FBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 10 ; length of list_pids : 10 ; length of list_args : 10 time to download the photos : 2.8647074699401855 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : 0 number of steps : 10 step1:mask_detect Wed Apr 9 10:30:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 6374 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-09 10:30:34.860155: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-09 10:30:34.887179: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-09 10:30:34.889457: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f4ae8000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-09 10:30:34.889493: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-09 10:30:34.894302: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-09 10:30:35.127863: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1593a5e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-09 10:30:35.127914: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-09 10:30:35.128545: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-09 10:30:35.128877: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 10:30:35.130827: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 10:30:35.133042: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 10:30:35.133387: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 10:30:35.135857: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 10:30:35.136961: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 10:30:35.142482: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 10:30:35.144597: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 10:30:35.144692: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 10:30:35.145400: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-09 10:30:35.145417: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-09 10:30:35.145426: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-09 10:30:35.146993: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5890 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-04-09 10:30:35.463045: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-09 10:30:35.463208: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 10:30:35.463244: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 10:30:35.463282: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 10:30:35.463314: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 10:30:35.463345: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 10:30:35.463382: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 10:30:35.463420: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 10:30:35.466162: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 10:30:35.467958: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-09 10:30:35.468029: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 10:30:35.468052: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 10:30:35.468072: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 10:30:35.468092: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 10:30:35.468111: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 10:30:35.468136: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 10:30:35.468171: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 10:30:35.469492: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 10:30:35.469544: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-09 10:30:35.469556: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-09 10:30:35.469566: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-09 10:30:35.471037: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5890 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl2847 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) {'thcl': {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'], 'list_hashtags_csv': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'svm_hashtag_type_desc': 5275, 'photo_desc_type': 5275, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME learn_RUBBIA_REFUS_AMIENS_23 NUM_CLASSES 9 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : learn_RUBBIA_REFUS_AMIENS_23 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-04-09 10:30:47.778540: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 10:30:47.988389: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23 /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23/mask_model.h5 size_local : 256009536 size in s3 : 256009536 create time local : 2021-08-09 09:43:22 create time in s3 : 2021-08-06 18:54:04 mask_model.h5 already exist and didn't need to update list_images length : 10 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 47 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 76 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 54 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 58 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 86 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 79 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 52 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 43 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 61 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 85 Detection mask done ! Trying to reset tf kernel 490216 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 3890 tf kernel not reseted sub process len(results) : 10 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 10 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 : 10593 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl2847 Catched exception ! Connect or reconnect ! thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] time for calcul the mask position with numpy : 0.005778789520263672 nb_pixel_total : 119652 time to create 1 rle with old method : 0.14728021621704102 length of segment : 575 time for calcul the mask position with numpy : 0.0004611015319824219 nb_pixel_total : 21630 time to create 1 rle with old method : 0.02538323402404785 length of segment : 165 time for calcul the mask position with numpy : 0.00034236907958984375 nb_pixel_total : 11863 time to create 1 rle with old method : 0.013782739639282227 length of segment : 105 time for calcul the mask position with numpy : 0.0003600120544433594 nb_pixel_total : 17278 time to create 1 rle with old method : 0.01976609230041504 length of segment : 131 time for calcul the mask position with numpy : 0.0004177093505859375 nb_pixel_total : 12805 time to create 1 rle with old method : 0.01589512825012207 length of segment : 152 time for calcul the mask position with numpy : 0.00033783912658691406 nb_pixel_total : 12377 time to create 1 rle with old method : 0.014208555221557617 length of segment : 141 time for calcul the mask position with numpy : 0.0010242462158203125 nb_pixel_total : 45814 time to create 1 rle with old method : 0.05178022384643555 length of segment : 271 time for calcul the mask position with numpy : 0.0015654563903808594 nb_pixel_total : 89021 time to create 1 rle with old method : 0.09719657897949219 length of segment : 558 time for calcul the mask position with numpy : 0.004544258117675781 nb_pixel_total : 176400 time to create 1 rle with new method : 0.0106201171875 length of segment : 556 time for calcul the mask position with numpy : 0.0003170967102050781 nb_pixel_total : 16086 time to create 1 rle with old method : 0.017009258270263672 length of segment : 110 time for calcul the mask position with numpy : 0.0016632080078125 nb_pixel_total : 83781 time to create 1 rle with old method : 0.09329342842102051 length of segment : 765 time for calcul the mask position with numpy : 0.0005917549133300781 nb_pixel_total : 19232 time to create 1 rle with old method : 0.023410797119140625 length of segment : 176 time for calcul the mask position with numpy : 0.0008115768432617188 nb_pixel_total : 16873 time to create 1 rle with old method : 0.01983499526977539 length of segment : 141 time for calcul the mask position with numpy : 0.019806861877441406 nb_pixel_total : 347028 time to create 1 rle with new method : 0.05170011520385742 length of segment : 1061 time for calcul the mask position with numpy : 0.29982447624206543 nb_pixel_total : 785080 time to create 1 rle with new method : 0.07286429405212402 length of segment : 1481 time for calcul the mask position with numpy : 0.00041484832763671875 nb_pixel_total : 9376 time to create 1 rle with old method : 0.01077890396118164 length of segment : 264 time for calcul the mask position with numpy : 0.0005421638488769531 nb_pixel_total : 16688 time to create 1 rle with old method : 0.018872976303100586 length of segment : 283 time for calcul the mask position with numpy : 0.0009503364562988281 nb_pixel_total : 50376 time to create 1 rle with old method : 0.059813737869262695 length of segment : 253 time for calcul the mask position with numpy : 0.001622915267944336 nb_pixel_total : 78740 time to create 1 rle with old method : 0.08797097206115723 length of segment : 193 time for calcul the mask position with numpy : 0.0017507076263427734 nb_pixel_total : 67764 time to create 1 rle with old method : 0.0800466537475586 length of segment : 405 time for calcul the mask position with numpy : 0.0002684593200683594 nb_pixel_total : 13719 time to create 1 rle with old method : 0.01539301872253418 length of segment : 142 time for calcul the mask position with numpy : 0.0016236305236816406 nb_pixel_total : 95806 time to create 1 rle with old method : 0.11039519309997559 length of segment : 408 time for calcul the mask position with numpy : 0.0005772113800048828 nb_pixel_total : 23829 time to create 1 rle with old method : 0.027540206909179688 length of segment : 163 time for calcul the mask position with numpy : 0.00011301040649414062 nb_pixel_total : 3290 time to create 1 rle with old method : 0.008797645568847656 length of segment : 63 time for calcul the mask position with numpy : 0.00038909912109375 nb_pixel_total : 9274 time to create 1 rle with old method : 0.010683536529541016 length of segment : 98 time for calcul the mask position with numpy : 0.002153158187866211 nb_pixel_total : 51761 time to create 1 rle with old method : 0.05825376510620117 length of segment : 348 time for calcul the mask position with numpy : 0.0007493495941162109 nb_pixel_total : 8232 time to create 1 rle with old method : 0.00953817367553711 length of segment : 141 time for calcul the mask position with numpy : 0.0019941329956054688 nb_pixel_total : 36430 time to create 1 rle with old method : 0.041875600814819336 length of segment : 206 time for calcul the mask position with numpy : 0.0007300376892089844 nb_pixel_total : 24115 time to create 1 rle with old method : 0.03235626220703125 length of segment : 192 time for calcul the mask position with numpy : 0.0012545585632324219 nb_pixel_total : 17067 time to create 1 rle with old method : 0.019745349884033203 length of segment : 172 time for calcul the mask position with numpy : 0.00035691261291503906 nb_pixel_total : 6104 time to create 1 rle with old method : 0.007259368896484375 length of segment : 79 time for calcul the mask position with numpy : 0.002207040786743164 nb_pixel_total : 37674 time to create 1 rle with old method : 0.04364204406738281 length of segment : 282 time for calcul the mask position with numpy : 0.0014355182647705078 nb_pixel_total : 27535 time to create 1 rle with old method : 0.03078174591064453 length of segment : 205 time for calcul the mask position with numpy : 0.008018732070922852 nb_pixel_total : 122802 time to create 1 rle with old method : 0.15065407752990723 length of segment : 609 time for calcul the mask position with numpy : 0.0016183853149414062 nb_pixel_total : 22773 time to create 1 rle with old method : 0.025284290313720703 length of segment : 178 time for calcul the mask position with numpy : 0.002233743667602539 nb_pixel_total : 33574 time to create 1 rle with old method : 0.037839412689208984 length of segment : 273 time for calcul the mask position with numpy : 0.002295970916748047 nb_pixel_total : 29323 time to create 1 rle with old method : 0.03340291976928711 length of segment : 191 time for calcul the mask position with numpy : 0.00069427490234375 nb_pixel_total : 10207 time to create 1 rle with old method : 0.018667221069335938 length of segment : 125 time for calcul the mask position with numpy : 0.0017619132995605469 nb_pixel_total : 19367 time to create 1 rle with old method : 0.02259683609008789 length of segment : 311 time for calcul the mask position with numpy : 0.004218101501464844 nb_pixel_total : 74673 time to create 1 rle with old method : 0.08308577537536621 length of segment : 418 time for calcul the mask position with numpy : 0.0012710094451904297 nb_pixel_total : 26108 time to create 1 rle with old method : 0.03430366516113281 length of segment : 181 time for calcul the mask position with numpy : 0.011269569396972656 nb_pixel_total : 190484 time to create 1 rle with new method : 0.018687963485717773 length of segment : 558 time for calcul the mask position with numpy : 0.0016007423400878906 nb_pixel_total : 22852 time to create 1 rle with old method : 0.02683711051940918 length of segment : 137 time for calcul the mask position with numpy : 0.008022785186767578 nb_pixel_total : 118032 time to create 1 rle with old method : 0.13353228569030762 length of segment : 445 time for calcul the mask position with numpy : 0.0013275146484375 nb_pixel_total : 22389 time to create 1 rle with old method : 0.024780750274658203 length of segment : 174 time for calcul the mask position with numpy : 0.0014314651489257812 nb_pixel_total : 26519 time to create 1 rle with old method : 0.0327000617980957 length of segment : 190 time for calcul the mask position with numpy : 0.0009660720825195312 nb_pixel_total : 14405 time to create 1 rle with old method : 0.016855239868164062 length of segment : 144 time for calcul the mask position with numpy : 0.004095792770385742 nb_pixel_total : 67132 time to create 1 rle with old method : 0.07622456550598145 length of segment : 282 time for calcul the mask position with numpy : 0.0026819705963134766 nb_pixel_total : 46347 time to create 1 rle with old method : 0.051897525787353516 length of segment : 228 time for calcul the mask position with numpy : 0.0024805068969726562 nb_pixel_total : 50419 time to create 1 rle with old method : 0.05861830711364746 length of segment : 232 time for calcul the mask position with numpy : 0.00034046173095703125 nb_pixel_total : 4949 time to create 1 rle with old method : 0.006074666976928711 length of segment : 60 time for calcul the mask position with numpy : 0.0013318061828613281 nb_pixel_total : 22730 time to create 1 rle with old method : 0.031890869140625 length of segment : 247 time for calcul the mask position with numpy : 0.004056453704833984 nb_pixel_total : 49176 time to create 1 rle with old method : 0.06786727905273438 length of segment : 370 time for calcul the mask position with numpy : 0.00037598609924316406 nb_pixel_total : 4710 time to create 1 rle with old method : 0.005823850631713867 length of segment : 79 time for calcul the mask position with numpy : 0.0022058486938476562 nb_pixel_total : 44613 time to create 1 rle with old method : 0.05294466018676758 length of segment : 411 time for calcul the mask position with numpy : 0.0016665458679199219 nb_pixel_total : 31938 time to create 1 rle with old method : 0.04265022277832031 length of segment : 227 time for calcul the mask position with numpy : 0.000949859619140625 nb_pixel_total : 15139 time to create 1 rle with old method : 0.017470359802246094 length of segment : 342 time for calcul the mask position with numpy : 0.0013155937194824219 nb_pixel_total : 18832 time to create 1 rle with old method : 0.021409988403320312 length of segment : 164 time for calcul the mask position with numpy : 0.001703023910522461 nb_pixel_total : 22347 time to create 1 rle with old method : 0.025739431381225586 length of segment : 168 time for calcul the mask position with numpy : 0.0010046958923339844 nb_pixel_total : 11760 time to create 1 rle with old method : 0.013209342956542969 length of segment : 116 time for calcul the mask position with numpy : 0.0017824172973632812 nb_pixel_total : 25507 time to create 1 rle with old method : 0.02965569496154785 length of segment : 116 time for calcul the mask position with numpy : 0.008354425430297852 nb_pixel_total : 147453 time to create 1 rle with old method : 0.1602320671081543 length of segment : 419 time for calcul the mask position with numpy : 0.0007512569427490234 nb_pixel_total : 28871 time to create 1 rle with old method : 0.034912109375 length of segment : 188 time for calcul the mask position with numpy : 0.001806020736694336 nb_pixel_total : 24690 time to create 1 rle with old method : 0.027347564697265625 length of segment : 361 time for calcul the mask position with numpy : 0.00691986083984375 nb_pixel_total : 169628 time to create 1 rle with new method : 0.008080244064331055 length of segment : 682 time for calcul the mask position with numpy : 0.003731250762939453 nb_pixel_total : 80478 time to create 1 rle with old method : 0.08719921112060547 length of segment : 647 time for calcul the mask position with numpy : 0.0029175281524658203 nb_pixel_total : 43078 time to create 1 rle with old method : 0.04815983772277832 length of segment : 241 time for calcul the mask position with numpy : 0.0011377334594726562 nb_pixel_total : 17168 time to create 1 rle with old method : 0.021419525146484375 length of segment : 157 time for calcul the mask position with numpy : 0.0019161701202392578 nb_pixel_total : 25099 time to create 1 rle with old method : 0.029009342193603516 length of segment : 295 time for calcul the mask position with numpy : 0.0077893733978271484 nb_pixel_total : 107230 time to create 1 rle with old method : 0.12173295021057129 length of segment : 331 time for calcul the mask position with numpy : 0.0007894039154052734 nb_pixel_total : 9727 time to create 1 rle with old method : 0.015175342559814453 length of segment : 122 time for calcul the mask position with numpy : 0.0006434917449951172 nb_pixel_total : 15937 time to create 1 rle with old method : 0.02611541748046875 length of segment : 193 time for calcul the mask position with numpy : 0.0005273818969726562 nb_pixel_total : 7097 time to create 1 rle with old method : 0.008228540420532227 length of segment : 168 time for calcul the mask position with numpy : 0.0011887550354003906 nb_pixel_total : 22404 time to create 1 rle with old method : 0.024838924407958984 length of segment : 234 time for calcul the mask position with numpy : 0.003635406494140625 nb_pixel_total : 62820 time to create 1 rle with old method : 0.07661700248718262 length of segment : 355 time for calcul the mask position with numpy : 0.0008742809295654297 nb_pixel_total : 22415 time to create 1 rle with old method : 0.02668905258178711 length of segment : 274 time for calcul the mask position with numpy : 0.0008819103240966797 nb_pixel_total : 16368 time to create 1 rle with old method : 0.019281387329101562 length of segment : 146 time for calcul the mask position with numpy : 0.00457453727722168 nb_pixel_total : 133836 time to create 1 rle with old method : 0.14899063110351562 length of segment : 487 time for calcul the mask position with numpy : 0.004166603088378906 nb_pixel_total : 90836 time to create 1 rle with old method : 0.10255050659179688 length of segment : 445 time for calcul the mask position with