python /home/admin/mtr/script_for_cron.py -j datou_current3 -m 20 -a ' -a 3318 ' -s datou_3318 -M 0 -S 0 -U 95,95,120 import MySQLdb succeeded Import error (python version) ['/Users/moilerat/Documents/Fotonower/install/caffe/distribute/python', '/home/admin/workarea/git/Velours/python/prod', '/home/admin/workarea/install/caffe_cuda8_python3/python', '/home/admin/workarea/install/darknet', '/home/admin/workarea/git/Velours/python', '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python', '/home/admin/mtr/.credentials', '/home/admin/workarea/install/caffe/python', '/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools', '/home/admin/workarea/git/fotonowerpip', '/home/admin/workarea/install/segment-anything', '/home/admin/workarea/git/pyfvs', '/usr/lib/python38.zip', '/usr/lib/python3.8', '/usr/lib/python3.8/lib-dynload', '/home/admin/.local/lib/python3.8/site-packages', '/usr/local/lib/python3.8/dist-packages', '/usr/lib/python3/dist-packages'] process id : 2177385 load datou : 3318 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected type for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? [(photo_id, hashtag_id, hashtag_type, x0, x1, y0, y1, score, seg_temp, polygons), ...] was removed should we ? chemin de la photo was removed should we ? [ (photo_id_loc, hashtag_id, hashtag_type, x0, x1, y0, y1, score, None), ...] was removed should we ? chemin de la photo was removed should we ? id de la photo (peut être local ou global) was removed should we ? chemin de la photo was removed should we ? (x0, y0, x1, y1) was removed should we ? chemin de la photo was removed should we ? donnée sous forme de texte was removed should we ? [ (photo_id, photo_id_loc, hashtag_type, x0, x1, y0, y1, score), ...] was removed should we ? None was removed should we ? donnée sous forme de texte was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? id de la photo (peut être local ou global) was removed should we ? donnée sous forme de texte was removed should we ? donnée sous forme de texte was removed should we ? donnée sous forme de texte was removed should we ? chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? None was removed should we ? donnée sous forme de nombre was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? donnée sous forme de texte was removed should we ? None was removed should we ? donnée sous forme de texte was removed should we ? [ptf_id0,ptf_id1...] was removed should we ? FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) load thcls load THCL from format json or kwargs add thcl : 2847 in CacheModelConfig load pdts add pdt : 5275 in CacheModelConfig Running datou job : batch_current TODO datou_current to load to do maybe to take outside batchDatouExec updating current state to 1 list_input_json: [] Current got : datou_id : 3318, datou_cur_ids : ['2711132'] with mtr_portfolio_ids : ['21929800'] and first list_photo_ids : [] new path : /proc/2177385/ Inside batchDatouExec : verbose : 0 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : mask_detect, crop_condition, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final, blur_detection, brightness, velours_tree, send_mail_cod, split_time_score over limit max, limiting to limit_max 40 list_input_json : [] origin We have 1 , BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 19 ; length of list_pids : 19 ; length of list_args : 19 time to download the photos : 3.8310651779174805 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : 0 number of steps : 10 step1:mask_detect Tue Apr 1 01:40:34 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10593 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-01 01:40:37.980250: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-01 01:40:37.995814: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-01 01:40:37.997523: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fd57c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-01 01:40:37.997549: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-01 01:40:38.001838: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-01 01:40:38.279901: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x13df70b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-01 01:40:38.279989: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-01 01:40:38.282496: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-01 01:40:38.283011: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 01:40:38.286843: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 01:40:38.290460: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-01 01:40:38.291245: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-01 01:40:38.294209: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-01 01:40:38.295480: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-01 01:40:38.300730: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 01:40:38.302403: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-01 01:40:38.303355: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 01:40:38.305433: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-01 01:40:38.305455: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-01 01:40:38.305469: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-01 01:40:38.307463: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9776 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-04-01 01:40:38.755802: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-01 01:40:38.755941: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 01:40:38.755982: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 01:40:38.756012: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-01 01:40:38.756042: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-01 01:40:38.756072: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-01 01:40:38.756096: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-01 01:40:38.756120: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 01:40:38.757914: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-01 01:40:38.759511: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-01 01:40:38.759574: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 01:40:38.759613: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 01:40:38.759636: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-01 01:40:38.759660: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-01 01:40:38.759683: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-01 01:40:38.759706: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-01 01:40:38.759729: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 01:40:38.761537: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-01 01:40:38.761584: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-01 01:40:38.761601: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-01 01:40:38.761615: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-01 01:40:38.763438: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9776 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl2847 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) {'thcl': {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'], 'list_hashtags_csv': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'svm_hashtag_type_desc': 5275, 'photo_desc_type': 5275, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME learn_RUBBIA_REFUS_AMIENS_23 NUM_CLASSES 9 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : learn_RUBBIA_REFUS_AMIENS_23 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-04-01 01:40:52.682452: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 01:40:52.949018: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23 /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23/mask_model.h5 size_local : 256009536 size in s3 : 256009536 create time local : 2021-08-09 09:43:22 create time in s3 : 2021-08-06 18:54:04 mask_model.h5 already exist and didn't need to update list_images length : 19 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 52 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 67 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 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, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 94 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 42 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 30 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 33 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 73 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 70 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 69 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 65 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 58 