python /home/admin/mtr/script_for_cron.py -j default -m 20 -a 'python3 ~/workarea/git/Velours/python/prod/datou.py -j batch_current -C 2543013' -s traitement_3459 -M 0 -S 0 -U 100,80,95 import MySQLdb succeeded Import error (python version) ['/Users/moilerat/Documents/Fotonower/install/caffe/distribute/python', '/home/admin/workarea/git/Velours/python/prod', '/home/admin/workarea/install/darknet', '/home/admin/workarea/git/Velours/python', '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python', '/home/admin/mtr/.credentials', '/home/admin/workarea/install/caffe/python', '/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools', '/home/admin/workarea/git/fotonowerpip', '/home/admin/workarea/install/segment-anything', '/home/admin/workarea/git/pyfvs', '/home/admin/workarea/git/apy', '/usr/lib/python38.zip', '/usr/lib/python3.8', '/usr/lib/python3.8/lib-dynload', '/home/admin/.local/lib/python3.8/site-packages', '/usr/local/lib/python3.8/dist-packages', '/usr/lib/python3/dist-packages'] process id : 4044648 load datou : 0 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 0 init_dummy_multi_datou is not linked in the step_by_step architecture ! WARNING : step 1294 init_dummy_multi_datou is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! Unexpected type for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : (photo_id, hashtag_id, score_max) was removed should we ? (x0, y0, x1, y1) was removed should we ? chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (x0, y0, x1, y1) was removed should we ? chemin de la photo was removed should we ? load thcls load pdts Running datou job : batch_current TODO datou_current to load to do maybe to take outside batchDatouExec updating current state to 1 list_input_json: [] Current got : datou_id : 4909, datou_cur_ids : ['2543013'] with mtr_portfolio_ids : ['20198530'] and first list_photo_ids : [] new path : /proc/4044648/ Inside batchDatouExec : verbose : 0 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already 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 13953 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 13961 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 13960 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 13957 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 13957 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 13966 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13959 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 13954 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 13954 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 13964 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 13964 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 13956 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 13955 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 13955 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 13963 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 13956 doesn't seem to be define in the database( WARNING : type of input 3 of step 13955 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 13953 doesn't seem to be define in the database( WARNING : type of input 2 of step 13957 doesn't seem to be define in the database( WARNING : output 1 of step 13953 have datatype=2 whereas input 1 of step 13959 have datatype=7 WARNING : type of output 2 of step 13959 doesn't seem to be define in the database( WARNING : type of input 1 of step 13954 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 13956 have datatype=10 whereas input 3 of step 13962 have datatype=6 WARNING : type of input 2 of step 13964 doesn't seem to be define in the database( WARNING : output 1 of step 13954 have datatype=7 whereas input 2 of step 13964 have datatype=None WARNING : type of output 3 of step 13964 doesn't seem to be define in the database( WARNING : type of input 1 of step 13956 doesn't seem to be define in the database( WARNING : output 0 of step 13956 have datatype=10 whereas input 0 of step 13965 have datatype=18 WARNING : type of input 5 of step 13962 doesn't seem to be define in the database( WARNING : output 0 of step 13965 have datatype=11 whereas input 5 of step 13962 have datatype=None WARNING : type of output 1 of step 13961 doesn't seem to be define in the database( WARNING : type of input 3 of step 13957 doesn't seem to be define in the database( WARNING : type of output 1 of step 13960 doesn't seem to be define in the database( WARNING : type of input 3 of step 13957 doesn't seem to be define in the database( WARNING : output 0 of step 13959 have datatype=1 whereas input 0 of step 13954 have datatype=2 WARNING : output 0 of step 13966 have datatype=6 whereas input 2 of step 13959 have datatype=5 WARNING : output 0 of step 13953 have datatype=16 whereas input 0 of step 13959 have datatype=1 DataTypes for each output/input checked ! List Step Type Loaded in datou : mask_detect, blur_detection, brightness, crop_condition, thcl, argmax, merge_mask_thcl_custom, rle_unique_nms_with_priority, crop_condition, ventilate_hashtags_in_portfolio, final, velours_tree, send_mail_cod, split_time_score over limit max, limiting to limit_max 20 list_input_json : [] origin We have 1 , WARNING: data may be incomplete, need to offset and complete ! BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 20 ; length of list_pids : 20 ; length of list_args : 20 time to download the photos : 4.421365261077881 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 : 14 step1:mask_detect Tue Feb 4 14:00:09 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 10998 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-02-04 14:00:12.587664: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-04 14:00:12.615167: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-04 14:00:12.617264: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f5478000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-04 14:00:12.617303: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-04 14:00:12.621109: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-04 14:00:12.915700: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x825c3c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-04 14:00:12.915749: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-04 14:00:12.917213: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-02-04 14:00:12.917595: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-04 14:00:12.920443: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-04 14:00:12.922543: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-04 14:00:12.922977: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-04 14:00:12.925434: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-04 14:00:12.926681: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-04 14:00:12.931731: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-04 14:00:12.933248: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-04 14:00:12.933319: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-04 14:00:12.934093: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-04 14:00:12.934109: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-04 14:00:12.934117: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-04 14:00:12.935535: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10193 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-02-04 14:00:13.198843: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-02-04 14:00:13.199020: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-04 14:00:13.199067: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-04 14:00:13.199096: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-04 14:00:13.199122: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-04 14:00:13.199144: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-04 14:00:13.199164: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-04 14:00:13.199185: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-04 14:00:13.200838: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-04 14:00:13.202172: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-02-04 14:00:13.202214: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-04 14:00:13.202233: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-04 14:00:13.202250: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-04 14:00:13.202267: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-04 14:00:13.202284: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-04 14:00:13.202301: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-04 14:00:13.202319: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-04 14:00:13.204153: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-04 14:00:13.204191: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-04 14:00:13.204214: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-04 14:00:13.204225: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-04 14:00:13.205613: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10193 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 : thcl2976 thcls : [{'id': 2976, 'mtr_user_id': 31, 'name': 'Mask_Limeil_Label_PEHD_080621', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,environnement,fibreux_cont,metal_cont,pet_clair_cont,pet_fonce_cont,pet_opaque_cont,barquette,film_plastique,autre_contaminant,etiquette_detachee', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3760, 'photo_desc_type': 5359, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 2976, 'mtr_user_id': 31, 'name': 'Mask_Limeil_Label_PEHD_080621', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,environnement,fibreux_cont,metal_cont,pet_clair_cont,pet_fonce_cont,pet_opaque_cont,barquette,film_plastique,autre_contaminant,etiquette_detachee', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3760, 'photo_desc_type': 5359, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5359 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5359, 'Mask_Limeil_Label_PEHD_080621', 16384, 25088, 'Mask_Limeil_Label_PEHD_080621', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 6, 9, 7, 22, 34), datetime.datetime(2021, 6, 9, 7, 22, 34)) {'thcl': {'id': 2976, 'mtr_user_id': 31, 'name': 'Mask_Limeil_Label_PEHD_080621', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,environnement,fibreux_cont,metal_cont,pet_clair_cont,pet_fonce_cont,pet_opaque_cont,barquette,film_plastique,autre_contaminant,etiquette_detachee', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3760, 'photo_desc_type': 5359, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'environnement', 'fibreux_cont', 'metal_cont', 'pet_clair_cont', 'pet_fonce_cont', 'pet_opaque_cont', 'barquette', 'film_plastique', 'autre_contaminant', 'etiquette_detachee'], 'list_hashtags_csv': 'background,environnement,fibreux_cont,metal_cont,pet_clair_cont,pet_fonce_cont,pet_opaque_cont,barquette,film_plastique,autre_contaminant,etiquette_detachee', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3760, 'svm_hashtag_type_desc': 5359, 'photo_desc_type': 5359, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'environnement', 'fibreux_cont', 'metal_cont', 'pet_clair_cont', 'pet_fonce_cont', 'pet_opaque_cont', 'barquette', 'film_plastique', 'autre_contaminant', 'etiquette_detachee'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME Mask_Limeil_Label_PEHD_080621 NUM_CLASSES 11 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : Mask_Limeil_Label_PEHD_080621 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-02-04 14:00:22.585393: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-04 14:00:22.762730: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/Mask_Limeil_Label_PEHD_080621 /data/models_weight/Mask_Limeil_Label_PEHD_080621/mask_model.h5 size_local : 256052544 size in s3 : 256052544 create time local : 2021-08-11 19:43:15 create time in s3 : 2021-08-06 17:21:30 mask_model.h5 already exist and didn't need to update list_images length : 20 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, 19) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3264.00000 nb d'objets trouves : 98 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, 19) min: 0.00000 max: 3264.00000 nb d'objets trouves : 62 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, 19) min: 0.00000 max: 3264.00000 nb d'objets trouves : 87 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3264.00000 nb d'objets trouves : 24 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, 19) min: 0.00000 max: 3264.00000 nb d'objets trouves : 75 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, 19) min: 0.00000 max: 3264.00000 nb d'objets trouves : 88 Detection mask done ! Trying to reset tf kernel 4049775 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5485 tf kernel not reseted sub process len(results) : 20 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 20 len(list_Values) 0 process is alive process is alive process is alive process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 10774 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl2976 Catched exception ! Connect or reconnect ! thcls : [{'id': 2976, 'mtr_user_id': 31, 'name': 'Mask_Limeil_Label_PEHD_080621', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,environnement,fibreux_cont,metal_cont,pet_clair_cont,pet_fonce_cont,pet_opaque_cont,barquette,film_plastique,autre_contaminant,etiquette_detachee', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3760, 'photo_desc_type': 5359, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 2976, 'mtr_user_id': 31, 'name': 'Mask_Limeil_Label_PEHD_080621', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,environnement,fibreux_cont,metal_cont,pet_clair_cont,pet_fonce_cont,pet_opaque_cont,barquette,film_plastique,autre_contaminant,etiquette_detachee', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3760, 'photo_desc_type': 5359, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5359 ['background', 'environnement', 'fibreux_cont', 'metal_cont', 'pet_clair_cont', 'pet_fonce_cont', 'pet_opaque_cont', 'barquette', 'film_plastique', 'autre_contaminant', 'etiquette_detachee'] time for calcul the mask position with numpy : 0.0586855411529541 nb_pixel_total : 8292 time to create 1 rle with old method : 0.011860132217407227 length of segment : 126 time for calcul the mask position with numpy : 0.021226882934570312 nb_pixel_total : 5135 time to create 1 rle with old method : 0.007494688034057617 length of segment : 68 time for calcul the mask position with numpy : 0.08330154418945312 nb_pixel_total : 32218 time to create 1 rle with old method : 0.04162931442260742 length of segment : 134 time for calcul the mask position with numpy : 0.05362558364868164 nb_pixel_total : 10000 time to create 1 rle with old method : 0.017166852951049805 length of segment : 128 time for calcul the mask position with numpy : 0.01715707778930664 nb_pixel_total : 2997 time to create 1 rle with old method : 0.006331443786621094 length of segment : 71 time for calcul the mask position with numpy : 0.08269286155700684 nb_pixel_total : 28112 time to create 1 rle with old method : 0.03804898262023926 length of segment : 149 time for calcul the mask position with numpy : 0.497434139251709 nb_pixel_total : 255137 time to create 1 rle with new method : 0.023331880569458008 length of segment : 466 time for calcul the mask position with numpy : 0.06281566619873047 nb_pixel_total : 27939 time to create 1 rle with old method : 0.03476905822753906 length of segment : 229 time for calcul the mask position with numpy : 0.15564823150634766 nb_pixel_total : 32126 time to create 1 rle with old method : 0.03697681427001953 length of segment : 296 time for calcul the mask position with numpy : 0.022433042526245117 nb_pixel_total : 18502 time to create 1 rle with old method : 0.022396564483642578 length of segment : 148 time for calcul the mask position with numpy : 0.20303964614868164 nb_pixel_total : 58495 time to create 1 rle with old method : 0.06285595893859863 length of segment : 295 time for calcul the mask position with numpy : 0.12544822692871094 nb_pixel_total : 58724 time to create 1 rle with old method : 0.07258081436157227 length of segment : 185 time for calcul the mask position with numpy : 0.10067486763000488 nb_pixel_total : 63070 time to create 1 rle with old method : 0.07267928123474121 length of segment : 172 time for calcul the mask position with numpy : 0.04057908058166504 nb_pixel_total : 11345 time to create 1 rle with old method : 0.020100116729736328 length of segment : 123 time for calcul the mask position with numpy : 0.06164360046386719 nb_pixel_total : 17969 time to create 1 rle with old method : 0.030315160751342773 length of segment : 216 time for calcul the mask position with numpy : 0.22406744956970215 nb_pixel_total : 76325 time to create 1 rle with old method : 0.09083199501037598 length of segment : 357 time for calcul the mask position with numpy : 0.10452985763549805 nb_pixel_total : 35940 time to create 1 rle with old method : 0.0453343391418457 length of segment : 140 time for calcul the mask position with numpy : 0.38783812522888184 nb_pixel_total : 145124 time to create 1 rle with old method : 0.1956775188446045 length of segment : 839 time for calcul the mask position with numpy : 0.07312917709350586 nb_pixel_total : 14641 time to create 1 rle with old method : 0.021509885787963867 length of segment : 153 time for calcul the mask position with numpy : 0.21323943138122559 nb_pixel_total : 101581 time to create 1 rle with old method : 0.11909031867980957 length of segment : 354 time for calcul the mask position with numpy : 0.03932690620422363 nb_pixel_total : 12989 time to create 1 rle with old method : 0.018248796463012695 length of segment : 126 time for calcul the mask position with numpy : 0.11986279487609863 nb_pixel_total : 28888 time to create 1 rle with old method : 0.035773277282714844 length of segment : 282 time for calcul the mask position with numpy : 0.08265399932861328 nb_pixel_total : 34646 time to create 1 rle with old method : 0.04312610626220703 length of segment : 223 time for calcul the mask position with numpy : 0.06404948234558105 nb_pixel_total : 14246 time to create 1 rle with old method : 0.02359294891357422 length of segment : 174 time for calcul the mask position with numpy : 0.07036495208740234 nb_pixel_total : 23475 time to create 1 rle with old method : 0.040669918060302734 length of segment : 181 time for calcul the mask position with numpy : 0.2862670421600342 nb_pixel_total : 138404 time to create 1 rle with old method : 0.15961933135986328 length of segment : 417 time for calcul the mask position with numpy : 0.21181702613830566 nb_pixel_total : 92589 time to create 1 rle with old method : 0.10857057571411133 length of segment : 392 time for calcul the mask position with numpy : 0.0695650577545166 nb_pixel_total : 32715 time to create 1 rle with old method : 0.0444033145904541 length of segment : 522 time for calcul the mask position with numpy : 0.2258148193359375 nb_pixel_total : 95484 time to create 1 rle with old method : 0.10847663879394531 length of segment : 475 time for calcul the mask position with numpy : 0.19905543327331543 nb_pixel_total : 119683 time to create 1 rle with old method : 0.1330864429473877 length of segment : 297 time for calcul the mask position with numpy : 0.05182766914367676 nb_pixel_total : 35749 time to create 1 rle with old method : 0.04767560958862305 length of segment : 241 time for calcul the mask position with numpy : 0.06626272201538086 nb_pixel_total : 27671 time to create 1 rle with old method : 0.03418231010437012 length of segment : 270 time for calcul the mask position with numpy : 0.01359415054321289 nb_pixel_total : 9781 time to create 1 rle with old method : 0.016077756881713867 length of segment : 118 time for calcul the mask position with numpy : 0.16230392456054688 nb_pixel_total : 44022 time to create 1 rle with old method : 0.05061912536621094 length of segment : 387 time for calcul the mask position with numpy : 0.029892444610595703 nb_pixel_total : 38562 time to create 1 rle with old method : 0.045885562896728516 length of segment : 461 time for calcul the mask position with numpy : 0.03829598426818848 nb_pixel_total : 5234 time to create 1 rle with old method : 0.009969234466552734 length of segment : 136 time for calcul the mask position with numpy : 0.014916419982910156 nb_pixel_total : 16307 time to create 1 rle with old method : 0.023529767990112305 length of segment : 142 time for calcul the mask position with numpy : 0.009540319442749023 nb_pixel_total : 10290 time to create 1 rle with old method : 0.01646137237548828 length of segment : 194 time for calcul the mask position with numpy : 0.1160118579864502 nb_pixel_total : 67971 time to create 1 rle with old method : 0.11088323593139648 length of segment : 254 time for calcul the mask position with numpy : 0.002209186553955078 nb_pixel_total : 3033 time to create 1 rle with old method : 0.004136085510253906 length of segment : 76 time for calcul the mask position with numpy : 0.010251522064208984 nb_pixel_total : 5703 time to create 1 rle with old method : 0.007546663284301758 length of segment : 97 time for calcul the mask position with numpy : 0.007180213928222656 nb_pixel_total : 9207 time to create 1 rle with old method : 0.01154017448425293 length of segment : 96 time for calcul the mask position with numpy : 0.06699514389038086 nb_pixel_total : 53671 time to create 1 rle with old method : 0.07237410545349121 length of segment : 278 time for calcul the mask position with numpy : 0.2310786247253418 nb_pixel_total : 93593 time to create 1 rle with old method : 0.12643170356750488 length of segment : 454 time for calcul the mask position with numpy : 0.02791309356689453 nb_pixel_total : 46447 time to create 1 rle with old method : 0.054343461990356445 length of segment : 265 time for calcul the mask position with numpy : 0.0014190673828125 nb_pixel_total : 39376 time to create 1 rle with old method : 0.04599618911743164 length of segment : 251 time for calcul the mask position with numpy : 0.04832959175109863 nb_pixel_total : 29239 time to create 1 rle with old method : 0.03578448295593262 length of segment : 215 time for calcul the mask position with numpy : 0.0013954639434814453 nb_pixel_total : 7503 time to create 1 rle with old method : 0.00859832763671875 length of segment : 110 time for calcul the mask position with numpy : 0.05983734130859375 nb_pixel_total : 9526 time to create 1 rle with old method : 0.01637554168701172 length of segment : 143 time for calcul the mask position with numpy : 0.1692521572113037 nb_pixel_total : 57337 time to create 1 rle with old method : 0.06667423248291016 length of segment : 239 time for calcul the mask position with numpy : 0.02192997932434082 nb_pixel_total : 5165 time to create 1 rle with old method : 0.009680747985839844 length of segment : 59 time for calcul the mask position with numpy : 0.6144304275512695 nb_pixel_total : 158528 time to create 1 rle with new method : 0.028714418411254883 length of segment : 713 time for calcul the mask position with numpy : 0.184051513671875 nb_pixel_total : 58332 time to create 1 rle with old method : 0.06725430488586426 length of segment : 295 time for calcul the mask position with numpy : 0.09825944900512695 nb_pixel_total : 26301 time to create 1 rle with old method : 0.033602237701416016 length of segment : 229 time for calcul the mask position with numpy : 0.12107968330383301 nb_pixel_total : 56352 time to create 1 rle with old method : 0.0662379264831543 length of segment : 165 time for calcul the mask position with numpy : 0.24664664268493652 nb_pixel_total : 107085 time to create 1 rle with old method : 0.12256264686584473 length of segment : 372 time for calcul the mask position with numpy : 0.09344887733459473 nb_pixel_total : 31079 time to create 1 rle with old method : 0.03573131561279297 length of segment : 183 time for calcul the mask position with numpy : 0.07935380935668945 nb_pixel_total : 19624 time to create 1 rle with old method : 0.024595975875854492 length of segment : 221 time for calcul the mask position with numpy : 0.3331434726715088 nb_pixel_total : 271389 time to create 1 rle with new method : 0.02574896812438965 length of segment : 517 time for calcul the mask position with numpy : 0.22178125381469727 nb_pixel_total : 79885 time to create 1 rle with old method : 0.10167884826660156 length of segment : 414 time for calcul the mask position with numpy : 0.012784242630004883 nb_pixel_total : 3026 time to create 1 rle with old method : 0.004485130310058594 length of segment : 70 time for calcul the mask position with numpy : 0.03815102577209473 nb_pixel_total : 5813 time to create 1 rle with old method : 0.010486125946044922 length of segment : 107 time for calcul the mask position with numpy : 0.021718978881835938 nb_pixel_total : 81561 time to create 1 rle with old method : 0.10786843299865723 length of segment : 307 time for calcul the mask position with numpy : 0.005959749221801758 nb_pixel_total : 21480 time to create 1 rle with old method : 0.02800726890563965 length of segment : 187 time for calcul the mask position with numpy : 0.5601353645324707 nb_pixel_total : 156763 time to create 1 rle with new method : 0.026458024978637695 length of segment : 727 time for calcul the mask position with numpy : 0.01902174949645996 nb_pixel_total : 7051 time to create 1 rle with old method : 0.010800838470458984 length of segment : 91 time for calcul the mask position with numpy : 0.05864572525024414 nb_pixel_total : 10213 time to create 1 rle with old method : 0.019240140914916992 length of segment : 158 time for calcul the mask position with numpy : 0.08733844757080078 nb_pixel_total : 25494 time to create 1 rle with old method : 0.034461021423339844 length of segment : 181 time for calcul the mask position with numpy : 0.028737783432006836 nb_pixel_total : 8475 time to create 1 rle with old method : 0.014783382415771484 length of segment : 105 time for calcul the mask position with numpy : 0.10311269760131836 nb_pixel_total : 59080 time to create 1 rle with old method : 0.08275175094604492 length of segment : 177 time for calcul the mask position with numpy : 0.00025725364685058594 nb_pixel_total : 6273 time to create 1 rle with old method : 0.0075283050537109375 length of segment : 88 time for calcul the mask position with numpy : 0.3655216693878174 nb_pixel_total : 211444 time to create 1 rle with new method : 0.019925594329833984 length of segment : 560 time for calcul the mask position with numpy : 0.0015785694122314453 nb_pixel_total : 38127 time to create 1 rle with old method : 0.0482635498046875 length of segment : 207 time for calcul the mask position with numpy : 0.024031400680541992 nb_pixel_total : 36740 time to create 1 rle with old method : 0.046434640884399414 length of segment : 232 time for calcul the mask position with numpy : 0.03534555435180664 nb_pixel_total : 13205 time to create 1 rle with old method : 0.018741846084594727 length of segment : 118 time for calcul the mask position with numpy : 0.00035071372985839844 nb_pixel_total : 14483 time to create 1 rle with old method : 0.016916275024414062 length of segment : 147 time for calcul the mask position with numpy : 0.08398771286010742 nb_pixel_total : 30951 time to create 1 rle with old method : 0.0365142822265625 length of segment : 332 time for calcul the mask position with numpy : 0.018276453018188477 nb_pixel_total : 10285 time to create 1 rle with old method : 0.016950368881225586 length of segment : 123 time for calcul the mask position with numpy : 0.0009114742279052734 nb_pixel_total : 28764 time to create 1 rle with old method : 0.0349118709564209 length of segment : 203 time for calcul the mask position with numpy : 0.01389312744140625 nb_pixel_total : 17051 time to create 1 rle with old method : 0.028400182723999023 length of segment : 193 time for calcul the mask position with numpy : 0.20807886123657227 nb_pixel_total : 79699 time to create 1 rle with old method : 0.10108399391174316 length of segment : 413 time for calcul the mask position with numpy : 0.026636123657226562 nb_pixel_total : 15068 time to create 1 rle with old method : 0.020920991897583008 length of segment : 289 time for calcul the mask position with numpy : 0.08245348930358887 nb_pixel_total : 43815 time to create 1 rle with old method : 0.0573573112487793 length of segment : 247 time for calcul the mask position with numpy : 0.036071062088012695 nb_pixel_total : 15278 time to create 1 rle with old method : 0.022294282913208008 length of segment : 377 time for calcul the mask position with numpy : 0.03762483596801758 nb_pixel_total : 3983 time to create 1 rle with old method : 0.009297370910644531 length of segment : 130 time for calcul the mask position with numpy : 0.013714075088500977 nb_pixel_total : 38437 time to create 1 rle with old method : 0.04330778121948242 length of segment : 145 time for calcul the mask position with numpy : 0.10206866264343262 nb_pixel_total : 22039 time to create 1 rle with old method : 0.02724146842956543 length of segment : 292 time for calcul the mask position with numpy : 0.035447120666503906 nb_pixel_total : 18180 time to create 1 rle with old method : 0.029349088668823242 length of segment : 248 time for calcul the mask position with numpy : 0.03011178970336914 nb_pixel_total : 50178 time to create 1 rle with old method : 0.058235883712768555 length of segment : 251 time for calcul the mask position with numpy : 0.0927131175994873 nb_pixel_total : 30236 time to create 1 rle with old method : 0.03645682334899902 length of segment : 257 time for calcul the mask position with numpy : 0.023537158966064453 nb_pixel_total : 33851 time to create 1 rle with old method : 0.0402989387512207 length of segment : 238 time for calcul the mask position with numpy : 0.0875101089477539 nb_pixel_total : 47815 time to create 1 rle with old method : 0.0568995475769043 length of segment : 254 time for calcul the mask position with numpy : 0.11714839935302734 nb_pixel_total : 52775 time to create 1 rle with old method : 0.0652613639831543 length of segment : 328 time for calcul the mask position with numpy : 0.08696269989013672 nb_pixel_total : 34700 time to create 1 rle with old method : 0.04056072235107422 length of segment : 306 time for calcul the mask position with numpy : 0.07049036026000977 nb_pixel_total : 27148 time to create 1 rle with old method : 0.03446054458618164 length of segment : 159 time for calcul the mask position with numpy : 0.0027382373809814453 nb_pixel_total : 2714 time to create 1 rle with old method : 0.0032837390899658203 length of segment : 78 time for calcul the mask position with numpy : 0.04480624198913574 nb_pixel_total : 56681 time to create 1 rle with old method : 0.06443190574645996 length of segment : 408 time for calcul the mask position with numpy : 0.11158323287963867 nb_pixel_total : 68716 time to create 1 rle with old method : 0.07827949523925781 length of segment : 258 time for calcul the mask position with numpy : 0.19167113304138184 nb_pixel_total : 69807 time to create 1 rle with old method : 0.08083486557006836 length of segment : 335 time for calcul the mask position with numpy : 0.02447795867919922 nb_pixel_total : 3278 time to create 1 rle with old method : 0.0054128170013427734 length of segment : 82 time for calcul the mask position with numpy : 0.04462432861328125 nb_pixel_total : 6514 time to create 1 rle with old method : 0.012144804000854492 length of segment : 106 time for calcul the mask position with numpy : 0.4021151065826416 nb_pixel_total : 99575 time to create 1 rle with old method : 0.11473751068115234 length of segment : 570 time for calcul the mask position with numpy : 0.14508676528930664 nb_pixel_total : 48796 time to create 1 rle with old method : 0.057790517807006836 length of segment : 248 time for calcul the mask position with numpy : 0.2821042537689209 nb_pixel_total : 68440 time to create 1 rle with old method : 0.07600641250610352 length of segment : 537 time for calcul the mask position with numpy : 0.21671128273010254 nb_pixel_total : 92777 time to create 1 rle with old method : 0.10206198692321777 length of segment : 427 time for calcul the mask position with numpy : 0.014093637466430664 nb_pixel_total : 7186 time to create 1 rle with old method : 0.011426448822021484 length of segment : 70 time for calcul the mask position with numpy : 0.21747541427612305 nb_pixel_total : 88525 time to create 1 rle with old method : 0.09959936141967773 length of segment : 515 time for calcul the mask position with numpy : 0.00431060791015625 nb_pixel_total : 6365 time to create 1 rle with old method : 0.006923198699951172 length of segment : 98 time for calcul the mask position with numpy : 0.11452460289001465 nb_pixel_total : 38673 time to create 1 rle with old method : 0.04606914520263672 length of segment : 202 time for calcul the mask position with numpy : 0.02146458625793457 nb_pixel_total : 4476 time to create 1 rle with old method : 0.00820779800415039 length of segment : 101 time for calcul the mask position with numpy : 0.10346150398254395 nb_pixel_total : 29794 time to create 1 rle with old method : 0.03990578651428223 length of segment : 196 time for calcul the mask position with numpy : 0.5114507675170898 nb_pixel_total : 118889 time to create 1 rle with old method : 0.12438201904296875 length of segment : 1296 time for calcul the mask position with numpy : 0.044327735900878906 nb_pixel_total : 10044 time to create 1 rle with old method : 0.016806840896606445 length of segment : 89 time for calcul the mask position with numpy : 0.07064962387084961 nb_pixel_total : 42042 time to create 1 rle with old method : 0.06155753135681152 length of segment : 296 time for calcul the mask position with numpy : 0.0012445449829101562 nb_pixel_total : 5935 time to create 1 rle with old method : 0.006302595138549805 length of segment : 50 time for calcul the mask position with numpy : 0.07223963737487793 nb_pixel_total : 18864 time to create 1 rle with old method : 0.02432417869567871 length of segment : 233 time for calcul the mask position with numpy : 0.055611371994018555 nb_pixel_total : 30698 time to create 1 rle with old method : 0.036249399185180664 length of segment : 335 time for calcul the mask position with numpy : 0.018217086791992188 nb_pixel_total : 5517 time to create 1 rle with old method : 0.010729551315307617 length of segment : 70 time for calcul the mask position with numpy : 0.09336733818054199 nb_pixel_total : 26052 time to create 1 rle with old method : 0.034343719482421875 length of segment : 241 time for calcul the mask position with numpy : 0.034311532974243164 nb_pixel_total : 10297 time to create 1 rle with old method : 0.016918182373046875 length of segment : 88 time for calcul the mask position with numpy : 0.039346933364868164 nb_pixel_total : 10474 time to create 1 rle with old method : 0.013970613479614258 length of segment : 178 time for calcul the mask position with numpy : 0.008940458297729492 nb_pixel_total : 6922 time to create 1 rle with old method : 0.01038980484008789 length of segment : 133 time for calcul the mask position with numpy : 0.057065486907958984 nb_pixel_total : 49236 time to create 1 rle with old method : 0.05586647987365723 length of segment : 144 time for calcul the mask position with numpy : 0.13059663772583008 nb_pixel_total : 62288 time to create 1 rle with old method : 0.06763815879821777 length of segment : 241 time for calcul the mask position with numpy : 0.008326292037963867 nb_pixel_total : 14342 time to create 1 rle with old method : 0.017253398895263672 length of segment : 172 time for calcul the mask position with numpy : 0.012414932250976562 nb_pixel_total : 12184 time to create 1 rle with old method : 0.0129852294921875 length of segment : 194 time for calcul the mask position with numpy : 0.1053466796875 nb_pixel_total : 81312 time to create 1 rle with old method : 0.0930938720703125 length of segment : 360 time for calcul the mask position with numpy : 0.0493011474609375 nb_pixel_total : 25952 time to create 1 rle with old method : 0.031310319900512695 length of segment : 165 time for calcul the mask position with numpy : 0.051827192306518555 nb_pixel_total : 24883 time to create 1 rle with old method : 0.032464027404785156 length of segment : 131 time for calcul the mask position with numpy : 0.00687098503112793 nb_pixel_total : 7188 time to create 1 rle with old method : 0.012743949890136719 length of segment : 61 time for calcul the mask position with numpy : 0.02545452117919922 nb_pixel_total : 41434 time to create 1 rle with old method : 0.04896664619445801 length of segment : 171 time for calcul the mask position with numpy : 0.16309571266174316 nb_pixel_total : 50657 time to create 1 rle with old method : 0.05824637413024902 length of segment : 288 time for calcul the mask position with numpy : 0.05907845497131348 nb_pixel_total : 32069 time to create 1 rle with old method : 0.03896498680114746 length of segment : 335 time for calcul the mask position with numpy : 0.1779038906097412 nb_pixel_total : 59489 time to create 1 rle with old method : 0.07321357727050781 length of segment : 324 time for calcul the mask position with numpy : 0.12720012664794922 nb_pixel_total : 26131 time to create 1 rle with old method : 0.03147387504577637 length of segment : 350 time for calcul the mask position with numpy : 0.0009698867797851562 nb_pixel_total : 1786 time to create 1 rle with old method : 0.0022864341735839844 length of segment : 48 time for calcul the mask position with numpy : 0.0011868476867675781 nb_pixel_total : 50511 time to create 1 rle with old method : 0.07158041000366211 length of segment : 282 time for calcul the mask position with numpy : 0.0012998580932617188 nb_pixel_total : 28497 time to create 1 rle with old method : 0.0301053524017334 length of segment : 176 time for calcul the mask position with numpy : 0.0750727653503418 nb_pixel_total : 45479 time to create 1 rle with old method : 0.07689476013183594 length of segment : 407 time for calcul the mask position with numpy : 0.0013833045959472656 nb_pixel_total : 27261 time to create 1 rle with old method : 0.032314300537109375 length of segment : 299 time for calcul the mask position with numpy : 0.1028299331665039 nb_pixel_total : 63405 time to create 1 rle with old method : 0.0780038833618164 length of segment : 273 time for calcul the mask position with numpy : 0.0026464462280273438 nb_pixel_total : 13870 time to create 1 rle with old method : 0.04171609878540039 length of segment : 182 time for calcul the mask position with numpy : 0.017895936965942383 nb_pixel_total : 2927 time to create 1 rle with old method : 0.006524801254272461 length of segment : 63 time for calcul the mask position with numpy : 0.18559002876281738 nb_pixel_total : 133889 time to create 1 rle with old method : 0.14613866806030273 length of segment : 442 time for calcul the mask position with numpy : 0.17371869087219238 nb_pixel_total : 41696 time to create 1 rle with old method : 0.04999899864196777 length of segment : 331 time for calcul the mask position with numpy : 0.4888286590576172 nb_pixel_total : 390761 time to create 1 rle with new method : 0.033135175704956055 length of segment : 775 time for calcul the mask position with numpy : 0.014893293380737305 nb_pixel_total : 7395 time to create 1 rle with old method : 0.010716915130615234 length of segment : 113 time for calcul the mask position with numpy : 0.051661014556884766 nb_pixel_total : 15737 time to create 1 rle with old method : 0.02222299575805664 length of segment : 148 time for calcul the mask position with numpy : 0.3405601978302002 nb_pixel_total : 140921 time to create 1 rle with old method : 0.14766311645507812 length of segment : 739 time for calcul the mask position with numpy : 0.35435056686401367 nb_pixel_total : 248082 time to create 1 rle with new method : 0.017515182495117188 length of segment : 944 time for calcul the mask position with numpy : 0.04189801216125488 nb_pixel_total : 4823 time to create 1 rle with old method : 0.008376598358154297 length of segment : 81 time for calcul the mask position with numpy : 0.20115280151367188 nb_pixel_total : 76345 time to create 1 rle with old method : 0.08500480651855469 length of segment : 464 time for calcul the mask position with numpy : 0.0007607936859130859 nb_pixel_total : 4300 time to create 1 rle with old method : 0.004979848861694336 length of segment : 90 time for calcul the mask position with numpy : 0.00011014938354492188 nb_pixel_total : 2764 time to create 1 rle with old method : 0.0032863616943359375 length of segment : 74 time for calcul the mask position with numpy : 0.38637256622314453 nb_pixel_total : 131626 time to create 1 rle with old method : 0.15283560752868652 length of segment : 531 time for calcul the mask position with numpy : 0.011166095733642578 nb_pixel_total : 17264 time to create 1 rle with old method : 0.022923707962036133 length of segment : 82 time for calcul the mask position with numpy : 0.20861554145812988 nb_pixel_total : 169899 time to create 1 rle with new method : 0.01307368278503418 length of segment : 536 time for calcul the mask position with numpy : 0.03756427764892578 nb_pixel_total : 25081 time to create 1 rle with old method : 0.030713558197021484 length of segment : 157 time for calcul the mask position with numpy : 0.11928939819335938 nb_pixel_total : 25515 time to create 1 rle with old method : 0.0406644344329834 length of segment : 306 time for calcul the mask position with numpy : 0.06413626670837402 nb_pixel_total : 18887 time to create 1 rle with old method : 0.024773120880126953 length of segment : 342 time for calcul the mask position with numpy : 0.08558058738708496 nb_pixel_total : 46174 time to create 1 rle with old method : 0.05562305450439453 length of segment : 206 time for calcul the mask position with numpy : 0.02041339874267578 nb_pixel_total : 2473 time to create 1 rle with old method : 0.005318641662597656 length of segment : 57 time for calcul the mask position with numpy : 0.00020194053649902344 nb_pixel_total : 6283 time to create 1 rle with old method : 0.007484912872314453 length of segment : 74 time for calcul the mask position with numpy : 0.04178738594055176 nb_pixel_total : 11595 time to create 1 rle with old method : 0.017781496047973633 length of segment : 138 time for calcul the mask position with numpy : 0.2446281909942627 nb_pixel_total : 225108 time to create 1 rle with new method : 0.030555248260498047 length of segment : 797 time for calcul the mask position with numpy : 0.023911237716674805 nb_pixel_total : 9789 time to create 1 rle with old method : 0.01597118377685547 length of segment : 193 time for calcul the mask position with numpy : 0.0027170181274414062 nb_pixel_total : 5097 time to create 1 rle with old method : 0.00601959228515625 length of segment : 87 time for calcul the mask position with numpy : 0.01115727424621582 nb_pixel_total : 3809 time to create 1 rle with old method : 0.005570173263549805 length of segment : 131 time for calcul the mask position with numpy : 9.465217590332031e-05 nb_pixel_total : 2050 time to create 1 rle with old method : 0.002680540084838867 length of segment : 48 time for calcul the mask position with numpy : 0.024697542190551758 nb_pixel_total : 43091 time to create 1 rle with old method : 0.0512540340423584 length of segment : 391 time for calcul the mask position with numpy : 0.001039266586303711 nb_pixel_total : 48328 time to create 1 rle with old method : 0.05505704879760742 length of segment : 212 time for calcul the mask position with numpy : 0.00020885467529296875 nb_pixel_total : 2609 time to create 1 rle with old method : 0.0031392574310302734 length of segment : 75 time for calcul the mask position with numpy : 0.0001614093780517578 nb_pixel_total : 4620 time to create 1 rle with old method : 0.006545543670654297 length of segment : 73 time for calcul the mask position with numpy : 0.0001919269561767578 nb_pixel_total : 6016 time to create 1 rle with old method : 0.00677037239074707 length of segment : 73 time for calcul the mask position with numpy : 0.0010142326354980469 nb_pixel_total : 3622 time to create 1 rle with old method : 0.004301548004150391 length of segment : 93 time for calcul the mask position with numpy : 0.009428262710571289 nb_pixel_total : 56824 time to create 1 rle with old method : 0.06482338905334473 length of segment : 380 time for calcul the mask position with numpy : 0.03761863708496094 nb_pixel_total : 58366 time to create 1 rle with old method : 0.07130956649780273 length of segment : 446 time for calcul the mask position with numpy : 0.051458120346069336 nb_pixel_total : 16689 time to create 1 rle with old method : 0.018185138702392578 length of segment : 179 time for calcul the mask position with numpy : 0.0016837120056152344 nb_pixel_total : 3917 time to create 1 rle with old method : 0.004464864730834961 length of segment : 52 time for calcul the mask position with numpy : 0.0052547454833984375 nb_pixel_total : 3576 time to create 1 rle with old method : 0.005408763885498047 length of segment : 54 time for calcul the mask position with numpy : 0.0015587806701660156 nb_pixel_total : 1377 time to create 1 rle with old method : 0.0016710758209228516 length of segment : 60 time for calcul the mask position with numpy : 0.06616449356079102 nb_pixel_total : 34170 time to create 1 rle with old method : 0.04099130630493164 length of segment : 255 time for calcul the mask position with numpy : 0.01873946189880371 nb_pixel_total : 23454 time to create 1 rle with old method : 0.031644582748413086 length of segment : 195 time for calcul the mask position with numpy : 0.014707326889038086 nb_pixel_total : 2940 time to create 1 rle with old method : 0.006084442138671875 length of segment : 54 time for calcul the mask position with numpy : 0.03647589683532715 nb_pixel_total : 21762 time to create 1 rle with old method : 0.02587604522705078 length of segment : 196 time for calcul the mask position with numpy : 0.09963321685791016 nb_pixel_total : 87949 time to create 1 rle with old method : 0.09399986267089844 length of segment : 348 time for calcul the mask position with numpy : 0.006356716156005859 nb_pixel_total : 8227 time to create 1 rle with old method : 0.011952877044677734 length of segment : 154 time for calcul the mask position with numpy : 0.005805492401123047 nb_pixel_total : 6009 time to create 1 rle with old method : 0.007579326629638672 length of segment : 122 time for calcul the mask position with numpy : 0.000316619873046875 nb_pixel_total : 15075 time to create 1 rle with old method : 0.016824722290039062 length of segment : 125 time for calcul the mask position with numpy : 0.03553199768066406 nb_pixel_total : 8851 time to create 1 rle with old method : 0.012088537216186523 length of segment : 155 time for calcul the mask position with numpy : 0.07365751266479492 nb_pixel_total : 39504 time to create 1 rle with old method : 0.04573369026184082 length of segment : 257 time for calcul the mask position with numpy : 0.034333229064941406 nb_pixel_total : 13199 time to create 1 rle with old method : 0.018688201904296875 length of segment : 135 time for calcul the mask position with numpy : 0.0007135868072509766 nb_pixel_total : 5527 time to create 1 rle with old method : 0.006509304046630859 length of segment : 104 time for calcul the mask position with numpy : 0.02870345115661621 nb_pixel_total : 11572 time to create 1 rle with old method : 0.0174252986907959 length of segment : 154 time for calcul the mask position with numpy : 0.09637713432312012 nb_pixel_total : 42629 time to create 1 rle with old method : 0.05074620246887207 length of segment : 321 time for calcul the mask position with numpy : 0.012219667434692383 nb_pixel_total : 68936 time to create 1 rle with old method : 0.0772714614868164 length of segment : 302 time for calcul the mask position with numpy : 0.07714724540710449 nb_pixel_total : 33265 time to create 1 rle with old method : 0.04071784019470215 length of segment : 230 time for calcul the mask position with numpy : 0.00461268424987793 nb_pixel_total : 1911 time to create 1 rle with old method : 0.002321004867553711 length of segment : 74 time for calcul the mask position with numpy : 0.002987384796142578 nb_pixel_total : 11683 time to create 1 rle with old method : 0.015837430953979492 length of segment : 138 time for calcul the mask position with numpy : 0.22950124740600586 nb_pixel_total : 250385 time to create 1 rle with new method : 0.014500856399536133 length of segment : 711 time for calcul the mask position with numpy : 0.16164159774780273 nb_pixel_total : 132382 time to create 1 rle with old method : 0.18490171432495117 length of segment : 482 time for calcul the mask position with numpy : 0.24485301971435547 nb_pixel_total : 337324 time to create 1 rle with new method : 0.02182769775390625 length of segment : 696 time for calcul the mask position with numpy : 0.379824161529541 nb_pixel_total : 328703 time to create 1 rle with new method : 0.025061368942260742 length of segment : 745 time for calcul the mask position with numpy : 0.2735416889190674 nb_pixel_total : 198024 time to create 1 rle with new method : 0.01463460922241211 length of segment : 817 time for calcul the mask position with numpy : 0.23958945274353027 nb_pixel_total : 448906 time to create 1 rle with new method : 0.02312779426574707 length of segment : 733 time for calcul the mask position with numpy : 0.006587028503417969 nb_pixel_total : 5064 time to create 1 rle with old method : 0.006143808364868164 length of segment : 73 time for calcul the mask position with numpy : 0.2563915252685547 nb_pixel_total : 356337 time to create 1 rle with new method : 0.02513265609741211 length of segment : 777 time for calcul the mask position with numpy : 0.060190677642822266 nb_pixel_total : 12412 time to create 1 rle with old method : 0.01935744285583496 length of segment : 287 time for calcul the mask position with numpy : 0.10252118110656738 nb_pixel_total : 56382 time to create 1 rle with old method : 0.06564903259277344 length of segment : 282 time for calcul the mask position with numpy : 0.06757783889770508 nb_pixel_total : 99363 time to create 1 rle with old method : 0.10908794403076172 length of segment : 365 time for calcul the mask position with numpy : 0.13005590438842773 nb_pixel_total : 190117 time to create 1 rle with new method : 0.01986098289489746 length of segment : 783 time for calcul the mask position with numpy : 0.0029022693634033203 nb_pixel_total : 26993 time to create 1 rle with old method : 0.02987504005432129 length of segment : 221 time for calcul the mask position with numpy : 0.003082275390625 nb_pixel_total : 22428 time to create 1 rle with old method : 0.024611949920654297 length of segment : 196 time for calcul the mask position with numpy : 0.026522397994995117 nb_pixel_total : 19850 time to create 1 rle with old method : 0.025244474411010742 length of segment : 163 time for calcul the mask position with numpy : 0.0019025802612304688 nb_pixel_total : 12028 time to create 1 rle with old method : 0.013521909713745117 length of segment : 122 time for calcul the mask position with numpy : 0.00403594970703125 nb_pixel_total : 2334 time to create 1 rle with old method : 0.0028450489044189453 length of segment : 45 time for calcul the mask position with numpy : 0.004929780960083008 nb_pixel_total : 13959 time to create 1 rle with old method : 0.018033504486083984 length of segment : 145 time for calcul the mask position with numpy : 0.004777431488037109 nb_pixel_total : 59026 time to create 1 rle with old method : 0.06405472755432129 length of segment : 184 time for calcul the mask position with numpy : 0.0002295970916748047 nb_pixel_total : 9246 time to create 1 rle with old method : 0.00974893569946289 length of segment : 131 time for calcul the mask position with numpy : 0.012801885604858398 nb_pixel_total : 8014 time to create 1 rle with old method : 0.011908531188964844 length of segment : 66 time for calcul the mask position with numpy : 0.00010728836059570312 nb_pixel_total : 3467 time to create 1 rle with old method : 0.004076242446899414 length of segment : 76 time for calcul the mask position with numpy : 0.03643512725830078 nb_pixel_total : 18542 time to create 1 rle with old method : 0.02414989471435547 length of segment : 170 time for calcul the mask position with numpy : 0.0006489753723144531 nb_pixel_total : 37345 time to create 1 rle with old method : 0.04285240173339844 length of segment : 202 time for calcul the mask position with numpy : 0.006227731704711914 nb_pixel_total : 92808 time to create 1 rle with old method : 0.1002340316772461 length of segment : 484 time for calcul the mask position with numpy : 0.005799770355224609 nb_pixel_total : 25018 time to create 1 rle with old method : 0.02936720848083496 length of segment : 263 time for calcul the mask position with numpy : 0.006433248519897461 nb_pixel_total : 50702 time to create 1 rle with old method : 0.057369232177734375 length of segment : 197 time for calcul the mask position with numpy : 0.010242223739624023 nb_pixel_total : 11737 time to create 1 rle with old method : 0.01719212532043457 length of segment : 120 time for calcul the mask position with numpy : 0.0003032684326171875 nb_pixel_total : 17036 time to create 1 rle with old method : 0.018834829330444336 length of segment : 127 time for calcul the mask position with numpy : 0.00029349327087402344 nb_pixel_total : 14157 time to create 1 rle with old method : 0.01676321029663086 length of segment : 108 time for calcul the mask position with numpy : 0.008039236068725586 nb_pixel_total : 43263 time to create 1 rle with old method : 0.04919719696044922 length of segment : 353 time for calcul the mask position with numpy : 0.003722667694091797 nb_pixel_total : 4343 time to create 1 rle with old method : 0.005486726760864258 length of segment : 104 time for calcul the mask position with numpy : 0.011665582656860352 nb_pixel_total : 11743 time to create 1 rle with old method : 0.016248226165771484 length of segment : 184 time for calcul the mask position with numpy : 0.06963515281677246 nb_pixel_total : 174069 time to create 1 rle with new method : 0.017962932586669922 length of segment : 592 time for calcul the mask position with numpy : 0.014653921127319336 nb_pixel_total : 41978 time to create 1 rle with old method : 0.050882816314697266 length of segment : 344 time for calcul the mask position with numpy : 0.008428096771240234 nb_pixel_total : 2453 time to create 1 rle with old method : 0.0036859512329101562 length of segment : 58 time for calcul the mask position with numpy : 0.07777047157287598 nb_pixel_total : 26533 time to create 1 rle with old method : 0.03948664665222168 length of segment : 123 time for calcul the mask position with numpy : 0.05204486846923828 nb_pixel_total : 15004 time to create 1 rle with old method : 0.020601749420166016 length of segment : 113 time for calcul the mask position with numpy : 0.3878591060638428 nb_pixel_total : 252571 time to create 1 rle with new method : 0.019251346588134766 length of segment : 728 time for calcul the mask position with numpy : 0.3391082286834717 nb_pixel_total : 207386 time to create 1 rle with new method : 0.019560813903808594 length of segment : 699 time for calcul the mask position with numpy : 0.023415565490722656 nb_pixel_total : 6477 time to create 1 rle with old method : 0.012408256530761719 length of segment : 93 time for calcul the mask position with numpy : 0.05532383918762207 nb_pixel_total : 81431 time to create 1 rle with old method : 0.09308505058288574 length of segment : 354 time for calcul the mask position with numpy : 0.06569528579711914 nb_pixel_total : 14777 time to create 1 rle with old method : 0.021250486373901367 length of segment : 266 time for calcul the mask position with numpy : 0.05436086654663086 nb_pixel_total : 22106 time to create 1 rle with old method : 0.02702188491821289 length of segment : 182 time for calcul the mask position with numpy : 0.019466638565063477 nb_pixel_total : 2831 time to create 1 rle with old method : 0.0058710575103759766 length of segment : 63 time for calcul the mask position with numpy : 0.14169931411743164 nb_pixel_total : 46451 time to create 1 rle with old method : 0.055771589279174805 length of segment : 353 time for calcul the mask position with numpy : 0.02927541732788086 nb_pixel_total : 4452 time to create 1 rle with old method : 0.016969680786132812 length of segment : 91 time for calcul the mask position with numpy : 0.03759646415710449 nb_pixel_total : 8158 time to create 1 rle with old method : 0.013099431991577148 length of segment : 135 time for calcul the mask position with numpy : 0.01313471794128418 nb_pixel_total : 1744 time to create 1 rle with old method : 0.003628253936767578 length of segment : 51 time for calcul the mask position with numpy : 0.005582094192504883 nb_pixel_total : 2424 time to create 1 rle with old method : 0.0029506683349609375 length of segment : 63 time for calcul the mask position with numpy : 0.08936786651611328 nb_pixel_total : 27494 time to create 1 rle with old method : 0.033495426177978516 length of segment : 201 time for calcul the mask position with numpy : 0.07999849319458008 nb_pixel_total : 17816 time to create 1 rle with old method : 0.024295806884765625 length of segment : 173 time for calcul the mask position with numpy : 0.23876619338989258 nb_pixel_total : 136638 time to create 1 rle with old method : 0.15972566604614258 length of segment : 524 time for calcul the mask position with numpy : 0.07364392280578613 nb_pixel_total : 28326 time to create 1 rle with old method : 0.032932281494140625 length of segment : 208 time for calcul the mask position with numpy : 0.16372203826904297 nb_pixel_total : 79320 time to create 1 rle with old method : 0.08652615547180176 length of segment : 550 time for calcul the mask position with numpy : 0.026612043380737305 nb_pixel_total : 8008 time to create 1 rle with old method : 0.013480901718139648 length of segment : 100 time for calcul the mask position with numpy : 0.0004181861877441406 nb_pixel_total : 16873 time to create 1 rle with old method : 0.018873214721679688 length of segment : 157 time for calcul the mask position with numpy : 0.017569780349731445 nb_pixel_total : 3716 time to create 1 rle with old method : 0.0074901580810546875 length of segment : 96 time for calcul the mask position with numpy : 0.0007047653198242188 nb_pixel_total : 6982 time to create 1 rle with old method : 0.007634639739990234 length of segment : 119 time for calcul the mask position with numpy : 0.10135316848754883 nb_pixel_total : 55924 time to create 1 rle with old method : 0.06638026237487793 length of segment : 286 time for calcul the mask position with numpy : 0.000335693359375 nb_pixel_total : 12520 time to create 1 rle with old method : 0.014356613159179688 length of segment : 122 time for calcul the mask position with numpy : 0.026958703994750977 nb_pixel_total : 29496 time to create 1 rle with old method : 0.036856651306152344 length of segment : 203 time for calcul the mask position with numpy : 0.43923330307006836 nb_pixel_total : 182392 time to create 1 rle with new method : 0.02210378646850586 length of segment : 662 time for calcul the mask position with numpy : 0.09135961532592773 nb_pixel_total : 81443 time to create 1 rle with old method : 0.08983016014099121 length of segment : 325 time for calcul the mask position with numpy : 0.02623724937438965 nb_pixel_total : 21304 time to create 1 rle with old method : 0.027187585830688477 length of segment : 286 time for calcul the mask position with numpy : 0.018726825714111328 nb_pixel_total : 20811 time to create 1 rle with old method : 0.028574466705322266 length of segment : 149 time for calcul the mask position with numpy : 0.0004916191101074219 nb_pixel_total : 21085 time to create 1 rle with old method : 0.02334904670715332 length of segment : 208 time for calcul the mask position with numpy : 0.010788679122924805 nb_pixel_total : 12819 time to create 1 rle with old method : 0.018970966339111328 length of segment : 149 time for calcul the mask position with numpy : 0.004707813262939453 nb_pixel_total : 7605 time to create 1 rle with old method : 0.013705730438232422 length of segment : 125 time for calcul the mask position with numpy : 0.061483144760131836 nb_pixel_total : 64400 time to create 1 rle with old method : 0.0819234848022461 length of segment : 261 time for calcul the mask position with numpy : 0.0005042552947998047 nb_pixel_total : 21937 time to create 1 rle with old method : 0.023606061935424805 length of segment : 164 time for calcul the mask position with numpy : 0.027171611785888672 nb_pixel_total : 40139 time to create 1 rle with old method : 0.04734969139099121 length of segment : 183 time for calcul the mask position with numpy : 0.00010275840759277344 nb_pixel_total : 2245 time to create 1 rle with old method : 0.0027282238006591797 length of segment : 41 time for calcul the mask position with numpy : 0.012538909912109375 nb_pixel_total : 10693 time to create 1 rle with old method : 0.016856908798217773 length of segment : 110 time for calcul the mask position with numpy : 0.014796733856201172 nb_pixel_total : 10330 time to create 1 rle with old method : 0.015729665756225586 length of segment : 234 time for calcul the mask position with numpy : 0.0059757232666015625 nb_pixel_total : 4711 time to create 1 rle with old method : 0.0057871341705322266 length of segment : 116 time for calcul the mask position with numpy : 0.08108687400817871 nb_pixel_total : 31416 time to create 1 rle with old method : 0.03668212890625 length of segment : 238 time for calcul the mask position with numpy : 0.030373811721801758 nb_pixel_total : 26836 time to create 1 rle with old method : 0.07223320007324219 length of segment : 192 time for calcul the mask position with numpy : 0.022860288619995117 nb_pixel_total : 5301 time to create 1 rle with old method : 0.00811004638671875 length of segment : 124 time for calcul the mask position with numpy : 0.032917022705078125 nb_pixel_total : 7030 time to create 1 rle with old method : 0.00921320915222168 length of segment : 123 time for calcul the mask position with numpy : 0.0002956390380859375 nb_pixel_total : 8107 time to create 1 rle with old method : 0.011044979095458984 length of segment : 87 time for calcul the mask position with numpy : 0.0009436607360839844 nb_pixel_total : 15443 time to create 1 rle with old method : 0.017954349517822266 length of segment : 167 time for calcul the mask position with numpy : 0.010703563690185547 nb_pixel_total : 19902 time to create 1 rle with old method : 0.025803089141845703 length of segment : 180 time for calcul the mask position with numpy : 0.20093059539794922 nb_pixel_total : 73305 time to create 1 rle with old method : 0.08440947532653809 length of segment : 246 time for calcul the mask position with numpy : 0.6620993614196777 nb_pixel_total : 228164 time to create 1 rle with new method : 0.023768186569213867 length of segment : 597 time for calcul the mask position with numpy : 0.2072463035583496 nb_pixel_total : 57044 time to create 1 rle with old method : 0.06565332412719727 length of segment : 245 time for calcul the mask position with numpy : 0.18402910232543945 nb_pixel_total : 47070 time to create 1 rle with old method : 0.05445122718811035 length of segment : 274 time for calcul the mask position with numpy : 0.26584529876708984 nb_pixel_total : 86797 time to create 1 rle with old method : 0.09517455101013184 length of segment : 356 time for calcul the mask position with numpy : 0.00908350944519043 nb_pixel_total : 15328 time to create 1 rle with old method : 0.01990509033203125 length of segment : 132 time for calcul the mask position with numpy : 0.06328105926513672 nb_pixel_total : 20804 time to create 1 rle with old method : 0.026784896850585938 length of segment : 178 time for calcul the mask position with numpy : 0.030375003814697266 nb_pixel_total : 22972 time to create 1 rle with old method : 0.02890944480895996 length of segment : 359 time for calcul the mask position with numpy : 0.3694312572479248 nb_pixel_total : 164338 time to create 1 rle with new method : 0.011178255081176758 length of segment : 371 time for calcul the mask position with numpy : 0.033885955810546875 nb_pixel_total : 8578 time to create 1 rle with old method : 0.014073610305786133 length of segment : 70 time for calcul the mask position with numpy : 0.02258920669555664 nb_pixel_total : 10549 time to create 1 rle with old method : 0.01598644256591797 length of segment : 138 time for calcul the mask position with numpy : 0.0450284481048584 nb_pixel_total : 8362 time to create 1 rle with old method : 0.015112161636352539 length of segment : 122 time for calcul the mask position with numpy : 0.037690162658691406 nb_pixel_total : 7344 time to create 1 rle with old method : 0.011530160903930664 length of segment : 97 time for calcul the mask position with numpy : 0.1485764980316162 nb_pixel_total : 41284 time to