with numpy : 0.03283047676086426 nb_pixel_total : 63941 time to create 1 rle with old method : 0.06910371780395508 time for calcul the mask position with numpy : 0.032419681549072266 nb_pixel_total : 7836 time to create 1 rle with old method : 0.008579492568969727 time for calcul the mask position with numpy : 0.032492637634277344 nb_pixel_total : 7460 time to create 1 rle with old method : 0.008011579513549805 time for calcul the mask position with numpy : 0.03255033493041992 nb_pixel_total : 44600 time to create 1 rle with old method : 0.050797224044799805 time for calcul the mask position with numpy : 0.03285384178161621 nb_pixel_total : 11879 time to create 1 rle with old method : 0.012887954711914062 time for calcul the mask position with numpy : 0.033582448959350586 nb_pixel_total : 44195 time to create 1 rle with old method : 0.048857927322387695 time for calcul the mask position with numpy : 0.033190250396728516 nb_pixel_total : 23652 time to create 1 rle with old method : 0.025918006896972656 time for calcul the mask position with numpy : 0.03357887268066406 nb_pixel_total : 30006 time to create 1 rle with old method : 0.03251075744628906 time for calcul the mask position with numpy : 0.033225059509277344 nb_pixel_total : 15880 time to create 1 rle with old method : 0.01747298240661621 time for calcul the mask position with numpy : 0.03283262252807617 nb_pixel_total : 29845 time to create 1 rle with old method : 0.03268289566040039 time for calcul the mask position with numpy : 0.03402090072631836 nb_pixel_total : 144263 time to create 1 rle with old method : 0.15684151649475098 create new chi : 5.328999757766724 proportion hashtag : error 0.14340033061450744 proportion hashtag : environment 0.209023722085841 proportion hashtag : pet_fonce 0.6475759472996516 time to delete rle : 0.5017991065979004 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 44 chid ids of type : 2805 Number RLEs to save : 27884 INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) first line : ('3813397138', '0', '0', '3280') ... last line : ('3813397181', '2459', '2462', '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 ! save time : 1.5360016822814941 INSERT IGNORE INTO MTRPhoto.photo_carac_ratio (`photo_id`, `hashtag_type`, `hashtag_id`, `ratio`) VALUES (%s, %s, %s, %s) on duplicate key update `ratio` = VALUES(`ratio`) map_output_result : {996751167: (1.0, 'Should be the crop_list due to order', 1.0)} End step rle-unique-nms After datou_step_exec type output : time spend for datou_step_exec : 21.958524227142334 time spend to save output : 0.00015854835510253906 total time spend for step 1 : 21.958682775497437 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {996751167: (1.0, 'Should be the crop_list due to order', 1.0)} batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 998957128,996751167) and `type` in (2805) Loaded 54 chid ids of type : 2805 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (3813397118,3813397119,3813397120,3813397121,3813397122,3813397123,3813397124,3813397125,3813397126,3813397127,3813397138,3813397139,3813397140,3813397141,3813397142,3813397143,3813397144,3813397145,3813397146,3813397147,3813397148,3813397149,3813397150,3813397151,3813397152,3813397153,3813397154,3813397155,3813397156,3813397157,3813397158,3813397159,3813397160,3813397161,3813397162,3813397163,3813397164,3813397165,3813397166,3813397167,3813397168,3813397169,3813397170,3813397171,3813397172,3813397173,3813397174,3813397175,3813397176,3813397177,3813397178,3813397179,3813397180,3813397181) SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (3813397118,3813397119,3813397120,3813397121,3813397122,3813397123,3813397124,3813397125,3813397126,3813397127,3813397138,3813397139,3813397140,3813397141,3813397142,3813397143,3813397144,3813397145,3813397146,3813397147,3813397148,3813397149,3813397150,3813397151,3813397152,3813397153,3813397154,3813397155,3813397156,3813397157,3813397158,3813397159,3813397160,3813397161,3813397162,3813397163,3813397164,3813397165,3813397166,3813397167,3813397168,3813397169,3813397170,3813397171,3813397172,3813397173,3813397174,3813397175,3813397176,3813397177,3813397178,3813397179,3813397180,3813397181) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (3813397118,3813397119,3813397120,3813397121,3813397122,3813397123,3813397124,3813397125,3813397126,3813397127,3813397138,3813397139,3813397140,3813397141,3813397142,3813397143,3813397144,3813397145,3813397146,3813397147,3813397148,3813397149,3813397150,3813397151,3813397152,3813397153,3813397154,3813397155,3813397156,3813397157,3813397158,3813397159,3813397160,3813397161,3813397162,3813397163,3813397164,3813397165,3813397166,3813397167,3813397168,3813397169,3813397170,3813397171,3813397172,3813397173,3813397174,3813397175,3813397176,3813397177,3813397178,3813397179,3813397180,3813397181) SELECT * FROM MTRPhoto.photo_carac_ratio WHERE hashtag_type=2805; ############################### TEST random_deformation ################################ Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2896 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=2896 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= 2896 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=2896 # 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 : random_deformation 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 (3288640) 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 (3288640) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 1006293201, 'portfolio_id': 3288640}] map_portfolio_id_photo_id: {3288640: [1006293201]} ##### Call download_photos : nb_thread : 5 begin to download photo : 1006293201 download finish for photo 1006293201 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 : 0.13317227363586426 #### 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:random_deformation Mon May 26 19:42:16 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/1748281336_935833_1006293201_ac133c4479fdab9f9d690f3bcbac83df.jpg': 1006293201} map_photo_id_path_extension : {1006293201: {'path': 'temp/1748281336_935833_1006293201_ac133c4479fdab9f9d690f3bcbac83df.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} beginning of datou step random deformation get user info for portfolio 3288640 on traite la photo : 1006293201 About to upload 4 photos upload in portfolio : 3287159 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1748281339_935833 we have uploaded 4 photos in the portfolio 3287159 time of upload the photos Elapsed time : 1.6304891109466553 After datou_step_exec type output : time spend for datou_step_exec : 4.348164319992065 time spend to save output : 7.081031799316406e-05 total time spend for step 1 : 4.348235130310059 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : random_deformation we use saveGeneral [1006293201] map_info['map_portfolio_photo'] : {3288640: [1006293201]} final : True mtd_id 2896 list_pids : [1006293201] Looping around the photos to save general results len do output : 4 /1361143213Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361143214Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361143215Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361143216Didn'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 ('2896', None, None, None, None, None, None, None, None) ('2896', '3288640', '1006293201', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 13 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 : [('2896', None, '1361143213', 'None', None, None, None, None, None), ('2896', None, '1361143214', 'None', None, None, None, None, None), ('2896', None, '1361143215', 'None', None, None, None, None, None), ('2896', None, '1361143216', 'None', None, None, None, None, None), ('2896', '3288640', '1006293201', None, None, None, None, None, None)] time used for this insertion : 0.015709400177001953 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1361143213: ['1006293201', 'temp/1006293201_random_deformation_0.png', []], 1361143214: ['1006293201', 'temp/1006293201_random_deformation_1.png', []], 1361143215: ['1006293201', 'temp/1006293201_random_deformation_2.png', []], 1361143216: ['1006293201', 'temp/1006293201_random_deformation_3.png', []]} name 'urllib' is not defined can't unload the photo : 1006293201 t ############################### TEST tile ################################ test tile avec chi rectangles, rles, polygones Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2985 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=2985 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= 2985 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=2985 # 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 : tile 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 (3341855) 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 (3341855) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 1008283903, 'portfolio_id': 3341855}] map_portfolio_id_photo_id: {3341855: [1008283903]} ##### Call download_photos : nb_thread : 5 begin to download photo : 1008283903 download finish for photo 1008283903 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 : 0.35155487060546875 #### 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:tile Mon May 26 19:42:21 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/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53.jpg': 1008283903} map_photo_id_path_extension : {1008283903: {'path': 'temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} verbose : True param_json : {'ETA': 3600, 'new_width': 640, 'new_height': 640, 'token': '5d93a4b2b749464f208c339a1324b78f', 'stride': 0, 'stride_relative': 0, 'portfolio_name': 'results_test_tile', 'crop_hashtag_type_tiled': 3243, 'crop_hashtag_type': 3242, 'arg_aux_upload': {'type_upload': 'python'}, 'host': 'www.fotonower.com'} type(crop_hashtag_type) : type(crop_hashtag_type_tiled) : We consider crop_hashtag_type is an integer ! map_chi_type_to_chi_type_cropped : {3242: 3243} TO DEPRECATE VR 14-6-18 map_filenames : {1008283903: 'temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53.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 ( 1008283903,1008283903) and `type` in (3242) Loaded 2 chid ids of type : 3242 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1929991390,1929991392) ++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1929991390,1929991392) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1929991390,1929991392) https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=results_test_tile&access_token=5d93a4b2b749464f208c339a1324b78f created feed_id_new_photos : 23354469 with name results_test_tile feed_id_new_photos : 23354469 filename : temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53.jpg photo_id : 1008283903 height_image_input : 2464 width_image_input : 3280 new_width : 640 new_height : 640 stride : 0 stride_relative : 0 chi to copy from the main photo to the tiled photo input_chi_for_this_image_as_chi : 2 list_bib_to_crops : 24 [(0, 640, 0, 640, 0), (0, 640, 640, 1280, 1), (0, 640, 1280, 1920, 2), (0, 640, 1824, 2464, 3), (640, 1280, 0, 640, 4), (640, 1280, 640, 1280, 5), (640, 1280, 1280, 1920, 6), (640, 1280, 1824, 2464, 7), (1280, 1920, 0, 640, 8), (1280, 1920, 640, 1280, 9), (1280, 1920, 1280, 1920, 10), (1280, 1920, 1824, 2464, 11), (1920, 2560, 0, 640, 12), (1920, 2560, 640, 1280, 13), (1920, 2560, 1280, 1920, 14), (1920, 2560, 1824, 2464, 15), (2560, 3200, 0, 640, 16), (2560, 3200, 640, 1280, 17), (2560, 3200, 1280, 1920, 18), (2560, 3200, 1824, 2464, 19), (2640, 3280, 0, 640, 20), (2640, 3280, 640, 1280, 21), (2640, 3280, 1280, 1920, 22), (2640, 3280, 1824, 2464, 23)] calcul des nouveaux crops pour le tile x0:0,x1:640,y0:0,y1:640 chi selectionnes : [] calcul des nouveaux crops pour le tile x0:0,x1:640,y0:640,y1:1280 chi selectionnes : [] calcul des nouveaux crops pour le tile x0:0,x1:640,y0:1280,y1:1920 chi selectionnes : [] calcul des nouveaux crops pour le tile x0:0,x1:640,y0:1824,y1:2464 chi selectionnes : [] calcul des nouveaux crops pour le tile x0:640,x1:1280,y0:0,y1:640 chi selectionnes : [] calcul des nouveaux crops pour le tile x0:640,x1:1280,y0:640,y1:1280 chi selectionnes : [] calcul des nouveaux crops pour le tile x0:640,x1:1280,y0:1280,y1:1920 chi selectionnes : [] calcul des nouveaux crops pour le tile x0:640,x1:1280,y0:1824,y1:2464 chi selectionnes : [] calcul des nouveaux crops pour le tile x0:1280,x1:1920,y0:0,y1:640 chi selectionnes : [] calcul des nouveaux crops pour le tile x0:1280,x1:1920,y0:640,y1:1280 calcul avec la methode originale chi selectionnes : [] calcul des nouveaux crops pour le tile x0:1280,x1:1920,y0:1280,y1:1920 calcul avec la methode originale chi selectionnes : [] calcul des nouveaux crops pour le tile x0:1280,x1:1920,y0:1824,y1:2464 calcul avec la methode originale chi selectionnes : [] calcul des nouveaux crops pour le tile x0:1920,x1:2560,y0:0,y1:640 calcul avec la methode originale chi selectionnes : [] calcul des nouveaux crops pour le tile x0:1920,x1:2560,y0:640,y1:1280 calcul avec la methode originale chi selectionnes : [] calcul des nouveaux crops pour le tile x0:1920,x1:2560,y0:1280,y1:1920 calcul avec la methode originale chi selectionnes : [] calcul des nouveaux crops pour le tile x0:1920,x1:2560,y0:1824,y1:2464 calcul avec la methode originale chi selectionnes : [] calcul des nouveaux crops pour le tile x0:2560,x1:3200,y0:0,y1:640 chi selectionnes : [] calcul des nouveaux crops pour le tile x0:2560,x1:3200,y0:640,y1:1280 chi selectionnes : [] calcul des nouveaux crops pour le tile x0:2560,x1:3200,y0:1280,y1:1920 chi selectionnes : [] calcul des nouveaux crops pour le tile x0:2560,x1:3200,y0:1824,y1:2464 chi selectionnes : [] calcul des nouveaux crops pour le tile x0:2640,x1:3280,y0:0,y1:640 chi selectionnes : [] calcul des nouveaux crops pour le tile x0:2640,x1:3280,y0:640,y1:1280 chi selectionnes : [] calcul des nouveaux crops pour le tile x0:2640,x1:3280,y0:1280,y1:1920 chi selectionnes : [] calcul des nouveaux crops pour le tile x0:2640,x1:3280,y0:1824,y1:2464 chi selectionnes : [] new_crops_tiles : 24 crop_transformed : 7 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) [(1008283903, 2090988864, 17, 0, 640, 0, 640, 1.0), (1008283903, 2090988864, 17, 0, 640, 640, 1280, 1.0), (1008283903, 2090988864, 17, 0, 640, 1280, 1920, 1.0), (1008283903, 2090988864, 17, 0, 640, 1824, 2464, 1.0), (1008283903, 2090988864, 17, 640, 1280, 0, 640, 1.0), (1008283903, 2090988864, 17, 640, 1280, 640, 1280, 1.0), (1008283903, 2090988864, 17, 640, 1280, 1280, 1920, 1.0), (1008283903, 2090988864, 17, 640, 1280, 1824, 2464, 1.0), (1008283903, 2090988864, 17, 1280, 1920, 0, 640, 1.0), (1008283903, 2090988864, 17, 1280, 1920, 640, 1280, 1.0), (1008283903, 2090988864, 17, 1280, 1920, 1280, 1920, 1.0), (1008283903, 2090988864, 17, 1280, 1920, 1824, 2464, 1.0), (1008283903, 2090988864, 17, 1920, 2560, 0, 640, 1.0), (1008283903, 2090988864, 17, 1920, 2560, 640, 1280, 1.0), (1008283903, 2090988864, 17, 1920, 2560, 1280, 1920, 1.0), (1008283903, 2090988864, 17, 1920, 2560, 1824, 2464, 1.0), (1008283903, 2090988864, 17, 2560, 3200, 0, 640, 1.0), (1008283903, 2090988864, 17, 2560, 3200, 640, 1280, 1.0), (1008283903, 2090988864, 17, 2560, 3200, 1280, 1920, 1.0), (1008283903, 2090988864, 17, 2560, 3200, 1824, 2464, 1.0), (1008283903, 2090988864, 17, 2640, 3280, 0, 640, 1.0), (1008283903, 2090988864, 17, 2640, 3280, 640, 1280, 1.0), (1008283903, 2090988864, 17, 2640, 3280, 1280, 1920, 1.0), (1008283903, 2090988864, 17, 2640, 3280, 1824, 2464, 1.0)] list_photo_ids_cropped : [1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903] batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903) and `type` in (17) Loaded 24 chid ids of type : 17 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1930014930,1930014931,1930014932,1930014933,1930014934,1930014935,1930014936,1930014937,1930014938,1930014939,1930014940,1930014941,1930014942,1930014943,1930014944,1930014945,1930014946,1930014947,1930014948,1930014949,1930014950,1930014951,1930014952,1930014953) SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1930014930,1930014931,1930014932,1930014933,1930014934,1930014935,1930014936,1930014937,1930014938,1930014939,1930014940,1930014941,1930014942,1930014943,1930014944,1930014945,1930014946,1930014947,1930014948,1930014949,1930014950,1930014951,1930014952,1930014953) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1930014930,1930014931,1930014932,1930014933,1930014934,1930014935,1930014936,1930014937,1930014938,1930014939,1930014940,1930014941,1930014942,1930014943,1930014944,1930014945,1930014946,1930014947,1930014948,1930014949,1930014950,1930014951,1930014952,1930014953) treat the image : temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53.jpg , 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 before upload mediasElapsed time : 0.32936644554138184 on upload les photos avec python init cache_photo without model_param we have 24 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1748281349_935833 INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `mtr_user_id`, `created_at`) VALUES (23354469, 1361143228, 0, NOW()),(23354469, 1361143229, 0, NOW()),(23354469, 1361143230, 0, NOW()),(23354469, 1361143231, 0, NOW()),(23354469, 1361143232, 0, NOW()),(23354469, 1361143233, 0, NOW()),(23354469, 1361143234, 0, NOW()),(23354469, 1361143235, 0, NOW()),(23354469, 1361143236, 0, NOW()),(23354469, 1361143237, 0, NOW()),(23354469, 1361143238, 0, NOW()),(23354469, 1361143239, 0, NOW()),(23354469, 1361143240, 0, NOW()),(23354469, 1361143241, 0, NOW()),(23354469, 1361143242, 0, NOW()),(23354469, 1361143243, 0, NOW()),(23354469, 1361143244, 0, NOW()),(23354469, 1361143245, 0, NOW()),(23354469, 1361143246, 0, NOW()),(23354469, 1361143247, 0, NOW()),(23354469, 1361143248, 0, NOW()),(23354469, 1361143250, 0, NOW()),(23354469, 1361143251, 0, NOW()),(23354469, 1361143252, 0, NOW()) 24 we have uploaded 24 photos in the portfolio 23354469 Importing ! upload mediasElapsed time : 6.544237852096558 , 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(1930014930, 1361143228, 0), (1930014931, 1361143229, 0), (1930014932, 1361143230, 0), (1930014933, 1361143231, 0), (1930014934, 1361143232, 0), (1930014935, 1361143233, 0), (1930014936, 1361143234, 0), (1930014937, 1361143235, 0), (1930014938, 1361143236, 0), (1930014939, 1361143237, 0), (1930014940, 1361143238, 0), (1930014941, 1361143239, 0), (1930014942, 1361143240, 0), (1930014943, 1361143241, 0), (1930014944, 1361143242, 0), (1930014945, 1361143243, 0), (1930014946, 1361143244, 0), (1930014947, 1361143245, 0), (1930014948, 1361143246, 0), (1930014949, 1361143247, 0), (1930014950, 1361143248, 0), (1930014951, 1361143250, 0), (1930014952, 1361143251, 0), (1930014953, 1361143252, 0)] Saving 7 CHIs. list_chi_tile : [": {'photo_id': 1361143237, 'hashtag_id': 511548407, 'type': 3243, 'x0': 180, 'x1': 640, 'y0': 611, 'y1': 640, 'score': 1.0, 'id': 0, 'points': ['311,640,292,640,268,640,240,640,229,640,223,640,188,640,181,640,180,640,186,640,211,640,216,640,211,640,211,640,244,640,242,640,261,640,275,640,298,640,357,640,380,640,409,640,548,640,557,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,635,640,628,640,628,576,621,543,612,490,611,424,621,352,640', '311,640,292,640,268,640,240,640,229,640,223,640,188,640,181,640,180,640,186,640,211,640,216,640,211,640,211,640,244,640,242,640,261,640,275,640,298,640,357,640,380,640,409,640,548,640,557,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,635,640,628,640,628,576,621,543,612,490,611,424,621,352,640'], 'sub_photo_id': 0, 'rles': [[1929991390, 487, 611, 30], [1929991390, 481, 612, 64], [1929991390, 474, 613, 75], [1929991390, 467, 614, 86], [1929991390, 461, 615, 95], [1929991390, 454, 616, 106], [1929991390, 448, 617, 116], [1929991390, 441, 618, 126], [1929991390, 434, 619, 137], [1929991390, 428, 620, 147], [1929991390, 423, 621, 158], [1929991390, 419, 622, 171], [1929991390, 415, 623, 185], [1929991390, 411, 624, 198], [1929991390, 408, 625, 210], [1929991390, 404, 626, 224], [1929991390, 400, 627, 237], [1929991390, 396, 628, 244], [1929991390, 392, 629, 248], [1929991390, 389, 630, 251], [1929991390, 385, 631, 255], [1929991390, 381, 632, 259], [1929991390, 377, 633, 263], [1929991390, 374, 634, 266], [1929991390, 370, 635, 270], [1929991390, 366, 636, 274], [1929991390, 362, 637, 278], [1929991390, 359, 638, 281], [1929991390, 355, 639, 285]], 'hashtag': '', 'sum_segment': 0}", ": {'photo_id': 1361143238, 'hashtag_id': 511548407, 'type': 3243, 'x0': 180, 'x1': 640, 'y0': 0, 'y1': 640, 'score': 1.0, 'id': 0, 'points': ['311,11,292,25,268,58,240,82,229,100,223,127,188,148,181,171,180,226,186,327,211,393,216,463,211,472,211,487,238,640,244,640,242,640,261,640,275,640,298,640,357,640,380,640,409,640,548,640,557,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,625,640,566,640,542,640,454,640,388,640,346,640,295,640,246,640,125,640,99,640,63,640,28,640,27,640,17,640,8,640,9,640,0,640,0,640,0,640,0,576,0,543,0,490,0,424,0,352,0', '311,11,292,25,268,58,240,82,229,100,223,127,188,148,181,171,180,226,186,327,211,393,216,463,211,472,211,487,238,640,244,640,242,640,261,640,275,640,298,640,357,640,380,640,409,640,548,640,557,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,625,640,566,640,542,640,454,640,388,640,346,640,295,640,246,640,125,640,99,640,63,640,28,640,27,640,17,640,8,640,9,640,0,640,0,640,0,640,0,576,0,543,0,490,0,424,0,352,0'], 'sub_photo_id': 0, 'rles': [[1929991390, 351, 0, 289], [1929991390, 347, 1, 293], [1929991390, 344, 2, 296], [1929991390, 340, 3, 300], [1929991390, 336, 4, 304], [1929991390, 332, 5, 308], [1929991390, 328, 6, 312], [1929991390, 325, 7, 315], [1929991390, 321, 8, 319], [1929991390, 317, 9, 323], [1929991390, 313, 10, 327], [1929991390, 311, 11, 329], [1929991390, 309, 12, 331], [1929991390, 308, 13, 332], [1929991390, 307, 14, 333], [1929991390, 305, 15, 335], [1929991390, 304, 16, 336], [1929991390, 303, 17, 337], [1929991390, 301, 18, 339], [1929991390, 300, 19, 340], [1929991390, 299, 20, 341], [1929991390, 297, 21, 343], [1929991390, 296, 22, 344], [1929991390, 295, 23, 345], [1929991390, 293, 24, 347], [1929991390, 292, 25, 348], [1929991390, 291, 26, 349], [1929991390, 291, 27, 349], [1929991390, 290, 28, 350], [1929991390, 289, 29, 351], [1929991390, 288, 30, 352], [1929991390, 288, 31, 352], [1929991390, 287, 32, 353], [1929991390, 286, 33, 354], [1929991390, 285, 34, 355], [1929991390, 285, 35, 355], [1929991390, 284, 36, 356], [1929991390, 283, 37, 357], [1929991390, 283, 38, 357], [1929991390, 282, 39, 358], [1929991390, 281, 40, 359], [1929991390, 280, 41, 360], [1929991390, 280, 42, 360], [1929991390, 279, 43, 361], [1929991390, 278, 44, 362], [1929991390, 277, 45, 363], [1929991390, 277, 46, 363], [1929991390, 276, 47, 364], [1929991390, 275, 48, 365], [1929991390, 275, 49, 365], [1929991390, 274, 50, 366], [1929991390, 273, 51, 367], [1929991390, 272, 52, 368], [1929991390, 272, 53, 368], [1929991390, 271, 54, 369], [1929991390, 270, 55, 370], [1929991390, 269, 56, 371], [1929991390, 269, 57, 371], [1929991390, 268, 58, 372], [1929991390, 267, 59, 373], [1929991390, 266, 60, 374], [1929991390, 264, 61, 376], [1929991390, 263, 62, 377], [1929991390, 262, 63, 378], [1929991390, 261, 64, 379], [1929991390, 260, 65, 380], [1929991390, 259, 66, 381], [1929991390, 257, 67, 383], [1929991390, 256, 68, 384], [1929991390, 255, 69, 385], [1929991390, 254, 70, 386], [1929991390, 253, 71, 387], [1929991390, 252, 72, 388], [1929991390, 250, 73, 390], [1929991390, 249, 74, 391], [1929991390, 248, 75, 392], [1929991390, 247, 76, 393], [1929991390, 246, 77, 394], [1929991390, 245, 78, 395], [1929991390, 243, 79, 397], [1929991390, 242, 80, 398], [1929991390, 241, 81, 399], [1929991390, 240, 82, 400], [1929991390, 239, 83, 401], [1929991390, 239, 84, 401], [1929991390, 238, 85, 402], [1929991390, 238, 86, 402], [1929991390, 237, 87, 403], [1929991390, 236, 88, 404], [1929991390, 236, 89, 404], [1929991390, 235, 90, 405], [1929991390, 234, 91, 406], [1929991390, 234, 92, 406], [1929991390, 233, 93, 407], [1929991390, 233, 94, 407], [1929991390, 232, 95, 408], [1929991390, 