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 91 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 71 Detection mask done ! Trying to reset tf kernel 2178124 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 15 tf kernel not reseted sub process len(results) : 19 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 19 len(list_Values) 0 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 : 5304 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl2847 Catched exception ! Connect or reconnect ! thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] time for calcul the mask position with numpy : 0.23521828651428223 nb_pixel_total : 85555 time to create 1 rle with old method : 0.12934541702270508 length of segment : 475 time for calcul the mask position with numpy : 0.6686892509460449 nb_pixel_total : 313282 time to create 1 rle with new method : 0.29954028129577637 length of segment : 713 time for calcul the mask position with numpy : 0.1378767490386963 nb_pixel_total : 59699 time to create 1 rle with old method : 0.07491040229797363 length of segment : 205 time for calcul the mask position with numpy : 0.054419517517089844 nb_pixel_total : 18737 time to create 1 rle with old method : 0.02459883689880371 length of segment : 165 time for calcul the mask position with numpy : 0.14501547813415527 nb_pixel_total : 37378 time to create 1 rle with old method : 0.051850318908691406 length of segment : 353 time for calcul the mask position with numpy : 0.10467743873596191 nb_pixel_total : 37673 time to create 1 rle with old method : 0.04941368103027344 length of segment : 195 time for calcul the mask position with numpy : 0.13287854194641113 nb_pixel_total : 69601 time to create 1 rle with old method : 0.08904886245727539 length of segment : 411 time for calcul the mask position with numpy : 0.09319877624511719 nb_pixel_total : 41602 time to create 1 rle with old method : 0.053054094314575195 length of segment : 302 time for calcul the mask position with numpy : 0.07240414619445801 nb_pixel_total : 32654 time to create 1 rle with old method : 0.04603934288024902 length of segment : 226 time for calcul the mask position with numpy : 0.17349672317504883 nb_pixel_total : 57766 time to create 1 rle with old method : 0.06708788871765137 length of segment : 329 time for calcul the mask position with numpy : 0.11836123466491699 nb_pixel_total : 37676 time to create 1 rle with old method : 0.05536007881164551 length of segment : 358 time for calcul the mask position with numpy : 0.04349207878112793 nb_pixel_total : 24177 time to create 1 rle with old method : 0.03619647026062012 length of segment : 195 time for calcul the mask position with numpy : 0.019162416458129883 nb_pixel_total : 17701 time to create 1 rle with old method : 0.02685379981994629 length of segment : 228 time for calcul the mask position with numpy : 0.05844688415527344 nb_pixel_total : 30728 time to create 1 rle with old method : 0.04095864295959473 length of segment : 184 time for calcul the mask position with numpy : 0.0018949508666992188 nb_pixel_total : 41215 time to create 1 rle with old method : 0.0568087100982666 length of segment : 299 time for calcul the mask position with numpy : 0.28058910369873047 nb_pixel_total : 162349 time to create 1 rle with new method : 0.020206451416015625 length of segment : 590 time for calcul the mask position with numpy : 0.045290231704711914 nb_pixel_total : 39969 time to create 1 rle with old method : 0.05440115928649902 length of segment : 331 time for calcul the mask position with numpy : 0.01159977912902832 nb_pixel_total : 7922 time to create 1 rle with old method : 0.014453649520874023 length of segment : 88 time for calcul the mask position with numpy : 0.2778632640838623 nb_pixel_total : 383603 time to create 1 rle with new method : 0.059966087341308594 length of segment : 799 time for calcul the mask position with numpy : 0.02984905242919922 nb_pixel_total : 13075 time to create 1 rle with old method : 0.021538972854614258 length of segment : 125 time for calcul the mask position with numpy : 0.0715494155883789 nb_pixel_total : 20207 time to create 1 rle with old method : 0.030923128128051758 length of segment : 187 time for calcul the mask position with numpy : 0.16761302947998047 nb_pixel_total : 61421 time to create 1 rle with old method : 0.10158562660217285 length of segment : 210 time for calcul the mask position with numpy : 0.1379563808441162 nb_pixel_total : 30760 time to create 1 rle with old method : 0.04927468299865723 length of segment : 323 time for calcul the mask position with numpy : 0.12417125701904297 nb_pixel_total : 34590 time to create 1 rle with old method : 0.04804396629333496 length of segment : 232 time for calcul the mask position with numpy : 0.14402031898498535 nb_pixel_total : 29009 time to create 1 rle with old method : 0.062468528747558594 length of segment : 192 time for calcul the mask position with numpy : 0.0553889274597168 nb_pixel_total : 24912 time to create 1 rle with old method : 0.03607654571533203 length of segment : 238 time for calcul the mask position with numpy : 0.08440828323364258 nb_pixel_total : 20026 time to create 1 rle with old method : 0.0296170711517334 length of segment : 171 time for calcul the mask position with numpy : 0.20949363708496094 nb_pixel_total : 43821 time to create 1 rle with old method : 0.0700535774230957 length of segment : 282 time for calcul the mask position with numpy : 0.19231295585632324 nb_pixel_total : 51854 time to create 1 rle with old method : 0.07065820693969727 length of segment : 359 time for calcul the mask position with numpy : 0.046900272369384766 nb_pixel_total : 14678 time to create 1 rle with old method : 0.022342205047607422 length of segment : 116 time for calcul the mask position with numpy : 0.6404821872711182 nb_pixel_total : 204186 time to create 1 rle with new method : 0.038994550704956055 length of segment : 1015 time for calcul the mask position with numpy : 0.08800244331359863 nb_pixel_total : 32287 time to create 1 rle with old method : 0.04274702072143555 length of segment : 179 time for calcul the mask position with numpy : 0.0005350112915039062 nb_pixel_total : 19895 time to create 1 rle with old method : 0.02426910400390625 length of segment : 201 time for calcul the mask position with numpy : 0.08347368240356445 nb_pixel_total : 29446 time to create 1 rle with old method : 0.04632115364074707 length of segment : 213 time for calcul the mask position with numpy : 0.19622111320495605 nb_pixel_total : 81697 time to create 1 rle with old method : 0.1040654182434082 length of segment : 347 time for calcul the mask position with numpy : 0.021910429000854492 nb_pixel_total : 6043 time to create 1 rle with old method : 0.009347915649414062 length of segment : 120 time for calcul the mask position with numpy : 0.07979655265808105 nb_pixel_total : 17636 time to create 1 rle with old method : 0.030817270278930664 length of segment : 172 time for calcul the mask position with numpy : 0.025224685668945312 nb_pixel_total : 10776 time to create 1 rle with old method : 0.019533157348632812 length of segment : 82 time for calcul the mask position with numpy : 0.030653953552246094 nb_pixel_total : 15440 time to create 1 rle with old method : 0.02483820915222168 length of segment : 247 time for calcul the mask position with numpy : 0.0376896858215332 nb_pixel_total : 19515 time to create 1 rle with old method : 0.024738788604736328 length of segment : 249 time for calcul the mask position with numpy : 0.1011807918548584 nb_pixel_total : 39902 time to create 1 rle with old method : 0.05622076988220215 length of segment : 265 time for calcul the mask position with numpy : 0.01509404182434082 nb_pixel_total : 7523 time to create 1 rle with old method : 0.011714935302734375 length of segment : 121 time for calcul the mask position with numpy : 0.04247856140136719 nb_pixel_total : 20835 time to create 1 rle with old method : 0.04032707214355469 length of segment : 135 time for calcul the mask position with numpy : 0.01666402816772461 nb_pixel_total : 12113 time to create 1 rle with old method : 0.01773691177368164 length of segment : 99 time for calcul the mask position with numpy : 0.09653663635253906 nb_pixel_total : 43174 time to create 1 rle with old method : 0.05827140808105469 length of segment : 324 time for calcul the mask position with numpy : 0.0007131099700927734 nb_pixel_total : 28044 time to create 1 rle with old method : 0.03627324104309082 length of segment : 197 time for calcul the mask position with numpy : 0.03302407264709473 nb_pixel_total : 15674 time to create 1 rle