numpy : 0.0004553794860839844 nb_pixel_total : 6778 time to create 1 rle with old method : 0.007708549499511719 length of segment : 99 time for calcul the mask position with numpy : 0.00040340423583984375 nb_pixel_total : 6546 time to create 1 rle with old method : 0.007597446441650391 length of segment : 85 time for calcul the mask position with numpy : 0.0005645751953125 nb_pixel_total : 6612 time to create 1 rle with old method : 0.007668495178222656 length of segment : 73 time for calcul the mask position with numpy : 0.0006110668182373047 nb_pixel_total : 12367 time to create 1 rle with old method : 0.01430201530456543 length of segment : 108 time for calcul the mask position with numpy : 0.003970146179199219 nb_pixel_total : 71038 time to create 1 rle with old method : 0.07862591743469238 length of segment : 241 time for calcul the mask position with numpy : 0.012992382049560547 nb_pixel_total : 273593 time to create 1 rle with new method : 0.01776909828186035 length of segment : 634 time for calcul the mask position with numpy : 0.0047914981842041016 nb_pixel_total : 80985 time to create 1 rle with old method : 0.10607409477233887 length of segment : 421 time for calcul the mask position with numpy : 0.0020601749420166016 nb_pixel_total : 38272 time to create 1 rle with old method : 0.04216623306274414 length of segment : 247 time for calcul the mask position with numpy : 0.0014748573303222656 nb_pixel_total : 36453 time to create 1 rle with old method : 0.042479753494262695 length of segment : 240 time for calcul the mask position with numpy : 0.0023474693298339844 nb_pixel_total : 33792 time to create 1 rle with old method : 0.042899131774902344 length of segment : 388 time for calcul the mask position with numpy : 0.004021406173706055 nb_pixel_total : 110780 time to create 1 rle with old method : 0.12268304824829102 length of segment : 254 time for calcul the mask position with numpy : 0.0008661746978759766 nb_pixel_total : 13628 time to create 1 rle with old method : 0.015745162963867188 length of segment : 146 time for calcul the mask position with numpy : 0.0018832683563232422 nb_pixel_total : 26091 time to create 1 rle with old method : 0.030046939849853516 length of segment : 230 time for calcul the mask position with numpy : 0.002915620803833008 nb_pixel_total : 69716 time to create 1 rle with old method : 0.10234642028808594 length of segment : 264 time for calcul the mask position with numpy : 0.0020508766174316406 nb_pixel_total : 29784 time to create 1 rle with old method : 0.033087730407714844 length of segment : 206 time for calcul the mask position with numpy : 0.0007739067077636719 nb_pixel_total : 14064 time to create 1 rle with old method : 0.01678323745727539 length of segment : 112 time for calcul the mask position with numpy : 0.0008556842803955078 nb_pixel_total : 14466 time to create 1 rle with old method : 0.01862955093383789 length of segment : 211 time for calcul the mask position with numpy : 0.004574775695800781 nb_pixel_total : 87160 time to create 1 rle with old method : 0.10338401794433594 length of segment : 331 time for calcul the mask position with numpy : 0.0016889572143554688 nb_pixel_total : 31680 time to create 1 rle with old method : 0.05245089530944824 length of segment : 274 time for calcul the mask position with numpy : 0.002713441848754883 nb_pixel_total : 36023 time to create 1 rle with old method : 0.04357624053955078 length of segment : 202 time for calcul the mask position with numpy : 0.00449824333190918 nb_pixel_total : 93182 time to create 1 rle with old method : 0.09986305236816406 length of segment : 666 time for calcul the mask position with numpy : 0.002184152603149414 nb_pixel_total : 45128 time to create 1 rle with old method : 0.04889559745788574 length of segment : 311 time for calcul the mask position with numpy : 0.001409292221069336 nb_pixel_total : 29485 time to create 1 rle with old method : 0.033113956451416016 length of segment : 177 time for calcul the mask position with numpy : 0.0014030933380126953 nb_pixel_total : 11476 time to create 1 rle with old method : 0.013295173645019531 length of segment : 209 time for calcul the mask position with numpy : 0.00437164306640625 nb_pixel_total : 113169 time to create 1 rle with old method : 0.12361741065979004 length of segment : 395 time for calcul the mask position with numpy : 0.0034515857696533203 nb_pixel_total : 57382 time to create 1 rle with old method : 0.06209206581115723 length of segment : 411 time for calcul the mask position with numpy : 0.0011763572692871094 nb_pixel_total : 29494 time to create 1 rle with old method : 0.03363680839538574 length of segment : 191 time for calcul the mask position with numpy : 0.0004673004150390625 nb_pixel_total : 10169 time to create 1 rle with old method : 0.011715888977050781 length of segment : 117 time for calcul the mask position with numpy : 0.00022363662719726562 nb_pixel_total : 6288 time to create 1 rle with old method : 0.0074465274810791016 length of segment : 136 time for calcul the mask position with numpy : 0.0033919811248779297 nb_pixel_total : 70725 time to create 1 rle with old method : 0.08008813858032227 length of segment : 348 time for calcul the mask position with numpy : 0.0008347034454345703 nb_pixel_total : 17139 time to create 1 rle with old method : 0.019891023635864258 length of segment : 98 time for calcul the mask position with numpy : 0.0006682872772216797 nb_pixel_total : 14699 time to create 1 rle with old method : 0.017345428466796875 length of segment : 105 time for calcul the mask position with numpy : 0.0026311874389648438 nb_pixel_total : 49127 time to create 1 rle with old method : 0.057294368743896484 length of segment : 442 time for calcul the mask position with numpy : 0.018012523651123047 nb_pixel_total : 332794 time to create 1 rle with new method : 0.023347139358520508 length of segment : 1041 time for calcul the mask position with numpy : 0.0016679763793945312 nb_pixel_total : 33019 time to create 1 rle with old method : 0.03849196434020996 length of segment : 291 time for calcul the mask position with numpy : 0.0047016143798828125 nb_pixel_total : 89794 time to create 1 rle with old method : 0.1004035472869873 length of segment : 480 time for calcul the mask position with numpy : 0.0036268234252929688 nb_pixel_total : 86325 time to create 1 rle with old method : 0.09547209739685059 length of segment : 694 time for calcul the mask position with numpy : 0.0015118122100830078 nb_pixel_total : 24305 time to create 1 rle with old method : 0.02748417854309082 length of segment : 162 time for calcul the mask position with numpy : 0.007611751556396484 nb_pixel_total : 123833 time to create 1 rle with old method : 0.13908863067626953 length of segment : 364 time for calcul the mask position with numpy : 0.008028268814086914 nb_pixel_total : 52748 time to create 1 rle with old method : 0.06628823280334473 length of segment : 398 time for calcul the mask position with numpy : 0.003568887710571289 nb_pixel_total : 101023 time to create 1 rle with old method : 0.11747550964355469 length of segment : 277 time for calcul the mask position with numpy : 0.0016100406646728516 nb_pixel_total : 20532 time to create 1 rle with old method : 0.025155067443847656 length of segment : 141 time for calcul the mask position with numpy : 0.005451679229736328 nb_pixel_total : 78958 time to create 1 rle with old method : 0.08798670768737793 length of segment : 263 time for calcul the mask position with numpy : 0.0004115104675292969 nb_pixel_total : 5837 time to create 1 rle with old method : 0.00682830810546875 length of segment : 84 time for calcul the mask position with numpy : 0.01051187515258789 nb_pixel_total : 181097 time to create 1 rle with new method : 0.010344505310058594 length of segment : 715 time for calcul the mask position with numpy : 0.0009322166442871094 nb_pixel_total : 17371 time to create 1 rle with old method : 0.02016162872314453 length of segment : 83 time for calcul the mask position with numpy : 0.0008087158203125 nb_pixel_total : 8250 time to create 1 rle with old method : 0.009750843048095703 length of segment : 203 time for calcul the mask position with numpy : 0.0011048316955566406 nb_pixel_total : 12021 time to create 1 rle with old method : 0.01979517936706543 length of segment : 138 time for calcul the mask position with numpy : 0.0016889572143554688 nb_pixel_total : 21209 time to create 1 rle with old method : 0.03098773956298828 length of segment : 133 time for calcul the mask position with numpy : 0.0008571147918701172 nb_pixel_total : 11858 time to create 1 rle with old method : 0.01669764518737793 length of segment : 152 time for calcul the mask position with numpy : 0.000553131103515625 nb_pixel_total : 9902 time to create 1 rle with old method : 0.011728525161743164 length of segment : 101 time for calcul the mask position with numpy : 0.0007863044738769531 nb_pixel_total : 9886 time to create 1 rle with old method : 0.01154327392578125 length of segment : 143 time for calcul the mask position with numpy : 0.0014488697052001953 nb_pixel_total : 24639 time to create 1 rle with old method : 0.028202056884765625 length of segment : 124 time for calcul the mask position with numpy : 0.004102230072021484 nb_pixel_total : 41157 time to create 1 rle with old method : 0.04661083221435547 length of segment : 426 time for calcul the mask position with numpy : 0.0006885528564453125 nb_pixel_total : 11973 time to create 1 rle with old method : 0.013636112213134766 length of segment : 185 time for calcul the mask position with numpy : 0.0009655952453613281 nb_pixel_total : 11010 time to create 1 rle with old method : 0.013155221939086914 length of segment : 123 time for calcul the mask position with numpy : 0.004137754440307617 nb_pixel_total : 40901 time to create 1 rle with old method : 0.04705309867858887 length of segment : 359 time for calcul the mask position with numpy : 0.0007121562957763672 nb_pixel_total : 10437 time to create 1 rle with old method : 0.01210641860961914 length of segment : 121 time for calcul the mask position with numpy : 0.001116037368774414 nb_pixel_total : 12253 time to create 1 rle with old method : 0.014528512954711914 length of segment : 223 time for calcul the mask position with numpy : 0.013672351837158203 nb_pixel_total : 213262 time to create 1 rle with new method : 0.01401972770690918 length of segment : 594 time for calcul the mask position with numpy : 0.0019075870513916016 nb_pixel_total : 24526 time to create 1 rle with old method : 0.02967691421508789 length of segment : 300 time for calcul the mask position with numpy : 0.003265380859375 nb_pixel_total : 31965 time to create 1 rle with old method : 0.037262678146362305 length of segment : 370 time for calcul the mask position with numpy : 0.0003209114074707031 nb_pixel_total : 7564 time to create 1 rle with old method : 0.008779048919677734 length of segment : 102 time for calcul the mask position with numpy : 0.001279592514038086 nb_pixel_total : 22363 time to create 1 rle with old method : 0.025478601455688477 length of segment : 157 time for calcul the mask position with numpy : 0.021570682525634766 nb_pixel_total : 436925 time to create 1 rle with new method : 0.024697542190551758 length of segment : 783 time for calcul the mask position with numpy : 0.004189729690551758 nb_pixel_total : 21622 time to create 1 rle with old method : 0.025010347366333008 length of segment : 245 time for calcul the mask position with numpy : 0.0029163360595703125 nb_pixel_total : 22746 time to create 1 rle with old method : 0.027645349502563477 length of segment : 208 time for calcul the mask position with numpy : 0.0002295970916748047 nb_pixel_total : 7258 time to create 1 rle with old method : 0.008441448211669922 length of segment : 120 time for calcul the mask position with numpy : 0.0059680938720703125 nb_pixel_total : 89126 time to create 1 rle with old method : 0.10024714469909668 length of segment : 471 time for calcul the mask position with numpy : 0.007723331451416016 nb_pixel_total : 184273 time to create 1 rle with new method : 0.013385534286499023 length of segment : 648 time for calcul the mask position with numpy : 0.0010216236114501953 nb_pixel_total : 31282 time to create 1 rle with old method : 0.035126447677612305 length of segment : 272 time for calcul the mask position with numpy : 0.0014965534210205078 nb_pixel_total : 15929 time to create 1 rle with old method : 0.02170729637145996 length of segment : 309 time for calcul the mask position with numpy : 0.002559185028076172 nb_pixel_total : 47618 time to create 1 rle with old method : 0.05482745170593262 length of segment : 356 time for calcul the mask position with numpy : 0.0005064010620117188 nb_pixel_total : 4894 time to create 1 rle with old method : 0.0060460567474365234 length of segment : 103 time for calcul the mask position with numpy : 0.0013499259948730469 nb_pixel_total : 22679 time to create 1 rle with old method : 0.027052640914916992 length of segment : 164 time for calcul the mask position with numpy : 0.0006132125854492188 nb_pixel_total : 16467 time to create 1 rle with old method : 0.01911306381225586 length of segment : 152 time for calcul the mask position with numpy : 0.0018451213836669922 nb_pixel_total : 22120 time to create 1 rle with old method : 0.026658058166503906 length of segment : 189 time for calcul the mask position with numpy : 0.0003032684326171875 nb_pixel_total : 5502 time to create 1 rle with old method : 0.007834434509277344 length of segment : 102 time for calcul the mask position with numpy : 0.0016939640045166016 nb_pixel_total : 29861 time to create 1 rle with old method : 0.03499937057495117 length of segment : 242 time for calcul the mask position with numpy : 0.0035424232482910156 nb_pixel_total : 70659 time to create 1 rle with old method : 0.07877635955810547 length of segment : 366 time for calcul the mask position with numpy : 0.001392364501953125 nb_pixel_total : 24602 time to create 1 rle with old method : 0.02749466896057129 length of segment : 143 time for calcul the mask position with numpy : 0.0034830570220947266 nb_pixel_total : 72796 time to create 1 rle with old method : 0.08037519454956055 length of segment : 646 time for calcul the mask position with numpy : 0.0026352405548095703 nb_pixel_total : 37352 time to create 1 rle with old method : 0.053185224533081055 length of segment : 298 time for calcul the mask position with numpy : 0.0010428428649902344 nb_pixel_total : 14166 time to create 1 rle with old method : 0.01571035385131836 length of segment : 193 time for calcul the mask position with numpy : 0.0006272792816162109 nb_pixel_total : 8036 time to create 1 rle with old method : 0.009294509887695312 length of segment : 104 time for calcul the mask position with numpy : 0.0019643306732177734 nb_pixel_total : 25935 time to create 1 rle with old method : 0.029227018356323242 length of segment : 315 time for calcul the mask position with numpy : 0.0010988712310791016 nb_pixel_total : 19786 time to create 1 rle with old method : 0.0223391056060791 length of segment : 189 time for calcul the mask position with numpy : 0.0011734962463378906 nb_pixel_total : 16633 time to create 1 rle with old method : 0.0187833309173584 length of segment : 221 time for calcul the mask position with numpy : 0.0018126964569091797 nb_pixel_total : 34691 time to create 1 rle with old method : 0.038903236389160156 length of segment : 359 time for calcul the mask position with numpy : 0.0006177425384521484 nb_pixel_total : 9495 time to create 1 rle with old method : 0.010970830917358398 length of segment : 112 time for calcul the mask position with numpy : 0.001712799072265625 nb_pixel_total : 24982 time to create 1 rle with old method : 0.028374910354614258 length of segment : 196 time for calcul the mask position with numpy : 0.0008246898651123047 nb_pixel_total : 13774 time to create 1 rle with old method : 0.015935897827148438 length of segment : 129 time for calcul the mask position with numpy : 0.001119375228881836 nb_pixel_total : 15587 time to create 1 rle with old method : 0.018364667892456055 length of segment : 156 time for calcul the mask position with numpy : 0.008312463760375977 nb_pixel_total : 175498 time to create 1 rle with new method : 0.009044170379638672 length of segment : 277 time for calcul the mask position with numpy : 0.0018069744110107422 nb_pixel_total : 19801 time to create 1 rle with old method : 0.03434562683105469 length of segment : 180 time for calcul the mask position with numpy : 0.0010364055633544922 nb_pixel_total : 16484 time to create 1 rle with old method : 0.01862502098083496 length of segment : 283 time for calcul the mask position with numpy : 0.0008962154388427734 nb_pixel_total : 11708 time to create 1 rle with old method : 0.013846874237060547 length of segment : 170 time for calcul the mask position with numpy : 0.0015687942504882812 nb_pixel_total : 34037 time to create 1 rle with old method : 0.038843393325805664 length of segment : 162 time for calcul the mask position with numpy : 0.001995563507080078 nb_pixel_total : 33519 time to create 1 rle with old method : 0.037596702575683594 length of segment : 233 time for calcul the mask position with numpy : 0.0011086463928222656 nb_pixel_total : 17745 time to create 1 rle with old method : 0.02027440071105957 length of segment : 175 time for calcul the mask position with numpy : 0.0004742145538330078 nb_pixel_total : 5465 time to create 1 rle with old method : 0.0063741207122802734 length of segment : 165 time for calcul the mask position with numpy : 0.0015168190002441406 nb_pixel_total : 24993 time to create 1 rle with old method : 0.02883148193359375 length of segment : 183 time for calcul the mask position with numpy : 0.012249946594238281 nb_pixel_total : 178329 time to create 1 rle with new method : 0.013926506042480469 length of segment : 480 time for calcul the mask position with numpy : 0.0021049976348876953 nb_pixel_total : 36858 time to create 1 rle with old method : 0.042677879333496094 length of segment : 178 time for calcul the mask position with numpy : 0.0004532337188720703 nb_pixel_total : 10811 time to create 1 rle with old method : 0.012556791305541992 length of segment : 90 time for calcul the mask position with numpy : 0.0007889270782470703 nb_pixel_total : 11998 time to create 1 rle with old method : 0.013687372207641602 length of segment : 167 time for calcul the mask position with numpy : 0.0021042823791503906 nb_pixel_total : 26974 time to create 1 rle with old method : 0.030320167541503906 length of segment : 423 time for calcul the mask position with numpy : 0.001010894775390625 nb_pixel_total : 15937 time to create 1 rle with old method : 0.01863884925842285 length of segment : 114 time for calcul the mask position with numpy : 0.001031637191772461 nb_pixel_total : 14190 time to create 1 rle with old method : 0.023871183395385742 length of segment : 171 time for calcul the mask position with numpy : 0.0070345401763916016 nb_pixel_total : 112512 time to create 1 rle with old method : 0.13297748565673828 length of segment : 611 time for calcul the mask position with numpy : 0.0006399154663085938 nb_pixel_total : 11244 time to create 1 rle with old method : 0.013124704360961914 length of segment : 106 time for calcul the mask position with numpy : 0.0008709430694580078 nb_pixel_total : 