create 1 rle with old method : 0.045771121978759766 length of segment : 479 time for calcul the mask position with numpy : 0.07001972198486328 nb_pixel_total : 31073 time to create 1 rle with old method : 0.03900647163391113 length of segment : 281 time for calcul the mask position with numpy : 0.0014376640319824219 nb_pixel_total : 13969 time to create 1 rle with old method : 0.015636444091796875 length of segment : 208 time for calcul the mask position with numpy : 0.022512435913085938 nb_pixel_total : 12754 time to create 1 rle with old method : 0.01853775978088379 length of segment : 230 time for calcul the mask position with numpy : 0.02559065818786621 nb_pixel_total : 4508 time to create 1 rle with old method : 0.00816655158996582 length of segment : 78 time for calcul the mask position with numpy : 0.22551536560058594 nb_pixel_total : 89967 time to create 1 rle with old method : 0.12625360488891602 length of segment : 545 time for calcul the mask position with numpy : 0.06123709678649902 nb_pixel_total : 22320 time to create 1 rle with old method : 0.03123784065246582 length of segment : 144 time for calcul the mask position with numpy : 0.03489255905151367 nb_pixel_total : 75204 time to create 1 rle with old method : 0.08334922790527344 length of segment : 262 time for calcul the mask position with numpy : 0.09058046340942383 nb_pixel_total : 63716 time to create 1 rle with old method : 0.07267093658447266 length of segment : 299 time for calcul the mask position with numpy : 0.12404847145080566 nb_pixel_total : 23697 time to create 1 rle with old method : 0.03027653694152832 length of segment : 174 time for calcul the mask position with numpy : 0.07489657402038574 nb_pixel_total : 20754 time to create 1 rle with old method : 0.025274991989135742 length of segment : 160 time for calcul the mask position with numpy : 0.016184091567993164 nb_pixel_total : 13812 time to create 1 rle with old method : 0.018285274505615234 length of segment : 108 time for calcul the mask position with numpy : 0.06613922119140625 nb_pixel_total : 23011 time to create 1 rle with old method : 0.030284404754638672 length of segment : 186 time for calcul the mask position with numpy : 0.02704763412475586 nb_pixel_total : 18874 time to create 1 rle with old method : 0.027185440063476562 length of segment : 293 time for calcul the mask position with numpy : 0.15143036842346191 nb_pixel_total : 36099 time to create 1 rle with old method : 0.042078495025634766 length of segment : 360 time for calcul the mask position with numpy : 0.019436359405517578 nb_pixel_total : 30153 time to create 1 rle with old method : 0.04012584686279297 length of segment : 364 time for calcul the mask position with numpy : 0.00432896614074707 nb_pixel_total : 18298 time to create 1 rle with old method : 0.021805763244628906 length of segment : 195 time for calcul the mask position with numpy : 0.1270430088043213 nb_pixel_total : 44982 time to create 1 rle with old method : 0.052874088287353516 length of segment : 229 time for calcul the mask position with numpy : 0.06883955001831055 nb_pixel_total : 12628 time to create 1 rle with old method : 0.021747589111328125 length of segment : 181 time for calcul the mask position with numpy : 0.0002613067626953125 nb_pixel_total : 12882 time to create 1 rle with old method : 0.013646364212036133 length of segment : 118 time for calcul the mask position with numpy : 0.08492517471313477 nb_pixel_total : 21608 time to create 1 rle with old method : 0.021936655044555664 length of segment : 202 time for calcul the mask position with numpy : 0.0006601810455322266 nb_pixel_total : 31056 time to create 1 rle with old method : 0.031847476959228516 length of segment : 196 time for calcul the mask position with numpy : 0.008636236190795898 nb_pixel_total : 36137 time to create 1 rle with old method : 0.0543360710144043 length of segment : 152 time for calcul the mask position with numpy : 0.13447332382202148 nb_pixel_total : 27616 time to create 1 rle with old method : 0.03722667694091797 length of segment : 321 time for calcul the mask position with numpy : 0.03658747673034668 nb_pixel_total : 16707 time to create 1 rle with old method : 0.023468971252441406 length of segment : 176 time for calcul the mask position with numpy : 0.04949808120727539 nb_pixel_total : 12198 time to create 1 rle with old method : 0.018213987350463867 length of segment : 163 time for calcul the mask position with numpy : 0.0022292137145996094 nb_pixel_total : 18142 time to create 1 rle with old method : 0.02047419548034668 length of segment : 191 time for calcul the mask position with numpy : 0.0688626766204834 nb_pixel_total : 18125 time to create 1 rle with old method : 0.025198698043823242 length of segment : 171 time for calcul the mask position with numpy : 0.05910372734069824 nb_pixel_total : 11675 time to create 1 rle with old method : 0.01737666130065918 length of segment : 175 time for calcul the mask position with numpy : 0.07375717163085938 nb_pixel_total : 26276 time to create 1 rle with old method : 0.03113245964050293 length of segment : 195 time for calcul the mask position with numpy : 0.06250357627868652 nb_pixel_total : 27280 time to create 1 rle with old method : 0.031130075454711914 length of segment : 217 time for calcul the mask position with numpy : 0.1083688735961914 nb_pixel_total : 38731 time to create 1 rle with old method : 0.04546380043029785 length of segment : 379 time for calcul the mask position with numpy : 0.10743856430053711 nb_pixel_total : 82711 time to create 1 rle with old method : 0.09322524070739746 length of segment : 364 time for calcul the mask position with numpy : 0.18848776817321777 nb_pixel_total : 90895 time to create 1 rle with old method : 0.10119962692260742 length of segment : 354 time for calcul the mask position with numpy : 0.031336069107055664 nb_pixel_total : 32812 time to create 1 rle with old method : 0.04239678382873535 length of segment : 376 time for calcul the mask position with numpy : 0.6013500690460205 nb_pixel_total : 327878 time to create 1 rle with new method : 0.021056413650512695 length of segment : 552 time for calcul the mask position with numpy : 0.3107268810272217 nb_pixel_total : 99399 time to create 1 rle with old method : 0.1120612621307373 length of segment : 438 time for calcul the mask position with numpy : 0.01786518096923828 nb_pixel_total : 3411 time to create 1 rle with old method : 0.003880739212036133 length of segment : 69 time for calcul the mask position with numpy : 0.11470246315002441 nb_pixel_total : 22831 time to create 1 rle with old method : 0.024606704711914062 length of segment : 207 time for calcul the mask position with numpy : 0.02147960662841797 nb_pixel_total : 3902 time to create 1 rle with old method : 0.007166624069213867 length of segment : 66 time for calcul the mask position with numpy : 0.014611959457397461 nb_pixel_total : 2449 time to create 1 rle with old method : 0.004981279373168945 length of segment : 58 time for calcul the mask position with numpy : 0.4603540897369385 nb_pixel_total : 142148 time to create 1 rle with old method : 0.15205860137939453 length of segment : 603 time for calcul the mask position with numpy : 0.026947736740112305 nb_pixel_total : 24776 time to create 1 rle with old method : 0.03197336196899414 length of segment : 164 time for calcul the mask position with numpy : 0.06235480308532715 nb_pixel_total : 38430 time to create 1 rle with old method : 0.043405771255493164 length of segment : 293 time for calcul the mask position with numpy : 0.12171339988708496 nb_pixel_total : 44201 time to create 1 rle with old method : 0.04974675178527832 length of segment : 432 time for calcul the mask position with numpy : 0.3144392967224121 nb_pixel_total : 79684 time to create 1 rle with old method : 0.08999109268188477 length of segment : 449 time for calcul the mask position with numpy : 0.05925798416137695 nb_pixel_total : 14115 time to create 1 rle with old method : 0.02290797233581543 length of segment : 152 time for calcul the mask position with numpy : 0.0233151912689209 nb_pixel_total : 6434 time to create 1 rle with old method : 0.010997295379638672 length of segment : 110 time for calcul the mask position with numpy : 0.010781049728393555 nb_pixel_total : 1529 time to create 1 rle with old method : 0.003319263458251953 length of segment : 52 time for calcul the mask position with numpy : 0.011210918426513672 nb_pixel_total : 5991 time to create 1 rle with old method : 0.012037038803100586 length of segment : 103 time for calcul the mask position with numpy : 0.06174516677856445 nb_pixel_total : 14381 time to create 1 rle with old method : 0.020377397537231445 length of segment : 157 time for calcul the mask position with numpy : 0.28790736198425293 nb_pixel_total : 47484 time to create 1 rle with old method : 0.05454850196838379 length of segment : 428 time for calcul the mask position with numpy : 0.10178732872009277 nb_pixel_total : 28632 time to create 1 rle with old method : 0.035500526428222656 length of segment : 244 time for calcul the mask position with numpy : 0.07386016845703125 nb_pixel_total : 36577 time to create 1 rle with old method : 0.055212974548339844 length of segment : 134 time for calcul the mask position with numpy : 0.018023252487182617 nb_pixel_total : 4279 time to create 1 rle with old method : 0.008733749389648438 length of segment : 63 time for calcul the mask position with numpy : 0.04326343536376953 nb_pixel_total : 5015 time to create 1 rle with old method : 0.009185552597045898 length of segment : 97 time for calcul the mask position with numpy : 0.11394357681274414 nb_pixel_total : 83304 time to create 1 rle with old method : 0.09879541397094727 length of segment : 297 time for calcul the mask position with numpy : 0.006806135177612305 nb_pixel_total : 2761 time to create 1 rle with old method : 0.0032515525817871094 length of segment : 43 time for calcul the mask position with numpy : 0.13122272491455078 nb_pixel_total : 45497 time to create 1 rle with old method : 0.05174994468688965 length of segment : 449 time for calcul the mask position with numpy : 0.01153707504272461 nb_pixel_total : 1912 time to create 1 rle with old method : 0.003871917724609375 length of segment : 43 time for calcul the mask position with numpy : 0.10593605041503906 nb_pixel_total : 28320 time to create 1 rle with old method : 0.03452873229980469 length of segment : 234 time for calcul the mask position with numpy : 0.27192115783691406 nb_pixel_total : 73559 time to create 1 rle with old method : 0.0854945182800293 length of segment : 314 time for calcul the mask position with numpy : 0.007179737091064453 nb_pixel_total : 18519 time to create 1 rle with old method : 0.02190852165222168 length of segment : 288 time for calcul the mask position with numpy : 0.017477989196777344 nb_pixel_total : 2065 time to create 1 rle with old method : 0.004469633102416992 length of segment : 47 time for calcul the mask position with numpy : 0.04144906997680664 nb_pixel_total : 4744 time to create 1 rle with old method : 0.008565187454223633 length of segment : 100 time for calcul the mask position with numpy : 0.18858861923217773 nb_pixel_total : 93957 time to create 1 rle with old method : 0.1000666618347168 length of segment : 641 time for calcul the mask position with numpy : 0.014234542846679688 nb_pixel_total : 10655 time to create 1 rle with old method : 0.016502857208251953 length of segment : 123 time for calcul the mask position with numpy : 0.0014514923095703125 nb_pixel_total : 3810 time to create 1 rle with old method : 0.0053708553314208984 length of segment : 100 time for calcul the mask position with numpy : 0.03106212615966797 nb_pixel_total : 10690 time to create 1 rle with old method : 0.015894174575805664 length of segment : 85 time for calcul the mask position with numpy : 0.0064067840576171875 nb_pixel_total : 7098 time to create 1 rle with old method : 0.010996341705322266 length of segment : 55 time for calcul the mask position with numpy : 0.044672489166259766 nb_pixel_total : 74389 time to create 1 rle with old method : 0.08122587203979492 length of segment : 373 time for calcul the mask position with numpy : 0.10622882843017578 nb_pixel_total : 43369 time to create 1 rle with old method : 0.04844856262207031 length of segment : 297 time for calcul the mask position with numpy : 0.04368710517883301 nb_pixel_total : 35820 time to create 1 rle with old method : 0.04166364669799805 length of segment : 290 time for calcul the mask position with numpy : 0.023691654205322266 nb_pixel_total : 25088 time to create 1 rle with old method : 0.03922438621520996 length of segment : 228 time for calcul the mask position with numpy : 0.046021223068237305 nb_pixel_total : 22527 time to create 1 rle with old method : 0.028411149978637695 length of segment : 158 time for calcul the mask position with numpy : 0.030187129974365234 nb_pixel_total : 16997 time to create 1 rle with old method : 0.02252030372619629 length of segment : 176 time for calcul the mask position with numpy : 0.02037525177001953 nb_pixel_total : 15043 time to create 1 rle with old method : 0.022490978240966797 length of segment : 160 time for calcul the mask position with numpy : 0.11872172355651855 nb_pixel_total : 54131 time to create 1 rle with old method : 0.06070995330810547 length of segment : 311 time for calcul the mask position with numpy : 0.0768594741821289 nb_pixel_total : 15759 time to create 1 rle with old method : 0.01838994026184082 length of segment : 235 time for calcul the mask position with numpy : 0.01063680648803711 nb_pixel_total : 23490 time to create 1 rle with old method : 0.0289306640625 length of segment : 201 time for calcul the mask position with numpy : 0.1507880687713623 nb_pixel_total : 27952 time to create 1 rle with old method : 0.03517007827758789 length of segment : 434 time for calcul the mask position with numpy : 0.017993450164794922 nb_pixel_total : 8221 time to create 1 rle with old method : 0.014224767684936523 length of segment : 138 time for calcul the mask position with numpy : 0.025732994079589844 nb_pixel_total : 16911 time to create 1 rle with old method : 0.022649765014648438 length of segment : 164 time for calcul the mask position with numpy : 0.24245882034301758 nb_pixel_total : 102182 time to create 1 rle with old method : 0.10881280899047852 length of segment : 391 time for calcul the mask position with numpy : 0.17453265190124512 nb_pixel_total : 205026 time to create 1 rle with new method : 0.020390987396240234 length of segment : 432 time for calcul the mask position with numpy : 0.0006136894226074219 nb_pixel_total : 3557 time to create 1 rle with old method : 0.004400014877319336 length of segment : 44 time for calcul the mask position with numpy : 0.0015740394592285156 nb_pixel_total : 5666 time to create 1 rle with old method : 0.006384849548339844 length of segment : 84 time for calcul the mask position with numpy : 0.05855989456176758 nb_pixel_total : 19599 time to create 1 rle with old method : 0.026963472366333008 length of segment : 125 time for calcul the mask position with numpy : 0.07982301712036133 nb_pixel_total : 20453 time to create 1 rle with old method : 0.04001259803771973 length of segment : 157 time for calcul the mask position with numpy : 0.04730796813964844 nb_pixel_total : 9215 time to create 1 rle with old method : 0.014701128005981445 length of segment : 111 time for calcul the mask position with numpy : 0.08750128746032715 nb_pixel_total : 28708 time to create 1 rle with old method : 0.04973196983337402 length of segment : 181 time for calcul the mask position with numpy : 0.07354211807250977 nb_pixel_total : 10464 time to create 1 rle with old method : 0.018857479095458984 length of segment : 148 time for calcul the mask position with numpy : 0.3415226936340332 nb_pixel_total : 136917 time to create 1 rle with old method : 0.1602344512939453 length of segment : 414 time for calcul the mask position with numpy : 0.08230924606323242 nb_pixel_total : 23074 time to create 1 rle with old method : 0.030309677124023438 length of segment : 174 time for calcul the mask position with numpy : 0.021686077117919922 nb_pixel_total : 2618 time to create 1 rle with old method : 0.005372285842895508 length of segment : 72 time for calcul the mask position with numpy : 0.15316057205200195 nb_pixel_total : 26034 time to create 1 rle with old method : 0.03154611587524414 length of segment : 236 time for calcul the mask position with numpy : 0.05085277557373047 nb_pixel_total : 26804 time to create 1 rle with old method : 0.03546905517578125 length of segment : 137 time for calcul the mask position with numpy : 0.09432005882263184 nb_pixel_total : 29727 time to create 1 rle with old method : 0.03575849533081055 length of segment : 186 time for calcul the mask position with numpy : 0.04076099395751953 nb_pixel_total : 26447 time to create 1 rle with old method : 0.034059762954711914 length of segment : 207 time for calcul the mask position with numpy : 0.037343502044677734 nb_pixel_total : 3855 time to create 1 rle with old method : 0.008027315139770508 length of segment : 106 time for calcul the mask position with numpy : 0.03965187072753906 nb_pixel_total : 10193 time to create 1 rle with old method : 0.01674485206604004 length of segment : 71 time for calcul the mask position with numpy : 0.04294943809509277 nb_pixel_total : 13719 time to create 1 rle with old method : 0.020429134368896484 length of segment : 168 time for calcul the mask position with numpy : 0.09362030029296875 nb_pixel_total : 33327 time to create 1 rle with old method : 0.04199075698852539 length of segment : 145 time for calcul the mask position with numpy : 0.04244589805603027 nb_pixel_total : 13550 time to create 1 rle with old method : 0.018504619598388672 length of segment : 154 time for calcul the mask position with numpy : 0.022039175033569336 nb_pixel_total : 6669 time to create 1 rle with old method : 0.011910200119018555 length of segment : 77 time for calcul the mask position with numpy : 0.2825887203216553 nb_pixel_total : 95732 time to create 1 rle with old method : 0.10420584678649902 length of segment : 332 time for calcul the mask position with numpy : 0.08683204650878906 nb_pixel_total : 25096 time to create 1 rle with old method : 0.030768871307373047 length of segment : 274 time for calcul the mask position with numpy : 0.058785200119018555 nb_pixel_total : 6231 time to create 1 rle with old method : 0.009454011917114258 length of segment : 96 time for calcul the mask position with numpy : 0.09843277931213379 nb_pixel_total : 23919 time to create 1 rle with old method : 0.029526948928833008 length of segment : 272 time for calcul the mask position with numpy : 0.08195018768310547 nb_pixel_total : 27940 time to create 1 rle with old method : 0.03439927101135254 length of segment : 245 time for calcul the mask position with numpy : 0.009559869766235352 nb_pixel_total : 9396 time to create 1 rle with old method : 0.015066146850585938 length of segment : 109 time for calcul the mask position with numpy : 0.20492076873779297 nb_pixel_total : 58187 time to create 1 rle with old method : 0.06396961212158203 length of segment : 371 time for calcul the mask position with numpy : 0.06049680709838867 nb_pixel_total : 15956 time to create 1 rle with old method : 0.020962953567504883 length of segment : 210 time for calcul the mask position with numpy : 0.16811871528625488 nb_pixel_total : 76170 time to create 1 rle with old method : 0.08519291877746582 length of segment : 347 time for calcul the mask position with numpy : 0.10936164855957031 nb_pixel_total : 71551 time to create 1 rle with old method : 0.07778429985046387 length of segment : 260 time for calcul the mask position with numpy : 0.03362298011779785 nb_pixel_total : 9960 time to create 1 rle with old method : 0.01495671272277832 length of segment : 151 time for calcul the mask position with numpy : 0.027505159378051758 nb_pixel_total : 4887 time to create 1 rle with old method : 0.008706092834472656 length of segment : 80 time for calcul the mask position with numpy : 0.07427525520324707 nb_pixel_total : 18144 time to create 1 rle with old method : 0.023128032684326172 length of segment : 187 time for calcul the mask position with numpy : 0.10650396347045898 nb_pixel_total : 31092 time to create 1 rle with old method : 0.03883671760559082 length of segment : 270 time for calcul the mask position with numpy : 0.002201080322265625 nb_pixel_total : 6863 time to create 1 rle with old method : 0.00747370719909668 length of segment : 98 time for calcul the mask position with numpy : 0.0028295516967773438 nb_pixel_total : 4201 time to create 1 rle with old method : 0.0046176910400390625 length of segment : 57 time for calcul the mask position with numpy : 0.043314218521118164 nb_pixel_total : 24127 time to create 1 rle with old method : 0.02890157699584961 length of segment : 211 time for calcul the mask position with numpy : 0.009828567504882812 nb_pixel_total : 1930 time to create 1 rle with old method : 0.0028302669525146484 length of segment : 52 time for calcul the mask position with numpy : 0.12864208221435547 nb_pixel_total : 44844 time to create 1 rle with old method : 0.05106067657470703 length of segment : 188 time for calcul the mask position with numpy : 0.003286600112915039 nb_pixel_total : 9100 time to create 1 rle with old method : 0.012896537780761719 length of segment : 81 time for calcul the mask position with numpy : 0.03105759620666504 nb_pixel_total : 9788 time to create 1 rle with old method : 0.01498866081237793 length of segment : 123 time for calcul the mask position with numpy : 0.011500120162963867 nb_pixel_total : 2311 time to create 1 rle with old method : 0.0047016143798828125 length of segment : 69 time for calcul the mask position with numpy : 0.38587355613708496 nb_pixel_total : 49341 time to create 1 rle with old method : 0.05417180061340332 length of segment : 595 time for calcul the mask position with numpy : 0.008843421936035156 nb_pixel_total : 3175 time to create 1 rle with old method : 0.004629611968994141 length of segment : 119 time for calcul the mask position with numpy : 0.08603668212890625 nb_pixel_total : 42364 time to create 1 rle with old method : 0.04690694808959961 length of segment : 320 time for calcul the mask position with numpy : 0.04835391044616699 nb_pixel_total : 13538 time to create 1 rle with old method : 0.019861459732055664 length of segment : 122 time for calcul the mask position with numpy : 0.05130338668823242 nb_pixel_total : 5027 time to create 1 rle with old method : 0.008895635604858398 length of segment : 114 time for calcul the mask position with numpy : 0.007251739501953125 nb_pixel_total : 5399 time to create 1 rle with old method : 0.006747245788574219 length of segment : 115 time for calcul the mask position with numpy : 0.05915999412536621 nb_pixel_total : 25506 time to create 1 rle with old method : 0.03229641914367676 length of segment : 196 time for calcul the mask position with numpy : 0.005597591400146484 nb_pixel_total : 11760 time to create 1 rle with old method : 0.015069246292114258 length of segment : 256 time for calcul the mask position with numpy : 0.0772860050201416 nb_pixel_total : 15876 time to create 1 rle with old method : 0.02138996124267578 length of segment : 188 time for calcul the mask position with numpy : 0.07633161544799805 nb_pixel_total : 54492 time to create 1 rle with old method : 0.058953046798706055 length of segment : 435 time for calcul the mask position with numpy : 0.11451601982116699 nb_pixel_total : 30335 time to create 1 rle with old method : 0.035565853118896484 length of segment : 327 time for calcul the mask position with numpy : 0.0034987926483154297 nb_pixel_total : 1708 time to create 1 rle with old method : 0.0022916793823242188 length of segment : 40 time for calcul the mask position with numpy : 0.08933782577514648 nb_pixel_total : 28741 time to create 1 rle with old method : 0.03887343406677246 length of segment : 176 time for calcul the mask position with numpy : 0.12152886390686035 nb_pixel_total : 72774 time to create 1 rle with old method : 0.08195352554321289 length of segment : 251 time for calcul the mask position with numpy : 0.09204959869384766 nb_pixel_total : 32057 time to create 1 rle with old method : 0.037653446197509766 length of segment : 163 time for calcul the mask position with numpy : 0.04611492156982422 nb_pixel_total : 14769 time to create 1 rle with old method : 0.0199737548828125 length of segment : 136 time for calcul the mask position with numpy : 0.11442875862121582 nb_pixel_total : 44272 time to create 1 rle with old method : 0.09778618812561035 length of segment : 259 time for calcul the mask position with numpy : 0.04362654685974121 nb_pixel_total : 7287 time to create 1 rle with old method : 0.008538961410522461 length of segment : 103 time for calcul the mask position with numpy : 0.09110426902770996 nb_pixel_total : 31075 time to create 1 rle with old method : 0.034438133239746094 length of segment : 195 time for calcul the mask position with numpy : 0.05076956748962402 nb_pixel_total : 9224 time to create 1 rle with old method : 0.010505914688110352 length of segment : 129 time for calcul the mask position with numpy : 0.30563807487487793 nb_pixel_total : 88899 time to create 1 rle with old method : 0.10013675689697266 length of segment : 371 time for calcul the mask position with numpy : 0.03917217254638672 nb_pixel_total : 11339 time to create 1 rle with old method : 0.016319990158081055 length of segment : 120 time for calcul the mask position with numpy : 0.022783994674682617 nb_pixel_total : 5433 time to create 1 rle with old method : 0.010719060897827148 length of segment : 71 time for calcul the mask position with numpy : 0.08202743530273438 nb_pixel_total : 24993 time to create 1 rle with old method : 0.03140592575073242 length of segment : 190 time for calcul the mask position with numpy : 0.480365514755249 nb_pixel_total : 186506 time to create 1 rle with new method : 0.014638662338256836 length of segment : 620 time for calcul the mask position with numpy : 0.37523508071899414 nb_pixel_total : 200096 time to create 1 rle with new method : 0.018424272537231445 length of segment : 532 time for calcul the mask position with numpy : 0.07100749015808105 nb_pixel_total : 29026 time to create 1 rle with old method : 0.03735494613647461 length of segment : 213 time for calcul the mask position with numpy : 0.10768485069274902 nb_pixel_total : 24272 time to create 1 rle with old method : 0.030452728271484375 length of segment : 337 time for calcul the mask position with numpy : 0.02467203140258789 nb_pixel_total : 7596 time to create 1 rle with old method : 0.013417482376098633 length of segment : 120 time for calcul the mask position with numpy : 0.08358526229858398 nb_pixel_total : 20683 time to create 1 rle with old method : 0.027014732360839844 length of segment : 184 time for calcul the mask position with numpy : 0.017412662506103516 nb_pixel_total : 4353 time to create 1 rle with old method : 0.008936882019042969 length of segment : 94 time for calcul the mask position with numpy : 0.044432878494262695 nb_pixel_total : 40359 time to create 1 rle with old method : 0.04955601692199707 length of segment : 312 time for calcul the mask position with numpy : 0.0582427978515625 nb_pixel_total : 14563 time to create 1 rle with old method : 0.018115758895874023 length of segment : 148 time for calcul the mask position with numpy : 0.09710478782653809 nb_pixel_total : 35817 time to create 1 rle with old method : 0.03802680969238281 length of segment : 159 time for calcul the mask position with numpy : 0.07901287078857422 nb_pixel_total : 41451 time to create 1 rle with old method : 0.04810023307800293 length of segment : 391 time for calcul the mask position with numpy : 0.0659022331237793 nb_pixel_total : 13271 time to create 1 rle with old method : 0.01820826530456543 length of segment : 152 time for calcul the mask position with numpy : 0.02162957191467285 nb_pixel_total : 6482 time to create 1 rle with old method : 0.011644124984741211 length of segment : 69 time for calcul the mask position with numpy : 0.025927066802978516 nb_pixel_total : 3257 time to create 1 rle with old method : 0.007608890533447266 length of segment : 58 time for calcul the mask position with numpy : 0.02379322052001953 nb_pixel_total : 2569 time to create 1 rle with old method : 0.00505828857421875 length of segment : 61 time for calcul the mask position with numpy : 0.05156421661376953 nb_pixel_total : 19704 time to create 1 rle with old method : 0.03680777549743652 length of segment : 92 time for calcul the mask position with numpy : 0.09483695030212402 nb_pixel_total : 40964 time to create 1 rle with old method : 0.07703280448913574 length of segment : 230 time for calcul the mask position with numpy : 0.1261122226715088 nb_pixel_total : 53309 time to create 1 rle with old method : 0.06995224952697754 length of segment : 281 time for calcul the mask position with numpy : 0.02434372901916504 nb_pixel_total : 4489 time to create 1 rle with old method : 0.0077190399169921875 length of segment : 65 time for calcul the mask position with numpy : 0.012890815734863281 nb_pixel_total : 13306 time to create 1 rle with old method : 0.026445865631103516 length of segment : 169 time for calcul the mask position with numpy : 0.015204668045043945 nb_pixel_total : 1926 time to create 1 rle with old method : 0.004197597503662109 length of segment : 54 time for calcul the mask position with numpy : 0.032202959060668945 nb_pixel_total : 5080 time to create 1 rle with old method : 0.009641885757446289 length of segment : 87 time for calcul the mask position with numpy : 0.02590465545654297 nb_pixel_total : 2024 time to create 1 rle with old method : 0.004385709762573242 length of segment : 60 time for calcul the mask position with numpy : 0.07729077339172363 nb_pixel_total : 16576 time to create 1 rle with old method : 0.023012876510620117 length of segment : 178 time for calcul the mask position with numpy : 0.02302265167236328 nb_pixel_total : 43143 time to create 1 rle with old method : 0.05023837089538574 length of segment : 482 time for calcul the mask position with numpy : 0.09584450721740723 nb_pixel_total : 26306 time to create 1 rle with old method : 0.040962934494018555 length of segment : 193 time for calcul the mask position with numpy : 0.019984960556030273 nb_pixel_total : 4226 time to create 1 rle with old method : 0.010117769241333008 length of segment : 64 time for calcul the mask position with numpy : 0.09327578544616699 nb_pixel_total : 50634 time to create 1 rle with old method : 0.07998847961425781 length of segment : 353 time for calcul the mask position with numpy : 0.04197072982788086 nb_pixel_total : 24035 time to create 1 rle with old method : 0.03012394905090332 length of segment : 191 time for calcul the mask position with numpy : 0.013958215713500977 nb_pixel_total : 3844 time to create 1 rle with old method : 0.007799386978149414 length of segment : 91 time for calcul the mask position with numpy : 0.0025293827056884766 nb_pixel_total : 21786 time to create 1 rle with old method : 0.02905130386352539 length of segment : 180 time for calcul the mask position with numpy : 0.10701727867126465 nb_pixel_total : 51237 time to create 1 rle with old method : 0.05895638465881348 length of segment : 352 time for calcul the mask position with numpy : 0.0014884471893310547 nb_pixel_total : 6067 time to create 1 rle with old method : 0.007164716720581055 length of segment : 79 time for calcul the mask position with numpy : 0.0028052330017089844 nb_pixel_total : 11992 time to create 1 rle with old method : 0.01616644859313965 length of segment : 137 time for calcul the mask position with numpy : 0.0961904525756836 nb_pixel_total : 57359 time to create 1 rle with old method : 0.08707332611083984 length of segment : 338 time for calcul the mask position with numpy : 0.054186344146728516 nb_pixel_total : 72495 time to create 1 rle with old method : 0.08417987823486328 length of segment : 352 time for calcul the mask position with numpy : 0.005701303482055664 nb_pixel_total : 31884 time to create 1 rle with old method : 0.035996437072753906 length of segment : 379 time for calcul the mask position with numpy : 0.0042247772216796875 nb_pixel_total : 32716 time to create 1 rle with old method : 0.03652787208557129 length of segment : 228 time for calcul the mask position with numpy : 0.0040090084075927734 nb_pixel_total : 47184 time to create 1 rle with old method : 0.05177927017211914 length of segment : 223 time for calcul the mask position with numpy : 0.0068511962890625 nb_pixel_total : 13929 time to create 1 rle with old method : 0.01959705352783203 length of segment : 108 time for calcul the mask position with numpy : 0.0312955379486084 nb_pixel_total : 175177 time to create 1 rle with new method : 0.015763044357299805 length of segment : 769 time for calcul the mask position with numpy : 0.00036144256591796875 nb_pixel_total : 3951 time to create 1 rle with old method : 0.004987001419067383 length of segment : 56 time for calcul the mask position with numpy : 0.008639097213745117 nb_pixel_total : 10375 time to create 1 rle with old method : 0.01747727394104004 length of segment : 127 time for calcul the mask position with numpy : 0.009985923767089844 nb_pixel_total : 40102 time to create 1 rle with old method : 0.05139923095703125 length of segment : 201 time for calcul the mask position with numpy : 0.015636205673217773 nb_pixel_total : 23128 time to create 1 rle with old method : 0.0274655818939209 length of segment : 228 time for calcul the mask position with numpy : 0.004021883010864258 nb_pixel_total : 12382 time to create 1 rle with old method : 0.016458749771118164 length of segment : 118 time for calcul the mask position with numpy : 0.0017364025115966797 nb_pixel_total : 18395 time to create 1 rle with old method : 0.020674705505371094 length of segment : 144 time for calcul the mask position with numpy : 0.00732731819152832 nb_pixel_total : 78781 time to create 1 rle with old method : 0.08837652206420898 length of segment : 336 time for calcul the mask position with numpy : 0.007059812545776367 nb_pixel_total : 65115 time to create 1 rle with old method : 0.06988239288330078 length of segment : 301 time for calcul the mask position with numpy : 0.012749910354614258 nb_pixel_total : 189703 time to create 1 rle with new method : 0.019542932510375977 length of segment : 1285 time for calcul the mask position with numpy : 0.023277759552001953 nb_pixel_total : 38806 time to create 1 rle with old method : 0.045882225036621094 length of segment : 351 time for calcul the mask position with numpy : 0.008381843566894531 nb_pixel_total : 31363 time to create 1 rle with old method : 0.0387117862701416 length of segment : 194 time for calcul the mask position with numpy : 0.0033850669860839844 nb_pixel_total : 16493 time to create 1 rle with old method : 0.02481389045715332 length of segment : 433 time for calcul the mask position with numpy : 0.0007989406585693359 nb_pixel_total : 7331 time to create 1 rle with old method : 0.009215831756591797 length of segment : 105 time for calcul the mask position with numpy : 0.002685070037841797 nb_pixel_total : 17507 time to create 1 rle with old method : 0.019804716110229492 length of segment : 206 time for calcul the mask position with numpy : 0.013390541076660156 nb_pixel_total : 37252 time to create 1 rle with old method : 0.044301748275756836 length of segment : 324 time for calcul the mask position with numpy : 0.0005192756652832031 nb_pixel_total : 8202 time to create 1 rle with old method : 0.009209394454956055 length of segment : 154 time for calcul the mask position with numpy : 0.0007567405700683594 nb_pixel_total : 2334 time to create 1 rle with old method : 0.0029413700103759766 length of segment : 46 time for calcul the mask position with numpy : 0.060930490493774414 nb_pixel_total : 33525 time to create 1 rle with old method : 0.05882525444030762 length of segment : 147 time for calcul the mask position with numpy : 0.00513911247253418 nb_pixel_total : 31387 time to create 1 rle with old method : 0.03725934028625488 length of segment : 217 time for calcul the mask position with numpy : 0.0002243518829345703 nb_pixel_total : 4195 time to create 1 rle with old method : 0.004798173904418945 length of segment : 62 time for calcul the mask position with numpy : 0.0047779083251953125 nb_pixel_total : 8022 time to create 1 rle with old method : 0.008989810943603516 length of segment : 121 time for calcul the mask position with numpy : 0.0005407333374023438 nb_pixel_total : 6313 time to create 1 rle with old method : 0.007114410400390625 length of segment : 70 time for calcul the mask position with numpy : 0.0027337074279785156 nb_pixel_total : 32621 time to create 1 rle with old method : 0.035488128662109375 length of segment : 241 time for calcul the mask position with numpy : 0.0003871917724609375 nb_pixel_total : 8642 time to create 1 rle with old method : 0.010121822357177734 length of segment : 85 time for calcul the mask position with numpy : 0.06774425506591797 nb_pixel_total : 106387 time to create 1 rle with old method : 0.13600707054138184 length of segment : 369 time for calcul the mask position with numpy : 0.0005452632904052734 nb_pixel_total : 7811 time to create 1 rle with old method : 0.009055376052856445 length of segment : 136 time for calcul the mask position with numpy : 0.03563976287841797 nb_pixel_total : 65200 time to create 1 rle with old method : 0.07002544403076172 length of segment : 250 time for calcul the mask position with numpy : 0.0001614093780517578 nb_pixel_total : 3568 time to create 1 rle with old method : 0.004347085952758789 length of segment : 89 time for calcul the mask position with numpy : 0.0030510425567626953 nb_pixel_total : 27004 time to create 1 rle with old method : 0.028705358505249023 length of segment : 263 time for calcul the mask position with numpy : 0.0031900405883789062 nb_pixel_total : 33641 time to create 1 rle with old method : 0.036240577697753906 length of segment : 214 time for calcul the mask position with numpy : 0.003858327865600586 nb_pixel_total : 10146 time to create 1 rle with old method : 0.012933015823364258 length of segment : 95 time for calcul the mask position with numpy : 0.0006587505340576172 nb_pixel_total : 25574 time to create 1 rle with old method : 0.027805328369140625 length of segment : 145 time for calcul the mask position with numpy : 0.016810178756713867 nb_pixel_total : 72623 time to create 1 rle with old method : 0.07980108261108398 length of segment : 358 time for calcul the mask position with numpy : 0.008999824523925781 nb_pixel_total : 27624 time to create 1 rle with old method : 0.03276705741882324 length of segment : 192 time for calcul the mask position with numpy : 0.002360105514526367 nb_pixel_total : 15390 time to create 1 rle with old method : 0.017267227172851562 length of segment : 133 time for calcul the mask position with numpy : 0.0032148361206054688 nb_pixel_total : 28230 time to create 1 rle with old method : 0.030630111694335938 length of segment : 169 time for calcul the mask position with numpy : 0.0021457672119140625 nb_pixel_total : 30316 time to create 1 rle with old method : 0.03244137763977051 length of segment : 304 time for calcul the mask position with numpy : 0.00434422492980957 nb_pixel_total : 5777 time to create 1 rle with old method : 0.0064487457275390625 length of segment : 133 time for calcul the mask position with numpy : 0.002774477005004883 nb_pixel_total : 18729 time to create 1 rle with old method : 0.0199735164642334 length of segment : 256 time for calcul the mask position with numpy : 0.0008499622344970703 nb_pixel_total : 7380 time to create 1 rle with old method : 0.008791208267211914 length of segment : 106 time for calcul the mask position with numpy : 0.0011055469512939453 nb_pixel_total : 17734 time to create 1 rle with old method : 0.019954442977905273 length of segment : 148 time for calcul the mask position with numpy : 0.002867460250854492 nb_pixel_total : 11056 time to create 1 rle with old method : 0.01223611831665039 length of segment : 148 time for calcul the mask position with numpy : 0.0009398460388183594 nb_pixel_total : 1920 time to create 1 rle with old method : 0.002345561981201172 length of segment : 54 time for calcul the mask position with numpy : 0.0074231624603271484 nb_pixel_total : 26670 time to create 1 rle with old method : 0.031958580017089844 length of segment : 270 time for calcul the mask position with numpy : 0.01874399185180664 nb_pixel_total : 151483 time to create 1 rle with new method : 0.01188206672668457 length of segment : 412 time for calcul the mask position with numpy : 0.0001373291015625 nb_pixel_total : 2674 time to create 1 rle with old method : 0.003190755844116211 length of segment : 40 time for calcul the mask position with numpy : 0.00020885467529296875 nb_pixel_total : 990 time to create 1 rle with old method : 0.0012631416320800781 length of segment : 39 time for calcul the mask position with numpy : 0.0030040740966796875 nb_pixel_total : 57842 time to create 1 rle with old method : 0.06258368492126465 length of segment : 208 time for calcul the mask position with numpy : 0.0008428096771240234 nb_pixel_total : 18279 time to create 1 rle with old method : 0.019658565521240234 length of segment : 187 time for calcul the mask position with numpy : 0.015780210494995117 nb_pixel_total : 13844 time to create 1 rle with old method : 0.018881559371948242 length of segment : 105 time for calcul the mask position with numpy : 0.0041484832763671875 nb_pixel_total : 33468 time to create 1 rle with old method : 0.036043405532836914 length of segment : 203 time for calcul the mask position with numpy : 0.00516200065612793 nb_pixel_total : 76448 time to create 1 rle with old method : 0.08103561401367188 length of segment : 361 time for calcul the mask position with numpy : 0.018596649169921875 nb_pixel_total : 45485 time to create 1 rle with old method : 0.05156898498535156 length of segment : 243 time for calcul the mask position with numpy : 0.0047304630279541016 nb_pixel_total : 81733 time to create 1 rle with old method : 0.08527684211730957 length of segment : 337 time for calcul the mask position with numpy : 0.00016570091247558594 nb_pixel_total : 2651 time to create 1 rle with old method : 0.0031898021697998047 length of segment : 60 time for calcul the mask position with numpy : 0.05463123321533203 nb_pixel_total : 29123 time to create 1 rle with old method : 0.0352177619934082 length of segment : 220 time for calcul the mask position with numpy : 0.0036232471466064453 nb_pixel_total : 60332 time to create 1 rle with old method : 0.06606078147888184 length of segment : 349 time for calcul the mask position with numpy : 0.01186680793762207 nb_pixel_total : 39443 time to create 1 rle with old method : 0.044293880462646484 length of segment : 230 time for calcul the mask position with numpy : 0.0012919902801513672 nb_pixel_total : 23041 time to create 1 rle with old method : 0.02484750747680664 length of segment : 196 time for calcul the mask position with numpy : 0.005142688751220703 nb_pixel_total : 15776 time to create 1 rle with old method : 0.020153522491455078 length of segment : 187 time for calcul the mask position with numpy : 0.007834434509277344 nb_pixel_total : 20617 time to create 1 rle with old method : 0.023529529571533203 length of segment : 480 time for calcul the mask position with numpy : 0.002356290817260742 nb_pixel_total : 20588 time to create 1 rle with old method : 0.02099609375 length of segment : 169 time for calcul the mask position with numpy : 0.053046464920043945 nb_pixel_total : 81529 time to create 1 rle with old method : 0.08726906776428223 length of segment : 367 time for calcul the mask position with numpy : 0.02973151206970215 nb_pixel_total : 170593 time to create 1 rle with new method : 0.0197293758392334 length of segment : 522 time for calcul the mask position with numpy : 0.0007901191711425781 nb_pixel_total : 8247 time to create 1 rle with old method : 0.009636163711547852 length of segment : 120 time for calcul the mask position with numpy : 0.0015952587127685547 nb_pixel_total : 18945 time to create 1 rle with old method : 0.02089667320251465 length of segment : 164 time for calcul the mask position with numpy : 0.019617795944213867 nb_pixel_total : 23469 time to create 1 rle with old method : 0.027437686920166016 length of segment : 321 time for calcul the mask position with numpy : 0.00472569465637207 nb_pixel_total : 38041 time to create 1 rle with old method : 0.040544748306274414 length of segment : 281 time for calcul the mask position with numpy : 0.010194778442382812 nb_pixel_total : 19377 time to create 1 rle with old method : 0.024213075637817383 length of segment : 218 time for calcul the mask position with numpy : 0.005631446838378906 nb_pixel_total : 22311 time to create 1 rle with old method : 0.02587747573852539 length of segment : 277 time for calcul the mask position with numpy : 0.011573553085327148 nb_pixel_total : 29259 time to create 1 rle with old method : 0.0368342399597168 length of segment : 189 time for calcul the mask position with numpy : 0.002638578414916992 nb_pixel_total : 7807 time to create 1 rle with old method : 0.009360790252685547 length of segment : 116 time for calcul the mask position with numpy : 0.000392913818359375 nb_pixel_total : 12858 time to create 1 rle with old method : 0.014446258544921875 length of segment : 124 time for calcul the mask position with numpy : 0.002073526382446289 nb_pixel_total : 7834 time to create 1 rle with old method : 0.008899688720703125 length of segment : 106 time for calcul the mask position with numpy : 0.0018646717071533203 nb_pixel_total : 32254 time to create 1 rle with old method : 0.03505206108093262 length of segment : 429 time for calcul the mask position with numpy : 0.0018000602722167969 nb_pixel_total : 14007 time to create 1 rle with old method : 0.015594959259033203 length of segment : 130 time for calcul the mask position with numpy : 0.00709223747253418 nb_pixel_total : 12630 time to create 1 rle with old method : 0.01603245735168457 length of segment : 149 time for calcul the mask position with numpy : 0.00029921531677246094 nb_pixel_total : 2499 time to create 1 rle with old method : 0.002797842025756836 length of segment : 73 time for calcul the mask position with numpy : 0.0006256103515625 nb_pixel_total : 2746 time to create 1 rle with old method : 0.0034058094024658203 length of segment : 52 time for calcul the mask position with numpy : 0.010231971740722656 nb_pixel_total : 26840 time to create 1 rle with old method : 0.043396949768066406 length of segment : 201 time for calcul the mask position with numpy : 0.0026581287384033203 nb_pixel_total : 20854 time to create 1 rle with old method : 0.026401281356811523 length of segment : 205 time for calcul the mask position with numpy : 0.022523164749145508 nb_pixel_total : 34336 time to create 1 rle with old method : 0.04221963882446289 length of segment : 259 time for calcul the mask position with numpy : 0.009188413619995117 nb_pixel_total : 33276 time to create 1 rle with old method : 0.04024243354797363 length of segment : 212 time for calcul the mask position with numpy : 0.0015540122985839844 nb_pixel_total : 11946 time to create 1 rle with old method : 0.01351022720336914 length of segment : 127 time for calcul the mask position with numpy : 0.0002980232238769531 nb_pixel_total : 2650 time to create 1 rle with old method : 0.003243684768676758 length of segment : 61 time for calcul the mask position with numpy : 0.003912925720214844 nb_pixel_total : 17002 time to create 1 rle with old method : 0.021851301193237305 length of segment : 128 time for calcul the mask position with numpy : 0.0004074573516845703 nb_pixel_total : 4251 time to create 1 rle with old method : 0.005144596099853516 length of segment : 55 time for calcul the mask position with numpy : 0.00749659538269043 nb_pixel_total : 34102 time to create 1 rle with old method : 0.03790402412414551 length of segment : 290 time for calcul the mask position with numpy : 0.0026459693908691406 nb_pixel_total : 9791 time to create 1 rle with old method : 0.012696027755737305 length of segment : 103 time for calcul the mask position with numpy : 0.0006642341613769531 nb_pixel_total : 12197 time to create 1 rle with old method : 0.014039278030395508 length of segment : 115 time for calcul the mask position with numpy : 0.008949756622314453 nb_pixel_total : 39402 time to create 1 rle with old method : 0.043586015701293945 length of segment : 213 time for calcul the mask position with numpy : 0.00515437126159668 nb_pixel_total : 32780 time to create 1 rle with old method : 0.0354619026184082 length of segment : 281 time for calcul the mask position with numpy : 0.00107574462890625 nb_pixel_total : 5808 time to create 1 rle with old method : 0.006524324417114258 length of segment : 77 time for calcul the mask position with numpy : 0.0016720294952392578 nb_pixel_total : 12890 time to create 1 rle with old method : 0.013904094696044922 length of segment : 115 time for calcul the mask position with numpy : 0.005915164947509766 nb_pixel_total : 20794 time to create 1 rle with old method : 0.023891210556030273 length of segment : 209 time for calcul the mask position with numpy : 0.0004506111145019531 nb_pixel_total : 4807 time to create 1 rle with old method : 0.005685567855834961 length of segment : 76 time for calcul the mask position with numpy : 0.0217132568359375 nb_pixel_total : 38975 time to create 1 rle with old method : 0.04436349868774414 length of segment : 371 time for calcul the mask position with numpy : 0.010165691375732422 nb_pixel_total : 41863 time to create 1 rle with old method : 0.04630398750305176 length of segment : 262 time for calcul the mask position with numpy : 0.0017333030700683594 nb_pixel_total : 3123 time to create 1 rle with old method : 0.003537416458129883 length of segment : 90 time for calcul the mask position with numpy : 0.007500171661376953 nb_pixel_total : 90203 time to create 1 rle with old method : 0.1209559440612793 length of segment : 314 time for calcul the mask position with numpy : 0.004965066909790039 nb_pixel_total : 59103 time to create 1 rle with old method : 0.06961274147033691 length of segment : 324 time for calcul the mask position with numpy : 0.00070953369140625 nb_pixel_total : 2930 time to create 1 rle with old method : 0.003439664840698242 length of segment : 61 time for calcul the mask position with numpy : 0.0077381134033203125 nb_pixel_total : 67691 time to create 1 rle with old method : 0.072845458984375 length of segment : 225 time for calcul the mask position with numpy : 0.010936260223388672 nb_pixel_total : 16252 time to create 1 rle with old method : 0.023017168045043945 length of segment : 164 time for calcul the mask position with numpy : 0.017500638961791992 nb_pixel_total : 46071 time to create 1 rle with old method : 0.053331851959228516 length of segment : 279 time for calcul the mask position with numpy : 0.0019254684448242188 nb_pixel_total : 24655 time to create 1 rle with old method : 0.02738475799560547 length of segment : 189 time for calcul the mask position with numpy : 0.001234292984008789 nb_pixel_total : 9275 time to create 1 rle with old method : 0.01140451431274414 length of segment : 85 time for calcul the mask position with numpy : 0.0028061866760253906 nb_pixel_total : 33602 time to create 1 rle with old method : 0.0368809700012207 length of segment : 235 time for calcul the mask position with numpy : 0.0026350021362304688 nb_pixel_total : 34075 time to create 1 rle with old method : 0.03722977638244629 length of segment : 250 time for calcul the mask position with numpy : 0.000988006591796875 nb_pixel_total : 12855 time to create 1 rle with old method : 0.015877485275268555 length of segment : 134 time for calcul the mask position with numpy : 0.0008885860443115234 nb_pixel_total : 14525 time to create 1 rle with old method : 0.015871286392211914 length of segment : 99 time for calcul the mask position with numpy : 0.004113197326660156 nb_pixel_total : 44550 time to create 1 rle with old method : 0.04832267761230469 length of segment : 760 time for calcul the mask position with numpy : 0.003874540328979492 nb_pixel_total : 19800 time to create 1 rle with old method : 0.021841764450073242 length of segment : 227 time for calcul the mask position with numpy : 0.003155946731567383 nb_pixel_total : 14788 time to create 1 rle with old method : 0.01642751693725586 length of segment : 113 time for calcul the mask position with numpy : 0.003337383270263672 nb_pixel_total : 13127 time to create 1 rle with old method : 0.017409563064575195 length of segment : 144 time for calcul the mask position with numpy : 0.018835783004760742 nb_pixel_total : 129557 time to create 1 rle with old method : 0.14239287376403809 length of segment : 587 time for calcul the mask position with numpy : 0.006231069564819336 nb_pixel_total : 30800 time to create 1 rle with old method : 0.033275604248046875 length of segment : 228 time for calcul the mask position with numpy : 0.0006005764007568359 nb_pixel_total : 5701 time to create 1 rle with old method : 0.00945901870727539 length of segment : 81 time for calcul the mask position with numpy : 0.0010917186737060547 nb_pixel_total : 5054 time to create 1 rle with old method : 0.00598454475402832 length of segment : 92 time for calcul the mask position with numpy : 0.0008299350738525391 nb_pixel_total : 4037 time to create 1 rle with old method : 0.004793643951416016 length of segment : 60 time for calcul the mask position with numpy : 0.005824565887451172 nb_pixel_total : 40676 time to create 1 rle with old method : 0.045729875564575195 length of segment : 210 time for calcul the mask position with numpy : 0.0024750232696533203 nb_pixel_total : 11328 time to create 1 rle with old method : 0.012287616729736328 length of segment : 137 time for calcul the mask position with numpy : 0.0038814544677734375 nb_pixel_total : 34976 time to create 1 rle with old method : 0.037201881408691406 length of segment : 356 time for calcul the mask position with numpy : 0.0008323192596435547 nb_pixel_total : 6609 time to create 1 rle with old method : 0.007282733917236328 length of segment : 91 time for calcul the mask position with numpy : 0.018965959548950195 nb_pixel_total : 94518 time to create 1 rle with old method : 0.09805655479431152 length of segment : 432 time for calcul the mask position with numpy : 0.009531497955322266 nb_pixel_total : 49035 time to create 1 rle with old method : 0.05346798896789551 length of segment : 351 time for calcul the mask position with numpy : 0.002756357192993164 nb_pixel_total : 31734 time to create 1 rle with old method : 0.03384232521057129 length of segment : 315 time for calcul the mask position with numpy : 0.0010402202606201172 nb_pixel_total : 7034 time to create 1 rle with old method : 0.008244991302490234 length of segment : 94 time for calcul the mask position with numpy : 0.0003275871276855469 nb_pixel_total : 1990 time to create 1 rle with old method : 0.002456188201904297 length of segment : 57 time for calcul the mask position with numpy : 0.004547595977783203 nb_pixel_total : 23850 time to create 1 rle with old method : 0.02601766586303711 length of segment : 349 time for calcul the mask position with numpy : 0.0006604194641113281 nb_pixel_total : 14705 time to create 1 rle with old method : 0.016444921493530273 length of segment : 217 time for calcul the mask position with numpy : 0.012536287307739258 nb_pixel_total : 103228 time to create 1 rle with old method : 0.10999250411987305 length of segment : 371 time for calcul the mask position with numpy : 0.0010848045349121094 nb_pixel_total : 21203 time to create 1 rle with old method : 0.02348947525024414 length of segment : 150 time for calcul the mask position with numpy : 0.0017840862274169922 nb_pixel_total : 18191 time to create 1 rle with old method : 0.02011561393737793 length of segment : 207 time for calcul the mask position with numpy : 0.0021343231201171875 nb_pixel_total : 9574 time to create 1 rle with old method : 0.013450860977172852 length of segment : 128 time for calcul the mask position with numpy : 0.00019288063049316406 nb_pixel_total : 5403 time to create 1 rle with old method : 0.0062694549560546875 length of segment : 104 time for calcul the mask position with numpy : 0.012566804885864258 nb_pixel_total : 105741 time to create 1 rle with old method : 0.10964655876159668 length of segment : 489 time for calcul the mask position with numpy : 0.00452113151550293 nb_pixel_total : 26483 time to create 1 rle with old method : 0.027898073196411133 length of segment : 186 time for calcul the mask position with numpy : 0.0008270740509033203 nb_pixel_total : 7639 time to create 1 rle with old method : 0.007956981658935547 length of segment : 107 time for calcul the mask position with numpy : 0.002747774124145508 nb_pixel_total : 17208 time to create 1 rle with old method : 0.017523527145385742 length of segment : 257 time for calcul the mask position with numpy : 0.0017445087432861328 nb_pixel_total : 13448 time to create 1 rle with old method : 0.014263391494750977 length of segment : 199 time for calcul the mask position with numpy : 0.0009806156158447266 nb_pixel_total : 40874 time to create 1 rle with old method : 0.04351234436035156 length of segment : 220 time for calcul the mask position with numpy : 0.004894256591796875 nb_pixel_total : 34288 time to create 1 rle with old method : 0.03702092170715332 length of segment : 243 time for calcul the mask position with numpy : 0.0008885860443115234 nb_pixel_total : 14839 time to create 1 rle with old method : 0.016658306121826172 length of segment : 154 time for calcul the mask position with numpy : 0.0018596649169921875 nb_pixel_total : 28438 time to create 1 rle with old method : 0.03066706657409668 length of segment : 239 time for calcul the mask position with numpy : 0.0032567977905273438 nb_pixel_total : 29806 time to create 1 rle with old method : 0.031767845153808594 length of segment : 225 time for calcul the mask position with numpy : 0.001485586166381836 nb_pixel_total : 7149 time to create 1 rle with old method : 0.008299827575683594 length of segment : 78 time for calcul the mask position with numpy : 0.0006699562072753906 nb_pixel_total : 8540 time to create 1 rle with old method : 0.010035514831542969 length of segment : 100 time for calcul the mask position with numpy : 0.003247976303100586 nb_pixel_total : 21781 time to create 1 rle with old method : 0.02368783950805664 length of segment : 305 time for calcul the mask position with numpy : 0.001898050308227539 nb_pixel_total : 22862 time to create 1 rle with old method : 0.02435922622680664 length of segment : 216 time for calcul the mask position with numpy : 0.0009067058563232422 nb_pixel_total : 5980 time to create 1 rle with old method : 0.0066874027252197266 length of segment : 102 time for calcul the mask position with numpy : 0.0003554821014404297 nb_pixel_total : 3208 time to create 1 rle with old method : 0.0039365291595458984 length of segment : 66 time for calcul the mask position with numpy : 0.0024042129516601562 nb_pixel_total : 43016 time to create 1 rle with old method : 0.046303749084472656 length of segment : 317 time for calcul the mask position with numpy : 0.0037789344787597656 nb_pixel_total : 76598 time to create 1 rle with old method : 0.08053803443908691 length of segment : 232 time for calcul the mask position with numpy : 0.0046465396881103516 nb_pixel_total : 28797 time to create 1 rle with old method : 0.035400390625 length of segment : 200 time for calcul the mask position with numpy : 0.0003871917724609375 nb_pixel_total : 9806 time to create 1 rle with old method : 0.011138916015625 length of segment : 133 time for calcul the mask position with numpy : 0.004310131072998047 nb_pixel_total : 40320 time to create 1 rle with old method : 0.041580915451049805 length of segment : 196 time for calcul the mask position with numpy : 0.009246826171875 nb_pixel_total : 91988 time to create 1 rle with old method : 0.09303426742553711 length of segment : 590 time for calcul the mask position with numpy : 0.001219034194946289 nb_pixel_total : 16633 time to create 1 rle with old method : 0.024370670318603516 length of segment : 189 time for calcul the mask position with numpy : 0.001605987548828125 nb_pixel_total : 23726 time to create 1 rle with old method : 0.025233745574951172 length of segment : 171 time for calcul the mask position with numpy : 0.0008606910705566406 nb_pixel_total : 19403 time to create 1 rle with old method : 0.020192861557006836 length of segment : 198 time for calcul the mask position with numpy : 0.015325307846069336 nb_pixel_total : 242604 time to create 1 rle with new method : 0.018820524215698242 length of segment : 545 time for calcul the mask position with numpy : 0.006878376007080078 nb_pixel_total : 85783 time to create 1 rle with old method : 0.09164810180664062 length of segment : 404 time for calcul the mask position with numpy : 0.00031113624572753906 nb_pixel_total : 3117 time to create 1 rle with old method : 0.0038111209869384766 length of segment : 45 time for calcul the mask position with numpy : 0.002498149871826172 nb_pixel_total : 31669 time to create 1 rle with old method : 0.03561735153198242 length of segment : 186 time for calcul the mask position with numpy : 0.0027980804443359375 nb_pixel_total : 49724 time to create 1 rle with old method : 0.05498361587524414 length of segment : 253 time for calcul the mask position with numpy : 0.0018672943115234375 nb_pixel_total : 28111 time to create 1 rle with old method : 0.030719757080078125 length of segment : 183 time for calcul the mask position with numpy : 0.0031991004943847656 nb_pixel_total : 55710 time to create 1 rle with old method : 0.05873727798461914 length of segment : 271 time for calcul the mask position with numpy : 0.0002162456512451172 nb_pixel_total : 4632 time to create 1 rle with old method : 0.00508427619934082 length of segment : 85 time for calcul the mask position with numpy : 0.0030434131622314453 nb_pixel_total : 20629 time to create 1 rle with old method : 0.02337932586669922 length of segment : 379 time for calcul the mask position with numpy : 0.003376483917236328 nb_pixel_total : 41963 time to create 1 rle with old method : 0.04314923286437988 length of segment : 283 time for calcul the mask position with numpy : 0.003986358642578125 nb_pixel_total : 40296 time to create 1 rle with old method : 0.044136762619018555 length of segment : 408 time for calcul the mask position with numpy : 0.003171682357788086 nb_pixel_total : 50161 time to create 1 rle with old method : 0.05976986885070801 length of segment : 255 time for calcul the mask position with numpy : 0.004576921463012695 nb_pixel_total : 52718 time to create 1 rle with old method : 0.06332516670227051 length of segment : 253 time for calcul the mask position with numpy : 0.0012099742889404297 nb_pixel_total : 12660 time to create 1 rle with old method : 0.014054298400878906 length of segment : 166 time for calcul the mask position with numpy : 0.0018410682678222656 nb_pixel_total : 27929 time to create 1 rle with old method : 0.02955031394958496 length of segment : 248 time for calcul the mask position with numpy : 0.0062863826751708984 nb_pixel_total : 96568 time to create 1 rle with old method : 0.1044614315032959 length of segment : 412 time for calcul the mask position with numpy : 0.0011448860168457031 nb_pixel_total : 14918 time to create 1 rle with old method : 0.017162561416625977 length of segment : 123 time for calcul the mask position with numpy : 0.00021505355834960938 nb_pixel_total : 2399 time to create 1 rle with old method : 0.0028448104858398438 length of segment : 50 time for calcul the mask position with numpy : 0.0006873607635498047 nb_pixel_total : 7034 time to create 1 rle with old method : 0.008193731307983398 length of segment : 87 time for calcul the mask position with numpy : 0.0026178359985351562 nb_pixel_total : 39768 time to create 1 rle with old method : 0.04355597496032715 length of segment : 240 time for calcul the mask position with numpy : 0.008219718933105469 nb_pixel_total : 107004 time to create 1 rle with old method : 0.11121273040771484 length of segment : 600 time for calcul the mask position with numpy : 0.002025604248046875 nb_pixel_total : 22933 time to create 1 rle with old method : 0.026917219161987305 length of segment : 140 time for calcul the mask position with numpy : 0.004556894302368164 nb_pixel_total : 106282 time to create 1 rle with old method : 0.10990762710571289 length of segment : 436 time for calcul the mask position with numpy : 6.723403930664062e-05 nb_pixel_total : 1421 time to create 