231, 96, 409], [1929991390, 231, 97, 409], [1929991390, 230, 98, 410], [1929991390, 230, 99, 410], [1929991390, 229, 100, 411], [1929991390, 229, 101, 411], [1929991390, 229, 102, 411], [1929991390, 228, 103, 412], [1929991390, 228, 104, 412], [1929991390, 228, 105, 412], [1929991390, 228, 106, 412], [1929991390, 227, 107, 413], [1929991390, 227, 108, 413], [1929991390, 227, 109, 413], [1929991390, 227, 110, 413], [1929991390, 227, 111, 413], [1929991390, 226, 112, 414], [1929991390, 226, 113, 414], [1929991390, 226, 114, 414], [1929991390, 226, 115, 414], [1929991390, 225, 116, 415], [1929991390, 225, 117, 415], [1929991390, 225, 118, 415], [1929991390, 225, 119, 415], [1929991390, 225, 120, 415], [1929991390, 224, 121, 416], [1929991390, 224, 122, 416], [1929991390, 224, 123, 416], [1929991390, 224, 124, 416], [1929991390, 223, 125, 417], [1929991390, 223, 126, 417], [1929991390, 223, 127, 417], [1929991390, 221, 128, 419], [1929991390, 219, 129, 421], [1929991390, 218, 130, 422], [1929991390, 216, 131, 424], [1929991390, 214, 132, 426], [1929991390, 213, 133, 427], [1929991390, 211, 134, 429], [1929991390, 209, 135, 431], [1929991390, 208, 136, 432], [1929991390, 206, 137, 434], [1929991390, 204, 138, 436], [1929991390, 203, 139, 437], [1929991390, 201, 140, 439], [1929991390, 199, 141, 441], [1929991390, 198, 142, 442], [1929991390, 196, 143, 444], [1929991390, 194, 144, 446], [1929991390, 193, 145, 447], [1929991390, 191, 146, 449], [1929991390, 189, 147, 451], [1929991390, 188, 148, 452], [1929991390, 188, 149, 452], [1929991390, 187, 150, 453], [1929991390, 187, 151, 453], [1929991390, 187, 152, 453], [1929991390, 186, 153, 454], [1929991390, 186, 154, 454], [1929991390, 186, 155, 454], [1929991390, 186, 156, 454], [1929991390, 185, 157, 455], [1929991390, 185, 158, 455], [1929991390, 185, 159, 455], [1929991390, 184, 160, 456], [1929991390, 184, 161, 456], [1929991390, 184, 162, 456], [1929991390, 183, 163, 457], [1929991390, 183, 164, 457], [1929991390, 183, 165, 457], [1929991390, 183, 166, 457], [1929991390, 182, 167, 458], [1929991390, 182, 168, 458], [1929991390, 182, 169, 458], [1929991390, 181, 170, 459], [1929991390, 181, 171, 459], [1929991390, 181, 172, 459], [1929991390, 181, 173, 459], [1929991390, 181, 174, 459], [1929991390, 181, 175, 459], [1929991390, 181, 176, 459], [1929991390, 181, 177, 459], [1929991390, 181, 178, 459], [1929991390, 181, 179, 459], [1929991390, 181, 180, 459], [1929991390, 181, 181, 459], [1929991390, 181, 182, 459], [1929991390, 181, 183, 459], [1929991390, 181, 184, 459], [1929991390, 181, 185, 459], [1929991390, 181, 186, 459], [1929991390, 181, 187, 459], [1929991390, 181, 188, 459], [1929991390, 181, 189, 459], [1929991390, 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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 7 chid ids of type : 3243 INSERT IGNORE INTO MTRPhoto.crop_polygon_points (`crop_hashtag_id`, `points`) VALUES (%s, %s) Number RLEs to save : 2937 INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) first line : ('3813397206', '487', '611', '30') ... last line : ('3813397212', '108', '531', '8') 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 : 6.795034170150757 map_pid_results : {'1361143228': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_0.jpg'], '1361143229': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_1.jpg'], '1361143230': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_2.jpg'], '1361143231': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_3.jpg'], '1361143232': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_4.jpg'], '1361143233': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_5.jpg'], '1361143234': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_6.jpg'], '1361143235': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_7.jpg'], '1361143236': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_8.jpg'], '1361143237': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_9.jpg'], '1361143238': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_10.jpg'], '1361143239': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_11.jpg'], '1361143240': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_12.jpg'], '1361143241': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_13.jpg'], '1361143242': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_14.jpg'], '1361143243': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_15.jpg'], '1361143244': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_16.jpg'], '1361143245': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_17.jpg'], '1361143246': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_18.jpg'], '1361143247': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_19.jpg'], '1361143248': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_20.jpg'], '1361143250': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_21.jpg'], '1361143251': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_22.jpg'], '1361143252': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_23.jpg']} After datou_step_exec type output : time spend for datou_step_exec : 13.18485713005066 time spend to save output : 6.866455078125e-05 total time spend for step 1 : 13.18492579460144 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1361143228': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_0.jpg'], '1361143229': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_1.jpg'], '1361143230': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_2.jpg'], '1361143231': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_3.jpg'], '1361143232': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_4.jpg'], '1361143233': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_5.jpg'], '1361143234': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_6.jpg'], '1361143235': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_7.jpg'], '1361143236': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_8.jpg'], '1361143237': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_9.jpg'], '1361143238': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_10.jpg'], '1361143239': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_11.jpg'], '1361143240': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_12.jpg'], '1361143241': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_13.jpg'], '1361143242': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_14.jpg'], '1361143243': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_15.jpg'], '1361143244': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_16.jpg'], '1361143245': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_17.jpg'], '1361143246': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_18.jpg'], '1361143247': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_19.jpg'], '1361143248': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_20.jpg'], '1361143250': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_21.jpg'], '1361143251': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_22.jpg'], '1361143252': ['temp/1748281341_935833_1008283903_6d008d31a1477b2e98cbafa96bd48e53_23.jpg']} batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 1361143228,1361143229,1361143230,1361143231,1361143232,1361143233,1361143234,1361143235,1361143236,1361143237,1361143238,1361143239,1361143240,1361143241,1361143242,1361143243,1361143244,1361143245,1361143246,1361143247,1361143248,1361143250,1361143251,1361143252) and `type` in (3243) Loaded 7 chid ids of type : 3243 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (3813397206,3813397207,3813397208,3813397209,3813397210,3813397211,3813397212) ++WARNING : duplicated polygon, we should remove this data for chi_id : 3813397206. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3813397207. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3813397208. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3813397209. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3813397210. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3813397211. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3813397212. Ignored now SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (3813397206,3813397207,3813397208,3813397209,3813397210,3813397211,3813397212) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (3813397206,3813397207,3813397208,3813397209,3813397210,3813397211,3813397212) fin du test de tile ############################### TEST rotate_chi ################################ test rotate avec chi rectangles, rles, polygones Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2970 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=2970 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= 2970 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=2970 # 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 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 (3337029) 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 (3337029) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 1003369118, 'portfolio_id': 3337029}] map_portfolio_id_photo_id: {3337029: [1003369118]} ##### Call download_photos : nb_thread : 5 begin to download photo : 1003369118 download finish for photo 1003369118 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 : 0.30612826347351074 #### 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 Mon May 26 19:42:35 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/1748281355_935833_1003369118_58171420504d0b5f05a1233b6c515509_65826337.jpg': 1003369118} map_photo_id_path_extension : {1003369118: {'path': 'temp/1748281355_935833_1003369118_58171420504d0b5f05a1233b6c515509_65826337.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step_rotate ! Warning, new_feed_id is empty ! We are in a linear step without datou_depend ! rotate photos of 0,90,180,270 degres batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 1003369118) and `type` in (3086) Loaded 16 chid ids of type : 3086 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1928951187,1928951188,1928951189,1928951190,1928951191,1928951192,1928951193,1928951194,1928951195,1928951196,1928951197,1928951198,1928951199,1928951200,1928951201,1928951202) ++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1928951187,1928951188,1928951189,1928951190,1928951191,1928951192,1928951193,1928951194,1928951195,1928951196,1928951197,1928951198,1928951199,1928951200,1928951201,1928951202) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1928951187,1928951188,1928951189,1928951190,1928951191,1928951192,1928951193,1928951194,1928951195,1928951196,1928951197,1928951198,1928951199,1928951200,1928951201,1928951202) map_chi : {1003369118: [, , , , , , , , , , , , , , , ]} https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc feed_id_new_photos : 23354474 photo_id in download_rotate_and_save : 1003369118 list_chi_loc : 16 Use all angle ! Rotation of photo 1003369118 of 0 degree temp/1748281355_935833_1003369118_58171420504d0b5f05a1233b6c515509_65826337.jpg [, , , , , , , , , , , , , , , ] 0 remove_crop_border : False version de PIL : 9.5.0 [[ 1. 0.] [-0. 1.]] 0 [[ 1. 0.] [-0. 1.]] shrink_image : False len(list_crops) : 16 time for calcul the mask position with numpy : 0.010335445404052734 nb_pixel_total : 110633 time to create 1 rle with old method : 0.12132477760314941 .time for calcul the mask position with numpy : 0.01049661636352539 nb_pixel_total : 15826 time to create 1 rle with old method : 0.023754596710205078 .time for calcul the mask position with numpy : 0.008863210678100586 nb_pixel_total : 5286 time to create 1 rle with old method : 0.0059053897857666016 .time for calcul the mask position with numpy : 0.009178876876831055 nb_pixel_total : 1633 time to create 1 rle with old method : 0.0017359256744384766 .time for calcul the mask position with numpy : 0.009585142135620117 nb_pixel_total : 105533 time to create 1 rle with old method : 0.12050342559814453 .time for calcul the mask position with numpy : 0.009964942932128906 nb_pixel_total : 4393 time to create 1 rle with old method : 0.004967212677001953 .time for calcul the mask position with numpy : 0.008567094802856445 nb_pixel_total : 632 time to create 1 rle with old method : 0.0007040500640869141 .time for calcul the mask position with numpy : 0.01280069351196289 nb_pixel_total : 62627 time to create 1 rle with old method : 0.0687706470489502 .time for calcul the mask position with numpy : 0.009112834930419922 nb_pixel_total : 33681 time to create 1 rle with old method : 0.03654885292053223 .time for calcul the mask position with numpy : 0.009103536605834961 nb_pixel_total : 37724 time to create 1 rle with old method : 0.04169273376464844 .time for calcul the mask position with numpy : 0.010039567947387695 nb_pixel_total : 48775 time to create 1 rle with old method : 0.055329084396362305 .time for calcul the mask position with numpy : 0.08136534690856934 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.16922855377197266 .time for calcul the mask position with numpy : 0.008761405944824219 nb_pixel_total : 2310 time to create 1 rle with old method : 0.0024919509887695312 .time for calcul the mask position with numpy : 0.008617877960205078 nb_pixel_total : 2256 time to create 1 rle with old method : 0.002657651901245117 .time for calcul the mask position with numpy : 0.008857011795043945 nb_pixel_total : 3112 time to create 1 rle with old method : 0.0035474300384521484 .time for calcul the mask position with numpy : 0.00872492790222168 nb_pixel_total : 1662 time to create 1 rle with old method : 0.001971721649169922 .len(list_crops_rotate) : 16 list_crops_rotate : : {'photo_id': 0, 'hashtag_id': 493012381, 'type': 3230, 'x0': 0, 'x1': 574, 'y0': 1038, 'y1': 1438, 'score': 1.0, 'id': None, 'points': ['1,1038,97,1108,179,1182,245,1230,274,1204,337,1245,310,1279,313,1288,577,1440,-1,1438'], 'sub_photo_id': 0, 'rles': [(-1, 1, 1038, 1), (-1, 1, 1039, 3), (-1, 1, 1040, 4), (-1, 1, 1041, 5), (-1, 1, 1042, 7), (-1, 1, 1043, 8), (-1, 1, 1044, 9), (-1, 1, 1045, 11), (-1, 1, 1046, 12), (-1, 1, 1047, 14), (-1, 1, 1048, 15), (-1, 1, 1049, 16), (-1, 1, 1050, 18), (-1, 1, 1051, 19), (-1, 1, 1052, 20), (-1, 1, 1053, 22), (-1, 1, 1054, 23), (-1, 1, 1055, 25), (-1, 1, 1056, 26), (-1, 1, 1057, 27), (-1, 1, 1058, 29), (-1, 1, 1059, 30), (-1, 1, 1060, 31), (-1, 1, 1061, 33), (-1, 1, 1062, 34), (-1, 1, 1063, 35), (-1, 1, 1064, 37), (-1, 1, 1065, 38), (-1, 1, 1066, 40), (-1, 1, 1067, 41), (-1, 1, 1068, 42), (-1, 1, 1069, 44), (-1, 1, 1070, 45), (-1, 1, 1071, 46), (-1, 1, 1072, 48), (-1, 1, 1073, 49), (-1, 1, 1074, 51), (-1, 1, 1075, 52), (-1, 1, 1076, 53), (-1, 1, 1077, 55), (-1, 1, 1078, 56), (-1, 1, 1079, 57), (-1, 1, 1080, 59), (-1, 1, 1081, 60), (-1, 1, 1082, 62), (-1, 1, 1083, 63), (-1, 1, 1084, 64), (-1, 1, 1085, 66), (-1, 1, 1086, 67), (-1, 1, 1087, 68), (-1, 1, 1088, 70), (-1, 1, 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(-1, 1124, 1267, 70), (-1, 1125, 1268, 69), (-1, 1125, 1269, 70), (-1, 1125, 1270, 70), (-1, 1125, 1271, 71), (-1, 1125, 1272, 71), (-1, 1126, 1273, 71), (-1, 1126, 1274, 71), (-1, 1126, 1275, 72), (-1, 1126, 1276, 72), (-1, 1127, 1277, 71), (-1, 1127, 1278, 72), (-1, 1127, 1279, 72), (-1, 1154, 1280, 45), (-1, 1156, 1281, 44), (-1, 1158, 1282, 42), (-1, 1160, 1283, 40), (-1, 1161, 1284, 39), (-1, 1163, 1285, 37), (-1, 1165, 1286, 35), (-1, 1166, 1287, 34), (-1, 1168, 1288, 32), (-1, 1170, 1289, 29), (-1, 1172, 1290, 27), (-1, 1173, 1291, 26), (-1, 1175, 1292, 24), (-1, 1176, 1293, 23), (-1, 1177, 1294, 22), (-1, 1179, 1295, 20), (-1, 1180, 1296, 19), (-1, 1181, 1297, 15), (-1, 1183, 1298, 9), (-1, 1184, 1299, 3)], 'hashtag': '', 'sum_segment': 0},: {'photo_id': 0, 'hashtag_id': 493012381, 'type': 3230, 'x0': 311, 'x1': 371, 'y0': 832, 'y1': 907, 'score': 1.0, 'id': None, 'points': ['311,841,362,907,371,901,347,864,356,855,352,841,346,832,328,837'], 'sub_photo_id': 0, 'rles': [(-1, 345, 832, 2), (-1, 341, 833, 7), (-1, 338, 834, 10), (-1, 334, 835, 15), (-1, 330, 836, 20), (-1, 326, 837, 24), (-1, 322, 838, 29), (-1, 318, 839, 34), (-1, 314, 840, 38), (-1, 311, 841, 42), (-1, 312, 842, 41), (-1, 313, 843, 41), (-1, 313, 844, 41), (-1, 314, 845, 40), (-1, 315, 846, 39), (-1, 316, 847, 39), (-1, 316, 848, 39), (-1, 317, 849, 38), (-1, 318, 850, 38), (-1, 319, 851, 37), (-1, 319, 852, 37), (-1, 320, 853, 36), (-1, 321, 854, 36), (-1, 322, 855, 35), (-1, 323, 856, 33), (-1, 323, 857, 32), (-1, 324, 858, 30), (-1, 325, 859, 28), (-1, 326, 860, 26), (-1, 326, 861, 25), (-1, 327, 862, 23), (-1, 328, 863, 21), (-1, 329, 864, 19), (-1, 330, 865, 19), (-1, 330, 866, 19), (-1, 331, 867, 19), (-1, 332, 868, 19), (-1, 333, 869, 18), (-1, 333, 870, 19), (-1, 334, 871, 19), (-1, 335, 872, 18), (-1, 336, 873, 18), (-1, 336, 874, 18), (-1, 337, 875, 18), (-1, 338, 876, 18), (-1, 339, 877, 17), (-1, 340, 878, 17), (-1, 340, 879, 18), (-1, 341, 880, 17), (-1, 342, 881, 17), (-1, 343, 882, 17), (-1, 343, 883, 17), (-1, 344, 884, 17), (-1, 345, 885, 17), (-1, 346, 886, 16), (-1, 347, 887, 16), (-1, 347, 888, 17), (-1, 348, 889, 16), (-1, 349, 890, 16), (-1, 350, 891, 16), (-1, 350, 892, 16), (-1, 351, 893, 16), (-1, 352, 894, 15), (-1, 353, 895, 15), (-1, 353, 896, 16), (-1, 354, 897, 15), (-1, 355, 898, 15), (-1, 356, 899, 15), (-1, 357, 900, 14), (-1, 357, 901, 15), (-1, 358, 902, 13), (-1, 359, 903, 10), (-1, 360, 904, 8), (-1, 360, 905, 6), (-1, 361, 906, 4), (-1, 362, 907, 1)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1003369118 of 90 degree temp/1748281355_935833_1003369118_58171420504d0b5f05a1233b6c515509_65826337.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 len(list_crops) : 16 time for calcul the mask position with numpy : 0.009262561798095703 nb_pixel_total : 110633 time to create 1 rle with old method : 0.11922383308410645 .time for calcul the mask position with numpy : 0.009510040283203125 nb_pixel_total : 15826 time to create 1 rle with old method : 0.017119884490966797 .time for calcul the mask position with numpy : 0.00867009162902832 nb_pixel_total : 5286 time to create 1 rle with old method : 0.0060613155364990234 .time for calcul the mask position with numpy : 0.00880122184753418 nb_pixel_total : 1633 time to create 1 rle with old method : 0.0018107891082763672 .time for calcul the mask position with numpy : 0.009190559387207031 nb_pixel_total : 105533 time to create 1 rle with old method : 0.11174678802490234 .time for calcul the mask position with numpy : 0.008971214294433594 nb_pixel_total : 4393 time to create 1 rle with old method : 0.004788637161254883 .time for calcul the mask position with numpy : 0.00915217399597168 nb_pixel_total : 632 time to create 1 rle with old method : 0.0007817745208740234 .time for calcul the mask position with numpy : 0.009321928024291992 nb_pixel_total : 62627 time to create 1 rle with old method : 0.06952095031738281 .time for calcul the mask position with numpy : 0.00923299789428711 nb_pixel_total : 33681 time to create 1 rle with old method : 0.03771662712097168 .time for calcul the mask position with numpy : 0.009139537811279297 nb_pixel_total : 37724 time to create 1 rle with old method : 0.04208540916442871 .time for calcul the mask position with numpy : 0.0091094970703125 nb_pixel_total : 48775 time to create 1 rle with old method : 0.05378079414367676 .time for calcul the mask position with numpy : 0.05376005172729492 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.3088698387145996 .time for calcul the mask position with numpy : 0.01036691665649414 nb_pixel_total : 2310 time to create 1 rle with old method : 0.003036975860595703 .time for calcul the mask position with numpy : 0.010379791259765625 nb_pixel_total : 2256 time to create 1 rle with old method : 0.0025479793548583984 .time for calcul the mask position with numpy : 0.009033441543579102 nb_pixel_total : 3112 time to create 1 rle with old method : 0.0035271644592285156 .time for calcul the mask position with numpy : 0.009729862213134766 nb_pixel_total : 1662 time to create 1 rle with old method : 0.0023849010467529297 .len(list_crops_rotate) : 16 list_crops_rotate : : {'photo_id': -1, 'hashtag_id': 493012381, 'type': 3230, 'x0': 1038, 'x1': 1438, 'y0': 1584, 'y1': 2158, 'score': 1.0, 'id': None, 'points': ['1038,2157,1108,2061,1182,1979,1230,1913,1204,1884,1245,1821,1279,1848,1288,1845,1440,1581,1438,2159'], 'sub_photo_id': 0, 'rles': [(-1, 1438, 1584, 1), (-1, 1437, 1585, 2), (-1, 1437, 1586, 2), (-1, 1436, 1587, 3), 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['1257,1046,1261,1035,1279,1031,1279,1014,1279,1005,1290,986,1299,974,1296,960,1282,959,1273,962,1266,966,1255,980,1234,1007,1234,1013,1239,1023,1242,1032,1248,1040'], 'sub_photo_id': 0, 'rles': [(-1, 1281, 959, 8), (-1, 1278, 960, 19), (-1, 1275, 961, 22), (-1, 1273, 962, 24), (-1, 1271, 963, 27), (-1, 1269, 964, 29), (-1, 1267, 965, 31), (-1, 1266, 966, 32), (-1, 1265, 967, 34), (-1, 1264, 968, 35), (-1, 1264, 969, 35), (-1, 1263, 970, 36), (-1, 1262, 971, 37), (-1, 1261, 972, 39), (-1, 1260, 973, 40), (-1, 1260, 974, 40), (-1, 1259, 975, 40), (-1, 1258, 976, 40), (-1, 1257, 977, 41), (-1, 1257, 978, 40), (-1, 1256, 979, 40), (-1, 1255, 980, 40), (-1, 1254, 981, 41), (-1, 1253, 982, 41), (-1, 1253, 983, 40), (-1, 1252, 984, 40), (-1, 1251, 985, 41), (-1, 1250, 986, 41), (-1, 1250, 987, 40), (-1, 1249, 988, 41), (-1, 1248, 989, 41), (-1, 1247, 990, 42), (-1, 1246, 991, 42), (-1, 1246, 992, 42), (-1, 1245, 993, 42), (-1, 1244, 994, 42), (-1, 1243, 995, 43), (-1, 1243, 996, 42), (-1, 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8), (-1, 1252, 1043, 7), (-1, 1254, 1044, 5), (-1, 1255, 1045, 3), (-1, 1257, 1046, 1)], 'hashtag': '', 'sum_segment': 0},: {'photo_id': -1, 'hashtag_id': 493012381, 'type': 3230, 'x0': 832, 'x1': 907, 'y0': 1787, 'y1': 1847, 'score': 1.0, 'id': None, 'points': ['841,1847,907,1796,901,1787,864,1811,855,1802,841,1806,832,1812,837,1830'], 'sub_photo_id': 0, 'rles': [(-1, 901, 1787, 1), (-1, 899, 1788, 4), (-1, 898, 1789, 5), (-1, 896, 1790, 8), (-1, 895, 1791, 10), (-1, 893, 1792, 12), (-1, 891, 1793, 15), (-1, 890, 1794, 17), (-1, 888, 1795, 19), (-1, 887, 1796, 21), (-1, 885, 1797, 22), (-1, 884, 1798, 22), (-1, 882, 1799, 22), (-1, 881, 1800, 22), (-1, 879, 1801, 23), (-1, 854, 1802, 2), (-1, 878, 1802, 22), (-1, 850, 1803, 7), (-1, 876, 1803, 23), (-1, 847, 1804, 11), (-1, 875, 1804, 23), (-1, 843, 1805, 16), (-1, 873, 1805, 24), (-1, 841, 1806, 19), (-1, 871, 1806, 24), (-1, 839, 1807, 22), (-1, 870, 1807, 24), (-1, 838, 1808, 24), (-1, 868, 1808, 25), (-1, 836, 1809, 27), (-1, 867, 1809, 24), (-1, 835, 1810, 29), (-1, 865, 1810, 25), (-1, 833, 1811, 56), (-1, 832, 1812, 55), (-1, 832, 1813, 54), (-1, 833, 1814, 52), (-1, 833, 1815, 51), (-1, 833, 1816, 49), (-1, 833, 1817, 48), (-1, 834, 1818, 46), (-1, 834, 1819, 44), (-1, 834, 1820, 43), (-1, 835, 1821, 41), (-1, 835, 1822, 40), (-1, 835, 1823, 38), (-1, 835, 1824, 37), (-1, 836, 1825, 35), (-1, 836, 1826, 33), (-1, 836, 1827, 32), (-1, 836, 1828, 31), (-1, 837, 1829, 28), (-1, 837, 1830, 27), (-1, 837, 1831, 26), (-1, 837, 1832, 25), (-1, 838, 1833, 22), (-1, 838, 1834, 21), (-1, 838, 1835, 20), (-1, 838, 1836, 18), (-1, 839, 1837, 16), (-1, 839, 1838, 15), (-1, 839, 1839, 14), (-1, 839, 1840, 12), (-1, 840, 1841, 10), (-1, 840, 1842, 9), (-1, 840, 1843, 7), (-1, 840, 1844, 6), (-1, 841, 1845, 4), (-1, 841, 1846, 2), (-1, 841, 1847, 1)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1003369118 of 180 degree temp/1748281355_935833_1003369118_58171420504d0b5f05a1233b6c515509_65826337.jpg [, , , , , , , , , , , , , , , ] 180 remove_crop_border : False version de PIL : 9.5.0 [[-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 len(list_crops) : 16 time for calcul the mask position with numpy : 0.011655569076538086 nb_pixel_total : 110633 time to create 1 rle with old method : 0.12763571739196777 .time for calcul the mask position with numpy : 0.008869171142578125 nb_pixel_total : 15826 time to create 1 rle with old method : 0.017416954040527344 .time for calcul the mask position with numpy : 0.0086669921875 nb_pixel_total : 5286 time to create 1 rle with old method : 0.0060656070709228516 .time for calcul the mask position with numpy : 0.009161233901977539 nb_pixel_total : 1633 time to create 1 rle with old method : 0.0019009113311767578 .time for calcul the mask position with numpy : 0.010352134704589844 nb_pixel_total : 105533 time to create 1 rle with old method : 0.11762237548828125 .time for calcul the mask position with numpy : 0.009520292282104492 nb_pixel_total : 4393 time to create 1 rle with old method : 0.005063056945800781 .time for calcul the mask position with numpy : 0.009068489074707031 nb_pixel_total : 632 time to create 1 rle with old method : 0.0007607936859130859 .time for calcul the mask position with numpy : 0.010000228881835938 nb_pixel_total : 62627 time to create 1 rle with old method : 0.07065033912658691 .time for calcul the mask position with numpy : 0.01015782356262207 nb_pixel_total : 33681 time to create 1 rle with old method : 0.03693389892578125 .time for calcul the mask position with numpy : 0.009378194808959961 nb_pixel_total : 37724 time to create 1 rle with old method : 0.04189634323120117 .time for calcul the mask position with numpy : 0.01100778579711914 nb_pixel_total : 48775 time to create 1 rle with old method : 0.05443215370178223 .time for calcul the mask position with numpy : 0.04859757423400879 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.3908700942993164 .time for calcul the mask position with numpy : 0.009210824966430664 nb_pixel_total : 2310 time to create 1 rle with old method : 0.0026712417602539062 .time for calcul the mask position with numpy : 0.008864402770996094 nb_pixel_total : 2256 time to create 1 rle with old method : 0.0025589466094970703 .time for calcul the mask position with numpy : 0.009508371353149414 nb_pixel_total : 3112 time to create 1 rle with old method : 0.003564596176147461 .time for calcul the mask position with numpy : 0.009251594543457031 nb_pixel_total : 1662 time to create 1 rle with old method : 0.0019600391387939453 .len(list_crops_rotate) : 16 list_crops_rotate : : {'photo_id': -2, 'hashtag_id': 493012381, 'type': 3230, 'x0': 1584, 'x1': 2158, 'y0': 0, 'y1': 400, 'score': 1.0, 'id': None, 'points': ['2157,400,2061,330,1979,256,1913,208,1884,234,1821,193,1848,159,1845,150,1581,-2,2159,0'], 'sub_photo_id': 0, 'rles': [(-1, 1584, 0, 575), (-1, 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(-1, 223, 263, 19), (-1, 222, 264, 21), (-1, 222, 265, 21), (-1, 221, 266, 23), (-1, 221, 267, 23), (-1, 220, 268, 25), (-1, 220, 269, 25), (-1, 219, 270, 27), (-1, 220, 271, 26), (-1, 222, 272, 25), (-1, 224, 273, 23), (-1, 226, 274, 22), (-1, 228, 275, 20), (-1, 230, 276, 19), (-1, 231, 277, 18), (-1, 232, 278, 18), (-1, 234, 279, 16), (-1, 235, 280, 16), (-1, 236, 281, 15), (-1, 237, 282, 14), (-1, 238, 283, 14), (-1, 240, 284, 12), (-1, 241, 285, 12), (-1, 242, 286, 11), (-1, 243, 287, 11), (-1, 245, 288, 9), (-1, 246, 289, 8), (-1, 247, 290, 8), (-1, 248, 291, 7), (-1, 250, 292, 6), (-1, 251, 293, 5), (-1, 252, 294, 4), (-1, 253, 295, 4), (-1, 254, 296, 3), (-1, 256, 297, 2), (-1, 257, 298, 1)], 'hashtag': '', 'sum_segment': 0},: {'photo_id': -2, 'hashtag_id': 2107752406, 'type': 3230, 'x0': 1487, 'x1': 1620, 'y0': 1392, 'y1': 1425, 'score': 1.0, 'id': None, 'points': ['1620,1425,1487,1417,1497,1392,1515,1392,1533,1396,1580,1411'], 'sub_photo_id': 0, 'rles': [(-1, 1497, 1392, 21), (-1, 1497, 1393, 25), (-1, 1496, 1394, 31), (-1, 1496, 1395, 35), (-1, 1495, 1396, 40), (-1, 1495, 1397, 43), (-1, 1495, 1398, 46), (-1, 1494, 1399, 50), (-1, 1494, 1400, 54), (-1, 1493, 1401, 58), (-1, 1493, 1402, 61), (-1, 1493, 1403, 64), (-1, 1492, 1404, 68), (-1, 1492, 1405, 71), (-1, 1491, 1406, 75), (-1, 1491, 1407, 79), (-1, 1491, 1408, 82), (-1, 1490, 1409, 86), (-1, 1490, 1410, 89), (-1, 1489, 1411, 93), (-1, 1489, 1412, 96), (-1, 1489, 1413, 99), (-1, 1488, 1414, 102), (-1, 1488, 1415, 105), (-1, 1487, 1416, 109), (-1, 1487, 1417, 112), (-1, 1496, 1418, 106), (-1, 1512, 1419, 93), (-1, 1529, 1420, 79), (-1, 1546, 1421, 64), (-1, 1562, 1422, 51), (-1, 1579, 1423, 37), (-1, 1596, 1424, 23), (-1, 1612, 1425, 9)], 'hashtag': '', 'sum_segment': 0},: {'photo_id': -2, 'hashtag_id': 2107752406, 'type': 3230, 'x0': 959, 'x1': 1046, 'y0': 139, 'y1': 204, 'score': 1.0, 'id': None, 'points': ['1046,180,1035,176,1031,158,1014,158,1005,158,986,147,974,138,960,141,959,155,962,164,966,171,980,182,1007,203,1013,203,1023,198,1032,195,1040,189'], 'sub_photo_id': 0, 'rles': [(-1, 972, 139, 3), (-1, 967, 140, 9), (-1, 963, 141, 15), (-1, 960, 142, 19), (-1, 960, 143, 20), (-1, 960, 144, 22), (-1, 960, 145, 23), (-1, 960, 146, 24), (-1, 960, 147, 26), (-1, 960, 148, 27), (-1, 960, 149, 29), (-1, 959, 150, 32), (-1, 959, 151, 34), (-1, 959, 152, 35), (-1, 959, 153, 37), (-1, 959, 154, 39), (-1, 959, 155, 40), (-1, 959, 156, 42), (-1, 959, 157, 44), (-1, 960, 158, 45), (-1, 960, 159, 72), (-1, 960, 160, 72), (-1, 961, 161, 71), (-1, 961, 162, 72), (-1, 961, 163, 72), (-1, 962, 164, 71), (-1, 962, 165, 71), (-1, 963, 166, 71), (-1, 963, 167, 71), (-1, 964, 168, 70), (-1, 964, 169, 70), (-1, 965, 170, 69), (-1, 965, 171, 70), (-1, 966, 172, 69), (-1, 967, 173, 68), (-1, 968, 174, 67), (-1, 970, 175, 66), (-1, 971, 176, 65), (-1, 972, 177, 65), (-1, 973, 178, 67), (-1, 975, 179, 67), (-1, 976, 180, 69), (-1, 977, 181, 70), (-1, 979, 182, 67), (-1, 980, 183, 66), (-1, 981, 184, 64), (-1, 982, 185, 62), (-1, 984, 186, 60), (-1, 985, 187, 58), (-1, 986, 188, 56), (-1, 988, 189, 54), (-1, 989, 190, 52), (-1, 990, 191, 50), (-1, 991, 192, 47), (-1, 993, 193, 44), (-1, 994, 194, 42), (-1, 995, 195, 39), (-1, 997, 196, 36), (-1, 998, 197, 33), (-1, 999, 198, 29), (-1, 1000, 199, 25), (-1, 1002, 200, 20), (-1, 1003, 201, 17), (-1, 1004, 202, 14), (-1, 1006, 203, 10), (-1, 1007, 204, 7)], 'hashtag': '', 'sum_segment': 0},: {'photo_id': -2, 'hashtag_id': 493012381, 'type': 3230, 'x0': 1787, 'x1': 1847, 'y0': 531, 'y1': 606, 'score': 1.0, 'id': None, 'points': ['1847,597,1796,531,1787,537,1811,574,1802,583,1806,597,1812,606,1830,601'], 'sub_photo_id': 0, 'rles': [(-1, 1796, 531, 1), (-1, 1794, 532, 4), (-1, 1793, 533, 6), (-1, 1791, 534, 8), (-1, 1790, 535, 10), (-1, 1788, 536, 13), (-1, 1787, 537, 15), (-1, 1788, 538, 14), (-1, 1788, 539, 15), (-1, 1789, 540, 15), (-1, 1790, 541, 15), (-1, 1790, 542, 16), (-1, 1791, 543, 15), (-1, 1792, 544, 15), (-1, 1792, 545, 16), (-1, 1793, 546, 16), (-1, 1793, 547, 16), (-1, 1794, 548, 16), (-1, 1795, 549, 16), (-1, 1795, 550, 17), (-1, 1796, 551, 16), (-1, 1797, 552, 16), (-1, 1797, 553, 17), (-1, 1798, 554, 17), (-1, 1799, 555, 17), (-1, 1799, 556, 17), (-1, 1800, 557, 17), (-1, 1801, 558, 17), (-1, 1801, 559, 18), (-1, 1802, 560, 17), (-1, 1803, 561, 17), (-1, 1803, 562, 18), (-1, 1804, 563, 18), (-1, 1805, 564, 18), (-1, 1805, 565, 18), (-1, 1806, 566, 18), (-1, 1806, 567, 19), (-1, 1807, 568, 19), (-1, 1808, 569, 18), (-1, 1808, 570, 19), (-1, 1809, 571, 19), (-1, 1810, 572, 19), (-1, 1810, 573, 19), (-1, 1811, 574, 19), (-1, 1810, 575, 21), (-1, 1809, 576, 23), (-1, 1808, 577, 25), (-1, 1807, 578, 26), (-1, 1806, 579, 28), (-1, 1805, 580, 30), (-1, 1804, 581, 32), (-1, 1803, 582, 33), (-1, 1802, 583, 35), (-1, 1802, 584, 36), (-1, 1803, 585, 36), (-1, 1803, 586, 37), (-1, 1803, 587, 37), (-1, 1803, 588, 38), (-1, 1804, 589, 38), (-1, 1804, 590, 39), (-1, 1804, 591, 39), (-1, 1805, 592, 39), (-1, 1805, 593, 40), (-1, 1805, 594, 41), (-1, 1805, 595, 41), (-1, 1806, 596, 41), (-1, 1806, 597, 42), (-1, 1807, 598, 38), (-1, 1807, 599, 34), (-1, 1808, 600, 29), (-1, 1809, 601, 24), (-1, 1809, 602, 20), (-1, 1810, 603, 15), (-1, 1811, 604, 10), (-1, 1811, 605, 7), (-1, 1812, 606, 2)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1003369118 of 270 degree temp/1748281355_935833_1003369118_58171420504d0b5f05a1233b6c515509_65826337.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 len(list_crops) : 16 time for calcul the mask position with numpy : 0.010370969772338867 nb_pixel_total : 110633 time to create 1 rle with old method : 0.1250905990600586 .time for calcul the mask position with numpy : 0.008994102478027344 nb_pixel_total : 15826 time to create 1 rle with old method : 0.018373727798461914 .time for calcul the mask position with numpy : 0.009282112121582031 nb_pixel_total : 5286 time to create 1 rle with old method : 0.005951404571533203 .time for calcul the mask position with numpy : 0.009139776229858398 nb_pixel_total : 1633 time to create 1 rle with old method : 0.001961231231689453 .time for calcul the mask position with numpy : 0.011041402816772461 nb_pixel_total : 105533 time to create 1 rle with old method : 0.11928486824035645 .time for calcul the mask position with numpy : 0.009256124496459961 nb_pixel_total : 4393 time to create 1 rle with old method : 0.005121707916259766 .time for calcul the mask position with numpy : 0.00878286361694336 nb_pixel_total : 632 time to create 1 rle with old method : 0.0007700920104980469 .time for calcul the mask position with numpy : 0.010109186172485352 nb_pixel_total : 62627 time to create 1 rle with old method : 0.07018804550170898 .time for calcul the mask position with numpy : 0.009238243103027344 nb_pixel_total : 33681 time to create 1 rle with old method : 0.039740562438964844 .time for calcul the mask position with numpy : 0.01021122932434082 nb_pixel_total : 37724 time to create 1 rle with old method : 0.043524980545043945 .time for calcul the mask position with numpy : 0.009604454040527344 nb_pixel_total : 48775 time to create 1 rle with old method : 0.05555534362792969 .time for calcul the mask position with numpy : 0.045500755310058594 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.15400290489196777 .time for calcul the mask position with numpy : 0.008708000183105469 nb_pixel_total : 2310 time to create 1 rle with old method : 0.0025482177734375 .time for calcul the mask position with numpy : 0.008947610855102539 nb_pixel_total : 2256 time to create 1 rle with old method : 0.002501249313354492 .time for calcul the mask position with numpy : 0.008915901184082031 nb_pixel_total : 3112 time to create 1 rle with old method : 0.0034759044647216797 .time for calcul the mask position with numpy : 0.00866389274597168 nb_pixel_total : 1662 time to create 1 rle with old method : 0.001916646957397461 .len(list_crops_rotate) : 16 list_crops_rotate : : {'photo_id': -3, 'hashtag_id': 493012381, 'type': 3230, 'x0': 0, 'x1': 400, 'y0': 0, 'y1': 574, 'score': 1.0, 'id': None, 'points': ['400,0,330,96,256,178,208,244,234,273,193,336,159,309,150,312,-2,576,0,-1'], 'sub_photo_id': 0, 'rles': [(-1, 0, 0, 301), (-1, 0, 1, 401), (-1, 0, 2, 400), (-1, 0, 3, 400), (-1, 0, 4, 399), (-1, 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23354474 time of upload the photos Elapsed time : 1.3632640838623047 map_filename_photo_id : 4 map_filename_photo_id : {'temp/1748281355_935833_1003369118_58171420504d0b5f05a1233b6c515509_658263370.jpg': 1361143335, 'temp/1748281355_935833_1003369118_58171420504d0b5f05a1233b6c515509_6582633790.jpg': 1361143336, 'temp/1748281355_935833_1003369118_58171420504d0b5f05a1233b6c515509_65826337180.jpg': 1361143337, 'temp/1748281355_935833_1003369118_58171420504d0b5f05a1233b6c515509_65826337270.jpg': 1361143338} Len new_chis : 4 Len list_new_chi_with_photo_id : 64 of type : 3230 list_new_chi_with_photo_id : [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ] batch 1 Loaded 64 chid ids of type : 3230 Number RLEs to save : 24654 TO DO : save crop sub photo not yet done ! batch 1 Loaded 64 chid ids of type : 3230 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 64 chid ids of type : 3230 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 64 chid ids of type : 3230 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++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 : 18.706146717071533 time spend to save output : 0.00014925003051757812 total time spend for step 1 : 18.70629596710205 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1361143335: ['1003369118', 'temp/1748281355_935833_1003369118_58171420504d0b5f05a1233b6c515509_658263370.jpg', [, , , , , , , , , , , , , , , ]], 1361143336: ['1003369118', 'temp/1748281355_935833_1003369118_58171420504d0b5f05a1233b6c515509_6582633790.jpg', [, , , , , , , , , , , , , , , ]], 1361143337: ['1003369118', 'temp/1748281355_935833_1003369118_58171420504d0b5f05a1233b6c515509_65826337180.jpg', [, , , , , , , , , , , , , , , ]], 1361143338: ['1003369118', 'temp/1748281355_935833_1003369118_58171420504d0b5f05a1233b6c515509_65826337270.jpg', [, , , , , , , , , , , , , , , ]]} batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 1361143335,1361143336,1361143337,1361143338) and `type` in (3230) Loaded 64 chid ids of type : 3230 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (3813397213,3813397214,3813397215,3813397216,3813397217,3813397218,3813397219,3813397220,3813397221,3813397222,3813397223,3813397224,3813397225,3813397226,3813397227,3813397228,3813397229,3813397230,3813397231,3813397232,3813397233,3813397234,3813397235,3813397236,3813397237,3813397238,3813397239,3813397240,3813397241,3813397242,3813397243,3813397244,3813397245,3813397246,3813397247,3813397248,3813397249,3813397250,3813397251,3813397252,3813397253,3813397254,3813397255,3813397256,3813397257,3813397258,3813397259,3813397260,3813397261,3813397262,3813397263,3813397264,3813397265,3813397266,3813397267,3813397268,3813397269,3813397270,3813397271,3813397272,3813397273,3813397274,3813397275,3813397276) ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (3813397213,3813397214,3813397215,3813397216,3813397217,3813397218,3813397219,3813397220,3813397221,3813397222,3813397223,3813397224,3813397225,3813397226,3813397227,3813397228,3813397229,3813397230,3813397231,3813397232,3813397233,3813397234,3813397235,3813397236,3813397237,3813397238,3813397239,3813397240,3813397241,3813397242,3813397243,3813397244,3813397245,3813397246,3813397247,3813397248,3813397249,3813397250,3813397251,3813397252,3813397253,3813397254,3813397255,3813397256,3813397257,3813397258,3813397259,3813397260,3813397261,3813397262,3813397263,3813397264,3813397265,3813397266,3813397267,3813397268,3813397269,3813397270,3813397271,3813397272,3813397273,3813397274,3813397275,3813397276) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (3813397213,3813397214,3813397215,3813397216,3813397217,3813397218,3813397219,3813397220,3813397221,3813397222,3813397223,3813397224,3813397225,3813397226,3813397227,3813397228,3813397229,3813397230,3813397231,3813397232,3813397233,3813397234,3813397235,3813397236,3813397237,3813397238,3813397239,3813397240,3813397241,3813397242,3813397243,3813397244,3813397245,3813397246,3813397247,3813397248,3813397249,3813397250,3813397251,3813397252,3813397253,3813397254,3813397255,3813397256,3813397257,3813397258,3813397259,3813397260,3813397261,3813397262,3813397263,3813397264,3813397265,3813397266,3813397267,3813397268,3813397269,3813397270,3813397271,3813397272,3813397273,3813397274,3813397275,3813397276) fin du test de rotate_chi Ayatollah of tests excluded it ! (Bon le prochain developpeur qui passe ici peut enlever ayatollah VR 11-2-21) name : rubbia_carac_pet_clair_0121 not run because too long ############################### TEST rubbia_carac_pet_clair_0121_no_cnn ################################ Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2719 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=2719 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= 2719 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=2719 # 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 ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 6479 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 6480 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7445 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 6509 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 6479 doesn't seem to be define in the database( WARNING : type of input 1 of step 6480 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 6479 doesn't seem to be define in the database( WARNING : type of input 1 of step 7445 doesn't seem to be define in the database( WARNING : type of output 1 of step 7445 doesn't seem to be define in the database( WARNING : type of input 3 of step 6509 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : merge_mask_thcl_custom, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final 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 (3373196) 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 (3373196) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 1009068724, 'portfolio_id': 3373196}, {'photo_id': 1009068683, 'portfolio_id': 3373196}] map_portfolio_id_photo_id: {3373196: [1009068724, 1009068683]} ##### Call download_photos : nb_thread : 5 begin to download photo : 1009068724 begin to download photo : 1009068683 download finish for photo 1009068683 download finish for photo 1009068724 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 : 2 ; length of list_pids : 2 ; length of list_args : 2 ##### After load_data_input time to download the photos : 0.21285128593444824 #### 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 : 4 step1:merge_mask_thcl_custom Mon May 26 19:42:54 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281374_935833_1009068683_4beb092fd180a8b620f754bb89015722.jpg': 1009068683, 'temp/1748281374_935833_1009068724_3e705379f97632c4a2fd676e348a335d.jpg': 1009068724} map_photo_id_path_extension : {1009068683: {'path': 'temp/1748281374_935833_1009068683_4beb092fd180a8b620f754bb89015722.jpg', 'extension': 'jpg'}, 1009068724: {'path': 'temp/1748281374_935833_1009068724_3e705379f97632c4a2fd676e348a335d.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Begin step merge_mask_thcl_custom args ['temp/1748281374_935833_1009068683_4beb092fd180a8b620f754bb89015722.jpg', 'temp/1748281374_935833_1009068724_3e705379f97632c4a2fd676e348a335d.jpg'] processing picture : temp/1748281374_935833_1009068683_4beb092fd180a8b620f754bb89015722.jpg processing picture : temp/1748281374_935833_1009068724_3e705379f97632c4a2fd676e348a335d.jpg map_subphoto_result {} batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 1009068683,1009068724) and `type` in (2800) Loaded 82 chid ids of type : 2800 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1941272807,1941272792,1941272797,1941272801,1941272788,1941272813,1941272802,1941272799,1941272815,1941272809,1941272786,1941272808,1941272791,1941272816,1941272796,1941272811,1941272817,1941272810,1941272814,1941272793,1941272803,1941272804,1941272805,1941272781,1941272789,1941272798,1941272812,1941272800,1941272784,1941272782,1941272794,1941272795,1941272806,1941272785,1941272790,1941272783,1941272787,1941270022,1941270015,1941270036,1941270021,1941270029,1941270018,1941270017,1941270025,1941607383,1941270019,1941270038,1941270040,1941270014,1941270039,1941270020,1941270030,1941270033,1941270028,1941270027,1941270026,1941270032,1941270031,1941270035,1941270037,1941270041,1941270012,1941270013,1941607380,1941607381,2202875936,1941270008,1941270016,1941607382,1941270034,2202875937,1941270010,1941270005,1941270007,1941270024,1941270011,2202875938,1941270023,1941607379,1941270006,1941270009) ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1941272807,1941272792,1941272797,1941272801,1941272788,1941272813,1941272802,1941272799,1941272815,1941272809,1941272786,1941272808,1941272791,1941272816,1941272796,1941272811,1941272817,1941272810,1941272814,1941272793,1941272803,1941272804,1941272805,1941272781,1941272789,1941272798,1941272812,1941272800,1941272784,1941272782,1941272794,1941272795,1941272806,1941272785,1941272790,1941272783,1941272787,1941270022,1941270015,1941270036,1941270021,1941270029,1941270018,1941270017,1941270025,1941607383,1941270019,1941270038,1941270040,1941270014,1941270039,1941270020,1941270030,1941270033,1941270028,1941270027,1941270026,1941270032,1941270031,1941270035,1941270037,1941270041,1941270012,1941270013,1941607380,1941607381,2202875936,1941270008,1941270016,1941607382,1941270034,2202875937,1941270010,1941270005,1941270007,1941270024,1941270011,2202875938,1941270023,1941607379,1941270006,1941270009) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1941272807,1941272792,1941272797,1941272801,1941272788,1941272813,1941272802,1941272799,1941272815,1941272809,1941272786,1941272808,1941272791,1941272816,1941272796,1941272811,1941272817,1941272810,1941272814,1941272793,1941272803,1941272804,1941272805,1941272781,1941272789,1941272798,1941272812,1941272800,1941272784,1941272782,1941272794,1941272795,1941272806,1941272785,1941272790,1941272783,1941272787,1941270022,1941270015,1941270036,1941270021,1941270029,1941270018,1941270017,1941270025,1941607383,1941270019,1941270038,1941270040,1941270014,1941270039,1941270020,1941270030,1941270033,1941270028,1941270027,1941270026,1941270032,1941270031,1941270035,1941270037,1941270041,1941270012,1941270013,1941607380,1941607381,2202875936,1941270008,1941270016,1941607382,1941270034,2202875937,1941270010,1941270005,1941270007,1941270024,1941270011,2202875938,1941270023,1941607379,1941270006,1941270009) As expected we have just one thcl present processing picture : 1009068683 begin to find the sub_photo_id : select crop_hashtag_id , sub_photo_id from MTRPhoto.crop_sub_photo_ids where crop_hashtag_id in (1941272807,1941272792,1941272797,1941272801,1941272788,1941272813,1941272802,1941272799,1941272815,1941272809,1941272786,1941272808,1941272791,1941272816,1941272796,1941272811,1941272817,1941272810,1941272814,1941272793,1941272803,1941272804,1941272805,1941272781,1941272789,1941272798,1941272812,1941272800,1941272784,1941272782,1941272794,1941272795,1941272806,1941272785,1941272790,1941272783,1941272787) ; chi 1941272807 saved with no changes chi 1941272792 saved with no changes chi 1941272797 saved with no changes chi 1941272801 saved with no changes chi 1941272788 saved with no changes chi 1941272813 saved with no changes chi 1941272802 saved with no changes chi 1941272799 saved with no changes chi 1941272815 saved with no changes chi 1941272809 saved with no changes chi 1941272786 saved with no changes chi 1941272808 saved with no changes chi 1941272791 saved with no changes select cps.photo_id, cps.thcl, h.hashtag, avg(cps.score), p.text,cps.hashtag_id FROM MTRPhoto.class_photo_score cps inner join MTRBack.hashtags h on h.hashtag_id = cps.hashtag_id inner join MTRBack.photos p on cps.photo_id = p.photo_id where cps.photo_id in(1009071263,1009221813,1009235446,1009235446,1009279340,1009279356,1009280654,1009282603,1009285753,1009285765,1009287138,1009288503,1009288546,1009289519,1009290969,1009291015,1009292124,1009293463,1009293494,1009302642,1009302924,1009314298,1009325392,1009325685,1009331808,1009337442,1009343208,1009348717,1009360426,1009360710,1009366170,1009370139,1009378373,1009378413,1009382207,1009387316,1009387465,1009397637,1009397719,1009407253,1009418244,1009418926,1009427413,1009430107,1009431383,1009433078,1009433144,1009434053,1009434384,1009434694,1009435681,1009436481,1009436964,1009437043,1009437239,1009438334,1009438590,1009440272,1009440278,1009442464,1009442509,1009442528,1009444076,1009444130,1009445413,1009445538,1009446902,1009448266,1009448396,1009449789,1009451629,1009451631,1009451750,1009452461,1009452961,1009452979,1009454196,1009454247,1009454337,1009455534,1009457025,1009457032,1009457228,1009460750,1009460967,1009466377,1009466452,1009467101,1009471967,1009472268,1009477695,1009478118,1009481722,1009482987,1009487453,1009487657,1009487950,1009489088,1009489218,1009489308,1009490315,1009490479,1009495060,1009495488,1009498944,1009500499,1009500766,1009506264,1009506270,1009506275,1009506746,1009509720,1009510178,1009511663,1009517339,1009517535,1009517849,1009521436,1009521607,1009525340,1009525354,1009525617,1009528261,1009529525,1009533039,1009533043,1009533518,1009536205,1009540619,1009540774,1009541035,1009543634,1009544885,1009545316,1009549107,1009549847,1009551213,1009552745,1009553914,1009557764,1009558260,1009560600,1009562085,1009566563,1009567165,1009570167,1009571289,1009576217,1009576836,1009579452,1009580609,1009585073,1009585473,1009587840,1009589122,1009593908,1009596967,1009597989,1009602701,1009603252,1009605651,1009606591,1009610545,1009610907,1009612085,1009612622,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,100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and