13840 time to create 1 rle with old method : 0.015883445739746094 length of segment : 175 time for calcul the mask position with numpy : 0.0006763935089111328 nb_pixel_total : 13747 time to create 1 rle with old method : 0.015640974044799805 length of segment : 141 time for calcul the mask position with numpy : 0.0007529258728027344 nb_pixel_total : 17504 time to create 1 rle with old method : 0.02034926414489746 length of segment : 130 time for calcul the mask position with numpy : 0.001001119613647461 nb_pixel_total : 21213 time to create 1 rle with old method : 0.024088382720947266 length of segment : 167 time for calcul the mask position with numpy : 0.0018754005432128906 nb_pixel_total : 25102 time to create 1 rle with old method : 0.028678178787231445 length of segment : 264 time for calcul the mask position with numpy : 0.0024423599243164062 nb_pixel_total : 46726 time to create 1 rle with old method : 0.05288267135620117 length of segment : 270 time for calcul the mask position with numpy : 0.0034193992614746094 nb_pixel_total : 82867 time to create 1 rle with old method : 0.09211039543151855 length of segment : 709 time for calcul the mask position with numpy : 0.0007627010345458984 nb_pixel_total : 12106 time to create 1 rle with old method : 0.013849020004272461 length of segment : 145 time for calcul the mask position with numpy : 0.0010204315185546875 nb_pixel_total : 14502 time to create 1 rle with old method : 0.016793489456176758 length of segment : 346 time for calcul the mask position with numpy : 0.0015060901641845703 nb_pixel_total : 25458 time to create 1 rle with old method : 0.02917194366455078 length of segment : 187 time for calcul the mask position with numpy : 0.0012364387512207031 nb_pixel_total : 24395 time to create 1 rle with old method : 0.027868032455444336 length of segment : 174 time for calcul the mask position with numpy : 0.0008652210235595703 nb_pixel_total : 15932 time to create 1 rle with old method : 0.018645524978637695 length of segment : 138 time for calcul the mask position with numpy : 0.001094818115234375 nb_pixel_total : 15949 time to create 1 rle with old method : 0.01913738250732422 length of segment : 137 time for calcul the mask position with numpy : 0.0004684925079345703 nb_pixel_total : 7676 time to create 1 rle with old method : 0.009184122085571289 length of segment : 166 time for calcul the mask position with numpy : 0.01780843734741211 nb_pixel_total : 348056 time to create 1 rle with new method : 0.16351938247680664 length of segment : 1095 time for calcul the mask position with numpy : 0.0002682209014892578 nb_pixel_total : 3354 time to create 1 rle with old method : 0.004112720489501953 length of segment : 61 time for calcul the mask position with numpy : 0.001863241195678711 nb_pixel_total : 29665 time to create 1 rle with old method : 0.03380322456359863 length of segment : 325 time for calcul the mask position with numpy : 0.00045180320739746094 nb_pixel_total : 11770 time to create 1 rle with old method : 0.013411521911621094 length of segment : 155 time for calcul the mask position with numpy : 0.05394864082336426 nb_pixel_total : 855985 time to create 1 rle with new method : 0.0826425552368164 length of segment : 1434 time for calcul the mask position with numpy : 0.012593746185302734 nb_pixel_total : 291868 time to create 1 rle with new method : 0.029761552810668945 length of segment : 1179 time for calcul the mask position with numpy : 0.0005147457122802734 nb_pixel_total : 17654 time to create 1 rle with old method : 0.020899534225463867 length of segment : 185 time for calcul the mask position with numpy : 0.0004754066467285156 nb_pixel_total : 9621 time to create 1 rle with old method : 0.011267423629760742 length of segment : 100 time for calcul the mask position with numpy : 0.0016222000122070312 nb_pixel_total : 38475 time to create 1 rle with old method : 0.043717145919799805 length of segment : 291 time for calcul the mask position with numpy : 0.0007483959197998047 nb_pixel_total : 12855 time to create 1 rle with old method : 0.015175819396972656 length of segment : 193 time for calcul the mask position with numpy : 0.0009188652038574219 nb_pixel_total : 18638 time to create 1 rle with old method : 0.021922826766967773 length of segment : 171 time for calcul the mask position with numpy : 0.002329587936401367 nb_pixel_total : 49042 time to create 1 rle with old method : 0.05497622489929199 length of segment : 347 time for calcul the mask position with numpy : 0.002688884735107422 nb_pixel_total : 81378 time to create 1 rle with old method : 0.0917508602142334 length of segment : 319 time for calcul the mask position with numpy : 0.0039026737213134766 nb_pixel_total : 105914 time to create 1 rle with old method : 0.11838364601135254 length of segment : 422 time for calcul the mask position with numpy : 0.0012257099151611328 nb_pixel_total : 36746 time to create 1 rle with old method : 0.04144406318664551 length of segment : 246 time for calcul the mask position with numpy : 0.0010461807250976562 nb_pixel_total : 30200 time to create 1 rle with old method : 0.0344998836517334 length of segment : 141 time for calcul the mask position with numpy : 0.0014965534210205078 nb_pixel_total : 34004 time to create 1 rle with old method : 0.03927445411682129 length of segment : 257 time for calcul the mask position with numpy : 0.0019440650939941406 nb_pixel_total : 46977 time to create 1 rle with old method : 0.05269122123718262 length of segment : 308 time for calcul the mask position with numpy : 0.0012485980987548828 nb_pixel_total : 38387 time to create 1 rle with old method : 0.04355573654174805 length of segment : 277 time for calcul the mask position with numpy : 0.0011622905731201172 nb_pixel_total : 24682 time to create 1 rle with old method : 0.028473615646362305 length of segment : 144 time for calcul the mask position with numpy : 0.0010218620300292969 nb_pixel_total : 26939 time to create 1 rle with old method : 0.030433177947998047 length of segment : 204 time for calcul the mask position with numpy : 0.0008494853973388672 nb_pixel_total : 24594 time to create 1 rle with old method : 0.028125286102294922 length of segment : 191 time for calcul the mask position with numpy : 0.0009458065032958984 nb_pixel_total : 22285 time to create 1 rle with old method : 0.025737285614013672 length of segment : 127 time for calcul the mask position with numpy : 0.006367921829223633 nb_pixel_total : 175525 time to create 1 rle with new method : 0.009704113006591797 length of segment : 519 time for calcul the mask position with numpy : 0.0014417171478271484 nb_pixel_total : 28282 time to create 1 rle with old method : 0.03309035301208496 length of segment : 206 time for calcul the mask position with numpy : 0.0004680156707763672 nb_pixel_total : 6952 time to create 1 rle with old method : 0.008082389831542969 length of segment : 119 time for calcul the mask position with numpy : 0.0013804435729980469 nb_pixel_total : 24926 time to create 1 rle with old method : 0.02844834327697754 length of segment : 203 time for calcul the mask position with numpy : 0.0013496875762939453 nb_pixel_total : 29872 time to create 1 rle with old method : 0.033734798431396484 length of segment : 228 time for calcul the mask position with numpy : 0.001155853271484375 nb_pixel_total : 28730 time to create 1 rle with old method : 0.03313040733337402 length of segment : 193 time for calcul the mask position with numpy : 0.0016086101531982422 nb_pixel_total : 37325 time to create 1 rle with old method : 0.0431218147277832 length of segment : 242 time for calcul the mask position with numpy : 0.004807233810424805 nb_pixel_total : 127178 time to create 1 rle with old method : 0.1710515022277832 length of segment : 436 time for calcul the mask position with numpy : 0.0008549690246582031 nb_pixel_total : 17558 time to create 1 rle with old method : 0.02066326141357422 length of segment : 126 time for calcul the mask position with numpy : 0.004024505615234375 nb_pixel_total : 117744 time to create 1 rle with old method : 0.14087414741516113 length of segment : 583 time for calcul the mask position with numpy : 0.002004384994506836 nb_pixel_total : 38388 time to create 1 rle with old method : 0.04277443885803223 length of segment : 300 time for calcul the mask position with numpy : 0.0016412734985351562 nb_pixel_total : 29764 time to create 1 rle with old method : 0.03415489196777344 length of segment : 171 time for calcul the mask position with numpy : 0.001497507095336914 nb_pixel_total : 35531 time to create 1 rle with old method : 0.04114031791687012 length of segment : 241 time for calcul the mask position with numpy : 0.0004909038543701172 nb_pixel_total : 8737 time to create 1 rle with old method : 0.01045989990234375 length of segment : 90 time for calcul the mask position with numpy : 0.004694223403930664 nb_pixel_total : 120429 time to create 1 rle with old method : 0.14711761474609375 length of segment : 470 time for calcul the mask position with numpy : 0.0016660690307617188 nb_pixel_total : 36639 time to create 1 rle with old method : 0.04106473922729492 length of segment : 280 time for calcul the mask position with numpy : 0.0013201236724853516 nb_pixel_total : 22697 time to create 1 rle with old method : 0.026463747024536133 length of segment : 129 time for calcul the mask position with numpy : 0.0023736953735351562 nb_pixel_total : 50027 time to create 1 rle with old method : 0.054892539978027344 length of segment : 334 time for calcul the mask position with numpy : 0.0006864070892333984 nb_pixel_total : 10236 time to create 1 rle with old method : 0.012102365493774414 length of segment : 123 time for calcul the mask position with numpy : 0.004914760589599609 nb_pixel_total : 72965 time to create 1 rle with old method : 0.08328509330749512 length of segment : 310 time for calcul the mask position with numpy : 0.0012645721435546875 nb_pixel_total : 24085 time to create 1 rle with old method : 0.02784562110900879 length of segment : 188 time for calcul the mask position with numpy : 0.0011966228485107422 nb_pixel_total : 15616 time to create 1 rle with old method : 0.02303791046142578 length of segment : 141 time for calcul the mask position with numpy : 0.001291036605834961 nb_pixel_total : 17044 time to create 1 rle with old method : 0.02580118179321289 length of segment : 165 time for calcul the mask position with numpy : 0.0025055408477783203 nb_pixel_total : 33216 time to create 1 rle with old method : 0.04879641532897949 length of segment : 250 time for calcul the mask position with numpy : 0.0020647048950195312 nb_pixel_total : 36148 time to create 1 rle with old method : 0.039948463439941406 length of segment : 323 time for calcul the mask position with numpy : 0.0028107166290283203 nb_pixel_total : 50134 time to create 1 rle with old method : 0.05679821968078613 length of segment : 326 time for calcul the mask position with numpy : 0.0012388229370117188 nb_pixel_total : 29168 time to create 1 rle with old method : 0.03343653678894043 length of segment : 230 time for calcul the mask position with numpy : 0.0014867782592773438 nb_pixel_total : 23175 time to create 1 rle with old method : 0.02596449851989746 length of segment : 244 time for calcul the mask position with numpy : 0.0013172626495361328 nb_pixel_total : 26448 time to create 1 rle with old method : 0.029052019119262695 length of segment : 189 time for calcul the mask position with numpy : 0.0012340545654296875 nb_pixel_total : 25904 time to create 1 rle with old method : 0.029645204544067383 length of segment : 119 time for calcul the mask position with numpy : 0.002115011215209961 nb_pixel_total : 43983 time to create 1 rle with old method : 0.05234265327453613 length of segment : 380 time for calcul the mask position with numpy : 0.0005559921264648438 nb_pixel_total : 9245 time to create 1 rle with old method : 0.010827064514160156 length of segment : 147 time for calcul the mask position with numpy : 0.0070230960845947266 nb_pixel_total : 188791 time to create 1 rle with new method : 0.009655952453613281 length of segment : 585 time for calcul the mask position with numpy : 0.0010080337524414062 nb_pixel_total : 16920 time to create 1 rle with old method : 0.01949143409729004 length of segment : 169 time for calcul the mask position with numpy : 0.0011548995971679688 nb_pixel_total : 28143 time to create 1 rle with old method : 0.03254508972167969 length of segment : 265 time for calcul the mask position with numpy : 0.0011641979217529297 nb_pixel_total : 23999 time to create 1 rle with old method : 0.027399063110351562 length of segment : 181 time for calcul the mask position with numpy : 0.0005083084106445312 nb_pixel_total : 9495 time to create 1 rle with old method : 0.010680913925170898 length of segment : 85 time for calcul the mask position with numpy : 0.000993490219116211 nb_pixel_total : 24822 time to create 1 rle with old method : 0.02791595458984375 length of segment : 241 time for calcul the mask position with numpy : 0.00028967857360839844 nb_pixel_total : 5744 time to create 1 rle with old method : 0.0065004825592041016 length of segment : 75 time for calcul the mask position with numpy : 0.0051572322845458984 nb_pixel_total : 122841 time to create 1 rle with old method : 0.13803887367248535 length of segment : 277 time for calcul the mask position with numpy : 0.005072832107543945 nb_pixel_total : 112466 time to create 1 rle with old method : 0.12323689460754395 length of segment : 468 time for calcul the mask position with numpy : 0.0013027191162109375 nb_pixel_total : 20996 time to create 1 rle with old method : 0.024310588836669922 length of segment : 175 time for calcul the mask position with numpy : 0.0006861686706542969 nb_pixel_total : 15905 time to create 1 rle with old method : 0.018847942352294922 length of segment : 108 time for calcul the mask position with numpy : 0.005091190338134766 nb_pixel_total : 117189 time to create 1 rle with old method : 0.13020801544189453 length of segment : 421 time for calcul the mask position with numpy : 0.0008432865142822266 nb_pixel_total : 14534 time to create 1 rle with old method : 0.01667308807373047 length of segment : 134 time for calcul the mask position with numpy : 0.0010178089141845703 nb_pixel_total : 14765 time to create 1 rle with old method : 0.01771378517150879 length of segment : 130 time for calcul the mask position with numpy : 0.0018792152404785156 nb_pixel_total : 30588 time to create 1 rle with old method : 0.03493189811706543 length of segment : 197 time for calcul the mask position with numpy : 0.0013680458068847656 nb_pixel_total : 22059 time to create 1 rle with old method : 0.02682805061340332 length of segment : 195 time for calcul the mask position with numpy : 0.0058171749114990234 nb_pixel_total : 86094 time to create 1 rle with old method : 0.09766173362731934 length of segment : 312 time for calcul the mask position with numpy : 0.0028607845306396484 nb_pixel_total : 27302 time to create 1 rle with old method : 0.0311124324798584 length of segment : 454 time for calcul the mask position with numpy : 0.008093595504760742 nb_pixel_total : 136022 time to create 1 rle with old method : 0.15254426002502441 length of segment : 346 time for calcul the mask position with numpy : 0.0008692741394042969 nb_pixel_total : 11368 time to create 1 rle with old method : 0.012422561645507812 length of segment : 141 time for calcul the mask position with numpy : 0.0028188228607177734 nb_pixel_total : 43616 time to create 1 rle with old method : 0.04891705513000488 length of segment : 386 time for calcul the mask position with numpy : 0.004830121994018555 nb_pixel_total : 47635 time to create 1 rle with old method : 0.056063175201416016 length of segment : 620 time for calcul the mask position with numpy : 0.0026116371154785156 nb_pixel_total : 22067 time to create 1 rle with old method : 0.025375843048095703 length of segment : 222 time for calcul the mask position with numpy : 0.004642963409423828 nb_pixel_total : 79763 time to create 1 rle with old method : 0.0856781005859375 length of segment : 314 time for calcul the mask position with numpy : 0.0028498172760009766 nb_pixel_total : 44159 time to create 1 rle with old method : 0.04996323585510254 length of segment : 282 time for calcul the mask position with numpy : 0.002495288848876953 nb_pixel_total : 52583 time to create 1 rle with old method : 0.062306880950927734 length of segment : 195 time for calcul the mask position with numpy : 0.0033845901489257812 nb_pixel_total : 47108 time to create 1 rle with old method : 0.061232566833496094 length of segment : 260 time for calcul the mask position with numpy : 0.006342172622680664 nb_pixel_total : 111549 time to create 1 rle with old method : 0.11951804161071777 length of segment : 369 time for calcul the mask position with numpy : 0.0012278556823730469 nb_pixel_total : 18639 time to create 1 rle with old method : 0.02086329460144043 length of segment : 130 time for calcul the mask position with numpy : 0.003392934799194336 nb_pixel_total : 55193 time to create 1 rle with old method : 0.06134390830993652 length of segment : 257 time for calcul the mask position with numpy : 0.0018742084503173828 nb_pixel_total : 28899 time to create 1 rle with old method : 0.032946109771728516 length of segment : 163 time for calcul the mask position with numpy : 0.0022308826446533203 nb_pixel_total : 26869 time to create 1 rle with old method : 0.029837369918823242 length of segment : 254 time for calcul the mask position with numpy : 0.004204988479614258 nb_pixel_total : 58850 time to create 1 rle with old method : 0.06285429000854492 length of segment : 274 time for calcul the mask position with numpy : 0.0005471706390380859 nb_pixel_total : 5035 time to create 1 rle with old method : 0.007712364196777344 length of segment : 68 time for calcul the mask position with numpy : 0.0015676021575927734 nb_pixel_total : 30959 time to create 1 rle with old method : 0.0336918830871582 length of segment : 247 time for calcul the mask position with numpy : 0.0017282962799072266 nb_pixel_total : 43617 time to create 1 rle with old method : 0.04671120643615723 length of segment : 275 time for calcul the mask position with numpy : 0.0034334659576416016 nb_pixel_total : 56215 time to create 1 rle with old method : 0.06223583221435547 length of segment : 280 time for calcul the mask position with numpy : 0.002022981643676758 nb_pixel_total : 28141 time to create 1 rle with old method : 0.04329848289489746 length of segment : 131 time for calcul the mask position with numpy : 0.0004863739013671875 nb_pixel_total : 16618 time to create 1 rle with old method : 0.02172088623046875 length of segment : 183 time for calcul the mask position with numpy : 0.006581306457519531 nb_pixel_total : 92216 time to create 1 rle with old method : 0.10600805282592773 length of segment : 514 time for calcul the mask position with numpy : 0.00023674964904785156 nb_pixel_total : 8839 time to create 1 rle with old method : 0.01038217544555664 length of segment : 112 time for calcul the mask position with numpy : 0.0050008296966552734 nb_pixel_total : 84424 time to create 1 rle with old method : 0.09470152854919434 length of segment : 317 time for calcul the mask position with numpy : 0.0022513866424560547 nb_pixel_total : 33404 time to create 1 rle with old