1 rle with old method : 0.0017173290252685547 length of segment : 31 time for calcul the mask position with numpy : 0.0020952224731445312 nb_pixel_total : 40348 time to create 1 rle with old method : 0.043109893798828125 length of segment : 224 time for calcul the mask position with numpy : 0.0029273033142089844 nb_pixel_total : 38636 time to create 1 rle with old method : 0.042284250259399414 length of segment : 317 time for calcul the mask position with numpy : 0.0015285015106201172 nb_pixel_total : 33414 time to create 1 rle with old method : 0.03544211387634277 length of segment : 185 time for calcul the mask position with numpy : 0.0001614093780517578 nb_pixel_total : 4214 time to create 1 rle with old method : 0.004868268966674805 length of segment : 81 time for calcul the mask position with numpy : 0.0015637874603271484 nb_pixel_total : 20067 time to create 1 rle with old method : 0.02203369140625 length of segment : 173 time for calcul the mask position with numpy : 0.0017554759979248047 nb_pixel_total : 26193 time to create 1 rle with old method : 0.028778791427612305 length of segment : 216 time for calcul the mask position with numpy : 0.0007636547088623047 nb_pixel_total : 6291 time to create 1 rle with old method : 0.006947517395019531 length of segment : 106 time for calcul the mask position with numpy : 0.0005977153778076172 nb_pixel_total : 5044 time to create 1 rle with old method : 0.005925416946411133 length of segment : 109 time for calcul the mask position with numpy : 0.0018246173858642578 nb_pixel_total : 33223 time to create 1 rle with old method : 0.03666114807128906 length of segment : 180 time for calcul the mask position with numpy : 0.0022623538970947266 nb_pixel_total : 40384 time to create 1 rle with old method : 0.04337430000305176 length of segment : 240 time for calcul the mask position with numpy : 0.0003180503845214844 nb_pixel_total : 8577 time to create 1 rle with old method : 0.009421825408935547 length of segment : 92 time for calcul the mask position with numpy : 0.0034286975860595703 nb_pixel_total : 49300 time to create 1 rle with old method : 0.054444074630737305 length of segment : 284 time for calcul the mask position with numpy : 0.002340078353881836 nb_pixel_total : 37306 time to create 1 rle with old method : 0.04051041603088379 length of segment : 276 time for calcul the mask position with numpy : 0.0032091140747070312 nb_pixel_total : 39112 time to create 1 rle with old method : 0.042696475982666016 length of segment : 220 time for calcul the mask position with numpy : 0.0037174224853515625 nb_pixel_total : 32246 time to create 1 rle with old method : 0.03572559356689453 length of segment : 504 time for calcul the mask position with numpy : 0.0007421970367431641 nb_pixel_total : 11015 time to create 1 rle with old method : 0.012834310531616211 length of segment : 68 time for calcul the mask position with numpy : 0.0018939971923828125 nb_pixel_total : 28120 time to create 1 rle with old method : 0.030848979949951172 length of segment : 247 time for calcul the mask position with numpy : 0.0018773078918457031 nb_pixel_total : 47282 time to create 1 rle with old method : 0.0500490665435791 length of segment : 459 time for calcul the mask position with numpy : 0.00012731552124023438 nb_pixel_total : 3340 time to create 1 rle with old method : 0.0038771629333496094 length of segment : 46 time for calcul the mask position with numpy : 0.002035379409790039 nb_pixel_total : 42100 time to create 1 rle with old method : 0.04493117332458496 length of segment : 370 time for calcul the mask position with numpy : 0.00031638145446777344 nb_pixel_total : 4708 time to create 1 rle with old method : 0.005561351776123047 length of segment : 62 time for calcul the mask position with numpy : 0.0010797977447509766 nb_pixel_total : 18734 time to create 1 rle with old method : 0.020950794219970703 length of segment : 203 time for calcul the mask position with numpy : 0.0028085708618164062 nb_pixel_total : 34591 time to create 1 rle with old method : 0.03875303268432617 length of segment : 233 time for calcul the mask position with numpy : 0.018762826919555664 nb_pixel_total : 286230 time to create 1 rle with new method : 0.023327350616455078 length of segment : 558 time for calcul the mask position with numpy : 0.0008246898651123047 nb_pixel_total : 10828 time to create 1 rle with old method : 0.012215852737426758 length of segment : 124 time for calcul the mask position with numpy : 0.0017158985137939453 nb_pixel_total : 15631 time to create 1 rle with old method : 0.017635822296142578 length of segment : 199 time for calcul the mask position with numpy : 0.0017096996307373047 nb_pixel_total : 19571 time to create 1 rle with old method : 0.021977901458740234 length of segment : 185 time for calcul the mask position with numpy : 0.003989458084106445 nb_pixel_total : 58577 time to create 1 rle with old method : 0.06076240539550781 length of segment : 266 time for calcul the mask position with numpy : 0.005396366119384766 nb_pixel_total : 55715 time to create 1 rle with old method : 0.05913686752319336 length of segment : 539 time for calcul the mask position with numpy : 0.004392147064208984 nb_pixel_total : 52748 time to create 1 rle with old method : 0.056133270263671875 length of segment : 379 time for calcul the mask position with numpy : 0.0001766681671142578 nb_pixel_total : 3319 time to create 1 rle with old method : 0.003764629364013672 length of segment : 82 time for calcul the mask position with numpy : 0.002368450164794922 nb_pixel_total : 28173 time to create 1 rle with old method : 0.030917882919311523 length of segment : 356 time for calcul the mask position with numpy : 0.00037550926208496094 nb_pixel_total : 2886 time to create 1 rle with old method : 0.003567934036254883 length of segment : 81 time for calcul the mask position with numpy : 0.0010328292846679688 nb_pixel_total : 24269 time to create 1 rle with old method : 0.02574014663696289 length of segment : 177 time for calcul the mask position with numpy : 0.0011725425720214844 nb_pixel_total : 21713 time to create 1 rle with old method : 0.02318739891052246 length of segment : 146 time for calcul the mask position with numpy : 0.0012781620025634766 nb_pixel_total : 21717 time to create 1 rle with old method : 0.023391008377075195 length of segment : 166 time for calcul the mask position with numpy : 0.00016307830810546875 nb_pixel_total : 1514 time to create 1 rle with old method : 0.0018613338470458984 length of segment : 32 time for calcul the mask position with numpy : 0.003145456314086914 nb_pixel_total : 49619 time to create 1 rle with old method : 0.05243492126464844 length of segment : 264 time for calcul the mask position with numpy : 0.00420689582824707 nb_pixel_total : 41155 time to create 1 rle with old method : 0.04587578773498535 length of segment : 302 time for calcul the mask position with numpy : 0.0013492107391357422 nb_pixel_total : 21128 time to create 1 rle with old method : 0.02300739288330078 length of segment : 127 time for calcul the mask position with numpy : 0.0002033710479736328 nb_pixel_total : 1899 time to create 1 rle with old method : 0.0022563934326171875 length of segment : 37 time for calcul the mask position with numpy : 0.002135753631591797 nb_pixel_total : 36347 time to create 1 rle with old method : 0.03964090347290039 length of segment : 227 time for calcul the mask position with numpy : 0.00046563148498535156 nb_pixel_total : 3817 time to create 1 rle with old method : 0.004282951354980469 length of segment : 102 time for calcul the mask position with numpy : 0.0008614063262939453 nb_pixel_total : 19142 time to create 1 rle with old method : 0.021126985549926758 length of segment : 173 time for calcul the mask position with numpy : 0.003833293914794922 nb_pixel_total : 49598 time to create 1 rle with old method : 0.051964521408081055 length of segment : 302 time for calcul the mask position with numpy : 0.0005562305450439453 nb_pixel_total : 20781 time to create 1 rle with old method : 0.0217587947845459 length of segment : 159 time for calcul the mask position with numpy : 0.0009486675262451172 nb_pixel_total : 7927 time to create 1 rle with old method : 0.009032964706420898 length of segment : 114 time for calcul the mask position with numpy : 0.0030469894409179688 nb_pixel_total : 41835 time to create 1 rle with old method : 0.04618096351623535 length of segment : 292 time for calcul the mask position with numpy : 0.0002727508544921875 nb_pixel_total : 2430 time to create 1 rle with old method : 0.002699613571166992 length of segment : 50 time for calcul the mask position with numpy : 0.0030210018157958984 nb_pixel_total : 7395 time to create 1 rle with old method : 0.00916433334350586 length of segment : 199 time for calcul the mask position with numpy : 0.0029747486114501953 nb_pixel_total : 25902 time to create 1 rle with old method : 0.02813124656677246 length of segment : 342 time for calcul the mask position with numpy : 0.0050776004791259766 nb_pixel_total : 60887 time to create 1 rle with old method : 0.06558847427368164 length of segment : 303 time for calcul the mask position with numpy : 0.0030918121337890625 nb_pixel_total : 42622 time to create 1 rle with old method : 0.04610610008239746 length of segment : 310 time for calcul the mask position with numpy : 0.0017521381378173828 nb_pixel_total : 20290 time to create 1 rle with old method : 0.022542953491210938 length of segment : 162 time for calcul the mask position with numpy : 0.0016973018646240234 nb_pixel_total : 20319 time to create 1 rle with old method : 0.0239870548248291 length of segment : 161 time for calcul the mask position with numpy : 0.0012547969818115234 nb_pixel_total : 18498 time to create 1 rle with old method : 0.02075958251953125 length of segment : 142 time for calcul the mask position with numpy : 0.0002720355987548828 nb_pixel_total : 1833 time to create 1 rle with old method : 0.002240419387817383 length of segment : 48 time for calcul the mask position with numpy : 0.008921623229980469 nb_pixel_total : 158072 time to create 1 rle with new method : 0.012989997863769531 length of segment : 505 time for calcul the mask position with numpy : 0.004134178161621094 nb_pixel_total : 61482 time to create 1 rle with old method : 0.06433343887329102 length of segment : 526 time for calcul the mask position with numpy : 0.0020706653594970703 nb_pixel_total : 21315 time to create 1 rle with old method : 0.023111581802368164 length of segment : 141 time for calcul the mask position with numpy : 0.00040984153747558594 nb_pixel_total : 5848 time to create 1 rle with old method : 0.0067102909088134766 length of segment : 115 time for calcul the mask position with numpy : 0.002099275588989258 nb_pixel_total : 23946 time to create 1 rle with old method : 0.026096105575561523 length of segment : 307 time for calcul the mask position with numpy : 0.000997304916381836 nb_pixel_total : 24818 time to create 1 rle with old method : 0.027123451232910156 length of segment : 324 time for calcul the mask position with numpy : 0.001312255859375 nb_pixel_total : 39188 time to create 1 rle with old method : 0.05910205841064453 length of segment : 287 time for calcul the mask position with numpy : 0.0009198188781738281 nb_pixel_total : 6461 time to create 1 rle with old method : 0.0076639652252197266 length of segment : 171 time for calcul the mask position with numpy : 0.0013904571533203125 nb_pixel_total : 48895 time to create 1 rle with old method : 0.05265927314758301 length of segment : 317 time for calcul the mask position with numpy : 0.0022313594818115234 nb_pixel_total : 46720 time to create 1 rle with old method : 0.05067181587219238 length of segment : 190 time for calcul the mask position with numpy : 0.0009245872497558594 nb_pixel_total : 37873 time to create 1 rle with old method : 0.04203033447265625 length of segment : 266 time for calcul the mask position with numpy : 0.0008683204650878906 nb_pixel_total : 13626 time to create 1 rle with old method : 0.015562772750854492 length of segment : 128 time for calcul the mask position with numpy : 0.0019049644470214844 nb_pixel_total : 34432 time to create 1 rle with old method : 0.03773641586303711 length of segment : 213 time for calcul the mask position with numpy : 0.003327608108520508 nb_pixel_total : 56277 time to create 1 rle with old method : 0.06294894218444824 length of segment : 276 time for calcul the mask position with numpy : 0.000637054443359375 nb_pixel_total : 4973 time to create 1 rle with old method : 0.005711078643798828 length of segment : 103 time for calcul the mask position with numpy : 0.003235340118408203 nb_pixel_total : 42744 time to create 1 rle with old method : 0.04711771011352539 length of segment : 208 time for calcul the mask position with numpy : 0.002969980239868164 nb_pixel_total : 34243 time to create 1 rle with old method : 0.037674665451049805 length of segment : 253 time for calcul the mask position with numpy : 0.015712738037109375 nb_pixel_total : 248205 time to create 1 rle with new method : 0.016420841217041016 length of segment : 501 time for calcul the mask position with numpy : 0.0013895034790039062 nb_pixel_total : 16472 time to create 1 rle with old method : 0.01805901527404785 length of segment : 129 time for calcul the mask position with numpy : 0.0013794898986816406 nb_pixel_total : 20250 time to create 1 rle with old method : 0.02388310432434082 length of segment : 146 time for calcul the mask position with numpy : 0.003126382827758789 nb_pixel_total : 45656 time to create 1 rle with old method : 0.04667973518371582 length of segment : 212 time for calcul the mask position with numpy : 0.0016608238220214844 nb_pixel_total : 30814 time to create 1 rle with old method : 0.033631324768066406 length of segment : 248 time for calcul the mask position with numpy : 0.0008366107940673828 nb_pixel_total : 11964 time to create 1 rle with old method : 0.013048410415649414 length of segment : 131 time for calcul the mask position with numpy : 0.005953788757324219 nb_pixel_total : 85640 time to create 1 rle with old method : 0.09276580810546875 length of segment : 386 time for calcul the mask position with numpy : 0.0004990100860595703 nb_pixel_total : 5922 time to create 1 rle with old method : 0.007263660430908203 length of segment : 107 time for calcul the mask position with numpy : 0.0018374919891357422 nb_pixel_total : 35660 time to create 1 rle with old method : 0.039253950119018555 length of segment : 208 time for calcul the mask position with numpy : 0.0023887157440185547 nb_pixel_total : 30700 time to create 1 rle with old method : 0.03403472900390625 length of segment : 391 time for calcul the mask position with numpy : 0.0009481906890869141 nb_pixel_total : 36732 time to create 1 rle with old method : 0.04268980026245117 length of segment : 175 time for calcul the mask position with numpy : 0.0007340908050537109 nb_pixel_total : 4888 time to create 1 rle with old method : 0.006355762481689453 length of segment : 70 time for calcul the mask position with numpy : 0.0035991668701171875 nb_pixel_total : 39702 time to create 1 rle with old method : 0.04530763626098633 length of segment : 367 time for calcul the mask position with numpy : 0.0038611888885498047 nb_pixel_total : 55162 time to create 1 rle with old method : 0.06014847755432129 length of segment : 248 time for calcul the mask position with numpy : 0.00028061866760253906 nb_pixel_total : 2045 time to create 1 rle with old method : 0.0024518966674804688 length of segment : 52 time for calcul the mask position with numpy : 0.001211404800415039 nb_pixel_total : 20234 time to create 1 rle with old method : 0.022558212280273438 length of segment : 204 time for calcul the mask position with numpy : 0.0010437965393066406 nb_pixel_total : 12714 time to create 1 rle with old method : 0.014650344848632812 length of segment : 133 time for calcul the mask position with numpy : 0.002402782440185547 nb_pixel_total : 38602 time to create 1 rle with old method : 0.04348587989807129 length of segment : 385 time for calcul the mask position with numpy : 0.00164031982421875 nb_pixel_total : 21017 time to create 1 rle with old method : 0.024270296096801758 length of segment : 204 time for calcul the mask position with numpy : 0.0005676746368408203 nb_pixel_total : 19390 time to create 1 rle with old method : 0.022504329681396484 length of segment : 233 time for calcul the mask position with numpy : 0.00023126602172851562 nb_pixel_total : 3044 time to create 1 rle with old method : 0.0036573410034179688 length of segment : 60 time for calcul the mask position with numpy : 0.0005786418914794922 nb_pixel_total : 7461 time to create 1 rle with old method : 0.008312702178955078 length of segment : 106 time for calcul the mask position with numpy : 0.0035257339477539062 nb_pixel_total : 59104 time to create 1 rle with old method : 0.06573724746704102 length of segment : 310 time for calcul the mask position with numpy : 0.008318662643432617 nb_pixel_total : 132376 time to create 1 rle with old method : 0.1402268409729004 length of segment : 547 time for calcul the mask position with numpy : 0.0008721351623535156 nb_pixel_total : 13141 time to create 1 rle with old method : 0.014859437942504883 length of segment : 97 time for calcul the mask position with numpy : 0.0014371871948242188 nb_pixel_total : 12882 time to create 1 rle with old method : 0.015209674835205078 length of segment : 224 time for calcul the mask position with numpy : 0.0014033317565917969 nb_pixel_total : 17281 time to create 1 rle with old method : 0.01938939094543457 length of segment : 258 time for calcul the mask position with numpy : 0.0006146430969238281 nb_pixel_total : 4653 time to create 1 rle with old method : 0.005626201629638672 length of segment : 109 time for calcul the mask position with numpy : 0.0020203590393066406 nb_pixel_total : 21589 time to create 1 rle with old method : 0.024831295013427734 length of segment : 254 time for calcul the mask position with numpy : 0.0060384273529052734 nb_pixel_total : 74570 time to create 1 rle with old method : 0.08331847190856934 length of segment : 430 time for calcul the mask position with numpy : 0.0019865036010742188 nb_pixel_total : 33450 time to create 1 rle with old method : 0.04016256332397461 length of segment : 270 time for calcul the mask position with numpy : 0.0006859302520751953 nb_pixel_total : 12762 time to create 1 rle with old method : 0.014274120330810547 length of segment : 278 time for calcul the mask position with numpy : 0.0026535987854003906 nb_pixel_total : 44885 time to create 1 rle with old method : 0.04881095886230469 length of segment : 273 time for calcul the mask position with numpy : 0.001390218734741211 nb_pixel_total : 17206 time to create 1 rle with old method : 0.0194244384765625 length of segment : 212 time for calcul the mask position with numpy : 0.0006630420684814453 nb_pixel_total : 12938 time to create 1 rle with old method : 0.014569282531738281 length of segment : 192 time for calcul the mask position with numpy : 0.0007810592651367188 nb_pixel_total : 32928 time to create 1 rle with old method : 0.036574602127075195 length of segment : 270 time for calcul the mask position with numpy : 0.003198385238647461 nb_pixel_total : 36347 time to create 1 rle with old method : 0.04179549217224121 length of segment : 218 time for calcul the mask position with numpy : 0.00017690658569335938 nb_pixel_total : 1278 time to create 1 rle with old method : 0.0015985965728759766 length of segment : 41 time for calcul the mask position with numpy : 0.015458106994628906 nb_pixel_total : 146142 time to create 1 rle with old method : 0.16031312942504883 length of segment : 528 time for calcul the mask position with numpy : 0.0033991336822509766 nb_pixel_total : 42828 time to create 1 rle with old method : 0.0475919246673584 length of segment : 431 time for calcul the mask position with numpy : 0.0021042823791503906 nb_pixel_total : 37661 time to create 1 rle with old method : 0.042919158935546875 length of segment : 236 time for calcul the mask position with numpy : 0.0005130767822265625 nb_pixel_total : 3250 time to create 1 rle with old method : 0.004148960113525391 length of segment : 77 time for calcul the mask position with numpy : 0.004072666168212891 nb_pixel_total : 72899 time to create 1 rle with old method : 0.08124351501464844 length of segment : 338 time for calcul the mask position with numpy : 0.002835988998413086 nb_pixel_total : 51962 time to create 1 rle with old method : 0.057828664779663086 length of segment : 333 time for calcul the mask position with numpy : 0.0001971721649169922 nb_pixel_total : 6165 time to create 1 rle with old method : 0.007295846939086914 length of segment : 86 time for calcul the mask position with numpy : 0.0026350021362304688 nb_pixel_total : 43948 time to create 1 rle with old method : 0.04845023155212402 length of segment : 358 time for calcul the mask position with numpy : 0.0014445781707763672 nb_pixel_total : 19776 time to create 1 rle with old method : 0.022432804107666016 length of segment : 156 time for calcul the mask position with numpy : 0.004001140594482422 nb_pixel_total : 44297 time to create 1 rle with old method : 0.05065321922302246 length of segment : 207 time for calcul the mask position with numpy : 0.0005412101745605469 nb_pixel_total : 7170 time to create 1 rle with old method : 0.008230924606323242 length of segment : 93 time for calcul the mask position with numpy : 0.0003910064697265625 nb_pixel_total : 3644 time to create 1 rle with old method : 0.0045015811920166016 length of segment : 87 time for calcul the mask position with numpy : 0.0008523464202880859 nb_pixel_total : 10545 time to create 1 rle with old method : 0.012026071548461914 length of segment : 109 time for calcul the mask position with numpy : 0.0027952194213867188 nb_pixel_total : 30566 time to create 1 rle with old method : 0.034291982650756836 length of segment : 228 time for calcul the mask position with numpy : 0.00035262107849121094 nb_pixel_total : 3033 time to create 1 rle with old method : 0.0035774707794189453 length of segment : 68 time for calcul the mask position with numpy : 0.0009510517120361328 nb_pixel_total : 10393 time to create 1 rle with old method : 0.012003898620605469 length of segment : 137 time for calcul the mask position with numpy : 0.0006618499755859375 nb_pixel_total : 9968 time to create 1 rle with old method : 0.011726140975952148 length of segment : 86 time for calcul the mask position with numpy : 0.0019042491912841797 nb_pixel_total : 24761 time to create 1 rle with old method : 0.02870345115661621 length of segment : 249 time for calcul the mask position with numpy : 0.0015125274658203125 nb_pixel_total : 31507 time to create 1 rle with old method : 0.03499770164489746 length of segment : 193 time for calcul the mask position with numpy : 0.004681587219238281 nb_pixel_total : 48492 time to create 1 rle with old method : 0.07322049140930176 length of segment : 310 time for calcul the mask position with numpy : 0.004522562026977539 nb_pixel_total : 73682 time to create 1 rle with old method : 0.08151888847351074 length of segment : 286 time for calcul the mask position with numpy : 0.0026848316192626953 nb_pixel_total : 32878 time to create 1 rle with old method : 0.03699636459350586 length of segment : 215 time for calcul the mask position with numpy : 0.00013780593872070312 nb_pixel_total : 2181 time to create 1 rle with old method : 0.00257110595703125 length of segment : 97 time for calcul the mask position with numpy : 0.00043272972106933594 nb_pixel_total : 3060 time to create 1 rle with old method : 0.0035703182220458984 length of segment : 68 time for calcul the mask position with numpy : 0.002333402633666992 nb_pixel_total : 32551 time to create 1 rle with old method : 0.03534388542175293 length of segment : 217 time for calcul the mask position with numpy : 0.0004818439483642578 nb_pixel_total : 3632 time to create 1 rle with old method : 0.004567861557006836 length of segment : 61 time for calcul the mask position with numpy : 0.004560947418212891 nb_pixel_total : 83808 time to create 1 rle with old method : 0.09122943878173828 length of segment : 313 time for calcul the mask position with numpy : 0.0006620883941650391 nb_pixel_total : 25417 time to create 1 rle with old method : 0.03190469741821289 length of segment : 162 time for calcul the mask position with numpy : 0.005880594253540039 nb_pixel_total : 50808 time to create 1 rle with old method : 0.06658148765563965 length of segment : 283 time for calcul the mask position with numpy : 0.0005745887756347656 nb_pixel_total : 8386 time to create 1 rle with old method : 0.009360790252685547 length of segment : 80 time for calcul the mask position with numpy : 7.963180541992188e-05 nb_pixel_total : 1866 time to create 1 rle with old method : 0.0022170543670654297 length of segment : 58 time for calcul the mask position with numpy : 0.0013413429260253906 nb_pixel_total : 15356 time to create 1 rle with old method : 0.017065048217773438 length of segment : 210 time for calcul the mask position with numpy : 0.00400996208190918 nb_pixel_total : 54252 time to create 1 rle with old method : 0.06173253059387207 length of segment : 388 time for calcul the mask position with numpy : 0.00197601318359375 nb_pixel_total : 42430 time to create 1 rle with old method : 0.047197818756103516 length of segment : 443 time for calcul the mask position with numpy : 0.0017170906066894531 nb_pixel_total : 23242 time to create 1 rle with old method : 0.027127981185913086 length of segment : 176 time for calcul the mask position with numpy : 0.0032100677490234375 nb_pixel_total : 55393 time to create 1 rle with old method : 0.06311821937561035 length of segment : 258 time for calcul the mask position with numpy : 0.0003342628479003906 nb_pixel_total : 9735 time to create 1 rle with old method : 0.011087417602539062 length of segment : 102 time for calcul the mask position with numpy : 0.0021948814392089844 nb_pixel_total : 41647 time to create 1 rle with old method : 0.04552865028381348 length of segment : 131 time for calcul the mask position with numpy : 0.000286102294921875 nb_pixel_total : 2522 time to create 1 rle with old method : 0.003029346466064453 length of segment : 58 time for calcul the mask position with numpy : 0.004262447357177734 nb_pixel_total : 188223 time to create 1 rle with new method : 0.007049083709716797 length of segment : 476 time for calcul the mask position with numpy : 0.007607221603393555 nb_pixel_total : 334302 time to create 1 rle with new method : 0.013762474060058594 length of segment : 601 time for calcul the mask position with numpy : 0.005795955657958984 nb_pixel_total : 289796 time to create 1 rle with new method : 0.010949134826660156 length of segment : 546 time for calcul the mask position with numpy : 0.006863117218017578 nb_pixel_total : 244937 time to create 1 rle with new method : 0.01410531997680664 length of segment : 722 time for calcul the mask position with numpy : 0.0061261653900146484 nb_pixel_total : 280821 time to create 1 rle with new method : 0.012634038925170898 length of segment : 663 time for calcul the mask position with numpy : 0.008811712265014648 nb_pixel_total : 307766 time to create 1 rle with new method : 0.017832517623901367 length of segment : 712 time for calcul the mask position with numpy : 0.004651069641113281 nb_pixel_total : 205843 time to create 1 rle with new method : 0.008460283279418945 length of segment : 476 time for calcul the mask position with numpy : 0.007050275802612305 nb_pixel_total : 306262 time to create 1 rle with new method : 0.013971805572509766 length of segment : 765 time for calcul the mask position with numpy : 0.004656314849853516 nb_pixel_total : 165601 time to create 1 rle with new method : 0.012862443923950195 length of segment : 542 time for calcul the mask position with numpy : 0.00567173957824707 nb_pixel_total : 138059 time to create 1 rle with old method : 0.15408086776733398 length of segment : 547 time for calcul the mask position with numpy : 0.014619112014770508 nb_pixel_total : 448939 time to create 1 rle with new method : 0.027996301651000977 length of segment : 848 time for calcul the mask position with numpy : 0.005904674530029297 nb_pixel_total : 274090 time to create 1 rle with new method : 0.018585681915283203 length of segment : 874 time for calcul the mask position with numpy : 0.0016827583312988281 nb_pixel_total : 81356 time to create 1 rle with old method : 0.09127497673034668 length of segment : 211 time for calcul the mask position with numpy : 0.004529237747192383 nb_pixel_total : 261814 time to create 1 rle with new method : 0.013908624649047852 length of segment : 637 time for calcul the mask position with numpy : 0.005711793899536133 nb_pixel_total : 265620 time to create 1 rle with new method : 0.01147317886352539 length of segment : 657 time for calcul the mask position with numpy : 0.0030269622802734375 nb_pixel_total : 136497 time to create 1 rle with old method : 0.1474759578704834 length of segment : 465 time for calcul the mask position with numpy : 0.004320859909057617 nb_pixel_total : 153420 time to create 1 rle with new method : 0.011281967163085938 length of segment : 439 time for calcul the mask position with numpy : 0.0010309219360351562 nb_pixel_total : 62551 time to create 1 rle with old method : 0.07129502296447754 length of segment : 196 time for calcul the mask position with numpy : 0.0031919479370117188 nb_pixel_total : 185104 time to create 1 rle with new method : 0.006639242172241211 length of segment : 628 time for calcul the mask position with numpy : 0.010877847671508789 nb_pixel_total : 235611 time to create 1 rle with new method : 0.010341644287109375 length of segment : 542 time for calcul the mask position with numpy : 0.0039212703704833984 nb_pixel_total : 73382 time to create 1 rle with old method : 0.08203673362731934 length of segment : 490 time for calcul the mask position with numpy : 0.0011050701141357422 nb_pixel_total : 15702 time to create 1 rle with old method : 0.025602340698242188 length of segment : 160 time for calcul the mask position with numpy : 0.008367776870727539 nb_pixel_total : 198455 time to create 1 rle with new method : 0.009064674377441406 length of segment : 489 time for calcul the mask position with numpy : 0.0035593509674072266 nb_pixel_total : 68799 time to create 1 rle with old method : 0.07507562637329102 length of segment : 447 time for calcul the mask position with numpy : 0.0032536983489990234 nb_pixel_total : 59573 time to create 1 rle with old method : 0.0653996467590332 length of segment : 221 time for calcul the mask position with numpy : 0.0036509037017822266 nb_pixel_total : 63873 time to create 1 rle with old method : 0.07007312774658203 length of segment : 263 time for calcul the mask position with numpy : 0.010282754898071289 nb_pixel_total : 152639 time to create 1 rle with new method : 0.011118888854980469 length of segment : 814 time for calcul the mask position with numpy : 0.0008573532104492188 nb_pixel_total : 12853 time to create 1 rle with old method : 0.014200687408447266 length of segment : 133 time for calcul the mask position with numpy : 0.0010025501251220703 nb_pixel_total : 6155 time to create 1 rle with old method : 0.007639408111572266 length of segment : 171 time for calcul the mask position with numpy : 0.0004818439483642578 nb_pixel_total : 4797 time to create 1 rle with old method : 0.0051746368408203125 length of segment : 80 time for calcul the mask position with numpy : 0.00026988983154296875 nb_pixel_total : 10192 time to create 1 rle with old method : 0.011762380599975586 length of segment : 162 time for calcul the mask position with numpy : 9.393692016601562e-05 nb_pixel_total : 1788 time to create 1 rle with old method : 0.002221822738647461 length of segment : 59 time for calcul the mask position with numpy : 0.00012874603271484375 nb_pixel_total : 2979 time to create 1 rle with old method : 0.003474712371826172 length of segment : 75 time for calcul the mask position with numpy : 0.002979278564453125 nb_pixel_total : 51403 time to create 1 rle with old method : 0.056137800216674805 length of segment : 204 time for calcul the mask position with numpy : 0.010099172592163086 nb_pixel_total : 194365 time to create 1 rle with new method : 0.009944677352905273 length of segment : 488 time for calcul the mask position with numpy : 0.0013048648834228516 nb_pixel_total : 17714 time to create 1 rle with old method : 0.019925355911254883 length of segment : 194 time for calcul the mask position with numpy : 0.00769495964050293 nb_pixel_total : 204085 time to create 1 rle with new method : 0.016789913177490234 length of segment : 364 time for calcul the mask position with numpy : 0.00532984733581543 nb_pixel_total : 110928 time to create 1 rle with old method : 0.11939215660095215 length of segment : 440 time for calcul the mask position with numpy : 0.000270843505859375 nb_pixel_total : 2694 time to create 1 rle with old method : 0.003039836883544922 length of segment : 69 time for calcul the mask position with numpy : 0.0002675056457519531 nb_pixel_total : 4831 time to create 1 rle with old method : 0.005399465560913086 length of segment : 104 time for calcul the mask position with numpy : 0.0002682209014892578 nb_pixel_total : 10503 time to create 1 rle with old method : 0.012097358703613281 length of segment : 67 time for calcul the mask position with numpy : 0.00019669532775878906 nb_pixel_total : 2076 time to create 1 rle with old method : 0.002540111541748047 length of segment : 69 time for calcul the mask position with numpy : 0.00047969818115234375 nb_pixel_total : 3925 time to create 1 rle with old method : 0.0046236515045166016 length of segment : 100 time for calcul the mask position with numpy : 0.0018432140350341797 nb_pixel_total : 43376 time to create 1 rle with old method : 0.047940969467163086 length of segment : 323 time for calcul the mask position with numpy : 0.002315044403076172 nb_pixel_total : 37224 time to create 1 rle with old method : 0.041037559509277344 length of segment : 271 time for calcul the mask position with numpy : 0.0032067298889160156 nb_pixel_total : 54400 time to create 1 rle with old method : 0.058608055114746094 length of segment : 212 time for calcul the mask position with numpy : 0.0030753612518310547 nb_pixel_total : 67272 time to create 1 rle with old method : 0.07221126556396484 length of segment : 362 time for calcul the mask position with numpy : 0.0017817020416259766 nb_pixel_total : 22385 time to create 1 rle with old method : 0.025246858596801758 length of segment : 260 time for calcul the mask position with numpy : 0.0009176731109619141 nb_pixel_total : 27872 time to create 1 rle with old method : 0.031365156173706055 length of segment : 539 time for calcul the mask position with numpy : 0.0012309551239013672 nb_pixel_total : 24073 time to create 1 rle with old method : 0.02627706527709961 length of segment : 241 time for calcul the mask position with numpy : 0.0053217411041259766 nb_pixel_total : 106396 time to create 1 rle with old method : 0.11401581764221191 length of segment : 418 time for calcul the mask position with numpy : 0.0017399787902832031 nb_pixel_total : 17868 time to create 1 rle with old method : 0.020711183547973633 length of segment : 215 time for calcul the mask position with numpy : 0.0003314018249511719 nb_pixel_total : 11072 time to create 1 rle with old method : 0.012839078903198242 length of segment : 158 time for calcul the mask position with numpy : 0.007483005523681641 nb_pixel_total : 123235 time to create 1 rle with old method : 0.1596541404724121 length of segment : 641 time for calcul the mask position with numpy : 0.018621444702148438 nb_pixel_total : 276969 time to create 1 rle with new method : 0.020570993423461914 length of segment : 677 time for calcul the mask position with numpy : 0.018097877502441406 nb_pixel_total : 320054 time to create 1 rle with new method : 0.02179574966430664 length of segment : 582 time for calcul the mask position with numpy : 0.013714075088500977 nb_pixel_total : 218715 time to create 1 rle with new method : 0.017066478729248047 length of segment : 529 time for calcul the mask position with numpy : 0.0019588470458984375 nb_pixel_total : 14243 time to create 1 rle with old method : 0.0181729793548584 length of segment : 206 time for calcul the mask position with numpy : 0.002941608428955078 nb_pixel_total : 13815 time to create 1 rle with old method : 0.015049457550048828 length of segment : 175 time for calcul the mask position with numpy : 0.0019686222076416016 nb_pixel_total : 32644 time to create 1 rle with old method : 0.03739643096923828 length of segment : 239 time for calcul the mask position with numpy : 0.0005934238433837891 nb_pixel_total : 8963 time to create 1 rle with old method : 0.010500907897949219 length of segment : 134 time for calcul the mask position with numpy : 0.008688211441040039 nb_pixel_total : 116929 time to create 1 rle with old method : 0.12633585929870605 length of segment : 399 time for calcul the mask position with numpy : 0.0075757503509521484 nb_pixel_total : 105154 time to create 1 rle with old method : 0.11200356483459473 length of segment : 463 time for calcul the mask position with numpy : 0.0031576156616210938 nb_pixel_total : 55882 time to create 1 rle with old method : 0.05999279022216797 length of segment : 295 time for calcul the mask position with numpy : 0.0033359527587890625 nb_pixel_total : 57817 time to create 1 rle with old method : 0.06378722190856934 length of segment : 247 time for calcul the mask position with numpy : 0.0009369850158691406 nb_pixel_total : 17349 time to create 1 rle with old method : 0.0193173885345459 length of segment : 86 time for calcul the mask position with numpy : 0.0009801387786865234 nb_pixel_total : 12589 time to create 1 rle with old method : 0.014230966567993164 length of segment : 153 time for calcul the mask position with numpy : 0.002442598342895508 nb_pixel_total : 32784 time to create 1 rle with old method : 0.035478830337524414 length of segment : 322 time for calcul the mask position with numpy : 0.0019137859344482422 nb_pixel_total : 33523 time to create 1 rle with old method : 0.036670684814453125 length of segment : 194 time for calcul the mask position with numpy : 0.0011625289916992188 nb_pixel_total : 20629 time to create 1 rle with old method : 0.022445201873779297 length of segment : 149 time for calcul the mask position with numpy : 0.00046825408935546875 nb_pixel_total : 5307 time to create 1 rle with old method : 0.006267070770263672 length of segment : 89 time for calcul the mask position with numpy : 0.002306699752807617 nb_pixel_total : 34240 time to create 1 rle with old method : 0.03746342658996582 length of segment : 254 time for calcul the mask position with numpy : 0.0005712509155273438 nb_pixel_total : 6557 time to create 1 rle with old method : 0.0075724124908447266 length of segment : 70 time for calcul the mask position with numpy : 0.01071476936340332 nb_pixel_total : 115872 time to create 1 rle with old method : 0.12272000312805176 length of segment : 739 time for calcul the mask position with numpy : 0.01437830924987793 nb_pixel_total : 13844 time to create 1 rle with old method : 0.024055957794189453 length of segment : 200 time for calcul the mask position with numpy : 0.000209808349609375 nb_pixel_total : 3766 time to create 1 rle with old method : 0.004680156707763672 length of segment : 94 time for calcul the mask position with numpy : 0.002046346664428711 nb_pixel_total : 29500 time to create 1 rle with old method : 0.0316166877746582 length of segment : 272 time for calcul the mask position with numpy : 0.0030889511108398438 nb_pixel_total : 66055 time to create 1 rle with old method : 0.06982564926147461 length of segment : 360 time for calcul the mask position with numpy : 0.006208658218383789 nb_pixel_total : 70882 time to create 1 rle with old method : 0.08879947662353516 length of segment : 475 time for calcul the mask position with numpy : 0.0007779598236083984 nb_pixel_total : 32143 time to create 1 rle with old method : 0.03466153144836426 length of segment : 181 time for calcul the mask position with numpy : 0.0009808540344238281 nb_pixel_total : 11380 time to create 1 rle with old method : 0.012546062469482422 length of segment : 107 time for calcul the mask position with numpy : 0.0036554336547851562 nb_pixel_total : 38550 time to create 1 rle with old method : 0.04276084899902344 length of segment : 526 time for calcul the mask position with numpy : 0.0005247592926025391 nb_pixel_total : 7259 time to create 1 rle with old method : 0.008472681045532227 length of segment : 101 time for calcul the mask position with numpy : 0.0015420913696289062 nb_pixel_total : 18840 time to create 1 rle with old method : 0.020645618438720703 length of segment : 299 time for calcul the mask position with numpy : 0.0020439624786376953 nb_pixel_total : 35028 time to create 1 rle with old method : 0.04272294044494629 length of segment : 302 time for calcul the mask position with numpy : 0.007205963134765625 nb_pixel_total : 121091 time to create 1 rle with old method : 0.13346266746520996 length of segment : 459 time for calcul the mask position with numpy : 0.005498409271240234 nb_pixel_total : 101988 time to create 1 rle with old method : 0.11071228981018066 length of segment : 432 time for calcul the mask position with numpy : 0.0007417201995849609 nb_pixel_total : 5033 time to create 1 rle with old method : 0.005793333053588867 length of segment : 101 time for calcul the mask position with numpy : 0.005769491195678711 nb_pixel_total : 95466 time to create 1 rle with old method : 0.10431671142578125 length of segment : 665 time for calcul the mask position with numpy : 0.0009446144104003906 nb_pixel_total : 35132 time to create 1 rle with old method : 0.037543535232543945 length of segment : 195 time for calcul the mask position with numpy : 9.5367431640625e-05 nb_pixel_total : 1778 time to create 1 rle with old method : 0.0020418167114257812 length of segment : 56 time for calcul the mask position with numpy : 0.0003476142883300781 nb_pixel_total : 6949 time to create 1 rle with old method : 0.008091211318969727 length of segment : 62 time spent for convertir_results : 105.0950255393982 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 956 chid ids of type : 3760 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 219885 save missing photos in datou_result : time spend for datou_step_exec : 469.3734984397888 time spend to save output : 17.716463565826416 total time spend for step 1 : 487.08996200561523 step2:blur_detection Tue Feb 4 14:08:16 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 inside step blur_detection methode: ratio et variance treat image : temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d.jpg resize: (2160, 3264) 1334416147 -5.666096234671825 treat image : temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa.jpg resize: (2160, 3264) 1334416146 -5.708156237290212 treat image : temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510.jpg resize: (2160, 3264) 1334416145 -4.815456407597747 treat image : temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced.jpg resize: (2160, 3264) 1334416109 -3.776201276033427 treat image : temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0.jpg resize: (2160, 3264) 1334416058 -4.8471468797094275 treat image : temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc.jpg resize: (2160, 3264) 1334194033 -4.176024405855606 treat image : temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc.jpg resize: (2160, 3264) 1334194028 -5.219630423003019 treat image : temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a.jpg resize: (2160, 3264) 1334194010 -5.189865102780668 treat image : temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f.jpg resize: (2160, 3264) 1334194006 -4.672563076193158 treat image : temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f.jpg resize: (2160, 3264) 1334194002 -5.23752644705515 treat image : temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a.jpg resize: (2160, 3264) 1334194000 -3.9924691344146597 treat image : temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0.jpg resize: (2160, 3264) 1334193886 -5.733144942582252 treat image : temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b.jpg resize: (2160, 3264) 1334193883 -5.539662795647853 treat image : temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3.jpg resize: (2160, 3264) 1334193847 -5.509290430607267 treat image : temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22.jpg resize: (2160, 3264) 1334193843 -5.655041125610419 treat image : temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea.jpg resize: (2160, 3264) 1334193840 -4.925104137530129 treat image : temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c.jpg resize: (2160, 3264) 1334193838 -5.795876552282547 treat image : temp/1738674005_4044648_1334193728_dcaf7feecc7ce8071774f70bd9bb8ca5.jpg resize: (2160, 3264) 1334193728 -2.548575432486161 treat image : temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271.jpg resize: (2160, 3264) 1334193389 -5.527034204905684 treat image : temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60.jpg resize: (2160, 3264) 1334193386 -4.833988194946205 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 : 20 time used for this insertion : 0.013015031814575195 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 20 time used for this insertion : 0.011645317077636719 save missing photos in datou_result : time spend for datou_step_exec : 53.85135269165039 time spend to save output : 0.029583215713500977 total time spend for step 2 : 53.88093590736389 step3:brightness Tue Feb 4 14:09:10 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 inside step calcul brightness treat image : temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d.jpg treat image : temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa.jpg treat image : temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510.jpg treat image : temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced.jpg treat image : temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0.jpg treat image : temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc.jpg treat image : temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc.jpg treat image : temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a.jpg treat image : temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f.jpg treat image : temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f.jpg treat image : temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a.jpg treat image : temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0.jpg treat image : temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b.jpg treat image : temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3.jpg treat image : temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22.jpg treat image : temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea.jpg treat image : temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c.jpg treat image : temp/1738674005_4044648_1334193728_dcaf7feecc7ce8071774f70bd9bb8ca5.jpg treat image : temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271.jpg treat image : temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60.jpg 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 : 20 time used for this insertion : 0.01336526870727539 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 20 time used for this insertion : 0.01204681396484375 save missing photos in datou_result : time spend for datou_step_exec : 15.91431999206543 time spend to save output : 0.03357052803039551 total time spend for step 3 : 15.947890520095825 step4:crop_condition Tue Feb 4 14:09:26 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 : 3760 Loading chi in step crop for list_pids : 20 ! batch 1 Loaded 956 chid ids of type : 3760 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : barquette param for this class : {'min_score': 0.7} filtre for class : barquette hashtag_id of this class : 492787675 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 97 About to insert : list_path_to_insert length 97 new photo from crops ! About to upload 97 photos upload in portfolio : 4219792 init cache_photo without model_param we have 97 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738674595_4044648 we have uploaded 97 photos in the portfolio 4219792 time of upload the photos Elapsed time : 25.27319622039795 we have finished the crop for the class : barquette begin to crop the class : fibreux_cont param for this class : {'min_score': 0.7} filtre for class : fibreux_cont hashtag_id of this class : 2107756748 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 98 About to insert : list_path_to_insert length 98 new photo from crops ! About to upload 98 photos upload in portfolio : 4219792 init cache_photo without model_param we have 98 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738674636_4044648 we have uploaded 98 photos in the portfolio 4219792 time of upload the photos Elapsed time : 24.113720893859863 we have finished the crop for the class : fibreux_cont begin to crop the class : film_plastique param for this class : {'min_score': 0.7} filtre for class : film_plastique hashtag_id of this class : 2107756122 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 60 About to insert : list_path_to_insert length 60 new photo from crops ! About to upload 60 photos upload in portfolio : 4219792 init cache_photo without model_param we have 60 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738674675_4044648 we have uploaded 60 photos in the portfolio 4219792 time of upload the photos Elapsed time : 14.875598430633545 we have finished the crop for the class : film_plastique begin to crop the class : autre_contaminant param for this class : {'min_score': 0.7} filtre for class : autre_contaminant hashtag_id of this class : 2107756781 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 212 About to insert : list_path_to_insert length 212 new photo from crops ! About to upload 212 photos upload in portfolio : 4219792 init cache_photo without model_param we have 212 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738674730_4044648 we have uploaded 212 photos in the portfolio 4219792 time of upload the photos Elapsed time : 48.46527338027954 we have finished the crop for the class : autre_contaminant begin to crop the class : etiquette_detachee param for this class : {'min_score': 0.7} filtre for class : etiquette_detachee hashtag_id of this class : 2107756860 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 34 About to insert : list_path_to_insert length 34 new photo from crops ! About to upload 34 photos upload in portfolio : 4219792 init cache_photo without model_param we have 34 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738674784_4044648 we have uploaded 34 photos in the portfolio 4219792 time of upload the photos Elapsed time : 8.123234510421753 we have finished the crop for the class : etiquette_detachee begin to crop the class : pet_clair_cont param for this class : {'min_score': 0.7} filtre for class : pet_clair_cont hashtag_id of this class : 2107758154 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 83 About to insert : list_path_to_insert length 83 new photo from crops ! About to upload 83 photos upload in portfolio : 4219792 init cache_photo without model_param we have 83 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738674807_4044648 we have uploaded 83 photos in the portfolio 4219792 time of upload the photos Elapsed time : 18.15085220336914 we have finished the crop for the class : pet_clair_cont begin to crop the class : metal_cont param for this class : {'min_score': 0.7} filtre for class : metal_cont hashtag_id of this class : 2107756749 begin to crop the class : pet_fonce_cont param for this class : {'min_score': 0.7} filtre for class : pet_fonce_cont hashtag_id of this class : 2107758155 begin to crop the class : pet_opaque_cont param for this class : {'min_score': 0.7} filtre for class : pet_opaque_cont hashtag_id of this class : 2107758156 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 69 About to insert : list_path_to_insert length 69 new photo from crops ! About to upload 69 photos upload in portfolio : 4219792 init cache_photo without model_param we have 69 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738674842_4044648 we have uploaded 69 photos in the portfolio 4219792 time of upload the photos Elapsed time : 16.68821620941162 we have finished the crop for the class : pet_opaque_cont delete rles for these photos Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1334416147, 1334416146, 1334416145, 1334416109, 1334416058, 1334194033, 1334194028, 1334194010, 1334194006, 1334194002, 1334194000, 1334193886, 1334193883, 1334193847, 1334193843, 1334193840, 1334193838, 1334193728, 1334193389, 1334193386] Looping around the photos to save general results len do output : 653 /1334560641Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560642Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560643Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560644Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560645Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560646Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560647Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560649Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560650Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560651Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560652Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560653Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560654Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560655Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560656Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560657Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560658Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560659Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560660Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560661Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560662Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560663Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560664Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560665Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560666Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560667Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560668Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560669Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560670Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560671Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560673Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560674Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560675Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560676Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560677Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560678Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560679Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560680Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560681Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560682Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560683Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560684Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560685Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560686Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560688Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560689Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560690Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560691Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560692Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560693Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560694Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560695Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560696Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560697Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560698Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560699Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560700Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560701Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560702Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560703Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560704Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560705Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560706Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560707Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560708Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560710Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560711Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560712Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560713Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560714Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560715Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560716Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560717Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560718Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560719Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560720Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560721Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560722Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560723Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560724Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560725Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560726Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560727Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560728Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560729Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560730Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560732Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560733Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560734Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560735Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560736Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560737Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560738Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560740Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560741Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560742Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560743Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560766Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560767Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560768Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560769Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560770Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560771Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560773Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560774Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560775Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560776Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560777Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560778Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560779Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560780Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560781Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560782Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560783Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560784Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560785Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560786Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560787Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560788Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560789Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560790Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560791Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560792Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560793Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560794Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560795Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560796Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560797Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560798Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560799Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560800Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560801Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560802Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560803Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560804Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560805Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560806Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560807Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560808Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560809Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560810Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560811Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560812Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560814Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560815Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560816Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560817Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560818Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560819Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560820Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560821Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560822Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560823Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560824Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560825Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560826Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560827Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560828Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560829Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560830Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560831Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560832Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560833Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560834Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560835Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560837Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560838Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560839Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560840Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560841Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560842Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560843Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560844Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560845Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560846Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560847Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560848Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560849Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560850Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560851Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560852Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560853Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560854Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560855Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560856Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560857Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560858Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560859Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560860Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560861Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560862Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560863Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560864Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560865Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560866Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560897Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560898Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560899Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560900Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560901Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560902Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560904Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560908Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560912Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560913Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560914Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560915Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560916Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560918Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560919Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560920Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560921Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560922Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560923Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560924Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560925Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560926Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560927Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560929Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560930Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560931Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560932Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560933Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560934Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560935Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560936Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560937Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560938Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560939Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560940Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560941Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560942Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560943Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560944Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560945Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560946Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560947Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560948Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560949Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560951Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560952Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560953Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560954Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560955Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560956Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560958Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560959Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560960Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560961Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560962Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560963Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560964Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560965Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560966Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334560967Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334561017Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334561018Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334561019Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416147', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416146', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416145', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416109', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416058', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194033', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194028', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194010', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194006', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194002', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194000', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193886', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193883', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193847', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193843', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193840', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193838', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193728', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193389', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193386', None, None, None, None, None, '2543013') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1979 