thcl in (2444) group by h.hashtag order by cps.photo_id chi 1941272796 updated with thcls [2444] select cps.photo_id, cps.thcl, h.hashtag, avg(cps.score), p.text,cps.hashtag_id FROM MTRPhoto.class_photo_score cps inner join MTRBack.hashtags h on h.hashtag_id = cps.hashtag_id inner join MTRBack.photos p on cps.photo_id = p.photo_id where cps.photo_id in(1009071264,1009221814,1009235447,1009235447,1009279341,1009279357,1009280655,1009282604,1009285754,1009285766,1009287139,1009288504,1009288547,1009289520,1009290970,1009291016,1009292125,1009293464,1009293495,1009302643,1009302925,1009314299,1009325393,1009325686,1009331809,1009337443,1009343209,1009348718,1009360428,1009360711,1009366171,1009370140,1009378374,1009378414,1009382208,1009387317,1009387466,1009397639,1009397720,1009407254,1009418245,1009418927,1009427414,1009430108,1009431384,1009433079,1009433145,1009434054,1009434385,1009434695,1009435682,1009436482,1009436965,1009437044,1009437240,1009438336,1009438591,1009440273,1009440279,1009442465,1009442510,1009442529,1009444077,1009444131,1009445414,1009445539,1009446903,1009448267,1009448397,1009449790,1009451630,1009451632,1009451751,1009452462,1009452962,1009452980,1009454197,1009454248,1009454338,1009455535,1009457026,1009457033,1009457229,1009460751,1009460968,1009466378,1009466453,1009467102,1009471968,1009472269,1009477696,1009478120,1009481723,1009482988,1009487454,1009487658,1009487951,1009489089,1009489219,1009489309,1009490316,1009490480,1009495061,1009495489,1009498945,1009500500,1009500767,1009506265,1009506271,1009506276,1009506747,1009509721,1009510179,1009511664,1009517341,1009517536,1009517850,1009521437,1009521608,1009525341,1009525355,1009525618,1009528262,1009529526,1009533040,1009533045,1009533519,1009536206,1009540620,1009540775,1009541036,1009543635,1009544886,1009545317,1009549108,1009549848,1009551214,1009552746,1009553915,1009557765,1009558261,1009560601,1009562086,1009566564,1009567166,1009570168,1009571290,1009576219,1009576837,1009579453,1009580610,1009585074,1009585474,1009587841,1009589123,1009593909,1009596968,1009597990,1009602702,1009603253,1009605652,1009606592,1009610546,1009610908,1009612086,1009612623,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,100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and thcl in (2444) group by h.hashtag order by cps.photo_id chi 1941272793 updated with thcls [2444] chi 1941272803 saved with no changes chi 1941272804 saved with no changes chi 1941272805 saved with no changes chi 1941272781 saved with no changes chi 1941272789 saved with no changes chi 1941272798 saved with no changes chi 1941272812 saved with no changes chi 1941272800 saved with no changes chi 1941272784 saved with no changes chi 1941272782 saved with no changes chi 1941272794 saved with no changes chi 1941272795 saved with no changes chi 1941272806 saved with no changes chi 1941272785 saved with no changes chi 1941272790 saved with no changes chi 1941272783 saved with no changes chi 1941272787 saved with no changes processing picture : 1009068724 begin to find the sub_photo_id : select crop_hashtag_id , sub_photo_id from MTRPhoto.crop_sub_photo_ids where crop_hashtag_id in (1941270022,1941270015,1941270036,1941270021,1941270029,1941270018,1941270017,1941270025,1941607383,1941270019,1941270038,1941270040,1941270014,1941270039,1941270020,1941270030,1941270033,1941270028,1941270027,1941270026,1941270032,1941270031,1941270035,1941270037,1941270041,1941270012,1941270013,1941607380,1941607381,2202875936,1941270008,1941270016,1941607382,1941270034,2202875937,1941270010,1941270005,1941270007,1941270024,1941270011,2202875938,1941270023,1941607379,1941270006,1941270009) ; chi 1941270022 saved with no changes chi 1941270015 saved with no changes chi 1941270036 saved with no changes chi 1941270021 saved with no changes chi 1941270029 saved with no changes chi 1941270018 saved with no changes chi 1941270017 saved with no changes chi 1941270025 saved with no changes chi 1941607383 saved with no changes chi 1941270019 saved with no changes chi 1941270038 saved with no changes chi 1941270040 saved with no changes chi 1941270014 saved with no changes chi 1941270039 saved with no changes chi 1941270020 saved with no changes select cps.photo_id, cps.thcl, h.hashtag, avg(cps.score), p.text,cps.hashtag_id FROM MTRPhoto.class_photo_score cps inner join MTRBack.hashtags h on h.hashtag_id = cps.hashtag_id inner join MTRBack.photos p on cps.photo_id = p.photo_id where cps.photo_id in(1009070856,1009221556,1009235000,1009235000,1009235000,1009279342,1009279352,1009280656,1009282608,1009285758,1009285767,1009287134,1009288505,1009288551,1009289512,1009290974,1009291020,1009292129,1009293459,1009293499,1009302644,1009302926,1009314291,1009325388,1009325687,1009331813,1009337444,1009343213,1009348719,1009360418,1009360712,1009366172,1009370141,1009378378,1009378418,1009382212,1009387309,1009387467,1009397623,1009397721,1009407255,1009418249,1009418931,1009427415,1009430103,1009431388,1009433083,1009433140,1009434049,1009434049,1009434049,1009434377,1009434377,1009434377,1009434690,1009434690,1009434690,1009435683,1009436483,1009436969,1009436969,1009436969,1009437039,1009437244,1009438330,1009438586,1009438586,1009438586,1009440274,1009440283,1009440274,1009440274,1009442466,1009442502,1009442533,1009442533,1009442533,1009444071,1009444126,1009444126,1009444126,1009445418,1009445543,1009446898,1009446898,1009446898,1009448271,1009448392,1009448392,1009448392,1009449794,1009449794,1009449794,1009451625,1009451636,1009451746,1009451746,1009451746,1009452466,1009452466,1009452466,1009452957,1009452957,1009452957,1009452981,1009454192,1009454249,1009454339,1009454339,1009454339,1009455527,1009457021,1009457034,1009457224,1009457224,1009457224,1009460746,1009460746,1009460746,1009460963,1009466379,1009466448,1009467103,1009467103,1009467103,1009471963,1009471963,1009471963,1009472264,1009477697,1009478110,1009478110,1009478110,1009481715,1009481715,1009481715,1009482983,1009482983,1009482983,1009487455,1009487650,1009487952,1009487952,1009487952,1009489081,1009489223,1009489310,1009489310,1009489310,1009490311,1009490311,1009490311,1009490481,1009495056,1009495484,1009495484,1009495484,1009498949,1009498949,1009498949,1009500504,1009500504,1009500504,1009500755,1009506260,1009506266,1009506266,1009506280,1009506742,1009506742,1009506742,1009509716,1009510174,1009510174,1009510174,1009511665,1009511665,1009511665,1009517335,1009517537,1009517845,1009517845,1009517845,1009521429,1009521429,1009521429,1009521603,1009525336,1009525350,1009525619,1009525619,1009525619,1009528263,1009529518,1009529518,1009529518,1009533041,1009533047,1009533520,1009533520,1009533520,1009536210,1009536210,1009536210,1009540621,1009540767,1009541040,1009541040,1009541040,1009543630,1009543630,1009543630,1009544881,1009545318,1009545318,1009545318,1009549109,1009549843,1009549843,1009549843,1009551218,1009551218,1009551218,1009552741,1009553916,1009553916,1009553916,1009557766,1009558262,1009558262,1009558262,1009560602,1009560602,1009560602,1009562081,1009562081,1009562081,1009566559,1009567161,1009567161,1009567161,1009570163,1009571294,1009571294,1009571294,1009576213,1009576838,1009576838,1009576838,1009579448,1009579448,1009579448,1009580611,1009580611,1009580611,1009585078,1009585478,1009585478,1009585478,1009587842,1009589124,1009589124,1009589124,1009593904,1009596963,1009597982,1009597982,1009597982,1009602703,1009603248,1009603248,1009603248,1009605647,1009605647,1009605647,1009606587,1009606587,1009606587,1009610541,1009610909,1009610909,1009610909,1009612090,1009612090,1009612090,1009612624,1009612624,1009612624,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,100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and thcl in (2444) group by h.hashtag order by cps.photo_id chi 1941270030 updated with thcls [2444] select cps.photo_id, cps.thcl, h.hashtag, avg(cps.score), p.text,cps.hashtag_id FROM MTRPhoto.class_photo_score cps inner join MTRBack.hashtags h on h.hashtag_id = cps.hashtag_id inner join MTRBack.photos p on cps.photo_id = p.photo_id where cps.photo_id 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and thcl in (2444) group by h.hashtag order by cps.photo_id chi 1941270028 updated with thcls [2444] select cps.photo_id, cps.thcl, h.hashtag, avg(cps.score), p.text,cps.hashtag_id FROM MTRPhoto.class_photo_score cps inner join MTRBack.hashtags h on h.hashtag_id = cps.hashtag_id inner join MTRBack.photos p on cps.photo_id = p.photo_id where cps.photo_id 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and thcl in (2444) group by h.hashtag order by cps.photo_id chi 1941270027 updated with thcls [2444] select cps.photo_id, cps.thcl, h.hashtag, avg(cps.score), p.text,cps.hashtag_id FROM MTRPhoto.class_photo_score cps inner join MTRBack.hashtags h on h.hashtag_id = cps.hashtag_id inner join MTRBack.photos p on cps.photo_id = p.photo_id where cps.photo_id 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and thcl in (2444) group by h.hashtag order by cps.photo_id chi 1941270026 updated with thcls [2444] chi 1941270032 saved with no changes chi 1941270031 saved with no changes chi 1941270035 saved with no changes chi 1941270037 saved with no changes chi 1941270041 saved with no changes chi 1941270012 saved with no changes chi 1941270013 saved with no changes chi 1941607380 saved with no changes chi 1941607381 saved with no changes chi 2202875936 saved with no changes chi 1941270008 saved with no changes chi 1941270016 saved with no changes chi 1941607382 saved with no changes chi 1941270034 saved with no changes chi 2202875937 saved with no changes chi 1941270010 saved with no changes chi 1941270005 saved with no changes chi 1941270007 saved with no changes chi 1941270024 saved with no changes chi 1941270011 saved with no changes chi 2202875938 saved with no changes chi 1941270023 saved with no changes chi 1941607379 saved with no changes chi 1941270006 saved with no changes chi 1941270009 saved with no changes 76 crops to save 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 76 chid ids of type : 2913 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++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 ! batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 1009068683,1009068724) and `type` in (2913) Loaded 76 chid ids of type : 2913 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (2099910103,2099910104,2099910105,2099910106,2099910107,2099910108,2099910109,2099910110,2099910111,2099910112,2099910113,2099910114,2099910115,2099910116,2099910117,2099910118,2099910119,2099910120,2099910121,2099910122,2099910123,2099910124,2099910125,2099910126,2099910127,2099910128,2099910129,2099910130,2099910131,2099910132,2099910133,2099910134,2099910135,2099910136,2099910137,2099910138,2099910139,2099910140,2099910141,2099910142,2099910143,2099910144,2099910145,2099910146,2099910147,2099910148,2099910149,2099910150,2099910151,2099910152,2099910153,2099910154,2099910155,2099910156,2099910157,2099910158,2099910159,2099910160,2099910161,2099910162,2202876163,2099910163,2099910164,2099910165,2099910166,2202876164,2099910167,2099910168,2099910169,2099910170,2099910171,2202876165,2099910172,2099910173,2099910174,2099910175) ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (2099910103,2099910104,2099910105,2099910106,2099910107,2099910108,2099910109,2099910110,2099910111,2099910112,2099910113,2099910114,2099910115,2099910116,2099910117,2099910118,2099910119,2099910120,2099910121,2099910122,2099910123,2099910124,2099910125,2099910126,2099910127,2099910128,2099910129,2099910130,2099910131,2099910132,2099910133,2099910134,2099910135,2099910136,2099910137,2099910138,2099910139,2099910140,2099910141,2099910142,2099910143,2099910144,2099910145,2099910146,2099910147,2099910148,2099910149,2099910150,2099910151,2099910152,2099910153,2099910154,2099910155,2099910156,2099910157,2099910158,2099910159,2099910160,2099910161,2099910162,2202876163,2099910163,2099910164,2099910165,2099910166,2202876164,2099910167,2099910168,2099910169,2099910170,2099910171,2202876165,2099910172,2099910173,2099910174,2099910175) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (2099910103,2099910104,2099910105,2099910106,2099910107,2099910108,2099910109,2099910110,2099910111,2099910112,2099910113,2099910114,2099910115,2099910116,2099910117,2099910118,2099910119,2099910120,2099910121,2099910122,2099910123,2099910124,2099910125,2099910126,2099910127,2099910128,2099910129,2099910130,2099910131,2099910132,2099910133,2099910134,2099910135,2099910136,2099910137,2099910138,2099910139,2099910140,2099910141,2099910142,2099910143,2099910144,2099910145,2099910146,2099910147,2099910148,2099910149,2099910150,2099910151,2099910152,2099910153,2099910154,2099910155,2099910156,2099910157,2099910158,2099910159,2099910160,2099910161,2099910162,2202876163,2099910163,2099910164,2099910165,2099910166,2202876164,2099910167,2099910168,2099910169,2099910170,2099910171,2202876165,2099910172,2099910173,2099910174,2099910175) insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(2099910116, 1056231396, 0), (2099910117, 1056231397, 0), (2099910150, 1056231391, 0), (2099910151, 1056231392, 0), (2099910152, 1056231393, 0), (2099910153, 1056231395, 0)] End of step merge_mask_thcl_custom After datou_step_exec type output : time spend for datou_step_exec : 0.7290604114532471 time spend to save output : 8.130073547363281e-05 total time spend for step 1 : 0.7291417121887207 step2:rle_unique_nms_with_priority Mon May 26 19:42:55 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 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/1748281374_935833_1009068683_4beb092fd180a8b620f754bb89015722.jpg', 'temp/1748281374_935833_1009068724_3e705379f97632c4a2fd676e348a335d.jpg'] We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :2, first value : ['temp/1748281374_935833_1009068683_4beb092fd180a8b620f754bb89015722.jpg'] After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281374_935833_1009068683_4beb092fd180a8b620f754bb89015722.jpg': 1009068683, 'temp/1748281374_935833_1009068724_3e705379f97632c4a2fd676e348a335d.jpg': 1009068724} map_photo_id_path_extension : {1009068683: {'path': 'temp/1748281374_935833_1009068683_4beb092fd180a8b620f754bb89015722.jpg', 'extension': 'jpg'}, 1009068724: {'path': 'temp/1748281374_935833_1009068724_3e705379f97632c4a2fd676e348a335d.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Begin step rle-unique-nms on traite la photo : temp/1748281374_935833_1009068683_4beb092fd180a8b620f754bb89015722.jpg on traite la photo : temp/1748281374_935833_1009068724_3e705379f97632c4a2fd676e348a335d.jpg batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 1009068683,1009068724) and `type` in (2913) and hashtag_id in (2107755846,492636447,492645504,681467679) Loaded 43 chid ids of type : 2913 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (2099910116,2099910118,2099910119,2099910120,2099910121,2099910122,2099910123,2099910124,2099910125,2099910126,2099910127,2099910128,2099910129,2099910130,2099910131,2099910132,2099910133,2099910134,2099910154,2099910155,2099910156,2099910157,2099910158,2099910159,2099910160,2099910161,2099910162,2202876163,2099910163,2099910164,2099910165,2099910166,2202876164,2099910167,2099910168,2099910169,2099910170,2099910171,2202876165,2099910172,2099910173,2099910174,2099910175) +++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (2099910116,2099910118,2099910119,2099910120,2099910121,2099910122,2099910123,2099910124,2099910125,2099910126,2099910127,2099910128,2099910129,2099910130,2099910131,2099910132,2099910133,2099910134,2099910154,2099910155,2099910156,2099910157,2099910158,2099910159,2099910160,2099910161,2099910162,2202876163,2099910163,2099910164,2099910165,2099910166,2202876164,2099910167,2099910168,2099910169,2099910170,2099910171,2202876165,2099910172,2099910173,2099910174,2099910175) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (2099910116,2099910118,2099910119,2099910120,2099910121,2099910122,2099910123,2099910124,2099910125,2099910126,2099910127,2099910128,2099910129,2099910130,2099910131,2099910132,2099910133,2099910134,2099910154,2099910155,2099910156,2099910157,2099910158,2099910159,2099910160,2099910161,2099910162,2202876163,2099910163,2099910164,2099910165,2099910166,2202876164,2099910167,2099910168,2099910169,2099910170,2099910171,2202876165,2099910172,2099910173,2099910174,2099910175) nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 2.357605218887329 create new chi : 2.956390380859375e-05 proportion hashtag : pet_clair 0.5317132844650205 proportion hashtag : contaminant 0.002766525205761317 time to delete rle : 0.039154767990112305 save time : 7.3909759521484375e-06 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 2.6933131217956543 create new chi : 5.53131103515625e-05 proportion hashtag : pet_clair 0.5582751414609054 time to delete rle : 0.013994455337524414 save time : 1.9550323486328125e-05 INSERT IGNORE INTO MTRPhoto.photo_carac_ratio (`photo_id`, `hashtag_type`, `hashtag_id`, `ratio`) VALUES (%s, %s, %s, %s) on duplicate key update `ratio` = VALUES(`ratio`) map_output_result : {1009068683: (0.002588053987919006, 'Should be the crop_list due to order', 0.005176107975838012), 1009068724: (0.002588053987919006, 'Should be the crop_list due to order', 0.0)} End step rle-unique-nms After datou_step_exec type output : time spend for datou_step_exec : 5.287399530410767 time spend to save output : 0.00021505355834960938 total time spend for step 2 : 5.287614583969116 step3:ventilate_hashtags_in_portfolio Mon May 26 19:43:00 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 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 :0, first value : [] After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281374_935833_1009068683_4beb092fd180a8b620f754bb89015722.jpg': 1009068683, 'temp/1748281374_935833_1009068724_3e705379f97632c4a2fd676e348a335d.jpg': 1009068724} map_photo_id_path_extension : {1009068683: {'path': 'temp/1748281374_935833_1009068683_4beb092fd180a8b620f754bb89015722.jpg', 'extension': 'jpg'}, 1009068724: {'path': 'temp/1748281374_935833_1009068724_3e705379f97632c4a2fd676e348a335d.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 3373196 get user id for portfolio 3373196 get sub ptf for main ptf 3373196, type 2913 and hashtags [] SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=3373196 AND mptpi.`type`=2913 AND mptpi.`min_score`=0.7 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=3373196 AND mptpi.`type`=2913 AND mptpi.`min_score`=0.7 To do returned map {'bouchon': {'main_port_id': 3373196, 'sub_port_id': 3373210, 'hashtag': 'bouchon', 'pht': 2913, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 408, 'last_updated_at': datetime.datetime(2025, 5, 26, 15, 43, 11), 'last_updated_at_desc': datetime.datetime(2025, 5, 26, 15, 43, 11), 'last_updated_at_asc': datetime.datetime(2021, 1, 26, 13, 9, 50)}, 'contaminant': {'main_port_id': 3373196, 'sub_port_id': 3373211, 'hashtag': 'contaminant', 'pht': 2913, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 409, 'last_updated_at': datetime.datetime(2025, 5, 26, 15, 43, 11), 'last_updated_at_desc': datetime.datetime(2025, 5, 26, 15, 43, 11), 'last_updated_at_asc': datetime.datetime(2021, 1, 26, 13, 9, 50)}, 'environement': {'main_port_id': 3373196, 'sub_port_id': 3373212, 'hashtag': 'environement', 'pht': 2913, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 410, 'last_updated_at': datetime.datetime(2025, 5, 26, 15, 43, 11), 'last_updated_at_desc': datetime.datetime(2025, 5, 26, 15, 43, 11), 'last_updated_at_asc': datetime.datetime(2021, 1, 26, 13, 9, 50)}, 'etiquette': {'main_port_id': 3373196, 'sub_port_id': 3373213, 'hashtag': 'etiquette', 'pht': 2913, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 411, 'last_updated_at': datetime.datetime(2025, 5, 26, 15, 43, 11), 'last_updated_at_desc': datetime.datetime(2025, 5, 26, 15, 43, 11), 'last_updated_at_asc': datetime.datetime(2021, 1, 26, 13, 9, 50)}, 'mal_croppe': {'main_port_id': 3373196, 'sub_port_id': 3373214, 'hashtag': 'mal_croppe', 'pht': 2913, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 412, 'last_updated_at': datetime.datetime(2025, 5, 26, 15, 43, 11), 'last_updated_at_desc': datetime.datetime(2025, 5, 26, 15, 43, 11), 'last_updated_at_asc': datetime.datetime(2021, 1, 26, 13, 9, 50)}, 'multiple_objets': {'main_port_id': 3373196, 'sub_port_id': 3373215, 'hashtag': 'multiple_objets', 'pht': 2913, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 413, 'last_updated_at': datetime.datetime(2025, 5, 26, 15, 43, 11), 'last_updated_at_desc': datetime.datetime(2025, 5, 26, 15, 43, 11), 'last_updated_at_asc': datetime.datetime(2021, 1, 26, 13, 9, 50)}, 'pet_clair': {'main_port_id': 3373196, 'sub_port_id': 3373216, 'hashtag': 'pet_clair', 'pht': 2913, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 414, 'last_updated_at': datetime.datetime(2025, 5, 26, 15, 43, 11), 'last_updated_at_desc': datetime.datetime(2025, 5, 26, 15, 43, 11), 'last_updated_at_asc': datetime.datetime(2021, 1, 26, 13, 9, 50)}} insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at) SELECT 3373210 , sub_photo_id, 31,now() FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at FROM MTRPhoto.crop_sub_photo_ids csp, MTRPhoto.crop_hashtag_ids chi, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp WHERE mpp.mtr_portfolio_id=3373196 AND mpp.mtr_photo_id=chi.photo_id AND chi.id=csp.crop_hashtag_id AND chi.score>0.7 AND chi.type=2913 AND chi.hashtag_id=h.hashtag_id AND h.hashtag='bouchon' GROUP BY csp.crop_hashtag_id, csp.created_at order by csp.created_at desc) as a group by crop_hashtag_id; insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at) SELECT 3373211 , sub_photo_id, 31,now() FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at FROM MTRPhoto.crop_sub_photo_ids csp, MTRPhoto.crop_hashtag_ids chi, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp WHERE mpp.mtr_portfolio_id=3373196 AND mpp.mtr_photo_id=chi.photo_id AND chi.id=csp.crop_hashtag_id AND chi.score>0.7 AND chi.type=2913 AND chi.hashtag_id=h.hashtag_id AND h.hashtag='contaminant' GROUP BY csp.crop_hashtag_id, csp.created_at order by csp.created_at desc) as a group by crop_hashtag_id; insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at) SELECT 3373212 , sub_photo_id, 31,now() FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at FROM MTRPhoto.crop_sub_photo_ids csp, MTRPhoto.crop_hashtag_ids chi, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp WHERE mpp.mtr_portfolio_id=3373196 AND mpp.mtr_photo_id=chi.photo_id AND chi.id=csp.crop_hashtag_id AND chi.score>0.7 AND chi.type=2913 AND chi.hashtag_id=h.hashtag_id AND h.hashtag='environement' GROUP BY csp.crop_hashtag_id, csp.created_at order by csp.created_at desc) as a group by crop_hashtag_id; insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at) SELECT 3373213 , sub_photo_id, 31,now() FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at FROM MTRPhoto.crop_sub_photo_ids csp, MTRPhoto.crop_hashtag_ids chi, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp WHERE mpp.mtr_portfolio_id=3373196 AND mpp.mtr_photo_id=chi.photo_id AND chi.id=csp.crop_hashtag_id AND chi.score>0.7 AND chi.type=2913 AND chi.hashtag_id=h.hashtag_id AND h.hashtag='etiquette' GROUP BY csp.crop_hashtag_id, csp.created_at order by csp.created_at desc) as a group by crop_hashtag_id; insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at) SELECT 3373214 , sub_photo_id, 31,now() FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at FROM MTRPhoto.crop_sub_photo_ids csp, MTRPhoto.crop_hashtag_ids chi, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp WHERE mpp.mtr_portfolio_id=3373196 AND mpp.mtr_photo_id=chi.photo_id AND chi.id=csp.crop_hashtag_id AND chi.score>0.7 AND chi.type=2913 AND chi.hashtag_id=h.hashtag_id AND h.hashtag='mal_croppe' GROUP BY csp.crop_hashtag_id, csp.created_at order by csp.created_at desc) as a group by crop_hashtag_id; insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at) SELECT 3373215 , sub_photo_id, 31,now() FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at FROM MTRPhoto.crop_sub_photo_ids csp, MTRPhoto.crop_hashtag_ids chi, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp WHERE mpp.mtr_portfolio_id=3373196 AND mpp.mtr_photo_id=chi.photo_id AND chi.id=csp.crop_hashtag_id AND chi.score>0.7 AND chi.type=2913 AND chi.hashtag_id=h.hashtag_id AND h.hashtag='multiple_objets' GROUP BY csp.crop_hashtag_id, csp.created_at order by csp.created_at desc) as a group by crop_hashtag_id; insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at) SELECT 3373216 , sub_photo_id, 31,now() FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at FROM MTRPhoto.crop_sub_photo_ids csp, MTRPhoto.crop_hashtag_ids chi, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp WHERE mpp.mtr_portfolio_id=3373196 AND mpp.mtr_photo_id=chi.photo_id AND chi.id=csp.crop_hashtag_id AND chi.score>0.7 AND chi.type=2913 AND chi.hashtag_id=h.hashtag_id AND h.hashtag='pet_clair' GROUP BY csp.crop_hashtag_id, csp.created_at order by csp.created_at desc) as a group by crop_hashtag_id; To do ! Use context local managing function ! After datou_step_exec type output : time spend for datou_step_exec : 2.116208076477051 time spend to save output : 4.4345855712890625e-05 total time spend for step 3 : 2.1162524223327637 step4:final Mon May 26 19:43:02 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 2 : {1009068683: (0.002588053987919006, 'Should be the crop_list due to order', 0.005176107975838012), 1009068724: (0.002588053987919006, 'Should be the crop_list due to order', 0.0)} input_args_next_step : {1009068683: ()} output_args : {1009068683: (0.002588053987919006, 'Should be the crop_list due to order', 0.005176107975838012), 1009068724: (0.002588053987919006, 'Should be the crop_list due to order', 0.0)} args : 1009068683 depend.output_id : 0 input_args_next_step : {1009068683: (0.002588053987919006,), 1009068724: ()} output_args : {1009068683: (0.002588053987919006, 'Should be the crop_list due to order', 0.005176107975838012), 1009068724: (0.002588053987919006, 'Should be the crop_list due to order', 0.0)} args : 1009068724 depend.output_id : 0 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :2, first value : (0.002588053987919006,) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281374_935833_1009068683_4beb092fd180a8b620f754bb89015722.jpg': 1009068683, 'temp/1748281374_935833_1009068724_3e705379f97632c4a2fd676e348a335d.jpg': 1009068724} map_photo_id_path_extension : {1009068683: {'path': 'temp/1748281374_935833_1009068683_4beb092fd180a8b620f754bb89015722.jpg', 'extension': 'jpg'}, 1009068724: {'path': 'temp/1748281374_935833_1009068724_3e705379f97632c4a2fd676e348a335d.