method : 0.03696632385253906 length of segment : 239 time for calcul the mask position with numpy : 0.003643035888671875 nb_pixel_total : 52262 time to create 1 rle with old method : 0.05722498893737793 length of segment : 342 time for calcul the mask position with numpy : 0.0021004676818847656 nb_pixel_total : 43341 time to create 1 rle with old method : 0.05001354217529297 length of segment : 361 time for calcul the mask position with numpy : 0.0022346973419189453 nb_pixel_total : 42954 time to create 1 rle with old method : 0.04656243324279785 length of segment : 421 time for calcul the mask position with numpy : 0.0011093616485595703 nb_pixel_total : 26392 time to create 1 rle with old method : 0.0284273624420166 length of segment : 175 time for calcul the mask position with numpy : 0.0008637905120849609 nb_pixel_total : 15140 time to create 1 rle with old method : 0.017159461975097656 length of segment : 123 time spent for convertir_results : 26.246262311935425 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 307 chid ids of type : 3594 Number RLEs to save : 84083 save missing photos in datou_result : time spend for datou_step_exec : 140.51359605789185 time spend to save output : 8.757721900939941 total time spend for step 1 : 149.2713179588318 step2:crop_condition Wed Apr 9 10:33:01 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure Loading chi in step crop with photo_hashtag_type : 3594 Loading chi in step crop for list_pids : 10 ! batch 1 Loaded 307 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : papier param for this class : {'min_score': 0.7} filtre for class : papier hashtag_id of this class : 492668766 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 209 About to insert : list_path_to_insert length 209 new photo from crops ! About to upload 209 photos upload in portfolio : 3736932 init cache_photo without model_param we have 209 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744187627_489391 we have uploaded 209 photos in the portfolio 3736932 time of upload the photos Elapsed time : 66.20282483100891 we have finished the crop for the class : papier begin to crop the class : carton param for this class : {'min_score': 0.7} filtre for class : carton hashtag_id of this class : 492774966 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 55 About to insert : list_path_to_insert length 55 new photo from crops ! About to upload 55 photos upload in portfolio : 3736932 init cache_photo without model_param we have 55 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744187708_489391 we have uploaded 55 photos in the portfolio 3736932 time of upload the photos Elapsed time : 21.65459704399109 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 1 About to insert : list_path_to_insert length 1 new photo from crops ! About to upload 1 photos upload in portfolio : 3736932 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744187732_489391 we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 5.796108722686768 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 36 About to insert : list_path_to_insert length 36 new photo from crops ! About to upload 36 photos upload in portfolio : 3736932 init cache_photo without model_param we have 36 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744187755_489391 we have uploaded 36 photos in the portfolio 3736932 time of upload the photos Elapsed time : 9.333101034164429 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 2 About to insert : list_path_to_insert length 2 new photo from crops ! About to upload 2 photos upload in portfolio : 3736932 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744187767_489391 we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.8207428455352783 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 1 About to insert : list_path_to_insert length 1 new photo from crops ! About to upload 1 photos upload in portfolio : 3736932 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744187770_489391 we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.6797337532043457 we have finished the crop for the class : pehd begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 3 new photo from crops ! About to upload 3 photos upload in portfolio : 3736932 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744187773_489391 we have uploaded 3 photos in the portfolio 3736932 time of upload the photos Elapsed time : 5.865644693374634 we have finished the crop for the class : pet_fonce delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1350350941, 1350350820, 1350350813, 1350350795, 1350350774, 1350350760, 1350350752, 1350350629, 1350350542, 1350350510] Looping around the photos to save general results len do output : 307 /1350720598Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720603Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720607Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720611Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720615Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720618Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720623Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720626Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720631Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720635Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720639Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720644Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720649Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720652Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720657Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720660Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720664Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720669Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720673Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720677Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720681Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720685Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720689Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720693Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720697Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720701Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720706Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720709Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720713Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720718Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720722Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720726Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720731Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720735Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720738Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720743Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720747Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720751Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720755Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720759Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720764Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720769Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720773Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720778Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720784Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720787Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720797Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720802Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720805Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720811Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720815Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720819Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720825Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720829Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720835Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720839Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720842Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720847Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720851Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720855Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720860Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720864Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720869Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720873Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720877Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720881Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720887Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720891Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720897Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720901Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720905Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720911Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720914Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720918Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720924Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720927Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720931Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720935Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720940Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720944Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720948Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720952Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720956Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720959Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720964Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720968Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720971Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720976Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720977Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720978Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720981Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720984Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720985Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720987Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720988Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720989Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720991Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720992Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720993Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720994Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720996Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720997Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350720998Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721000Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721001Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721002Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721003Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721006Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721007Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721008Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721010Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721011Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721012Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721014Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721015Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721016Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721019Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721020Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721021Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721022Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721024Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721025Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721026Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721028Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721029Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721030Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721032Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721033Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721034Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721036Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721037Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721038Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721039Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721041Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721042Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721043Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721045Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721046Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721047Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721048Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721050Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721051Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721052Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721054Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721055Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721056Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721058Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721060Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721061Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721062Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721064Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721065Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721066Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721068Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721069Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721071Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721073Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721074Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721075Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721076Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721078Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721079Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721080Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721082Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721083Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350721084Didn't 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/1350721988Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350941', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350820', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350813', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350795', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350774', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350760', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350752', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350629', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350542', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350510', None, None, None, None, None, '2733675') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 931 time used for this insertion : 0.08324599266052246 save_final save missing photos in datou_result : time spend for datou_step_exec : 197.59341144561768 time spend to save output : 0.08990287780761719 total time spend for step 2 : 197.6833143234253 step3:rle_unique_nms_with_priority Wed Apr 9 10:36:18 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 307 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 24 nb_hashtags : 4 time to prepare the origin masks : 8.02615737915039 time for calcul the mask position with numpy : 0.8091311454772949 nb_pixel_total : 4959497 time to create 1 rle with new method : 0.6261460781097412 time for calcul the mask position with numpy : 0.03321671485900879 nb_pixel_total : 3290 time to create 1 rle with old method : 0.0037717819213867188 time for calcul the mask position with numpy : 0.032851457595825195 nb_pixel_total : 17866 time to create 1 rle with old method : 0.01995849609375 time for calcul the mask position with numpy : 0.034150123596191406 nb_pixel_total : 70703 time to create 1 rle with old method : 0.0788259506225586 time for calcul the mask position with numpy : 0.03243756294250488 nb_pixel_total : 13719 time to create 1 rle with old method : 0.015563249588012695 time for calcul the mask position with numpy : 0.0344085693359375 nb_pixel_total : 67764 time to create 1 rle with old method : 0.07474541664123535 time for calcul the mask position with numpy : 0.03373312950134277 nb_pixel_total : 78740 time to create 1 rle with old method : 0.08735132217407227 time for calcul the mask position with numpy : 0.03270697593688965 nb_pixel_total : 50376 time to create 1 rle with old method : 0.05569100379943848 time for calcul the mask position with numpy : 0.03708934783935547 nb_pixel_total : 16549 time to create 1 rle with old method : 0.018317222595214844 time for calcul the mask position with numpy : 0.03246784210205078 nb_pixel_total : 9376 time to create 1 rle with old method : 0.010678529739379883 time for calcul the mask position with numpy : 0.0403902530670166 nb_pixel_total : 784558 time to create 1 rle with new method : 0.5814459323883057 time for calcul the mask position with numpy : 0.03647160530090332 nb_pixel_total : 334990 time to create 1 rle with new method : 0.5441999435424805 time for calcul the mask position with numpy : 0.038614749908447266 nb_pixel_total : 16873 time to create 1 rle with old method : 0.018964052200317383 time for calcul the mask position with numpy : 0.03553915023803711 nb_pixel_total : 19232 time to create 1 rle with old method : 0.023079395294189453 time for calcul the mask position with numpy : 0.03373861312866211 nb_pixel_total : 83781 time to create 1 rle with old method : 0.0940558910369873 time for calcul the mask position with numpy : 0.03357887268066406 nb_pixel_total : 16086 time to create 1 rle with old method : 0.018205642700195312 time for calcul the mask position with numpy : 0.0351107120513916 nb_pixel_total : 176400 time to create 1 rle with new method : 0.6414999961853027 time for calcul the mask position with numpy : 0.03921699523925781 nb_pixel_total : 89021 time to create 1 rle with old method : 0.09757089614868164 time for calcul the mask position with numpy : 0.03400826454162598 nb_pixel_total : 45814 time to create 1 rle with old method : 0.05373883247375488 time for calcul the mask position with numpy : 0.03496670722961426 nb_pixel_total : 12377 time to create 1 rle with old method : 0.013923883438110352 time for calcul the mask position with numpy : 0.03571772575378418 nb_pixel_total : 12805 time to create 1 rle with old method : 0.01411747932434082 time for calcul the mask position with numpy : 0.03402376174926758 nb_pixel_total : 17278 time to create 1 rle with old method : 0.019661426544189453 time for calcul the mask position with numpy : 0.03499174118041992 nb_pixel_total : 11863 time to create 1 rle with old method : 0.013549566268920898 time for calcul the mask position with numpy : 0.0349581241607666 nb_pixel_total : 21630 time to create 1 rle with old method : 0.02454829216003418 time for calcul the mask position with numpy : 0.0354924201965332 nb_pixel_total : 119652 time to create 1 rle with old method : 0.1343669891357422 create