time used for this insertion : 0.11004400253295898 save_final save missing photos in datou_result : time spend for datou_step_exec : 294.064945936203 time spend to save output : 0.12413740158081055 total time spend for step 4 : 294.1890833377838 step5:thcl Tue Feb 4 14:14:20 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step Thcl ! we are using the classfication for only one thcl 3233 time to import caffe and check if the image exist : 0.06872963905334473 time to convert the images to numpy array : 0.4189445972442627 time to import caffe and check if the image exist : 0.061103105545043945 time to convert the images to numpy array : 0.5079638957977295 time to import caffe and check if the image exist : 0.03602933883666992 time to convert the images to numpy array : 0.5338490009307861 time to import caffe and check if the image exist : 0.060077667236328125 time to convert the images to numpy array : 0.5285203456878662 time to import caffe and check if the image exist : 0.0700690746307373 time to convert the images to numpy array : 0.5461175441741943 time to import caffe and check if the image exist : 0.042762041091918945 time to convert the images to numpy array : 0.5916769504547119 time to import caffe and check if the image exist : 0.0444793701171875 time to convert the images to numpy array : 0.626997709274292 time to import caffe and check if the image exist : 0.053487300872802734 time to convert the images to numpy array : 0.6206016540527344 time to import caffe and check if the image exist : 0.06681180000305176 time to convert the images to numpy array : 0.6077792644500732 time to import caffe and check if the image exist : 0.05826449394226074 time to convert the images to numpy array : 0.6579186916351318 total time to convert the images to numpy array : 0.9020638465881348 list photo_ids error: [] list photo_ids correct : [1334561230, 1334561231, 1334561232, 1334561233, 1334561234, 1334561239, 1334561240, 1334561241, 1334561242, 1334561243, 1334561244, 1334561245, 1334561246, 1334561247, 1334561248, 1334561249, 1334561250, 1334561251, 1334561252, 1334561253, 1334561254, 1334561255, 1334561256, 1334561257, 1334561258, 1334561259, 1334561260, 1334561261, 1334561262, 1334561263, 1334561264, 1334561265, 1334561266, 1334561267, 1334561268, 1334561270, 1334561272, 1334561273, 1334561274, 1334561292, 1334561293, 1334561294, 1334561295, 1334561296, 1334561297, 1334561298, 1334561299, 1334561300, 1334561301, 1334561302, 1334561303, 1334561304, 1334561305, 1334561307, 1334561308, 1334561309, 1334561310, 1334561311, 1334561312, 1334561313, 1334561315, 1334561316, 1334561317, 1334561318, 1334561319, 1334561320, 1334561026, 1334561027, 1334561028, 1334561029, 1334561030, 1334561031, 1334561032, 1334561033, 1334561034, 1334561035, 1334561036, 1334561037, 1334561038, 1334561039, 1334561040, 1334561041, 1334561042, 1334561044, 1334561045, 1334561046, 1334561047, 1334561048, 1334561049, 1334561050, 1334561051, 1334561052, 1334561053, 1334561054, 1334561055, 1334561056, 1334561057, 1334561058, 1334561059, 1334561060, 1334561061, 1334561062, 1334561063, 1334561064, 1334561065, 1334561066, 1334561067, 1334561068, 1334561069, 1334561070, 1334561071, 1334561072, 1334561074, 1334561075, 1334561077, 1334561078, 1334561079, 1334561080, 1334561081, 1334561082, 1334561083, 1334561084, 1334561085, 1334561086, 1334561087, 1334561088, 1334561089, 1334561090, 1334561091, 1334561092, 1334561093, 1334561094, 1334561321, 1334561322, 1334561323, 1334561324, 1334561325, 1334561326, 1334561327, 1334561328, 1334561329, 1334561330, 1334561331, 1334561332, 1334561333, 1334561334, 1334561335, 1334561336, 1334561337, 1334561338, 1334561339, 1334561340, 1334561341, 1334561342, 1334561343, 1334561344, 1334561345, 1334561346, 1334561347, 1334561348, 1334561349, 1334561350, 1334561351, 1334561352, 1334561353, 1334561354, 1334561355, 1334561356, 1334561357, 1334561358, 1334561359, 1334561360, 1334561361, 1334561362, 1334561363, 1334561364, 1334561365, 1334561366, 1334561367, 1334561368, 1334561369, 1334561370, 1334561371, 1334561372, 1334561373, 1334561374, 1334561375, 1334561376, 1334561402, 1334561403, 1334561404, 1334561405, 1334561406, 1334561407, 1334561408, 1334561409, 1334561411, 1334561412, 1334560802, 1334560803, 1334560804, 1334560805, 1334560806, 1334560807, 1334560808, 1334560809, 1334560810, 1334560811, 1334560812, 1334560814, 1334560815, 1334560816, 1334560817, 1334560818, 1334560819, 1334560820, 1334560821, 1334560822, 1334560823, 1334560824, 1334560825, 1334560826, 1334560827, 1334560828, 1334560829, 1334560830, 1334560831, 1334560832, 1334560833, 1334560834, 1334560835, 1334560837, 1334560838, 1334560839, 1334560840, 1334560841, 1334560842, 1334560843, 1334560844, 1334560845, 1334560846, 1334560847, 1334560848, 1334560849, 1334560850, 1334560851, 1334560852, 1334560853, 1334560854, 1334560855, 1334560856, 1334560857, 1334560858, 1334560859, 1334560860, 1334560861, 1334560862, 1334560863, 1334560864, 1334560865, 1334560866, 1334560897, 1334560898, 1334560899, 1334561095, 1334561096, 1334561097, 1334561098, 1334561099, 1334561100, 1334561101, 1334561102, 1334561103, 1334561104, 1334561105, 1334561106, 1334561107, 1334561108, 1334561109, 1334561110, 1334561111, 1334561112, 1334561113, 1334561114, 1334561115, 1334561116, 1334561117, 1334561118, 1334561119, 1334561120, 1334561121, 1334561122, 1334561123, 1334561124, 1334561125, 1334561126, 1334561127, 1334561128, 1334561129, 1334561130, 1334561131, 1334561132, 1334561133, 1334561134, 1334561135, 1334561136, 1334561137, 1334561138, 1334561139, 1334561140, 1334561141, 1334561142, 1334561143, 1334561144, 1334561145, 1334561146, 1334561147, 1334561148, 1334561149, 1334561150, 1334561151, 1334561152, 1334561153, 1334561154, 1334561156, 1334561157, 1334561158, 1334561159, 1334561160, 1334561161, 1334561413, 1334561415, 1334561416, 1334561417, 1334561418, 1334561419, 1334561420, 1334561421, 1334561422, 1334561423, 1334561424, 1334561425, 1334561426, 1334561427, 1334561428, 1334561429, 1334561430, 1334561431, 1334561432, 1334561433, 1334561434, 1334561435, 1334561436, 1334561437, 1334561438, 1334561439, 1334561440, 1334561441, 1334561442, 1334561443, 1334561444, 1334561445, 1334561446, 1334561447, 1334561448, 1334561449, 1334561450, 1334561451, 1334561452, 1334561453, 1334561455, 1334561456, 1334561457, 1334561458, 1334561459, 1334561460, 1334561461, 1334561462, 1334561463, 1334561464, 1334561465, 1334561466, 1334561467, 1334561468, 1334561469, 1334561470, 1334561471, 1334561472, 1334561473, 1334560711, 1334560712, 1334560713, 1334560714, 1334560715, 1334560716, 1334560717, 1334560718, 1334560719, 1334560720, 1334560721, 1334560722, 1334560723, 1334560724, 1334560725, 1334560726, 1334560727, 1334560728, 1334560729, 1334560730, 1334560732, 1334560733, 1334560734, 1334560735, 1334560736, 1334560737, 1334560738, 1334560740, 1334560741, 1334560742, 1334560743, 1334560766, 1334560767, 1334560768, 1334560769, 1334560770, 1334560771, 1334560773, 1334560774, 1334560775, 1334560776, 1334560777, 1334560778, 1334560779, 1334560780, 1334560781, 1334560782, 1334560783, 1334560784, 1334560785, 1334560786, 1334560787, 1334560788, 1334560789, 1334560790, 1334560791, 1334560792, 1334560793, 1334560794, 1334560795, 1334560796, 1334560797, 1334560798, 1334560799, 1334560800, 1334560801, 1334561162, 1334561163, 1334561165, 1334561166, 1334561167, 1334561168, 1334561169, 1334561170, 1334561171, 1334561172, 1334561173, 1334561174, 1334561176, 1334561177, 1334561178, 1334561179, 1334561180, 1334561181, 1334561182, 1334561183, 1334561184, 1334561185, 1334561186, 1334561187, 1334561188, 1334561189, 1334561190, 1334561191, 1334561192, 1334561193, 1334561194, 1334561195, 1334561196, 1334561197, 1334561198, 1334561199, 1334561200, 1334561201, 1334561202, 1334561203, 1334561204, 1334561205, 1334561206, 1334561207, 1334561208, 1334561209, 1334561210, 1334561211, 1334561212, 1334561213, 1334561214, 1334561215, 1334561216, 1334561217, 1334561218, 1334561219, 1334561220, 1334561221, 1334561222, 1334561223, 1334561224, 1334561225, 1334561226, 1334561227, 1334561228, 1334561229, 1334560900, 1334560901, 1334560902, 1334560904, 1334560908, 1334560912, 1334560913, 1334560914, 1334560915, 1334560916, 1334560918, 1334560919, 1334560920, 1334560921, 1334560922, 1334560923, 1334560924, 1334560925, 1334560926, 1334560927, 1334560929, 1334560930, 1334560931, 1334560932, 1334560933, 1334560934, 1334560935, 1334560936, 1334560937, 1334560938, 1334560939, 1334560940, 1334560941, 1334560942, 1334560943, 1334560944, 1334560945, 1334560946, 1334560947, 1334560948, 1334560949, 1334560951, 1334560952, 1334560953, 1334560954, 1334560955, 1334560956, 1334560958, 1334560959, 1334560960, 1334560961, 1334560962, 1334560963, 1334560964, 1334560965, 1334560966, 1334560967, 1334561017, 1334561018, 1334561019, 1334561020, 1334561021, 1334561022, 1334561023, 1334561024, 1334561025, 1334560641, 1334560642, 1334560643, 1334560644, 1334560645, 1334560646, 1334560647, 1334560649, 1334560650, 1334560651, 1334560652, 1334560653, 1334560654, 1334560655, 1334560656, 1334560657, 1334560658, 1334560659, 1334560660, 1334560661, 1334560662, 1334560663, 1334560664, 1334560665, 1334560666, 1334560667, 1334560668, 1334560669, 1334560670, 1334560671, 1334560673, 1334560674, 1334560675, 1334560676, 1334560677, 1334560678, 1334560679, 1334560680, 1334560681, 1334560682, 1334560683, 1334560684, 1334560685, 1334560686, 1334560688, 1334560689, 1334560690, 1334560691, 1334560692, 1334560693, 1334560694, 1334560695, 1334560696, 1334560697, 1334560698, 1334560699, 1334560700, 1334560701, 1334560702, 1334560703, 1334560704, 1334560705, 1334560706, 1334560707, 1334560708, 1334560710] number of photos to traite : 653 try to delete the photos incorrect in DB tagging for thcl : 3233 To do loadFromThcl(), then load ParamDescType : thcl3233 thcls : [{'id': 3233, 'mtr_user_id': 31, 'name': 'learn_generique_01122021_6000_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'barquette_opaque,carton,ela,environnement,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_fonce,pet_opaque,textiles_sanitaires', 'svm_portfolios_learning': '4825680,4829009,4825557,4824709,4824714,4824733,4825676,4824737,4824753,4829002,4824848,4824863,4825667,4825677', 'photo_hashtag_type': 4151, 'photo_desc_type': 5557, 'type_classification': 'caffe', 'hashtag_id_list': '2107760128,492774966,492741797,493012381,492636447,2107756122,493202403,492628673,492668766,628944319,2107755846,2107755900,2107759152,2107760129'}] thcl {'id': 3233, 'mtr_user_id': 31, 'name': 'learn_generique_01122021_6000_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'barquette_opaque,carton,ela,environnement,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_fonce,pet_opaque,textiles_sanitaires', 'svm_portfolios_learning': '4825680,4829009,4825557,4824709,4824714,4824733,4825676,4824737,4824753,4829002,4824848,4824863,4825667,4825677', 'photo_hashtag_type': 4151, 'photo_desc_type': 5557, 'type_classification': 'caffe', 'hashtag_id_list': '2107760128,492774966,492741797,493012381,492636447,2107756122,493202403,492628673,492668766,628944319,2107755846,2107755900,2107759152,2107760129'} Update svm_hashtag_type_desc : 5557 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5557, 'learn_generique_01122021_6000_v2', 2048, 2048, 'learn_generique_01122021_6000_v2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2021, 12, 1, 19, 59, 17), datetime.datetime(2021, 12, 1, 19, 59, 17)) To loadFromThcl() : net_5557 begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 2500 l 3632 free memory gpu now : 1929 wait 20 seconds l 3637 free memory gpu now : 1929 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5557, 'learn_generique_01122021_6000_v2', 2048, 2048, 'learn_generique_01122021_6000_v2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2021, 12, 1, 19, 59, 17), datetime.datetime(2021, 12, 1, 19, 59, 17)) None mean_file_type : mean_file_path : prototxt_file_path : model : learn_generique_01122021_6000_v2 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : learn_generique_01122021_6000_v2 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_generique_01122021_6000_v2 /data/models_weight/learn_generique_01122021_6000_v2/caffemodel size_local : 94383085 size in s3 : 94383085 create time local : 2021-12-09 17:03:46 create time in s3 : 2021-12-01 18:45:54 caffemodel already exist and didn't need to update /data/models_weight/learn_generique_01122021_6000_v2/deploy.prototxt size_local : 32544 size in s3 : 32544 create time local : 2021-12-09 17:03:46 create time in s3 : 2021-12-01 18:45:53 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_generique_01122021_6000_v2/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-12-09 17:03:47 create time in s3 : 2021-12-01 18:59:02 mean.npy already exist and didn't need to update /data/models_weight/learn_generique_01122021_6000_v2/synset_words.txt size_local : 454 size in s3 : 454 create time local : 2021-12-09 17:03:47 create time in s3 : 2021-12-01 18:59:15 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/:/home/admin/workarea/git/apy/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/learn_generique_01122021_6000_v2/deploy.prototxt caffemodel_filename : /data/models_weight/learn_generique_01122021_6000_v2/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10555 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'pool5']) time used to do the prepocess of the images : 4.633809328079224 time used to do the prediction : 2.255094051361084 save descriptor for thcl : 3233 time to traite the descriptors : 4.021695852279663 Catched exception ! Connect or reconnect ! storage_type for insertDescriptorsMulti : 3 To insert : 1334561230 To insert : 1334561231 To insert : 1334561232 To insert : 1334561233 To insert : 1334561234 To insert : 1334561239 To insert : 1334561240 To insert : 1334561241 To insert : 1334561242 To insert : 1334561243 To insert : 1334561244 To insert : 1334561245 To insert : 1334561246 To insert : 1334561247 To insert : 1334561248 To insert : 1334561249 To insert : 1334561250 To insert : 1334561251 To insert : 1334561252 To insert : 1334561253 To insert : 1334561254 To insert : 1334561255 To insert : 1334561256 To insert : 1334561257 To insert : 1334561258 To insert : 1334561259 To insert : 1334561260 To insert : 1334561261 To insert : 1334561262 To insert : 1334561263 To insert : 1334561264 To insert : 1334561265 To insert : 1334561266 To insert : 1334561267 To insert : 1334561268 To insert : 1334561270 To insert : 1334561272 To insert : 1334561273 To insert : 1334561274 To insert : 1334561292 To insert : 1334561293 To insert : 1334561294 To insert : 1334561295 To insert : 1334561296 To insert : 1334561297 To insert : 1334561298 To insert : 1334561299 To insert : 1334561300 To insert : 1334561301 To insert : 1334561302 To insert : 1334561303 To insert : 1334561304 To insert : 1334561305 To insert : 1334561307 To insert : 1334561308 To insert : 1334561309 To insert : 1334561310 To insert : 1334561311 To insert : 1334561312 To insert : 1334561313 To insert : 1334561315 To insert : 1334561316 To insert : 1334561317 To insert : 1334561318 To insert : 1334561319 To insert : 1334561320 To insert : 1334561026 To insert : 1334561027 To insert : 1334561028 To insert : 1334561029 To insert : 1334561030 To insert : 1334561031 To insert : 1334561032 To insert : 1334561033 To insert : 1334561034 To insert : 1334561035 To insert : 1334561036 To insert : 1334561037 To insert : 1334561038 To insert : 1334561039 To insert : 1334561040 To insert : 1334561041 To insert : 1334561042 To insert : 1334561044 To insert : 1334561045 To insert : 1334561046 To insert : 1334561047 To insert : 1334561048 To insert : 1334561049 To insert : 1334561050 To insert : 1334561051 To insert : 1334561052 To insert : 1334561053 To insert : 1334561054 To insert : 1334561055 To insert : 1334561056 To insert : 1334561057 To insert : 1334561058 To insert : 1334561059 To insert : 1334561060 To insert : 1334561061 To insert : 1334561062 To insert : 1334561063 To insert : 1334561064 To insert : 1334561065 To insert : 1334561066 To insert : 1334561067 To insert : 1334561068 To insert : 1334561069 To insert : 1334561070 To insert : 1334561071 To insert : 1334561072 To insert : 1334561074 To insert : 1334561075 To insert : 1334561077 To insert : 1334561078 To insert : 1334561079 To insert : 1334561080 To insert : 1334561081 To insert : 1334561082 To insert : 1334561083 To insert : 1334561084 To insert : 1334561085 To insert : 1334561086 To insert : 1334561087 To insert : 1334561088 To insert : 1334561089 To insert : 1334561090 To insert : 1334561091 To insert : 1334561092 To insert : 1334561093 To insert : 1334561094 To insert : 1334561321 To insert : 1334561322 To insert : 1334561323 To insert : 1334561324 To insert : 1334561325 To insert : 1334561326 To insert : 1334561327 To insert : 1334561328 To insert : 1334561329 To insert : 1334561330 To insert : 1334561331 To insert : 1334561332 To insert : 1334561333 To insert : 1334561334 To insert : 1334561335 To insert : 1334561336 To insert : 1334561337 To insert : 1334561338 To insert : 1334561339 To insert : 1334561340 To insert : 1334561341 To insert : 1334561342 To insert : 1334561343 To insert : 1334561344 To insert : 1334561345 To insert : 1334561346 To insert : 1334561347 To insert : 1334561348 To insert : 1334561349 To insert : 1334561350 To insert : 1334561351 To insert : 1334561352 To insert : 1334561353 To insert : 1334561354 To insert : 1334561355 To insert : 1334561356 To insert : 1334561357 To insert : 1334561358 To insert : 1334561359 To insert : 1334561360 To insert : 1334561361 To insert : 1334561362 To insert : 1334561363 To insert : 1334561364 To insert : 1334561365 To insert : 1334561366 To insert : 1334561367 To insert : 1334561368 To insert : 1334561369 To insert : 1334561370 To insert : 1334561371 To insert : 1334561372 To insert : 1334561373 To insert : 1334561374 To insert : 1334561375 To insert : 1334561376 To insert : 1334561402 To insert : 1334561403 To insert : 1334561404 To insert : 1334561405 To insert : 1334561406 To insert : 1334561407 To insert : 1334561408 To insert : 1334561409 To insert : 1334561411 To insert : 1334561412 To insert : 1334560802 To insert : 1334560803 To insert : 1334560804 To insert : 1334560805 To insert : 1334560806 To insert : 1334560807 To insert : 1334560808 To insert : 1334560809 To insert : 1334560810 To insert : 1334560811 To insert : 1334560812 To insert : 1334560814 To insert : 1334560815 To insert : 1334560816 To insert : 1334560817 To insert : 1334560818 To insert : 1334560819 To insert : 1334560820 To insert : 1334560821 To insert : 1334560822 To insert : 1334560823 To insert : 1334560824 To insert : 1334560825 To insert : 1334560826 To insert : 1334560827 To insert : 1334560828 To insert : 1334560829 To insert : 1334560830 To insert : 1334560831 To insert : 1334560832 To insert : 1334560833 To insert : 1334560834 To insert : 1334560835 To insert : 1334560837 To insert : 1334560838 To insert : 1334560839 To insert : 1334560840 To insert : 1334560841 To insert : 1334560842 To insert : 1334560843 To insert : 1334560844 To insert : 1334560845 To insert : 1334560846 To insert : 1334560847 To insert : 1334560848 To insert : 1334560849 To insert : 1334560850 To insert : 1334560851 To insert : 1334560852 To insert : 1334560853 To insert : 1334560854 To insert : 1334560855 To insert : 1334560856 To insert : 1334560857 To insert : 1334560858 To insert : 1334560859 To insert : 1334560860 To insert : 1334560861 To insert : 1334560862 To insert : 1334560863 To insert : 1334560864 To insert : 1334560865 To insert : 1334560866 To insert : 1334560897 To insert : 1334560898 To insert : 1334560899 To insert : 1334561095 To insert : 1334561096 To insert : 1334561097 To insert : 1334561098 To insert : 1334561099 To insert : 1334561100 To insert : 1334561101 To insert : 1334561102 To insert : 1334561103 To insert : 1334561104 To insert : 1334561105 To insert : 1334561106 To insert : 1334561107 To insert : 1334561108 To insert : 1334561109 To insert : 1334561110 To insert : 1334561111 To insert : 1334561112 To insert : 1334561113 To insert : 1334561114 To insert : 1334561115 To insert : 1334561116 To insert : 1334561117 To insert : 1334561118 To insert : 1334561119 To insert : 1334561120 To insert : 1334561121 To insert : 1334561122 To insert : 1334561123 To insert : 1334561124 To insert : 1334561125 To insert : 1334561126 To insert : 1334561127 To insert : 1334561128 To insert : 1334561129 To insert : 1334561130 To insert : 1334561131 To insert : 1334561132 To insert : 1334561133 To insert : 1334561134 To insert : 1334561135 To insert : 1334561136 To insert : 1334561137 To insert : 1334561138 To insert : 1334561139 To insert : 1334561140 To insert : 1334561141 To insert : 1334561142 To insert : 1334561143 To insert : 1334561144 To insert : 1334561145 To insert : 1334561146 To insert : 1334561147 To insert : 1334561148 To insert : 1334561149 To insert : 1334561150 To insert : 1334561151 To insert : 1334561152 To insert : 1334561153 To insert : 1334561154 To insert : 1334561156 To insert : 1334561157 To insert : 1334561158 To insert : 1334561159 To insert : 1334561160 To insert : 1334561161 To insert : 1334561413 To insert : 1334561415 To insert : 1334561416 To insert : 1334561417 To insert : 1334561418 To insert : 1334561419 To insert : 1334561420 To insert : 1334561421 To insert : 1334561422 To insert : 1334561423 To insert : 1334561424 To insert : 1334561425 To insert : 1334561426 To insert : 1334561427 To insert : 1334561428 To insert : 1334561429 To insert : 1334561430 To insert : 1334561431 To insert : 1334561432 To insert : 1334561433 To insert : 1334561434 To insert : 1334561435 To insert : 1334561436 To insert : 1334561437 To insert : 1334561438 To insert : 1334561439 To insert : 1334561440 To insert : 1334561441 To insert : 1334561442 To insert : 1334561443 To insert : 1334561444 To insert : 1334561445 To insert : 1334561446 To insert : 1334561447 To insert : 1334561448 To insert : 1334561449 To insert : 1334561450 To insert : 1334561451 To insert : 1334561452 To insert : 1334561453 To insert : 1334561455 To insert : 1334561456 To insert : 1334561457 To insert : 1334561458 To insert : 1334561459 To insert : 1334561460 To insert : 1334561461 To insert : 1334561462 To insert : 1334561463 To insert : 1334561464 To insert : 1334561465 To insert : 1334561466 To insert : 1334561467 To insert : 1334561468 To insert : 1334561469 To insert : 1334561470 To insert : 1334561471 To insert : 1334561472 To insert : 1334561473 To insert : 1334560711 To insert : 1334560712 To insert : 1334560713 To insert : 1334560714 To insert : 1334560715 To insert : 1334560716 To insert : 1334560717 To insert : 1334560718 To insert : 1334560719 To insert : 1334560720 To insert : 1334560721 To insert : 1334560722 To insert : 1334560723 To insert : 1334560724 To insert : 1334560725 To insert : 1334560726 To insert : 1334560727 To insert : 1334560728 To insert : 1334560729 To insert : 1334560730 To insert : 1334560732 To insert : 1334560733 To insert : 1334560734 To insert : 1334560735 To insert : 1334560736 To insert : 1334560737 To insert : 1334560738 To insert : 1334560740 To insert : 1334560741 To insert : 1334560742 To insert : 1334560743 To insert : 1334560766 To insert : 1334560767 To insert : 1334560768 To insert : 1334560769 To insert : 1334560770 To insert : 1334560771 To insert : 1334560773 To insert : 1334560774 To insert : 1334560775 To insert : 1334560776 To insert : 1334560777 To insert : 1334560778 To insert : 1334560779 To insert : 1334560780 To insert : 1334560781 To insert : 1334560782 To insert : 1334560783 To insert : 1334560784 To insert : 1334560785 To insert : 1334560786 To insert : 1334560787 To insert : 1334560788 To insert : 1334560789 To insert : 1334560790 To insert : 1334560791 To insert : 1334560792 To insert : 1334560793 To insert : 1334560794 To insert : 1334560795 To insert : 1334560796 To insert : 1334560797 To insert : 1334560798 To insert : 1334560799 To insert : 1334560800 To insert : 1334560801 To insert : 1334561162 To insert : 1334561163 To insert : 1334561165 To insert : 1334561166 To insert : 1334561167 To insert : 1334561168 To insert : 1334561169 To insert : 1334561170 To insert : 1334561171 To insert : 1334561172 To insert : 1334561173 To insert : 1334561174 To insert : 1334561176 To insert : 1334561177 To insert : 1334561178 To insert : 1334561179 To insert : 1334561180 To insert : 1334561181 To insert : 1334561182 To insert : 1334561183 To insert : 1334561184 To insert : 1334561185 To insert : 1334561186 To insert : 1334561187 To insert : 1334561188 To insert : 1334561189 To insert : 1334561190 To insert : 1334561191 To insert : 1334561192 To insert : 1334561193 To insert : 1334561194 To insert : 1334561195 To insert : 1334561196 To insert : 1334561197 To insert : 1334561198 To insert : 1334561199 To insert : 1334561200 To insert : 1334561201 To insert : 1334561202 To insert : 1334561203 To insert : 1334561204 To insert : 1334561205 To insert : 1334561206 To insert : 1334561207 To insert : 1334561208 To insert : 1334561209 To insert : 1334561210 To insert : 1334561211 To insert : 1334561212 To insert : 1334561213 To insert : 1334561214 To insert : 1334561215 To insert : 1334561216 To insert : 1334561217 To insert : 1334561218 To insert : 1334561219 To insert : 1334561220 To insert : 1334561221 To insert : 1334561222 To insert : 1334561223 To insert : 1334561224 To insert : 1334561225 To insert : 1334561226 To insert : 1334561227 To insert : 1334561228 To insert : 1334561229 To insert : 1334560900 To insert : 1334560901 To insert : 1334560902 To insert : 1334560904 To insert : 1334560908 To insert : 1334560912 To insert : 1334560913 To insert : 1334560914 To insert : 1334560915 To insert : 1334560916 To insert : 1334560918 To insert : 1334560919 To insert : 1334560920 To insert : 1334560921 To insert : 1334560922 To insert : 1334560923 To insert : 1334560924 To insert : 1334560925 To insert : 1334560926 To insert : 1334560927 To insert : 1334560929 To insert : 1334560930 To insert : 1334560931 To insert : 1334560932 To insert : 1334560933 To insert : 1334560934 To insert : 1334560935 To insert : 1334560936 To insert : 1334560937 To insert : 1334560938 To insert : 1334560939 To insert : 1334560940 To insert : 1334560941 To insert : 1334560942 To insert : 1334560943 To insert : 1334560944 To insert : 1334560945 To insert : 1334560946 To insert : 1334560947 To insert : 1334560948 To insert : 1334560949 To insert : 1334560951 To insert : 1334560952 To insert : 1334560953 To insert : 1334560954 To insert : 1334560955 To insert : 1334560956 To insert : 1334560958 To insert : 1334560959 To insert : 1334560960 To insert : 1334560961 To insert : 1334560962 To insert : 1334560963 To insert : 1334560964 To insert : 1334560965 To insert : 1334560966 To insert : 1334560967 To insert : 1334561017 To insert : 1334561018 To insert : 1334561019 To insert : 1334561020 To insert : 1334561021 To insert : 1334561022 To insert : 1334561023 To insert : 1334561024 To insert : 1334561025 To insert : 1334560641 To insert : 1334560642 To insert : 1334560643 To insert : 1334560644 To insert : 1334560645 To insert : 1334560646 To insert : 1334560647 To insert : 1334560649 To insert : 1334560650 To insert : 1334560651 To insert : 1334560652 To insert : 1334560653 To insert : 1334560654 To insert : 1334560655 To insert : 1334560656 To insert : 1334560657 To insert : 1334560658 To insert : 1334560659 To insert : 1334560660 To insert : 1334560661 To insert : 1334560662 To insert : 1334560663 To insert : 1334560664 To insert : 1334560665 To insert : 1334560666 To insert : 1334560667 To insert : 1334560668 To insert : 1334560669 To insert : 1334560670 To insert : 1334560671 To insert : 1334560673 To insert : 1334560674 To insert : 1334560675 To insert : 1334560676 To insert : 1334560677 To insert : 1334560678 To insert : 1334560679 To insert : 1334560680 To insert : 1334560681 To insert : 1334560682 To insert : 1334560683 To insert : 1334560684 To insert : 1334560685 To insert : 1334560686 To insert : 1334560688 To insert : 1334560689 To insert : 1334560690 To insert : 1334560691 To insert : 1334560692 To insert : 1334560693 To insert : 1334560694 To insert : 1334560695 To insert : 1334560696 To insert : 1334560697 To insert : 1334560698 To insert : 1334560699 To insert : 1334560700 To insert : 1334560701 To insert : 1334560702 To insert : 1334560703 To insert : 1334560704 To insert : 1334560705 To insert : 1334560706 To insert : 1334560707 To insert : 1334560708 To insert : 1334560710 time to insert the descriptors : 121.4824550151825 Inside saveOutput : final : False verbose : 0 time used to find the portfolios of the photos SAVE THCL : begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 954 time used for this insertion : 0.0701284408569336 save missing photos in datou_result : time spend for datou_step_exec : 157.2533745765686 time spend to save output : 0.1495506763458252 total time spend for step 5 : 157.40292525291443 step6:argmax Tue Feb 4 14:16:58 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou_step Argmax ! calculate argmax for thcl : 3233 Inside saveOutput : final : False verbose : 0 photo_id : 1334561230 output[photo_id] : [('1334561230', 'metal', 0.92379475, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056200_0.png'] photo_id : 1334561231 output[photo_id] : [('1334561231', 'barquette_opaque', 0.60134995, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056199_0.png'] photo_id : 1334561232 output[photo_id] : [('1334561232', 'papier', 0.54963636, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056201_0.png'] photo_id : 1334561233 output[photo_id] : [('1334561233', 'carton', 0.6924505, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056218_0.png'] photo_id : 1334561234 output[photo_id] : [('1334561234', 'etiquette', 0.6998738, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056210_0.png'] photo_id : 1334561239 output[photo_id] : [('1334561239', 'papier', 0.6205702, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571453_0.png'] photo_id : 1334561240 output[photo_id] : [('1334561240', 'etiquette', 0.7486675, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571555_0.png'] photo_id : 1334561241 output[photo_id] : [('1334561241', 'etiquette', 0.86591923, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571633_0.png'] photo_id : 1334561242 output[photo_id] : [('1334561242', 'pet_opaque', 0.5313354, 4151, '3233'), 'temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0_rle_crop_3653571656_0.png'] photo_id : 1334561243 output[photo_id] : [('1334561243', 'etiquette', 0.30350354, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982723_0.png'] photo_id : 1334561244 output[photo_id] : [('1334561244', 'etiquette', 0.35585505, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982715_0.png'] photo_id : 1334561245 output[photo_id] : [('1334561245', 'etiquette', 0.8770276, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982765_0.png'] photo_id : 1334561246 output[photo_id] : [('1334561246', 'etiquette', 0.57206655, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3653571684_0.png'] photo_id : 1334561247 output[photo_id] : [('1334561247', 'etiquette', 0.4429206, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982767_0.png'] photo_id : 1334561248 output[photo_id] : [('1334561248', 'papier', 0.5981178, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982757_0.png'] photo_id : 1334561249 output[photo_id] : [('1334561249', 'barquette_opaque', 0.50545454, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982824_0.png'] photo_id : 1334561250 output[photo_id] : [('1334561250', 'etiquette', 0.39105618, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982843_0.png'] photo_id : 1334561251 output[photo_id] : [('1334561251', 'etiquette', 0.5005067, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982911_0.png'] photo_id : 1334561252 output[photo_id] : [('1334561252', 'etiquette', 0.30695885, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982895_0.png'] photo_id : 1334561253 output[photo_id] : [('1334561253', 'etiquette', 0.80495375, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982971_0.png'] photo_id : 1334561254 output[photo_id] : [('1334561254', 'etiquette', 0.45800287, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983015_0.png'] photo_id : 1334561255 output[photo_id] : [('1334561255', 'etiquette', 0.96870273, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983005_0.png'] photo_id : 1334561256 output[photo_id] : [('1334561256', 'etiquette', 0.6115823, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983057_0.png'] photo_id : 1334561257 output[photo_id] : [('1334561257', 'papier', 0.66546136, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983062_0.png'] photo_id : 1334561258 output[photo_id] : [('1334561258', 'etiquette', 0.99444747, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983076_0.png'] photo_id : 1334561259 output[photo_id] : [('1334561259', 'papier', 0.52757376, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983069_0.png'] photo_id : 1334561260 output[photo_id] : [('1334561260', 'etiquette', 0.8432287, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983125_0.png'] photo_id : 1334561261 output[photo_id] : [('1334561261', 'etiquette', 0.9709316, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983140_0.png'] photo_id : 1334561262 output[photo_id] : [('1334561262', 'etiquette', 0.9406739, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983182_0.png'] photo_id : 1334561263 output[photo_id] : [('1334561263', 'etiquette', 0.6893004, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983141_0.png'] photo_id : 1334561264 output[photo_id] : [('1334561264', 'papier', 0.260517, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983169_0.png'] photo_id : 1334561265 output[photo_id] : [('1334561265', 'pet_fonce', 0.42214245, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983139_0.png'] photo_id : 1334561266 output[photo_id] : [('1334561266', 'papier', 0.51924306, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983175_0.png'] photo_id : 1334561267 output[photo_id] : [('1334561267', 'etiquette', 0.42757523, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056070_0.png'] photo_id : 1334561268 output[photo_id] : [('1334561268', 'etiquette', 0.8213901, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056072_0.png'] photo_id : 1334561270 output[photo_id] : [('1334561270', 'etiquette', 0.8347358, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056077_0.png'] photo_id : 1334561272 output[photo_id] : [('1334561272', 'papier', 0.27189678, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056099_0.png'] photo_id : 1334561273 output[photo_id] : [('1334561273', 'etiquette', 0.54673696, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056103_0.png'] photo_id : 1334561274 output[photo_id] : [('1334561274', 'etiquette', 0.9173585, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056190_0.png'] photo_id : 1334561292 output[photo_id] : [('1334561292', 'pet_clair', 0.39745525, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571484_0.png'] photo_id : 1334561293 output[photo_id] : [('1334561293', 'papier', 0.40647617, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571486_0.png'] photo_id : 1334561294 output[photo_id] : [('1334561294', 'pet_fonce', 0.55862314, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571463_0.png'] photo_id : 1334561295 output[photo_id] : [('1334561295', 'film_plastique', 0.43874982, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571534_0.png'] photo_id : 1334561296 output[photo_id] : [('1334561296', 'barquette_opaque', 0.82937646, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571529_0.png'] photo_id : 1334561297 output[photo_id] : [('1334561297', 'papier', 0.8465917, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571507_0.png'] photo_id : 1334561298 output[photo_id] : [('1334561298', 'pet_clair', 0.32102633, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571522_0.png'] photo_id : 1334561299 output[photo_id] : [('1334561299', 'barquette_opaque', 0.5433995, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571628_0.png'] photo_id : 1334561300 output[photo_id] : [('1334561300', 'etiquette', 0.4383251, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571617_0.png'] photo_id : 1334561301 output[photo_id] : [('1334561301', 'film_plastique', 0.63043135, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571616_0.png'] photo_id : 1334561302 output[photo_id] : [('1334561302', 'barquette_opaque', 0.5162695, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571630_0.png'] photo_id : 1334561303 output[photo_id] : [('1334561303', 'film_plastique', 0.43997604, 4151, '3233'), 'temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0_rle_crop_3653571652_0.png'] photo_id : 1334561304 output[photo_id] : [('1334561304', 'pet_opaque', 0.30265936, 4151, '3233'), 'temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0_rle_crop_3653571669_0.png'] photo_id : 1334561305 output[photo_id] : [('1334561305', 'barquette_opaque', 0.82410336, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982712_0.png'] photo_id : 1334561307 output[photo_id] : [('1334561307', 'film_plastique', 0.58696306, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982692_0.png'] photo_id : 1334561308 output[photo_id] : [('1334561308', 'etiquette', 0.2689781, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982721_0.png'] photo_id : 1334561309 output[photo_id] : [('1334561309', 'barquette_opaque', 0.29275325, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982748_0.png'] photo_id : 1334561310 output[photo_id] : [('1334561310', 'etiquette', 0.37896916, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982750_0.png'] photo_id : 1334561311 output[photo_id] : [('1334561311', 'metal', 0.35843685, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3653571680_0.png'] photo_id : 1334561312 output[photo_id] : [('1334561312', 'film_plastique', 0.22118828, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3653571677_0.png'] photo_id : 1334561313 output[photo_id] : [('1334561313', 'pehd', 0.44812948, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982759_0.png'] photo_id : 1334561315 output[photo_id] : [('1334561315', 'metal', 0.63135695, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982760_0.png'] photo_id : 1334561316 output[photo_id] : [('1334561316', 'barquette_opaque', 0.30397415, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982747_0.png'] photo_id : 1334561317 output[photo_id] : [('1334561317', 'etiquette', 0.92246884, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982819_0.png'] photo_id : 1334561318 output[photo_id] : [('1334561318', 'pet_opaque', 0.39775625, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982812_0.png'] photo_id : 1334561319 output[photo_id] : [('1334561319', 'pet_fonce', 0.5639024, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982833_0.png'] photo_id : 1334561320 output[photo_id] : [('1334561320', 'etiquette', 0.72012174, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982875_0.png'] photo_id : 1334561026 output[photo_id] : [('1334561026', 'papier', 0.7313967, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571520_0.png'] photo_id : 1334561027 output[photo_id] : [('1334561027', 'pet_opaque', 0.44136292, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571494_0.png'] photo_id : 1334561028 output[photo_id] : [('1334561028', 'pet_opaque', 0.56490517, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571516_0.png'] photo_id : 1334561029 output[photo_id] : [('1334561029', 'pet_opaque', 0.3306477, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571536_0.png'] photo_id : 1334561030 output[photo_id] : [('1334561030', 'barquette_opaque', 0.6313666, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571515_0.png'] photo_id : 1334561031 output[photo_id] : [('1334561031', 'pet_opaque', 0.7657403, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571533_0.png'] photo_id : 1334561032 output[photo_id] : [('1334561032', 'barquette_opaque', 0.8043112, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571505_0.png'] photo_id : 1334561033 output[photo_id] : [('1334561033', 'carton', 0.39084202, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571519_0.png'] photo_id : 1334561034 output[photo_id] : [('1334561034', 'metal', 0.32143745, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571523_0.png'] photo_id : 1334561035 output[photo_id] : [('1334561035', 'etiquette', 0.47090203, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571501_0.png'] photo_id : 1334561036 output[photo_id] : [('1334561036', 'etiquette', 0.49072534, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571577_0.png'] photo_id : 1334561037 output[photo_id] : [('1334561037', 'barquette_opaque', 0.6294597, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571571_0.png'] photo_id : 1334561038 output[photo_id] : [('1334561038', 'carton', 0.4886905, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571556_0.png'] photo_id : 1334561039 output[photo_id] : [('1334561039', 'carton', 0.5685681, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571578_0.png'] photo_id : 1334561040 output[photo_id] : [('1334561040', 'pet_opaque', 0.81669426, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571546_0.png'] photo_id : 1334561041 output[photo_id] : [('1334561041', 'film_plastique', 0.32424152, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571581_0.png'] photo_id : 1334561042 output[photo_id] : [('1334561042', 'papier', 0.5216898, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571538_0.png'] photo_id : 1334561044 output[photo_id] : [('1334561044', 'pet_opaque', 0.7215526, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571542_0.png'] photo_id : 1334561045 output[photo_id] : [('1334561045', 'papier', 0.25897983, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571551_0.png'] photo_id : 1334561046 output[photo_id] : [('1334561046', 'papier', 0.9200248, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571569_0.png'] photo_id : 1334561047 output[photo_id] : [('1334561047', 'barquette_opaque', 0.67139816, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571564_0.png'] photo_id : 1334561048 output[photo_id] : [('1334561048', 'etiquette', 0.9693758, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571545_0.png'] photo_id : 1334561049 output[photo_id] : [('1334561049', 'barquette_opaque', 0.7361459, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571540_0.png'] photo_id : 1334561050 output[photo_id] : [('1334561050', 'barquette_opaque', 0.92168194, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571558_0.png'] photo_id : 1334561051 output[photo_id] : [('1334561051', 'etiquette', 0.20378785, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571572_0.png'] photo_id : 1334561052 output[photo_id] : [('1334561052', 'metal', 0.5350699, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571547_0.png'] photo_id : 1334561053 output[photo_id] : [('1334561053', 'papier', 0.32979223, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571589_0.png'] photo_id : 1334561054 output[photo_id] : [('1334561054', 'etiquette', 0.3758159, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571603_0.png'] photo_id : 1334561055 output[photo_id] : [('1334561055', 'barquette_opaque', 0.33581114, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571598_0.png'] photo_id : 1334561056 output[photo_id] : [('1334561056', 'environnement', 0.5725334, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571613_0.png'] photo_id : 1334561057 output[photo_id] : [('1334561057', 'etiquette', 0.40305224, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571587_0.png'] photo_id : 1334561058 output[photo_id] : [('1334561058', 'metal', 0.40582672, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571636_0.png'] photo_id : 1334561059 output[photo_id] : [('1334561059', 'papier', 0.33913115, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571609_0.png'] photo_id : 1334561060 output[photo_id] : [('1334561060', 'etiquette', 0.56131697, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571626_0.png'] photo_id : 1334561061 output[photo_id] : [('1334561061', 'barquette_opaque', 0.6609892, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571622_0.png'] photo_id : 1334561062 output[photo_id] : [('1334561062', 'pet_fonce', 0.46220833, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571602_0.png'] photo_id : 1334561063 output[photo_id] : [('1334561063', 'metal', 0.4554622, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571612_0.png'] photo_id : 1334561064 output[photo_id] : [('1334561064', 'environnement', 0.36228833, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571605_0.png'] photo_id : 1334561065 output[photo_id] : [('1334561065', 'barquette_opaque', 0.81449026, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571586_0.png'] photo_id : 1334561066 output[photo_id] : [('1334561066', 'papier', 0.6992819, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571614_0.png'] photo_id : 1334561067 output[photo_id] : [('1334561067', 'barquette_opaque', 0.48178196, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571638_0.png'] photo_id : 1334561068 output[photo_id] : [('1334561068', 'etiquette', 0.36945835, 4151, '3233'), 'temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0_rle_crop_3653571670_0.png'] photo_id : 1334561069 output[photo_id] : [('1334561069', 'papier', 0.89144963, 4151, '3233'), 'temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0_rle_crop_3653571657_0.png'] photo_id : 1334561070 output[photo_id] : [('1334561070', 'barquette_opaque', 0.5334366, 4151, '3233'), 'temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0_rle_crop_3653571653_0.png'] photo_id : 1334561071 output[photo_id] : [('1334561071', 'pehd', 0.5717668, 4151, '3233'), 'temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0_rle_crop_3653571651_0.png'] photo_id : 1334561072 output[photo_id] : [('1334561072', 'pet_opaque', 0.36636275, 4151, '3233'), 'temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0_rle_crop_3653571654_0.png'] photo_id : 1334561074 output[photo_id] : [('1334561074', 'barquette_opaque', 0.92854077, 4151, '3233'), 'temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0_rle_crop_3653571666_0.png'] photo_id : 1334561075 output[photo_id] : [('1334561075', 'barquette_opaque', 0.30249506, 4151, '3233'), 'temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0_rle_crop_3653571662_0.png'] photo_id : 1334561077 output[photo_id] : [('1334561077', 'etiquette', 0.49077895, 4151, '3233'), 'temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0_rle_crop_3653571660_0.png'] photo_id : 1334561078 output[photo_id] : [('1334561078', 'pet_opaque', 0.37459955, 4151, '3233'), 'temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0_rle_crop_3653571671_0.png'] photo_id : 1334561079 output[photo_id] : [('1334561079', 'papier', 0.42248523, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982711_0.png'] photo_id : 1334561080 output[photo_id] : [('1334561080', 'etiquette', 0.73926747, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982703_0.png'] photo_id : 1334561081 output[photo_id] : [('1334561081', 'barquette_opaque', 0.21359949, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982695_0.png'] photo_id : 1334561082 output[photo_id] : [('1334561082', 'environnement', 0.24607958, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982708_0.png'] photo_id : 1334561083 output[photo_id] : [('1334561083', 'carton', 0.68675554, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982693_0.png'] photo_id : 1334561084 output[photo_id] : [('1334561084', 'papier', 0.6334843, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982688_0.png'] photo_id : 1334561085 output[photo_id] : [('1334561085', 'papier', 0.2196072, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982706_0.png'] photo_id : 1334561086 output[photo_id] : [('1334561086', 'papier', 0.9085105, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982696_0.png'] photo_id : 1334561087 output[photo_id] : [('1334561087', 'metal', 0.36914885, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982685_0.png'] photo_id : 1334561088 output[photo_id] : [('1334561088', 'papier', 0.4731777, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982716_0.png'] photo_id : 1334561089 output[photo_id] : [('1334561089', 'etiquette', 0.76419127, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982697_0.png'] photo_id : 1334561090 output[photo_id] : [('1334561090', 'barquette_opaque', 0.5398694, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982720_0.png'] photo_id : 1334561091 output[photo_id] : [('1334561091', 'barquette_opaque', 0.3538653, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3653571676_0.png'] photo_id : 1334561092 output[photo_id] : [('1334561092', 'pet_opaque', 0.5382314, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3653571683_0.png'] photo_id : 1334561093 output[photo_id] : [('1334561093', 'carton', 0.7632738, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3653571679_0.png'] photo_id : 1334561094 output[photo_id] : [('1334561094', 'etiquette', 0.6773486, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3653571686_0.png'] photo_id : 1334561321 output[photo_id] : [('1334561321', 'barquette_opaque', 0.8924256, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982870_0.png'] photo_id : 1334561322 output[photo_id] : [('1334561322', 'film_plastique', 0.45817316, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982877_0.png'] photo_id : 1334561323 output[photo_id] : [('1334561323', 'carton', 0.6205454, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982849_0.png'] photo_id : 1334561324 output[photo_id] : [('1334561324', 'papier', 0.44541854, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982832_0.png'] photo_id : 1334561325 output[photo_id] : [('1334561325', 'pet_fonce', 0.22763121, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982838_0.png'] photo_id : 1334561326 output[photo_id] : [('1334561326', 'etiquette', 0.27120298, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982918_0.png'] photo_id : 1334561327 output[photo_id] : [('1334561327', 'barquette_opaque', 0.802662, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982904_0.png'] photo_id : 1334561328 output[photo_id] : [('1334561328', 'film_plastique', 0.60793513, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982893_0.png'] photo_id : 1334561329 output[photo_id] : [('1334561329', 'film_plastique', 0.45740423, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982887_0.png'] photo_id : 1334561330 output[photo_id] : [('1334561330', 'etiquette', 0.29380947, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982883_0.png'] photo_id : 1334561331 output[photo_id] : [('1334561331', 'environnement', 0.66117454, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982978_0.png'] photo_id : 1334561332 output[photo_id] : [('1334561332', 'metal', 0.25456628, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982964_0.png'] photo_id : 1334561333 output[photo_id] : [('1334561333', 'pet_clair', 0.39461035, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982947_0.png'] photo_id : 1334561334 output[photo_id] : [('1334561334', 'barquette_opaque', 0.3393536, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982962_0.png'] photo_id : 1334561335 output[photo_id] : [('1334561335', 'film_plastique', 0.6288556, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982958_0.png'] photo_id : 1334561336 output[photo_id] : [('1334561336', 'pet_clair', 0.2795537, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982932_0.png'] photo_id : 1334561337 output[photo_id] : [('1334561337', 'film_plastique', 0.39137402, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983038_0.png'] photo_id : 1334561338 output[photo_id] : [('1334561338', 'barquette_opaque', 0.38263485, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983043_0.png'] photo_id : 1334561339 output[photo_id] : [('1334561339', 'barquette_opaque', 0.9830427, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983042_0.png'] photo_id : 1334561340 output[photo_id] : [('1334561340', 'film_plastique', 0.47361425, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983034_0.png'] photo_id : 1334561341 output[photo_id] : [('1334561341', 'metal', 0.20050338, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983017_0.png'] photo_id : 1334561342 output[photo_id] : [('1334561342', 'papier', 0.48993665, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983027_0.png'] photo_id : 1334561343 output[photo_id] : [('1334561343', 'pet_clair', 0.3012189, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651982998_0.png'] photo_id : 1334561344 output[photo_id] : [('1334561344', 'pet_opaque', 0.34069377, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983007_0.png'] photo_id : 1334561345 output[photo_id] : [('1334561345', 'etiquette', 0.7578286, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983085_0.png'] photo_id : 1334561346 output[photo_id] : [('1334561346', 'papier', 0.2275253, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983075_0.png'] photo_id : 1334561347 output[photo_id] : [('1334561347', 'pet_clair', 0.5940251, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983050_0.png'] photo_id : 1334561348 output[photo_id] : [('1334561348', 'pet_opaque', 0.88126737, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983082_0.png'] photo_id : 1334561349 output[photo_id] : [('1334561349', 'barquette_opaque', 0.60446984, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983067_0.png'] photo_id : 1334561350 output[photo_id] : [('1334561350', 'film_plastique', 0.38952798, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983074_0.png'] photo_id : 1334561351 output[photo_id] : [('1334561351', 'papier', 0.8650576, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983124_0.png'] photo_id : 1334561352 output[photo_id] : [('1334561352', 'barquette_opaque', 0.31512114, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983110_0.png'] photo_id : 1334561353 output[photo_id] : [('1334561353', 'barquette_opaque', 0.94871634, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983111_0.png'] photo_id : 1334561354 output[photo_id] : [('1334561354', 'barquette_opaque', 0.80286366, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983119_0.png'] photo_id : 1334561355 output[photo_id] : [('1334561355', 'etiquette', 0.87408537, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983096_0.png'] photo_id : 1334561356 output[photo_id] : [('1334561356', 'film_plastique', 0.48025998, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983126_0.png'] photo_id : 1334561357 output[photo_id] : [('1334561357', 'film_plastique', 0.20929034, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983120_0.png'] photo_id : 1334561358 output[photo_id] : [('1334561358', 'papier', 0.4881169, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983105_0.png'] photo_id : 1334561359 output[photo_id] : [('1334561359', 'barquette_opaque', 0.4614175, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983136_0.png'] photo_id : 1334561360 output[photo_id] : [('1334561360', 'film_plastique', 0.44853672, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983090_0.png'] photo_id : 1334561361 output[photo_id] : [('1334561361', 'film_plastique', 0.28511488, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983149_0.png'] photo_id : 1334561362 output[photo_id] : [('1334561362', 'papier', 0.5544574, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983148_0.png'] photo_id : 1334561363 output[photo_id] : [('1334561363', 'papier', 0.70044065, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983167_0.png'] photo_id : 1334561364 output[photo_id] : [('1334561364', 'metal', 0.8614515, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983160_0.png'] photo_id : 1334561365 output[photo_id] : [('1334561365', 'carton', 0.30832782, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983177_0.png'] photo_id : 1334561366 output[photo_id] : [('1334561366', 'pet_clair', 0.3147812, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983156_0.png'] photo_id : 1334561367 output[photo_id] : [('1334561367', 'film_plastique', 0.58307254, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056065_0.png'] photo_id : 1334561368 output[photo_id] : [('1334561368', 'metal', 0.49970907, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056127_0.png'] photo_id : 1334561369 output[photo_id] : [('1334561369', 'metal', 0.3827284, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056122_0.png'] photo_id : 1334561370 output[photo_id] : [('1334561370', 'papier', 0.33329955, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056116_0.png'] photo_id : 1334561371 output[photo_id] : [('1334561371', 'pet_fonce', 0.7362733, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3653571952_0.png'] photo_id : 1334561372 output[photo_id] : [('1334561372', 'barquette_opaque', 0.5111251, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056109_0.png'] photo_id : 1334561373 output[photo_id] : [('1334561373', 'barquette_opaque', 0.85021675, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056093_0.png'] photo_id : 1334561374 output[photo_id] : [('1334561374', 'pet_fonce', 0.32562068, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056098_0.png'] photo_id : 1334561375 output[photo_id] : [('1334561375', 'barquette_opaque', 0.63953304, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056182_0.png'] photo_id : 1334561376 output[photo_id] : [('1334561376', 'papier', 0.21863975, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056189_0.png'] photo_id : 1334561402 output[photo_id] : [('1334561402', 'papier', 0.608958, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571440_0.png'] photo_id : 1334561403 output[photo_id] : [('1334561403', 'pet_opaque', 0.4223163, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571531_0.png'] photo_id : 1334561404 output[photo_id] : [('1334561404', 'etiquette', 0.2437116, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571521_0.png'] photo_id : 1334561405 output[photo_id] : [('1334561405', 'papier', 0.7305571, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571488_0.png'] photo_id : 1334561406 output[photo_id] : [('1334561406', 'papier', 0.601497, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571510_0.png'] photo_id : 1334561407 output[photo_id] : [('1334561407', 'etiquette', 0.45244515, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571550_0.png'] photo_id : 1334561408 output[photo_id] : [('1334561408', 'pet_opaque', 0.9326205, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571553_0.png'] photo_id : 1334561409 output[photo_id] : [('1334561409', 'metal', 0.31914744, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571625_0.png'] photo_id : 1334561411 output[photo_id] : [('1334561411', 'papier', 0.85376257, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571624_0.png'] photo_id : 1334561412 output[photo_id] : [('1334561412', 'papier', 0.29147536, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571621_0.png'] photo_id : 1334560802 output[photo_id] : [('1334560802', 'etiquette', 0.32564348, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982722_0.png'] photo_id : 1334560803 output[photo_id] : [('1334560803', 'pet_opaque', 0.79819953, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982687_0.png'] photo_id : 1334560804 output[photo_id] : [('1334560804', 'papier', 0.8514685, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982744_0.png'] photo_id : 1334560805 output[photo_id] : [('1334560805', 'pet_opaque', 0.747887, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982769_0.png'] photo_id : 1334560806 output[photo_id] : [('1334560806', 'pehd', 0.5834723, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982727_0.png'] photo_id : 1334560807 output[photo_id] : [('1334560807', 'papier', 0.8311121, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982740_0.png'] photo_id : 1334560808 output[photo_id] : [('1334560808', 'papier', 0.495739, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3653571681_0.png'] photo_id : 1334560809 output[photo_id] : [('1334560809', 'papier', 0.5698361, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3653571678_0.png'] photo_id : 1334560810 output[photo_id] : [('1334560810', 'papier', 0.38329428, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982731_0.png'] photo_id : 1334560811 output[photo_id] : [('1334560811', 'papier', 