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step final ! query to retrieve results for portfolio 3373196 : SELECT photo_id, hashtag_id, ratio from MTRPhoto.photo_carac_ratio where hashtag_type = 2914 and photo_id in (1009068683,1009068724); Catched exception ! Connect or reconnect ! insert ignore into MTRUser.portfolio_carac_ratio (portfolio_id, hashtag_type, hashtag_id, value) values (%s,%s,%s,%s) on DUPLICATE KEY UPDATE value=VALUES(value) After datou_step_exec type output : time spend for datou_step_exec : 0.09324908256530762 time spend to save output : 0.00010466575622558594 total time spend for step 4 : 0.0933537483215332 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True original output for save of step final : {1009068683: ('0.0025316977085267163',), 1009068724: ('0.0025316977085267163',)} new output for save of step final : {1009068683: ('0.0025316977085267163',), 1009068724: ('0.0025316977085267163',)} [1009068683, 1009068724] map_info['map_portfolio_photo'] : {3373196: [1009068724, 1009068683]} final : True mtd_id 2719 list_pids : [1009068683, 1009068724] Looping around the photos to save general results len do output : 2 /1009068683.Didn't retrieve data . /1009068724.Didn't retrieve data . before output type Used above Used above Managing all output in save final without adding information in the mtr_datou_result ('2719', None, None, None, None, None, None, None, None) ('2719', '3373196', '1009068683', None, None, None, None, None, None) ('2719', None, None, None, None, None, None, None, None) ('2719', '3373196', '1009068724', 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 : [('2719', '3373196', '1009068683', '0.0025316977085267163', None, None, None, None, None), ('2719', '3373196', '1009068724', '0.0025316977085267163', None, None, None, None, None)] time used for this insertion : 0.01251673698425293 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 4 output : {1009068683: ('0.0025316977085267163',), 1009068724: ('0.0025316977085267163',)} {1009068683: ('0.0025316977085267163',), 1009068724: ('0.0025316977085267163',)} ############################### TEST rubbia_carac_jrm_no_mask_detect ################################ Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=3164 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=3164 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= 3164 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=3164 # 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 7557 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 7556 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 7561 merge_mask_and_thcl is not consistent : 3 used against 1 in the step definition ! WARNING : number of inputs for step 7558 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7560 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7559 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7560 doesn't seem to be define in the database( WARNING : type of input 3 of step 7559 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7556 have datatype=6 whereas input 0 of step 7561 have datatype=20 WARNING : type of output 1 of step 7561 doesn't seem to be define in the database( WARNING : type of input 1 of step 7558 doesn't seem to be define in the database( WARNING : type of output 2 of step 7561 doesn't seem to be define in the database( WARNING : type of input 1 of step 7560 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 : crop_condition, thcl, argmax, merge_mask_and_thcl, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final 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 (3364276) 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 (3364276) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 1008922130, 'portfolio_id': 3364276}, {'photo_id': 1008922101, 'portfolio_id': 3364276}, {'photo_id': 1008922097, 'portfolio_id': 3364276}, {'photo_id': 1008922095, 'portfolio_id': 3364276}, {'photo_id': 1008922073, 'portfolio_id': 3364276}, {'photo_id': 1008922072, 'portfolio_id': 3364276}, {'photo_id': 1008922003, 'portfolio_id': 3364276}, {'photo_id': 1008922002, 'portfolio_id': 3364276}, {'photo_id': 1008921786, 'portfolio_id': 3364276}, {'photo_id': 1008921657, 'portfolio_id': 3364276}, {'photo_id': 1008921656, 'portfolio_id': 3364276}, {'photo_id': 1008921602, 'portfolio_id': 3364276}, {'photo_id': 1008921601, 'portfolio_id': 3364276}, {'photo_id': 1008921600, 'portfolio_id': 3364276}] map_portfolio_id_photo_id: {3364276: [1008922130, 1008922101, 1008922097, 1008922095, 1008922073, 1008922072, 1008922003, 1008922002, 1008921786, 1008921657, 1008921656, 1008921602, 1008921601, 1008921600]} ##### Call download_photos : nb_thread : 5 begin to download photo : 1008922130 begin to download photo : 1008922095 begin to download photo : 1008922003 begin to download photo : 1008921657 begin to download photo : 1008921601 download finish for photo 1008922130 begin to download photo : 1008922101 download finish for photo 1008921657 begin to download photo : 1008921656 download finish for photo 1008921601 begin to download photo : 1008921600 download finish for photo 1008922003 begin to download photo : 1008922002 download finish for photo 1008922095 begin to download photo : 1008922073 download finish for photo 1008921656 begin to download photo : 1008921602 download finish for photo 1008921600 download finish for photo 1008922002 begin to download photo : 1008921786 download finish for photo 1008922101 begin to download photo : 1008922097 download finish for photo 1008922073 begin to download photo : 1008922072 download finish for photo 1008921786 download finish for photo 1008922072 download finish for photo 1008921602 download finish for photo 1008922097 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 : 14 ; length of list_pids : 14 ; length of list_args : 14 ##### After load_data_input time to download the photos : 1.3206753730773926 #### 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:crop_condition Mon May 26 19:43:04 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281382_935833_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg': 1008921601, 'temp/1748281382_935833_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg': 1008921600, 'temp/1748281382_935833_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg': 1008922003, 'temp/1748281382_935833_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg': 1008922002, 'temp/1748281382_935833_1008921786_066e79494b5536cc7d2f3293519cd617.jpg': 1008921786, 'temp/1748281382_935833_1008922095_dc858351f179fe112b62a458b452c880.jpg': 1008922095, 'temp/1748281382_935833_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg': 1008922073, 'temp/1748281382_935833_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg': 1008922072, 'temp/1748281382_935833_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg': 1008921657, 'temp/1748281382_935833_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg': 1008921656, 'temp/1748281382_935833_1008921602_383b6a7758931a2dff9de560df82456c.jpg': 1008921602, 'temp/1748281382_935833_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg': 1008922130, 'temp/1748281382_935833_1008922101_1a025189b8603388d54304e6754e9df9.jpg': 1008922101, 'temp/1748281382_935833_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg': 1008922097} map_photo_id_path_extension : {1008921601: {'path': 'temp/1748281382_935833_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg', 'extension': 'jpg'}, 1008921600: {'path': 'temp/1748281382_935833_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg', 'extension': 'jpg'}, 1008922003: {'path': 'temp/1748281382_935833_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg', 'extension': 'jpg'}, 1008922002: {'path': 'temp/1748281382_935833_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg', 'extension': 'jpg'}, 1008921786: {'path': 'temp/1748281382_935833_1008921786_066e79494b5536cc7d2f3293519cd617.jpg', 'extension': 'jpg'}, 1008922095: {'path': 'temp/1748281382_935833_1008922095_dc858351f179fe112b62a458b452c880.jpg', 'extension': 'jpg'}, 1008922073: {'path': 'temp/1748281382_935833_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg', 'extension': 'jpg'}, 1008922072: {'path': 'temp/1748281382_935833_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg', 'extension': 'jpg'}, 1008921657: {'path': 'temp/1748281382_935833_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg', 'extension': 'jpg'}, 1008921656: {'path': 'temp/1748281382_935833_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg', 'extension': 'jpg'}, 1008921602: {'path': 'temp/1748281382_935833_1008921602_383b6a7758931a2dff9de560df82456c.jpg', 'extension': 'jpg'}, 1008922130: {'path': 'temp/1748281382_935833_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg', 'extension': 'jpg'}, 1008922101: {'path': 'temp/1748281382_935833_1008922101_1a025189b8603388d54304e6754e9df9.jpg', 'extension': 'jpg'}, 1008922097: {'path': 'temp/1748281382_935833_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} param_json : {'upload_type': 'python', 'photo_hashtag_type': 3336, 'token': '7c6bc181426bc5913186d8428a0fbc15', 'feed_id_new_photos': 3364311, 'crop_type': 'rle_png_transparent', 'host': 'www.fotonower.com', 'filter': {'teint_dans_la_masse': {'min_score': 0.7}, 'autre_refus': {'min_score': 0.7}, 'carton_gris': {'min_score': 0.7}, 'cartonnette': {'min_score': 0.7}, 'carton_brun': {'min_score': 0.7}, 'plastique': {'min_score': 0.7}, 'kraft': {'min_score': 0.7}, 'metal': {'min_score': 0.7}}} Loading chi in step crop with photo_hashtag_type : 3336 Loading chi in step crop for list_pids : 14 ! batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008921657,1008921656,1008921602,1008922130,1008922101,1008922097) and `type` in (3336) Loaded 121 chid ids of type : 3336 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (2099961860,2099961861,2099961862,2099961863,2099961864,2099961865,2099961866,2099961867,2099961868,2099961869,2099961870,2099961871,2099961872,2099961873,2099961874,2099961875,2099961876,2099961877,2099961878,2099961879,2099961880,2099961881,2099961882,2099961883,2099961884,2099961885,2099961886,2099961887,2099961888,2099961889,2099961890,2099961891,2099961892,2099961893,2099961894,2099961895,2099961896,2099961897,2099961898,2099961899,2099961900,2099961901,2099961902,2099961903,2099961904,2099961905,2099961906,2099961907,2099961908,2099961909,2099961910,2099961911,2099961912,2099961913,2099961914,2099961915,2099961916,2099961917,2099961918,2099961919,2099961920,2099961921,2099961922,2099961923,2099961924,2099961925,2099961926,2099961927,2099961928,2099961929,2099961930,2099961931,2099961932,2099961933,2099961934,2099961935,2099961936,2099961937,2099961938,2099961939,2099961940,2099961941,2099961942,2099961943,2099961944,2099961945,2099961946,2099961947,2099961948,2099961949,2099961950,2099961951,2099961952,2099961953,2099961954,2099961955,2099961956,2099961957,2099961958,2099961959,2099961960,2099961961,2099961962,2099961963,2099961964,2099961965,2099961966,2099961967,2099961968,2099961969,2099961970,2099961971,2099961972,2099961973,2099961974,2099961975,2099961976,2099961977,2099961978,2099961979,2099961980) ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (2099961860,2099961861,2099961862,2099961863,2099961864,2099961865,2099961866,2099961867,2099961868,2099961869,2099961870,2099961871,2099961872,2099961873,2099961874,2099961875,2099961876,2099961877,2099961878,2099961879,2099961880,2099961881,2099961882,2099961883,2099961884,2099961885,2099961886,2099961887,2099961888,2099961889,2099961890,2099961891,2099961892,2099961893,2099961894,2099961895,2099961896,2099961897,2099961898,2099961899,2099961900,2099961901,2099961902,2099961903,2099961904,2099961905,2099961906,2099961907,2099961908,2099961909,2099961910,2099961911,2099961912,2099961913,2099961914,2099961915,2099961916,2099961917,2099961918,2099961919,2099961920,2099961921,2099961922,2099961923,2099961924,2099961925,2099961926,2099961927,2099961928,2099961929,2099961930,2099961931,2099961932,2099961933,2099961934,2099961935,2099961936,2099961937,2099961938,2099961939,2099961940,2099961941,2099961942,2099961943,2099961944,2099961945,2099961946,2099961947,2099961948,2099961949,2099961950,2099961951,2099961952,2099961953,2099961954,2099961955,2099961956,2099961957,2099961958,2099961959,2099961960,2099961961,2099961962,2099961963,2099961964,2099961965,2099961966,2099961967,2099961968,2099961969,2099961970,2099961971,2099961972,2099961973,2099961974,2099961975,2099961976,2099961977,2099961978,2099961979,2099961980) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (2099961860,2099961861,2099961862,2099961863,2099961864,2099961865,2099961866,2099961867,2099961868,2099961869,2099961870,2099961871,2099961872,2099961873,2099961874,2099961875,2099961876,2099961877,2099961878,2099961879,2099961880,2099961881,2099961882,2099961883,2099961884,2099961885,2099961886,2099961887,2099961888,2099961889,2099961890,2099961891,2099961892,2099961893,2099961894,2099961895,2099961896,2099961897,2099961898,2099961899,2099961900,2099961901,2099961902,2099961903,2099961904,2099961905,2099961906,2099961907,2099961908,2099961909,2099961910,2099961911,2099961912,2099961913,2099961914,2099961915,2099961916,2099961917,2099961918,2099961919,2099961920,2099961921,2099961922,2099961923,2099961924,2099961925,2099961926,2099961927,2099961928,2099961929,2099961930,2099961931,2099961932,2099961933,2099961934,2099961935,2099961936,2099961937,2099961938,2099961939,2099961940,2099961941,2099961942,2099961943,2099961944,2099961945,2099961946,2099961947,2099961948,2099961949,2099961950,2099961951,2099961952,2099961953,2099961954,2099961955,2099961956,2099961957,2099961958,2099961959,2099961960,2099961961,2099961962,2099961963,2099961964,2099961965,2099961966,2099961967,2099961968,2099961969,2099961970,2099961971,2099961972,2099961973,2099961974,2099961975,2099961976,2099961977,2099961978,2099961979,2099961980) 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 ( 1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008921657,1008921656,1008921602,1008922130,1008922101,1008922097) begin to crop the class : teint_dans_la_masse param for this class : {'min_score': 0.7} filtre for class : teint_dans_la_masse hashtag_id of this class : 2107752385 select * from (select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge, IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on chi.id =css.crop_hashtag_id where type = 3336 and photo_id in (1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008921657,1008921656,1008921602,1008922130,1008922101,1008922097) and hashtag_id = 2107752385) as a where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 and coverage >= 0 and surface_rectangle >= 0; chi_id interessant : [2099961965] chi_id interessant : [2099961965, 2099961952] chi_id interessant : [2099961965, 2099961952, 2099961898] chi_id interessant : [2099961965, 2099961952, 2099961898, 2099961863] type of cropped photo chosen : rle_png_transparent we resize croppped photo by 1 on x axis and by 1 on y axis map_result returned by crop_photo_return_map_crop : length : 0 map_result after crop : {} About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! map_result_insert : {} insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [] insert ignore into MTRPhoto.photo_sub_photos (photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s) : [] map of cropped photos with some data : {} we have finished the crop for the class : teint_dans_la_masse begin to crop the class : autre_refus param for this class : {'min_score': 0.7} filtre for class : autre_refus hashtag_id of this class : 2107752406 select * from (select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge, IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on chi.id =css.crop_hashtag_id where type = 3336 and photo_id in (1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008921657,1008921656,1008921602,1008922130,1008922101,1008922097) and hashtag_id = 2107752406) as a where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 and coverage >= 0 and surface_rectangle >= 0; chi_id interessant : [2099961980] chi_id interessant : [2099961980, 2099961933] chi_id interessant : [2099961980, 2099961933, 2099961880] chi_id interessant : [2099961980, 2099961933, 2099961880, 2099961870] type of cropped photo chosen : rle_png_transparent we resize croppped photo by 1 on x axis and by 1 on y axis map_result returned by crop_photo_return_map_crop : length : 0 map_result after crop : {} About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! map_result_insert : {} insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [] insert ignore into MTRPhoto.photo_sub_photos (photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s) : [] map of cropped photos with some data : {} we have finished the crop for the class : autre_refus begin to crop the class : carton_gris param for this class : {'min_score': 0.7} filtre for class : carton_gris hashtag_id of this class : 2107753020 select * from (select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge, IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on chi.id =css.crop_hashtag_id where type = 3336 and photo_id in (1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008921657,1008921656,1008921602,1008922130,1008922101,1008922097) and hashtag_id = 2107753020) as a where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 and coverage >= 0 and surface_rectangle >= 0; chi_id interessant : [2099961976] chi_id interessant : [2099961976, 2099961975] chi_id interessant : [2099961976, 2099961975, 2099961971] chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964] chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960] chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950] chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949] chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937] chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934] chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915] chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913] chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912] chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912, 2099961910] chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912, 2099961910, 2099961908] chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912, 2099961910, 2099961908, 2099961906] chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912, 2099961910, 2099961908, 2099961906, 2099961903] chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912, 2099961910, 2099961908, 2099961906, 2099961903, 2099961890] chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912, 2099961910, 2099961908, 2099961906, 2099961903, 2099961890, 2099961889] chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912, 2099961910, 2099961908, 2099961906, 2099961903, 2099961890, 2099961889, 2099961882] chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912, 2099961910, 2099961908, 2099961906, 2099961903, 2099961890, 2099961889, 2099961882, 2099961876] chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912, 2099961910, 2099961908, 2099961906, 2099961903, 2099961890, 2099961889, 2099961882, 2099961876, 2099961867] chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912, 2099961910, 2099961908, 2099961906, 2099961903, 2099961890, 2099961889, 2099961882, 2099961876, 2099961867, 2099961862] type of cropped photo chosen : rle_png_transparent we resize croppped photo by 1 on x axis and by 1 on y axis map_result returned by crop_photo_return_map_crop : length : 0 map_result after crop : {} About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! map_result_insert : {} insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [] insert ignore into MTRPhoto.photo_sub_photos (photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s) : [] map of cropped photos with some data : {} we have finished the crop for the class : carton_gris begin to crop the class : cartonnette param for this class : {'min_score': 0.7} filtre for class : cartonnette hashtag_id of this class : 702398920 select * from (select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge, IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on chi.id =css.crop_hashtag_id where type = 3336 and photo_id in (1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008921657,1008921656,1008921602,1008922130,1008922101,1008922097) and hashtag_id = 702398920) as a where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 and coverage >= 0 and surface_rectangle >= 0; chi_id interessant : [2099961953] chi_id interessant : [2099961953, 2099961954] chi_id interessant : [2099961953, 2099961954, 2099961944] chi_id interessant : [2099961953, 2099961954, 2099961944, 2099961945] chi_id interessant : [2099961953, 2099961954, 2099961944, 2099961945, 2099961922] chi_id interessant : [2099961953, 2099961954, 2099961944, 2099961945, 2099961922, 2099961932] chi_id interessant : [2099961953, 2099961954, 2099961944, 2099961945, 2099961922, 2099961932, 2099961892] chi_id interessant : [2099961953, 2099961954, 2099961944, 2099961945, 2099961922, 2099961932, 2099961892, 2099961877] chi_id interessant : [2099961953, 2099961954, 