with old method : 0.025301218032836914 length of segment : 124 time for calcul the mask position with numpy : 0.07919549942016602 nb_pixel_total : 19246 time to create 1 rle with old method : 0.027127504348754883 length of segment : 194 time for calcul the mask position with numpy : 0.00043511390686035156 nb_pixel_total : 11181 time to create 1 rle with old method : 0.013569355010986328 length of segment : 185 time for calcul the mask position with numpy : 0.08395671844482422 nb_pixel_total : 23836 time to create 1 rle with old method : 0.03411412239074707 length of segment : 226 time for calcul the mask position with numpy : 0.0503995418548584 nb_pixel_total : 7059 time to create 1 rle with old method : 0.01288914680480957 length of segment : 100 time for calcul the mask position with numpy : 0.0692451000213623 nb_pixel_total : 31541 time to create 1 rle with old method : 0.041882991790771484 length of segment : 156 time for calcul the mask position with numpy : 0.050302743911743164 nb_pixel_total : 10511 time to create 1 rle with old method : 0.0182039737701416 length of segment : 118 time for calcul the mask position with numpy : 0.0359036922454834 nb_pixel_total : 10363 time to create 1 rle with old method : 0.01759028434753418 length of segment : 96 time for calcul the mask position with numpy : 0.1756877899169922 nb_pixel_total : 48295 time to create 1 rle with old method : 0.05999040603637695 length of segment : 331 time for calcul the mask position with numpy : 0.14188861846923828 nb_pixel_total : 59100 time to create 1 rle with old method : 0.0849769115447998 length of segment : 313 time for calcul the mask position with numpy : 0.12147903442382812 nb_pixel_total : 45623 time to create 1 rle with old method : 0.05963468551635742 length of segment : 313 time for calcul the mask position with numpy : 0.10689043998718262 nb_pixel_total : 19691 time to create 1 rle with old method : 0.028519392013549805 length of segment : 136 time for calcul the mask position with numpy : 0.3046705722808838 nb_pixel_total : 104694 time to create 1 rle with old method : 0.13180923461914062 length of segment : 265 time for calcul the mask position with numpy : 0.0405576229095459 nb_pixel_total : 11846 time to create 1 rle with old method : 0.027498960494995117 length of segment : 116 time for calcul the mask position with numpy : 0.04966568946838379 nb_pixel_total : 12226 time to create 1 rle with old method : 0.021106243133544922 length of segment : 96 time for calcul the mask position with numpy : 8.136520624160767 nb_pixel_total : 1210187 time to create 1 rle with new method : 0.4105370044708252 length of segment : 1431 time for calcul the mask position with numpy : 0.173203706741333 nb_pixel_total : 12464 time to create 1 rle with old method : 0.019092798233032227 length of segment : 120 time for calcul the mask position with numpy : 0.025347471237182617 nb_pixel_total : 13396 time to create 1 rle with old method : 0.023658037185668945 length of segment : 144 time for calcul the mask position with numpy : 0.5083084106445312 nb_pixel_total : 65731 time to create 1 rle with old method : 0.0845494270324707 length of segment : 503 time for calcul the mask position with numpy : 0.008166790008544922 nb_pixel_total : 3754 time to create 1 rle with old method : 0.006579875946044922 length of segment : 91 time for calcul the mask position with numpy : 0.4371051788330078 nb_pixel_total : 67815 time to create 1 rle with old method : 0.08506178855895996 length of segment : 350 time for calcul the mask position with numpy : 0.5995221138000488 nb_pixel_total : 102626 time to create 1 rle with old method : 0.13029885292053223 length of segment : 310 time for calcul the mask position with numpy : 0.13053655624389648 nb_pixel_total : 77169 time to create 1 rle with old method : 0.09638285636901855 length of segment : 224 time for calcul the mask position with numpy : 0.09110689163208008 nb_pixel_total : 30954 time to create 1 rle with old method : 0.03984332084655762 length of segment : 176 time for calcul the mask position with numpy : 0.04956221580505371 nb_pixel_total : 25460 time to create 1 rle with old method : 0.03705859184265137 length of segment : 276 time for calcul the mask position with numpy : 0.12921452522277832 nb_pixel_total : 46791 time to create 1 rle with old method : 0.06329059600830078 length of segment : 143 time for calcul the mask position with numpy : 0.09007930755615234 nb_pixel_total : 20041 time to create 1 rle with old method : 0.03541421890258789 length of segment : 214 time for calcul the mask position with numpy : 0.1773076057434082 nb_pixel_total : 73719 time to create 1 rle with old method : 0.09312081336975098 length of segment : 516 time for calcul the mask position with numpy : 0.02959275245666504 nb_pixel_total : 9101 time to create 1 rle with old method : 0.015539169311523438 length of segment : 127 time for calcul the mask position with numpy : 0.0004971027374267578 nb_pixel_total : 15056 time to create 1 rle with old method : 0.026537179946899414 length of segment : 106 time for calcul the mask position with numpy : 0.13633012771606445 nb_pixel_total : 24039 time to create 1 rle with old method : 0.02820277214050293 length of segment : 217 time for calcul the mask position with numpy : 0.029164791107177734 nb_pixel_total : 14984 time to create 1 rle with old method : 0.026372432708740234 length of segment : 91 time for calcul the mask position with numpy : 0.10660099983215332 nb_pixel_total : 47831 time to create 1 rle with old method : 0.06304717063903809 length of segment : 203 time for calcul the mask position with numpy : 0.009599924087524414 nb_pixel_total : 23949 time to create 1 rle with old method : 0.03319239616394043 length of segment : 230 time for calcul the mask position with numpy : 0.1446526050567627 nb_pixel_total : 19266 time to create 1 rle with old method : 0.027391433715820312 length of segment : 218 time for calcul the mask position with numpy : 0.002460002899169922 nb_pixel_total : 1863 time to create 1 rle with old method : 0.002341747283935547 length of segment : 67 time for calcul the mask position with numpy : 0.11354470252990723 nb_pixel_total : 55460 time to create 1 rle with old method : 0.08327007293701172 length of segment : 458 time for calcul the mask position with numpy : 0.11879396438598633 nb_pixel_total : 41205 time to create 1 rle with old method : 0.05345511436462402 length of segment : 329 time for calcul the mask position with numpy : 0.012349367141723633 nb_pixel_total : 12609 time to create 1 rle with old method : 0.020908355712890625 length of segment : 114 time for calcul the mask position with numpy : 0.07174134254455566 nb_pixel_total : 25359 time to create 1 rle with old method : 0.03849434852600098 length of segment : 211 time for calcul the mask position with numpy : 0.1289975643157959 nb_pixel_total : 34490 time to create 1 rle with old method : 0.04045557975769043 length of segment : 338 time for calcul the mask position with numpy : 0.016527652740478516 nb_pixel_total : 14705 time to create 1 rle with old method : 0.01741623878479004 length of segment : 175 time for calcul the mask position with numpy : 1.0490987300872803 nb_pixel_total : 103564 time to create 1 rle with old method : 0.14767670631408691 length of segment : 433 time for calcul the mask position with numpy : 0.16936421394348145 nb_pixel_total : 19806 time to create 1 rle with old method : 0.023966312408447266 length of segment : 149 time for calcul the mask position with numpy : 0.36943817138671875 nb_pixel_total : 78768 time to create 1 rle with old method : 0.10605692863464355 length of segment : 275 time for calcul the mask position with numpy : 0.03694510459899902 nb_pixel_total : 9280 time to create 1 rle with old method : 0.011317729949951172 length of segment : 97 time for calcul the mask position with numpy : 0.04910016059875488 nb_pixel_total : 10127 time to create 1 rle with old method : 0.01631307601928711 length of segment : 143 time for calcul the mask position with numpy : 0.03546714782714844 nb_pixel_total : 8955 time to create 1 rle with old method : 0.013310909271240234 length of segment : 85 time for calcul the mask position with numpy : 0.0254361629486084 nb_pixel_total : 7268 time to create 1 rle with old method : 0.013444900512695312 length of segment : 107 time for calcul the mask position with numpy : 0.07454061508178711 nb_pixel_total : 13957 time to create 1 rle with old method : 0.01988959312438965 length of segment : 130 time for calcul the mask position with numpy : 0.13243412971496582 nb_pixel_total : 26691 time to create 1 rle with old