0.881218, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982730_0.png'] photo_id : 1334560812 output[photo_id] : [('1334560812', 'papier', 0.15851527, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982749_0.png'] photo_id : 1334560814 output[photo_id] : [('1334560814', 'papier', 0.3425151, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982756_0.png'] photo_id : 1334560815 output[photo_id] : [('1334560815', 'barquette_opaque', 0.8278872, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982753_0.png'] photo_id : 1334560816 output[photo_id] : [('1334560816', 'etiquette', 0.3348856, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982794_0.png'] photo_id : 1334560817 output[photo_id] : [('1334560817', 'etiquette', 0.5811966, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982779_0.png'] photo_id : 1334560818 output[photo_id] : [('1334560818', 'papier', 0.87604094, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982818_0.png'] photo_id : 1334560819 output[photo_id] : [('1334560819', 'papier', 0.60126156, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982809_0.png'] photo_id : 1334560820 output[photo_id] : [('1334560820', 'pet_opaque', 0.41538125, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982789_0.png'] photo_id : 1334560821 output[photo_id] : [('1334560821', 'barquette_opaque', 0.5758619, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982810_0.png'] photo_id : 1334560822 output[photo_id] : [('1334560822', 'environnement', 0.48465717, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982872_0.png'] photo_id : 1334560823 output[photo_id] : [('1334560823', 'barquette_opaque', 0.41205785, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982878_0.png'] photo_id : 1334560824 output[photo_id] : [('1334560824', 'papier', 0.8054551, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982844_0.png'] photo_id : 1334560825 output[photo_id] : [('1334560825', 'environnement', 0.6435761, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982865_0.png'] photo_id : 1334560826 output[photo_id] : [('1334560826', 'etiquette', 0.6580916, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982856_0.png'] photo_id : 1334560827 output[photo_id] : [('1334560827', 'carton', 0.5720856, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982901_0.png'] photo_id : 1334560828 output[photo_id] : [('1334560828', 'metal', 0.24012648, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982892_0.png'] photo_id : 1334560829 output[photo_id] : [('1334560829', 'barquette_opaque', 0.3932186, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982879_0.png'] photo_id : 1334560830 output[photo_id] : [('1334560830', 'papier', 0.6761144, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3653571695_0.png'] photo_id : 1334560831 output[photo_id] : [('1334560831', 'etiquette', 0.3776189, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982988_0.png'] photo_id : 1334560832 output[photo_id] : [('1334560832', 'papier', 0.5799397, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982981_0.png'] photo_id : 1334560833 output[photo_id] : [('1334560833', 'barquette_opaque', 0.30799904, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982926_0.png'] photo_id : 1334560834 output[photo_id] : [('1334560834', 'etiquette', 0.94570345, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982966_0.png'] photo_id : 1334560835 output[photo_id] : [('1334560835', 'etiquette', 0.46611005, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982968_0.png'] photo_id : 1334560837 output[photo_id] : [('1334560837', 'etiquette', 0.44971058, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982965_0.png'] photo_id : 1334560838 output[photo_id] : [('1334560838', 'etiquette', 0.8823742, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983039_0.png'] photo_id : 1334560839 output[photo_id] : [('1334560839', 'papier', 0.63340193, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983036_0.png'] photo_id : 1334560840 output[photo_id] : [('1334560840', 'papier', 0.46694207, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983012_0.png'] photo_id : 1334560841 output[photo_id] : [('1334560841', 'ela', 0.5131654, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983008_0.png'] photo_id : 1334560842 output[photo_id] : [('1334560842', 'pet_opaque', 0.33658746, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651982997_0.png'] photo_id : 1334560843 output[photo_id] : [('1334560843', 'papier', 0.86626214, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983035_0.png'] photo_id : 1334560844 output[photo_id] : [('1334560844', 'papier', 0.56342226, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983025_0.png'] photo_id : 1334560845 output[photo_id] : [('1334560845', 'papier', 0.42118412, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3653571942_0.png'] photo_id : 1334560846 output[photo_id] : [('1334560846', 'ela', 0.8705966, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983131_0.png'] photo_id : 1334560847 output[photo_id] : [('1334560847', 'barquette_opaque', 0.37087175, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983089_0.png'] photo_id : 1334560848 output[photo_id] : [('1334560848', 'barquette_opaque', 0.37582415, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983134_0.png'] photo_id : 1334560849 output[photo_id] : [('1334560849', 'barquette_opaque', 0.6367362, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983127_0.png'] photo_id : 1334560850 output[photo_id] : [('1334560850', 'barquette_opaque', 0.77078635, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983088_0.png'] photo_id : 1334560851 output[photo_id] : [('1334560851', 'papier', 0.95706195, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983143_0.png'] photo_id : 1334560852 output[photo_id] : [('1334560852', 'barquette_opaque', 0.48427194, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983174_0.png'] photo_id : 1334560853 output[photo_id] : [('1334560853', 'etiquette', 0.33926383, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056086_0.png'] photo_id : 1334560854 output[photo_id] : [('1334560854', 'carton', 0.29344866, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056054_0.png'] photo_id : 1334560855 output[photo_id] : [('1334560855', 'papier', 0.2519282, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056071_0.png'] photo_id : 1334560856 output[photo_id] : [('1334560856', 'etiquette', 0.8909877, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056069_0.png'] photo_id : 1334560857 output[photo_id] : [('1334560857', 'papier', 0.5033314, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056104_0.png'] photo_id : 1334560858 output[photo_id] : [('1334560858', 'etiquette', 0.51565737, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056107_0.png'] photo_id : 1334560859 output[photo_id] : [('1334560859', 'papier', 0.69998664, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056110_0.png'] photo_id : 1334560860 output[photo_id] : [('1334560860', 'barquette_opaque', 0.89656323, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3653571955_0.png'] photo_id : 1334560861 output[photo_id] : [('1334560861', 'papier', 0.6117042, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056112_0.png'] photo_id : 1334560862 output[photo_id] : [('1334560862', 'etiquette', 0.90228933, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056106_0.png'] photo_id : 1334560863 output[photo_id] : [('1334560863', 'papier', 0.81886137, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056101_0.png'] photo_id : 1334560864 output[photo_id] : [('1334560864', 'metal', 0.69897825, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056154_0.png'] photo_id : 1334560865 output[photo_id] : [('1334560865', 'etiquette', 0.5581098, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056165_0.png'] photo_id : 1334560866 output[photo_id] : [('1334560866', 'papier', 0.34896037, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056188_0.png'] photo_id : 1334560897 output[photo_id] : [('1334560897', 'film_plastique', 0.5879675, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571478_0.png'] photo_id : 1334560898 output[photo_id] : [('1334560898', 'film_plastique', 0.75206363, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571468_0.png'] photo_id : 1334560899 output[photo_id] : [('1334560899', 'film_plastique', 0.4998502, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571470_0.png'] photo_id : 1334561095 output[photo_id] : [('1334561095', 'papier', 0.7513199, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982754_0.png'] photo_id : 1334561096 output[photo_id] : [('1334561096', 'etiquette', 0.6624727, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982763_0.png'] photo_id : 1334561097 output[photo_id] : [('1334561097', 'metal', 0.50616646, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982732_0.png'] photo_id : 1334561098 output[photo_id] : [('1334561098', 'metal', 0.42249632, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982799_0.png'] photo_id : 1334561099 output[photo_id] : [('1334561099', 'barquette_opaque', 0.40136668, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982811_0.png'] photo_id : 1334561100 output[photo_id] : [('1334561100', 'barquette_opaque', 0.6834451, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982781_0.png'] photo_id : 1334561101 output[photo_id] : [('1334561101', 'pet_opaque', 0.34898156, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3653571688_0.png'] photo_id : 1334561102 output[photo_id] : [('1334561102', 'metal', 0.3648307, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982805_0.png'] photo_id : 1334561103 output[photo_id] : [('1334561103', 'papier', 0.5225008, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982823_0.png'] photo_id : 1334561104 output[photo_id] : [('1334561104', 'etiquette', 0.56784695, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982814_0.png'] photo_id : 1334561105 output[photo_id] : [('1334561105', 'papier', 0.91841733, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982821_0.png'] photo_id : 1334561106 output[photo_id] : [('1334561106', 'barquette_opaque', 0.66982836, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982816_0.png'] photo_id : 1334561107 output[photo_id] : [('1334561107', 'film_plastique', 0.7710644, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982788_0.png'] photo_id : 1334561108 output[photo_id] : [('1334561108', 'papier', 0.8830874, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982793_0.png'] photo_id : 1334561109 output[photo_id] : [('1334561109', 'ela', 0.7152607, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982798_0.png'] photo_id : 1334561110 output[photo_id] : [('1334561110', 'papier', 0.38779932, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982815_0.png'] photo_id : 1334561111 output[photo_id] : [('1334561111', 'pet_opaque', 0.4921203, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982817_0.png'] photo_id : 1334561112 output[photo_id] : [('1334561112', 'metal', 0.88626575, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982822_0.png'] photo_id : 1334561113 output[photo_id] : [('1334561113', 'papier', 0.73002714, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982808_0.png'] photo_id : 1334561114 output[photo_id] : [('1334561114', 'etiquette', 0.9828058, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982802_0.png'] photo_id : 1334561115 output[photo_id] : [('1334561115', 'environnement', 0.6946671, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982784_0.png'] photo_id : 1334561116 output[photo_id] : [('1334561116', 'pet_opaque', 0.594413, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982854_0.png'] photo_id : 1334561117 output[photo_id] : [('1334561117', 'pet_opaque', 0.755805, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982867_0.png'] photo_id : 1334561118 output[photo_id] : [('1334561118', 'pet_fonce', 0.9214081, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982853_0.png'] photo_id : 1334561119 output[photo_id] : [('1334561119', 'film_plastique', 0.3029379, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982845_0.png'] photo_id : 1334561120 output[photo_id] : [('1334561120', 'papier', 0.78493077, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982871_0.png'] photo_id : 1334561121 output[photo_id] : [('1334561121', 'pet_fonce', 0.85969734, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982847_0.png'] photo_id : 1334561122 output[photo_id] : [('1334561122', 'barquette_opaque', 0.34349206, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982866_0.png'] photo_id : 1334561123 output[photo_id] : [('1334561123', 'barquette_opaque', 0.5665962, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982858_0.png'] photo_id : 1334561124 output[photo_id] : [('1334561124', 'papier', 0.24494626, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982842_0.png'] photo_id : 1334561125 output[photo_id] : [('1334561125', 'etiquette', 0.3700662, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982846_0.png'] photo_id : 1334561126 output[photo_id] : [('1334561126', 'papier', 0.55456865, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982840_0.png'] photo_id : 1334561127 output[photo_id] : [('1334561127', 'etiquette', 0.3126535, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982857_0.png'] photo_id : 1334561128 output[photo_id] : [('1334561128', 'film_plastique', 0.34486225, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982862_0.png'] photo_id : 1334561129 output[photo_id] : [('1334561129', 'etiquette', 0.39312688, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982905_0.png'] photo_id : 1334561130 output[photo_id] : [('1334561130', 'papier', 0.48996052, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982915_0.png'] photo_id : 1334561131 output[photo_id] : [('1334561131', 'pet_opaque', 0.96491617, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982898_0.png'] photo_id : 1334561132 output[photo_id] : [('1334561132', 'carton', 0.3508715, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982891_0.png'] photo_id : 1334561133 output[photo_id] : [('1334561133', 'etiquette', 0.46773857, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982899_0.png'] photo_id : 1334561134 output[photo_id] : [('1334561134', 'etiquette', 0.5331324, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982907_0.png'] photo_id : 1334561135 output[photo_id] : [('1334561135', 'etiquette', 0.20840847, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982990_0.png'] photo_id : 1334561136 output[photo_id] : [('1334561136', 'environnement', 0.5172014, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982937_0.png'] photo_id : 1334561137 output[photo_id] : [('1334561137', 'papier', 0.26372385, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982936_0.png'] photo_id : 1334561138 output[photo_id] : [('1334561138', 'film_plastique', 0.5914809, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982956_0.png'] photo_id : 1334561139 output[photo_id] : [('1334561139', 'papier', 0.67103136, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982982_0.png'] photo_id : 1334561140 output[photo_id] : [('1334561140', 'barquette_opaque', 0.7235987, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982934_0.png'] photo_id : 1334561141 output[photo_id] : [('1334561141', 'ela', 0.65344703, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982977_0.png'] photo_id : 1334561142 output[photo_id] : [('1334561142', 'papier', 0.7651962, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982986_0.png'] photo_id : 1334561143 output[photo_id] : [('1334561143', 'etiquette', 0.894151, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982961_0.png'] photo_id : 1334561144 output[photo_id] : [('1334561144', 'etiquette', 0.5010954, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982967_0.png'] photo_id : 1334561145 output[photo_id] : [('1334561145', 'papier', 0.31561798, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3653571940_0.png'] photo_id : 1334561146 output[photo_id] : [('1334561146', 'barquette_opaque', 0.93266374, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982948_0.png'] photo_id : 1334561147 output[photo_id] : [('1334561147', 'metal', 0.25719872, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982925_0.png'] photo_id : 1334561148 output[photo_id] : [('1334561148', 'pet_fonce', 0.70009303, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982949_0.png'] photo_id : 1334561149 output[photo_id] : [('1334561149', 'papier', 0.68512523, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982973_0.png'] photo_id : 1334561150 output[photo_id] : [('1334561150', 'film_plastique', 0.6890339, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982944_0.png'] photo_id : 1334561151 output[photo_id] : [('1334561151', 'barquette_opaque', 0.94945526, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982933_0.png'] photo_id : 1334561152 output[photo_id] : [('1334561152', 'barquette_opaque', 0.41645458, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982959_0.png'] photo_id : 1334561153 output[photo_id] : [('1334561153', 'etiquette', 0.5188712, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982945_0.png'] photo_id : 1334561154 output[photo_id] : [('1334561154', 'papier', 0.53886074, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982930_0.png'] photo_id : 1334561156 output[photo_id] : [('1334561156', 'etiquette', 0.7103969, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983033_0.png'] photo_id : 1334561157 output[photo_id] : [('1334561157', 'barquette_opaque', 0.5901331, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983001_0.png'] photo_id : 1334561158 output[photo_id] : [('1334561158', 'papier', 0.59507203, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983044_0.png'] photo_id : 1334561159 output[photo_id] : [('1334561159', 'etiquette', 0.23182648, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983018_0.png'] photo_id : 1334561160 output[photo_id] : [('1334561160', 'barquette_opaque', 0.5586722, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983009_0.png'] photo_id : 1334561161 output[photo_id] : [('1334561161', 'pet_opaque', 0.21033055, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651982995_0.png'] photo_id : 1334561413 output[photo_id] : [('1334561413', 'etiquette', 0.96654695, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571631_0.png'] photo_id : 1334561415 output[photo_id] : [('1334561415', 'barquette_opaque', 0.9900413, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571635_0.png'] photo_id : 1334561416 output[photo_id] : [('1334561416', 'barquette_opaque', 0.9617251, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571596_0.png'] photo_id : 1334561417 output[photo_id] : [('1334561417', 'pet_opaque', 0.64797395, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571615_0.png'] photo_id : 1334561418 output[photo_id] : [('1334561418', 'film_plastique', 0.54261816, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571610_0.png'] photo_id : 1334561419 output[photo_id] : [('1334561419', 'papier', 0.3867993, 4151, '3233'), 'temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0_rle_crop_3653571659_0.png'] photo_id : 1334561420 output[photo_id] : [('1334561420', 'barquette_opaque', 0.52954483, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982705_0.png'] photo_id : 1334561421 output[photo_id] : [('1334561421', 'etiquette', 0.3383545, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982678_0.png'] photo_id : 1334561422 output[photo_id] : [('1334561422', 'pet_opaque', 0.52694553, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982701_0.png'] photo_id : 1334561423 output[photo_id] : [('1334561423', 'papier', 0.399702, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3653571685_0.png'] photo_id : 1334561424 output[photo_id] : [('1334561424', 'papier', 0.49427575, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3653571687_0.png'] photo_id : 1334561425 output[photo_id] : [('1334561425', 'etiquette', 0.41385087, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982741_0.png'] photo_id : 1334561426 output[photo_id] : [('1334561426', 'pet_opaque', 0.9974504, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982725_0.png'] photo_id : 1334561427 output[photo_id] : [('1334561427', 'barquette_opaque', 0.9995691, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982729_0.png'] photo_id : 1334561428 output[photo_id] : [('1334561428', 'metal', 0.4530673, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982734_0.png'] photo_id : 1334561429 output[photo_id] : [('1334561429', 'barquette_opaque', 0.5530018, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982813_0.png'] photo_id : 1334561430 output[photo_id] : [('1334561430', 'barquette_opaque', 0.6496992, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982825_0.png'] photo_id : 1334561431 output[photo_id] : [('1334561431', 'barquette_opaque', 0.6522189, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3653571691_0.png'] photo_id : 1334561432 output[photo_id] : [('1334561432', 'papier', 0.5069217, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982827_0.png'] photo_id : 1334561433 output[photo_id] : [('1334561433', 'barquette_opaque', 0.6246694, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982829_0.png'] photo_id : 1334561434 output[photo_id] : [('1334561434', 'pet_opaque', 0.6448973, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982830_0.png'] photo_id : 1334561435 output[photo_id] : [('1334561435', 'papier', 0.33592996, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982860_0.png'] photo_id : 1334561436 output[photo_id] : [('1334561436', 'barquette_opaque', 0.95540315, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982850_0.png'] photo_id : 1334561437 output[photo_id] : [('1334561437', 'pet_opaque', 0.6896365, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982890_0.png'] photo_id : 1334561438 output[photo_id] : [('1334561438', 'pet_opaque', 0.70037633, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982880_0.png'] photo_id : 1334561439 output[photo_id] : [('1334561439', 'pet_opaque', 0.31391272, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982882_0.png'] photo_id : 1334561440 output[photo_id] : [('1334561440', 'barquette_opaque', 0.42701155, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982913_0.png'] photo_id : 1334561441 output[photo_id] : [('1334561441', 'pet_opaque', 0.789556, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982912_0.png'] photo_id : 1334561442 output[photo_id] : [('1334561442', 'barquette_opaque', 0.30073297, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982931_0.png'] photo_id : 1334561443 output[photo_id] : [('1334561443', 'barquette_opaque', 0.61714256, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982954_0.png'] photo_id : 1334561444 output[photo_id] : [('1334561444', 'papier', 0.6205888, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982989_0.png'] photo_id : 1334561445 output[photo_id] : [('1334561445', 'pet_opaque', 0.9984371, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982927_0.png'] photo_id : 1334561446 output[photo_id] : [('1334561446', 'barquette_opaque', 0.98740226, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982983_0.png'] photo_id : 1334561447 output[photo_id] : [('1334561447', 'etiquette', 0.5422607, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982960_0.png'] photo_id : 1334561448 output[photo_id] : [('1334561448', 'carton', 0.38157418, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982980_0.png'] photo_id : 1334561449 output[photo_id] : [('1334561449', 'barquette_opaque', 0.7162807, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982963_0.png'] photo_id : 1334561450 output[photo_id] : [('1334561450', 'papier', 0.5461266, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983020_0.png'] photo_id : 1334561451 output[photo_id] : [('1334561451', 'barquette_opaque', 0.71935964, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983032_0.png'] photo_id : 1334561452 output[photo_id] : [('1334561452', 'pet_opaque', 0.9527772, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983066_0.png'] photo_id : 1334561453 output[photo_id] : [('1334561453', 'barquette_opaque', 0.9545212, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983071_0.png'] photo_id : 1334561455 output[photo_id] : [('1334561455', 'pet_opaque', 0.50559664, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983077_0.png'] photo_id : 1334561456 output[photo_id] : [('1334561456', 'papier', 0.37151733, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983154_0.png'] photo_id : 1334561457 output[photo_id] : [('1334561457', 'pet_opaque', 0.526036, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983152_0.png'] photo_id : 1334561458 output[photo_id] : [('1334561458', 'etiquette', 0.3574057, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983153_0.png'] photo_id : 1334561459 output[photo_id] : [('1334561459', 'etiquette', 0.56255597, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983150_0.png'] photo_id : 1334561460 output[photo_id] : [('1334561460', 'papier', 0.5120297, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056056_0.png'] photo_id : 1334561461 output[photo_id] : [('1334561461', 'papier', 0.8641425, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056058_0.png'] photo_id : 1334561462 output[photo_id] : [('1334561462', 'carton', 0.6836078, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3653571949_0.png'] photo_id : 1334561463 output[photo_id] : [('1334561463', 'papier', 0.7167381, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056057_0.png'] photo_id : 1334561464 output[photo_id] : [('1334561464', 'papier', 0.9798753, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056055_0.png'] photo_id : 1334561465 output[photo_id] : [('1334561465', 'papier', 0.39111036, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3653571951_0.png'] photo_id : 1334561466 output[photo_id] : [('1334561466', 'pet_opaque', 0.94261235, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056051_0.png'] photo_id : 1334561467 output[photo_id] : [('1334561467', 'barquette_opaque', 0.743232, 4151, '3233'), 'temp/1738674005_4044648_1334193728_dcaf7feecc7ce8071774f70bd9bb8ca5_rle_crop_3652056142_0.png'] photo_id : 1334561468 output[photo_id] : [('1334561468', 'papier', 0.7484784, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056180_0.png'] photo_id : 1334561469 output[photo_id] : [('1334561469', 'barquette_opaque', 0.64579433, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056152_0.png'] photo_id : 1334561470 output[photo_id] : [('1334561470', 'barquette_opaque', 0.70927876, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056173_0.png'] photo_id : 1334561471 output[photo_id] : [('1334561471', 'pet_opaque', 0.56572205, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056151_0.png'] photo_id : 1334561472 output[photo_id] : [('1334561472', 'barquette_opaque', 0.45136806, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3653571958_0.png'] photo_id : 1334561473 output[photo_id] : [('1334561473', 'barquette_opaque', 0.36457455, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056216_0.png'] photo_id : 1334560711 output[photo_id] : [('1334560711', 'barquette_opaque', 0.6433743, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3653571948_0.png'] photo_id : 1334560712 output[photo_id] : [('1334560712', 'film_plastique', 0.72195673, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983100_0.png'] photo_id : 1334560713 output[photo_id] : [('1334560713', 'barquette_opaque', 0.32233334, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983117_0.png'] photo_id : 1334560714 output[photo_id] : [('1334560714', 'papier', 0.5816732, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983128_0.png'] photo_id : 1334560715 output[photo_id] : [('1334560715', 'film_plastique', 0.9245486, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983092_0.png'] photo_id : 1334560716 output[photo_id] : [('1334560716', 'papier', 0.79065734, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983178_0.png'] photo_id : 1334560717 output[photo_id] : [('1334560717', 'barquette_opaque', 0.8428382, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983181_0.png'] photo_id : 1334560718 output[photo_id] : [('1334560718', 'barquette_opaque', 0.44401053, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983180_0.png'] photo_id : 1334560719 output[photo_id] : [('1334560719', 'papier', 0.5355044, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983173_0.png'] photo_id : 1334560720 output[photo_id] : [('1334560720', 'pet_opaque', 0.44956747, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983183_0.png'] photo_id : 1334560721 output[photo_id] : [('1334560721', 'papier', 0.45944428, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983158_0.png'] photo_id : 1334560722 output[photo_id] : [('1334560722', 'pet_opaque', 0.6864424, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983137_0.png'] photo_id : 1334560723 output[photo_id] : [('1334560723', 'carton', 0.48094103, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983176_0.png'] photo_id : 1334560724 output[photo_id] : [('1334560724', 'barquette_opaque', 0.74106264, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056066_0.png'] photo_id : 1334560725 output[photo_id] : [('1334560725', 'film_plastique', 0.7363539, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056082_0.png'] photo_id : 1334560726 output[photo_id] : [('1334560726', 'etiquette', 0.55510163, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056052_0.png'] photo_id : 1334560727 output[photo_id] : [('1334560727', 'pet_clair', 0.63288105, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056129_0.png'] photo_id : 1334560728 output[photo_id] : [('1334560728', 'metal', 0.55055285, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056118_0.png'] photo_id : 1334560729 output[photo_id] : [('1334560729', 'barquette_opaque', 0.2519477, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056174_0.png'] photo_id : 1334560730 output[photo_id] : [('1334560730', 'barquette_opaque', 0.55582124, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056164_0.png'] photo_id : 1334560732 output[photo_id] : [('1334560732', 'environnement', 0.3483307, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056181_0.png'] photo_id : 1334560733 output[photo_id] : [('1334560733', 'barquette_opaque', 0.65096974, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056163_0.png'] photo_id : 1334560734 output[photo_id] : [('1334560734', 'film_plastique', 0.34440002, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056179_0.png'] photo_id : 1334560735 output[photo_id] : [('1334560735', 'barquette_opaque', 0.59157807, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056167_0.png'] photo_id : 1334560736 output[photo_id] : [('1334560736', 'film_plastique', 0.38688743, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056153_0.png'] photo_id : 1334560737 output[photo_id] : [('1334560737', 'film_plastique', 0.5471057, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056191_0.png'] photo_id : 1334560738 output[photo_id] : [('1334560738', 'papier', 0.34570754, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056208_0.png'] photo_id : 1334560740 output[photo_id] : [('1334560740', 'film_plastique', 0.7773255, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056192_0.png'] photo_id : 1334560741 output[photo_id] : [('1334560741', 'barquette_opaque', 0.93732136, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056209_0.png'] photo_id : 1334560742 output[photo_id] : [('1334560742', 'film_plastique', 0.7414283, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056219_0.png'] photo_id : 1334560743 output[photo_id] : [('1334560743', 'film_plastique', 0.6420752, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056215_0.png'] photo_id : 1334560766 output[photo_id] : [('1334560766', 'pet_opaque', 0.5211467, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571473_0.png'] photo_id : 1334560767 output[photo_id] : [('1334560767', 'barquette_opaque', 0.25782603, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571481_0.png'] photo_id : 1334560768 output[photo_id] : [('1334560768', 'etiquette', 0.23082533, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571445_0.png'] photo_id : 1334560769 output[photo_id] : [('1334560769', 'ela', 0.34929898, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571482_0.png'] photo_id : 1334560770 output[photo_id] : [('1334560770', 'film_plastique', 0.6778253, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571449_0.png'] photo_id : 1334560771 output[photo_id] : [('1334560771', 'etiquette', 0.39912593, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571472_0.png'] photo_id : 1334560773 output[photo_id] : [('1334560773', 'etiquette', 0.4017152, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571447_0.png'] photo_id : 1334560774 output[photo_id] : [('1334560774', 'papier', 0.9019116, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571526_0.png'] photo_id : 1334560775 output[photo_id] : [('1334560775', 'barquette_opaque', 0.4954491, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571517_0.png'] photo_id : 1334560776 output[photo_id] : [('1334560776', 'papier', 0.30405426, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571496_0.png'] photo_id : 1334560777 output[photo_id] : [('1334560777', 'carton', 0.64102536, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571530_0.png'] photo_id : 1334560778 output[photo_id] : [('1334560778', 'pet_opaque', 0.33261362, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571532_0.png'] photo_id : 1334560779 output[photo_id] : [('1334560779', 'etiquette', 0.6937861, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571508_0.png'] photo_id : 1334560780 output[photo_id] : [('1334560780', 'film_plastique', 0.37526497, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571503_0.png'] photo_id : 1334560781 output[photo_id] : [('1334560781', 'etiquette', 0.7101104, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571493_0.png'] photo_id : 1334560782 output[photo_id] : [('1334560782', 'pet_opaque', 0.5797431, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571543_0.png'] photo_id : 1334560783 output[photo_id] : [('1334560783', 'metal', 0.19088672, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571559_0.png'] photo_id : 1334560784 output[photo_id] : [('1334560784', 'barquette_opaque', 0.9301217, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571570_0.png'] photo_id : 1334560785 output[photo_id] : [('1334560785', 'papier', 0.9871066, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571567_0.png'] photo_id : 1334560786 output[photo_id] : [('1334560786', 'metal', 0.4641185, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571573_0.png'] photo_id : 1334560787 output[photo_id] : [('1334560787', 'etiquette', 0.44265217, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571544_0.png'] photo_id : 1334560788 output[photo_id] : [('1334560788', 'etiquette', 0.7027844, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571565_0.png'] photo_id : 1334560789 output[photo_id] : [('1334560789', 'papier', 0.42317435, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571600_0.png'] photo_id : 1334560790 output[photo_id] : [('1334560790', 'metal', 0.5849382, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571632_0.png'] photo_id : 1334560791 output[photo_id] : [('1334560791', 'papier', 0.27239764, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571595_0.png'] photo_id : 1334560792 output[photo_id] : [('1334560792', 'etiquette', 0.75974196, 4151, '3233'), 'temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0_rle_crop_3653571661_0.png'] photo_id : 1334560793 output[photo_id] : [('1334560793', 'papier', 0.33955976, 4151, '3233'), 'temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0_rle_crop_3653571664_0.png'] photo_id : 1334560794 output[photo_id] : [('1334560794', 'carton', 0.8259633, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982718_0.png'] photo_id : 1334560795 output[photo_id] : [('1334560795', 'etiquette', 0.44678295, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982724_0.png'] photo_id : 1334560796 output[photo_id] : [('1334560796', 'papier', 0.37203932, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982710_0.png'] photo_id : 1334560797 output[photo_id] : [('1334560797', 'papier', 0.5536646, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982702_0.png'] photo_id : 1334560798 output[photo_id] : [('1334560798', 'barquette_opaque', 0.549016, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982709_0.png'] photo_id : 1334560799 output[photo_id] : [('1334560799', 'etiquette', 0.8509246, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982707_0.png'] photo_id : 1334560800 output[photo_id] : [('1334560800', 'papier', 0.44196537, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982683_0.png'] photo_id : 1334560801 output[photo_id] : [('1334560801', 'papier', 0.7551686, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982698_0.png'] photo_id : 1334561162 output[photo_id] : [('1334561162', 'papier', 0.2524656, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983019_0.png'] photo_id : 1334561163 output[photo_id] : [('1334561163', 'metal', 0.42155698, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983030_0.png'] photo_id : 1334561165 output[photo_id] : [('1334561165', 'papier', 0.41806966, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651982999_0.png'] photo_id : 1334561166 output[photo_id] : [('1334561166', 'pet_opaque', 0.23818335, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651982996_0.png'] photo_id : 1334561167 output[photo_id] : [('1334561167', 'barquette_opaque', 0.7336959, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983023_0.png'] photo_id : 1334561168 output[photo_id] : [('1334561168', 'etiquette', 0.94439834, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983063_0.png'] photo_id : 1334561169 output[photo_id] : [('1334561169', 'ela', 0.20485407, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983061_0.png'] photo_id : 1334561170 output[photo_id] : [('1334561170', 'papier', 0.42837486, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983072_0.png'] photo_id : 1334561171 output[photo_id] : [('1334561171', 'pet_fonce', 0.5847811, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983046_0.png'] photo_id : 1334561172 output[photo_id] : [('1334561172', 'barquette_opaque', 0.80592054, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983068_0.png'] photo_id : 1334561173 output[photo_id] : [('1334561173', 'ela', 0.93501794, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983054_0.png'] photo_id : 1334561174 output[photo_id] : [('1334561174', 'pet_opaque', 0.2979292, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983060_0.png'] photo_id : 1334561176 output[photo_id] : [('1334561176', 'pet_opaque', 0.77096945, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983079_0.png'] photo_id : 1334561177 output[photo_id] : [('1334561177', 'pet_opaque', 0.43062967, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983055_0.png'] photo_id : 1334561178 output[photo_id] : [('1334561178', 'papier', 0.48278412, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983052_0.png'] photo_id : 1334561179 output[photo_id] : [('1334561179', 'carton', 0.61562604, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983059_0.png'] photo_id : 1334561180 output[photo_id] : [('1334561180', 'barquette_opaque', 0.62870467, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983056_0.png'] photo_id : 1334561181 output[photo_id] : [('1334561181', 'pet_opaque', 0.37001145, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983051_0.png'] photo_id : 1334561182 output[photo_id] : [('1334561182', 'metal', 0.4264477, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983107_0.png'] photo_id : 1334561183 output[photo_id] : [('1334561183', 'barquette_opaque', 0.5253997, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983123_0.png'] photo_id : 1334561184 output[photo_id] : [('1334561184', 'barquette_opaque', 0.6875883, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983112_0.png'] photo_id : 1334561185 output[photo_id] : [('1334561185', 'pet_opaque', 0.9509708, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983132_0.png'] photo_id : 1334561186 output[photo_id] : [('1334561186', 'papier', 0.43819267, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983099_0.png'] photo_id : 1334561187 output[photo_id] : [('1334561187', 'barquette_opaque', 0.68251187, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983104_0.png'] photo_id : 1334561188 output[photo_id] : [('1334561188', 'pet_clair', 0.33493426, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3653571946_0.png'] photo_id : 1334561189 output[photo_id] : [('1334561189', 'papier', 0.5403347, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983095_0.png'] photo_id : 1334561190 output[photo_id] : [('1334561190', 'etiquette', 0.36910254, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983098_0.png'] photo_id : 1334561191 output[photo_id] : [('1334561191', 'papier', 0.5268024, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983159_0.png'] photo_id : 1334561192 output[photo_id] : [('1334561192', 'papier', 0.5570017, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983165_0.png'] photo_id : 1334561193 output[photo_id] : [('1334561193', 'barquette_opaque', 0.96876043, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983172_0.png'] photo_id : 1334561194 output[photo_id] : [('1334561194', 'papier', 0.8034556, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983185_0.png'] photo_id : 1334561195 output[photo_id] : [('1334561195', 'film_plastique', 0.2810794, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983166_0.png'] photo_id : 1334561196 output[photo_id] : [('1334561196', 'barquette_opaque', 0.6245492, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983162_0.png'] photo_id : 1334561197 output[photo_id] : [('1334561197', 'pet_fonce', 0.761628, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983168_0.png'] photo_id : 1334561198 output[photo_id] : [('1334561198', 'pet_fonce', 0.8301512, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983142_0.png'] photo_id : 1334561199 output[photo_id] : [('1334561199', 'papier', 0.26723373, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983170_0.png'] photo_id : 1334561200 output[photo_id] : [('1334561200', 'barquette_opaque', 0.9306547, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056068_0.png'] photo_id : 1334561201 output[photo_id] : [('1334561201', 'papier', 0.59672546, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056060_0.png'] photo_id : 1334561202 output[photo_id] : [('1334561202', 'environnement', 0.4987011, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056064_0.png'] photo_id : 1334561203 output[photo_id] : [('1334561203', 'papier', 0.912631, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056078_0.png'] photo_id : 1334561204 output[photo_id] : [('1334561204', 'papier', 0.73034376, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056083_0.png'] photo_id : 1334561205 output[photo_id] : [('1334561205', 'etiquette', 0.66989654, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056081_0.png'] photo_id : 1334561206 output[photo_id] : [('1334561206', 'etiquette', 0.54614526, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056089_0.png'] photo_id : 1334561207 output[photo_id] : [('1334561207', 'environnement', 0.58446074, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056061_0.png'] photo_id : 1334561208 output[photo_id] : [('1334561208', 'papier', 0.6755264, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056080_0.png'] photo_id : 1334561209 output[photo_id] : [('1334561209', 'metal', 0.25603184, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056079_0.png'] photo_id : 1334561210 output[photo_id] : [('1334561210', 'carton', 0.3694263, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056063_0.png'] photo_id : 1334561211 output[photo_id] : [('1334561211', 'pet_fonce', 0.7405836, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056087_0.png'] photo_id : 1334561212 output[photo_id] : [('1334561212', 'etiquette', 0.67590123, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056074_0.png'] photo_id : 1334561213 output[photo_id] : [('1334561213', 'pet_fonce', 0.6310938, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056125_0.png'] photo_id : 1334561214 output[photo_id] : [('1334561214', 'pet_opaque', 0.3729448, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056100_0.png'] photo_id : 1334561215 output[photo_id] : [('1334561215', 'film_plastique', 0.26406053, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056126_0.png'] photo_id : 1334561216 output[photo_id] : [('1334561216', 'papier', 0.40616286, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3653571953_0.png'] photo_id : 1334561217 output[photo_id] : [('1334561217', 'pet_opaque', 0.63210183, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056108_0.png'] photo_id : 1334561218 output[photo_id] : [('1334561218', 'barquette_opaque', 0.5134759, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056097_0.png'] photo_id : 1334561219 output[photo_id] : [('1334561219', 'papier', 0.9840491, 4151, '3233'), 'temp/1738674005_4044648_1334193728_dcaf7feecc7ce8071774f70bd9bb8ca5_rle_crop_3652056140_0.png'] photo_id : 1334561220 output[photo_id] : [('1334561220', 'papier', 0.6921782, 4151, '3233'), 'temp/1738674005_4044648_1334193728_dcaf7feecc7ce8071774f70bd9bb8ca5_rle_crop_3652056147_0.png'] photo_id : 1334561221 output[photo_id] : [('1334561221', 'papier', 0.8595817, 4151, '3233'), 'temp/1738674005_4044648_1334193728_dcaf7feecc7ce8071774f70bd9bb8ca5_rle_crop_3652056135_0.png'] photo_id : 1334561222 output[photo_id] : [('1334561222', 'papier', 0.5124707, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056166_0.png'] photo_id : 1334561223 output[photo_id] : [('1334561223', 'carton', 0.5151766, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056156_0.png'] photo_id : 1334561224 output[photo_id] : [('1334561224', 'environnement', 0.41778862, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056178_0.png'] photo_id : 1334561225 output[photo_id] : [('1334561225', 'papier', 0.45649862, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056175_0.png'] photo_id : 1334561226 output[photo_id] : [('1334561226', 'etiquette', 0.38214052, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056157_0.png'] photo_id : 1334561227 output[photo_id] : [('1334561227', 'etiquette', 0.46481726, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056160_0.png'] photo_id : 1334561228 output[photo_id] : [('1334561228', 'papier', 0.5401705, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056195_0.png'] photo_id : 1334561229 output[photo_id] : [('1334561229', 'pet_opaque', 0.39180464, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056196_0.png'] photo_id : 1334560900 output[photo_id] : [('1334560900', 'film_plastique', 0.70099103, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571511_0.png'] photo_id : 1334560901 output[photo_id] : [('1334560901', 'film_plastique', 0.5269945, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571537_0.png'] photo_id : 1334560902 output[photo_id] : [('1334560902', 'barquette_opaque', 0.6941941, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571528_0.png'] photo_id : 1334560904 output[photo_id] : [('1334560904', 'film_plastique', 0.5024206, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571513_0.png'] photo_id : 1334560908 output[photo_id] : [('1334560908', 'pet_clair', 0.4892287, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571568_0.png'] photo_id : 1334560912 output[photo_id] : [('1334560912', 'metal', 0.2922794, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571576_0.png'] photo_id : 1334560913 output[photo_id] : [('1334560913', 'papier', 0.77705544, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571579_0.png'] photo_id : 1334560914 output[photo_id] : [('1334560914', 'papier', 0.47309065, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571634_0.png'] photo_id : 1334560915 output[photo_id] : [('1334560915', 'pet_opaque', 0.5557369, 4151, '3233'), 'temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0_rle_crop_3653571673_0.png'] photo_id : 1334560916 output[photo_id] : [('1334560916', 'papier', 0.57917035, 4151, '3233'), 'temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0_rle_crop_3653571667_0.png'] photo_id : 1334560918 output[photo_id] : [('1334560918', 'film_plastique', 0.5534161, 4151, '3233'), 'temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0_rle_crop_3653571663_0.png'] photo_id : 1334560919 output[photo_id] : [('1334560919', 'etiquette', 0.8530318, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982719_0.png'] photo_id : 1334560920 output[photo_id] : [('1334560920', 'barquette_opaque', 0.56267005, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982713_0.png'] photo_id : 1334560921 output[photo_id] : [('1334560921', 'film_plastique', 0.7697116, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982746_0.png'] photo_id : 1334560922 output[photo_id] : [('1334560922', 'papier', 0.36355537, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982762_0.png'] photo_id : 1334560923 output[photo_id] : [('1334560923', 'film_plastique', 0.6019959, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982778_0.png'] photo_id : 1334560924 output[photo_id] : [('1334560924', 'film_plastique', 0.37729686, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982795_0.png'] photo_id : 1334560925 output[photo_id] : [('1334560925', 'pet_opaque', 0.1937278, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982820_0.png'] photo_id : 1334560926 output[photo_id] : [('1334560926', 'film_plastique', 0.50166994, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982839_0.png'] photo_id : 1334560927 output[photo_id] : [('1334560927', 'metal', 0.7950487, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982903_0.png'] photo_id : 1334560929 output[photo_id] : [('1334560929', 'etiquette', 0.42887655, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982885_0.png'] photo_id : 1334560930 output[photo_id] : [('1334560930', 'papier', 0.80846953, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982897_0.png'] photo_id : 1334560931 output[photo_id] : [('1334560931', 'pet_clair', 0.3111952, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982935_0.png'] photo_id : 1334560932 output[photo_id] : [('1334560932', 'papier', 0.50080395, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982939_0.png'] photo_id : 1334560933 output[photo_id] : [('1334560933', 'pet_clair', 0.30479735, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982979_0.png'] photo_id : 1334560934 output[photo_id] : [('1334560934', 'papier', 0.19497542, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3653571943_0.png'] photo_id : 1334560935 output[photo_id] : [('1334560935', 'papier', 0.26303068, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3653571945_0.png'] photo_id : 1334560936 output[photo_id] : [('1334560936', 'film_plastique', 0.4791637, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983041_0.png'] photo_id : 1334560937 output[photo_id] : [('1334560937', 'barquette_opaque', 0.660918, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983086_0.png'] photo_id : 1334560938 output[photo_id] : [('1334560938', 'barquette_opaque', 0.3044725, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983078_0.png'] photo_id : 1334560939 output[photo_id] : [('1334560939', 'film_plastique', 0.31170714, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983084_0.png'] photo_id : 1334560940 output[photo_id] : [('1334560940', 'metal', 0.4591649, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983102_0.png'] photo_id : 1334560941 output[photo_id] : [('1334560941', 'papier', 0.30436194, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983113_0.png'] photo_id : 1334560942 output[photo_id] : [('1334560942', 'papier', 0.45795304, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983115_0.png'] photo_id : 1334560943 output[photo_id] : [('1334560943', 'pet_opaque', 0.8784244, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983116_0.png'] photo_id : 1334560944 output[photo_id] : [('1334560944', 'papier', 0.43791658, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983129_0.png'] photo_id : 1334560945 output[photo_id] : [('1334560945', 'pet_opaque', 0.41180068, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983101_0.png'] photo_id : 1334560946 output[photo_id] : [('1334560946', 'film_plastique', 0.25206843, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983184_0.png'] photo_id : 1334560947 output[photo_id] : [('1334560947', 'environnement', 0.24399664, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983164_0.png'] photo_id : 1334560948 output[photo_id] : [('1334560948', 'etiquette', 0.52129483, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983146_0.png'] photo_id : 1334560949 output[photo_id] : [('1334560949', 'barquette_opaque', 0.4930772, 4151, '3233'), 'temp/1738674005_4044648_1334193843_19147ce6dd7d7d6bf125a339bf969b22_rle_crop_3651983161_0.png'] photo_id : 1334560951 output[photo_id] : [('1334560951', 'papier', 0.6967074, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056088_0.png'] photo_id : 1334560952 output[photo_id] : [('1334560952', 'film_plastique', 0.3343787, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3653571950_0.png'] photo_id : 1334560953 output[photo_id] : [('1334560953', 'barquette_opaque', 0.35043705, 4151, '3233'), 'temp/1738674005_4044648_1334193840_1fb8e1ea4f97a4987b4f0734d4fd52ea_rle_crop_3652056085_0.png'] photo_id : 1334560954 output[photo_id] : [('1334560954', 'papier', 0.86401105, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056120_0.png'] photo_id : 1334560955 output[photo_id] : [('1334560955', 'papier', 0.5327283, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3653571956_0.png'] photo_id : 1334560956 output[photo_id] : [('1334560956', 'pet_opaque', 0.9854236, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3653571954_0.png'] photo_id : 1334560958 output[photo_id] : [('1334560958', 'pet_opaque', 0.28001937, 4151, '3233'), 'temp/1738674005_4044648_1334193838_3670f9ed40a25162645c795654c7689c_rle_crop_3652056092_0.png'] photo_id : 1334560959 output[photo_id] : [('1334560959', 'pet_opaque', 0.20575942, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056176_0.png'] photo_id : 1334560960 output[photo_id] : [('1334560960', 'papier', 0.2842232, 4151, '3233'), 'temp/1738674005_4044648_1334193389_de8e42d7bf51a4f67ec1af682204d271_rle_crop_3652056177_0.png'] photo_id : 1334560961 output[photo_id] : [('1334560961', 'metal', 0.30312994, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056194_0.png'] photo_id : 1334560962 output[photo_id] : [('1334560962', 'barquette_opaque', 0.71770114, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056197_0.png'] photo_id : 1334560963 output[photo_id] : [('1334560963', 'metal', 0.4087154, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3653571959_0.png'] photo_id : 1334560964 output[photo_id] : [('1334560964', 'papier', 0.81380373, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056206_0.png'] photo_id : 1334560965 output[photo_id] : [('1334560965', 'etiquette', 0.20102409, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056213_0.png'] photo_id : 1334560966 output[photo_id] : [('1334560966', 'film_plastique', 0.65820396, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056207_0.png'] photo_id : 1334560967 output[photo_id] : [('1334560967', 'film_plastique', 0.6271964, 4151, '3233'), 'temp/1738674005_4044648_1334193386_d1b38c39364d91a73db2cb40c7816c60_rle_crop_3652056198_0.png'] photo_id : 1334561017 output[photo_id] : [('1334561017', 'film_plastique', 0.21236117, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571451_0.png'] photo_id : 1334561018 output[photo_id] : [('1334561018', 'pet_opaque', 0.7316506, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571461_0.png'] photo_id : 1334561019 output[photo_id] : [('1334561019', 'papier', 0.30953458, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571467_0.png'] photo_id : 1334561020 output[photo_id] : [('1334561020', 'carton', 0.41772866, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571476_0.png'] photo_id : 1334561021 output[photo_id] : [('1334561021', 'papier', 0.98972625, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571474_0.png'] photo_id : 1334561022 output[photo_id] : [('1334561022', 'pet_opaque', 0.80892146, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571448_0.png'] photo_id : 1334561023 output[photo_id] : [('1334561023', 'barquette_opaque', 0.92338204, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571487_0.png'] photo_id : 1334561024 output[photo_id] : [('1334561024', 'metal', 0.29960895, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571454_0.png'] photo_id : 1334561025 output[photo_id] : [('1334561025', 'etiquette', 0.43325537, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571443_0.png'] photo_id : 1334560641 output[photo_id] : [('1334560641', 'pet_opaque', 0.70888764, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571452_0.png'] photo_id : 1334560642 output[photo_id] : [('1334560642', 'barquette_opaque', 0.79040575, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571469_0.png'] photo_id : 1334560643 output[photo_id] : [('1334560643', 'barquette_opaque', 0.5453113, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571465_0.png'] photo_id : 1334560644 output[photo_id] : [('1334560644', 'barquette_opaque', 0.9770114, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571483_0.png'] photo_id : 1334560645 output[photo_id] : [('1334560645', 'barquette_opaque', 0.68833214, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571456_0.png'] photo_id : 1334560646 output[photo_id] : [('1334560646', 'barquette_opaque', 0.6793653, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571442_0.png'] photo_id : 1334560647 output[photo_id] : [('1334560647', 'barquette_opaque', 0.29936484, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571459_0.png'] photo_id : 1334560649 output[photo_id] : [('1334560649', 'papier', 0.29889774, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571485_0.png'] photo_id : 1334560650 output[photo_id] : [('1334560650', 'barquette_opaque', 0.49688244, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571466_0.png'] photo_id : 1334560651 output[photo_id] : [('1334560651', 'film_plastique', 0.8821173, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571471_0.png'] photo_id : 1334560652 output[photo_id] : [('1334560652', 'barquette_opaque', 0.9350011, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571450_0.png'] photo_id : 1334560653 output[photo_id] : [('1334560653', 'barquette_opaque', 0.60697585, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571462_0.png'] photo_id : 1334560654 output[photo_id] : [('1334560654', 'barquette_opaque', 0.9004456, 4151, '3233'), 'temp/1738674005_4044648_1334416147_0bfff2d61035f0291d66acc6b343250d_rle_crop_3653571455_0.png'] photo_id : 1334560655 output[photo_id] : [('1334560655', 'pet_opaque', 0.622245, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571509_0.png'] photo_id : 1334560656 output[photo_id] : [('1334560656', 'pet_fonce', 0.31708497, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571518_0.png'] photo_id : 1334560657 output[photo_id] : [('1334560657', 'pet_clair', 0.61273444, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571489_0.png'] photo_id : 1334560658 output[photo_id] : [('1334560658', 'barquette_opaque', 0.783448, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571525_0.png'] photo_id : 1334560659 output[photo_id] : [('1334560659', 'barquette_opaque', 0.52288735, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571495_0.png'] photo_id : 1334560660 output[photo_id] : [('1334560660', 'barquette_opaque', 0.28994712, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571512_0.png'] photo_id : 1334560661 output[photo_id] : [('1334560661', 'barquette_opaque', 0.71468866, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571502_0.png'] photo_id : 1334560662 output[photo_id] : [('1334560662', 'barquette_opaque', 0.9296726, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571492_0.png'] photo_id : 1334560663 output[photo_id] : [('1334560663', 'barquette_opaque', 0.8137486, 4151, '3233'), 'temp/1738674005_4044648_1334416146_de405353d98d3cc0372bc18e4625f1fa_rle_crop_3653571499_0.png'] photo_id : 1334560664 output[photo_id] : [('1334560664', 'film_plastique', 0.5703692, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571580_0.png'] photo_id : 1334560665 output[photo_id] : [('1334560665', 'film_plastique', 0.56620777, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571583_0.png'] photo_id : 1334560666 output[photo_id] : [('1334560666', 'barquette_opaque', 0.9966647, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571563_0.png'] photo_id : 1334560667 output[photo_id] : [('1334560667', 'pet_clair', 0.26936817, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571562_0.png'] photo_id : 1334560668 output[photo_id] : [('1334560668', 'film_plastique', 0.4634163, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571548_0.png'] photo_id : 1334560669 output[photo_id] : [('1334560669', 'film_plastique', 0.55754143, 4151, '3233'), 'temp/1738674005_4044648_1334416145_9f5555cb4e614437c14c4e4638e75510_rle_crop_3653571566_0.png'] photo_id : 1334560670 output[photo_id] : [('1334560670', 'film_plastique', 0.6662849, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571597_0.png'] photo_id : 1334560671 output[photo_id] : [('1334560671', 'barquette_opaque', 0.41190445, 4151, '3233'), 'temp/1738674005_4044648_1334416109_61dde2cfebdcd19de1b467419937cced_rle_crop_3653571591_0.png'] photo_id : 1334560673 output[photo_id] : [('1334560673', 'barquette_opaque', 0.3867623, 4151, '3233'), 'temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0_rle_crop_3653571665_0.png'] photo_id : 1334560674 output[photo_id] : [('1334560674', 'film_plastique', 0.48005533, 4151, '3233'), 'temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0_rle_crop_3653571649_0.png'] photo_id : 1334560675 output[photo_id] : [('1334560675', 'pehd', 0.47126636, 4151, '3233'), 'temp/1738674005_4044648_1334416058_0af337c725df9f4dcf8003007d3e17d0_rle_crop_3653571648_0.png'] photo_id : 1334560676 output[photo_id] : [('1334560676', 'barquette_opaque', 0.7463449, 4151, '3233'), 'temp/1738674005_4044648_1334194033_fbd009d820aa16ae0bacacba82d84fbc_rle_crop_3651982717_0.png'] photo_id : 1334560677 output[photo_id] : [('1334560677', 'papier', 0.4926635, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982770_0.png'] photo_id : 1334560678 output[photo_id] : [('1334560678', 'pet_opaque', 0.51981556, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982771_0.png'] photo_id : 1334560679 output[photo_id] : [('1334560679', 'papier', 0.5942175, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982728_0.png'] photo_id : 1334560680 output[photo_id] : [('1334560680', 'film_plastique', 0.20586358, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3653571682_0.png'] photo_id : 1334560681 output[photo_id] : [('1334560681', 'film_plastique', 0.7254818, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982768_0.png'] photo_id : 1334560682 output[photo_id] : [('1334560682', 'pet_fonce', 0.51303166, 4151, '3233'), 'temp/1738674005_4044648_1334194028_73136e7ca75b7b942b0826be660ce6bc_rle_crop_3651982738_0.png'] photo_id : 1334560683 output[photo_id] : [('1334560683', 'papier', 0.59258366, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982780_0.png'] photo_id : 1334560684 output[photo_id] : [('1334560684', 'pet_clair', 0.47012243, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982790_0.png'] photo_id : 1334560685 output[photo_id] : [('1334560685', 'carton', 0.29577753, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982807_0.png'] photo_id : 1334560686 output[photo_id] : [('1334560686', 'pehd', 0.2707469, 4151, '3233'), 'temp/1738674005_4044648_1334194010_0a6cd611a8bb559361b43c03d4a3904a_rle_crop_3651982782_0.png'] photo_id : 1334560688 output[photo_id] : [('1334560688', 'film_plastique', 0.9018358, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3653571692_0.png'] photo_id : 1334560689 output[photo_id] : [('1334560689', 'barquette_opaque', 0.4973683, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982876_0.png'] photo_id : 1334560690 output[photo_id] : [('1334560690', 'pet_opaque', 0.2569767, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982873_0.png'] photo_id : 1334560691 output[photo_id] : [('1334560691', 'barquette_opaque', 0.5684534, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982851_0.png'] photo_id : 1334560692 output[photo_id] : [('1334560692', 'metal', 0.76122105, 4151, '3233'), 'temp/1738674005_4044648_1334194006_97c6fc138b61a9fcb47e2196de03c18f_rle_crop_3651982835_0.png'] photo_id : 1334560693 output[photo_id] : [('1334560693', 'papier', 