2099961944, 2099961945, 2099961922, 2099961932, 2099961892, 2099961877, 2099961871] type of cropped photo chosen : rle_png_transparent we resize croppped photo by 1 on x axis and by 1 on y axis map_result returned by crop_photo_return_map_crop : length : 0 map_result after crop : {} About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! map_result_insert : {} insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [] insert ignore into MTRPhoto.photo_sub_photos (photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s) : [] map of cropped photos with some data : {} we have finished the crop for the class : cartonnette begin to crop the class : carton_brun param for this class : {'min_score': 0.7} filtre for class : carton_brun hashtag_id of this class : 2107753024 select * from (select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge, IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on chi.id =css.crop_hashtag_id where type = 3336 and photo_id in (1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008921657,1008921656,1008921602,1008922130,1008922101,1008922097) and hashtag_id = 2107753024) as a where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 and coverage >= 0 and surface_rectangle >= 0; chi_id interessant : [2099961974] chi_id interessant : [2099961974, 2099961969] chi_id interessant : [2099961974, 2099961969, 2099961979] chi_id interessant : [2099961974, 2099961969, 2099961979, 2099961957] chi_id interessant : [2099961974, 2099961969, 2099961979, 2099961957, 2099961963] chi_id interessant : [2099961974, 2099961969, 2099961979, 2099961957, 2099961963, 2099961955] chi_id interessant : [2099961974, 2099961969, 2099961979, 2099961957, 2099961963, 2099961955, 2099961942] chi_id interessant : [2099961974, 2099961969, 2099961979, 2099961957, 2099961963, 2099961955, 2099961942, 2099961931] chi_id interessant : [2099961974, 2099961969, 2099961979, 2099961957, 2099961963, 2099961955, 2099961942, 2099961931, 2099961916] chi_id interessant : [2099961974, 2099961969, 2099961979, 2099961957, 2099961963, 2099961955, 2099961942, 2099961931, 2099961916, 2099961900] chi_id interessant : [2099961974, 2099961969, 2099961979, 2099961957, 2099961963, 2099961955, 2099961942, 2099961931, 2099961916, 2099961900, 2099961901] chi_id interessant : [2099961974, 2099961969, 2099961979, 2099961957, 2099961963, 2099961955, 2099961942, 2099961931, 2099961916, 2099961900, 2099961901, 2099961902] chi_id interessant : [2099961974, 2099961969, 2099961979, 2099961957, 2099961963, 2099961955, 2099961942, 2099961931, 2099961916, 2099961900, 2099961901, 2099961902, 2099961872] type of cropped photo chosen : rle_png_transparent we resize croppped photo by 1 on x axis and by 1 on y axis map_result returned by crop_photo_return_map_crop : length : 0 map_result after crop : {} About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! map_result_insert : {} insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [] insert ignore into MTRPhoto.photo_sub_photos (photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s) : [] map of cropped photos with some data : {} we have finished the crop for the class : carton_brun begin to crop the class : plastique param for this class : {'min_score': 0.7} filtre for class : plastique hashtag_id of this class : 492725882 select * from (select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge, IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on chi.id =css.crop_hashtag_id where type = 3336 and photo_id in (1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008921657,1008921656,1008921602,1008922130,1008922101,1008922097) and hashtag_id = 492725882) as a where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 and coverage >= 0 and surface_rectangle >= 0; chi_id interessant : [2099961978] begin to crop the class : kraft param for this class : {'min_score': 0.7} filtre for class : kraft hashtag_id of this class : 493202403 select * from (select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge, IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on chi.id =css.crop_hashtag_id where type = 3336 and photo_id in (1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008921657,1008921656,1008921602,1008922130,1008922101,1008922097) and hashtag_id = 493202403) as a where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 and coverage >= 0 and surface_rectangle >= 0; begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 select * from (select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge, IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on chi.id =css.crop_hashtag_id where type = 3336 and photo_id in (1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008921657,1008921656,1008921602,1008922130,1008922101,1008922097) and hashtag_id = 492628673) as a where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 and coverage >= 0 and surface_rectangle >= 0; map of total cropped photos with some data : {} After datou_step_exec type output : time spend for datou_step_exec : 9.919448614120483 time spend to save output : 0.0004508495330810547 total time spend for step 1 : 9.919899463653564 step2:thcl Mon May 26 19:43:13 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 : {} VR 22-3-18 : For now we do not clean correctly the datou structure No keys ! input_args_next_step, len :0 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281382_935833_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg': 1008921601, 'temp/1748281382_935833_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg': 1008921600, 'temp/1748281382_935833_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg': 1008922003, 'temp/1748281382_935833_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg': 1008922002, 'temp/1748281382_935833_1008921786_066e79494b5536cc7d2f3293519cd617.jpg': 1008921786, 'temp/1748281382_935833_1008922095_dc858351f179fe112b62a458b452c880.jpg': 1008922095, 'temp/1748281382_935833_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg': 1008922073, 'temp/1748281382_935833_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg': 1008922072, 'temp/1748281382_935833_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg': 1008921657, 'temp/1748281382_935833_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg': 1008921656, 'temp/1748281382_935833_1008921602_383b6a7758931a2dff9de560df82456c.jpg': 1008921602, 'temp/1748281382_935833_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg': 1008922130, 'temp/1748281382_935833_1008922101_1a025189b8603388d54304e6754e9df9.jpg': 1008922101, 'temp/1748281382_935833_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg': 1008922097} map_photo_id_path_extension : {1008921601: {'path': 'temp/1748281382_935833_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg', 'extension': 'jpg'}, 1008921600: {'path': 'temp/1748281382_935833_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg', 'extension': 'jpg'}, 1008922003: {'path': 'temp/1748281382_935833_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg', 'extension': 'jpg'}, 1008922002: {'path': 'temp/1748281382_935833_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg', 'extension': 'jpg'}, 1008921786: {'path': 'temp/1748281382_935833_1008921786_066e79494b5536cc7d2f3293519cd617.jpg', 'extension': 'jpg'}, 1008922095: {'path': 'temp/1748281382_935833_1008922095_dc858351f179fe112b62a458b452c880.jpg', 'extension': 'jpg'}, 1008922073: {'path': 'temp/1748281382_935833_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg', 'extension': 'jpg'}, 1008922072: {'path': 'temp/1748281382_935833_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg', 'extension': 'jpg'}, 1008921657: {'path': 'temp/1748281382_935833_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg', 'extension': 'jpg'}, 1008921656: {'path': 'temp/1748281382_935833_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg', 'extension': 'jpg'}, 1008921602: {'path': 'temp/1748281382_935833_1008921602_383b6a7758931a2dff9de560df82456c.jpg', 'extension': 'jpg'}, 1008922130: {'path': 'temp/1748281382_935833_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg', 'extension': 'jpg'}, 1008922101: {'path': 'temp/1748281382_935833_1008922101_1a025189b8603388d54304e6754e9df9.jpg', 'extension': 'jpg'}, 1008922097: {'path': 'temp/1748281382_935833_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Thcl ! no input After datou_step_exec type output : time spend for datou_step_exec : 0.0010426044464111328 time spend to save output : 3.910064697265625e-05 total time spend for step 2 : 0.001081705093383789 step3:argmax Mon May 26 19:43:13 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 : {} VR 22-3-18 : For now we do not clean correctly the datou structure No keys ! input_args_next_step, len :0 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281382_935833_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg': 1008921601, 'temp/1748281382_935833_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg': 1008921600, 'temp/1748281382_935833_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg': 1008922003, 'temp/1748281382_935833_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg': 1008922002, 'temp/1748281382_935833_1008921786_066e79494b5536cc7d2f3293519cd617.jpg': 1008921786, 'temp/1748281382_935833_1008922095_dc858351f179fe112b62a458b452c880.jpg': 1008922095, 'temp/1748281382_935833_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg': 1008922073, 'temp/1748281382_935833_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg': 1008922072, 'temp/1748281382_935833_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg': 1008921657, 'temp/1748281382_935833_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg': 1008921656, 'temp/1748281382_935833_1008921602_383b6a7758931a2dff9de560df82456c.jpg': 1008921602, 'temp/1748281382_935833_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg': 1008922130, 'temp/1748281382_935833_1008922101_1a025189b8603388d54304e6754e9df9.jpg': 1008922101, 'temp/1748281382_935833_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg': 1008922097} map_photo_id_path_extension : {1008921601: {'path': 'temp/1748281382_935833_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg', 'extension': 'jpg'}, 1008921600: {'path': 'temp/1748281382_935833_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg', 'extension': 'jpg'}, 1008922003: {'path': 'temp/1748281382_935833_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg', 'extension': 'jpg'}, 1008922002: {'path': 'temp/1748281382_935833_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg', 'extension': 'jpg'}, 1008921786: {'path': 'temp/1748281382_935833_1008921786_066e79494b5536cc7d2f3293519cd617.jpg', 'extension': 'jpg'}, 1008922095: {'path': 'temp/1748281382_935833_1008922095_dc858351f179fe112b62a458b452c880.jpg', 'extension': 'jpg'}, 1008922073: {'path': 'temp/1748281382_935833_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg', 'extension': 'jpg'}, 1008922072: {'path': 'temp/1748281382_935833_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg', 'extension': 'jpg'}, 1008921657: {'path': 'temp/1748281382_935833_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg', 'extension': 'jpg'}, 1008921656: {'path': 'temp/1748281382_935833_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg', 'extension': 'jpg'}, 1008921602: {'path': 'temp/1748281382_935833_1008921602_383b6a7758931a2dff9de560df82456c.jpg', 'extension': 'jpg'}, 1008922130: {'path': 'temp/1748281382_935833_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg', 'extension': 'jpg'}, 1008922101: {'path': 'temp/1748281382_935833_1008922101_1a025189b8603388d54304e6754e9df9.jpg', 'extension': 'jpg'}, 1008922097: {'path': 'temp/1748281382_935833_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! no input After datou_step_exec type output : time spend for datou_step_exec : 0.000102996826171875 time spend to save output : 2.956390380859375e-05 total time spend for step 3 : 0.00013256072998046875 step4:merge_mask_and_thcl Mon May 26 19:43:13 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 : {} VR 22-3-18 : For now we do not clean correctly the datou structure No keys ! input_args_next_step, len :0 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281382_935833_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg': 1008921601, 'temp/1748281382_935833_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg': 1008921600, 'temp/1748281382_935833_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg': 1008922003, 'temp/1748281382_935833_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg': 1008922002, 'temp/1748281382_935833_1008921786_066e79494b5536cc7d2f3293519cd617.jpg': 1008921786, 'temp/1748281382_935833_1008922095_dc858351f179fe112b62a458b452c880.jpg': 1008922095, 'temp/1748281382_935833_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg': 1008922073, 'temp/1748281382_935833_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg': 1008922072, 'temp/1748281382_935833_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg': 1008921657, 'temp/1748281382_935833_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg': 1008921656, 'temp/1748281382_935833_1008921602_383b6a7758931a2dff9de560df82456c.jpg': 1008921602, 'temp/1748281382_935833_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg': 1008922130, 'temp/1748281382_935833_1008922101_1a025189b8603388d54304e6754e9df9.jpg': 1008922101, 'temp/1748281382_935833_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg': 1008922097} map_photo_id_path_extension : {1008921601: {'path': 'temp/1748281382_935833_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg', 'extension': 'jpg'}, 1008921600: {'path': 'temp/1748281382_935833_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg', 'extension': 'jpg'}, 1008922003: {'path': 'temp/1748281382_935833_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg', 'extension': 'jpg'}, 1008922002: {'path': 'temp/1748281382_935833_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg', 'extension': 'jpg'}, 1008921786: {'path': 'temp/1748281382_935833_1008921786_066e79494b5536cc7d2f3293519cd617.jpg', 'extension': 'jpg'}, 1008922095: {'path': 'temp/1748281382_935833_1008922095_dc858351f179fe112b62a458b452c880.jpg', 'extension': 'jpg'}, 1008922073: {'path': 'temp/1748281382_935833_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg', 'extension': 'jpg'}, 1008922072: {'path': 'temp/1748281382_935833_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg', 'extension': 'jpg'}, 1008921657: {'path': 'temp/1748281382_935833_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg', 'extension': 'jpg'}, 1008921656: {'path': 'temp/1748281382_935833_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg', 'extension': 'jpg'}, 1008921602: {'path': 'temp/1748281382_935833_1008921602_383b6a7758931a2dff9de560df82456c.jpg', 'extension': 'jpg'}, 1008922130: {'path': 'temp/1748281382_935833_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg', 'extension': 'jpg'}, 1008922101: {'path': 'temp/1748281382_935833_1008922101_1a025189b8603388d54304e6754e9df9.jpg', 'extension': 'jpg'}, 1008922097: {'path': 'temp/1748281382_935833_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} debut de la step merge mask and classif [] crops sont a sauvegarder After datou_step_exec type output : time spend for datou_step_exec : 0.00012135505676269531 time spend to save output : 3.0279159545898438e-05 total time spend for step 4 : 0.00015163421630859375 step5:rle_unique_nms_with_priority Mon May 26 19:43:13 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/1748281382_935833_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg', 'temp/1748281382_935833_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg', 'temp/1748281382_935833_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg', 'temp/1748281382_935833_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg', 'temp/1748281382_935833_1008921786_066e79494b5536cc7d2f3293519cd617.jpg', 'temp/1748281382_935833_1008922095_dc858351f179fe112b62a458b452c880.jpg', 'temp/1748281382_935833_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg', 'temp/1748281382_935833_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg', 'temp/1748281382_935833_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg', 'temp/1748281382_935833_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg', 'temp/1748281382_935833_1008921602_383b6a7758931a2dff9de560df82456c.jpg', 'temp/1748281382_935833_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg', 'temp/1748281382_935833_1008922101_1a025189b8603388d54304e6754e9df9.jpg', 'temp/1748281382_935833_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg'] We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :14, first value : ['temp/1748281382_935833_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg'] After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281382_935833_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg': 1008921601, 'temp/1748281382_935833_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg': 1008921600, 'temp/1748281382_935833_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg': 1008922003, 'temp/1748281382_935833_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg': 1008922002, 'temp/1748281382_935833_1008921786_066e79494b5536cc7d2f3293519cd617.jpg': 1008921786, 'temp/1748281382_935833_1008922095_dc858351f179fe112b62a458b452c880.jpg': 1008922095, 'temp/1748281382_935833_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg': 1008922073, 'temp/1748281382_935833_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg': 1008922072, 'temp/1748281382_935833_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg': 1008921657, 'temp/1748281382_935833_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg': 1008921656, 'temp/1748281382_935833_1008921602_383b6a7758931a2dff9de560df82456c.jpg': 1008921602, 'temp/1748281382_935833_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg': 1008922130, 'temp/1748281382_935833_1008922101_1a025189b8603388d54304e6754e9df9.jpg': 1008922101, 'temp/1748281382_935833_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg': 1008922097} map_photo_id_path_extension : {1008921601: {'path': 'temp/1748281382_935833_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg', 'extension': 'jpg'}, 1008921600: {'path': 'temp/1748281382_935833_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg', 'extension': 'jpg'}, 1008922003: {'path': 'temp/1748281382_935833_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg', 'extension': 'jpg'}, 1008922002: {'path': 'temp/1748281382_935833_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg', 'extension': 'jpg'}, 1008921786: {'path': 'temp/1748281382_935833_1008921786_066e79494b5536cc7d2f3293519cd617.jpg', 'extension': 'jpg'}, 1008922095: {'path': 'temp/1748281382_935833_1008922095_dc858351f179fe112b62a458b452c880.jpg', 'extension': 'jpg'}, 1008922073: {'path': 'temp/1748281382_935833_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg', 'extension': 'jpg'}, 1008922072: {'path': 'temp/1748281382_935833_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg', 'extension': 'jpg'}, 1008921657: {'path': 'temp/1748281382_935833_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg', 'extension': 'jpg'}, 1008921656: {'path': 'temp/1748281382_935833_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg', 'extension': 'jpg'}, 1008921602: {'path': 'temp/1748281382_935833_1008921602_383b6a7758931a2dff9de560df82456c.jpg', 'extension': 'jpg'}, 1008922130: {'path': 'temp/1748281382_935833_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg', 'extension': 'jpg'}, 1008922101: {'path': 'temp/1748281382_935833_1008922101_1a025189b8603388d54304e6754e9df9.jpg', 'extension': 'jpg'}, 1008922097: {'path': 'temp/1748281382_935833_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Begin step rle-unique-nms on traite la photo : temp/1748281382_935833_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg on traite la photo : temp/1748281382_935833_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg on traite la photo : temp/1748281382_935833_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg on traite la photo : temp/1748281382_935833_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg on traite la photo : temp/1748281382_935833_1008921786_066e79494b5536cc7d2f3293519cd617.jpg on traite la photo : temp/1748281382_935833_1008922095_dc858351f179fe112b62a458b452c880.jpg on traite la photo : temp/1748281382_935833_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg on traite la photo : temp/1748281382_935833_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg on traite la photo : temp/1748281382_935833_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg on traite la photo : temp/1748281382_935833_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg on traite la photo : temp/1748281382_935833_1008921602_383b6a7758931a2dff9de560df82456c.jpg on traite la photo : temp/1748281382_935833_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg on traite la photo : temp/1748281382_935833_1008922101_1a025189b8603388d54304e6754e9df9.jpg on traite la photo : temp/1748281382_935833_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008921657,1008921656,1008921602,1008922130,1008922101,1008922097) and `type` in (3418) and hashtag_id in (492668766,538914404) Loaded 26 chid ids of type : 3418 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (2099963667,2099963668,2099963669,2099963670,2099963671,2099963672,2099963673,2099963674,2099963675,2099963676,2099963677,2099963678,2099963679,2099963680,2099963681,2099963682,2099963683,2099963684,2099963685,2099963686,2099963687,2099963688,2099963689,2099963690,2099963691,2099963692) ++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (2099963667,2099963668,2099963669,2099963670,2099963671,2099963672,2099963673,2099963674,2099963675,2099963676,2099963677,2099963678,2099963679,2099963680,2099963681,2099963682,2099963683,2099963684,2099963685,2099963686,2099963687,2099963688,2099963689,2099963690,2099963691,2099963692) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (2099963667,2099963668,2099963669,2099963670,2099963671,2099963672,2099963673,2099963674,2099963675,2099963676,2099963677,2099963678,2099963679,2099963680,2099963681,2099963682,2099963683,2099963684,2099963685,2099963686,2099963687,2099963688,2099963689,2099963690,2099963691,2099963692) nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 2.3936879634857178 create new chi : 0.006476640701293945 proportion hashtag : refus 0.011822314499524865 proportion hashtag : papier 0.9824540455733292 time to delete rle : 0.5303606986999512 save time : 8.106231689453125e-06 nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 2.8032162189483643 create new chi : 0.006556034088134766 proportion hashtag : refus 0.01255295771301869 proportion hashtag : papier 0.9863322081881533 time to delete rle : 0.7487051486968994 save time : 3.0040740966796875e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.2565433979034424 create new chi : 2.7179718017578125e-05 proportion hashtag : refus 0.010260185698447893 proportion hashtag : papier 0.9897398143015521 time to delete rle : 0.496105432510376 save time : 5.245208740234375e-06 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 0.9404516220092773 create new chi : 0.008579492568969727 proportion hashtag : papier 0.98686463117675 time to delete rle : 0.5447499752044678 save time : 3.814697265625e-06 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.9777615070343018 create new chi : 4.601478576660156e-05 proportion hashtag : refus 0.0024127929996832437 proportion hashtag : papier 0.9975872070003168 time to delete rle : 0.9432454109191895 save time : 3.24249267578125e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.4068095684051514 create new chi : 0.006907224655151367 proportion hashtag : papier 0.9585115913050364 time to delete rle : 0.5005524158477783 save time : 4.76837158203125e-06 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.3201563358306885 create new chi : 2.0265579223632812e-05 proportion hashtag : refus 0.0034527686490338928 