method : 0.0333249568939209 length of segment : 207 time for calcul the mask position with numpy : 0.18373870849609375 nb_pixel_total : 31088 time to create 1 rle with old method : 0.05388379096984863 length of segment : 200 time for calcul the mask position with numpy : 0.07900762557983398 nb_pixel_total : 13161 time to create 1 rle with old method : 0.023899078369140625 length of segment : 119 time for calcul the mask position with numpy : 0.09811258316040039 nb_pixel_total : 8881 time to create 1 rle with old method : 0.01607227325439453 length of segment : 102 time for calcul the mask position with numpy : 0.05898880958557129 nb_pixel_total : 10473 time to create 1 rle with old method : 0.01769566535949707 length of segment : 184 time for calcul the mask position with numpy : 0.2793428897857666 nb_pixel_total : 63418 time to create 1 rle with old method : 0.07843780517578125 length of segment : 312 time for calcul the mask position with numpy : 0.11750030517578125 nb_pixel_total : 26799 time to create 1 rle with old method : 0.03717994689941406 length of segment : 265 time for calcul the mask position with numpy : 0.10069727897644043 nb_pixel_total : 24786 time to create 1 rle with old method : 0.02962946891784668 length of segment : 225 time for calcul the mask position with numpy : 0.05974268913269043 nb_pixel_total : 20488 time to create 1 rle with old method : 0.028556108474731445 length of segment : 253 time for calcul the mask position with numpy : 0.20343303680419922 nb_pixel_total : 43988 time to create 1 rle with old method : 0.05509638786315918 length of segment : 326 time for calcul the mask position with numpy : 0.32295870780944824 nb_pixel_total : 46882 time to create 1 rle with old method : 0.06253981590270996 length of segment : 293 time for calcul the mask position with numpy : 0.061789512634277344 nb_pixel_total : 4951 time to create 1 rle with old method : 0.011438131332397461 length of segment : 81 time for calcul the mask position with numpy : 0.7209310531616211 nb_pixel_total : 176674 time to create 1 rle with new method : 0.020795345306396484 length of segment : 627 time for calcul the mask position with numpy : 0.05556631088256836 nb_pixel_total : 22199 time to create 1 rle with old method : 0.03149700164794922 length of segment : 198 time for calcul the mask position with numpy : 0.16079163551330566 nb_pixel_total : 12028 time to create 1 rle with old method : 0.02184152603149414 length of segment : 111 time for calcul the mask position with numpy : 0.18154549598693848 nb_pixel_total : 64063 time to create 1 rle with old method : 0.08093428611755371 length of segment : 534 time for calcul the mask position with numpy : 0.29960060119628906 nb_pixel_total : 12583 time to create 1 rle with old method : 0.018120288848876953 length of segment : 188 time for calcul the mask position with numpy : 0.14473748207092285 nb_pixel_total : 11956 time to create 1 rle with old method : 0.020016193389892578 length of segment : 90 time for calcul the mask position with numpy : 0.4979870319366455 nb_pixel_total : 34835 time to create 1 rle with old method : 0.04632854461669922 length of segment : 264 time for calcul the mask position with numpy : 0.05683445930480957 nb_pixel_total : 13937 time to create 1 rle with old method : 0.02236318588256836 length of segment : 135 time for calcul the mask position with numpy : 0.22664284706115723 nb_pixel_total : 81349 time to create 1 rle with old method : 0.11218929290771484 length of segment : 335 time for calcul the mask position with numpy : 0.043004512786865234 nb_pixel_total : 11328 time to create 1 rle with old method : 0.020510435104370117 length of segment : 129 time for calcul the mask position with numpy : 0.07935667037963867 nb_pixel_total : 10806 time to create 1 rle with old method : 0.021242618560791016 length of segment : 140 time for calcul the mask position with numpy : 0.01586604118347168 nb_pixel_total : 88608 time to create 1 rle with old method : 0.1701641082763672 length of segment : 401 time for calcul the mask position with numpy : 0.049147605895996094 nb_pixel_total : 16759 time to create 1 rle with old method : 0.025749921798706055 length of segment : 142 time for calcul the mask position with numpy : 0.05605602264404297 nb_pixel_total : 19255 time to create 1 rle with old method : 0.03028273582458496 length of segment : 139 time for calcul the mask position with numpy : 0.01290273666381836 nb_pixel_total : 12247 time to create 1 rle with old method : 0.020119190216064453 length of segment : 166 time for calcul the mask position with numpy : 0.03759884834289551 nb_pixel_total : 6030 time to create 1 rle with old method : 0.01165151596069336 length of segment : 90 time for calcul the mask position with numpy : 0.3967578411102295 nb_pixel_total : 79603 time to create 1 rle with old method : 0.0999288558959961 length of segment : 307 time for calcul the mask position with numpy : 0.1514143943786621 nb_pixel_total : 25503 time to create 1 rle with old method : 0.04166746139526367 length of segment : 223 time for calcul the mask position with numpy : 0.0076634883880615234 nb_pixel_total : 15405 time to create 1 rle with old method : 0.025847911834716797 length of segment : 141 time for calcul the mask position with numpy : 0.028845548629760742 nb_pixel_total : 4148 time to create 1 rle with old method : 0.007216215133666992 length of segment : 77 time for calcul the mask position with numpy : 0.43365001678466797 nb_pixel_total : 48156 time to create 1 rle with old method : 0.07120108604431152 length of segment : 196 time for calcul the mask position with numpy : 0.10495114326477051 nb_pixel_total : 32750 time to create 1 rle with old method : 0.043508291244506836 length of segment : 219 time for calcul the mask position with numpy : 0.021919965744018555 nb_pixel_total : 39549 time to create 1 rle with old method : 0.06833410263061523 length of segment : 272 time for calcul the mask position with numpy : 0.021433591842651367 nb_pixel_total : 4389 time to create 1 rle with old method : 0.008629322052001953 length of segment : 79 time for calcul the mask position with numpy : 0.1556103229522705 nb_pixel_total : 41517 time to create 1 rle with old method : 0.05290842056274414 length of segment : 278 time for calcul the mask position with numpy : 0.00641322135925293 nb_pixel_total : 5636 time to create 1 rle with old method : 0.009083747863769531 length of segment : 77 time for calcul the mask position with numpy : 0.10326290130615234 nb_pixel_total : 36723 time to create 1 rle with old method : 0.051934003829956055 length of segment : 258 time for calcul the mask position with numpy : 0.003537893295288086 nb_pixel_total : 22998 time to create 1 rle with old method : 0.03058791160583496 length of segment : 190 time for calcul the mask position with numpy : 0.14214825630187988 nb_pixel_total : 33120 time to create 1 rle with old method : 0.06679558753967285 length of segment : 215 time for calcul the mask position with numpy : 0.07660794258117676 nb_pixel_total : 17461 time to create 1 rle with old method : 0.027386188507080078 length of segment : 135 time for calcul the mask position with numpy : 0.036910295486450195 nb_pixel_total : 27428 time to create 1 rle with old method : 0.039649248123168945 length of segment : 98 time for calcul the mask position with numpy : 0.06054997444152832 nb_pixel_total : 16657 time to create 1 rle with old method : 0.020509719848632812 length of segment : 198 time for calcul the mask position with numpy : 0.05351543426513672 nb_pixel_total : 7363 time to create 1 rle with old method : 0.0159299373626709 length of segment : 242 time for calcul the mask position with numpy : 0.03031468391418457 nb_pixel_total : 9586 time to create 1 rle with old method : 0.014615058898925781 length of segment : 170 time for calcul the mask position with numpy : 0.16267061233520508 nb_pixel_total : 56405 time to create 1 rle with old method : 0.0701894760131836 length of segment : 335 time for calcul the mask position with numpy : 0.2375500202178955 nb_pixel_total : 119513 time to create 1 rle with old method : 0.14249086380004883 length of segment : 541 time for calcul the mask position with numpy : 0.2926476001739502 nb_pixel_total : 163937 time to create 1 rle with new method : 0.016260862350463867 length of segment : 555 time for calcul the mask position with numpy : 0.04582500457763672 nb_pixel_total : 27945 time to create 1 rle with old method : 0.03903698921203613 length of segment : 208 time for calcul the mask position with numpy : 0.03693866729736328 nb_pixel_total : 9134 time to create 1 rle with old method : 0.013921260833740234 length of segment : 142 time for calcul the mask position with numpy : 0.06381487846374512 nb_pixel_total : 25064 time to create 1 rle with old method : 0.034219980239868164 length of segment : 151 time for calcul the mask position with numpy : 0.012947320938110352 nb_pixel_total : 42600 time to create 1 rle with old method : 0.05683326721191406 length of segment : 235 time for calcul the mask position with numpy : 0.04799675941467285 nb_pixel_total : 18802 time to create 1 rle with old method : 0.03184247016906738 length of segment : 165 time for calcul the mask position with numpy : 0.18388676643371582 nb_pixel_total : 50529 time to create 1 rle with old method : 0.08685588836669922 length of segment : 214 time for calcul the mask position with numpy : 0.047231197357177734 nb_pixel_total : 7583 time to create 1 rle with old method : 0.014238119125366211 length of segment : 99 time for calcul the mask position with numpy : 0.09087514877319336 nb_pixel_total : 22195 time to create 1 rle with old method : 0.0287172794342041 length of segment : 141 time for calcul the mask position with numpy : 0.055466651916503906 nb_pixel_total : 10412 time to create 1 rle with old method : 0.025799989700317383 length of segment : 98 time for calcul the mask position with numpy : 0.0711827278137207 nb_pixel_total : 8050 time to create 1 rle with old method : 0.013536453247070312 length of segment : 369 time for calcul the mask position with numpy : 0.0760660171508789 nb_pixel_total : 24171 time to create 1 rle with old method : 0.033598899841308594 length of segment : 141 time for calcul the mask position with numpy : 0.051004648208618164 nb_pixel_total : 8977 time to create 1 rle with old method : 0.015444040298461914 length of segment : 130 time for calcul the mask position with numpy : 0.08104944229125977 nb_pixel_total : 21605 time to create 1 rle with old method : 0.030530214309692383 length of segment : 146 time for calcul the mask position with numpy : 0.08565497398376465 nb_pixel_total : 22052 time to create 1 rle with old method : 0.02863144874572754 length of segment : 223 time for calcul the mask position with numpy : 0.08075451850891113 nb_pixel_total : 14754 time to create 1 rle with old method : 0.02241230010986328 length of segment : 115 time for calcul the mask position with numpy : 0.05929374694824219 nb_pixel_total : 8247 time to create 1 rle with old method : 0.016632795333862305 length of segment : 155 time for calcul the mask position with numpy : 0.16405868530273438 nb_pixel_total : 50616 time to create 1 rle with old method : 0.06162309646606445 length of segment : 234 time for calcul the mask position with numpy : 0.014482498168945312 nb_pixel_total : 5500 time to create 1 rle with old method : 0.01136159896850586 length of segment : 108 time for calcul the mask position with numpy : 0.04450058937072754 nb_pixel_total : 9646 time to create 1 rle with old method : 0.017646312713623047 length of segment : 102 time for calcul the mask position with numpy : 0.1430191993713379 nb_pixel_total : 49986 time to create 1 rle with old method : 0.08033585548400879 length of segment : 256 time for calcul the mask position with numpy : 0.07088923454284668 nb_pixel_total : 14974 time to create 1 rle with old method : 0.023151397705078125 length of segment : 176 time for calcul the mask position with numpy : 0.2096109390258789 nb_pixel_total : 58371 time to create 1 rle with old method : 0.0985565185546875 length of segment : 280 time for calcul the mask position with numpy : 0.02515101432800293 nb_pixel_total : 6150 time to create 1 rle with old method : 0.012299299240112305 length of segment : 88 time for calcul the mask position with numpy : 0.6123185157775879 nb_pixel_total : 180983 time to create 1 rle with new method : 0.01902008056640625 length of segment : 412 time for calcul the mask position with numpy : 0.039313316345214844 nb_pixel_total : 8930 time to create 1 rle with old method : 0.015120506286621094 length of segment : 109 time for calcul the mask position with numpy : 0.06869339942932129 nb_pixel_total : 10401 time to create 1 rle with old method : 0.017522096633911133 length of segment : 147 time for calcul the mask position with numpy : 0.09366297721862793 nb_pixel_total : 28078 time to create 1 rle with old method : 0.0439605712890625 length of segment : 188 time for calcul the mask position with numpy : 0.0685572624206543 nb_pixel_total : 26583 time to create 1 rle with old method : 0.035552024841308594 length of segment : 239 time for calcul the mask position with numpy : 0.18340110778808594 nb_pixel_total : 80149 time to create 1 rle with old method : 0.09771108627319336 length of segment : 369 time for calcul the mask position with numpy : 0.11636114120483398 nb_pixel_total : 20833 time to create 1 rle with old method : 0.03464102745056152 length of segment : 194 time for calcul the mask position with numpy : 0.029146909713745117 nb_pixel_total : 7390 time to create 1 rle with old method : 0.012701988220214844 length of segment : 91 time for calcul the mask position with numpy : 0.314150333404541 nb_pixel_total : 165883 time to create 1 rle with new method : 0.011080741882324219 length of segment : 364 time for calcul the mask position with numpy : 0.06349754333496094 nb_pixel_total : 30043 time to create 1 rle with old method : 0.0377955436706543 length of segment : 178 time for calcul the mask position with numpy : 0.16833138465881348 nb_pixel_total : 28508 time to create 1 rle with old method : 0.03908967971801758 length of segment : 135 time for calcul the mask position with numpy : 0.42044925689697266 nb_pixel_total : 52233 time to create 1 rle with old method : 0.06427502632141113 length of segment : 408 time for calcul the mask position with numpy : 0.08575224876403809 nb_pixel_total : 35028 time to create 1 rle with old method : 0.04540705680847168 length of segment : 205 time for calcul the mask position with numpy : 0.04495382308959961 nb_pixel_total : 13463 time to create 1 rle with old method : 0.021118879318237305 length of segment : 325 time for calcul the mask position with numpy : 0.5140376091003418 nb_pixel_total : 91873 time to create 1 rle with old method : 0.11378884315490723 length of segment : 672 time for calcul the mask position with numpy : 0.056478261947631836 nb_pixel_total : 18485 time to create 1 rle with old method : 0.029842138290405273 length of segment : 121 time for calcul the mask position with numpy : 0.021409988403320312 nb_pixel_total : 10257 time to create 1 rle with old method : 0.015410900115966797 length of segment : 115 time for calcul the mask position with numpy : 0.16962742805480957 nb_pixel_total : 42368 time to create 1 rle with old method : 0.05608081817626953 length of segment : 364 time for calcul the mask position with numpy : 0.02103710174560547 nb_pixel_total : 4531 time to create 1 rle with old method : 0.0077972412109375 length of segment : 87 time for calcul the mask position with numpy : 0.005745649337768555 nb_pixel_total : 7741 time to create 1 rle with old method : 0.01662421226501465 length of segment : 114 time for calcul the mask position with numpy : 0.05046534538269043 nb_pixel_total : 12200 time to create 1 rle with old method : 0.020701169967651367 length of segment : 146 time for calcul the mask position with numpy : 0.01724982261657715 nb_pixel_total : 10977 time to create 1 rle with old method : 0.01604318618774414 length of segment : 237 time for calcul the mask position with numpy : 0.07024168968200684 nb_pixel_total : 21032 time to create 1 rle with old method : 0.02829122543334961 length of segment : 212 time for calcul the mask position with numpy : 0.030079126358032227 nb_pixel_total : 4867 time to create 1 rle with old method : 0.0062787532806396484 length of segment : 58 time for calcul the mask position with numpy : 0.1097402572631836 nb_pixel_total : 15672 time to create 1 rle with old method : 0.022568225860595703 length of segment : 186 time for calcul the mask position with numpy : 0.0018663406372070312 nb_pixel_total : 11518 time to create 1 rle with old method : 0.014008283615112305 length of segment : 94 time for calcul the mask position with numpy : 0.11792802810668945 nb_pixel_total : 42974 time to create 1 rle with old method : 0.06117558479309082 length of segment : 222 time for calcul the mask position with numpy : 0.025455713272094727 nb_pixel_total : 5000 time to create 1 rle with old method : 0.010277509689331055 length of segment : 70 time for calcul the mask position with numpy : 0.17922472953796387 nb_pixel_total : 74807 time to create 1 rle with old method : 