new chi : 5.032907485961914 time to delete rle : 0.018898487091064453 batch 1 Loaded 49 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++Number RLEs to save : 19053 TO DO : save crop sub photo not yet done ! save time : 5.66708517074585 nb_obj : 39 nb_hashtags : 3 time to prepare the origin masks : 4.501277446746826 time for calcul the mask position with numpy : 0.3911886215209961 nb_pixel_total : 5510903 time to create 1 rle with new method : 0.4575498104095459 time for calcul the mask position with numpy : 0.0291898250579834 nb_pixel_total : 74673 time to create 1 rle with old method : 0.0837247371673584 time for calcul the mask position with numpy : 0.02884364128112793 nb_pixel_total : 33574 time to create 1 rle with old method : 0.03650045394897461 time for calcul the mask position with numpy : 0.028676748275756836 nb_pixel_total : 118032 time to create 1 rle with old method : 0.12835001945495605 time for calcul the mask position with numpy : 0.028186798095703125 nb_pixel_total : 26519 time to create 1 rle with old method : 0.028864622116088867 time for calcul the mask position with numpy : 0.028374195098876953 nb_pixel_total : 11760 time to create 1 rle with old method : 0.012715816497802734 time for calcul the mask position with numpy : 0.02874922752380371 nb_pixel_total : 22389 time to create 1 rle with old method : 0.024997949600219727 time for calcul the mask position with numpy : 0.028820276260375977 nb_pixel_total : 67132 time to create 1 rle with old method : 0.07341146469116211 time for calcul the mask position with numpy : 0.031966209411621094 nb_pixel_total : 28871 time to create 1 rle with old method : 0.031403541564941406 time for calcul the mask position with numpy : 0.02873706817626953 nb_pixel_total : 18832 time to create 1 rle with old method : 0.021027565002441406 time for calcul the mask position with numpy : 0.02946019172668457 nb_pixel_total : 50419 time to create 1 rle with old method : 0.05509757995605469 time for calcul the mask position with numpy : 0.028301477432250977 nb_pixel_total : 19367 time to create 1 rle with old method : 0.020930767059326172 time for calcul the mask position with numpy : 0.02840423583984375 nb_pixel_total : 14405 time to create 1 rle with old method : 0.016879558563232422 time for calcul the mask position with numpy : 0.029230594635009766 nb_pixel_total : 40400 time to create 1 rle with old method : 0.043679237365722656 time for calcul the mask position with numpy : 0.033187150955200195 nb_pixel_total : 190414 time to create 1 rle with new method : 0.6793889999389648 time for calcul the mask position with numpy : 0.02917790412902832 nb_pixel_total : 36430 time to create 1 rle with old method : 0.04199719429016113 time for calcul the mask position with numpy : 0.02910923957824707 nb_pixel_total : 26108 time to create 1 rle with old method : 0.030440568923950195 time for calcul the mask position with numpy : 0.029993534088134766 nb_pixel_total : 8232 time to create 1 rle with old method : 0.009187936782836914 time for calcul the mask position with numpy : 0.028930187225341797 nb_pixel_total : 22773 time to create 1 rle with old method : 0.025949954986572266 time for calcul the mask position with numpy : 0.029122114181518555 nb_pixel_total : 31938 time to create 1 rle with old method : 0.03554582595825195 time for calcul the mask position with numpy : 0.03029036521911621 nb_pixel_total : 25507 time to create 1 rle with old method : 0.028664112091064453 time for calcul the mask position with numpy : 0.029719829559326172 nb_pixel_total : 6104 time to create 1 rle with old method : 0.006764411926269531 time for calcul the mask position with numpy : 0.029272079467773438 nb_pixel_total : 15136 time to create 1 rle with old method : 0.01715254783630371 time for calcul the mask position with numpy : 0.03128790855407715 nb_pixel_total : 122802 time to create 1 rle with old method : 0.14082074165344238 time for calcul the mask position with numpy : 0.02913689613342285 nb_pixel_total : 22347 time to create 1 rle with old method : 0.027036428451538086 time for calcul the mask position with numpy : 0.029392004013061523 nb_pixel_total : 37674 time to create 1 rle with old method : 0.04425311088562012 time for calcul the mask position with numpy : 0.03287839889526367 nb_pixel_total : 27535 time to create 1 rle with old method : 0.04202842712402344 time for calcul the mask position with numpy : 0.02947854995727539 nb_pixel_total : 22852 time to create 1 rle with old method : 0.026002883911132812 time for calcul the mask position with numpy : 0.029593467712402344 nb_pixel_total : 51761 time to create 1 rle with old method : 0.05882406234741211 time for calcul the mask position with numpy : 0.02974867820739746 nb_pixel_total : 49176 time to create 1 rle with old method : 0.05639791488647461 time for calcul the mask position with numpy : 0.030903339385986328 nb_pixel_total : 147453 time to create 1 rle with old method : 0.20020413398742676 time for calcul the mask position with numpy : 0.029161930084228516 nb_pixel_total : 9274 time to create 1 rle with old method : 0.010710716247558594 time for calcul the mask position with numpy : 0.029291629791259766 nb_pixel_total : 24115 time to create 1 rle with old method : 0.02783060073852539 time for calcul the mask position with numpy : 0.029410839080810547 nb_pixel_total : 4710 time to create 1 rle with old method : 0.006976604461669922 time for calcul the mask position with numpy : 0.02916264533996582 nb_pixel_total : 22730 time to create 1 rle with old method : 0.02591705322265625 time for calcul the mask position with numpy : 0.029451370239257812 nb_pixel_total : 10207 time to create 1 rle with old method : 0.01271820068359375 time for calcul the mask position with numpy : 0.03268742561340332 nb_pixel_total : 29323 time to create 1 rle with old method : 0.033740997314453125 time for calcul the mask position with numpy : 0.030150175094604492 nb_pixel_total : 46347 time to create 1 rle with old method : 0.05446338653564453 time for calcul the mask position with numpy : 0.02962636947631836 nb_pixel_total : 17067 time to create 1 rle with old method : 0.01903700828552246 time for calcul the mask position with numpy : 0.029407978057861328 nb_pixel_total : 4949 time to create 1 rle with old method : 0.005571603775024414 create new chi : 4.3121607303619385 time to delete rle : 0.004586696624755859 batch 1 Loaded 79 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 20477 TO DO : save crop sub photo not yet done ! save time : 1.4056382179260254 nb_obj : 29 nb_hashtags : 5 time to prepare the origin masks : 4.307041168212891 time for calcul the mask position with numpy : 0.40207767486572266 nb_pixel_total : 5505451 time to create 1 rle with new method : 0.6961545944213867 time for calcul the mask position with numpy : 0.03153061866760254 nb_pixel_total : 133836 time to create 1 rle with old method : 0.15124082565307617 time for calcul the mask position with numpy : 0.03058338165283203 nb_pixel_total : 24690 time to create 1 rle with old method : 0.05240058898925781 time for calcul the mask position with numpy : 0.029572486877441406 nb_pixel_total : 25099 time to create 1 rle with old method : 0.028149127960205078 time for calcul the mask position with numpy : 0.031113862991333008 nb_pixel_total : 62820 time to create 1 rle with old method : 0.0698385238647461 time for calcul the mask position with numpy : 0.029310941696166992 nb_pixel_total : 22404 time to create 1 rle with old method : 0.02503347396850586 time for calcul the mask position with numpy : 0.03000950813293457 nb_pixel_total : 22415 time to create 1 rle with old method : 0.02531909942626953 time for calcul the mask position with numpy : 0.0291595458984375 nb_pixel_total : 38272 time to create 1 rle with old method : 0.04287910461425781 time for calcul the mask position with numpy : 0.029274940490722656 nb_pixel_total : 43078 time to create 1 rle with old method : 0.04744434356689453 time for calcul the mask position with numpy : 0.029035329818725586 nb_pixel_total : 6612 time to create 1 rle with old method : 0.0074329376220703125 time for calcul the mask position with numpy : 0.028918981552124023 nb_pixel_total : 17168 time to create 1 rle with old method : 0.01918339729309082 time for calcul the mask position with numpy : 0.028835535049438477 nb_pixel_total : 7097 time to create 1 rle with old method : 0.008008718490600586 time for calcul the mask position with numpy : 0.029072999954223633 nb_pixel_total : 6520 time to create 1 rle with old method : 0.008645057678222656 time for calcul the mask position with numpy : 0.032651662826538086 nb_pixel_total : 90836 time to create 1 rle with old method : 0.10721445083618164 time for calcul the mask position with numpy : 0.030284404754638672 nb_pixel_total : 80985 time to create 1 rle with old method : 0.1009054183959961 time for calcul the mask position with numpy : 0.029106855392456055 nb_pixel_total : 33632 time to create 1 rle with old method : 0.0375218391418457 time for calcul the mask position with numpy : 0.030651330947875977 nb_pixel_total : 71038 time to create 1 rle with old method : 0.07827019691467285 time for calcul the mask position with numpy : 0.02752685546875 nb_pixel_total : 12367 time to create 1 rle with old method : 0.013400077819824219 time for calcul the mask position with numpy : 0.028604507446289062 nb_pixel_total : 169628 time to create 1 rle with new method : 0.5789525508880615 time for calcul the mask position with numpy : 0.02957940101623535 nb_pixel_total : 80478 time to create 1 rle with old method : 0.09774637222290039 time for calcul the mask position with numpy : 0.03011918067932129 nb_pixel_total : 26091 time to create 1 rle with old method : 0.03479337692260742 time for calcul the mask position with numpy : 0.029702425003051758 nb_pixel_total : 16368 time to create 1 rle with old method : 0.01844334602355957 time for calcul the mask position with numpy : 0.03211808204650879 nb_pixel_total : 265296 time to create 1 rle with new method : 0.35970377922058105 time for calcul the mask position with numpy : 0.029292821884155273 nb_pixel_total : 3695 time to create 1 rle with old method : 0.004231452941894531 time for calcul the mask position with numpy : 0.030288219451904297 nb_pixel_total : 107230 time to create 1 rle with old method : 0.11959481239318848 time for calcul the mask position with numpy : 0.029311656951904297 nb_pixel_total : 36453 time to create 1 rle with old method : 0.04068326950073242 time for calcul the mask position with numpy : 0.0296628475189209 nb_pixel_total : 110780 time to create 1 rle with old method : 0.1255638599395752 time for calcul the mask position with numpy : 0.029773473739624023 nb_pixel_total : 9727 time to create 1 rle with old method : 0.011089801788330078 time for calcul the mask position with numpy : 0.031528472900390625 nb_pixel_total : 6546 time to create 1 rle with old method : 0.010753631591796875 time for calcul the mask position with numpy : 0.034654855728149414 nb_pixel_total : 13628 time to create 1 rle with old method : 0.023309946060180664 create new chi : 4.3048741817474365 time to delete rle : 0.0037326812744140625 batch 1 Loaded 59 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 18432 TO DO : save crop sub photo not yet done ! save time : 5.213632822036743 nb_obj : 24 nb_hashtags : 3 time to prepare the origin masks : 8.702336311340332 time for calcul the mask position with numpy : 0.21617698669433594 nb_pixel_total : 5677952 time to create 1 rle with new method : 0.7307877540588379 time for calcul the mask position with numpy : 0.021503925323486328 nb_pixel_total : 86325 time to create 1 rle with old method : 0.09486532211303711 time for calcul the mask position with numpy : 0.023395061492919922 nb_pixel_total : 89794 time to create 1 rle with old method : 0.09968328475952148 time for calcul the mask position with numpy : 0.03529977798461914 nb_pixel_total : 33019 time to create 1 rle with old method : 0.035631656646728516 time for calcul the mask position with numpy : 0.03864645957946777 nb_pixel_total : 332794 time to create 1 rle with new method : 0.5737416744232178 time for calcul the mask position with numpy : 0.03501629829406738 nb_pixel_total : 49127 time to create 1 rle with old method : 0.05399155616760254 time for calcul the mask position with numpy : 0.03665971755981445 nb_pixel_total : 14699 time to create 1 rle with old method : 0.018899917602539062 time for calcul the mask position with numpy : 0.03419661521911621 nb_pixel_total : 17139 time to create 1 rle with old method : 0.018752336502075195 time for calcul the mask position with numpy : 0.03303718566894531 nb_pixel_total : 70725 time to create 1 rle with old method : 0.07684850692749023 time for calcul the mask position with numpy : 0.032919883728027344 nb_pixel_total : 6288 time to create 1 rle with old method : 0.00715327262878418 time for calcul the mask position with numpy : 0.029148340225219727 nb_pixel_total : 10169 time to create 1 rle with old method : 0.011201858520507812 time for calcul the mask position with numpy : 0.022477388381958008 nb_pixel_total : 29494 time to create 1 rle with old method : 0.04381608963012695 time for calcul the mask position with numpy : 0.02508544921875 nb_pixel_total : 57382 time to create 1 rle with old method : 0.07074975967407227 time for calcul the mask position with numpy : 0.0236356258392334 nb_pixel_total : 113169 time to create 1 rle with old method : 0.12459802627563477 time for calcul the mask position with numpy : 0.023244142532348633 nb_pixel_total : 11476 time to create 1 rle with old method : 0.013093948364257812 time for calcul the mask position with numpy : 0.025702238082885742 nb_pixel_total : 29485 time to create 1 rle with old method : 0.03627276420593262 time for calcul the mask position with numpy : 0.022383689880371094 nb_pixel_total : 45128 time to create 1 rle with old method : 0.04907083511352539 time for calcul the mask position with numpy : 0.022064924240112305 nb_pixel_total : 93182 time to create 1 rle with old method : 0.12841153144836426 time for calcul the mask position with numpy : 0.02174520492553711 nb_pixel_total : 36023 time to create 1 rle with old method : 0.03901028633117676 time for calcul the mask position with numpy : 0.021607637405395508 nb_pixel_total : 31680 time to create 1 rle with old method : 0.04070448875427246 time for calcul the mask position with numpy : 0.024704933166503906 nb_pixel_total : 87160 time to create 1 rle with old method : 0.09466671943664551 time for calcul the mask position with numpy : 0.02255558967590332 nb_pixel_total : 14466 time to create 1 rle with old method : 0.015903234481811523 time for calcul the mask position with numpy : 0.021849393844604492 nb_pixel_total : 14064 time to create 1 rle with old method : 0.015341520309448242 time for calcul the mask position with numpy : 0.02266716957092285 nb_pixel_total : 29784 time to create 1 rle with old method : 0.03250432014465332 time for calcul the mask position with numpy : 0.022945880889892578 nb_pixel_total : 69716 time to create 1 rle with old method : 0.0782155990600586 create new chi : 3.4217941761016846 time to delete rle : 0.0035886764526367188 batch 1 Loaded 49 chid ids of type : 3594 +++++++++++++++++++++++++++Number RLEs to save : 17584 TO DO : save crop sub photo not yet done ! save time : 1.0981309413909912 nb_obj : 43 nb_hashtags : 3 time to prepare the origin masks : 4.829037427902222 time for calcul the mask position with numpy : 0.5790407657623291 nb_pixel_total : 4896989 time to create 1 rle with new method : 0.5059494972229004 time for calcul the mask position with numpy : 0.028213024139404297 nb_pixel_total : 24639 time to create 1 rle with old method : 0.029708385467529297 time for calcul the mask position with numpy : 0.029094934463500977 nb_pixel_total : 101023 time to create 1 rle with old method : 0.10916829109191895 time for calcul the mask position with numpy : 0.027886390686035156 nb_pixel_total : 52748 time to create 1 rle with old method : 0.05737638473510742 time for calcul the mask position with numpy : 0.031984806060791016 nb_pixel_total : 180419 time to create 1 rle with new method : 0.7270076274871826 time for calcul the mask position with numpy : 0.02928328514099121 nb_pixel_total : 22746 time to create 1 rle with old method : 0.02627277374267578 time for calcul the mask position with numpy : 0.03482246398925781 nb_pixel_total : 123833 time to create 1 rle with old method : 0.13656234741210938 time for calcul the mask position with numpy : 0.03350496292114258 nb_pixel_total : 3657 time to create 1 rle with old method : 0.004540681838989258 time for calcul the mask position with numpy : 0.029362201690673828 nb_pixel_total : 16467 time to create 1 rle with old method : 0.019351482391357422 time for calcul the mask position with numpy : 0.029667377471923828 nb_pixel_total : 7258 time to create 1 rle with old method : 0.007722139358520508 time for calcul the mask position with numpy : 0.029061317443847656 nb_pixel_total : 11858 time to create 1 rle with old method : 0.014693498611450195 time for calcul the mask position with numpy : 0.02960991859436035 nb_pixel_total : 22679 time to create 1 rle with old method : 0.026378870010375977 time for calcul the mask position with numpy : 0.03291893005371094 nb_pixel_total : 213262 time to create 1 rle with new method : 0.379439115524292 time for calcul the mask position with numpy : 0.02833247184753418 nb_pixel_total : 12021 time to create 1 rle with old method : 0.013099908828735352 time for calcul the mask position with numpy : 0.028115510940551758 nb_pixel_total : 70659 time to create 1 rle with old method : 0.07801413536071777 time for calcul the mask position with numpy : 0.029025554656982422 nb_pixel_total : 31196 time to create 1 rle with old method : 0.03398847579956055 time for calcul the mask position with numpy : 0.028598785400390625 nb_pixel_total : 17371 time to create 1 rle with old method : 0.01910090446472168 time for calcul the mask position with numpy : 0.02903294563293457 nb_pixel_total : 89126 time to create 1 rle with old method : 0.09813714027404785 time for calcul the mask position with numpy : 0.03286910057067871 nb_pixel_total : 436925 time to create 1 rle with new method : 0.587108850479126 time for calcul the mask position with numpy : 0.028091907501220703 nb_pixel_total : 21209 time to create 1 rle with old method : 0.02440810203552246 time for calcul the mask position with numpy : 0.02869558334350586 nb_pixel_total : 24526 time to create 1 rle with old method : 0.028180837631225586 time for calcul the mask position with numpy : 0.02901625633239746 nb_pixel_total : 29861 time to create 1 rle with old method : 0.03294968605041504 time for calcul the mask position with numpy : 0.029783248901367188 nb_pixel_total : 40901 time to create 1 rle with old method : 0.0465092658996582 time for calcul the mask position with numpy : 0.02905750274658203 nb_pixel_total : 12253 time to create 1 rle with old method : 0.013596773147583008 time for calcul the mask position with numpy : 0.029326677322387695 nb_pixel_total : 47618 time to create 1 rle with old method : 0.0531010627746582 time for calcul the mask position with numpy : 0.02926015853881836 nb_pixel_total : 78958 time to create 1 rle with old method : 0.09263443946838379 time for calcul the mask position with numpy : 0.0315091609954834 nb_pixel_total : 24305 time to create 1 rle with old method : 0.029567718505859375 time for calcul the mask position with numpy : 0.02999114990234375 nb_pixel_total : 181097 time to create 1 rle with new method : 0.38013625144958496 time for calcul the mask position with numpy : 0.02929830551147461 nb_pixel_total : 15929 time to create 1 rle with old method : 0.01797652244567871 time for calcul the mask position with numpy : 0.030477046966552734 nb_pixel_total : 41157 time to create 1 rle with old method : 0.057694435119628906 time for calcul the mask position with numpy : 0.03318309783935547 nb_pixel_total : 22363 time to create 1 rle with old method : 0.029534101486206055 time for calcul the mask position with numpy : 0.029984474182128906 nb_pixel_total : 21259 time to create 1 rle with old method : 0.024930953979492188 time for calcul the mask position with numpy : 0.030588150024414062 nb_pixel_total : 8250 time to create 1 rle with old method : 0.00948953628540039 time for calcul the mask position with numpy : 0.03105759620666504 nb_pixel_total : 10437 time to create 1 rle with old method : 0.011871814727783203 time for calcul the mask position with numpy : 0.03146052360534668 nb_pixel_total : 22120 time to create 1 rle with old method : 0.05689215660095215 time for calcul the mask position with numpy : 0.035428762435913086 nb_pixel_total : 9886 time to create 1 rle with old method : 0.01117396354675293 time for calcul the mask position with numpy : 0.029452085494995117 nb_pixel_total : 20532 time to create 1 rle with old method : 0.022762775421142578 time for calcul the mask position with numpy : 0.029475688934326172 nb_pixel_total : 5837 time to create 1 rle with old method : 0.006549358367919922 time for calcul the mask position with numpy : 0.02922224998474121 nb_pixel_total : 31965 time to create 1 rle with old method : 0.03691458702087402 time for calcul the mask position with numpy : 0.02994823455810547 nb_pixel_total : 4894 time to create 1 rle with old method : 0.005629539489746094 time for calcul the mask position with numpy : 0.032521963119506836 nb_pixel_total : 11973 time to create 1 rle with old method : 0.013358592987060547 time for calcul the mask position with numpy : 0.03334784507751465 nb_pixel_total : 11010 time to create 1 rle with old method : 0.012504100799560547 time for calcul the mask position with numpy : 0.030476808547973633 nb_pixel_total : 7122 time to create 1 rle with old method : 0.0075893402099609375 time for calcul the mask position with numpy : 0.028728246688842773 nb_pixel_total : 9902 time to create 1 rle with old method : 0.011226177215576172 create new chi : 5.9240875244140625 time to delete rle : 0.003839731216430664 batch 1 Loaded 87 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 24282 TO DO : save crop sub photo not yet done ! save time : 2.2423970699310303 nb_obj : 28 nb_hashtags : 4 time to prepare the origin masks : 3.77107572555542 time for calcul the mask position with numpy : 0.780498743057251 nb_pixel_total : 6112307 time to create 1 rle with new method : 0.701509952545166 time for calcul the mask position with numpy : 0.029233694076538086 nb_pixel_total : 9495 time to create 1 rle with old method : 0.010744333267211914 time for calcul the mask position with numpy : 0.02943110466003418 nb_pixel_total : 34037 time to create 1 rle with old method : 0.03795027732849121 time for calcul the mask position with numpy : 0.029992341995239258 nb_pixel_total : 10752 time to create 1 rle with old method : 0.01212930679321289 time for calcul the mask position with numpy : 0.029356718063354492 nb_pixel_total : 5465 time to create 1 rle with old method : 0.006285667419433594 time for calcul the mask position with numpy : 0.02946186065673828 nb_pixel_total : 36858 time to create 1 rle with old method : 0.0407261848449707 time for calcul the mask position with numpy : 0.0289456844329834 nb_pixel_total : 25935 time to create 1 rle with old method : 0.028934717178344727 time for calcul the mask position with numpy : 0.028808116912841797 nb_pixel_total : 34691 time to create 1 rle with old method : 0.03875088691711426 time for calcul the mask position with numpy : 0.02901291847229004 nb_pixel_total : 14166 time to create 1 rle with old method : 0.015784025192260742 time for calcul the mask position with numpy : 0.030956268310546875 nb_pixel_total : 178329 time to create 1 rle with new method : 1.0695199966430664 time for calcul the mask position with numpy : 0.033411264419555664 nb_pixel_total : 13774 time to create 1 rle with old method : 0.022402286529541016 time for calcul the mask position with numpy : 0.029277324676513672 nb_pixel_total : 11708 time to create 1 rle with old method : 0.012943029403686523 time for calcul the mask position with numpy : 0.02853560447692871 nb_pixel_total : 19801 time to create 1 rle with old method : 0.021552562713623047 time for calcul the mask position with numpy : 0.028276681900024414 nb_pixel_total : 33519 time to create 1 rle with old method : 0.035881996154785156 time for calcul the mask position with numpy : 0.028319358825683594 nb_pixel_total : 11998 time to create 1 rle with old method : 0.013112783432006836 time for calcul the mask position with numpy : 0.02729010581970215 nb_pixel_total : 8036 time to create 1 rle with old method : 0.008489370346069336 time for calcul the mask position with numpy : 0.02789592742919922 nb_pixel_total : 37352 time to create 1 rle with old method : 0.03990960121154785 time for calcul the mask position with numpy : 0.028804302215576172 nb_pixel_total : 16633 time to create 1 rle with old method : 0.01769280433654785 time for calcul the mask position with numpy : 0.026822805404663086 nb_pixel_total : 26974 time to create 1 rle with old method : 0.029547452926635742 time for calcul the mask position with numpy : 0.028544187545776367 nb_pixel_total : 16484 time to create 1 rle with old method : 0.01788616180419922 time for calcul the mask position with numpy : 0.02733135223388672 nb_pixel_total : 19786 time to create 1 rle with old method : 0.020991086959838867 time for calcul the mask position with numpy : 0.027805566787719727 nb_pixel_total : 24982 time to create 1 rle with old method : 0.026363849639892578 time for calcul the mask position with numpy : 0.02811884880065918 nb_pixel_total : 24993 time to create 1 rle with old method : 0.02689051628112793 time for calcul the mask position with numpy : 0.028081417083740234 nb_pixel_total : 72796 time to create 1 rle with old method : 0.0791933536529541 time for calcul the mask position with numpy : 0.029637575149536133 nb_pixel_total : 175498 time to create 1 rle with new method : 0.8201711177825928 time for calcul the mask position with numpy : 0.02895355224609375 nb_pixel_total : 17745 time to create 1 rle with old method : 0.019533872604370117 time for calcul the mask position with numpy : 0.028842687606811523 nb_pixel_total : 15587 time to create 1 rle with old method : 0.01739788055419922 time for calcul the mask position with numpy : 0.028862714767456055 nb_pixel_total : 24602 time to create 1 rle with old method : 0.02746748924255371 time for calcul the mask position with numpy : 0.028771638870239258 nb_pixel_total : 15937 time to create 1 rle with old method : 0.017853975296020508 create new chi : 4.918076276779175 time to delete rle : 0.0033593177795410156 batch 1 Loaded 57 chid ids of type : 3594 ++++++++++++++++++++++++++++++++Number RLEs to save : 14839 TO DO : save crop sub photo not yet done ! save time : 1.0030591487884521 nb_obj : 24 nb_hashtags : 4 time to prepare the origin masks : 11.875057458877563 time for calcul the mask position with numpy : 0.42313194274902344 nb_pixel_total : 5056428 time to create 1 rle with new method : 0.7330951690673828 time for calcul the mask position with numpy : 0.024416208267211914 nb_pixel_total : 17654 time to create 1 rle with old method : 0.023226261138916016 time for calcul the mask position with numpy : 0.02492070198059082 nb_pixel_total : 265071 time to create 1 rle with new method : 1.490116834640503 time for calcul the mask position with numpy : 0.033232688903808594 nb_pixel_total : 854519 time to create 1 rle with new method : 0.9801280498504639 time for calcul the mask position with numpy : 0.03595733642578125 nb_pixel_total : 11770 time to create 1 rle with old method : 0.01279306411743164 time for calcul the mask position with numpy : 0.03524971008300781 nb_pixel_total : 29665 time to create 1 rle with old method : 0.033495187759399414 time for calcul the mask position with numpy : 0.03904128074645996 nb_pixel_total : 3354 time to create 1 rle with old method : 0.005555868148803711 time for calcul the mask position with numpy : 0.04430675506591797 nb_pixel_total : 337106 time to create 1 rle with new method : 0.808276891708374 time for calcul the mask position with numpy : 0.03452920913696289 nb_pixel_total : 7676 time to create 1 rle with old method : 0.008620262145996094 time for calcul the mask position with numpy : 0.03469371795654297 nb_pixel_total : 15949 time to create 1 rle with old method : 0.01783013343811035 time for calcul the mask position with numpy : 0.034633636474609375 nb_pixel_total : 15932 time to create 1 rle with old method : 0.01834273338317871 time for calcul the mask position with numpy : 0.03549027442932129 nb_pixel_total : 24395 time to create 1 rle with old method : 0.027358531951904297 time for calcul the mask position with numpy : 0.02697896957397461 nb_pixel_total : 25458 time to create 1 rle with old method : 0.028294801712036133 time for calcul the mask position with numpy : 0.025809764862060547 nb_pixel_total : 14212 time to create 1 rle with old method : 0.023511171340942383 time for calcul the mask position with numpy : 0.022860288619995117 nb_pixel_total : 12106 time to create 1 rle with old method : 0.013786792755126953 time for calcul the mask position with numpy : 0.02474808692932129 nb_pixel_total : 82867 time to create 1 rle with old method : 0.0920870304107666 time for calcul the mask position with numpy : 0.022199392318725586 nb_pixel_total : 46726 time to create 1 rle with old method : 0.0507044792175293 time for calcul the mask position with numpy : 0.023538589477539062 nb_pixel_total : 25102 time to create 1 rle with old method : 0.02951979637145996 time for calcul the mask position with numpy : 0.02375960350036621 nb_pixel_total : 21213 time to create 1 rle with old method : 0.022874832153320312 time for calcul the mask position with numpy : 0.022508859634399414 nb_pixel_total : 17504 time to create 1 rle with old method : 0.018915414810180664 time for calcul the mask position with numpy : 0.021535873413085938 nb_pixel_total : 13747 time to create 1 rle with old method : 0.015243291854858398 time for calcul the mask position with numpy : 0.02223062515258789 nb_pixel_total : 13840 time to create 1 rle with old method : 0.016061067581176758 time for calcul the mask position with numpy : 0.029921293258666992 nb_pixel_total : 11244 time to create 1 rle with old method : 0.01264333724975586 time for calcul the mask position with numpy : 0.027270793914794922 nb_pixel_total : 112512 time to create 1 rle with old method : 0.12553191184997559 time for calcul the mask position with numpy : 0.02241039276123047 nb_pixel_total : 14190 time to create 1 rle with old method : 0.015952587127685547 create new chi : 5.848013877868652 time to delete rle : 0.0029230117797851562 batch 1 Loaded 49 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 18667 TO DO : save crop sub photo not yet done ! save time : 3.295086622238159 nb_obj : 26 nb_hashtags : 3 time to prepare the origin masks : 3.8613381385803223 time for calcul the mask position with numpy : 0.5665669441223145 nb_pixel_total : 5855417 time to create 1 rle with new method : 0.4877500534057617 time for calcul the mask position with numpy : 0.029466629028320312 nb_pixel_total : 30200 time to create 1 rle with old method : 0.0372319221496582 time for calcul the mask position with numpy : 0.029963970184326172 nb_pixel_total : 24926 time to create 1 rle with old method : 0.041410207748413086 time for calcul the mask position with numpy : 0.03317594528198242 nb_pixel_total : 28730 time to create 1 rle with old method : 0.04447603225708008 time for calcul the mask position with numpy : 0.030782222747802734 nb_pixel_total : 105914 time to create 1 rle with old method : 0.12259769439697266 time for calcul the mask position with numpy : 0.02982187271118164 nb_pixel_total : 17558 time to create 1 rle with old method : 0.02073812484741211 time for calcul the mask position with numpy : 0.030567407608032227 nb_pixel_total : 127178 time to create 1 rle with old method : 0.20252037048339844 time for calcul the mask position with numpy : 0.035039663314819336 nb_pixel_total : 49042 time to create 1 rle with old method : 0.05721449851989746 time for calcul the mask position with numpy : 0.029473543167114258 nb_pixel_total : 34004 time to create 1 rle with old method : 0.0420687198638916 time for calcul the mask position with numpy : 0.028988361358642578 nb_pixel_total : 18638 time to create 1 rle with old method : 0.02097320556640625 time for calcul the mask position with numpy : 0.029949426651000977 nb_pixel_total : 26939 time to create 1 rle with old method : 0.030876636505126953 time for calcul the mask position with numpy : 0.03229188919067383 nb_pixel_total : 175519 time to create 1 rle with new method : 0.5557811260223389 time for calcul the mask position with numpy : 0.029073476791381836 nb_pixel_total : 6952 time to create 1 rle with old method : 0.007944583892822266 time for calcul the mask position with numpy : 0.031241416931152344 nb_pixel_total : 29872 time to create 1 rle with old method : 0.0339357852935791 time for calcul the mask position with numpy : 0.030684232711791992 nb_pixel_total : 37325 time to create 1 rle with old method : 0.04444718360900879 time for calcul the mask position with numpy : 0.03671121597290039 nb_pixel_total : 117744 time to create 1 rle with old method : 0.13544821739196777 time for calcul the mask position with numpy : 0.03125643730163574 nb_pixel_total : 81378 time to create 1 rle with old method : 0.09288835525512695 time for calcul the mask position with numpy : 0.02945423126220703 nb_pixel_total : 38475 time to create 1 rle with old method : 0.042810678482055664 time for calcul the mask position with numpy : 0.02910447120666504 nb_pixel_total : 38387 time to create 1 rle with old method : 0.04286527633666992 time for calcul the mask position with numpy : 0.029237747192382812 nb_pixel_total : 22285 time to create 1 rle with old method : 0.025385141372680664 time for calcul the mask position with numpy : 0.029586315155029297 nb_pixel_total : 46977 time to create 1 rle with old method : 0.05520510673522949 time for calcul the mask position with numpy : 0.029503345489501953 nb_pixel_total : 24682 time to create 1 rle with old method : 0.02828812599182129 time for calcul the mask position with numpy : 0.0331573486328125 nb_pixel_total : 9621 time to create 1 rle with old method : 0.011982440948486328 time for calcul the mask position with numpy : 0.03273153305053711 nb_pixel_total : 24594 time to create 1 rle with old method : 0.02837371826171875 time for calcul the mask position with numpy : 0.029015779495239258 nb_pixel_total : 12855 time to create 1 rle with old method : 0.014469623565673828 time for calcul the mask position with numpy : 0.028995513916015625 nb_pixel_total : 36746 time to create 1 rle with old method : 0.042244911193847656 time for calcul the mask position with numpy : 0.029629945755004883 nb_pixel_total : 28282 time to create 1 rle with old method : 0.031815290451049805 create new chi : 3.7282590866088867 time to delete rle : 0.0025522708892822266 batch 1 Loaded 53 chid ids of type : 3594 +++++++++++++++++++++++++++++++Number RLEs to save : 15328 TO DO : save crop sub photo not yet done ! save time : 2.0354437828063965 nb_obj : 37 nb_hashtags : 3 time to prepare the origin masks : 4.111762046813965 time for calcul the mask position with numpy : 0.6525547504425049 nb_pixel_total : 5543492 time to create 1 rle with new method : 0.7123260498046875 time for calcul the mask position with numpy : 0.029277801513671875 nb_pixel_total : 9495 time to create 1 rle with old method : 0.010307788848876953 time for calcul the mask position with numpy : 0.02817058563232422 nb_pixel_total : 25904 time to create 1 rle with old method : 0.02929401397705078 time for calcul the mask position with numpy : 0.030165433883666992 nb_pixel_total : 112466 time to create 1 rle with old method : 0.12835097312927246 time for calcul the mask position with numpy : 0.034279823303222656 nb_pixel_total : 30588 time to create 1 rle with old method : 0.03474164009094238 time for calcul the mask position with numpy : 0.030718326568603516 nb_pixel_total : 117189 time to create 1 rle with old method : 0.14502310752868652 time for calcul the mask position with numpy : 0.04277682304382324 nb_pixel_total : 26448 time to create 1 rle with old method : 0.030005931854248047 time for calcul the mask position with numpy : 0.029532194137573242 nb_pixel_total : 15905 time to create 1 rle with old method : 0.01785731315612793 time for calcul the mask position with numpy : 0.029288530349731445 nb_pixel_total : 23175 time to create 1 rle with old method : 0.027791500091552734 time for calcul the mask position with numpy : 0.02936863899230957 nb_pixel_total : 16920 time to create 1 rle with old method : 0.0188753604888916 time for calcul the mask position with numpy : 0.029671907424926758 nb_pixel_total : 72965 time to create 1 rle with old method : 0.08117842674255371 time for calcul the mask position with numpy : 0.03506064414978027 nb_pixel_total : 15616 time to create 1 rle with old method : 0.01798558235168457 time for calcul the mask position with numpy : 0.03383946418762207 nb_pixel_total : 28114 time to create 1 rle with old method : 0.03737163543701172 time for calcul the mask position with numpy : 0.029906511306762695 nb_pixel_total : 36148 time to create 1 rle with old method : 0.04430389404296875 time for calcul the mask position with numpy : 0.029242515563964844 nb_pixel_total : 24822 time to create 1 rle with old method : 0.030249834060668945 time for calcul the mask position with numpy : 0.03201174736022949 nb_pixel_total : 188791 time to create 1 rle with new method : 0.5174880027770996 time for calcul the mask position with numpy : 0.029773235321044922 nb_pixel_total : 14534 time to create 1 rle with old method : 0.024634838104248047 time for calcul the mask position with numpy : 0.033215999603271484 nb_pixel_total : 35531 time to create 1 rle with old method : 0.0434575080871582 time for calcul the mask position with numpy : 0.03174996376037598 nb_pixel_total : 9245 time to create 1 rle with old method : 0.010394096374511719 time for calcul the mask position with numpy : 0.029346227645874023 nb_pixel_total : 23999 time to create 1 rle with old method : 0.02666187286376953 time for calcul the mask position with numpy : 0.02889847755432129 nb_pixel_total : 33216 time to create 1 rle with old method : 0.03638577461242676 time for calcul the mask position with numpy : 0.028554201126098633 nb_pixel_total : 5744 time to create 1 rle with old method : 0.006311893463134766 time for calcul the mask position with numpy : 0.029088258743286133 nb_pixel_total : 120429 time to create 1 rle with old method : 0.13286995887756348 time for calcul the mask position with numpy : 0.030353069305419922 nb_pixel_total : 20996 time to create 1 rle with old method : 0.023191452026367188 time for calcul the mask position with numpy : 0.028816699981689453 nb_pixel_total : 38388 time to create 1 rle with old method : 0.04238247871398926 time for calcul the mask position with numpy : 0.028722763061523438 nb_pixel_total : 36639 time to create 1 rle with old method : 0.040601253509521484 time for calcul the mask position with numpy : 0.028795719146728516 nb_pixel_total : 43983 time to create 1 rle with old method : 0.047981977462768555 time for calcul the mask position with numpy : 0.02846527099609375 nb_pixel_total : 22697 time to create 1 rle with old method : 0.024922609329223633 time for calcul the mask position with numpy : 0.02868175506591797 nb_pixel_total : 50134 time to create 1 rle with old method : 0.05441451072692871 time for calcul the mask position with numpy : 0.027634620666503906 nb_pixel_total : 50027 time to create 1 rle with old method : 0.06681299209594727 time for calcul the mask position with numpy : 0.03305554389953613 nb_pixel_total : 8737 time to create 1 rle with old method : 0.013996124267578125 time for calcul the mask position with numpy : 0.029143095016479492 nb_pixel_total : 24085 time to create 1 rle with old method : 0.026993274688720703 time for calcul the mask position with numpy : 0.02947688102722168 nb_pixel_total : 122841 time to create 1 rle with old method : 0.13624906539916992 time for calcul the mask position with numpy : 0.02877211570739746 nb_pixel_total : 29168 time to create 1 rle with old method : 0.03228116035461426 time for calcul the mask position with numpy : 0.028879880905151367 nb_pixel_total : 10236 time to create 1 rle with old method : 0.011426448822021484 time for calcul the mask position with numpy : 0.02900552749633789 nb_pixel_total : 29764 time to create 1 rle with old method : 0.03399467468261719 time for calcul the mask position with numpy : 0.029175758361816406 nb_pixel_total : 14765 time to create 1 rle with old method : 0.016411781311035156 time for calcul the mask position with numpy : 0.029204845428466797 nb_pixel_total : 17044 time to create 1 rle with old method : 0.018794536590576172 create new chi : 4.588321208953857 time to delete rle : 0.004820823669433594 batch 1 Loaded 75 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 19453 TO DO : save crop sub photo not yet done ! save time : 2.4188873767852783 nb_obj : 33 nb_hashtags : 5 time to prepare the origin masks : 4.289728403091431 time for calcul the mask position with numpy : 0.3712759017944336 nb_pixel_total : 5550908 time to create 1 rle with new method : 0.9289441108703613 time for calcul the mask position with numpy : 0.033735036849975586 nb_pixel_total : 52583 time to create 1 rle with old method : 0.06421613693237305 time for calcul the mask position with numpy : 0.029621601104736328 nb_pixel_total : 16618 time to create 1 rle with old method : 0.018757343292236328 time for calcul the mask position with numpy : 0.02939462661743164 nb_pixel_total : 18639 time to create 1 rle with old method : 0.020826101303100586 time for calcul the mask position with numpy : 0.03220105171203613 nb_pixel_total : 58850 time to create 1 rle with old method : 0.06740260124206543 time for calcul the mask position with numpy : 0.030107975006103516 nb_pixel_total : 84424 time to create 1 rle with old method : 0.13081955909729004 time for calcul the mask position with numpy : 0.0382840633392334 nb_pixel_total : 47635 time to create 1 rle with old method : 0.12366271018981934 time for calcul the mask position with numpy : 0.03606057167053223 nb_pixel_total : 27302 time to create 1 rle with old method : 0.03144693374633789 time for calcul the mask position with numpy : 0.030009746551513672 nb_pixel_total : 47108 time to create 1 rle with old method : 0.056095123291015625 time for calcul the mask position with numpy : 0.030623197555541992 nb_pixel_total : 28899 time to create 1 rle with old method : 0.034621477127075195 time for calcul the mask position with numpy : 0.03389430046081543 nb_pixel_total : 30959 time to create 1 rle with old method : 0.03684258460998535 time for calcul the mask position with numpy : 0.03199505805969238 nb_pixel_total : 33404 time to create 1 rle with old method : 0.05308413505554199 time for calcul the mask position with numpy : 0.03425168991088867 nb_pixel_total : 26392 time to create 1 rle with old method : 0.029692649841308594 time for calcul the mask position with numpy : 0.029252290725708008 nb_pixel_total : 26869 time to create 1 rle with old method : 0.0304412841796875 time for calcul the mask position with numpy : 0.02925729751586914 nb_pixel_total : 22059 time to create 1 rle with old method : 0.024827957153320312 time for calcul the mask position with numpy : 0.028966903686523438 nb_pixel_total : 55193 time to create 1 rle with old method : 0.06139254570007324 time for calcul the mask position with numpy : 0.02933359146118164 nb_pixel_total : 43341 time to create 1 rle with old method : 0.050231218338012695 time for calcul the mask position with numpy : 0.029581785202026367 nb_pixel_total : 52262 time to create 1 rle with old method : 0.06725692749023438 time for calcul the mask position with numpy : 0.029267311096191406 nb_pixel_total : 92216 time to create 1 rle with old method : 0.10428476333618164 time for calcul the mask position with numpy : 0.028497934341430664 nb_pixel_total : 8839 time to create 1 rle with old method : 0.00977015495300293 time for calcul the mask position with numpy : 0.028730154037475586 nb_pixel_total : 22067 time to create 1 rle with old method : 0.023895978927612305 time for calcul the mask position with numpy : 0.027777671813964844 nb_pixel_total : 79763 time to create 1 rle with old method : 0.08686447143554688 time for calcul the mask position with numpy : 0.028493642807006836 nb_pixel_total : 111549 time to create 1 rle with old method : 0.1236429214477539 time for calcul the mask position with numpy : 0.02908015251159668 nb_pixel_total : 44159 time to create 1 rle with old method : 0.04944038391113281 time for calcul the mask position with numpy : 0.03097820281982422 nb_pixel_total : 43617 time to create 1 rle with old method : 0.0480198860168457 time for calcul the mask position with numpy : 0.02952289581298828 nb_pixel_total : 43616 time to create 1 rle with old method : 0.048522233963012695 time for calcul the mask position with numpy : 0.02906322479248047 nb_pixel_total : 136022 time to create 1 rle with old method : 0.14967918395996094 time for calcul the mask position with numpy : 0.0290682315826416 nb_pixel_total : 11368 time to create 1 rle with old method : 0.01275324821472168 time for calcul the mask position with numpy : 0.028856754302978516 nb_pixel_total : 15140 time to create 1 rle with old method : 0.0194699764251709 time for calcul the mask position with numpy : 0.03495502471923828 nb_pixel_total : 86094 time to create 1 rle with old method : 0.0959632396697998 time for calcul the mask position with numpy : 0.028468608856201172 nb_pixel_total : 42954 time to create 1 rle with old method : 0.045200347900390625 time for calcul the mask position with numpy : 0.02792215347290039 nb_pixel_total : 56215 time to create 1 rle with old method : 0.05988669395446777 time for calcul the mask position with numpy : 0.027491331100463867 nb_pixel_total : 5035 time to create 1 rle with old method : 0.005409717559814453 time for calcul the mask position with numpy : 0.027530670166015625 nb_pixel_total : 28141 time to create 1 rle with old method : 0.029686927795410156 create new chi : 4.164511203765869 time to delete rle : 0.003077983856201172 batch 1 Loaded 67 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 20084 TO DO : save crop sub photo not yet done ! save time : 5.205524444580078 map_output_result : {1350350941: (0.0, 'Should be the crop_list due to order', 0), 1350350820: (0.0, 'Should be the crop_list due to order', 0), 1350350813: (0.0, 'Should be the crop_list due to order', 0), 1350350795: (0.0, 'Should be the crop_list due to order', 0), 1350350774: (0.0, 'Should be the crop_list due to order', 0), 1350350760: (0.0, 'Should be the crop_list due to order', 0), 1350350752: (0.0, 'Should be the crop_list due to order', 0), 1350350629: (0.0, 'Should be the crop_list due to order', 0), 1350350542: (0.0, 'Should be the crop_list due to order', 0), 1350350510: (0.0, 'Should be the crop_list due to order', 0)} End step rle-unique-nms Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : rle_unique_nms_with_priority we use saveGeneral [1350350941, 1350350820, 1350350813, 1350350795, 1350350774, 1350350760, 1350350752, 1350350629, 1350350542, 1350350510] Looping around the photos to save general results len do output : 10 /1350350941.Didn't retrieve data . /1350350820.Didn't retrieve data . /1350350813.Didn't retrieve data . /1350350795.Didn't retrieve data . /1350350774.Didn't retrieve data . /1350350760.Didn't retrieve data . /1350350752.Didn't retrieve data . /1350350629.Didn't retrieve data . /1350350542.Didn't retrieve data . /1350350510.Didn't retrieve data . before output type Used above Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350941', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350820', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350813', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350795', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350774', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350760', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350752', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350629', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350542', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350510', None, None, None, None, None, '2733675') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 30 time used for this insertion : 0.06387090682983398 save_final save missing photos in datou_result : time spend for datou_step_exec : 135.55578899383545 time spend to save output : 0.06435632705688477 total time spend for step 3 : 135.62014532089233 step4:ventilate_hashtags_in_portfolio Wed Apr 9 10:38:34 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 22153594 get user id for portfolio 22153594 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`=22153594 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('autre','pehd','pet_clair','environnement','pet_fonce','metal','flou','mal_croppe','background','papier','carton')) AND mptpi.`min_score`=0.5 To do To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22153594 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('autre','pehd','pet_clair','environnement','pet_fonce','metal','flou','mal_croppe','background','papier','carton')) AND mptpi.`min_score`=0.5 To do Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22153594 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('autre','pehd','pet_clair','environnement','pet_fonce','metal','flou','mal_croppe','background','papier','carton')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/22156752,22156753,22156754,22156755,22156756,22156757,22156758,22156759,22156760,22156761,22156762?tags=autre,pehd,pet_clair,environnement,pet_fonce,metal,flou,mal_croppe,background,papier,carton Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1350350941, 1350350820, 1350350813, 1350350795, 1350350774, 1350350760, 1350350752, 1350350629, 1350350542, 1350350510] Looping around the photos to save general results len do output : 1 /22153594. before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350941', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350820', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350813', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350795', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350774', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350760', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350752', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350629', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350542', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350510', None, None, None, None, None, '2733675') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 11 time used for this insertion : 0.38791346549987793 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.806852102279663 time spend to save output : 0.3881816864013672 total time spend for step 4 : 2.1950337886810303 step5:final Wed Apr 9 10:38:36 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 Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step final ! Catched exception ! Connect or reconnect ! Inside saveOutput : final : False verbose : 0 original output for save of step final : {1350350941: ('0.2245747095134351',), 1350350820: ('0.2245747095134351',), 1350350813: ('0.2245747095134351',), 1350350795: ('0.2245747095134351',), 1350350774: ('0.2245747095134351',), 1350350760: ('0.2245747095134351',), 1350350752: ('0.2245747095134351',), 1350350629: ('0.2245747095134351',), 1350350542: ('0.2245747095134351',), 1350350510: ('0.2245747095134351',)} new output for save of step final : {1350350941: ('0.2245747095134351',), 1350350820: ('0.2245747095134351',), 1350350813: ('0.2245747095134351',), 1350350795: ('0.2245747095134351',), 1350350774: ('0.2245747095134351',), 1350350760: ('0.2245747095134351',), 1350350752: ('0.2245747095134351',), 1350350629: ('0.2245747095134351',), 1350350542: ('0.2245747095134351',), 1350350510: ('0.2245747095134351',)} [1350350941, 1350350820, 1350350813, 1350350795, 1350350774, 1350350760, 1350350752, 1350350629, 1350350542, 1350350510] Looping around the photos to save general results len do output : 10 /1350350941.Didn't retrieve data . /1350350820.Didn't retrieve data . /1350350813.Didn't retrieve data . /1350350795.Didn't retrieve data . /1350350774.Didn't retrieve data . /1350350760.Didn't retrieve data . /1350350752.Didn't retrieve data . /1350350629.Didn't retrieve data . /1350350542.Didn't retrieve data . /1350350510.Didn't retrieve data . before output type Used above Used above Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350941', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350820', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350813', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350795', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350774', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350760', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350752', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350629', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350542', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350510', None, None, None, None, None, '2733675') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 30 time used for this insertion : 0.0136871337890625 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.1064598560333252 time spend to save output : 0.014238357543945312 total time spend for step 5 : 0.12069821357727051 step6:blur_detection Wed Apr 9 10:38:36 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 Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection methode: ratio et variance treat image : temp/1744187429_489391_1350350941_c94f8ca44af77526ade75e311e223587.jpg resize: (2160, 3264) 1350350941 -5.919430388194657 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5.jpg resize: (2160, 3264) 1350350820 -3.874050884109253 treat image : temp/1744187429_489391_1350350813_85b3a764084b470d02af2c9f1ebcee4c.jpg resize: (2160, 3264) 1350350813 -4.11272447042474 treat image : temp/1744187429_489391_1350350795_1fd87ed50fec96e615d8535a49b1d160.jpg resize: (2160, 3264) 1350350795 -6.202025074805631 treat image : temp/1744187429_489391_1350350774_ef8aac449f1a40bdd8418608e80528b5.jpg resize: (2160, 3264) 1350350774 -4.631646990532043 treat image : temp/1744187429_489391_1350350760_5df569a28e7a89edcd6487dab5089e2f.jpg resize: (2160, 3264) 1350350760 -4.158122433948496 treat image : temp/1744187429_489391_1350350752_116338e98f7afe6b02a58ab437923d69.jpg resize: (2160, 3264) 1350350752 -5.701553733672763 treat image : temp/1744187429_489391_1350350629_2dce2ba54579371e63fecfa033a9bf66.jpg resize: (2160, 3264) 1350350629 -1.628383661638516 treat image : temp/1744187429_489391_1350350542_e701d2405fbcf52284155d70855c4297.jpg resize: (2160, 3264) 1350350542 -4.978613530557983 treat image : temp/1744187429_489391_1350350510_beafde6ed77f8cab6d4b3695f11fc3ab.jpg resize: (2160, 3264) 1350350510 -3.636648807086212 treat image : temp/1744187429_489391_1350350941_c94f8ca44af77526ade75e311e223587_rle_crop_3751219901_0.png resize: (63, 85) 1350720598 -3.367093448795379 treat image : temp/1744187429_489391_1350350941_c94f8ca44af77526ade75e311e223587_rle_crop_3751219893_0.png resize: (167, 134) 1350720603 -3.313130142017397 treat image : temp/1744187429_489391_1350350941_c94f8ca44af77526ade75e311e223587_rle_crop_3751219892_0.png resize: (1151, 1093) 1350720607 -4.819869410673256 treat image : temp/1744187429_489391_1350350941_c94f8ca44af77526ade75e311e223587_rle_crop_3751219881_0.png resize: (130, 154) 1350720611 -2.9321325179452957 treat image : temp/1744187429_489391_1350350941_c94f8ca44af77526ade75e311e223587_rle_crop_3751219899_0.png resize: (408, 357) 1350720615 -3.152404000564858 treat image : temp/1744187429_489391_1350350941_c94f8ca44af77526ade75e311e223587_rle_crop_3751219884_0.png resize: (255, 296) 1350720618 -3.8775129584212684 treat image : temp/1744187429_489391_1350350941_c94f8ca44af77526ade75e311e223587_rle_crop_3751219887_0.png resize: (95, 200) 1350720623 -2.7652272325984026 treat image : temp/1744187429_489391_1350350941_c94f8ca44af77526ade75e311e223587_rle_crop_3751219880_0.png resize: (105, 165) 1350720626 -4.602719649596559 treat image : temp/1744187429_489391_1350350941_c94f8ca44af77526ade75e311e223587_rle_crop_3751219879_0.png resize: (164, 187) 1350720631 -4.8148169510365255 treat image : temp/1744187429_489391_1350350941_c94f8ca44af77526ade75e311e223587_rle_crop_3751219891_0.png resize: (1059, 462) 1350720635 -6.165188688819839 treat image : temp/1744187429_489391_1350350941_c94f8ca44af77526ade75e311e223587_rle_crop_3751219878_0.png resize: (537, 369) 1350720639 -4.3766797977083485 treat image : temp/1744187429_489391_1350350941_c94f8ca44af77526ade75e311e223587_rle_crop_3751219897_0.png resize: (379, 289) 1350720644 -5.217236914782035 treat image : temp/1744187429_489391_1350350941_c94f8ca44af77526ade75e311e223587_rle_crop_3751219900_0.png resize: (163, 199) 1350720649 -1.1848470981170345 treat image : temp/1744187429_489391_1350350941_c94f8ca44af77526ade75e311e223587_rle_crop_3751219894_0.png resize: (261, 140) 1350720652 -4.6670056541502065 treat image : temp/1744187429_489391_1350350941_c94f8ca44af77526ade75e311e223587_rle_crop_3751219882_0.png resize: (147, 177) 1350720657 -2.212358712144342 treat image : temp/1744187429_489391_1350350941_c94f8ca44af77526ade75e311e223587_rle_crop_3751219895_0.png resize: (239, 306) 1350720660 -5.036680202062465 treat image : temp/1744187429_489391_1350350941_c94f8ca44af77526ade75e311e223587_rle_crop_3751219888_0.png resize: (387, 400) 1350720664 -5.400351600350529 treat image : temp/1744187429_489391_1350350941_c94f8ca44af77526ade75e311e223587_rle_crop_3751219898_0.png resize: (142, 124) 1350720669 -1.164589315288863 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219940_0.png