proportion hashtag : papier 0.9965472313509661 time to delete rle : 0.5656383037567139 save time : 3.5762786865234375e-06 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 2.3719117641448975 create new chi : 4.982948303222656e-05 proportion hashtag : refus 0.006595957396262274 proportion hashtag : papier 0.9934040426037377 time to delete rle : 0.3743922710418701 save time : 2.6464462280273438e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 0.8988573551177979 create new chi : 0.006773471832275391 proportion hashtag : papier 0.994315707158695 time to delete rle : 0.405411958694458 save time : 3.5762786865234375e-06 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 0.8835551738739014 create new chi : 2.4080276489257812e-05 proportion hashtag : refus 0.0018834633354450428 proportion hashtag : papier 0.9981165366645549 time to delete rle : 0.46138834953308105 save time : 3.814697265625e-06 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.3025808334350586 create new chi : 6.771087646484375e-05 proportion hashtag : refus 0.011059624445676274 proportion hashtag : papier 0.9889403755543237 time to delete rle : 0.4983656406402588 save time : 2.6702880859375e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.3902525901794434 create new chi : 2.0265579223632812e-05 proportion hashtag : refus 0.009172696586949636 proportion hashtag : papier 0.9908273034130504 time to delete rle : 0.6776633262634277 save time : 3.0994415283203125e-06 nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 1.6173181533813477 create new chi : 0.006799459457397461 proportion hashtag : papier 0.9896722560975609 proportion hashtag : refus 0.001785095620842572 time to delete rle : 0.5798437595367432 save time : 3.814697265625e-06 nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 1.4696869850158691 create new chi : 0.00793766975402832 proportion hashtag : papier 0.9968715354767184 proportion hashtag : refus 0.00213167168197656 time to delete rle : 0.4056823253631592 save time : 3.337860107421875e-06 INSERT IGNORE INTO MTRPhoto.photo_carac_ratio (`photo_id`, `hashtag_type`, `hashtag_id`, `ratio`) VALUES (%s, %s, %s, %s) on duplicate key update `ratio` = VALUES(`ratio`) map_output_result : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908)} End step rle-unique-nms After datou_step_exec type output : time spend for datou_step_exec : 31.837664365768433 time spend to save output : 0.0001475811004638672 total time spend for step 5 : 31.837811946868896 step6:ventilate_hashtags_in_portfolio Mon May 26 19:43:45 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 Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :0, first value : [] After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281382_935833_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg': 1008921601, 'temp/1748281382_935833_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg': 1008921600, 'temp/1748281382_935833_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg': 1008922003, 'temp/1748281382_935833_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg': 1008922002, 'temp/1748281382_935833_1008921786_066e79494b5536cc7d2f3293519cd617.jpg': 1008921786, 'temp/1748281382_935833_1008922095_dc858351f179fe112b62a458b452c880.jpg': 1008922095, 'temp/1748281382_935833_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg': 1008922073, 'temp/1748281382_935833_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg': 1008922072, 'temp/1748281382_935833_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg': 1008921657, 'temp/1748281382_935833_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg': 1008921656, 'temp/1748281382_935833_1008921602_383b6a7758931a2dff9de560df82456c.jpg': 1008921602, 'temp/1748281382_935833_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg': 1008922130, 'temp/1748281382_935833_1008922101_1a025189b8603388d54304e6754e9df9.jpg': 1008922101, 'temp/1748281382_935833_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg': 1008922097} map_photo_id_path_extension : {1008921601: {'path': 'temp/1748281382_935833_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg', 'extension': 'jpg'}, 1008921600: {'path': 'temp/1748281382_935833_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg', 'extension': 'jpg'}, 1008922003: {'path': 'temp/1748281382_935833_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg', 'extension': 'jpg'}, 1008922002: {'path': 'temp/1748281382_935833_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg', 'extension': 'jpg'}, 1008921786: {'path': 'temp/1748281382_935833_1008921786_066e79494b5536cc7d2f3293519cd617.jpg', 'extension': 'jpg'}, 1008922095: {'path': 'temp/1748281382_935833_1008922095_dc858351f179fe112b62a458b452c880.jpg', 'extension': 'jpg'}, 1008922073: {'path': 'temp/1748281382_935833_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg', 'extension': 'jpg'}, 1008922072: {'path': 'temp/1748281382_935833_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg', 'extension': 'jpg'}, 1008921657: {'path': 'temp/1748281382_935833_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg', 'extension': 'jpg'}, 1008921656: {'path': 'temp/1748281382_935833_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg', 'extension': 'jpg'}, 1008921602: {'path': 'temp/1748281382_935833_1008921602_383b6a7758931a2dff9de560df82456c.jpg', 'extension': 'jpg'}, 1008922130: {'path': 'temp/1748281382_935833_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg', 'extension': 'jpg'}, 1008922101: {'path': 'temp/1748281382_935833_1008922101_1a025189b8603388d54304e6754e9df9.jpg', 'extension': 'jpg'}, 1008922097: {'path': 'temp/1748281382_935833_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} beginning of datou step ventilate_hashtags_in_portfolio : To implement ! To do loadFromThcl(), then load ParamDescType : thcl2456 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 (2456) thcls : [{'id': 2456, 'mtr_user_id': 31, 'name': 'learn_qualipapia_papier_refus_from_vlg_data_aug', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'papier,refus', 'svm_portfolios_learning': '3028087,3028251', 'photo_hashtag_type': 3049, 'photo_desc_type': 4999, 'type_classification': 'caffe', 'hashtag_id_list': '492668766,538914404'}] thcl {'id': 2456, 'mtr_user_id': 31, 'name': 'learn_qualipapia_papier_refus_from_vlg_data_aug', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'papier,refus', 'svm_portfolios_learning': '3028087,3028251', 'photo_hashtag_type': 3049, 'photo_desc_type': 4999, 'type_classification': 'caffe', 'hashtag_id_list': '492668766,538914404'} Update svm_hashtag_type_desc : 4999 Iterating over portfolio : 3364276 get user id for portfolio 3364276 get sub ptf for main ptf 3535038, type 3418 and hashtags ['refus', 'papier'] SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=3535038 AND mptpi.`type`=3418 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('refus','papier')) AND mptpi.`min_score`=0.7 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=3535038 AND mptpi.`type`=3418 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('refus','papier')) AND mptpi.`min_score`=0.7 To do returned map {'refus': {'main_port_id': 3535038, 'sub_port_id': 3535090, 'hashtag': 'refus', 'pht': 3418, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 8903, 'last_updated_at': datetime.datetime(2025, 5, 26, 15, 44, 7), 'last_updated_at_desc': datetime.datetime(2025, 5, 26, 15, 44, 7), 'last_updated_at_asc': datetime.datetime(2021, 3, 5, 16, 19, 20)}, 'papier': {'main_port_id': 3535038, 'sub_port_id': 3535091, 'hashtag': 'papier', 'pht': 3418, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 8904, 'last_updated_at': datetime.datetime(2025, 5, 26, 15, 44, 7), 'last_updated_at_desc': datetime.datetime(2025, 5, 26, 15, 44, 7), 'last_updated_at_asc': datetime.datetime(2021, 3, 5, 16, 19, 20)}} insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at) SELECT 3535090 , sub_photo_id, 739,now() FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at FROM MTRPhoto.crop_sub_photo_ids csp, MTRPhoto.crop_hashtag_ids chi, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp WHERE mpp.mtr_portfolio_id=3364276 AND mpp.mtr_photo_id=chi.photo_id AND chi.id=csp.crop_hashtag_id AND chi.score>0.7 AND chi.type=3418 AND chi.hashtag_id=h.hashtag_id AND h.hashtag='refus' GROUP BY csp.crop_hashtag_id, csp.created_at order by csp.created_at desc) as a group by crop_hashtag_id; insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at) SELECT 3535091 , sub_photo_id, 739,now() FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at FROM MTRPhoto.crop_sub_photo_ids csp, MTRPhoto.crop_hashtag_ids chi, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp WHERE mpp.mtr_portfolio_id=3364276 AND mpp.mtr_photo_id=chi.photo_id AND chi.id=csp.crop_hashtag_id AND chi.score>0.7 AND chi.type=3418 AND chi.hashtag_id=h.hashtag_id AND h.hashtag='papier' GROUP BY csp.crop_hashtag_id, csp.created_at order by csp.created_at desc) as a group by crop_hashtag_id; To do ! Use context local managing function ! get sub ptf for main ptf 3364276, type 3418 and hashtags ['refus', 'papier'] SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=3364276 AND mptpi.`type`=3418 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('refus','papier')) AND mptpi.`min_score`=0.7 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=3364276 AND mptpi.`type`=3418 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('refus','papier')) AND mptpi.`min_score`=0.7 To do returned map {'refus': {'main_port_id': 3364276, 'sub_port_id': 3534897, 'hashtag': 'refus', 'pht': 3418, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 8901, 'last_updated_at': datetime.datetime(2025, 5, 26, 15, 44, 7), 'last_updated_at_desc': datetime.datetime(2025, 5, 26, 15, 44, 7), 'last_updated_at_asc': datetime.datetime(2021, 3, 5, 15, 22, 49)}, 'papier': {'main_port_id': 3364276, 'sub_port_id': 3534898, 'hashtag': 'papier', 'pht': 3418, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 8902, 'last_updated_at': datetime.datetime(2025, 5, 26, 15, 44, 7), 'last_updated_at_desc': datetime.datetime(2025, 5, 26, 15, 44, 7), 'last_updated_at_asc': datetime.datetime(2021, 3, 5, 15, 22, 49)}} insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at) SELECT 3534897 , sub_photo_id, 739,now() FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at FROM MTRPhoto.crop_sub_photo_ids csp, MTRPhoto.crop_hashtag_ids chi, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp WHERE mpp.mtr_portfolio_id=3364276 AND mpp.mtr_photo_id=chi.photo_id AND chi.id=csp.crop_hashtag_id AND chi.score>0.7 AND chi.type=3418 AND chi.hashtag_id=h.hashtag_id AND h.hashtag='refus' GROUP BY csp.crop_hashtag_id, csp.created_at order by csp.created_at desc) as a group by crop_hashtag_id; insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at) SELECT 3534898 , sub_photo_id, 739,now() FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at FROM MTRPhoto.crop_sub_photo_ids csp, MTRPhoto.crop_hashtag_ids chi, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp WHERE mpp.mtr_portfolio_id=3364276 AND mpp.mtr_photo_id=chi.photo_id AND chi.id=csp.crop_hashtag_id AND chi.score>0.7 AND chi.type=3418 AND chi.hashtag_id=h.hashtag_id AND h.hashtag='papier' GROUP BY csp.crop_hashtag_id, csp.created_at order by csp.created_at desc) as a group by crop_hashtag_id; To do ! Use context local managing function ! After datou_step_exec type output : time spend for datou_step_exec : 0.15987944602966309 time spend to save output : 0.00010800361633300781 total time spend for step 6 : 0.1599874496459961 step7:final Mon May 26 19:43:45 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 Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 2 : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908)} input_args_next_step : {1008921601: ()} output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908)} args : 1008921601 depend.output_id : 0 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: ()} output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908)} args : 1008921600 depend.output_id : 0 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: ()} output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908)} args : 1008922003 depend.output_id : 0 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: ()} output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908)} args : 1008922002 depend.output_id : 0 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: (0.005230650193135238,), 1008921786: ()} output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908)} args : 1008921786 depend.output_id : 0 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: (0.005230650193135238,), 1008921786: (0.005230650193135238,), 1008922095: ()} output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908)} args : 1008922095 depend.output_id : 0 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: (0.005230650193135238,), 1008921786: (0.005230650193135238,), 1008922095: (0.005230650193135238,), 1008922073: ()} output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908)} args : 1008922073 depend.output_id : 0 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: (0.005230650193135238,), 1008921786: (0.005230650193135238,), 1008922095: (0.005230650193135238,), 1008922073: (0.005230650193135238,), 1008922072: ()} output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908)} args : 1008922072 depend.output_id : 0 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: (0.005230650193135238,), 1008921786: (0.005230650193135238,), 1008922095: (0.005230650193135238,), 1008922073: (0.005230650193135238,), 1008922072: (0.005230650193135238,), 1008921657: ()} output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908)} args : 1008921657 depend.output_id : 0 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: (0.005230650193135238,), 1008921786: (0.005230650193135238,), 1008922095: (0.005230650193135238,), 1008922073: (0.005230650193135238,), 1008922072: (0.005230650193135238,), 1008921657: (0.005230650193135238,), 1008921656: ()} output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908)} args : 1008921656 depend.output_id : 0 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: (0.005230650193135238,), 1008921786: (0.005230650193135238,), 1008922095: (0.005230650193135238,), 1008922073: (0.005230650193135238,), 1008922072: (0.005230650193135238,), 1008921657: (0.005230650193135238,), 1008921656: (0.005230650193135238,), 1008921602: ()} output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908)} args : 1008921602 depend.output_id : 0 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: (0.005230650193135238,), 1008921786: (0.005230650193135238,), 1008922095: (0.005230650193135238,), 1008922073: (0.005230650193135238,), 1008922072: (0.005230650193135238,), 1008921657: (0.005230650193135238,), 1008921656: (0.005230650193135238,), 1008921602: (0.005230650193135238,), 1008922130: ()} output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908)} args : 1008922130 depend.output_id : 0 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: (0.005230650193135238,), 1008921786: (0.005230650193135238,), 1008922095: (0.005230650193135238,), 1008922073: (0.005230650193135238,), 1008922072: (0.005230650193135238,), 1008921657: (0.005230650193135238,), 1008921656: (0.005230650193135238,), 1008921602: (0.005230650193135238,), 1008922130: (0.005230650193135238,), 1008922101: ()} output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908)} args : 1008922101 depend.output_id : 0 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: (0.005230650193135238,), 1008921786: (0.005230650193135238,), 1008922095: (0.005230650193135238,), 1008922073: (0.005230650193135238,), 1008922072: (0.005230650193135238,), 1008921657: (0.005230650193135238,), 1008921656: (0.005230650193135238,), 1008921602: (0.005230650193135238,), 1008922130: (0.005230650193135238,), 1008922101: (0.005230650193135238,), 1008922097: ()} output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908)} args : 1008922097 depend.output_id : 0 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :14, first value : (0.005230650193135238,) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1748281382_935833_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg': 1008921601, 'temp/1748281382_935833_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg': 1008921600, 'temp/1748281382_935833_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg': 1008922003, 'temp/1748281382_935833_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg': 1008922002, 'temp/1748281382_935833_1008921786_066e79494b5536cc7d2f3293519cd617.jpg': 1008921786, 'temp/1748281382_935833_1008922095_dc858351f179fe112b62a458b452c880.jpg': 1008922095, 'temp/1748281382_935833_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg': 1008922073, 'temp/1748281382_935833_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg': 1008922072, 'temp/1748281382_935833_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg': 1008921657, 'temp/1748281382_935833_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg': 1008921656, 'temp/1748281382_935833_1008921602_383b6a7758931a2dff9de560df82456c.jpg': 1008921602, 'temp/1748281382_935833_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg': 1008922130, 'temp/1748281382_935833_1008922101_1a025189b8603388d54304e6754e9df9.jpg': 1008922101, 'temp/1748281382_935833_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg': 1008922097} map_photo_id_path_extension : {1008921601: {'path': 'temp/1748281382_935833_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg', 'extension': 'jpg'}, 1008921600: {'path': 'temp/1748281382_935833_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg', 'extension': 'jpg'}, 1008922003: {'path': 'temp/1748281382_935833_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg', 'extension': 'jpg'}, 1008922002: {'path': 'temp/1748281382_935833_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg', 'extension': 'jpg'}, 1008921786: {'path': 'temp/1748281382_935833_1008921786_066e79494b5536cc7d2f3293519cd617.jpg', 'extension': 'jpg'}, 1008922095: {'path': 'temp/1748281382_935833_1008922095_dc858351f179fe112b62a458b452c880.jpg', 'extension': 'jpg'}, 1008922073: {'path': 'temp/1748281382_935833_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg', 'extension': 'jpg'}, 1008922072: {'path': 'temp/1748281382_935833_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg', 'extension': 'jpg'}, 1008921657: {'path': 'temp/1748281382_935833_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg', 'extension': 'jpg'}, 1008921656: {'path': 'temp/1748281382_935833_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg', 'extension': 'jpg'}, 1008921602: {'path': 'temp/1748281382_935833_1008921602_383b6a7758931a2dff9de560df82456c.jpg', 'extension': 'jpg'}, 1008922130: {'path': 'temp/1748281382_935833_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg', 'extension': 'jpg'}, 1008922101: {'path': 'temp/1748281382_935833_1008922101_1a025189b8603388d54304e6754e9df9.jpg', 'extension': 'jpg'}, 1008922097: {'path': 'temp/1748281382_935833_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step final ! query to retrieve results for portfolio 3364276 : SELECT photo_id, hashtag_id, ratio from MTRPhoto.photo_carac_ratio where hashtag_type = 3418 and photo_id in (1008921602,1008921601,1008921600,1008921657,1008921656,1008921786,1008922003,1008922002,1008922073,1008922072,1008922097,1008922095,1008922101,1008922130); insert ignore into MTRUser.portfolio_carac_ratio (portfolio_id, hashtag_type, hashtag_id, value) values (%s,%s,%s,%s) on DUPLICATE KEY UPDATE value=VALUES(value) After datou_step_exec type output : time spend for datou_step_exec : 0.02059173583984375 time spend to save output : 5.555152893066406e-05 total time spend for step 7 : 0.020647287368774414 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True original output for save of step final : {1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',)} new output for save of step final : {1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',)} [1008921601, 1008921600, 1008922003, 1008922002, 1008921786, 1008922095, 1008922073, 1008922072, 1008921657, 1008921656, 1008921602, 1008922130, 1008922101, 1008922097] map_info['map_portfolio_photo'] : {3364276: [1008922130, 1008922101, 1008922097, 1008922095, 1008922073, 1008922072, 1008922003, 1008922002, 1008921786, 1008921657, 1008921656, 1008921602, 1008921601, 1008921600]} final : True mtd_id 3164 list_pids : [1008921601, 1008921600, 1008922003, 1008922002, 1008921786, 1008922095, 1008922073, 1008922072, 1008921657, 1008921656, 1008921602, 1008922130, 1008922101, 1008922097] Looping around the photos to save general results len do output : 14 /1008921601.Didn't retrieve data . /1008921600.Didn't retrieve data . /1008922003.Didn't retrieve data . /1008922002.Didn't retrieve data . /1008921786.Didn't retrieve data . /1008922095.Didn't retrieve data . /1008922073.Didn't retrieve data . /1008922072.Didn't retrieve data . /1008921657.Didn't retrieve data . /1008921656.Didn't retrieve data . /1008921602.Didn't retrieve data . /1008922130.Didn't retrieve data . /1008922101.Didn't retrieve data . /1008922097.Didn't retrieve data . before output type Used above Used above Managing all output in save final without adding information in the mtr_datou_result ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921601', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921600', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922003', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922002', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921786', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922095', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922073', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922072', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921657', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921656', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921602', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922130', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922101', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922097', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 42 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 : [('3164', '3364276', '1008921601', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008921600', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008922003', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008922002', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008921786', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008922095', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008922073', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008922072', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008921657', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008921656', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008921602', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008922130', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008922101', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008922097', '0.005252307643909098', None, None, None, None, None)] time used for this insertion : 0.016996383666992188 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 7 output : {1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',)} {1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',)} ############################### TEST ventilate_hashtags_in_portfolio ################################ DELETE FROM MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 5486631; Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=3070 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=3070 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= 3070 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=3070 # 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 : ventilate_hashtags_in_portfolio 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 (5363525) 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 (5363525) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 1075306598, 'portfolio_id': 5363525}, {'photo_id': 1075306564, 'portfolio_id': 5363525}, {'photo_id': 1075306534, 'portfolio_id': 5363525}, {'photo_id': 1075306522, 'portfolio_id': 5363525}, {'photo_id': 1075304668, 'portfolio_id': 5363525}] map_portfolio_id_photo_id: {5363525: [1075306598, 1075306564, 1075306534, 1075306522, 1075304668]} ##### 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 : 0.017143726348876953 #### 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:ventilate_hashtags_in_portfolio Mon May 26 19:43:46 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 datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 5363525 get user id for portfolio 5363525 get sub ptf for main ptf 5363525, type 4268 and hashtags ['pet_clair', 'error', 'environment'] SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=5363525 AND mptpi.