0.09557461738586426 length of segment : 312 time for calcul the mask position with numpy : 0.02595973014831543 nb_pixel_total : 9911 time to create 1 rle with old method : 0.020450592041015625 length of segment : 100 time for calcul the mask position with numpy : 0.160125732421875 nb_pixel_total : 70543 time to create 1 rle with old method : 0.08371782302856445 length of segment : 257 time for calcul the mask position with numpy : 0.07565808296203613 nb_pixel_total : 26756 time to create 1 rle with old method : 0.035317420959472656 length of segment : 240 time for calcul the mask position with numpy : 0.09253549575805664 nb_pixel_total : 28478 time to create 1 rle with old method : 0.039862632751464844 length of segment : 249 time for calcul the mask position with numpy : 0.1308290958404541 nb_pixel_total : 22544 time to create 1 rle with old method : 0.02950429916381836 length of segment : 204 time for calcul the mask position with numpy : 0.09378814697265625 nb_pixel_total : 21692 time to create 1 rle with old method : 0.030343294143676758 length of segment : 154 time for calcul the mask position with numpy : 0.05010628700256348 nb_pixel_total : 12520 time to create 1 rle with old method : 0.019741058349609375 length of segment : 80 time for calcul the mask position with numpy : 0.13964104652404785 nb_pixel_total : 30438 time to create 1 rle with old method : 0.04249143600463867 length of segment : 176 time for calcul the mask position with numpy : 0.10260701179504395 nb_pixel_total : 29134 time to create 1 rle with old method : 0.04112529754638672 length of segment : 195 time for calcul the mask position with numpy : 0.10384202003479004 nb_pixel_total : 19399 time to create 1 rle with old method : 0.025623798370361328 length of segment : 144 time for calcul the mask position with numpy : 0.17424345016479492 nb_pixel_total : 17825 time to create 1 rle with old method : 0.026000261306762695 length of segment : 120 time for calcul the mask position with numpy : 0.011943340301513672 nb_pixel_total : 5653 time to create 1 rle with old method : 0.011451959609985352 length of segment : 87 time for calcul the mask position with numpy : 0.1697988510131836 nb_pixel_total : 13656 time to create 1 rle with old method : 0.022051334381103516 length of segment : 128 time for calcul the mask position with numpy : 0.11959099769592285 nb_pixel_total : 18189 time to create 1 rle with old method : 0.024491310119628906 length of segment : 168 time for calcul the mask position with numpy : 0.10384202003479004 nb_pixel_total : 20291 time to create 1 rle with old method : 0.029828786849975586 length of segment : 188 time for calcul the mask position with numpy : 0.5972890853881836 nb_pixel_total : 134837 time to create 1 rle with old method : 0.1631941795349121 length of segment : 440 time for calcul the mask position with numpy : 0.40975022315979004 nb_pixel_total : 75268 time to create 1 rle with old method : 0.08875203132629395 length of segment : 367 time for calcul the mask position with numpy : 0.1021721363067627 nb_pixel_total : 18763 time to create 1 rle with old method : 0.026964187622070312 length of segment : 186 time for calcul the mask position with numpy : 0.037531375885009766 nb_pixel_total : 6688 time to create 1 rle with old method : 0.011775016784667969 length of segment : 102 time for calcul the mask position with numpy : 0.07494020462036133 nb_pixel_total : 15168 time to create 1 rle with old method : 0.022282838821411133 length of segment : 145 time for calcul the mask position with numpy : 0.044814109802246094 nb_pixel_total : 11708 time to create 1 rle with old method : 0.02206110954284668 length of segment : 101 time for calcul the mask position with numpy : 0.8792967796325684 nb_pixel_total : 19567 time to create 1 rle with old method : 0.02670431137084961 length of segment : 244 time for calcul the mask position with numpy : 0.14057016372680664 nb_pixel_total : 24960 time to create 1 rle with old method : 0.03577613830566406 length of segment : 215 time for calcul the mask position with numpy : 0.07775521278381348 nb_pixel_total : 20413 time to create 1 rle with old method : 0.02580738067626953 length of segment : 292 time for calcul the mask position with numpy : 0.05155467987060547 nb_pixel_total : 10972 time to create 1 rle with old method : 0.021842241287231445 length of segment : 122 time for calcul the mask position with numpy : 0.06000232696533203 nb_pixel_total : 14557 time to create 1 rle with old method : 0.02160191535949707 length of segment : 151 time for calcul the mask position with numpy : 0.06969833374023438 nb_pixel_total : 16235 time to create 1 rle with old method : 0.024281740188598633 length of segment : 211 time for calcul the mask position with numpy : 0.029030799865722656 nb_pixel_total : 7948 time to create 1 rle with old method : 0.014714717864990234 length of segment : 104 time for calcul the mask position with numpy : 0.20626211166381836 nb_pixel_total : 81185 time to create 1 rle with old method : 0.1143944263458252 length of segment : 313 time for calcul the mask position with numpy : 0.0790250301361084 nb_pixel_total : 14667 time to create 1 rle with old method : 0.04943394660949707 length of segment : 165 time for calcul the mask position with numpy : 0.16646265983581543 nb_pixel_total : 36246 time to create 1 rle with old method : 0.04625654220581055 length of segment : 374 time for calcul the mask position with numpy : 0.09118103981018066 nb_pixel_total : 14447 time to create 1 rle with old method : 0.0238187313079834 length of segment : 214 time for calcul the mask position with numpy : 0.0722358226776123 nb_pixel_total : 17062 time to create 1 rle with old method : 0.02891087532043457 length of segment : 165 time for calcul the mask position with numpy : 0.27573323249816895 nb_pixel_total : 48961 time to create 1 rle with old method : 0.07662296295166016 length of segment : 352 time for calcul the mask position with numpy : 0.038064002990722656 nb_pixel_total : 6979 time to create 1 rle with old method : 0.010903596878051758 length of segment : 87 time for calcul the mask position with numpy : 0.0261380672454834 nb_pixel_total : 10427 time to create 1 rle with old method : 0.01627373695373535 length of segment : 180 time for calcul the mask position with numpy : 0.034168243408203125 nb_pixel_total : 8732 time to create 1 rle with old method : 0.014875411987304688 length of segment : 115 time for calcul the mask position with numpy : 0.010022878646850586 nb_pixel_total : 4471 time to create 1 rle with old method : 0.0070955753326416016 length of segment : 78 time for calcul the mask position with numpy : 0.043749094009399414 nb_pixel_total : 9372 time to create 1 rle with old method : 0.015906810760498047 length of segment : 118 time for calcul the mask position with numpy : 0.011481523513793945 nb_pixel_total : 10006 time to create 1 rle with old method : 0.015575885772705078 length of segment : 129 time for calcul the mask position with numpy : 0.11892938613891602 nb_pixel_total : 36603 time to create 1 rle with old method : 0.0554966926574707 length of segment : 227 time for calcul the mask position with numpy : 0.12450480461120605 nb_pixel_total : 36402 time to create 1 rle with old method : 0.045587778091430664 length of segment : 225 time for calcul the mask position with numpy : 0.0073354244232177734 nb_pixel_total : 9830 time to create 1 rle with old method : 0.015100479125976562 length of segment : 119 time for calcul the mask position with numpy : 0.08587455749511719 nb_pixel_total : 25478 time to create 1 rle with old method : 0.036805152893066406 length of segment : 396 time for calcul the mask position with numpy : 0.08283805847167969 nb_pixel_total : 14468 time to create 1 rle with old method : 0.02422308921813965 length of segment : 152 time for calcul the mask position with numpy : 0.03862571716308594 nb_pixel_total : 15276 time to create 1 rle with old method : 0.022658109664916992 length of segment : 166 time for calcul the mask position with numpy : 0.07399106025695801 nb_pixel_total : 24596 time to create 1 rle with old method : 0.03421282768249512 length of segment : 192 time for calcul the mask position with numpy : 0.011524200439453125 nb_pixel_total : 4339 time to create 1 rle with old method : 0.009465694427490234 length of segment : 82 time for calcul the mask position with numpy : 0.13459396362304688 nb_pixel_total : 45556 time to create 1 rle with old method : 0.05546998977661133 length of segment : 317 time for calcul the mask position with numpy : 0.1585702896118164 nb_pixel_total : 47908 time to create 1 rle with old method : 0.06162214279174805 length of segment : 233 time for calcul the mask position with numpy : 0.14117169380187988 nb_pixel_total : 35854 time to create 1 rle with old method : 0.04791855812072754 length of segment : 286 time for calcul the mask position with numpy : 0.03281831741333008 nb_pixel_total : 5698 time to create 1 rle with old method : 0.009207487106323242 length of segment : 96 time for calcul the mask position with numpy : 0.0446324348449707 nb_pixel_total : 7981 time to create 1 rle with old method : 0.01336216926574707 length of segment : 142 time for calcul the mask position with numpy : 0.029884815216064453 nb_pixel_total : 7112 time to create 1 rle with old method : 0.014027118682861328 length of segment : 108 time for calcul the mask position with numpy : 0.0004711151123046875 nb_pixel_total : 17322 time to create 1 rle with old method : 0.03336834907531738 length of segment : 158 time for calcul the mask position with numpy : 0.025440454483032227 nb_pixel_total : 7870 time to create 1 rle with old method : 0.014728546142578125 length of segment : 117 time for calcul the mask position with numpy : 0.05469036102294922 nb_pixel_total : 15581 time to create 1 rle with old method : 0.02422475814819336 length of segment : 166 time for calcul the mask position with numpy : 0.06847310066223145 nb_pixel_total : 21526 time to create 1 rle with old method : 0.029874086380004883 length of segment : 206 time for calcul the mask position with numpy : 0.021170377731323242 nb_pixel_total : 9225 time to create 1 rle with old method : 0.011146068572998047 length of segment : 133 time for calcul the mask position with numpy : 0.6780524253845215 nb_pixel_total : 291631 time to create 1 rle with new method : 0.028096914291381836 length of segment : 737 time for calcul the mask position with numpy : 0.3966696262359619 nb_pixel_total : 129572 time to create 1 rle with old method : 0.16580653190612793 length of segment : 881 time for calcul the mask position with numpy : 0.04991292953491211 nb_pixel_total : 19987 time to create 1 rle with old method : 0.022318601608276367 length of segment : 165 time for calcul the mask position with numpy : 0.1827702522277832 nb_pixel_total : 101440 time to create 1 rle with old method : 0.11823368072509766 length of segment : 448 time for calcul the mask position with numpy : 0.27007102966308594 nb_pixel_total : 186322 time to create 1 rle with new method : 0.013350725173950195 length of segment : 679 time for calcul the mask position with numpy : 0.03973102569580078 nb_pixel_total : 19218 time to create 1 rle with old method : 0.027157068252563477 length of segment : 209 time for calcul the mask position with numpy : 0.0006995201110839844 nb_pixel_total : 29872 time to create 1 rle with old method : 0.04031062126159668 length of segment : 198 time for calcul the mask position with numpy : 0.09158539772033691 nb_pixel_total : 35602 time to create 1 rle with old method : 0.042214393615722656 length of segment : 240 time for calcul the mask position with numpy : 0.001737833023071289 nb_pixel_total : 19698 time to create 1 rle with old method : 0.02360987663269043 length of segment : 323 time for calcul the mask position with numpy : 0.026090145111083984 nb_pixel_total : 9290 time to create 1 rle with old method : 0.014475345611572266 length of segment : 143 time for calcul the mask position with numpy : 0.020917415618896484 nb_pixel_total : 24067 time to create 1 rle with old method : 0.031960487365722656 length of segment : 218 time for calcul the mask position with numpy : 0.004915952682495117 nb_pixel_total : 4447 time to create 1 rle with old method : 0.006654500961303711 length of segment : 65 time for calcul the mask position with numpy : 0.09507131576538086 nb_pixel_total : 45431 time to create 1 rle with old method : 0.057503461837768555 length of segment : 316 time for calcul the mask position with numpy : 0.01576066017150879 nb_pixel_total : 17396 time to create 1 rle with old method : 0.031996726989746094 length of segment : 124 time for calcul the mask position with numpy : 0.061154842376708984 nb_pixel_total : 64866 time to create 1 rle with old method : 0.08250832557678223 length of segment : 483 time for calcul the mask position with numpy : 0.015738725662231445 nb_pixel_total : 7737 time to create 1 rle with old method : 0.013120412826538086 length of segment : 105 time for calcul the mask position with numpy : 0.08890247344970703 nb_pixel_total : 29515 time to create 1 rle with old method : 0.039936065673828125 length of segment : 431 time for calcul the mask position with numpy : 0.015876054763793945 nb_pixel_total : 1536 time to create 1 rle with old method : 0.0033228397369384766 length of segment : 132 time for calcul the mask position with numpy : 0.027724266052246094 nb_pixel_total : 14964 time to create 1 rle with old method : 0.021208763122558594 length of segment : 89 time for calcul the mask position with numpy : 0.0886237621307373 nb_pixel_total : 68298 time to create 1 rle with old method : 0.1005258560180664 length of segment : 429 time for calcul the mask position with numpy : 0.025314807891845703 nb_pixel_total : 23608 time to create 1 rle with old method : 0.03227877616882324 length of segment : 208 time for calcul the mask position with numpy : 0.03040909767150879 nb_pixel_total : 13622 time to create 1 rle with old method : 0.020567893981933594 length of segment : 160 time for calcul the mask position with numpy : 0.025621891021728516 nb_pixel_total : 18297 time to create 1 rle with old method : 0.02525925636291504 length of segment : 105 time for calcul the mask position with numpy : 0.571056604385376 nb_pixel_total : 727651 time to create 1 rle with new method : 0.06723618507385254 length of segment : 907 time for calcul the mask position with numpy : 0.043341636657714844 nb_pixel_total : 177447 time to create 1 rle with new method : 0.008043050765991211 length of segment : 618 time for calcul the mask position with numpy : 0.07580065727233887 nb_pixel_total : 94460 time to create 1 rle with old method : 0.1285710334777832 length of segment : 279 time for calcul the mask position with numpy : 0.1317453384399414 nb_pixel_total : 49590 time to create 1 rle with old method : 0.07018399238586426 length of segment : 280 time for calcul the mask position with numpy : 0.03969120979309082 nb_pixel_total : 27190 time to create 1 rle with old method : 0.03677487373352051 length of segment : 330 time for calcul the mask position with numpy : 0.11347723007202148 nb_pixel_total : 8958 time to create 1 rle with old method : 0.014910459518432617 length of segment : 84 time for calcul the mask position with numpy : 0.11385583877563477 nb_pixel_total : 25905 time to create 1 rle with old method : 0.03737187385559082 length of segment : 226 time for calcul the mask position with numpy : 0.3553776741027832 nb_pixel_total : 284938 time to create 1 rle with new method : 0.017750263214111328 length of segment : 613 time for calcul the mask position with numpy : 0.15954375267028809 nb_pixel_total : 126201 time to create 1 rle with old method : 0.148695707321167 length of segment : 458 time for calcul the mask position with numpy : 0.20182514190673828 nb_pixel_total : 75938 time to create 1 rle with old method : 0.10066604614257812 length of segment : 372 time for calcul the mask position with numpy : 0.005320310592651367 nb_pixel_total : 7503 time to create 1 rle with old method : 0.011573076248168945 length of segment : 91 time for calcul the mask position with numpy : 0.012714624404907227 nb_pixel_total : 14630 time to create 1 rle with old method : 0.023746728897094727 length of segment : 135 time for calcul the mask position with numpy : 0.021364212036132812 nb_pixel_total : 13323 time to create 1 rle with old method : 0.021913766860961914 length of segment : 100 time for calcul the mask position with numpy : 0.22282814979553223 nb_pixel_total : 23166 time to create 1 rle with old method : 0.033690690994262695 length of segment : 358 time for calcul the mask position with numpy : 0.06970548629760742 nb_pixel_total : 44094 time to create 1 rle with old method : 0.05379796028137207 length of segment : 338 time for calcul the mask position with numpy : 0.0025534629821777344 nb_pixel_total : 11870 time to create 1 rle with old method : 0.014237403869628906 length of segment : 156 time for calcul the mask position with numpy : 0.015803813934326172 nb_pixel_total : 11137 time to create 1 rle with old method : 0.020425081253051758 length of segment : 436 time for calcul the mask position with numpy : 0.07819223403930664 nb_pixel_total : 61846 time to create 