0.33389625, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982916_0.png'] photo_id : 1334560694 output[photo_id] : [('1334560694', 'pet_opaque', 0.34209695, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982919_0.png'] photo_id : 1334560695 output[photo_id] : [('1334560695', 'film_plastique', 0.83171916, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3651982888_0.png'] photo_id : 1334560696 output[photo_id] : [('1334560696', 'barquette_opaque', 0.73203886, 4151, '3233'), 'temp/1738674005_4044648_1334194002_02bd5d7ea056be668a55815bbf01c70f_rle_crop_3653571694_0.png'] photo_id : 1334560697 output[photo_id] : [('1334560697', 'papier', 0.21651103, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982938_0.png'] photo_id : 1334560698 output[photo_id] : [('1334560698', 'film_plastique', 0.77113926, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982987_0.png'] photo_id : 1334560699 output[photo_id] : [('1334560699', 'barquette_opaque', 0.561047, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982974_0.png'] photo_id : 1334560700 output[photo_id] : [('1334560700', 'barquette_opaque', 0.54107803, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982923_0.png'] photo_id : 1334560701 output[photo_id] : [('1334560701', 'pet_clair', 0.23270719, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982952_0.png'] photo_id : 1334560702 output[photo_id] : [('1334560702', 'pet_clair', 0.35277337, 4151, '3233'), 'temp/1738674005_4044648_1334194000_188dc2dc3a66d5d70846bf67b466b23a_rle_crop_3651982969_0.png'] photo_id : 1334560703 output[photo_id] : [('1334560703', 'pet_opaque', 0.79068077, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983026_0.png'] photo_id : 1334560704 output[photo_id] : [('1334560704', 'film_plastique', 0.84573525, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651982992_0.png'] photo_id : 1334560705 output[photo_id] : [('1334560705', 'pet_fonce', 0.34124482, 4151, '3233'), 'temp/1738674005_4044648_1334193886_b0f61046233e6112358c70ba8aeb58e0_rle_crop_3651983021_0.png'] photo_id : 1334560706 output[photo_id] : [('1334560706', 'etiquette', 0.96827835, 4151, '3233'), 'temp/1738674005_4044648_1334193883_b1e8e6f223f7618521235d357c13de5b_rle_crop_3651983070_0.png'] photo_id : 1334560707 output[photo_id] : [('1334560707', 'pet_opaque', 0.40313867, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983094_0.png'] photo_id : 1334560708 output[photo_id] : [('1334560708', 'pet_opaque', 0.5275508, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983097_0.png'] photo_id : 1334560710 output[photo_id] : [('1334560710', 'barquette_opaque', 0.6569336, 4151, '3233'), 'temp/1738674005_4044648_1334193847_173fd6a321e8121afacf630ab94444d3_rle_crop_3651983103_0.png'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 653 time used for this insertion : 0.10791397094726562 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 653 time used for this insertion : 0.06905031204223633 save missing photos in datou_result : time spend for datou_step_exec : 0.007895708084106445 time spend to save output : 0.1854996681213379 total time spend for step 6 : 0.19339537620544434 step7:merge_mask_thcl_custom Tue Feb 4 14:16:58 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 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 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step merge_mask_thcl_custom this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , 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sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we 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sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we 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sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it this is a sub image , we ignore it batch 1 Loaded 956 chid ids of type : 3760 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++As expected we have just one thcl present begin to find the sub_photo_id : begin to find the sub_photo_id : begin to find the sub_photo_id : begin to find the sub_photo_id : begin to find the sub_photo_id : begin to find the sub_photo_id : begin to find the sub_photo_id : begin to find the sub_photo_id : begin to find the sub_photo_id : begin to find the sub_photo_id : begin to find the sub_photo_id : begin to find the sub_photo_id : begin to find the sub_photo_id : begin to find the sub_photo_id : begin to find the sub_photo_id : begin to find the sub_photo_id : begin to find the sub_photo_id : begin to find the sub_photo_id : begin to find the sub_photo_id : begin to find the sub_photo_id : batch 1 Loaded 690 chid ids of type : 4210 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 690 chid ids of type : 4210 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++End of step merge_mask_thcl_custom Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : merge_mask_thcl_custom we use saveGeneral [1334416147, 1334416146, 1334416145, 1334416109, 1334416058, 1334194033, 1334194028, 1334194010, 1334194006, 1334194002, 1334194000, 1334193886, 1334193883, 1334193847, 1334193843, 1334193840, 1334193838, 1334193728, 1334193389, 1334193386] Looping around the photos to save general results len do output : 20 /1334416147Didn't retrieve data .Didn't retrieve data . /1334416146Didn't retrieve data .Didn't retrieve data . /1334416145Didn't retrieve data .Didn't retrieve data . /1334416109Didn't retrieve data .Didn't retrieve data . /1334416058Didn't retrieve data .Didn't retrieve data . /1334194033Didn't retrieve data .Didn't retrieve data . /1334194028Didn't retrieve data .Didn't retrieve data . /1334194010Didn't retrieve data .Didn't retrieve data . /1334194006Didn't retrieve data .Didn't retrieve data . /1334194002Didn't retrieve data .Didn't retrieve data . /1334194000Didn't retrieve data .Didn't retrieve data . /1334193886Didn't retrieve data .Didn't retrieve data . /1334193883Didn't retrieve data .Didn't retrieve data . /1334193847Didn't retrieve data .Didn't retrieve data . /1334193843Didn't retrieve data .Didn't retrieve data . /1334193840Didn't retrieve data .Didn't retrieve data . /1334193838Didn't retrieve data .Didn't retrieve data . /1334193728Didn't retrieve data .Didn't retrieve data . /1334193389Didn't retrieve data .Didn't retrieve data . /1334193386Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416147', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416146', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416145', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416109', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416058', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194033', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194028', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194010', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194006', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194002', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194000', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193886', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193883', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193847', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193843', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193840', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193838', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193728', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193389', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193386', None, None, None, None, None, '2543013') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 60 time used for this insertion : 0.014673233032226562 save_final save missing photos in datou_result : time spend for datou_step_exec : 4.0677642822265625 time spend to save output : 0.01609516143798828 total time spend for step 7 : 4.083859443664551 step8:rle_unique_nms_with_priority Tue Feb 4 14:17:02 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms nb_obj : 37 nb_hashtags : 10 time to prepare the origin masks : 10.94664216041565 time for calcul the mask position with numpy : 0.07492303848266602 nb_pixel_total : 5399773 time to create 1 rle with new method : 0.17638158798217773 time for calcul the mask position with numpy : 0.0289459228515625 nb_pixel_total : 11318 time to create 1 rle with old method : 0.012624979019165039 time for calcul the mask position with numpy : 0.02898883819580078 nb_pixel_total : 35834 time to create 1 rle with old method : 0.03714489936828613 time for calcul the mask position with numpy : 0.028140544891357422 nb_pixel_total : 29159 time to create 1 rle with old method : 0.03096175193786621 time for calcul the mask position with numpy : 0.02863335609436035 nb_pixel_total : 14206 time to create 1 rle with old method : 0.015422344207763672 time for calcul the mask position with numpy : 0.02815866470336914 nb_pixel_total : 8268 time to create 1 rle with old method : 0.009060859680175781 time for calcul the mask position with numpy : 0.029055356979370117 nb_pixel_total : 95334 time to create 1 rle with old method : 0.10021471977233887 time for calcul the mask position with numpy : 0.028261661529541016 nb_pixel_total : 38389 time to create 1 rle with old method : 0.04026341438293457 time for calcul the mask position with numpy : 0.028168678283691406 nb_pixel_total : 32058 time to create 1 rle with old method : 0.03301405906677246 time for calcul the mask position with numpy : 0.028186798095703125 nb_pixel_total : 76279 time to create 1 rle with old method : 0.07937741279602051 time for calcul the mask position with numpy : 0.028048038482666016 nb_pixel_total : 46377 time to create 1 rle with old method : 0.04878950119018555 time for calcul the mask position with numpy : 0.02852654457092285 nb_pixel_total : 138061 time to create 1 rle with old method : 0.147979736328125 time for calcul the mask position with numpy : 0.029421567916870117 nb_pixel_total : 53606 time to create 1 rle with old method : 0.057224273681640625 time for calcul the mask position with numpy : 0.029749155044555664 nb_pixel_total : 119511 time to create 1 rle with old method : 0.12679433822631836 time for calcul the mask position with numpy : 0.029808759689331055 nb_pixel_total : 101415 time to create 1 rle with old method : 0.10999059677124023 time for calcul the mask position with numpy : 0.029946327209472656 nb_pixel_total : 35639 time to create 1 rle with old method : 0.03829240798950195 time for calcul the mask position with numpy : 0.02935504913330078 nb_pixel_total : 56989 time to create 1 rle with old method : 0.06018662452697754 time for calcul the mask position with numpy : 0.028772592544555664 nb_pixel_total : 9164 time to create 1 rle with old method : 0.009876012802124023 time for calcul the mask position with numpy : 0.029131174087524414 nb_pixel_total : 58458 time to create 1 rle with old method : 0.06154489517211914 time for calcul the mask position with numpy : 0.02868795394897461 nb_pixel_total : 67808 time to create 1 rle with old method : 0.07049274444580078 time for calcul the mask position with numpy : 0.02785038948059082 nb_pixel_total : 34405 time to create 1 rle with old method : 0.03678321838378906 time for calcul the mask position with numpy : 0.030463218688964844 nb_pixel_total : 27965 time to create 1 rle with old method : 0.029900550842285156 time for calcul the mask position with numpy : 0.029265165328979492 nb_pixel_total : 27605 time to create 1 rle with old method : 0.03080582618713379 time for calcul the mask position with numpy : 0.02908492088317871 nb_pixel_total : 43975 time to create 1 rle with old method : 0.04885601997375488 time for calcul the mask position with numpy : 0.029390811920166016 nb_pixel_total : 93502 time to create 1 rle with old method : 0.10099124908447266 time for calcul the mask position with numpy : 0.02887272834777832 nb_pixel_total : 17633 time to create 1 rle with old method : 0.0192105770111084 time for calcul the mask position with numpy : 0.02931690216064453 nb_pixel_total : 92454 time to create 1 rle with old method : 0.09789490699768066 time for calcul the mask position with numpy : 0.028587818145751953 nb_pixel_total : 9970 time to create 1 rle with old method : 0.010519742965698242 time for calcul the mask position with numpy : 0.028543472290039062 nb_pixel_total : 7481 time to create 1 rle with old method : 0.008523702621459961 time for calcul the mask position with numpy : 0.02882218360900879 nb_pixel_total : 9751 time to create 1 rle with old method : 0.011121034622192383 time for calcul the mask position with numpy : 0.028494596481323242 nb_pixel_total : 27873 time to create 1 rle with old method : 0.029268264770507812 time for calcul the mask position with numpy : 0.030046463012695312 nb_pixel_total : 18464 time to create 1 rle with old method : 0.019828081130981445 time for calcul the mask position with numpy : 0.02886343002319336 nb_pixel_total : 32078 time to create 1 rle with old method : 0.03421592712402344 time for calcul the mask position with numpy : 0.028563499450683594 nb_pixel_total : 39081 time to create 1 rle with old method : 0.04113340377807617 time for calcul the mask position with numpy : 0.028620243072509766 nb_pixel_total : 28838 time to create 1 rle with old method : 0.03233027458190918 time for calcul the mask position with numpy : 0.028497695922851562 nb_pixel_total : 32370 time to create 1 rle with old method : 0.03416800498962402 time for calcul the mask position with numpy : 0.028437137603759766 nb_pixel_total : 16208 time to create 1 rle with old method : 0.017388343811035156 time for calcul the mask position with numpy : 0.030056238174438477 nb_pixel_total : 62941 time to create 1 rle with old method : 0.06986880302429199 create new chi : 3.100510835647583 time to delete rle : 0.05825161933898926 batch 1 Loaded 38 chid ids of type : 4211 Number RLEs to save : 20877 TO DO : save crop sub photo not yet done ! save time : 1.2070586681365967 nb_obj : 39 nb_hashtags : 9 time to prepare the origin masks : 13.997062683105469 time for calcul the mask position with numpy : 0.10045838356018066 nb_pixel_total : 5426913 time to create 1 rle with new method : 0.17560100555419922 time for calcul the mask position with numpy : 0.027950286865234375 nb_pixel_total : 38265 time to create 1 rle with old method : 0.038819313049316406 time for calcul the mask position with numpy : 0.028098583221435547 nb_pixel_total : 27075 time to create 1 rle with old method : 0.027672529220581055 time for calcul the mask position with numpy : 0.029977798461914062 nb_pixel_total : 49312 time to create 1 rle with old method : 0.052471160888671875 time for calcul the mask position with numpy : 0.028372526168823242 nb_pixel_total : 9478 time to create 1 rle with old method : 0.010349512100219727 time for calcul the mask position with numpy : 0.029650449752807617 nb_pixel_total : 13632 time to create 1 rle with old method : 0.015038728713989258 time for calcul the mask position with numpy : 0.02992415428161621 nb_pixel_total : 22469 time to create 1 rle with old method : 0.024141311645507812 time for calcul the mask position with numpy : 0.03009510040283203 nb_pixel_total : 34656 time to create 1 rle with old method : 0.04598855972290039 time for calcul the mask position with numpy : 0.033297061920166016 nb_pixel_total : 6235 time to create 1 rle with old method : 0.006845951080322266 time for calcul the mask position with numpy : 0.02853560447692871 nb_pixel_total : 47513 time to create 1 rle with old method : 0.049500465393066406 time for calcul the mask position with numpy : 0.031124591827392578 nb_pixel_total : 79759 time to create 1 rle with old method : 0.08598542213439941 time for calcul the mask position with numpy : 0.030258655548095703 nb_pixel_total : 43712 time to create 1 rle with old method : 0.04745769500732422 time for calcul the mask position with numpy : 0.029773712158203125 nb_pixel_total : 52599 time to create 1 rle with old method : 0.06067371368408203 time for calcul the mask position with numpy : 0.02978658676147461 nb_pixel_total : 30166 time to create 1 rle with old method : 0.03351140022277832 time for calcul the mask position with numpy : 0.030674457550048828 nb_pixel_total : 106946 time to create 1 rle with old method : 0.11271429061889648 time for calcul the mask position with numpy : 0.02947711944580078 nb_pixel_total : 25365 time to create 1 rle with old method : 0.027376174926757812 time for calcul the mask position with numpy : 0.029974699020385742 nb_pixel_total : 36631 time to create 1 rle with old method : 0.037734031677246094 time for calcul the mask position with numpy : 0.028166770935058594 nb_pixel_total : 55842 time to create 1 rle with old method : 0.05748796463012695 time for calcul the mask position with numpy : 0.027661561965942383 nb_pixel_total : 10227 time to create 1 rle with old method : 0.011131048202514648 time for calcul the mask position with numpy : 0.028916120529174805 nb_pixel_total : 58233 time to create 1 rle with old method : 0.061171770095825195 time for calcul the mask position with numpy : 0.02871394157409668 nb_pixel_total : 68502 time to create 1 rle with old method : 0.07122802734375 time for calcul the mask position with numpy : 0.0278780460357666 nb_pixel_total : 30951 time to create 1 rle with old method : 0.03175997734069824 time for calcul the mask position with numpy : 0.027574539184570312 nb_pixel_total : 79520 time to create 1 rle with old method : 0.08170318603515625 time for calcul the mask position with numpy : 0.027158021926879883 nb_pixel_total : 12721 time to create 1 rle with old method : 0.012794256210327148 time for calcul the mask position with numpy : 0.02960062026977539 nb_pixel_total : 211930 time to create 1 rle with new method : 0.14469289779663086 time for calcul the mask position with numpy : 0.028356313705444336 nb_pixel_total : 16278 time to create 1 rle with old method : 0.017429113388061523 time for calcul the mask position with numpy : 0.028591156005859375 nb_pixel_total : 5779 time to create 1 rle with old method : 0.006390810012817383 time for calcul the mask position with numpy : 0.02777385711669922 nb_pixel_total : 7038 time to create 1 rle with old method : 0.007753849029541016 time for calcul the mask position with numpy : 0.028024673461914062 nb_pixel_total : 81425 time to create 1 rle with old method : 0.08569884300231934 time for calcul the mask position with numpy : 0.02747201919555664 nb_pixel_total : 14448 time to create 1 rle with old method : 0.016062259674072266 time for calcul the mask position with numpy : 0.029527902603149414 nb_pixel_total : 8422 time to create 1 rle with old method : 0.009553670883178711 time for calcul the mask position with numpy : 0.029431819915771484 nb_pixel_total : 19955 time to create 1 rle with old method : 0.021889448165893555 time for calcul the mask position with numpy : 0.029418230056762695 nb_pixel_total : 26250 time to create 1 rle with old method : 0.0281674861907959 time for calcul the mask position with numpy : 0.030503511428833008 nb_pixel_total : 21403 time to create 1 rle with old method : 0.023932933807373047 time for calcul the mask position with numpy : 0.02939772605895996 nb_pixel_total : 28701 time to create 1 rle with old method : 0.03156590461730957 time for calcul the mask position with numpy : 0.02935194969177246 nb_pixel_total : 30901 time to create 1 rle with old method : 0.03419780731201172 time for calcul the mask position with numpy : 0.02971792221069336 nb_pixel_total : 37920 time to create 1 rle with old method : 0.04222869873046875 time for calcul the mask position with numpy : 0.029435157775878906 nb_pixel_total : 56946 time to create 1 rle with old method : 0.060640573501586914 time for calcul the mask position with numpy : 0.029493093490600586 nb_pixel_total : 57181 time to create 1 rle with old method : 0.061510324478149414 time for calcul the mask position with numpy : 0.029813766479492188 nb_pixel_total : 58941 time to create 1 rle with old method : 0.06580400466918945 create new chi : 3.1107418537139893 time to delete rle : 0.002122163772583008 batch 1 Loaded 40 chid ids of type : 4211 Number RLEs to save : 20152 TO DO : save crop sub photo not yet done ! save time : 2.055205821990967 nb_obj : 35 nb_hashtags : 8 time to prepare the origin masks : 11.70686149597168 time for calcul the mask position with numpy : 0.10057449340820312 nb_pixel_total : 5595896 time to create 1 rle with new method : 0.17088556289672852 time for calcul the mask position with numpy : 0.02717757225036621 nb_pixel_total : 18781 time to create 1 rle with old method : 0.019403934478759766 time for calcul the mask position with numpy : 0.027377605438232422 nb_pixel_total : 71823 time to create 1 rle with old method : 0.07210874557495117 time for calcul the mask position with numpy : 0.027646780014038086 nb_pixel_total : 38562 time to create 1 rle with old method : 0.04106426239013672 time for calcul the mask position with numpy : 0.028411388397216797 nb_pixel_total : 30426 time to create 1 rle with old method : 0.03192853927612305 time for calcul the mask position with numpy : 0.02842545509338379 nb_pixel_total : 41324 time to create 1 rle with old method : 0.04328775405883789 time for calcul the mask position with numpy : 0.029056549072265625 nb_pixel_total : 94860 time to create 1 rle with old method : 0.09932518005371094 time for calcul the mask position with numpy : 0.028516530990600586 nb_pixel_total : 41910 time to create 1 rle with old method : 0.04348134994506836 time for calcul the mask position with numpy : 0.027902841567993164 nb_pixel_total : 14301 time to create 1 rle with old method : 0.01558375358581543 time for calcul the mask position with numpy : 0.028864145278930664 nb_pixel_total : 119917 time to create 1 rle with old method : 0.12447571754455566 time for calcul the mask position with numpy : 0.027723312377929688 nb_pixel_total : 26030 time to create 1 rle with old method : 0.027470111846923828 time for calcul the mask position with numpy : 0.0280606746673584 nb_pixel_total : 63200 time to create 1 rle with old method : 0.06612825393676758 time for calcul the mask position with numpy : 0.027996540069580078 nb_pixel_total : 69429 time to create 1 rle with old method : 0.0720207691192627 time for calcul the mask position with numpy : 0.02817988395690918 nb_pixel_total : 13468 time to create 1 rle with old method : 0.014232158660888672 time for calcul the mask position with numpy : 0.028037071228027344 nb_pixel_total : 25885 time to create 1 rle with old method : 0.02752518653869629 time for calcul the mask position with numpy : 0.028594493865966797 nb_pixel_total : 81187 time to create 1 rle with old method : 0.08554911613464355 time for calcul the mask position with numpy : 0.028664112091064453 nb_pixel_total : 133616 time to create 1 rle with old method : 0.13764524459838867 time for calcul the mask position with numpy : 0.02844834327697754 nb_pixel_total : 50596 time to create 1 rle with old method : 0.05135083198547363 time for calcul the mask position with numpy : 0.027413606643676758 nb_pixel_total : 45276 time to create 1 rle with old method : 0.04812169075012207 time for calcul the mask position with numpy : 0.027664899826049805 nb_pixel_total : 12159 time to create 1 rle with old method : 0.013233661651611328 time for calcul the mask position with numpy : 0.028284311294555664 nb_pixel_total : 92616 time to create 1 rle with old method : 0.09696626663208008 time for calcul the mask position with numpy : 0.027722597122192383 nb_pixel_total : 27130 time to create 1 rle with old method : 0.028847217559814453 time for calcul the mask position with numpy : 0.028069496154785156 nb_pixel_total : 48667 time to create 1 rle with old method : 0.051531314849853516 time for calcul the mask position with numpy : 0.028487682342529297 nb_pixel_total : 31986 time to create 1 rle with old method : 0.03350377082824707 time for calcul the mask position with numpy : 0.02832627296447754 nb_pixel_total : 6497 time to create 1 rle with old method : 0.007194042205810547 time for calcul the mask position with numpy : 0.028010845184326172 nb_pixel_total : 7125 time to create 1 rle with old method : 0.007449150085449219 time for calcul the mask position with numpy : 0.02854299545288086 nb_pixel_total : 59425 time to create 1 rle with old method : 0.06191444396972656 time for calcul the mask position with numpy : 0.027869224548339844 nb_pixel_total : 4825 time to create 1 rle with old method : 0.005301475524902344 time for calcul the mask position with numpy : 0.027655363082885742 nb_pixel_total : 2150 time to create 1 rle with old method : 0.0024034976959228516 time for calcul the mask position with numpy : 0.027755260467529297 nb_pixel_total : 28398 time to create 1 rle with old method : 0.02978062629699707 time for calcul the mask position with numpy : 0.028971195220947266 nb_pixel_total : 48989 time to create 1 rle with old method : 0.05109453201293945 time for calcul the mask position with numpy : 0.028241872787475586 nb_pixel_total : 6345 time to create 1 rle with old method : 0.0070378780364990234 time for calcul the mask position with numpy : 0.028543949127197266 nb_pixel_total : 62073 time to create 1 rle with old method : 0.06462597846984863 time for calcul the mask position with numpy : 0.030498504638671875 nb_pixel_total : 326 time to create 1 rle with old method : 0.0005993843078613281 time for calcul the mask position with numpy : 0.028055191040039062 nb_pixel_total : 24775 time to create 1 rle with old method : 0.026680946350097656 time for calcul the mask position with numpy : 0.028376340866088867 nb_pixel_total : 10267 time to create 1 rle with old method : 0.01089930534362793 create new chi : 2.7950594425201416 time to delete rle : 0.0019299983978271484 batch 1 Loaded 36 chid ids of type : 4211 Number RLEs to save : 20312 TO DO : save crop sub photo not yet done ! save time : 1.072418451309204 nb_obj : 34 nb_hashtags : 8 time to prepare the origin masks : 10.690812349319458 time for calcul the mask position with numpy : 0.08710575103759766 nb_pixel_total : 5753711 time to create 1 rle with new method : 0.1615312099456787 time for calcul the mask position with numpy : 0.027508974075317383 nb_pixel_total : 33243 time to create 1 rle with old method : 0.03348374366760254 time for calcul the mask position with numpy : 0.02739238739013672 nb_pixel_total : 25051 time to create 1 rle with old method : 0.02541971206665039 time for calcul the mask position with numpy : 0.02874755859375 nb_pixel_total : 42539 time to create 1 rle with old method : 0.05729532241821289 time for calcul the mask position with numpy : 0.027340173721313477 nb_pixel_total : 58217 time to create 1 rle with old method : 0.06094551086425781 time for calcul the mask position with numpy : 0.027135372161865234 nb_pixel_total : 21700 time to create 1 rle with old method : 0.021439075469970703 time for calcul the mask position with numpy : 0.0270082950592041 nb_pixel_total : 75712 time to create 1 rle with old method : 0.07730555534362793 time for calcul the mask position with numpy : 0.028514385223388672 nb_pixel_total : 56889 time to create 1 rle with old method : 0.06452560424804688 time for calcul the mask position with numpy : 0.028111696243286133 nb_pixel_total : 25486 time to create 1 rle with old method : 0.026787519454956055 time for calcul the mask position with numpy : 0.02805495262145996 nb_pixel_total : 46029 time to create 1 rle with old method : 0.04908037185668945 time for calcul the mask position with numpy : 0.026759862899780273 nb_pixel_total : 23276 time to create 1 rle with old method : 0.02326345443725586 time for calcul the mask position with numpy : 0.0284426212310791 nb_pixel_total : 39404 time to create 1 rle with old method : 0.041570425033569336 time for calcul the mask position with numpy : 0.028380155563354492 nb_pixel_total : 8196 time to create 1 rle with old method : 0.009169816970825195 time for calcul the mask position with numpy : 0.02881932258605957 nb_pixel_total : 234946 time to create 1 rle with new method : 0.13654518127441406 time for calcul the mask position with numpy : 0.029541969299316406 nb_pixel_total : 34065 time to create 1 rle with old method : 0.03961896896362305 time for calcul the mask position with numpy : 0.027849197387695312 nb_pixel_total : 16618 time to create 1 rle with old method : 0.01766800880432129 time for calcul the mask position with numpy : 0.028838634490966797 nb_pixel_total : 169670 time to create 1 rle with new method : 0.14359235763549805 time for calcul the mask position with numpy : 0.02775430679321289 nb_pixel_total : 13137 time to create 1 rle with old method : 0.013433694839477539 time for calcul the mask position with numpy : 0.02689528465270996 nb_pixel_total : 15029 time to create 1 rle with old method : 0.017760753631591797 time for calcul the mask position with numpy : 0.027364492416381836 nb_pixel_total : 68891 time to create 1 rle with old method : 0.06783223152160645 time for calcul the mask position with numpy : 0.026628971099853516 nb_pixel_total : 9750 time to create 1 rle with old method : 0.009878396987915039 time for calcul the mask position with numpy : 0.02669501304626465 nb_pixel_total : 11531 time to create 1 rle with old method : 0.011964797973632812 time for calcul the mask position with numpy : 0.02814483642578125 nb_pixel_total : 7376 time to create 1 rle with old method : 0.00799250602722168 time for calcul the mask position with numpy : 0.027637481689453125 nb_pixel_total : 3597 time to create 1 rle with old method : 0.0038330554962158203 time for calcul the mask position with numpy : 0.02727794647216797 nb_pixel_total : 3939 time to create 1 rle with old method : 0.004347085952758789 time for calcul the mask position with numpy : 0.02711009979248047 nb_pixel_total : 6231 time to create 1 rle with old method : 0.006625175476074219 time for calcul the mask position with numpy : 0.027008056640625 nb_pixel_total : 11559 time to create 1 rle with old method : 0.011563777923583984 time for calcul the mask position with numpy : 0.030449867248535156 nb_pixel_total : 11572 time to create 1 rle with old method : 0.014713525772094727 time for calcul the mask position with numpy : 0.029447078704833984 nb_pixel_total : 48114 time to create 1 rle with old method : 0.051666259765625 time for calcul the mask position with numpy : 0.030118227005004883 nb_pixel_total : 87546 time to create 1 rle with old method : 0.09345817565917969 time for calcul the mask position with numpy : 0.029348134994506836 nb_pixel_total : 15637 time to create 1 rle with old method : 0.0206143856048584 time for calcul the mask position with numpy : 0.0334017276763916 nb_pixel_total : 42862 time to create 1 rle with old method : 0.05666995048522949 time for calcul the mask position with numpy : 0.02932286262512207 nb_pixel_total : 5503 time to create 1 rle with old method : 0.00605010986328125 time for calcul the mask position with numpy : 0.029179811477661133 nb_pixel_total : 5997 time to create 1 rle with old method : 0.006761789321899414 time for calcul the mask position with numpy : 0.02899479866027832 nb_pixel_total : 17217 time to create 1 rle with old method : 0.018804311752319336 create new chi : 2.5059618949890137 time to delete rle : 0.001972198486328125 batch 1 Loaded 35 chid ids of type : 4211 Number RLEs to save : 18170 TO DO : save crop sub photo not yet done ! save time : 0.9883882999420166 nb_obj : 21 nb_hashtags : 6 time to prepare the origin masks : 4.85571026802063 time for calcul the mask position with numpy : 0.11906576156616211 nb_pixel_total : 6384960 time to create 1 rle with new method : 0.17756104469299316 time for calcul the mask position with numpy : 0.02463984489440918 nb_pixel_total : 37216 time to create 1 rle with old method : 0.041982412338256836 time for calcul the mask position with numpy : 0.023622751235961914 nb_pixel_total : 43213 time to create 1 rle with old method : 0.048837900161743164 time for calcul the mask position with numpy : 0.023470401763916016 nb_pixel_total : 24957 time to create 1 rle with old method : 0.026764392852783203 time for calcul the mask position with numpy : 0.023358821868896484 nb_pixel_total : 11989 time to create 1 rle with old method : 0.015454292297363281 time for calcul the mask position with numpy : 0.024073123931884766 nb_pixel_total : 4327 time to create 1 rle with old method : 0.006931781768798828 time for calcul the mask position with numpy : 0.023576974868774414 nb_pixel_total : 11723 time to create 1 rle with old method : 0.012819528579711914 time for calcul the mask position with numpy : 0.02300858497619629 nb_pixel_total : 50648 time to create 1 rle with old method : 0.0538182258605957 time for calcul the mask position with numpy : 0.023183107376098633 nb_pixel_total : 7998 time to create 1 rle with old method : 0.008329629898071289 time for calcul the mask position with numpy : 0.02285909652709961 nb_pixel_total : 22395 time to create 1 rle with old method : 0.02330160140991211 time for calcul the mask position with numpy : 0.022540807723999023 nb_pixel_total : 56267 time to create 1 rle with old method : 0.057381629943847656 time for calcul the mask position with numpy : 0.02347540855407715 nb_pixel_total : 99217 time to create 1 rle with old method : 0.10555887222290039 time for calcul the mask position with numpy : 0.022520065307617188 nb_pixel_total : 3451 time to create 1 rle with old method : 0.0037314891815185547 time for calcul the mask position with numpy : 0.02207779884338379 nb_pixel_total : 13918 time to create 1 rle with old method : 0.014042377471923828 time for calcul the mask position with numpy : 0.022905826568603516 nb_pixel_total : 19759 time to create 1 rle with old method : 0.021207809448242188 time for calcul the mask position with numpy : 0.022487640380859375 nb_pixel_total : 92665 time to create 1 rle with old method : 0.09387779235839844 time for calcul the mask position with numpy : 0.02689814567565918 nb_pixel_total : 32446 time to create 1 rle with old method : 0.034302711486816406 time for calcul the mask position with numpy : 0.022757291793823242 nb_pixel_total : 26954 time to create 1 rle with old method : 0.02715587615966797 time for calcul the mask position with numpy : 0.022101640701293945 nb_pixel_total : 16976 time to create 1 rle with old method : 0.01688408851623535 time for calcul the mask position with numpy : 0.022573232650756836 nb_pixel_total : 18523 time to create 1 rle with old method : 0.018822669982910156 time for calcul the mask position with numpy : 0.022480010986328125 nb_pixel_total : 58942 time to create 1 rle with old method : 0.05986833572387695 time for calcul the mask position with numpy : 0.021831989288330078 nb_pixel_total : 11696 time to create 1 rle with old method : 0.01210641860961914 create new chi : 1.5045979022979736 time to delete rle : 0.0011179447174072266 batch 1 Loaded 22 chid ids of type : 4211 Number RLEs to save : 10423 TO DO : save crop sub photo not yet done ! save time : 0.5916528701782227 nb_obj : 33 nb_hashtags : 8 time to prepare the origin masks : 9.814767360687256 time for calcul the mask position with numpy : 0.07863211631774902 nb_pixel_total : 6184278 time to create 1 rle with new method : 0.14079046249389648 time for calcul the mask position with numpy : 0.03177618980407715 nb_pixel_total : 10612 time to create 1 rle with old method : 0.01193380355834961 time for calcul the mask position with numpy : 0.03377962112426758 nb_pixel_total : 2087 time to create 1 rle with old method : 0.0038013458251953125 time for calcul the mask position with numpy : 0.0375516414642334 nb_pixel_total : 26758 time to create 1 rle with old method : 0.045052289962768555 time for calcul the mask position with numpy : 0.03417658805847168 nb_pixel_total : 79396 time to create 1 rle with old method : 0.13984107971191406 time for calcul the mask position with numpy : 0.03855013847351074 nb_pixel_total : 64278 time to create 1 rle with old method : 0.11480116844177246 time for calcul the mask position with numpy : 0.047295570373535156 nb_pixel_total : 20717 time to create 1 rle with old method : 0.03619122505187988 time for calcul the mask position with numpy : 0.03795433044433594 nb_pixel_total : 22006 time to create 1 rle with old method : 0.038745880126953125 time for calcul the mask position with numpy : 0.03845405578613281 nb_pixel_total : 4439 time to create 1 rle with old method : 0.008115530014038086 time for calcul the mask position with numpy : 0.03802895545959473 nb_pixel_total : 2055 time to create 1 rle with old method : 0.0038073062896728516 time for calcul the mask position with numpy : 0.037894248962402344 nb_pixel_total : 7607 time to create 1 rle with old method : 0.013572216033935547 time for calcul the mask position with numpy : 0.036687374114990234 nb_pixel_total : 19885 time to create 1 rle with old method : 0.028868913650512695 time for calcul the mask position with numpy : 0.029251813888549805 nb_pixel_total : 7000 time to create 1 rle with old method : 0.012101411819458008 time for calcul the mask position with numpy : 0.03708243370056152 nb_pixel_total : 7970 time to create 1 rle with old method : 0.014592170715332031 time for calcul the mask position with numpy : 0.03657102584838867 nb_pixel_total : 29464 time to create 1 rle with old method : 0.05350375175476074 time for calcul the mask position with numpy : 0.03679180145263672 nb_pixel_total : 12785 time to create 1 rle with old method : 0.02331852912902832 time for calcul the mask position with numpy : 0.03699135780334473 nb_pixel_total : 27404 time to create 1 rle with old method : 0.04998135566711426 time for calcul the mask position with numpy : 0.03670549392700195 nb_pixel_total : 28248 time to create 1 rle with old method : 0.05154228210449219 time for calcul the mask position with numpy : 0.037218570709228516 nb_pixel_total : 5278 time to create 1 rle with old method : 0.005777120590209961 time for calcul the mask position with numpy : 0.02901601791381836 nb_pixel_total : 21263 time to create 1 rle with old method : 0.023046016693115234 time for calcul the mask position with numpy : 0.029256105422973633 nb_pixel_total : 17773 time to create 1 rle with old method : 0.019484996795654297 time for calcul the mask position with numpy : 0.029018402099609375 nb_pixel_total : 21886 time to create 1 rle with old method : 0.023760318756103516 time for calcul the mask position with numpy : 0.030777692794799805 nb_pixel_total : 46298 time to create 1 rle with old method : 0.05298495292663574 time for calcul the mask position with numpy : 0.029317617416381836 nb_pixel_total : 55872 time to create 1 rle with old method : 0.06110644340515137 time for calcul the mask position with numpy : 0.029056549072265625 nb_pixel_total : 10282 time to create 1 rle with old method : 0.014058113098144531 time for calcul the mask position with numpy : 0.030980825424194336 nb_pixel_total : 39922 time to create 1 rle with old method : 0.04270577430725098 time for calcul the mask position with numpy : 0.028858423233032227 nb_pixel_total : 21025 time to create 1 rle with old method : 0.022284984588623047 time for calcul the mask position with numpy : 0.02933359146118164 nb_pixel_total : 80478 time to create 1 rle with old method : 0.08649587631225586 time for calcul the mask position with numpy : 0.029111146926879883 nb_pixel_total : 81364 time to create 1 rle with old method : 0.08735132217407227 time for calcul the mask position with numpy : 0.029236316680908203 nb_pixel_total : 12489 time to create 1 rle with old method : 0.013457536697387695 time for calcul the mask position with numpy : 0.02911663055419922 nb_pixel_total : 16797 time to create 1 rle with old method : 0.019193172454833984 time for calcul the mask position with numpy : 0.02913069725036621 nb_pixel_total : 31370 time to create 1 rle with old method : 0.03375864028930664 time for calcul the mask position with numpy : 0.029853343963623047 nb_pixel_total : 26463 time to create 1 rle with old method : 0.028711795806884766 time for calcul the mask position with numpy : 0.0288846492767334 nb_pixel_total : 4691 time to create 1 rle with old method : 0.0051288604736328125 create new chi : 2.5274055004119873 time to delete rle : 0.0014598369598388672 batch 1 Loaded 34 chid ids of type : 4211 Number RLEs to save : 14760 TO DO : save crop sub photo not yet done ! save time : 0.8329367637634277 nb_obj : 54 nb_hashtags : 10 time to prepare the origin masks : 18.716615200042725 time for calcul the mask position with numpy : 0.0855710506439209 nb_pixel_total : 5480068 time to create 1 rle with new method : 0.17424607276916504 time for calcul the mask position with numpy : 0.02865743637084961 nb_pixel_total : 8558 time to create 1 rle with old method : 0.009416341781616211 time for calcul the mask position with numpy : 0.028326034545898438 nb_pixel_total : 20702 time to create 1 rle with old method : 0.021958112716674805 time for calcul the mask position with numpy : 0.028460264205932617 nb_pixel_total : 44867 time to create 1 rle with old method : 0.04677462577819824 time for calcul the mask position with numpy : 0.027609586715698242 nb_pixel_total : 90726 time to create 1 rle with old method : 0.09292984008789062 time for calcul the mask position with numpy : 0.027381181716918945 nb_pixel_total : 12701 time to create 1 rle with old method : 0.014026165008544922 time for calcul the mask position with numpy : 0.028490543365478516 nb_pixel_total : 86669 time to create 1 rle with old method : 0.09151959419250488 time for calcul the mask position with numpy : 0.02909374237060547 nb_pixel_total : 23641 time to create 1 rle with old method : 0.02546381950378418 time for calcul the mask position with numpy : 0.028154611587524414 nb_pixel_total : 18095 time to create 1 rle with old method : 0.019834280014038086 time for calcul the mask position with numpy : 0.028764009475708008 nb_pixel_total : 12609 time to create 1 rle with old method : 0.013437509536743164 time for calcul the mask position with numpy : 0.028741121292114258 nb_pixel_total : 8392 time to create 1 rle with old method : 0.009106874465942383 time for calcul the mask position with numpy : 0.028638124465942383 nb_pixel_total : 77 time to create 1 rle with old method : 0.0001876354217529297 time for calcul the mask position with numpy : 0.028447389602661133 nb_pixel_total : 27164 time to create 1 rle with old method : 0.029882431030273438 time for calcul the mask position with numpy : 0.028652191162109375 nb_pixel_total : 22140 time to create 1 rle with old method : 0.02434682846069336 time for calcul the mask position with numpy : 0.03177309036254883 nb_pixel_total : 191 time to create 1 rle with old method : 0.0005457401275634766 time for calcul the mask position with numpy : 0.033785104751586914 nb_pixel_total : 22558 time to create 1 rle with old method : 0.03014373779296875 time for calcul the mask position with numpy : 0.0289766788482666 nb_pixel_total : 9426 time to create 1 rle with old method : 0.010905027389526367 time for calcul the mask position with numpy : 0.02880096435546875 nb_pixel_total : 32411 time to create 1 rle with old method : 0.03500938415527344 time for calcul the mask position with numpy : 0.02909088134765625 nb_pixel_total : 28069 time to create 1 rle with old method : 0.0298769474029541 time for calcul the mask position with numpy : 0.029407978057861328 nb_pixel_total : 36411 time to create 1 rle with old method : 0.0386655330657959 time for calcul the mask position with numpy : 0.02870345115661621 nb_pixel_total : 403 time to create 1 rle with old method : 0.0006995201110839844 time for calcul the mask position with numpy : 0.02869105339050293 nb_pixel_total : 353 time to create 1 rle with old method : 0.0005764961242675781 time for calcul the mask position with numpy : 0.02884674072265625 nb_pixel_total : 2222 time to create 1 rle with old method : 0.0027375221252441406 time for calcul the mask position with numpy : 0.02809906005859375 nb_pixel_total : 12169 time to create 1 rle with old method : 0.014925003051757812 time for calcul the mask position with numpy : 0.03307366371154785 nb_pixel_total : 18699 time to create 1 rle with old method : 0.029935359954833984 time for calcul the mask position with numpy : 0.03044867515563965 nb_pixel_total : 38450 time to create 1 rle with old method : 0.039553165435791016 time for calcul the mask position with numpy : 0.028783321380615234 nb_pixel_total : 43873 time to create 1 rle with old method : 0.04525947570800781 time for calcul the mask position with numpy : 0.029178142547607422 nb_pixel_total : 13919 time to create 1 rle with old method : 0.0147705078125 time for calcul the mask position with numpy : 0.02884387969970703 nb_pixel_total : 664 time to create 1 rle with old method : 0.0009832382202148438 time for calcul the mask position with numpy : 0.02901601791381836 nb_pixel_total : 30531 time to create 1 rle with old method : 0.031797170639038086 time for calcul the mask position with numpy : 0.02906179428100586 nb_pixel_total : 26270 time to create 1 rle with old method : 0.027646303176879883 time for calcul the mask position with numpy : 0.0287935733795166 nb_pixel_total : 1510 time to create 1 rle with old method : 0.0021347999572753906 time for calcul the mask position with numpy : 0.028777122497558594 nb_pixel_total : 89065 time to create 1 rle with old method : 0.09537100791931152 time for calcul the mask position with numpy : 0.029181241989135742 nb_pixel_total : 41198 time to create 1 rle with old method : 0.04408979415893555 time for calcul the mask position with numpy : 0.028290271759033203 nb_pixel_total : 75029 time to create 1 rle with old method : 0.07842850685119629 time for calcul the mask position with numpy : 0.028946638107299805 nb_pixel_total : 195 time to create 1 rle with old method : 0.0007905960083007812 time for calcul the mask position with numpy : 0.03223443031311035 nb_pixel_total : 164112 time to create 1 rle with new method : 0.15439438819885254 time for calcul the mask position with numpy : 0.029411792755126953 nb_pixel_total : 46872 time to create 1 rle with old method : 0.05145716667175293 time for calcul the mask position with numpy : 0.02980971336364746 nb_pixel_total : 63586 time to create 1 rle with old method : 0.06862044334411621 time for calcul the mask position with numpy : 0.029294252395629883 nb_pixel_total : 426 time to create 1 rle with old method : 0.0006473064422607422 time for calcul the mask position with numpy : 0.02915501594543457 nb_pixel_total : 18267 time to create 1 rle with old method : 0.01979374885559082 time for calcul the mask position with numpy : 0.029347896575927734 nb_pixel_total : 20753 time to create 1 rle with old method : 0.03196263313293457 time for calcul the mask position with numpy : 0.03321361541748047 nb_pixel_total : 23314 time to create 1 rle with old method : 0.03449416160583496 time for calcul the mask position with numpy : 0.07682228088378906 nb_pixel_total : 28850 time to create 1 rle with old method : 0.03229236602783203 time for calcul the mask position with numpy : 0.02925848960876465 nb_pixel_total : 12852 time to create 1 rle with old method : 0.014228343963623047 time for calcul the mask position with numpy : 0.029271364212036133 nb_pixel_total : 13757 time to create 1 rle with old method : 0.015279769897460938 time for calcul the mask position with numpy : 0.029600858688354492 nb_pixel_total : 82551 time to create 1 rle with old method : 0.08884692192077637 time for calcul the mask position with numpy : 0.029521703720092773 nb_pixel_total : 30077 time to create 1 rle with old method : 0.03299212455749512 time for calcul the mask position with numpy : 0.02966475486755371 nb_pixel_total : 538 time to create 1 rle with old method : 0.0007455348968505859 time for calcul the mask position with numpy : 0.027570247650146484 nb_pixel_total : 21601 time to create 1 rle with old method : 0.022246122360229492 time for calcul the mask position with numpy : 0.027752399444580078 nb_pixel_total : 15226 time to create 1 rle with old method : 0.01587367057800293 time for calcul the mask position with numpy : 0.02748847007751465 nb_pixel_total : 73203 time to create 1 rle with old method : 0.07490181922912598 time for calcul the mask position with numpy : 0.02747511863708496 nb_pixel_total : 18031 time to create 1 rle with old method : 0.019124746322631836 time for calcul the mask position with numpy : 0.029294967651367188 nb_pixel_total : 149 time to create 1 rle with old method : 0.0003180503845214844 time for calcul the mask position with numpy : 0.027178049087524414 nb_pixel_total : 36050 time to create 1 rle with old method : 0.03632330894470215 create new chi : 3.6075782775878906 time to delete rle : 0.0023174285888671875 batch 1 Loaded 59 chid ids of type : 4211 Number RLEs to save : 24944 TO DO : save crop sub photo not yet done ! save time : 2.24837327003479 nb_obj : 36 nb_hashtags : 11 time to prepare the origin masks : 11.15592336654663 time for calcul the mask position with numpy : 0.09356141090393066 nb_pixel_total : 5629316 time to create 1 rle with new method : 0.16945171356201172 time for calcul the mask position with numpy : 0.027464866638183594 nb_pixel_total : 33063 time to create 1 rle with old method : 0.03564119338989258 time for calcul the mask position with numpy : 0.027948617935180664 nb_pixel_total : 47393 time to create 1 rle with old method : 0.047944068908691406 time for calcul the mask position with numpy : 0.028200626373291016 nb_pixel_total : 16991 time to create 1 rle with old method : 0.018607616424560547 time for calcul the mask position with numpy : 0.026987791061401367 nb_pixel_total : 35800 time to create 1 rle with old method : 0.03637266159057617 time for calcul the mask position with numpy : 0.028617143630981445 nb_pixel_total : 204572 time to create 1 rle with new method : 0.1308581829071045 time for calcul the mask position with numpy : 0.027150392532348633 nb_pixel_total : 3547 time to create 1 rle with old method : 0.003925800323486328 time for calcul the mask position with numpy : 0.02753424644470215 nb_pixel_total : 22433 time to create 1 rle with old method : 0.02255845069885254 time for calcul the mask position with numpy : 0.02729201316833496 nb_pixel_total : 463 time to create 1 rle with old method : 0.0007572174072265625 time for calcul the mask position with numpy : 0.02730393409729004 nb_pixel_total : 27867 time to create 1 rle with old method : 0.028432130813598633 time for calcul the mask position with numpy : 0.02784895896911621 nb_pixel_total : 45411 time to create 1 rle with old method : 0.047742366790771484 time for calcul the mask position with numpy : 0.02802133560180664 nb_pixel_total : 28590 time to create 1 rle with old method : 0.029512643814086914 time for calcul the mask position with numpy : 0.028151750564575195 nb_pixel_total : 15757 time to create 1 rle with old method : 0.01714181900024414 time for calcul the mask position with numpy : 0.02817511558532715 nb_pixel_total : 5642 time to create 1 rle with old method : 0.006008148193359375 time for calcul the mask position with numpy : 0.029302597045898438 nb_pixel_total : 27773 time to create 1 rle with old method : 0.02934741973876953 time for calcul the mask position with numpy : 0.028579235076904297 nb_pixel_total : 43344 time to create 1 rle with old method : 0.045958757400512695 time for calcul the mask position with numpy : 0.028243064880371094 nb_pixel_total : 21214 time to create 1 rle with old method : 0.022208690643310547 time for calcul the mask position with numpy : 0.027962684631347656 nb_pixel_total : 14990 time to create 1 rle with old method : 0.01634836196899414 time for calcul the mask position with numpy : 0.028472185134887695 nb_pixel_total : 38319 time to create 1 rle with old method : 0.040251970291137695 time for calcul the mask position with numpy : 0.02904057502746582 nb_pixel_total : 140017 time to create 1 rle with old method : 0.14851117134094238 time for calcul the mask position with numpy : 0.029713153839111328 nb_pixel_total : 54024 time to create 1 rle with old method : 0.07561588287353516 time for calcul the mask position with numpy : 0.034899234771728516 nb_pixel_total : 24734 time to create 1 rle with old method : 0.026600360870361328 time for calcul the mask position with numpy : 0.030570030212402344 nb_pixel_total : 101938 time to create 1 rle with old method : 0.10825514793395996 time for calcul the mask position with numpy : 0.030670642852783203 nb_pixel_total : 78405 time to create 1 rle with old method : 0.08522963523864746 time for calcul the mask position with numpy : 0.029391050338745117 nb_pixel_total : 18462 time to create 1 rle with old method : 0.027444124221801758 time for calcul the mask position with numpy : 0.029744625091552734 nb_pixel_total : 79517 time to create 1 rle with old method : 0.08194184303283691 time for calcul the mask position with numpy : 0.027842044830322266 nb_pixel_total : 19514 time to create 1 rle with old method : 0.0204923152923584 time for calcul the mask position with numpy : 0.027634859085083008 nb_pixel_total : 16891 time to create 1 rle with old method : 0.017431974411010742 time for calcul the mask position with numpy : 0.029742717742919922 nb_pixel_total : 73396 time to create 1 rle with old method : 0.07573604583740234 time for calcul the mask position with numpy : 0.02778458595275879 nb_pixel_total : 8189 time to create 1 rle with old method : 0.00861501693725586 time for calcul the mask position with numpy : 0.027787446975708008 nb_pixel_total : 10581 time to create 1 rle with old method : 0.010922670364379883 time for calcul the mask position with numpy : 0.027201175689697266 nb_pixel_total : 6403 time to create 1 rle with old method : 0.006515026092529297 time for calcul the mask position with numpy : 0.027126789093017578 nb_pixel_total : 23836 time to create 1 rle with old method : 0.0241243839263916 time for calcul the mask position with numpy : 0.027185440063476562 nb_pixel_total : 44122 time to create 1 rle with old method : 0.045627593994140625 time for calcul the mask position with numpy : 0.02725052833557129 nb_pixel_total : 74297 time to create 1 rle with old method : 0.07378196716308594 time for calcul the mask position with numpy : 0.02719283103942871 nb_pixel_total : 10671 time to create 1 rle with old method : 0.011130332946777344 time for calcul the mask position with numpy : 0.027019023895263672 nb_pixel_total : 2758 time to create 1 rle with old method : 0.002890348434448242 create new chi : 2.742581605911255 time to delete rle : 0.0018434524536132812 batch 1 Loaded 38 chid ids of type : 4211 Number RLEs to save : 19562 TO DO : save crop sub photo not yet done ! save time : 1.18849515914917 nb_obj : 41 nb_hashtags : 9 time to prepare the origin masks : 12.409684658050537 time for calcul the mask position with numpy : 0.07089447975158691 nb_pixel_total : 5825540 time to create 1 rle with new method : 0.13641619682312012 time for calcul the mask position with numpy : 0.0284574031829834 nb_pixel_total : 44734 time to create 1 rle with old method : 0.06455659866333008 time for calcul the mask position with numpy : 0.033919334411621094 nb_pixel_total : 136841 time to create 1 rle with old method : 0.14050865173339844 time for calcul the mask position with numpy : 0.030022859573364258 nb_pixel_total : 54408 time to create 1 rle with old method : 0.05624842643737793 time for calcul the mask position with numpy : 0.027678489685058594 nb_pixel_total : 11738 time to create 1 rle with old method : 0.012444496154785156 time for calcul the mask position with numpy : 0.028385162353515625 nb_pixel_total : 586 time to create 1 rle with old method : 0.0008065700531005859 time for calcul the mask position with numpy : 0.028992176055908203 nb_pixel_total : 23837 time to create 1 rle with old method : 0.026187658309936523 time for calcul the mask position with numpy : 0.029088973999023438 nb_pixel_total : 15829 time to create 1 rle with old method : 0.017449140548706055 time for calcul the mask position with numpy : 0.029080867767333984 nb_pixel_total : 4880 time to create 1 rle with old method : 0.00559544563293457 time for calcul the mask position with numpy : 0.029250144958496094 nb_pixel_total : 24112 time to create 1 rle with old method : 0.026633739471435547 time for calcul the mask position with numpy : 0.028978586196899414 nb_pixel_total : 6175 time to create 1 rle with old method : 0.007001161575317383 time for calcul the mask position with numpy : 0.02895069122314453 nb_pixel_total : 18103 time to create 1 rle with old method : 0.019695043563842773 time for calcul the mask position with numpy : 0.02892446517944336 nb_pixel_total : 13489 time to create 1 rle with old method : 0.014403820037841797 time for calcul the mask position with numpy : 0.028622150421142578 nb_pixel_total : 9768 time to create 1 rle with old method : 0.010145425796508789 time for calcul the mask position with numpy : 0.029396533966064453 nb_pixel_total : 23035 time to create 1 rle with old method : 0.025701045989990234 time for calcul the mask position with numpy : 0.028793811798095703 nb_pixel_total : 15930 time to create 1 rle with old method : 0.01728677749633789 time for calcul the mask position with numpy : 0.027951955795288086 nb_pixel_total : 95569 time to create 1 rle with old method : 0.09931707382202148 time for calcul the mask position with numpy : 0.02865457534790039 nb_pixel_total : 709 time to create 1 rle with old method : 0.0011301040649414062 time for calcul the mask position with numpy : 0.029544353485107422 nb_pixel_total : 76132 time to create 1 rle with old method : 0.0805521011352539 time for calcul the mask position with numpy : 0.029096603393554688 nb_pixel_total : 27869 time to create 1 rle with old method : 0.029944658279418945 time for calcul the mask position with numpy : 0.029345989227294922 nb_pixel_total : 71454 time to create 1 rle with old method : 0.07539582252502441 time for calcul the mask position with numpy : 0.030727386474609375 nb_pixel_total : 3145 time to create 1 rle with old method : 0.005082845687866211 time for calcul the mask position with numpy : 0.032819271087646484 nb_pixel_total : 58096 time to create 1 rle with old method : 0.0661625862121582 time for calcul the mask position with numpy : 0.028562068939208984 nb_pixel_total : 25455 time to create 1 rle with old method : 0.026636838912963867 time for calcul the mask position with numpy : 0.028461456298828125 nb_pixel_total : 42310 time to create 1 rle with old method : 0.04487013816833496 time for calcul the mask position with numpy : 0.02821946144104004 nb_pixel_total : 30180 time to create 1 rle with old method : 0.032677412033081055 time for calcul the mask position with numpy : 0.028348207473754883 nb_pixel_total : 29655 time to create 1 rle with old method : 0.03175497055053711 time for calcul the mask position with numpy : 0.02901744842529297 nb_pixel_total : 219 time to create 1 rle with old method : 0.0004374980926513672 time for calcul the mask position with numpy : 0.029082775115966797 nb_pixel_total : 26422 time to create 1 rle with old method : 0.0321042537689209 time for calcul the mask position with numpy : 0.030660390853881836 nb_pixel_total : 25229 time to create 1 rle with old method : 0.027229785919189453 time for calcul the mask position with numpy : 0.02921915054321289 nb_pixel_total : 72506 time to create 1 rle with old method : 0.08014392852783203 time for calcul the mask position with numpy : 0.028966665267944336 nb_pixel_total : 9382 time to create 1 rle with old method : 0.010144710540771484 time for calcul the mask position with numpy : 0.0288236141204834 nb_pixel_total : 4179 time to create 1 rle with old method : 0.004445791244506836 time for calcul the mask position with numpy : 0.02781057357788086 nb_pixel_total : 25939 time to create 1 rle with old method : 0.027314424514770508 time for calcul the mask position with numpy : 0.028581619262695312 nb_pixel_total : 20391 time to create 1 rle with old method : 0.02253890037536621 time for calcul the mask position with numpy : 0.028696537017822266 nb_pixel_total : 13450 time to create 1 rle with old method : 0.014700174331665039 time for calcul the mask position with numpy : 0.027840375900268555 nb_pixel_total : 31922 time to create 1 rle with old method : 0.03390860557556152 time for calcul the mask position with numpy : 0.028933048248291016 nb_pixel_total : 33184 time to create 1 rle with old method : 0.03550124168395996 time for calcul the mask position with numpy : 0.02897787094116211 nb_pixel_total : 13672 time to create 1 rle with old method : 0.015051126480102539 time for calcul the mask position with numpy : 0.029065608978271484 nb_pixel_total : 26771 time to create 1 rle with old method : 0.028574228286743164 time for calcul the mask position with numpy : 0.027530670166015625 nb_pixel_total : 28693 time to create 1 rle with old method : 0.030316591262817383 time for calcul the mask position with numpy : 0.02823019027709961 nb_pixel_total : 28702 time to create 1 rle with old method : 0.030306100845336914 create new chi : 2.7449777126312256 time to delete rle : 0.0017054080963134766 batch 1 Loaded 44 chid ids of type : 4211 Number RLEs to save : 18637 TO DO : save crop sub photo not yet done ! save time : 1.0371067523956299 nb_obj : 31 nb_hashtags : 7 time to prepare the origin masks : 8.77317190170288 time for calcul the mask position with numpy : 0.07229828834533691 nb_pixel_total : 6088164 time to create 1 rle with new method : 0.14180660247802734 time for calcul the mask position with numpy : 0.029145240783691406 nb_pixel_total : 19603 time to create 1 rle with old method : 0.0210115909576416 time for calcul the mask position with numpy : 0.028650760650634766 nb_pixel_total : 26217 time to create 1 rle with old method : 0.02874135971069336 time for calcul the mask position with numpy : 0.0283355712890625 nb_pixel_total : 9211 time to create 1 rle with old method : 0.009792566299438477 time for calcul the mask position with numpy : 0.02881789207458496 nb_pixel_total : 6435 time to create 1 rle with old method : 0.007261037826538086 time for calcul the mask position with numpy : 0.029081106185913086 nb_pixel_total : 50665 time to create 1 rle with old method : 0.07549428939819336 time for calcul the mask position with numpy : 0.03214311599731445 nb_pixel_total : 88746 time to create 1 rle with old method : 0.140061616897583 time for calcul the mask position with numpy : 0.032726287841796875 nb_pixel_total : 233 time to create 1 rle with old method : 0.0007283687591552734 time for calcul the mask position with numpy : 0.030896663665771484 nb_pixel_total : 51116 time to create 1 rle with old method : 0.05887556076049805 time for calcul the mask position with numpy : 0.028589725494384766 nb_pixel_total : 34955 time to create 1 rle with old method : 0.03798031806945801 time for calcul the mask position with numpy : 0.028408527374267578 nb_pixel_total : 16522 time to create 1 rle with old method : 0.017646312713623047 time for calcul the mask position with numpy : 0.028871536254882812 nb_pixel_total : 20614 time to create 1 rle with old method : 0.023762941360473633 time for calcul the mask position with numpy : 0.031313419342041016 nb_pixel_total : 53174 time to create 1 rle with old method : 0.08047604560852051 time for calcul the mask position with numpy : 0.029430866241455078 nb_pixel_total : 13239 time to create 1 rle with old method : 0.014473915100097656 time for calcul the mask position with numpy : 0.028638124465942383 nb_pixel_total : 37468 time to create 1 rle with old method : 0.0390620231628418 time for calcul the mask position with numpy : 0.029102563858032227 nb_pixel_total : 28976 time to create 1 rle with old method : 0.030466318130493164 time for calcul the mask position with numpy : 0.029392719268798828 nb_pixel_total : 41448 time to create 1 rle with old method : 0.04416036605834961 time for calcul the mask position with numpy : 0.029285669326782227 nb_pixel_total : 24946 time to create 1 rle with old method : 0.027088642120361328 time for calcul the mask position with numpy : 0.029109477996826172 nb_pixel_total : 23965 time to create 1 rle with old method : 0.02582263946533203 time for calcul the mask position with numpy : 0.028981924057006836 nb_pixel_total : 410 time to create 1 rle with old method : 0.0005819797515869141 time for calcul the mask position with numpy : 0.029317617416381836 nb_pixel_total : 24177 time to create 1 rle with old method : 0.026291608810424805 time for calcul the mask position with numpy : 0.029577970504760742 nb_pixel_total : 186323 time to create 1 rle with new method : 0.12694048881530762 time for calcul the mask position with numpy : 0.02909231185913086 nb_pixel_total : 35618 time to create 1 rle with old method : 0.037812232971191406 time for calcul the mask position with numpy : 0.02916264533996582 nb_pixel_total : 158 time to create 1 rle with old method : 0.0002872943878173828 time for calcul the mask position with numpy : 0.029062747955322266 nb_pixel_total : 4952 time to create 1 rle with old method : 0.0056171417236328125 time for calcul the mask position with numpy : 0.02916574478149414 