resize: (179, 213) 1350720673 -1.5049670821999348 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219912_0.png resize: (176, 178) 1350720677 -1.9860843314348673 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219921_0.png resize: (348, 512) 1350720681 -2.682722950475736 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219902_0.png resize: (97, 127) 1350720685 -2.59745777511479 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219932_0.png resize: (263, 326) 1350720689 -2.9105348433311766 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219931_0.png resize: (76, 90) 1350720693 -2.4694568814675857 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219903_0.png resize: (346, 202) 1350720697 -1.54059101595876 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219909_0.png resize: (282, 206) 1350720701 -3.1335667850938567 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219915_0.png resize: (125, 129) 1350720706 -1.8081502044880537 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219936_0.png resize: (138, 286) 1350720709 -3.1327028759499846 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219928_0.png resize: (60, 105) 1350720713 -0.9801329329128858 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219906_0.png resize: (155, 225) 1350720718 -2.888836757809591 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219924_0.png resize: (138, 161) 1350720722 -2.794573048426916 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219910_0.png resize: (191, 212) 1350720726 -3.07963119731667 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219933_0.png resize: (227, 188) 1350720731 -2.171353300269181 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219920_0.png resize: (137, 244) 1350720735 -3.49809829110669 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219923_0.png resize: (190, 211) 1350720738 -0.6800403822804688 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219905_0.png resize: (183, 255) 1350720743 -2.0731414734471505 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219930_0.png resize: (242, 436) 1350720747 -3.166386631095932 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219908_0.png resize: (79, 97) 1350720751 -0.45890650094825736 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219918_0.png resize: (181, 180) 1350720755 0.19306245341252581 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219935_0.png resize: (145, 202) 1350720759 -2.550518424610739 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219934_0.png resize: (252, 115) 1350720764 -3.480332394465965 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219916_0.png resize: (223, 225) 1350720769 -1.7716106575903576 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219922_0.png resize: (174, 201) 1350720773 -0.20738185998980552 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219911_0.png resize: (583, 321) 1350720778 -2.4125509694001592 treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5_rle_crop_3751219913_0.png resize: (216, 231) 1350720784 -0.7428289449838499 treat image : 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missing photos in datou_result : time spend for datou_step_exec : 41.574907064437866 time spend to save output : 0.09572935104370117 total time spend for step 6 : 41.67063641548157 step7:brightness Wed Apr 9 10:39:18 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness treat image : temp/1744187429_489391_1350350941_c94f8ca44af77526ade75e311e223587.jpg treat image : temp/1744187429_489391_1350350820_50ec86e5575fdff7e68818cd9756c3c5.jpg treat image : temp/1744187429_489391_1350350813_85b3a764084b470d02af2c9f1ebcee4c.jpg treat image : temp/1744187429_489391_1350350795_1fd87ed50fec96e615d8535a49b1d160.jpg treat image : temp/1744187429_489391_1350350774_ef8aac449f1a40bdd8418608e80528b5.jpg treat image : temp/1744187429_489391_1350350760_5df569a28e7a89edcd6487dab5089e2f.jpg treat image : temp/1744187429_489391_1350350752_116338e98f7afe6b02a58ab437923d69.jpg treat image : temp/1744187429_489391_1350350629_2dce2ba54579371e63fecfa033a9bf66.jpg treat image : temp/1744187429_489391_1350350542_e701d2405fbcf52284155d70855c4297.jpg treat image : temp/1744187429_489391_1350350510_beafde6ed77f8cab6d4b3695f11fc3ab.jpg treat image : 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temp/1744187429_489391_1350350510_beafde6ed77f8cab6d4b3695f11fc3ab_rle_crop_3751220176_0.png treat image : temp/1744187429_489391_1350350510_beafde6ed77f8cab6d4b3695f11fc3ab_rle_crop_3751220165_0.png treat image : temp/1744187429_489391_1350350510_beafde6ed77f8cab6d4b3695f11fc3ab_rle_crop_3751220167_0.png treat image : temp/1744187429_489391_1350350510_beafde6ed77f8cab6d4b3695f11fc3ab_rle_crop_3751220183_0.png treat image : temp/1744187429_489391_1350350813_85b3a764084b470d02af2c9f1ebcee4c_rle_crop_3751219967_0.png treat image : temp/1744187429_489391_1350350510_beafde6ed77f8cab6d4b3695f11fc3ab_rle_crop_3751220178_0.png treat image : temp/1744187429_489391_1350350510_beafde6ed77f8cab6d4b3695f11fc3ab_rle_crop_3751220160_0.png treat image : temp/1744187429_489391_1350350941_c94f8ca44af77526ade75e311e223587_rle_crop_3751219889_0.png treat image : temp/1744187429_489391_1350350760_5df569a28e7a89edcd6487dab5089e2f_rle_crop_3751220058_0.png treat image : temp/1744187429_489391_1350350752_116338e98f7afe6b02a58ab437923d69_rle_crop_3751220079_0.png Inside saveOutput : final : False verbose : 0 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 317 time used for this insertion : 0.024101972579956055 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 317 time used for this insertion : 0.17786741256713867 save missing photos in datou_result : time spend for datou_step_exec : 10.560792446136475 time spend to save output : 0.2081913948059082 total time spend for step 7 : 10.768983840942383 step8:velours_tree Wed Apr 9 10:39:29 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure can't find the photo_desc_type Inside saveOutput : final : False verbose : 0 ouput is None No outpout to save, returning out of save general time spend for datou_step_exec : 0.5746417045593262 time spend to save output : 4.1961669921875e-05 total time spend for step 8 : 0.574683666229248 step9:send_mail_cod Wed Apr 9 10:39:29 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 2 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 3 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure dans la step send mail cod work_area: /home/admin/workarea/git/Velours/python in order to get the selector url, please entre the license of selector results_Auto_P22153594_09-04-2025_10_39_29.pdf 22156752 change filename to text .change filename to text .imagette221567521744187969 22156753 change filename to text .imagette221567531744187970 22156754 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette221567541744187970 22156756 change filename to text .change filename to text .change filename to text .imagette221567561744187971 22156757 change filename to text .imagette221567571744187971 22156758 imagette221567581744187972 22156759 imagette221567591744187972 22156760 imagette221567601744187972 22156761 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette221567611744187972 22156762 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette221567621744187973 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=22153594 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/22156752,22156753,22156754,22156755,22156756,22156757,22156758,22156759,22156760,22156761,22156762?tags=autre,pehd,pet_clair,environnement,pet_fonce,metal,flou,mal_croppe,background,papier,carton args[1350350941] : ((1350350941, -5.919430388194657, 492609224), (1350350941, -0.253972946672427, 496442774), '0.2245747095134351') We are sending mail with results at report@fotonower.com args[1350350820] : ((1350350820, -3.874050884109253, 492609224), (1350350820, -0.12326912927374489, 496442774), '0.2245747095134351') We are sending mail with results at report@fotonower.com args[1350350813] : ((1350350813, -4.11272447042474, 492609224), (1350350813, -0.3377816935712494, 496442774), '0.2245747095134351') We are sending mail with results at report@fotonower.com args[1350350795] : ((1350350795, -6.202025074805631, 492609224), (1350350795, -0.1871680074716537, 496442774), '0.2245747095134351') We are sending mail with results at report@fotonower.com args[1350350774] : ((1350350774, -4.631646990532043, 492609224), (1350350774, -0.08941721412777688, 496442774), '0.2245747095134351') We are sending mail with results at report@fotonower.com args[1350350760] : ((1350350760, -4.158122433948496, 492609224), (1350350760, -0.28846871515896916, 496442774), '0.2245747095134351') We are sending mail with results at report@fotonower.com args[1350350752] : ((1350350752, -5.701553733672763, 492609224), (1350350752, -0.27919173834228694, 496442774), '0.2245747095134351') We are sending mail with results at report@fotonower.com args[1350350629] : ((1350350629, -1.628383661638516, 492688767), (1350350629, 0.03267503697816284, 2107752395), '0.2245747095134351') We are sending mail with results at report@fotonower.com args[1350350542] : ((1350350542, -4.978613530557983, 492609224), (1350350542, -0.10212531307254967, 496442774), '0.2245747095134351') We are sending mail with results at report@fotonower.com args[1350350510] : ((1350350510, -3.636648807086212, 492609224), (1350350510, 0.09357675895327788, 2107752395), '0.2245747095134351') We are sending mail with results at report@fotonower.com refus_total : 0.2245747095134351 2022-04-13 10:29:59 0 SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=22153594 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1350350774,1350350795,1350350542,1350350629,1350350813,1350350510,1350350752,1350350760,1350350820,1350350941) Found this number of photos: 10 begin to download photo : 1350350774 begin to download photo : 1350350629 begin to download photo : 1350350752 begin to download photo : 1350350941 download finish for photo 1350350752 begin to download photo : 1350350760 download finish for photo 1350350774 begin to download photo : 1350350795 download finish for photo 1350350629 begin to download photo : 1350350813 download finish for photo 1350350760 begin to download photo : 1350350820 download finish for photo 1350350795 begin to download photo : 1350350542 download finish for photo 1350350941 download finish for photo 1350350820 download finish for photo 1350350542 download finish for photo 1350350813 begin to download photo : 1350350510 download finish for photo 1350350510 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153594_09-04-2025_10_39_29.pdf results_Auto_P22153594_09-04-2025_10_39_29.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153594_09-04-2025_10_39_29.pdf start insert file to database insert into MTRUser.mtr_files (mtd_id,mtr_portfolio_id,text,url,format,tags,file_size,value) values ('3318','22153594','results_Auto_P22153594_09-04-2025_10_39_29.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153594_09-04-2025_10_39_29.pdf','pdf','','0.87','0.2245747095134351') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/22153594

https://www.fotonower.com/image?json=false&list_photos_id=1350350941
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350350820
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350350813
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350350795
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350350774
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350350760
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350350752
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350350629
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350350542
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350350510
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 22.46%
Veuillez trouver les photos des contaminants.

exemples de contaminants: autre: https://www.fotonower.com/view/22156752?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/22156753?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/22156754?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/22156756?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/22156757?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/22156761?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/22156762?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153594_09-04-2025_10_39_29.pdf.

Lien vers velours :https://www.fotonower.com/velours/22156752,22156753,22156754,22156755,22156756,22156757,22156758,22156759,22156760,22156761,22156762?tags=autre,pehd,pet_clair,environnement,pet_fonce,metal,flou,mal_croppe,background,papier,carton.


L'équipe Fotonower 202 b'' Server: nginx Date: Wed, 09 Apr 2025 08:39:39 GMT Content-Length: 0 Connection: close X-Message-Id: RE-Ms049SBCCsQJ7l7e0wg Access-Control-Allow-Origin: https://sendgrid.api-docs.io Access-Control-Allow-Methods: POST Access-Control-Allow-Headers: Authorization, Content-Type, On-behalf-of, x-sg-elas-acl Access-Control-Max-Age: 600 X-No-CORS-Reason: https://sendgrid.com/docs/Classroom/Basics/API/cors.html Strict-Transport-Security: max-age=31536000; includeSubDomains Content-Security-Policy: frame-ancestors 'none' Cache-Control: no-cache X-Content-Type-Options: no-sniff Referrer-Policy: strict-origin-when-cross-origin Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : send_mail_cod we use saveGeneral [1350350941, 1350350820, 1350350813, 1350350795, 1350350774, 1350350760, 1350350752, 1350350629, 1350350542, 1350350510] Looping around the photos to save general results len do output : 0 before output type Used above Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350941', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350820', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350813', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350795', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350774', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350760', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350752', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350629', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350542', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350510', None, None, None, None, None, '2733675') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 10 time used for this insertion : 0.017794370651245117 save_final save missing photos in datou_result : time spend for datou_step_exec : 9.229139804840088 time spend to save output : 0.01797795295715332 total time spend for step 9 : 9.247117757797241 step10:split_time_score Wed Apr 9 10:39:39 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure begin split time score Catched exception ! Connect or reconnect ! TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('16', 10),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 07042025 22153594 Nombre de photos uploadées : 10 / 23040 (0%) 07042025 22153594 Nombre de photos taguées (types de déchets): 0 / 10 (0%) 07042025 22153594 Nombre de photos taguées (volume) : 0 / 10 (0%) elapsed_time : load_data_split_time_score 1.9073486328125e-06 elapsed_time : order_list_meta_photo_and_scores 6.198883056640625e-06 ?????????? elapsed_time : fill_and_build_computed_from_old_data 0.0003619194030761719 elapsed_time : insert_dashboard_record_day_entry 0.024320602416992188 We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153527 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153533 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153536 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153537 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153567 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153572 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153573 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153575 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153579 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153585 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153590 order by id desc limit 1 Qualite : 0.2245747095134351 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153594_09-04-2025_10_39_29.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153594 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22153594 AND mptpi.`type`=3594 To do Qualite : 0.18817508340141612 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153599_09-04-2025_10_31_18.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153599 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22153599 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153631 order by id desc limit 1 Qualite : 0.2387647538497724 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153644_09-04-2025_10_11_42.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153644 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22153644 AND mptpi.`type`=3594 To do Qualite : 0.20050398026424382 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153647_09-04-2025_10_19_51.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153647 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22153647 AND mptpi.`type`=3594 To do Qualite : 0.16284528966389794 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153651_09-04-2025_09_56_05.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153651 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22153651 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153655 order by id desc limit 1 Qualite : 0.13395730297527286 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153656_09-04-2025_09_56_03.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153656 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22153656 AND mptpi.`type`=3726 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'07042025': {'nb_upload': 10, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} Inside saveOutput : final : True verbose : 0 saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1350350941, 1350350820, 1350350813, 1350350795, 1350350774, 1350350760, 1350350752, 1350350629, 1350350542, 1350350510] Looping around the photos to save general results len do output : 1 /22153594Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350941', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350820', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350813', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350795', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350774', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350760', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350752', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350629', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350542', None, None, None, None, None, '2733675') ('3318', None, None, None, None, None, None, None, '2733675') ('3318', '22153594', '1350350510', None, None, None, None, None, '2733675') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 11 time used for this insertion : 0.015805482864379883 save_final save missing photos in datou_result : time spend for datou_step_exec : 11.328015327453613 time spend to save output : 0.016025781631469727 total time spend for step 10 : 11.344041109085083 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 10 set_done_treatment 269.72user 138.54system 9:24.31elapsed 72%CPU (0avgtext+0avgdata 7673856maxresident)k 740920inputs+212480outputs (13027major+24678196minor)pagefaults 0swaps