`type`=4268 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','error','environment')) AND mptpi.`min_score`=0.3 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=5363525 AND mptpi.`type`=4268 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','error','environment')) AND mptpi.`min_score`=0.3 To do returned map {'pet_clair': {'main_port_id': 5363525, 'sub_port_id': 5486630, 'hashtag': 'pet_clair', 'pht': 4268, 'min_score': 0.3, 'mtr_user_id': 31, 'id': 1060439, 'last_updated_at': datetime.datetime(2025, 5, 26, 15, 44, 7), 'last_updated_at_desc': datetime.datetime(2025, 5, 26, 15, 44, 7), 'last_updated_at_asc': datetime.datetime(2022, 2, 16, 13, 28, 54)}, 'error': {'main_port_id': 5363525, 'sub_port_id': 5486631, 'hashtag': 'error', 'pht': 4268, 'min_score': 0.3, 'mtr_user_id': 31, 'id': 1060440, 'last_updated_at': datetime.datetime(2025, 5, 26, 15, 44, 7), 'last_updated_at_desc': datetime.datetime(2025, 5, 26, 15, 44, 7), 'last_updated_at_asc': datetime.datetime(2022, 2, 16, 13, 28, 54)}, 'environment': {'main_port_id': 5363525, 'sub_port_id': 5486632, 'hashtag': 'environment', 'pht': 4268, 'min_score': 0.3, 'mtr_user_id': 31, 'id': 1060441, 'last_updated_at': datetime.datetime(2025, 5, 26, 15, 44, 7), 'last_updated_at_desc': datetime.datetime(2025, 5, 26, 15, 44, 7), 'last_updated_at_asc': datetime.datetime(2022, 2, 16, 13, 28, 54)}} insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at) SELECT 5486630 , sub_photo_id, 31,now() FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at FROM MTRPhoto.crop_sub_photo_ids csp, MTRPhoto.crop_hashtag_ids chi, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp WHERE mpp.mtr_portfolio_id=5363525 AND mpp.mtr_photo_id=chi.photo_id AND chi.id=csp.crop_hashtag_id AND chi.score>0.3 AND chi.type=4268 AND chi.hashtag_id=h.hashtag_id AND h.hashtag='pet_clair' GROUP BY csp.crop_hashtag_id, csp.created_at order by csp.created_at desc) as a group by crop_hashtag_id; insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at) SELECT 5486631 , sub_photo_id, 31,now() FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at FROM MTRPhoto.crop_sub_photo_ids csp, MTRPhoto.crop_hashtag_ids chi, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp WHERE mpp.mtr_portfolio_id=5363525 AND mpp.mtr_photo_id=chi.photo_id AND chi.id=csp.crop_hashtag_id AND chi.score>0.3 AND chi.type=4268 AND chi.hashtag_id=h.hashtag_id AND h.hashtag='error' GROUP BY csp.crop_hashtag_id, csp.created_at order by csp.created_at desc) as a group by crop_hashtag_id; insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at) SELECT 5486632 , sub_photo_id, 31,now() FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at FROM MTRPhoto.crop_sub_photo_ids csp, MTRPhoto.crop_hashtag_ids chi, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp WHERE mpp.mtr_portfolio_id=5363525 AND mpp.mtr_photo_id=chi.photo_id AND chi.id=csp.crop_hashtag_id AND chi.score>0.3 AND chi.type=4268 AND chi.hashtag_id=h.hashtag_id AND h.hashtag='environment' GROUP BY csp.crop_hashtag_id, csp.created_at order by csp.created_at desc) as a group by crop_hashtag_id; To do ! Use context local managing function ! After datou_step_exec type output : time spend for datou_step_exec : 0.07306265830993652 time spend to save output : 7.2479248046875e-05 total time spend for step 1 : 0.0731351375579834 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1075306598, 1075306564, 1075306534, 1075306522, 1075304668] map_info['map_portfolio_photo'] : {5363525: [1075306598, 1075306564, 1075306534, 1075306522, 1075304668]} final : True mtd_id 3070 list_pids : [1075306598, 1075306564, 1075306534, 1075306522, 1075304668] Looping around the photos to save general results len do output : 1 /5363525. 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 ('3070', None, None, None, None, None, None, None, None) ('3070', '5363525', '1075306598', None, None, None, None, None, None) ('3070', None, None, None, None, None, None, None, None) ('3070', '5363525', '1075306564', None, None, None, None, None, None) ('3070', None, None, None, None, None, None, None, None) ('3070', '5363525', '1075306534', None, None, None, None, None, None) ('3070', None, None, None, None, None, None, None, None) ('3070', '5363525', '1075306522', None, None, None, None, None, None) ('3070', None, None, None, None, None, None, None, None) ('3070', '5363525', '1075304668', 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 : [('3070', None, '5363525', "{'pet_clair': 5486630, 'error': 5486631, 'environment': 5486632}", None, None, None, None, None), ('3070', '5363525', '1075306598', None, None, None, None, None, None), ('3070', '5363525', '1075306564', None, None, None, None, None, None), ('3070', '5363525', '1075306534', None, None, None, None, None, None), ('3070', '5363525', '1075306522', None, None, None, None, None, None), ('3070', '5363525', '1075304668', None, None, None, None, None, None)] time used for this insertion : 0.013444185256958008 save_final save missing photos in datou_result : After save, about to update current ! SELECT count(*) from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id = 5486631 and hide_status = 0 Ayatollah of tests excluded it ! (Bon le prochain developpeur qui passe ici peut enlever ayatollah VR 11-2-21) name : merge_qualipapia_like not run because too long ############################### TEST poly_ro_rle ################################ test creation de rle a partir de polygon Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=3138 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=3138 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= 3138 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=3138 # 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 : poly_to_rle 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 (3337029) 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 (3337029) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 1003369118, 'portfolio_id': 3337029}] map_portfolio_id_photo_id: {3337029: [1003369118]} ##### Call download_photos : nb_thread : 5 begin to download photo : 1003369118 download finish for photo 1003369118 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 : 0.27091360092163086 #### 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:poly_to_rle Mon May 26 19:43:46 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/1748281426_935833_1003369118_58171420504d0b5f05a1233b6c515509_65826337.jpg': 1003369118} map_photo_id_path_extension : {1003369118: {'path': 'temp/1748281426_935833_1003369118_58171420504d0b5f05a1233b6c515509_65826337.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} can't find the hashtag_type_input ,set the output_type same as the input_type on traite la photo : temp/1748281426_935833_1003369118_58171420504d0b5f05a1233b6c515509_65826337.jpg batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 1003369118) and `type` in (3391) Loaded 16 chid ids of type : 3391 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1967620930,1967620931,1967620932,1967620933,1967620934,1967620935,1967620936,1967620937,1967620938,1967620939,1967620940,1967620941,1967620942,1967620943,1967620944,1967620945) ++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1967620930,1967620931,1967620932,1967620933,1967620934,1967620935,1967620936,1967620937,1967620938,1967620939,1967620940,1967620941,1967620942,1967620943,1967620944,1967620945) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1967620930,1967620931,1967620932,1967620933,1967620934,1967620935,1967620936,1967620937,1967620938,1967620939,1967620940,1967620941,1967620942,1967620943,1967620944,1967620945) time for calcul the mask position with numpy : 0.009273529052734375 nb_pixel_total : 110633 time to create 1 rle with old method : 0.12316012382507324 time for calcul the mask position with numpy : 0.007639646530151367 nb_pixel_total : 15826 time to create 1 rle with old method : 0.018106698989868164 time for calcul the mask position with numpy : 0.007462024688720703 nb_pixel_total : 5286 time to create 1 rle with old method : 0.006834506988525391 time for calcul the mask position with numpy : 0.007525444030761719 nb_pixel_total : 1633 time to create 1 rle with old method : 0.0020983219146728516 time for calcul the mask position with numpy : 0.008028984069824219 nb_pixel_total : 105533 time to create 1 rle with old method : 0.12390851974487305 time for calcul the mask position with numpy : 0.007519960403442383 nb_pixel_total : 4393 time to create 1 rle with old method : 0.005451679229736328 time for calcul the mask position with numpy : 0.007607460021972656 nb_pixel_total : 632 time to create 1 rle with old method : 0.0009019374847412109 time for calcul the mask position with numpy : 0.008172750473022461 nb_pixel_total : 62627 time to create 1 rle with old method : 0.072235107421875 time for calcul the mask position with numpy : 0.007740497589111328 nb_pixel_total : 33681 time to create 1 rle with old method : 0.03935360908508301 time for calcul the mask position with numpy : 0.007904529571533203 nb_pixel_total : 37724 time to create 1 rle with old method : 0.04327726364135742 time for calcul the mask position with numpy : 0.007820606231689453 nb_pixel_total : 48775 time to create 1 rle with old method : 0.056574106216430664 time for calcul the mask position with numpy : 0.047139883041381836 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.21947669982910156 time for calcul the mask position with numpy : 0.007259368896484375 nb_pixel_total : 2310 time to create 1 rle with old method : 0.0027565956115722656 time for calcul the mask position with numpy : 0.00723719596862793 nb_pixel_total : 2256 time to create 1 rle with old method : 0.0030198097229003906 time for calcul the mask position with numpy : 0.007216215133666992 nb_pixel_total : 3112 time to create 1 rle with old method : 0.0039598941802978516 time for calcul the mask position with numpy : 0.007339954376220703 nb_pixel_total : 1662 time to create 1 rle with old method : 0.0021276473999023438 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 16 chid ids of type : 3391 ++++++++++++++++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 : 1.1506450176239014 time spend to save output : 0.00016236305236816406 total time spend for step 1 : 1.1508073806762695 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1003369118: 'temp/1748281426_935833_1003369118_58171420504d0b5f05a1233b6c515509_65826337.jpg'} batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 1003369118) and `type` in (3391) Loaded 16 chid ids of type : 3391 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1967620930,1967620931,1967620932,1967620933,1967620934,1967620935,1967620936,1967620937,1967620938,1967620939,1967620940,1967620941,1967620942,1967620943,1967620944,1967620945) ++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1967620930,1967620931,1967620932,1967620933,1967620934,1967620935,1967620936,1967620937,1967620938,1967620939,1967620940,1967620941,1967620942,1967620943,1967620944,1967620945) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1967620930,1967620931,1967620932,1967620933,1967620934,1967620935,1967620936,1967620937,1967620938,1967620939,1967620940,1967620941,1967620942,1967620943,1967620944,1967620945) fin du test de poly_to_rle ############################### TEST cod_sts ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=3781 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=3781 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= 3781 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=3781 # 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 (4453840) 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 (4453840) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 1050302186, 'portfolio_id': 4453840}, {'photo_id': 1050302153, 'portfolio_id': 4453840}, {'photo_id': 1050302152, 'portfolio_id': 4453840}, {'photo_id': 1050302146, 'portfolio_id': 4453840}, {'photo_id': 1050302113, 'portfolio_id': 4453840}, {'photo_id': 1050302110, 'portfolio_id': 4453840}, {'photo_id': 1050302106, 'portfolio_id': 4453840}] map_portfolio_id_photo_id: {4453840: [1050302186, 1050302153, 1050302152, 1050302146, 1050302113, 1050302110, 1050302106]} ##### 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 : 0.015370368957519531 #### fin chargement data Blocking on flush ? No conitnuing About to test input to load Calling datou_exec Inside datou_exec : verbose : True we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save number of steps : 1 step1:split_time_score Mon May 26 19:43:47 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 : {} map_photo_id_path_extension : {} map_subphoto_mainphoto : {} SELECT app_name, token FROM MTRUser.mtr_app_api_token WHERE mtr_user_id=739 AND app_name="token_split_time_score" AND expire_at > NOW() TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] select SUBSTRING(ph.text,16,2) as h, count(*) from MTRUser.mtr_portfolio_photos mpp inner join MTRBack.photos ph on mpp.mtr_photo_id = ph.photo_id where mpp.mtr_portfolio_id = 4453840 group by h (('48', 4), ('42', 3)) SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4453840 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 100000 SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1 where mpp1.mtr_photo_id=cps.photo_id AND h.hashtag_id=cps.hashtag_id AND mpp1.hide_status=0 AND mpp1.mtr_portfolio_id in (4453840) AND cps.thcl in (861) order by cps.score desc LIMIT 0, 100000 SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1 where mpp1.mtr_photo_id=cps.photo_id AND h.hashtag_id=cps.hashtag_id AND mpp1.hide_status=0 AND mpp1.mtr_portfolio_id in (4453840) AND cps.thcl in (758) order by cps.score desc LIMIT 0, 100000 ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 17082021 4453840 Nombre de photos uploadées : 7 / 23040 (0%) 17082021 4453840 Nombre de photos taguées (types de déchets): 0 / 7 (0%) 17082021 4453840 Nombre de photos taguées (volume) : 0 / 7 (0%) [{'photo_id': 1050302113, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/9/13/8b089b6e181d22a2bb6883e02fd3a110.jpg', 'username': None, 'uploaded_at': 1631534001, 'text': 'IMG_20210817_104213.jpg'}, {'photo_id': 1050302153, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/9/13/fd7af64d2c8fdd6083cf479b54278cc7.jpg', 'username': None, 'uploaded_at': 1631534029, 'text': 'IMG_20210817_104213.jpg'}, {'photo_id': 1050302186, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/9/13/c60917e9be6d6a9f1fa465e963bfa064.jpg', 'username': None, 'uploaded_at': 1631534095, 'text': 'IMG_20210817_104216.jpg'}, {'photo_id': 1050302110, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/9/13/314ca6fa7e24f250d19c3f32c9493132.jpg', 'username': None, 'uploaded_at': 1631534001, 'text': 'IMG_20210817_094832.jpg'}] 0 [] elapsed_time : load_data_split_time_score 3.5762786865234375e-06 elapsed_time : order_list_meta_photo_and_scores 5.9604644775390625e-06 ??????? elapsed_time : fill_and_build_computed_from_old_data 0.0002999305725097656 INSERT INTO `MTRPhoto`.`dashboard_entry_day` (`dashboard_place_id`, `mtr_portfolio_id`, `date`) VALUES ( 34, 4453840, '2021-08-17' ) ON DUPLICATE KEY UPDATE mtr_portfolio_id=VALUES(mtr_portfolio_id), updated_at=NOW(); INSERT INTO `MTRPhoto`.`dashboard_run_ids` (`dashboard_entry_day`, `mtr_user_id`, `misc_info`) VALUES (173725,739,"{}"); elapsed_time : insert_dashboard_record_day_entry 0.02542877197265625 TODO 20-09-21 https://github.com/fotonower/raspi-fotonower-x/issues/253#issuecomment-923099773 TODO 20-9-21 TODO 20-9-21 ***** BEGIN SPLIT TIME ***** ```````list printed: [[0, 1, 2, 3], [4, 5, 6]] forced_hashtag: jrm force hashtag to jrm elapsed_time : SPLIT_TIME 0.005145072937011719 ***** END SPLIT TIME ***** NUMBER BATCH : 2 list_ponderation used : [0.001, 0.001, 0.001, 0.001, 0.001] , list_hashtag_class_create_as_list : ['jrm'] Listed one port to create portfolio : jrm_diff_batch__17082021_09_48_08_000000 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info Nombres de balles jrm_diff_batch__17082021_09_48_08_000000 : 0 duration : 24.0 update_text_in_photos list_photo_id_text len : 0 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_jrm:{'day': '17082021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 24.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20210817_094808.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 Listed one port to create portfolio : jrm_diff_batch__17082021_10_42_13_000000 ERROR missing amount info ERROR missing amount info ERROR missing amount info Nombres de balles jrm_diff_batch__17082021_10_42_13_000000 : 0 duration : 3.0 update_text_in_photos list_photo_id_text len : 0 , 100 first caracter of query INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update None result_one_balle_Type_jrm:{'day': '17082021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 3.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20210817_104213.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 We have rejected 0 photos because of the batch_size condition ! NUMBER BATCH list_of_portfolios_to_create : 2 list_same_port_ids : [4453926] find same portfolio which already exist 4453926 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 4453926 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4453926 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 list_same_port_ids : [4652336] find same portfolio which already exist 4652336 , we will use it INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 4652336 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) + 100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4652336 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`); 0 SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4453926 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=3543 ORDER BY id desc LIMIT 1 SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4453926 AND result is not null and result<>"None" and result=0.0 AND mtd_id=3543 ORDER BY created_at desc LIMIT 1 list_result_datou : [] select url from MTRUser.mtr_files where mtd_id = 3543 and mtr_portfolio_id = 4453926 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4453926 AND mptpi.`type`=4038 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844253, '_______jrm', '2021-08-17 09:48:08', '2021-08-17 09:48:32', 4, 4453926, -1, -1, '-1', "{'max_time_prod_two_photos': 0, 'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P4453926_21-11-2024_01_57_11.pdf', 'Teint_Dans_La_Masse': {'hashtag': 'Teint_Dans_La_Masse', 'sub_port_id': 18491127, 'pht': 4038}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 18491128, 'pht': 4038}, 'cartonnette': {'hashtag': 'cartonnette', 'sub_port_id': 18491129, 'pht': 4038}, 'papier': {'hashtag': 'papier', 'sub_port_id': 18491130, 'pht': 4038}, 'plastique': {'hashtag': 'plastique', 'sub_port_id': 18491131, 'pht': 4038}, 'autre_refus': {'hashtag': 'autre_refus', 'sub_port_id': 18491132, 'pht': 4038}, 'Carton_gris': {'hashtag': 'Carton_gris', 'sub_port_id': 18491133, 'pht': 4038}, 'Carton_brun': {'hashtag': 'Carton_brun', 'sub_port_id': 18491134, 'pht': 4038}, 'kraft': {'hashtag': 'kraft', 'sub_port_id': 18491135, 'pht': 4038}, 'metal': {'hashtag': 'metal', 'sub_port_id': 18491136, 'pht': 4038}}" ); SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4652336 AND result is not null and result<>"None" and result<>0.0 AND mtd_id=3543 ORDER BY id desc LIMIT 1 SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double FROM MTRPhoto.mtr_datou_result WHERE mtr_portfolio_id = 4652336 AND result is not null and result<>"None" and result=0.0 AND mtd_id=3543 ORDER BY created_at desc LIMIT 1 list_result_datou : [] select url from MTRUser.mtr_files where mtd_id = 3543 and mtr_portfolio_id = 4652336 order by file_id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4652336 AND mptpi.`type`=4038 To do INSERT INTO `MTRPhoto`.`dashboard_results` (`dashboard_run_id`, `hashtag`, `date_debut`, `date_fin`, `nombre_balle`, `mtr_portfolio_id`, `qualite`, `datou_result_id_qualite`, `completion_percentage`, `completion_json`) VALUES (1844253, '_______jrm', '2021-08-17 10:42:13', '2021-08-17 10:42:16', 3, 4652336, -1, -1, '-1', "{'max_time_prod_two_photos': 0, 'autre_refus': {'hashtag': 'autre_refus', 'sub_port_id': 18491234, 'pht': 4038}, 'Carton_gris': {'hashtag': 'Carton_gris', 'sub_port_id': 18491235, 'pht': 4038}, 'cartonnette': {'hashtag': 'cartonnette', 'sub_port_id': 18491236, 'pht': 4038}, 'Carton_brun': {'hashtag': 'Carton_brun', 'sub_port_id': 18491237, 'pht': 4038}, 'plastique': {'hashtag': 'plastique', 'sub_port_id': 18491238, 'pht': 4038}, 'papier': {'hashtag': 'papier', 'sub_port_id': 18491239, 'pht': 4038}, 'Teint_Dans_La_Masse': {'hashtag': 'Teint_Dans_La_Masse', 'sub_port_id': 18491240, 'pht': 4038}, 'metal': {'hashtag': 'metal', 'sub_port_id': 18491241, 'pht': 4038}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 18491242, 'pht': 4038}, 'kraft': {'hashtag': 'kraft', 'sub_port_id': 18491243, 'pht': 4038}}" ); elapsed_time : count_nb_balles_and_create_portfolio 1.113816738128662 # DISPLAY ALL COLLECTED DATA : {'17082021': {'nb_upload': 7, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} After datou_step_exec type output : time spend for datou_step_exec : 1.1889076232910156 time spend to save output : 5.364418029785156e-05 total time spend for step 1 : 1.1889612674713135 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1050302186, 1050302153, 1050302152, 1050302146, 1050302113, 1050302110, 1050302106] map_info['map_portfolio_photo'] : {4453840: [1050302186, 1050302153, 1050302152, 1050302146, 1050302113, 1050302110, 1050302106]} final : True mtd_id 3781 list_pids : [1050302186, 1050302153, 1050302152, 1050302146, 1050302113, 1050302110, 1050302106] Looping around the photos to save general results len do output : 1 /4453840Didn'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 ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302186', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302153', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302152', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302146', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302113', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302110', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302106', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 8 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 : [('3781', None, '4453840', 'None', None, None, None, None, None), ('3781', '4453840', '1050302186', None, None, None, None, None, None), ('3781', '4453840', '1050302153', None, None, None, None, None, None), ('3781', '4453840', '1050302152', None, None, None, None, None, None), ('3781', '4453840', '1050302146', None, None, None, None, None, None), ('3781', '4453840', '1050302113', None, None, None, None, None, None), ('3781', '4453840', '1050302110', None, None, None, None, None, None), ('3781', '4453840', '1050302106', None, None, None, None, None, None)] time used for this insertion : 0.013398170471191406 Command terminated by signal 15 192.35user 145.25system 21:35:29elapsed 0%CPU (0avgtext+0avgdata 5390124maxresident)k 7554080inputs+869000outputs (21719major+10975067minor)pagefaults 0swaps