nb_pixel_total : 40946 time to create 1 rle with old method : 0.04423856735229492 time for calcul the mask position with numpy : 0.029357433319091797 nb_pixel_total : 14677 time to create 1 rle with old method : 0.016532182693481445 time for calcul the mask position with numpy : 0.029189586639404297 nb_pixel_total : 44228 time to create 1 rle with old method : 0.04834103584289551 time for calcul the mask position with numpy : 0.02899456024169922 nb_pixel_total : 11277 time to create 1 rle with old method : 0.012337684631347656 time for calcul the mask position with numpy : 0.029240846633911133 nb_pixel_total : 13271 time to create 1 rle with old method : 0.014206171035766602 time for calcul the mask position with numpy : 0.029122591018676758 nb_pixel_total : 30924 time to create 1 rle with old method : 0.03364896774291992 time for calcul the mask position with numpy : 0.029141902923583984 nb_pixel_total : 7582 time to create 1 rle with old method : 0.008551597595214844 create new chi : 2.210550308227539 time to delete rle : 0.0015380382537841797 batch 1 Loaded 33 chid ids of type : 4211 Number RLEs to save : 15353 TO DO : save crop sub photo not yet done ! save time : 0.8438150882720947 nb_obj : 51 nb_hashtags : 11 time to prepare the origin masks : 15.689561128616333 time for calcul the mask position with numpy : 0.10312819480895996 nb_pixel_total : 5040696 time to create 1 rle with new method : 0.17325043678283691 time for calcul the mask position with numpy : 0.02944469451904297 nb_pixel_total : 31586 time to create 1 rle with old method : 0.03521442413330078 time for calcul the mask position with numpy : 0.029388427734375 nb_pixel_total : 13865 time to create 1 rle with old method : 0.01497340202331543 time for calcul the mask position with numpy : 0.02925896644592285 nb_pixel_total : 10100 time to create 1 rle with old method : 0.011337757110595703 time for calcul the mask position with numpy : 0.029245615005493164 nb_pixel_total : 5697 time to create 1 rle with old method : 0.006571054458618164 time for calcul the mask position with numpy : 0.029620647430419922 nb_pixel_total : 72494 time to create 1 rle with old method : 0.07587718963623047 time for calcul the mask position with numpy : 0.028627395629882812 nb_pixel_total : 4178 time to create 1 rle with old method : 0.004591703414916992 time for calcul the mask position with numpy : 0.028778076171875 nb_pixel_total : 33257 time to create 1 rle with old method : 0.03506755828857422 time for calcul the mask position with numpy : 0.028686046600341797 nb_pixel_total : 31339 time to create 1 rle with old method : 0.03390336036682129 time for calcul the mask position with numpy : 0.02980208396911621 nb_pixel_total : 72423 time to create 1 rle with old method : 0.07790732383728027 time for calcul the mask position with numpy : 0.030241727828979492 nb_pixel_total : 45246 time to create 1 rle with old method : 0.04918241500854492 time for calcul the mask position with numpy : 0.030211448669433594 nb_pixel_total : 20572 time to create 1 rle with old method : 0.023072004318237305 time for calcul the mask position with numpy : 0.03015613555908203 nb_pixel_total : 26432 time to create 1 rle with old method : 0.028952360153198242 time for calcul the mask position with numpy : 0.030855894088745117 nb_pixel_total : 37284 time to create 1 rle with old method : 0.040637969970703125 time for calcul the mask position with numpy : 0.030866622924804688 nb_pixel_total : 38769 time to create 1 rle with old method : 0.04147529602050781 time for calcul the mask position with numpy : 0.03089308738708496 nb_pixel_total : 64971 time to create 1 rle with old method : 0.07248806953430176 time for calcul the mask position with numpy : 0.034076690673828125 nb_pixel_total : 27549 time to create 1 rle with old method : 0.029692411422729492 time for calcul the mask position with numpy : 0.027895689010620117 nb_pixel_total : 39317 time to create 1 rle with old method : 0.04251503944396973 time for calcul the mask position with numpy : 0.03163599967956543 nb_pixel_total : 151326 time to create 1 rle with new method : 0.15113496780395508 time for calcul the mask position with numpy : 0.02950596809387207 nb_pixel_total : 11029 time to create 1 rle with old method : 0.012420654296875 time for calcul the mask position with numpy : 0.02941727638244629 nb_pixel_total : 39654 time to create 1 rle with old method : 0.043259382247924805 time for calcul the mask position with numpy : 0.029796838760375977 nb_pixel_total : 12350 time to create 1 rle with old method : 0.013632059097290039 time for calcul the mask position with numpy : 0.02851104736328125 nb_pixel_total : 18645 time to create 1 rle with old method : 0.019521474838256836 time for calcul the mask position with numpy : 0.02860283851623535 nb_pixel_total : 81522 time to create 1 rle with old method : 0.08444333076477051 time for calcul the mask position with numpy : 0.029554367065429688 nb_pixel_total : 30285 time to create 1 rle with old method : 0.03280305862426758 time for calcul the mask position with numpy : 0.02916717529296875 nb_pixel_total : 59747 time to create 1 rle with old method : 0.08915376663208008 time for calcul the mask position with numpy : 0.03963494300842285 nb_pixel_total : 76302 time to create 1 rle with old method : 0.1076507568359375 time for calcul the mask position with numpy : 0.03032827377319336 nb_pixel_total : 78545 time to create 1 rle with old method : 0.08339142799377441 time for calcul the mask position with numpy : 0.030646085739135742 nb_pixel_total : 198127 time to create 1 rle with new method : 0.16733121871948242 time for calcul the mask position with numpy : 0.028208494186401367 nb_pixel_total : 32577 time to create 1 rle with old method : 0.033736228942871094 time for calcul the mask position with numpy : 0.02842998504638672 nb_pixel_total : 47049 time to create 1 rle with old method : 0.04812502861022949 time for calcul the mask position with numpy : 0.028098344802856445 nb_pixel_total : 23007 time to create 1 rle with old method : 0.024372577667236328 time for calcul the mask position with numpy : 0.027866601943969727 nb_pixel_total : 64963 time to create 1 rle with old method : 0.06673526763916016 time for calcul the mask position with numpy : 0.027793407440185547 nb_pixel_total : 17673 time to create 1 rle with old method : 0.01842641830444336 time for calcul the mask position with numpy : 0.027209758758544922 nb_pixel_total : 8197 time to create 1 rle with old method : 0.00855875015258789 time for calcul the mask position with numpy : 0.02774810791015625 nb_pixel_total : 57715 time to create 1 rle with old method : 0.05747222900390625 time for calcul the mask position with numpy : 0.028122901916503906 nb_pixel_total : 26962 time to create 1 rle with old method : 0.02819538116455078 time for calcul the mask position with numpy : 0.02793741226196289 nb_pixel_total : 32614 time to create 1 rle with old method : 0.03367471694946289 time for calcul the mask position with numpy : 0.028110742568969727 nb_pixel_total : 106189 time to create 1 rle with old method : 0.1112067699432373 time for calcul the mask position with numpy : 0.028873682022094727 nb_pixel_total : 25519 time to create 1 rle with old method : 0.027489900588989258 time for calcul the mask position with numpy : 0.028490304946899414 nb_pixel_total : 18362 time to create 1 rle with old method : 0.019393205642700195 time for calcul the mask position with numpy : 0.028818368911743164 nb_pixel_total : 33547 time to create 1 rle with old method : 0.03644442558288574 time for calcul the mask position with numpy : 0.028956174850463867 nb_pixel_total : 23822 time to create 1 rle with old method : 0.02582693099975586 time for calcul the mask position with numpy : 0.02907276153564453 nb_pixel_total : 28122 time to create 1 rle with old method : 0.02962779998779297 time for calcul the mask position with numpy : 0.028570890426635742 nb_pixel_total : 417 time to create 1 rle with old method : 0.0008211135864257812 time for calcul the mask position with numpy : 0.03264784812927246 nb_pixel_total : 28717 time to create 1 rle with old method : 0.03036952018737793 time for calcul the mask position with numpy : 0.02785778045654297 nb_pixel_total : 18251 time to create 1 rle with old method : 0.018999099731445312 time for calcul the mask position with numpy : 0.028131961822509766 nb_pixel_total : 15694 time to create 1 rle with old method : 0.016514062881469727 time for calcul the mask position with numpy : 0.028155088424682617 nb_pixel_total : 10094 time to create 1 rle with old method : 0.011244535446166992 time for calcul the mask position with numpy : 0.02770686149597168 nb_pixel_total : 10270 time to create 1 rle with old method : 0.011654138565063477 time for calcul the mask position with numpy : 0.027854442596435547 nb_pixel_total : 33374 time to create 1 rle with old method : 0.035454511642456055 time for calcul the mask position with numpy : 0.029357194900512695 nb_pixel_total : 13798 time to create 1 rle with old method : 0.015127420425415039 create new chi : 3.9578864574432373 time to delete rle : 0.003136157989501953 batch 1 Loaded 52 chid ids of type : 4211 Number RLEs to save : 26634 TO DO : save crop sub photo not yet done ! save time : 1.4870531558990479 nb_obj : 40 nb_hashtags : 10 time to prepare the origin masks : 12.771461248397827 time for calcul the mask position with numpy : 0.3382236957550049 nb_pixel_total : 5974073 time to create 1 rle with new method : 0.3871617317199707 time for calcul the mask position with numpy : 0.03324437141418457 nb_pixel_total : 9768 time to create 1 rle with old method : 0.015877246856689453 time for calcul the mask position with numpy : 0.03430747985839844 nb_pixel_total : 33168 time to create 1 rle with old method : 0.03769087791442871 time for calcul the mask position with numpy : 0.029412508010864258 nb_pixel_total : 19297 time to create 1 rle with old method : 0.020940303802490234 time for calcul the mask position with numpy : 0.03115057945251465 nb_pixel_total : 20697 time to create 1 rle with old method : 0.022995948791503906 time for calcul the mask position with numpy : 0.02990579605102539 nb_pixel_total : 33796 time to create 1 rle with old method : 0.0362849235534668 time for calcul the mask position with numpy : 0.029320478439331055 nb_pixel_total : 25110 time to create 1 rle with old method : 0.02678513526916504 time for calcul the mask position with numpy : 0.031131744384765625 nb_pixel_total : 23386 time to create 1 rle with old method : 0.02668023109436035 time for calcul the mask position with numpy : 0.029938936233520508 nb_pixel_total : 18547 time to create 1 rle with old method : 0.01977705955505371 time for calcul the mask position with numpy : 0.030172348022460938 nb_pixel_total : 38885 time to create 1 rle with old method : 0.04461240768432617 time for calcul the mask position with numpy : 0.031378984451293945 nb_pixel_total : 81332 time to create 1 rle with old method : 0.08744931221008301 time for calcul the mask position with numpy : 0.02954554557800293 nb_pixel_total : 12582 time to create 1 rle with old method : 0.013355731964111328 time for calcul the mask position with numpy : 0.02932596206665039 nb_pixel_total : 39322 time to create 1 rle with old method : 0.042578697204589844 time for calcul the mask position with numpy : 0.03059101104736328 nb_pixel_total : 29184 time to create 1 rle with old method : 0.03235745429992676 time for calcul the mask position with numpy : 0.030414104461669922 nb_pixel_total : 16159 time to create 1 rle with old method : 0.01778388023376465 time for calcul the mask position with numpy : 0.030079364776611328 nb_pixel_total : 45978 time to create 1 rle with old method : 0.050665855407714844 time for calcul the mask position with numpy : 0.0297849178314209 nb_pixel_total : 34300 time to create 1 rle with old method : 0.03766775131225586 time for calcul the mask position with numpy : 0.03097677230834961 nb_pixel_total : 13046 time to create 1 rle with old method : 0.015450000762939453 time for calcul the mask position with numpy : 0.03030991554260254 nb_pixel_total : 16936 time to create 1 rle with old method : 0.018407583236694336 time for calcul the mask position with numpy : 0.03340625762939453 nb_pixel_total : 34047 time to create 1 rle with old method : 0.0417637825012207 time for calcul the mask position with numpy : 0.029684782028198242 nb_pixel_total : 41777 time to create 1 rle with old method : 0.04602503776550293 time for calcul the mask position with numpy : 0.030440092086791992 nb_pixel_total : 58989 time to create 1 rle with old method : 0.0681455135345459 time for calcul the mask position with numpy : 0.02989363670349121 nb_pixel_total : 67457 time to create 1 rle with old method : 0.09076666831970215 time for calcul the mask position with numpy : 0.03329586982727051 nb_pixel_total : 18853 time to create 1 rle with old method : 0.021506309509277344 time for calcul the mask position with numpy : 0.029476404190063477 nb_pixel_total : 20796 time to create 1 rle with old method : 0.022657155990600586 time for calcul the mask position with numpy : 0.029563426971435547 nb_pixel_total : 171 time to create 1 rle with old method : 0.0003917217254638672 time for calcul the mask position with numpy : 0.029363632202148438 nb_pixel_total : 13828 time to create 1 rle with old method : 0.015150785446166992 time for calcul the mask position with numpy : 0.02850651741027832 nb_pixel_total : 44365 time to create 1 rle with old method : 0.047734975814819336 time for calcul the mask position with numpy : 0.02963423728942871 nb_pixel_total : 19651 time to create 1 rle with old method : 0.02147984504699707 time for calcul the mask position with numpy : 0.029880523681640625 nb_pixel_total : 37981 time to create 1 rle with old method : 0.040264129638671875 time for calcul the mask position with numpy : 0.02947211265563965 nb_pixel_total : 12906 time to create 1 rle with old method : 0.01441502571105957 time for calcul the mask position with numpy : 0.029648303985595703 nb_pixel_total : 1359 time to create 1 rle with old method : 0.0017507076263427734 time for calcul the mask position with numpy : 0.02959752082824707 nb_pixel_total : 34624 time to create 1 rle with old method : 0.03758049011230469 time for calcul the mask position with numpy : 0.029467105865478516 nb_pixel_total : 12768 time to create 1 rle with old method : 0.014694690704345703 time for calcul the mask position with numpy : 0.02955007553100586 nb_pixel_total : 33665 time to create 1 rle with old method : 0.035861968994140625 time for calcul the mask position with numpy : 0.02928781509399414 nb_pixel_total : 12811 time to create 1 rle with old method : 0.014266729354858398 time for calcul the mask position with numpy : 0.02941417694091797 nb_pixel_total : 32699 time to create 1 rle with old method : 0.03507590293884277 time for calcul the mask position with numpy : 0.02947092056274414 nb_pixel_total : 24581 time to create 1 rle with old method : 0.02693963050842285 time for calcul the mask position with numpy : 0.029471635818481445 nb_pixel_total : 12121 time to create 1 rle with old method : 0.013092041015625 time for calcul the mask position with numpy : 0.029083728790283203 nb_pixel_total : 14721 time to create 1 rle with old method : 0.01556849479675293 time for calcul the mask position with numpy : 0.029060840606689453 nb_pixel_total : 14504 time to create 1 rle with old method : 0.015845775604248047 create new chi : 3.1808321475982666 time to delete rle : 0.0024390220642089844 batch 1 Loaded 43 chid ids of type : 4211 Number RLEs to save : 19593 TO DO : save crop sub photo not yet done ! save time : 1.133030652999878 nb_obj : 30 nb_hashtags : 9 time to prepare the origin masks : 10.941867589950562 time for calcul the mask position with numpy : 0.478285551071167 nb_pixel_total : 6086476 time to create 1 rle with new method : 0.4978957176208496 time for calcul the mask position with numpy : 0.030865907669067383 nb_pixel_total : 7123 time to create 1 rle with old method : 0.012258291244506836 time for calcul the mask position with numpy : 0.03750133514404297 nb_pixel_total : 30751 time to create 1 rle with old method : 0.0514369010925293 time for calcul the mask position with numpy : 0.037197113037109375 nb_pixel_total : 28679 time to create 1 rle with old method : 0.046080827713012695 time for calcul the mask position with numpy : 0.03336000442504883 nb_pixel_total : 7011 time to create 1 rle with old method : 0.011102437973022461 time for calcul the mask position with numpy : 0.03312492370605469 nb_pixel_total : 48898 time to create 1 rle with old method : 0.071197509765625 time for calcul the mask position with numpy : 0.03401470184326172 nb_pixel_total : 34197 time to create 1 rle with old method : 0.0431513786315918 time for calcul the mask position with numpy : 0.04654240608215332 nb_pixel_total : 17638 time to create 1 rle with old method : 0.03541874885559082 time for calcul the mask position with numpy : 0.0392148494720459 nb_pixel_total : 103023 time to create 1 rle with old method : 0.20306658744812012 time for calcul the mask position with numpy : 0.03999900817871094 nb_pixel_total : 11291 time to create 1 rle with old method : 0.020907878875732422 time for calcul the mask position with numpy : 0.039327383041381836 nb_pixel_total : 21131 time to create 1 rle with old method : 0.03943467140197754 time for calcul the mask position with numpy : 0.03976750373840332 nb_pixel_total : 13908 time to create 1 rle with old method : 0.025002479553222656 time for calcul the mask position with numpy : 0.037949323654174805 nb_pixel_total : 13341 time to create 1 rle with old method : 0.024004220962524414 time for calcul the mask position with numpy : 0.0382838249206543 nb_pixel_total : 94200 time to create 1 rle with old method : 0.16807055473327637 time for calcul the mask position with numpy : 0.038313865661621094 nb_pixel_total : 40157 time to create 1 rle with old method : 0.04890704154968262 time for calcul the mask position with numpy : 0.029816150665283203 nb_pixel_total : 21631 time to create 1 rle with old method : 0.024021387100219727 time for calcul the mask position with numpy : 0.03099346160888672 nb_pixel_total : 31708 time to create 1 rle with old method : 0.03396797180175781 time for calcul the mask position with numpy : 0.02979445457458496 nb_pixel_total : 34892 time to create 1 rle with old method : 0.0373685359954834 time for calcul the mask position with numpy : 0.03011775016784668 nb_pixel_total : 22831 time to create 1 rle with old method : 0.02533411979675293 time for calcul the mask position with numpy : 0.029849529266357422 nb_pixel_total : 23717 time to create 1 rle with old method : 0.0258786678314209 time for calcul the mask position with numpy : 0.029991865158081055 nb_pixel_total : 42996 time to create 1 rle with old method : 0.04833412170410156 time for calcul the mask position with numpy : 0.028065919876098633 nb_pixel_total : 40473 time to create 1 rle with old method : 0.04162883758544922 time for calcul the mask position with numpy : 0.028255462646484375 nb_pixel_total : 105614 time to create 1 rle with old method : 0.10732412338256836 time for calcul the mask position with numpy : 0.027035951614379883 nb_pixel_total : 26385 time to create 1 rle with old method : 0.026852846145629883 time for calcul the mask position with numpy : 0.026506423950195312 nb_pixel_total : 7627 time to create 1 rle with old method : 0.00792837142944336 time for calcul the mask position with numpy : 0.0271451473236084 nb_pixel_total : 9762 time to create 1 rle with old method : 0.010608434677124023 time for calcul the mask position with numpy : 0.027426481246948242 nb_pixel_total : 18096 time to create 1 rle with old method : 0.018811464309692383 time for calcul the mask position with numpy : 0.027619361877441406 nb_pixel_total : 29572 time to create 1 rle with old method : 0.03072977066040039 time for calcul the mask position with numpy : 0.027309894561767578 nb_pixel_total : 8515 time to create 1 rle with old method : 0.009003877639770508 time for calcul the mask position with numpy : 0.028722524642944336 nb_pixel_total : 28194 time to create 1 rle with old method : 0.030133485794067383 time for calcul the mask position with numpy : 0.028232574462890625 nb_pixel_total : 40403 time to create 1 rle with old method : 0.041373252868652344 create new chi : 3.3240766525268555 time to delete rle : 0.0016541481018066406 batch 1 Loaded 32 chid ids of type : 4211 Number RLEs to save : 16174 TO DO : save crop sub photo not yet done ! save time : 0.8872301578521729 nb_obj : 41 nb_hashtags : 8 time to prepare the origin masks : 13.976036310195923 time for calcul the mask position with numpy : 0.09765911102294922 nb_pixel_total : 5488956 time to create 1 rle with new method : 0.1741173267364502 time for calcul the mask position with numpy : 0.02933216094970703 nb_pixel_total : 32541 time to create 1 rle with old method : 0.07172918319702148 time for calcul the mask position with numpy : 0.05337691307067871 nb_pixel_total : 41948 time to create 1 rle with old method : 0.044823646545410156 time for calcul the mask position with numpy : 0.029576778411865234 nb_pixel_total : 37251 time to create 1 rle with old method : 0.04621458053588867 time for calcul the mask position with numpy : 0.0295412540435791 nb_pixel_total : 20598 time to create 1 rle with old method : 0.022606372833251953 time for calcul the mask position with numpy : 0.03038763999938965 nb_pixel_total : 40052 time to create 1 rle with old method : 0.0434262752532959 time for calcul the mask position with numpy : 0.029679059982299805 nb_pixel_total : 23524 time to create 1 rle with old method : 0.02544856071472168 time for calcul the mask position with numpy : 0.032788753509521484 nb_pixel_total : 14863 time to create 1 rle with old method : 0.016170263290405273 time for calcul the mask position with numpy : 0.03044438362121582 nb_pixel_total : 22779 time to create 1 rle with old method : 0.02456498146057129 time for calcul the mask position with numpy : 0.029602527618408203 nb_pixel_total : 19997 time to create 1 rle with old method : 0.021654605865478516 time for calcul the mask position with numpy : 0.029811382293701172 nb_pixel_total : 39055 time to create 1 rle with old method : 0.043761491775512695 time for calcul the mask position with numpy : 0.0336451530456543 nb_pixel_total : 18658 time to create 1 rle with old method : 0.028914213180541992 time for calcul the mask position with numpy : 0.03272891044616699 nb_pixel_total : 33149 time to create 1 rle with old method : 0.03526878356933594 time for calcul the mask position with numpy : 0.030027151107788086 nb_pixel_total : 49219 time to create 1 rle with old method : 0.053070783615112305 time for calcul the mask position with numpy : 0.03012704849243164 nb_pixel_total : 39693 time to create 1 rle with old method : 0.04204964637756348 time for calcul the mask position with numpy : 0.029956817626953125 nb_pixel_total : 4611 time to create 1 rle with old method : 0.005218505859375 time for calcul the mask position with numpy : 0.030058622360229492 nb_pixel_total : 26105 time to create 1 rle with old method : 0.029278278350830078 time for calcul the mask position with numpy : 0.030973434448242188 nb_pixel_total : 49530 time to create 1 rle with old method : 0.05582261085510254 time for calcul the mask position with numpy : 0.03139615058898926 nb_pixel_total : 50101 time to create 1 rle with old method : 0.053843021392822266 time for calcul the mask position with numpy : 0.030451536178588867 nb_pixel_total : 680 time to create 1 rle with old method : 0.0011415481567382812 time for calcul the mask position with numpy : 0.03119945526123047 nb_pixel_total : 103565 time to create 1 rle with old method : 0.11275982856750488 time for calcul the mask position with numpy : 0.031998395919799805 nb_pixel_total : 96052 time to create 1 rle with old method : 0.10212397575378418 time for calcul the mask position with numpy : 0.029778718948364258 nb_pixel_total : 14398 time to create 1 rle with old method : 0.015506744384765625 time for calcul the mask position with numpy : 0.03350520133972168 nb_pixel_total : 47256 time to create 1 rle with old method : 0.04914450645446777 time for calcul the mask position with numpy : 0.029544830322265625 nb_pixel_total : 55542 time to create 1 rle with old method : 0.05863142013549805 time for calcul the mask position with numpy : 0.030576229095458984 nb_pixel_total : 91871 time to create 1 rle with old method : 0.09602618217468262 time for calcul the mask position with numpy : 0.02926158905029297 nb_pixel_total : 28101 time to create 1 rle with old method : 0.030727863311767578 time for calcul the mask position with numpy : 0.028904438018798828 nb_pixel_total : 395 time to create 1 rle with old method : 0.0007808208465576172 time for calcul the mask position with numpy : 0.02906489372253418 nb_pixel_total : 38622 time to create 1 rle with old method : 0.04073810577392578 time for calcul the mask position with numpy : 0.028835535049438477 nb_pixel_total : 31584 time to create 1 rle with old method : 0.03963732719421387 time for calcul the mask position with numpy : 0.03043389320373535 nb_pixel_total : 85704 time to create 1 rle with old method : 0.09342145919799805 time for calcul the mask position with numpy : 0.02994513511657715 nb_pixel_total : 16578 time to create 1 rle with old method : 0.018296480178833008 time for calcul the mask position with numpy : 0.030263900756835938 nb_pixel_total : 31110 time to create 1 rle with old method : 0.03304648399353027 time for calcul the mask position with numpy : 0.029497861862182617 nb_pixel_total : 28057 time to create 1 rle with old method : 0.030700206756591797 time for calcul the mask position with numpy : 0.03533220291137695 nb_pixel_total : 106850 time to create 1 rle with old method : 0.11616802215576172 time for calcul the mask position with numpy : 0.02974414825439453 nb_pixel_total : 27858 time to create 1 rle with old method : 0.030165910720825195 time for calcul the mask position with numpy : 0.029555797576904297 nb_pixel_total : 40292 time to create 1 rle with old method : 0.04470062255859375 time for calcul the mask position with numpy : 0.029676437377929688 nb_pixel_total : 19365 time to create 1 rle with old method : 0.030751705169677734 time for calcul the mask position with numpy : 0.033956050872802734 nb_pixel_total : 40206 time to create 1 rle with old method : 0.04773139953613281 time for calcul the mask position with numpy : 0.029410123825073242 nb_pixel_total : 52540 time to create 1 rle with old method : 0.056601762771606445 time for calcul the mask position with numpy : 0.029494762420654297 nb_pixel_total : 32428 time to create 1 rle with old method : 0.035091400146484375 time for calcul the mask position with numpy : 0.029436826705932617 nb_pixel_total : 8556 time to create 1 rle with old method : 0.009761333465576172 create new chi : 3.3311245441436768 time to delete rle : 0.003340005874633789 batch 1 Loaded 43 chid ids of type : 4211 Number RLEs to save : 23384 TO DO : save crop sub photo not yet done ! save time : 1.2823829650878906 nb_obj : 39 nb_hashtags : 10 time to prepare the origin masks : 14.02355670928955 time for calcul the mask position with numpy : 0.17853713035583496 nb_pixel_total : 5807370 time to create 1 rle with new method : 0.5632894039154053 time for calcul the mask position with numpy : 0.029694795608520508 nb_pixel_total : 49443 time to create 1 rle with old method : 0.053177595138549805 time for calcul the mask position with numpy : 0.028673648834228516 nb_pixel_total : 36175 time to create 1 rle with old method : 0.0387876033782959 time for calcul the mask position with numpy : 0.029707908630371094 nb_pixel_total : 60625 time to create 1 rle with old method : 0.06473159790039062 time for calcul the mask position with numpy : 0.029050588607788086 nb_pixel_total : 20197 time to create 1 rle with old method : 0.02351522445678711 time for calcul the mask position with numpy : 0.029278278350830078 nb_pixel_total : 13571 time to create 1 rle with old method : 0.014614343643188477 time for calcul the mask position with numpy : 0.02889108657836914 nb_pixel_total : 7876 time to create 1 rle with old method : 0.008775949478149414 time for calcul the mask position with numpy : 0.029636383056640625 nb_pixel_total : 49450 time to create 1 rle with old method : 0.053096771240234375 time for calcul the mask position with numpy : 0.03000497817993164 nb_pixel_total : 19519 time to create 1 rle with old method : 0.024692296981811523 time for calcul the mask position with numpy : 0.034142494201660156 nb_pixel_total : 21659 time to create 1 rle with old method : 0.023827075958251953 time for calcul the mask position with numpy : 0.030559539794921875 nb_pixel_total : 42448 time to create 1 rle with old method : 0.04640936851501465 time for calcul the mask position with numpy : 0.03042316436767578 nb_pixel_total : 55516 time to create 1 rle with old method : 0.05975842475891113 time for calcul the mask position with numpy : 0.030539751052856445 nb_pixel_total : 28085 time to create 1 rle with old method : 0.03044414520263672 time for calcul the mask position with numpy : 0.030823230743408203 nb_pixel_total : 19112 time to create 1 rle with old method : 0.02141261100769043 time for calcul the mask position with numpy : 0.029863357543945312 nb_pixel_total : 7234 time to create 1 rle with old method : 0.00855875015258789 time for calcul the mask position with numpy : 0.03178548812866211 nb_pixel_total : 41042 time to create 1 rle with old method : 0.04429173469543457 time for calcul the mask position with numpy : 0.03078746795654297 nb_pixel_total : 34350 time to create 1 rle with old method : 0.03760838508605957 time for calcul the mask position with numpy : 0.03050398826599121 nb_pixel_total : 18483 time to create 1 rle with old method : 0.023096084594726562 time for calcul the mask position with numpy : 0.030234813690185547 nb_pixel_total : 24208 time to create 1 rle with old method : 0.026211977005004883 time for calcul the mask position with numpy : 0.030338048934936523 nb_pixel_total : 58334 time to create 1 rle with old method : 0.06244802474975586 time for calcul the mask position with numpy : 0.029410123825073242 nb_pixel_total : 7474 time to create 1 rle with old method : 0.00876927375793457 time for calcul the mask position with numpy : 0.02934718132019043 nb_pixel_total : 56206 time to create 1 rle with old method : 0.05928373336791992 time for calcul the mask position with numpy : 0.030520915985107422 nb_pixel_total : 41662 time to create 1 rle with old method : 0.04506850242614746 time for calcul the mask position with numpy : 0.02939128875732422 nb_pixel_total : 5837 time to create 1 rle with old method : 0.006549358367919922 time for calcul the mask position with numpy : 0.030900955200195312 nb_pixel_total : 10812 time to create 1 rle with old method : 0.012344598770141602 time for calcul the mask position with numpy : 0.030785322189331055 nb_pixel_total : 21660 time to create 1 rle with old method : 0.026612043380737305 time for calcul the mask position with numpy : 0.0296323299407959 nb_pixel_total : 61888 time to create 1 rle with old method : 0.06836056709289551 time for calcul the mask position with numpy : 0.029592275619506836 nb_pixel_total : 2150 time to create 1 rle with old method : 0.002801656723022461 time for calcul the mask position with numpy : 0.02993917465209961 nb_pixel_total : 23934 time to create 1 rle with old method : 0.03801679611206055 time for calcul the mask position with numpy : 0.030307769775390625 nb_pixel_total : 20178 time to create 1 rle with old method : 0.02217721939086914 time for calcul the mask position with numpy : 0.03125190734863281 nb_pixel_total : 15654 time to create 1 rle with old method : 0.02094268798828125 time for calcul the mask position with numpy : 0.03167533874511719 nb_pixel_total : 157832 time to create 1 rle with new method : 0.17705750465393066 time for calcul the mask position with numpy : 0.03006577491760254 nb_pixel_total : 20758 time to create 1 rle with old method : 0.02243494987487793 time for calcul the mask position with numpy : 0.02984929084777832 nb_pixel_total : 21237 time to create 1 rle with old method : 0.023135662078857422 time for calcul the mask position with numpy : 0.0311586856842041 nb_pixel_total : 34469 time to create 1 rle with old method : 0.03808760643005371 time for calcul the mask position with numpy : 0.03004598617553711 nb_pixel_total : 26657 time to create 1 rle with old method : 0.028714418411254883 time for calcul the mask position with numpy : 0.029772043228149414 nb_pixel_total : 1698 time to create 1 rle with old method : 0.002206563949584961 time for calcul the mask position with numpy : 0.030011892318725586 nb_pixel_total : 46539 time to create 1 rle with old method : 0.05032491683959961 time for calcul the mask position with numpy : 0.029790401458740234 nb_pixel_total : 37837 time to create 1 rle with old method : 0.04185175895690918 time for calcul the mask position with numpy : 0.029551982879638672 nb_pixel_total : 21061 time to create 1 rle with old method : 0.022978544235229492 create new chi : 3.3506245613098145 time to delete rle : 0.0019850730895996094 batch 1 Loaded 41 chid ids of type : 4211 Number RLEs to save : 20363 TO DO : save crop sub photo not yet done ! save time : 1.1455411911010742 nb_obj : 37 nb_hashtags : 9 time to prepare the origin masks : 12.529675245285034 time for calcul the mask position with numpy : 0.09424138069152832 nb_pixel_total : 6103683 time to create 1 rle with new method : 0.16736745834350586 time for calcul the mask position with numpy : 0.027639150619506836 nb_pixel_total : 871 time to create 1 rle with old method : 0.0012052059173583984 time for calcul the mask position with numpy : 0.027761220932006836 nb_pixel_total : 35328 time to create 1 rle with old method : 0.03757524490356445 time for calcul the mask position with numpy : 0.02781081199645996 nb_pixel_total : 497 time to create 1 rle with old method : 0.0007946491241455078 time for calcul the mask position with numpy : 0.02782440185546875 nb_pixel_total : 33416 time to create 1 rle with old method : 0.03477168083190918 time for calcul the mask position with numpy : 0.027854204177856445 nb_pixel_total : 13099 time to create 1 rle with old method : 0.01372838020324707 time for calcul the mask position with numpy : 0.02776360511779785 nb_pixel_total : 20117 time to create 1 rle with old method : 0.021668434143066406 time for calcul the mask position with numpy : 0.027924776077270508 nb_pixel_total : 30775 time to create 1 rle with old method : 0.03197312355041504 time for calcul the mask position with numpy : 0.02814483642578125 nb_pixel_total : 7427 time to create 1 rle with old method : 0.007807254791259766 time for calcul the mask position with numpy : 0.02777886390686035 nb_pixel_total : 41294 time to create 1 rle with old method : 0.04432225227355957 time for calcul the mask position with numpy : 0.029147624969482422 nb_pixel_total : 34142 time to create 1 rle with old method : 0.03802776336669922 time for calcul the mask position with numpy : 0.029348373413085938 nb_pixel_total : 11369 time to create 1 rle with old method : 0.012658357620239258 time for calcul the mask position with numpy : 0.02911853790283203 nb_pixel_total : 220 time to create 1 rle with old method : 0.0003886222839355469 time for calcul the mask position with numpy : 0.02742934226989746 nb_pixel_total : 16422 time to create 1 rle with old method : 0.017313003540039062 time for calcul the mask position with numpy : 0.027565479278564453 nb_pixel_total : 44795 time to create 1 rle with old method : 0.04513978958129883 time for calcul the mask position with numpy : 0.028844118118286133 nb_pixel_total : 4621 time to create 1 rle with old method : 0.0050694942474365234 time for calcul the mask position with numpy : 0.02883601188659668 nb_pixel_total : 13220 time to create 1 rle with old method : 0.014972686767578125 time for calcul the mask position with numpy : 0.02919483184814453 nb_pixel_total : 17122 time to create 1 rle with old method : 0.019372940063476562 time for calcul the mask position with numpy : 0.029074668884277344 nb_pixel_total : 36239 time to create 1 rle with old method : 0.038244009017944336 time for calcul the mask position with numpy : 0.028109312057495117 nb_pixel_total : 11935 time to create 1 rle with old method : 0.012771844863891602 time for calcul the mask position with numpy : 0.02805495262145996 nb_pixel_total : 20207 time to create 1 rle with old method : 0.020914554595947266 time for calcul the mask position with numpy : 0.0289919376373291 nb_pixel_total : 20941 time to create 1 rle with old method : 0.021617650985717773 time for calcul the mask position with numpy : 0.028920412063598633 nb_pixel_total : 85145 time to create 1 rle with old method : 0.08862709999084473 time for calcul the mask position with numpy : 0.029731273651123047 nb_pixel_total : 12912 time to create 1 rle with old method : 0.013936996459960938 time for calcul the mask position with numpy : 0.03024435043334961 nb_pixel_total : 73694 time to create 1 rle with old method : 0.08198666572570801 time for calcul the mask position with numpy : 0.03042316436767578 nb_pixel_total : 12677 time to create 1 rle with old method : 0.014243602752685547 time for calcul the mask position with numpy : 0.029163360595703125 nb_pixel_total : 2187 time to create 1 rle with old method : 0.002946138381958008 time for calcul the mask position with numpy : 0.029852628707885742 nb_pixel_total : 57510 time to create 1 rle with old method : 0.06331658363342285 time for calcul the mask position with numpy : 0.02977919578552246 nb_pixel_total : 17239 time to create 1 rle with old method : 0.01938915252685547 time for calcul the mask position with numpy : 0.029917001724243164 nb_pixel_total : 45486 time to create 1 rle with old method : 0.04709362983703613 time for calcul the mask position with numpy : 0.028757810592651367 nb_pixel_total : 42616 time to create 1 rle with old method : 0.04494071006774902 time for calcul the mask position with numpy : 0.028531312942504883 nb_pixel_total : 39871 time to create 1 rle with old method : 0.04257965087890625 time for calcul the mask position with numpy : 0.029837369918823242 nb_pixel_total : 21567 time to create 1 rle with old method : 0.023941993713378906 time for calcul the mask position with numpy : 0.02963089942932129 nb_pixel_total : 19323 time to create 1 rle with old method : 0.02092599868774414 time for calcul the mask position with numpy : 0.029587507247924805 nb_pixel_total : 55024 time to create 1 rle with old method : 0.05830049514770508 time for calcul the mask position with numpy : 0.028197050094604492 nb_pixel_total : 4806 time to create 1 rle with old method : 0.005415916442871094 time for calcul the mask position with numpy : 0.029786348342895508 nb_pixel_total : 36611 time to create 1 rle with old method : 0.05747485160827637 time for calcul the mask position with numpy : 0.03221487998962402 nb_pixel_total : 5832 time to create 1 rle with old method : 0.006394386291503906 create new chi : 2.381054162979126 time to delete rle : 0.0027534961700439453 batch 1 Loaded 38 chid ids of type : 4211 Number RLEs to save : 17065 TO DO : save crop sub photo not yet done ! save time : 0.9227523803710938 nb_obj : 33 nb_hashtags : 8 time to prepare the origin masks : 13.066044569015503 time for calcul the mask position with numpy : 0.10521674156188965 nb_pixel_total : 6073811 time to create 1 rle with new method : 0.17883968353271484 time for calcul the mask position with numpy : 0.02974867820739746 nb_pixel_total : 41504 time to create 1 rle with old method : 0.044450998306274414 time for calcul the mask position with numpy : 0.028559446334838867 nb_pixel_total : 7175 time to create 1 rle with old method : 0.007904767990112305 time for calcul the mask position with numpy : 0.0273435115814209 nb_pixel_total : 10312 time to create 1 rle with old method : 0.011119842529296875 time for calcul the mask position with numpy : 0.0276947021484375 nb_pixel_total : 24754 time to create 1 rle with old method : 0.025819063186645508 time for calcul the mask position with numpy : 0.028125524520874023 nb_pixel_total : 55205 time to create 1 rle with old method : 0.05657672882080078 time for calcul the mask position with numpy : 0.0283358097076416 nb_pixel_total : 19711 time to create 1 rle with old method : 0.020899057388305664 time for calcul the mask position with numpy : 0.028403043746948242 nb_pixel_total : 10489 time to create 1 rle with old method : 0.011776924133300781 time for calcul the mask position with numpy : 0.030719280242919922 nb_pixel_total : 37607 time to create 1 rle with old method : 0.04022097587585449 time for calcul the mask position with numpy : 0.0320894718170166 nb_pixel_total : 83651 time to create 1 rle with old method : 0.0879511833190918 time for calcul the mask position with numpy : 0.028378725051879883 nb_pixel_total : 44107 time to create 1 rle with old method : 0.04584360122680664 time for calcul the mask position with numpy : 0.028336048126220703 nb_pixel_total : 42576 time to create 1 rle with old method : 0.04350924491882324 time for calcul the mask position with numpy : 0.02754497528076172 nb_pixel_total : 2177 time to create 1 rle with old method : 0.0024271011352539062 time for calcul the mask position with numpy : 0.028385400772094727 nb_pixel_total : 50415 time to create 1 rle with old method : 0.05170917510986328 time for calcul the mask position with numpy : 0.027928590774536133 nb_pixel_total : 9687 time to create 1 rle with old method : 0.010956048965454102 time for calcul the mask position with numpy : 0.02897334098815918 nb_pixel_total : 8371 time to create 1 rle with old method : 0.00945138931274414 time for calcul the mask position with numpy : 0.02935934066772461 nb_pixel_total : 32441 time to create 1 rle with old method : 0.0360407829284668 time for calcul the mask position with numpy : 0.029523134231567383 nb_pixel_total : 48079 time to create 1 rle with old method : 0.049837589263916016 time for calcul the mask position with numpy : 0.027778148651123047 nb_pixel_total : 136 time to create 1 rle with old method : 0.00041985511779785156 time for calcul the mask position with numpy : 0.027420997619628906 nb_pixel_total : 33110 time to create 1 rle with old method : 0.03418755531311035 time for calcul the mask position with numpy : 0.027937889099121094 nb_pixel_total : 1256 time to create 1 rle with old method : 0.0016217231750488281 time for calcul the mask position with numpy : 0.028463363647460938 nb_pixel_total : 43910 time to create 1 rle with old method : 0.0461580753326416 time for calcul the mask position with numpy : 0.027955293655395508 nb_pixel_total : 783 time to create 1 rle with old method : 0.0012366771697998047 time for calcul the mask position with numpy : 0.027707576751708984 nb_pixel_total : 57493 time to create 1 rle with old method : 0.07950353622436523 time for calcul the mask position with numpy : 0.03708195686340332 nb_pixel_total : 51915 time to create 1 rle with old method : 0.06255412101745605 time for calcul the mask position with numpy : 0.03378486633300781 nb_pixel_total : 73552 time to create 1 rle with old method : 0.1021575927734375 time for calcul the mask position with numpy : 0.02972412109375 nb_pixel_total : 25364 time to create 1 rle with old method : 0.027617931365966797 time for calcul the mask position with numpy : 0.029335975646972656 nb_pixel_total : 55 time to create 1 rle with old method : 0.0002396106719970703 time for calcul the mask position with numpy : 0.03190040588378906 nb_pixel_total : 30457 time to create 1 rle with old method : 0.03262686729431152 time for calcul the mask position with numpy : 0.029558420181274414 nb_pixel_total : 72787 time to create 1 rle with old method : 0.09108161926269531 time for calcul the mask position with numpy : 0.03312993049621582 nb_pixel_total : 1538 time to create 1 rle with old method : 0.002782106399536133 time for calcul the mask position with numpy : 0.0295412540435791 nb_pixel_total : 23126 time to create 1 rle with old method : 0.024785995483398438 time for calcul the mask position with numpy : 0.029566287994384766 nb_pixel_total : 9861 time to create 1 rle with old method : 0.01063084602355957 time for calcul the mask position with numpy : 0.0292813777923584 nb_pixel_total : 22825 time to create 1 rle with old method : 0.02446722984313965 create new chi : 2.3750743865966797 time to delete rle : 0.0017209053039550781 batch 1 Loaded 37 chid ids of type : 4211 Number RLEs to save : 15166 TO DO : save crop sub photo not yet done ! save time : 0.8935492038726807 nb_obj : 4 nb_hashtags : 2 time to prepare the origin masks : 0.9826428890228271 time for calcul the mask position with numpy : 0.07219338417053223 nb_pixel_total : 6150951 time to create 1 rle with new method : 0.13954687118530273 time for calcul the mask position with numpy : 0.023340463638305664 nb_pixel_total : 62433 time to create 1 rle with old method : 0.06617283821105957 time for calcul the mask position with numpy : 0.02318263053894043 nb_pixel_total : 81237 time to create 1 rle with old method : 0.09208130836486816 time for calcul the mask position with numpy : 0.026979923248291016 nb_pixel_total : 307677 time to create 1 rle with new method : 0.13607001304626465 time for calcul the mask position with numpy : 0.02344655990600586 nb_pixel_total : 447942 time to create 1 rle with new method : 0.14309287071228027 create new chi : 0.7876300811767578 time to delete rle : 0.0006570816040039062 batch 1 Loaded 5 chid ids of type : 4211 Number RLEs to save : 6136 TO DO : save crop sub photo not yet done ! save time : 0.355266809463501 nb_obj : 22 nb_hashtags : 8 time to prepare the origin masks : 7.226922273635864 time for calcul the mask position with numpy : 0.46616125106811523 nb_pixel_total : 5541381 time to create 1 rle with new method : 0.5354039669036865 time for calcul the mask position with numpy : 0.028682708740234375 nb_pixel_total : 22368 time to create 1 rle with old method : 0.0232696533203125 time for calcul the mask position with numpy : 0.02456188201904297 nb_pixel_total : 54165 time to create 1 rle with old method : 0.05888104438781738 time for calcul the mask position with numpy : 0.026392221450805664 nb_pixel_total : 67063 time to create 1 rle with old method : 0.10694003105163574 time for calcul the mask position with numpy : 0.02427840232849121 nb_pixel_total : 21429 time to create 1 rle with old method : 0.02339911460876465 time for calcul the mask position with numpy : 0.02398848533630371 nb_pixel_total : 17823 time to create 1 rle with old method : 0.019344091415405273 time for calcul the mask position with numpy : 0.02439093589782715 nb_pixel_total : 12806 time to create 1 rle with old method : 0.014388084411621094 time for calcul the mask position with numpy : 0.02755570411682129 nb_pixel_total : 68741 time to create 1 rle with old method : 0.07599306106567383 time for calcul the mask position with numpy : 0.024827003479003906 nb_pixel_total : 33389 time to create 1 rle with old method : 0.03644156455993652 time for calcul the mask position with numpy : 0.02745962142944336 nb_pixel_total : 37147 time to create 1 rle with old method : 0.03947758674621582 time for calcul the mask position with numpy : 0.02291417121887207 nb_pixel_total : 10154 time to create 1 rle with old method : 0.01096487045288086 time for calcul the mask position with numpy : 0.02357006072998047 nb_pixel_total : 152111 time to create 1 rle with new method : 0.1585686206817627 time for calcul the mask position with numpy : 0.02404642105102539 nb_pixel_total : 203817 time to create 1 rle with new method : 0.16201186180114746 time for calcul the mask position with numpy : 0.02539348602294922 nb_pixel_total : 194060 time to create 1 rle with new method : 0.1530923843383789 time for calcul the mask position with numpy : 0.022680997848510742 nb_pixel_total : 17660 time to create 1 rle with old method : 0.018270492553710938 time for calcul the mask position with numpy : 0.023233890533447266 nb_pixel_total : 15679 time to create 1 rle with old method : 0.016618728637695312 time for calcul the mask position with numpy : 0.024924278259277344 nb_pixel_total : 110776 time to create 1 rle with old method : 0.11439728736877441 time for calcul the mask position with numpy : 0.02311539649963379 nb_pixel_total : 11053 time to create 1 rle with old method : 0.013839006423950195 time for calcul the mask position with numpy : 0.02586960792541504 nb_pixel_total : 198211 time to create 1 rle with new method : 0.1522226333618164 time for calcul the mask position with numpy : 0.023005247116088867 nb_pixel_total : 51108 time to create 1 rle with old method : 0.05596017837524414 time for calcul the mask position with numpy : 0.02543163299560547 nb_pixel_total : 59337 time to create 1 rle with old method : 0.06419181823730469 time for calcul the mask position with numpy : 0.025583982467651367 nb_pixel_total : 43610 time to create 1 rle with old method : 0.05227375030517578 time for calcul the mask position with numpy : 0.03624367713928223 nb_pixel_total : 106352 time to create 1 rle with old method : 0.11132407188415527 create new chi : 3.141174554824829 time to delete rle : 0.0018930435180664062 batch 1 Loaded 23 chid ids of type : 4211 Number RLEs to save : 16036 TO DO : save crop sub photo not yet done ! save time : 0.825141429901123 nb_obj : 27 nb_hashtags : 7 time to prepare the origin masks : 8.676522016525269 time for calcul the mask position with numpy : 0.08284401893615723 nb_pixel_total : 5972270 time to create 1 rle with new method : 0.13637423515319824 time for calcul the mask position with numpy : 0.027788400650024414 nb_pixel_total : 35046 time to create 1 rle with old method : 0.03595542907714844 time for calcul the mask position with numpy : 0.030427932739257812 nb_pixel_total : 116823 time to create 1 rle with old method : 0.1211235523223877 time for calcul the mask position with numpy : 0.027901172637939453 nb_pixel_total : 165 time to create 1 rle with old method : 0.00039386749267578125 time for calcul the mask position with numpy : 0.02955317497253418 nb_pixel_total : 95251 time to create 1 rle with old method : 0.10010671615600586 time for calcul the mask position with numpy : 0.028416872024536133 nb_pixel_total : 34947 time to create 1 rle with old method : 0.03677678108215332 time for calcul the mask position with numpy : 0.02867889404296875 nb_pixel_total : 8952 time to create 1 rle with old method : 0.010133504867553711 time for calcul the mask position with numpy : 0.030204296112060547 nb_pixel_total : 57694 time to create 1 rle with old method : 0.06245112419128418 time for calcul the mask position with numpy : 0.02825784683227539 nb_pixel_total : 13702 time to create 1 rle with old method : 0.014796972274780273 time for calcul the mask position with numpy : 0.028696775436401367 nb_pixel_total : 29274 time to create 1 rle with old method : 0.03140544891357422 time for calcul the mask position with numpy : 0.02838921546936035 nb_pixel_total : 1141 time to create 1 rle with old method : 0.0016248226165771484 time for calcul the mask position with numpy : 0.0289151668548584 nb_pixel_total : 120010 time to create 1 rle with old method : 0.12465119361877441 time for calcul the mask position with numpy : 0.028027772903442383 nb_pixel_total : 18195 time to create 1 rle with old method : 0.01880621910095215 time for calcul the mask position with numpy : 0.02814626693725586 nb_pixel_total : 32563 time to create 1 rle with old method : 0.03421950340270996 time for calcul the mask position with numpy : 0.028778791427612305 nb_pixel_total : 101927 time to create 1 rle with old method : 0.10431504249572754 time for calcul the mask position with numpy : 0.029872417449951172 nb_pixel_total : 32754 time to create 1 rle with old method : 0.03513622283935547 time for calcul the mask position with numpy : 0.028472900390625 nb_pixel_total : 20527 time to create 1 rle with old method : 0.021735668182373047 time for calcul the mask position with numpy : 0.028034448623657227 nb_pixel_total : 34220 time to create 1 rle with old method : 0.03451728820800781 time for calcul the mask position with numpy : 0.02785205841064453 nb_pixel_total : 5283 time to create 1 rle with old method : 0.005723714828491211 time for calcul the mask position with numpy : 0.03101658821105957 nb_pixel_total : 12558 time to create 1 rle with old method : 0.013562917709350586 time for calcul the mask position with numpy : 0.028438568115234375 nb_pixel_total : 104828 time to create 1 rle with old method : 0.1086418628692627 time for calcul the mask position with numpy : 0.028906583786010742 nb_pixel_total : 11313 time to create 1 rle with old method : 0.012834548950195312 time for calcul the mask position with numpy : 0.03106212615966797 nb_pixel_total : 65987 time to create 1 rle with old method : 0.07153081893920898 time for calcul the mask position with numpy : 0.030507326126098633 nb_pixel_total : 32051 time to create 1 rle with old method : 0.03481459617614746 time for calcul the mask position with numpy : 0.02898693084716797 nb_pixel_total : 68918 time to create 1 rle with old method : 0.07457685470581055 time for calcul the mask position with numpy : 0.029452800750732422 nb_pixel_total : 17350 time to create 1 rle with old method : 0.018958330154418945 time for calcul the mask position with numpy : 0.029394149780273438 nb_pixel_total : 37 time to create 1 rle with old method : 0.0001800060272216797 time for calcul the mask position with numpy : 0.02928924560546875 nb_pixel_total : 6454 time to create 1 rle with old method : 0.007071733474731445 create new chi : 2.1529669761657715 time to delete rle : 0.0014643669128417969 batch 1 Loaded 29 chid ids of type : 4211 Number RLEs to save : 15303 TO DO : save crop sub photo not yet done ! save time : 0.8361773490905762 map_output_result : {1334416147: (0.0, 'Should be the crop_list due to order', 0.0), 1334416146: (0.0, 'Should be the crop_list due to order', 0.0), 1334416145: (0.0, 'Should be the crop_list due to order', 0.0), 1334416109: (0.0, 'Should be the crop_list due to order', 0.0), 1334416058: (0.0, 'Should be the crop_list due to order', 0.0), 1334194033: (0.0, 'Should be the crop_list due to order', 0.0), 1334194028: (0.0, 'Should be the crop_list due to order', 0.0), 1334194010: (0.0, 'Should be the crop_list due to order', 0.0), 1334194006: (0.0, 'Should be the crop_list due to order', 0.0), 1334194002: (0.0, 'Should be the crop_list due to order', 0.0), 1334194000: (0.0, 'Should be the crop_list due to order', 0.0), 1334193886: (0.0, 'Should be the crop_list due to order', 0.0), 1334193883: (0.0, 'Should be the crop_list due to order', 0.0), 1334193847: (0.0, 'Should be the crop_list due to order', 0.0), 1334193843: (0.0, 'Should be the crop_list due to order', 0.0), 1334193840: (0.0, 'Should be the crop_list due to order', 0.0), 1334193838: (0.0, 'Should be the crop_list due to order', 0.0), 1334193728: (0.0, 'Should be the crop_list due to order', 0.0), 1334193389: (0.0, 'Should be the crop_list due to order', 0.0), 1334193386: (0.0, 'Should be the crop_list due to order', 0.0)} 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 [1334416147, 1334416146, 1334416145, 1334416109, 1334416058, 1334194033, 1334194028, 1334194010, 1334194006, 1334194002, 1334194000, 1334193886, 1334193883, 1334193847, 1334193843, 1334193840, 1334193838, 1334193728, 1334193389, 1334193386] Looping around the photos to save general results len do output : 20 /1334416147.Didn't retrieve data . /1334416146.Didn't retrieve data . /1334416145.Didn't retrieve data . /1334416109.Didn't retrieve data . /1334416058.Didn't retrieve data . /1334194033.Didn't retrieve data . /1334194028.Didn't retrieve data . /1334194010.Didn't retrieve data . /1334194006.Didn't retrieve data . /1334194002.Didn't retrieve data . /1334194000.Didn't retrieve data . /1334193886.Didn't retrieve data . /1334193883.Didn't retrieve data . /1334193847.Didn't retrieve data . /1334193843.Didn't retrieve data . /1334193840.Didn't retrieve data . /1334193838.Didn't retrieve data . /1334193728.Didn't retrieve data . /1334193389.Didn't retrieve data . /1334193386.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 ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416147', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416146', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416145', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416109', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416058', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194033', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194028', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194010', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194006', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194002', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194000', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193886', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193883', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193847', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193843', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193840', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193838', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193728', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193389', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193386', None, None, None, None, None, '2543013') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 60 time used for this insertion : 0.013057231903076172 save_final save missing photos in datou_result : time spend for datou_step_exec : 301.4699342250824 time spend to save output : 0.013765096664428711 total time spend for step 8 : 301.4836993217468 step9:crop_condition Tue Feb 4 14:22:04 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 some photos are not treated, begin crop_condition Loading chi in step crop with photo_hashtag_type : 4211 Loading chi in step crop for list_pids : 20 ! batch 1 Loaded 722 chid ids of type : 4211 begin to crop the class : barquette_opaque param for this class : {'min_score': 0.5} filtre for class : barquette_opaque hashtag_id of this class : 2107760128 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 100 About to insert : list_path_to_insert length 100 new photo from crops ! About to upload 100 photos upload in portfolio : 4869462 init cache_photo without model_param we have 100 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738675351_4044648 we have uploaded 100 photos in the portfolio 4869462 time of upload the photos Elapsed time : 22.682030200958252 we have finished the crop for the class : barquette_opaque begin to crop the class : carton param for this class : {'min_score': 0.5} filtre for class : carton hashtag_id of this class : 492774966 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 10 About to insert : list_path_to_insert length 10 new photo from crops ! About to upload 10 photos upload in portfolio : 4869462 init cache_photo without model_param we have 10 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738675378_4044648 we have uploaded 10 photos in the portfolio 4869462 time of upload the photos Elapsed time : 2.1990435123443604 we have finished the crop for the class : carton begin to crop the class : ela param for this class : {'min_score': 0.5} filtre for class : ela hashtag_id of this class : 492741797 Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 5 About to insert : list_path_to_insert length 5 new photo from crops ! About to upload 5 photos upload in portfolio : 4869462 init cache_photo without model_param we have 5 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738675384_4044648 we have uploaded 5 photos in the portfolio 4869462 time of upload the photos Elapsed time : 1.5626728534698486 we have finished the crop for the class : ela begin to crop the class : environnement param for this class : {'min_score': 0.5} filtre for class : environnement hashtag_id of this class : 493012381 Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 5 About to insert : list_path_to_insert length 5 new photo from crops ! About to upload 5 photos upload in portfolio : 4869462 init cache_photo without model_param we have 5 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738675389_4044648 we have uploaded 5 photos in the portfolio 4869462 time of upload the photos Elapsed time : 1.5605323314666748 we have finished the crop for the class : environnement begin to crop the class : etiquette param for this class : {'min_score': 0.5} filtre for class : etiquette hashtag_id of this class : 492636447 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 59 About to insert : list_path_to_insert length 59 new photo from crops ! About to upload 59 photos upload in portfolio : 4869462 init cache_photo without model_param we have 59 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738675399_4044648 we have uploaded 59 photos in the portfolio 4869462 time of upload the photos Elapsed time : 14.497555494308472 we have finished the crop for the class : etiquette begin to crop the class : film_plastique param for this class : {'min_score': 0.5} filtre for class : film_plastique hashtag_id of this class : 2107756122 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 37 About to insert : list_path_to_insert length 37 new photo from crops ! About to upload 37 photos upload in portfolio : 4869462 init cache_photo without model_param we have 37 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738675427_4044648 we have uploaded 37 photos in the portfolio 4869462 time of upload the photos Elapsed time : 10.235474586486816 we have finished the crop for the class : film_plastique begin to crop the class : kraft param for this class : {'min_score': 0.5} filtre for class : kraft hashtag_id of this class : 493202403 begin to crop the class : metal param for this class : {'min_score': 0.5} filtre for class : metal hashtag_id of this class : 492628673 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 10 About to insert : list_path_to_insert length 10 new photo from crops ! About to upload 10 photos upload in portfolio : 4869462 init cache_photo without model_param we have 10 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738675442_4044648 we have uploaded 10 photos in the portfolio 4869462 time of upload the photos Elapsed time : 2.3033251762390137 we have finished the crop for the class : metal begin to crop the class : pehd param for this class : {'min_score': 0.5} filtre for class : pehd hashtag_id of this class : 628944319 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 22 About to insert : list_path_to_insert length 22 new photo from crops ! About to upload 22 photos upload in portfolio : 4869462 init cache_photo without model_param we have 22 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738675902_4044648 we have uploaded 22 photos in the portfolio 4869462 time of upload the photos Elapsed time : 9.778030633926392 we have finished the crop for the class : pehd begin to crop the class : pet_clair param for this class : {'min_score': 0.5} filtre for class : pet_clair hashtag_id of this class : 2107755846 Next one ! Next one ! 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 : 4869462 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738675918_4044648 we have uploaded 3 photos in the portfolio 4869462 time of upload the photos Elapsed time : 1.0040910243988037 we have finished the crop for the class : pet_clair begin to crop the class : pet_opaque param for this class : {'min_score': 0.5} filtre for class : pet_opaque hashtag_id of this class : 2107759152 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 43 About to insert : list_path_to_insert length 43 new photo from crops ! About to upload 43 photos upload in portfolio : 4869462 init cache_photo without model_param we have 43 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738675932_4044648 we have uploaded 43 photos in the portfolio 4869462 time of upload the photos Elapsed time : 9.958505868911743 we have finished the crop for the class : pet_opaque begin to crop the class : textiles_sanitaires param for this class : {'min_score': 0.5} filtre for class : textiles_sanitaires hashtag_id of this class : 2107760129 begin to crop the class : pet_fonce param for this class : {'min_score': 0.5} filtre for class : pet_fonce hashtag_id of this class : 2107755900 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 13 About to insert : list_path_to_insert length 13 new photo from crops ! About to upload 13 photos upload in portfolio : 4869462 init cache_photo without model_param we have 13 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738675950_4044648 we have uploaded 13 photos in the portfolio 4869462 time of upload the photos Elapsed time : 3.095346450805664 we have finished the crop for the class : pet_fonce begin to crop the class : papier param for this class : {'min_score': 0.5} filtre for class : papier hashtag_id of this class : 492668766 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 85 About to insert : list_path_to_insert length 85 new photo from crops ! About to upload 85 photos upload in portfolio : 4869462 init cache_photo without model_param we have 85 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738675976_4044648 we have uploaded 85 photos in the portfolio 4869462 time of upload the photos Elapsed time : 19.659066915512085 we have finished the crop for the class : papier delete rles for these photos Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1334416147, 1334416146, 1334416145, 1334416109, 1334416058, 1334194033, 1334194028, 1334194010, 1334194006, 1334194002, 1334194000, 1334193886, 1334193883, 1334193847, 1334193843, 1334193840, 1334193838, 1334193728, 1334193389, 1334193386] Looping around the photos to save general results len do output : 392 /1334563316Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563317Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563318Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563319Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563320Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563321Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563322Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563323Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563324Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563325Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563326Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563327Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563328Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563329Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563330Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563331Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563332Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563333Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563335Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563336Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563337Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563338Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563339Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563340Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563341Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563342Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563343Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563344Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563345Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563346Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563347Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563348Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563349Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563350Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563351Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563352Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563353Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563354Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563355Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563356Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563357Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563358Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563359Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563360Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563361Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563362Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563363Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563364Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563365Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563366Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563367Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563368Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563369Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563370Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563371Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563372Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563373Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563374Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563375Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563376Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563377Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563378Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563379Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563380Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563381Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563382Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563383Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563384Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563385Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563386Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563387Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563388Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563389Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563390Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563391Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563392Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563393Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563395Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563396Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563397Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563398Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563399Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563400Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563401Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563402Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563403Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563404Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563405Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563406Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563407Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563408Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563409Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563410Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563411Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563412Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563413Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563414Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563415Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563416Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563417Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563427Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563428Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563429Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563430Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563431Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563432Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563434Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563435Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563436Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563437Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563440Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563441Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563442Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563443Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563444Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563446Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563447Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563448Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563449Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563450Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563487Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563488Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563489Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563490Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563491Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563492Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563493Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563494Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563495Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563496Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563497Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563498Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563499Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563500Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563501Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563502Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563503Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563504Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563505Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563507Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563509Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563510Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563511Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563512Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563513Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563514Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563515Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563516Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563517Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563518Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563519Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563520Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563521Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563523Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563526Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563530Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563534Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563537Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563538Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563539Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563540Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563541Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563542Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563543Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563544Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563545Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563546Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563547Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563548Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563549Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563550Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563551Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563552Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563553Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563554Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563555Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563556Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563557Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563558Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563597Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563598Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563599Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563600Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563601Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563602Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563603Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563604Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563605Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563606Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563607Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563608Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563609Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563610Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563611Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563612Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563613Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563614Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563615Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563616Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563617Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563618Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563619Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563620Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563621Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563623Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563624Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563625Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563626Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563627Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563628Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563629Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563630Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563631Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563632Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563633Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563634Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563639Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563640Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563641Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563642Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563643Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563644Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563645Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563646Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563647Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334563648Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565357Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565358Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565359Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565360Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565361Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565362Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565363Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565364Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565365Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565366Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565367Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565368Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565370Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565372Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565373Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565374Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565375Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565376Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565377Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565378Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565379Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565380Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565387Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565388Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334565389Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566119Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566120Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566122Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566123Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566124Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566125Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566126Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566127Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566128Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566129Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566130Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566131Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566132Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566133Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566134Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566135Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566136Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566137Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566138Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566139Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566140Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566141Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566142Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566144Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566145Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566146Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566147Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566148Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566149Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566150Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566151Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566152Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566153Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566154Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566155Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566156Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566157Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566158Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566159Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566160Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566161Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566162Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566163Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566265Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566266Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566267Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566268Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566269Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566270Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566271Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566272Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566273Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566274Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566275Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566276Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566277Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566603Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566604Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566605Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566606Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566607Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566608Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566609Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566610Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566611Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566612Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566613Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566614Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566615Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566616Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566617Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566618Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566619Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566620Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566621Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566622Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566623Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566624Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566625Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566626Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566627Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566628Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566629Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566630Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566631Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566632Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566633Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566634Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566635Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566636Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566637Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566638Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566639Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566642Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566646Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566650Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566654Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566657Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566661Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566665Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566669Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566673Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566676Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566680Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566684Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566687Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566691Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566694Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566698Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566701Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566702Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566703Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566704Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566705Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566706Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566707Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566708Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566709Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566710Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566711Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566712Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566713Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566714Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566715Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566716Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566717Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566719Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566720Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566721Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566722Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566723Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566724Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566725Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566726Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566727Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566728Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566729Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566730Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566731Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566732Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334566733Didn'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 ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416147', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416146', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416145', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416109', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416058', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194033', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194028', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194010', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194006', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194002', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194000', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193886', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193883', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193847', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193843', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193840', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193838', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193728', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193389', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193386', None, None, None, None, None, '2543013') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1196 time used for this insertion : 0.0852971076965332 save_final save missing photos in datou_result : time spend for datou_step_exec : 678.5700888633728 time spend to save output : 0.09355807304382324 total time spend for step 9 : 678.6636469364166 step10:ventilate_hashtags_in_portfolio Tue Feb 4 14:33:22 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 20198530 get user id for portfolio 20198530 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`=20198530 AND mptpi.`type`=4211 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('environnement','carton','textiles_sanitaires','papier','barquette_opaque','etiquette','metal','pet_clair','pet_fonce','mal_croppe','film_plastique','pehd','flou','pet_opaque','kraft','ela')) AND mptpi.`min_score`=0.5 To do 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`=20198530 AND mptpi.`type`=4211 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('environnement','carton','textiles_sanitaires','papier','barquette_opaque','etiquette','metal','pet_clair','pet_fonce','mal_croppe','film_plastique','pehd','flou','pet_opaque','kraft','ela')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/20198999,20199000,20199001,20199002,20199003,20199004,20199005,20199006,20199007,20199008,20199009,20199010,20199011,20199012,20199013,20199014?tags=environnement,papier,pet_clair,film_plastique,ela,pehd,kraft,etiquette,pet_opaque,metal,textiles_sanitaires,mal_croppe,flou,barquette_opaque,carton,pet_fonce Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1334416147, 1334416146, 1334416145, 1334416109, 1334416058, 1334194033, 1334194028, 1334194010, 1334194006, 1334194002, 1334194000, 1334193886, 1334193883, 1334193847, 1334193843, 1334193840, 1334193838, 1334193728, 1334193389, 1334193386] Looping around the photos to save general results len do output : 1 /20198530. 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 ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416147', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416146', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416145', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416109', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416058', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194033', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194028', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194010', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194006', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194002', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194000', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193886', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193883', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193847', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193843', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193840', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193838', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193728', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193389', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193386', None, None, None, None, None, '2543013') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 21 time used for this insertion : 0.015228271484375 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.9400451183319092 time spend to save output : 0.015527486801147461 total time spend for step 10 : 0.9555726051330566 step11:final Tue Feb 4 14:33:23 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : {1334416147: ('0.08133199781811382',), 1334416146: ('0.08133199781811382',), 1334416145: ('0.08133199781811382',), 1334416109: ('0.08133199781811382',), 1334416058: ('0.08133199781811382',), 1334194033: ('0.08133199781811382',), 1334194028: ('0.08133199781811382',), 1334194010: ('0.08133199781811382',), 1334194006: ('0.08133199781811382',), 1334194002: ('0.08133199781811382',), 1334194000: ('0.08133199781811382',), 1334193886: ('0.08133199781811382',), 1334193883: ('0.08133199781811382',), 1334193847: ('0.08133199781811382',), 1334193843: ('0.08133199781811382',), 1334193840: ('0.08133199781811382',), 1334193838: ('0.08133199781811382',), 1334193728: ('0.08133199781811382',), 1334193389: ('0.08133199781811382',), 1334193386: ('0.08133199781811382',)} new output for save of step final : {1334416147: ('0.08133199781811382',), 1334416146: ('0.08133199781811382',), 1334416145: ('0.08133199781811382',), 1334416109: ('0.08133199781811382',), 1334416058: ('0.08133199781811382',), 1334194033: ('0.08133199781811382',), 1334194028: ('0.08133199781811382',), 1334194010: ('0.08133199781811382',), 1334194006: ('0.08133199781811382',), 1334194002: ('0.08133199781811382',), 1334194000: ('0.08133199781811382',), 1334193886: ('0.08133199781811382',), 1334193883: ('0.08133199781811382',), 1334193847: ('0.08133199781811382',), 1334193843: ('0.08133199781811382',), 1334193840: ('0.08133199781811382',), 1334193838: ('0.08133199781811382',), 1334193728: ('0.08133199781811382',), 1334193389: ('0.08133199781811382',), 1334193386: ('0.08133199781811382',)} [1334416147, 1334416146, 1334416145, 1334416109, 1334416058, 1334194033, 1334194028, 1334194010, 1334194006, 1334194002, 1334194000, 1334193886, 1334193883, 1334193847, 1334193843, 1334193840, 1334193838, 1334193728, 1334193389, 1334193386] Looping around the photos to save general results len do output : 20 /1334416147.Didn't retrieve data . /1334416146.Didn't retrieve data . /1334416145.Didn't retrieve data . /1334416109.Didn't retrieve data . /1334416058.Didn't retrieve data . /1334194033.Didn't retrieve data . /1334194028.Didn't retrieve data . /1334194010.Didn't retrieve data . /1334194006.Didn't retrieve data . /1334194002.Didn't retrieve data . /1334194000.Didn't retrieve data . /1334193886.Didn't retrieve data . /1334193883.Didn't retrieve data . /1334193847.Didn't retrieve data . /1334193843.Didn't retrieve data . /1334193840.Didn't retrieve data . /1334193838.Didn't retrieve data . /1334193728.Didn't retrieve data . /1334193389.Didn't retrieve data . /1334193386.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 ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416147', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416146', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416145', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416109', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416058', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194033', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194028', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194010', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194006', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194002', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194000', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193886', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193883', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193847', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193843', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193840', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193838', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193728', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193389', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193386', None, None, None, None, None, '2543013') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 60 time used for this insertion : 0.01405477523803711 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.23530173301696777 time spend to save output : 0.01488637924194336 total time spend for step 11 : 0.25018811225891113 step12:velours_tree Tue Feb 4 14:33:24 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 : 0.04018712043762207 time spend to save output : 4.029273986816406e-05 total time spend for step 12 : 0.040227413177490234 step13:send_mail_cod Tue Feb 4 14:33:24 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 complete output_args for input 2 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 3 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure dans la step send mail cod work_area: /home/admin in order to get the selector url, please entre the license of selector results_Auto_P20198530_04-02-2025_14_33_24.pdf 20199000 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 .imagette201990001738676004 20199001 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 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imagette201990091738676017 20199010 imagette201990101738676017 20199011 imagette201990111738676017 20199012 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 .imagette201990121738676017 20199013 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 .imagette201990131738676019 20199014 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 .imagette201990141738676021 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=20198530 and hashtag_type = 4211 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/20198999,20199000,20199001,20199002,20199003,20199004,20199005,20199006,20199007,20199008,20199009,20199010,20199011,20199012,20199013,20199014?tags=environnement,papier,pet_clair,film_plastique,ela,pehd,kraft,etiquette,pet_opaque,metal,textiles_sanitaires,mal_croppe,flou,barquette_opaque,carton,pet_fonce your option no_mail is active, we will not send the real mail to your client args[1334416147] : ((1334416147, -5.666096234671825, 492609224), (1334416147, -0.17214759773270363, 496442774), '0.08133199781811382') We are sending mail with results at report@fotonower.com args[1334416146] : ((1334416146, -5.708156237290212, 492609224), (1334416146, -0.1846156743380615, 496442774), '0.08133199781811382') We are sending mail with results at report@fotonower.com args[1334416145] : ((1334416145, -4.815456407597747, 492609224), (1334416145, -0.08445989740565886, 496442774), '0.08133199781811382') We are sending mail with results at report@fotonower.com args[1334416109] : ((1334416109, -3.776201276033427, 492609224), (1334416109, -0.12011498078529927, 496442774), '0.08133199781811382') We are sending mail with results at report@fotonower.com args[1334416058] : ((1334416058, -4.8471468797094275, 492609224), (1334416058, -0.16623224703972675, 496442774), '0.08133199781811382') We are sending mail with results at report@fotonower.com args[1334194033] : ((1334194033, -4.176024405855606, 492609224), (1334194033, -0.22482804690713587, 496442774), '0.08133199781811382') We are sending mail with results at report@fotonower.com args[1334194028] : ((1334194028, -5.219630423003019, 492609224), (1334194028, 0.1551134357583091, 2107752395), '0.08133199781811382') We are sending mail with results at report@fotonower.com args[1334194010] : ((1334194010, -5.189865102780668, 492609224), (1334194010, -0.19214167045484776, 496442774), '0.08133199781811382') We are sending mail with results at report@fotonower.com args[1334194006] : ((1334194006, -4.672563076193158, 492609224), (1334194006, -0.1081186182604217, 496442774), '0.08133199781811382') We are sending mail with results at report@fotonower.com args[1334194002] : ((1334194002, -5.23752644705515, 492609224), (1334194002, 0.04944995067003112, 2107752395), '0.08133199781811382') We are sending mail with results at report@fotonower.com args[1334194000] : ((1334194000, -3.9924691344146597, 492609224), (1334194000, -0.1414135884755413, 496442774), '0.08133199781811382') We are sending mail with results at report@fotonower.com args[1334193886] : ((1334193886, -5.733144942582252, 492609224), (1334193886, -0.03598314191229639, 2107752395), '0.08133199781811382') We are sending mail with results at report@fotonower.com args[1334193883] : ((1334193883, -5.539662795647853, 492609224), (1334193883, -0.02171453320367586, 2107752395), '0.08133199781811382') We are sending mail with results at report@fotonower.com args[1334193847] : ((1334193847, -5.509290430607267, 492609224), (1334193847, -0.09291759583513073, 496442774), '0.08133199781811382') We are sending mail with results at report@fotonower.com args[1334193843] : ((1334193843, -5.655041125610419, 492609224), (1334193843, -0.12354342697725822, 496442774), '0.08133199781811382') We are sending mail with results at report@fotonower.com args[1334193840] : ((1334193840, -4.925104137530129, 492609224), (1334193840, 0.1184652872843382, 2107752395), '0.08133199781811382') We are sending mail with results at report@fotonower.com args[1334193838] : ((1334193838, -5.795876552282547, 492609224), (1334193838, -0.048885880838569006, 2107752395), '0.08133199781811382') We are sending mail with results at report@fotonower.com args[1334193728] : ((1334193728, -2.548575432486161, 492609224), (1334193728, 0.6226911698172449, 2107752395), '0.08133199781811382') We are sending mail with results at report@fotonower.com args[1334193389] : ((1334193389, -5.527034204905684, 492609224), (1334193389, -0.015842109325680503, 2107752395), '0.08133199781811382') We are sending mail with results at report@fotonower.com args[1334193386] : ((1334193386, -4.833988194946205, 492609224), (1334193386, -0.04245960755629055, 2107752395), '0.08133199781811382') We are sending mail with results at report@fotonower.com refus_total : 0.08133199781811382 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=20198530 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1334416147,1334416248,1334416244,1334416341,1334416403,1334416435,1334416444,1334416449,1334416459,1334416380,1334416242,1334416743,1334416767,1334416772,1334416777,1334194010,1334416780,1334194000,1334193886,1334416796) Found this number of photos: 20 begin to download photo : 1334416147 begin to download photo : 1334416435 begin to download photo : 1334416242 begin to download photo : 1334194010 download finish for photo 1334416435 begin to download photo : 1334416444 download finish for photo 1334194010 begin to download photo : 1334416780 download finish for photo 1334416242 begin to download photo : 1334416743 download finish for photo 1334416147 begin to download photo : 1334416248 download finish for photo 1334416444 begin to download photo : 1334416449 download finish for photo 1334416743 begin to download photo : 1334416767 download finish for photo 1334416780 begin to download photo : 1334194000 download finish for photo 1334416248 begin to download photo : 1334416244 download finish for photo 1334416449 begin to download photo : 1334416459 download finish for photo 1334194000 begin to download photo : 1334193886 download finish for photo 1334416244 begin to download photo : 1334416341 download finish for photo 1334416767 begin to download photo : 1334416772 download finish for photo 1334416459 begin to download photo : 1334416380 download finish for photo 1334193886 begin to download photo : 1334416796 download finish for photo 1334416341 begin to download photo : 1334416403 download finish for photo 1334416380 download finish for photo 1334416772 begin to download photo : 1334416777 download finish for photo 1334416403 download finish for photo 1334416796 download finish for photo 1334416777 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198530_04-02-2025_14_33_24.pdf results_Auto_P20198530_04-02-2025_14_33_24.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198530_04-02-2025_14_33_24.pdf start insert file to database insert into MTRUser.mtr_files (mtd_id,mtr_portfolio_id,text,url,format,tags,file_size,value) values ('4909','20198530','results_Auto_P20198530_04-02-2025_14_33_24.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198530_04-02-2025_14_33_24.pdf','pdf','','1.83','0.08133199781811382') Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : send_mail_cod we use saveGeneral [1334416147, 1334416146, 1334416145, 1334416109, 1334416058, 1334194033, 1334194028, 1334194010, 1334194006, 1334194002, 1334194000, 1334193886, 1334193883, 1334193847, 1334193843, 1334193840, 1334193838, 1334193728, 1334193389, 1334193386] 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 ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416147', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416146', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416145', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416109', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416058', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194033', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194028', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194010', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194006', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194002', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194000', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193886', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193883', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193847', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193843', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193840', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193838', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193728', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193389', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193386', None, None, None, None, None, '2543013') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 20 time used for this insertion : 0.014565229415893555 save_final save missing photos in datou_result : time spend for datou_step_exec : 23.453864336013794 time spend to save output : 0.014770030975341797 total time spend for step 13 : 23.468634366989136 step14:split_time_score Tue Feb 4 14:33:47 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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'}] (('13', 76),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 03022025 20198530 Nombre de photos uploadées : 76 / 23040 (0%) 03022025 20198530 Nombre de photos taguées (types de déchets): 0 / 76 (0%) 03022025 20198530 Nombre de photos taguées (volume) : 0 / 76 (0%) elapsed_time : load_data_split_time_score 2.86102294921875e-06 elapsed_time : order_list_meta_photo_and_scores 7.867813110351562e-06 ???????????????????????????????????????????????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0056018829345703125 elapsed_time : insert_dashboard_record_day_entry 0.022214174270629883 We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps Qualite : 0.23880805633724686 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20193381_03-02-2025_22_47_37.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20193381 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`=20193381 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20194251 order by id desc limit 1 Qualite : 0.11198983077154488 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198508_04-02-2025_08_41_30.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198508 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 ! 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 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 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 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 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 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 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`=20198508 AND mptpi.`type`=4200 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198509 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198510 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198511 order by id desc limit 1 Qualite : 0.11949896059037385 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198512_04-02-2025_12_38_15.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198512 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 ! 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 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 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 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 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`=20198512 AND mptpi.`type`=3327 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198513 order by id desc limit 1 Qualite : 0.19758238726624902 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198514_04-02-2025_06_49_22.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198514 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`=20198514 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198515 order by id desc limit 1 Qualite : 0.18838532124760682 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198516_04-02-2025_06_31_39.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198516 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`=20198516 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198517 order by id desc limit 1 Qualite : 0.13010448873463787 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198518_04-02-2025_08_44_13.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198518 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 ! 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 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 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 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 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 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 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`=20198518 AND mptpi.`type`=4200 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198519 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198520 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198521 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198522 order by id desc limit 1 Qualite : 0.04519510886558702 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198523_04-02-2025_08_35_44.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198523 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 ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 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 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 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`=20198523 AND mptpi.`type`=4207 To do Qualite : 0.0476497064530327 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198524_04-02-2025_08_10_37.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198524 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 ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 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 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 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`=20198524 AND mptpi.`type`=4207 To do Qualite : 0.056109846212566754 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198525_04-02-2025_07_58_43.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198525 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 ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 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 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 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`=20198525 AND mptpi.`type`=4207 To do Qualite : 0.0807318979113116 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198526_04-02-2025_07_26_17.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198526 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`=20198526 AND mptpi.`type`=3726 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198527 order by id desc limit 1 Qualite : 0.05629553772725869 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198528_04-02-2025_07_54_07.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198528 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 ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 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 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 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`=20198528 AND mptpi.`type`=4207 To do Qualite : 0.03708480878785295 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198529_04-02-2025_07_30_42.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198529 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 ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 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 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 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`=20198529 AND mptpi.`type`=4207 To do Qualite : 0.08314464406567418 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198530_04-02-2025_14_33_24.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198530 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 ! 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 13953 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 13961 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 13960 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 13957 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 13957 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 13966 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13959 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 13954 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 13954 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 13964 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 13964 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 13956 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 13955 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 13955 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 13963 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 13956 doesn't seem to be define in the database( WARNING : type of input 3 of step 13955 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 13953 doesn't seem to be define in the database( WARNING : type of input 2 of step 13957 doesn't seem to be define in the database( WARNING : output 1 of step 13953 have datatype=2 whereas input 1 of step 13959 have datatype=7 WARNING : type of output 2 of step 13959 doesn't seem to be define in the database( WARNING : type of input 1 of step 13954 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 13956 have datatype=10 whereas input 3 of step 13962 have datatype=6 WARNING : type of input 2 of step 13964 doesn't seem to be define in the database( WARNING : output 1 of step 13954 have datatype=7 whereas input 2 of step 13964 have datatype=None WARNING : type of output 3 of step 13964 doesn't seem to be define in the database( WARNING : type of input 1 of step 13956 doesn't seem to be define in the database( WARNING : output 0 of step 13956 have datatype=10 whereas input 0 of step 13965 have datatype=18 WARNING : type of input 5 of step 13962 doesn't seem to be define in the database( WARNING : output 0 of step 13965 have datatype=11 whereas input 5 of step 13962 have datatype=None WARNING : type of output 1 of step 13961 doesn't seem to be define in the database( WARNING : type of input 3 of step 13957 doesn't seem to be define in the database( WARNING : type of output 1 of step 13960 doesn't seem to be define in the database( WARNING : type of input 3 of step 13957 doesn't seem to be define in the database( WARNING : output 0 of step 13959 have datatype=1 whereas input 0 of step 13954 have datatype=2 WARNING : output 0 of step 13966 have datatype=6 whereas input 2 of step 13959 have datatype=5 WARNING : output 0 of step 13953 have datatype=16 whereas input 0 of step 13959 have datatype=1 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`=20198530 AND mptpi.`type`=4211 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198531 order by id desc limit 1 Qualite : 0.21320275840036873 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198532_04-02-2025_08_11_44.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198532 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`=20198532 AND mptpi.`type`=3594 To do Qualite : 0.21369050320745575 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198533_04-02-2025_06_10_38.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198533 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`=20198533 AND mptpi.`type`=3594 To do Qualite : 0.09553809795998332 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198534_04-02-2025_07_19_33.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198534 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 ! 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 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 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 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 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 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 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`=20198534 AND mptpi.`type`=4200 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198535 order by id desc limit 1 Qualite : 0.2458472243514416 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198536_04-02-2025_06_59_45.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198536 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 ! 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 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 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 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 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 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 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`=20198536 AND mptpi.`type`=4200 To do Qualite : 0.1742342450429037 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198537_04-02-2025_13_05_45.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198537 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 ! 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 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 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 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 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`=20198537 AND mptpi.`type`=3327 To do Qualite : 0.07624353351013721 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198538_04-02-2025_06_48_44.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198538 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 ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 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 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 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`=20198538 AND mptpi.`type`=4207 To do Qualite : 0.0422901641171671 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198539_04-02-2025_07_17_09.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198539 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 ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 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 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 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`=20198539 AND mptpi.`type`=4207 To do Qualite : 0.07434428453272496 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198540_04-02-2025_07_09_16.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198540 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 ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 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 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 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`=20198540 AND mptpi.`type`=4207 To do Qualite : 0.0436304380458182 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198541_04-02-2025_06_06_04.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198541 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 ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 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 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 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`=20198541 AND mptpi.`type`=4207 To do Qualite : 0.027081377536696854 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198542_04-02-2025_05_33_23.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198542 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 ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 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 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 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`=20198542 AND mptpi.`type`=4207 To do Qualite : 0.23644752776952532 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198543_04-02-2025_06_14_54.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198543 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`=20198543 AND mptpi.`type`=3594 To do Qualite : 0.1798517182078592 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198544_04-02-2025_05_49_08.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198544 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`=20198544 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198545 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198546 order by id desc limit 1 Qualite : 0.1963322477910917 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198547_04-02-2025_06_50_35.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198547 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`=20198547 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198548 order by id desc limit 1 Qualite : 0.07430193663599202 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198549_04-02-2025_05_54_59.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198549 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`=20198549 AND mptpi.`type`=3726 To do Qualite : 0.0 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198550_04-02-2025_06_35_52.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198550 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 ! 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 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 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 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 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 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 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`=20198550 AND mptpi.`type`=4209 To do Qualite : 0.07647708590482732 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198551_04-02-2025_07_03_08.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198551 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 ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 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 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 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`=20198551 AND mptpi.`type`=4207 To do Qualite : 0.046256483004091464 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20198552_04-02-2025_06_26_42.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20198552 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 ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 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 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 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`=20198552 AND mptpi.`type`=4207 To do Qualite : 0.04743174615925559 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20193388_04-02-2025_00_46_50.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20193388 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 ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 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 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 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`=20193388 AND mptpi.`type`=4207 To do Qualite : 0.04306949273557367 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20193389_03-02-2025_22_59_59.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20193389 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 ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 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 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 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`=20193389 AND mptpi.`type`=4207 To do Qualite : 0.041028691593019265 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20193390_03-02-2025_23_11_13.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20193390 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 ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 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 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 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`=20193390 AND mptpi.`type`=4207 To do Qualite : 0.05486121472800447 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20193391_03-02-2025_22_43_51.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20193391 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 ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 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 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 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`=20193391 AND mptpi.`type`=4207 To do Qualite : 0.2441252962411816 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20194258_03-02-2025_23_50_56.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20194258 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`=20194258 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20193392 order by id desc limit 1 NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'03022025': {'nb_upload': 76, '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 [1334416147, 1334416146, 1334416145, 1334416109, 1334416058, 1334194033, 1334194028, 1334194010, 1334194006, 1334194002, 1334194000, 1334193886, 1334193883, 1334193847, 1334193843, 1334193840, 1334193838, 1334193728, 1334193389, 1334193386] Looping around the photos to save general results len do output : 1 /20198530Didn'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 ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416147', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416146', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416145', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416109', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334416058', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194033', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194028', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194010', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194006', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194002', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334194000', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193886', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193883', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193847', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193843', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193840', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193838', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193728', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193389', None, None, None, None, None, '2543013') ('4909', None, None, None, None, None, None, None, '2543013') ('4909', None, '1334193386', None, None, None, None, None, '2543013') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 21 time used for this insertion : 0.01515507698059082 save_final save missing photos in datou_result : time spend for datou_step_exec : 18.104289531707764 time spend to save output : 0.015496015548706055 total time spend for step 14 : 18.11978554725647 caffe_path_current : About to save ! 2 After save, about to update current ! update_current_state 1295.07user 249.58system 34:03.38elapsed 75%CPU (0avgtext+0avgdata 12113292maxresident)k 35975504inputs+1477552outputs (887290major+57466754minor)pagefaults 0swaps