1 rle with old method : 0.0977935791015625 length of segment : 229 time for calcul the mask position with numpy : 0.022094249725341797 nb_pixel_total : 54074 time to create 1 rle with old method : 0.06686234474182129 length of segment : 408 time for calcul the mask position with numpy : 0.10464715957641602 nb_pixel_total : 3197 time to create 1 rle with old method : 0.010040521621704102 length of segment : 119 time for calcul the mask position with numpy : 0.12992000579833984 nb_pixel_total : 38089 time to create 1 rle with old method : 0.054450273513793945 length of segment : 314 time for calcul the mask position with numpy : 0.2772483825683594 nb_pixel_total : 187561 time to create 1 rle with new method : 0.015468597412109375 length of segment : 622 time for calcul the mask position with numpy : 0.1372668743133545 nb_pixel_total : 68700 time to create 1 rle with old method : 0.0843820571899414 length of segment : 388 time for calcul the mask position with numpy : 1.4237666130065918 nb_pixel_total : 1104221 time to create 1 rle with new method : 0.35358119010925293 length of segment : 1511 time for calcul the mask position with numpy : 0.17208385467529297 nb_pixel_total : 115781 time to create 1 rle with old method : 0.17502665519714355 length of segment : 490 time for calcul the mask position with numpy : 0.00490880012512207 nb_pixel_total : 14540 time to create 1 rle with old method : 0.028307437896728516 length of segment : 211 time for calcul the mask position with numpy : 0.3365490436553955 nb_pixel_total : 29107 time to create 1 rle with old method : 0.04004812240600586 length of segment : 425 time for calcul the mask position with numpy : 0.0256805419921875 nb_pixel_total : 129695 time to create 1 rle with old method : 0.16187024116516113 length of segment : 292 time for calcul the mask position with numpy : 0.4509730339050293 nb_pixel_total : 66310 time to create 1 rle with old method : 0.09558677673339844 length of segment : 570 time for calcul the mask position with numpy : 0.0069844722747802734 nb_pixel_total : 7940 time to create 1 rle with old method : 0.01662921905517578 length of segment : 85 time for calcul the mask position with numpy : 0.06549859046936035 nb_pixel_total : 16738 time to create 1 rle with old method : 0.024791955947875977 length of segment : 283 time for calcul the mask position with numpy : 0.00041556358337402344 nb_pixel_total : 6495 time to create 1 rle with old method : 0.008924484252929688 length of segment : 278 time for calcul the mask position with numpy : 0.02905416488647461 nb_pixel_total : 32629 time to create 1 rle with old method : 0.055086612701416016 length of segment : 539 time for calcul the mask position with numpy : 0.04600691795349121 nb_pixel_total : 117432 time to create 1 rle with old method : 0.14256882667541504 length of segment : 307 time for calcul the mask position with numpy : 0.7795765399932861 nb_pixel_total : 218221 time to create 1 rle with new method : 0.02437734603881836 length of segment : 625 time for calcul the mask position with numpy : 0.04388833045959473 nb_pixel_total : 3842 time to create 1 rle with old method : 0.007634162902832031 length of segment : 52 time for calcul the mask position with numpy : 0.11966609954833984 nb_pixel_total : 117867 time to create 1 rle with old method : 0.16651630401611328 length of segment : 634 time for calcul the mask position with numpy : 0.030600786209106445 nb_pixel_total : 70276 time to create 1 rle with old method : 0.10284233093261719 length of segment : 370 time for calcul the mask position with numpy : 0.08767986297607422 nb_pixel_total : 84308 time to create 1 rle with old method : 0.12026071548461914 length of segment : 269 time for calcul the mask position with numpy : 0.5021088123321533 nb_pixel_total : 125356 time to create 1 rle with old method : 0.18395590782165527 length of segment : 484 time for calcul the mask position with numpy : 0.17417120933532715 nb_pixel_total : 49308 time to create 1 rle with old method : 0.07492184638977051 length of segment : 342 time for calcul the mask position with numpy : 0.039081573486328125 nb_pixel_total : 6725 time to create 1 rle with old method : 0.011059045791625977 length of segment : 109 time for calcul the mask position with numpy : 0.12003922462463379 nb_pixel_total : 27507 time to create 1 rle with old method : 0.037926673889160156 length of segment : 173 time for calcul the mask position with numpy : 0.16987228393554688 nb_pixel_total : 36401 time to create 1 rle with old method : 0.11663413047790527 length of segment : 214 time for calcul the mask position with numpy : 0.05698847770690918 nb_pixel_total : 25156 time to create 1 rle with old method : 0.044300079345703125 length of segment : 242 time for calcul the mask position with numpy : 0.06434774398803711 nb_pixel_total : 16813 time to create 1 rle with old method : 0.027128219604492188 length of segment : 170 time for calcul the mask position with numpy : 0.07119059562683105 nb_pixel_total : 9177 time to create 1 rle with old method : 0.018370866775512695 length of segment : 176 time for calcul the mask position with numpy : 0.07663583755493164 nb_pixel_total : 15298 time to create 1 rle with old method : 0.023369789123535156 length of segment : 179 time for calcul the mask position with numpy : 0.14605069160461426 nb_pixel_total : 38582 time to create 1 rle with old method : 0.06590771675109863 length of segment : 277 time for calcul the mask position with numpy : 0.059571027755737305 nb_pixel_total : 17544 time to create 1 rle with old method : 0.026236534118652344 length of segment : 160 time for calcul the mask position with numpy : 0.08739995956420898 nb_pixel_total : 17066 time to create 1 rle with old method : 0.02719855308532715 length of segment : 205 time for calcul the mask position with numpy : 0.14179754257202148 nb_pixel_total : 41587 time to create 1 rle with old method : 0.09903073310852051 length of segment : 403 time for calcul the mask position with numpy : 0.07047009468078613 nb_pixel_total : 19417 time to create 1 rle with old method : 0.028573036193847656 length of segment : 146 time for calcul the mask position with numpy : 0.1410055160522461 nb_pixel_total : 18173 time to create 1 rle with old method : 0.028093576431274414 length of segment : 201 time for calcul the mask position with numpy : 0.0685737133026123 nb_pixel_total : 9107 time to create 1 rle with old method : 0.01693248748779297 length of segment : 225 time for calcul the mask position with numpy : 0.08377528190612793 nb_pixel_total : 12848 time to create 1 rle with old method : 0.02231311798095703 length of segment : 288 time for calcul the mask position with numpy : 0.1014561653137207 nb_pixel_total : 21101 time to create 1 rle with old method : 0.03810405731201172 length of segment : 237 time for calcul the mask position with numpy : 0.0015828609466552734 nb_pixel_total : 8123 time to create 1 rle with old method : 0.013760566711425781 length of segment : 116 time for calcul the mask position with numpy : 0.023185253143310547 nb_pixel_total : 3100 time to create 1 rle with old method : 0.005529165267944336 length of segment : 71 time for calcul the mask position with numpy : 0.1531829833984375 nb_pixel_total : 15794 time to create 1 rle with old method : 0.020520448684692383 length of segment : 193 time for calcul the mask position with numpy : 0.12568116188049316 nb_pixel_total : 21625 time to create 1 rle with old method : 0.032361745834350586 length of segment : 196 time for calcul the mask position with numpy : 0.04259920120239258 nb_pixel_total : 12011 time to create 1 rle with old method : 0.017546892166137695 length of segment : 181 time for calcul the mask position with numpy : 0.19335603713989258 nb_pixel_total : 35148 time to create 1 rle with old method : 0.0470423698425293 length of segment : 346 time for calcul the mask position with numpy : 0.051537275314331055 nb_pixel_total : 18182 time to create 1 rle with old method : 0.02736973762512207 length of segment : 166 time for calcul the mask position with numpy : 0.3378455638885498 nb_pixel_total : 67982 time to create 1 rle with old method : 0.08556365966796875 length of segment : 487 time for calcul the mask position with numpy : 0.2148427963256836 nb_pixel_total : 57777 time to create 1 rle with old method : 0.06981420516967773 length of segment : 259 time for calcul the mask position with numpy : 0.11416316032409668 nb_pixel_total : 44680 time to create 1 rle with old method : 0.05572247505187988 length of segment : 228 time for calcul the mask position with numpy : 0.12688136100769043 nb_pixel_total : 24337 time to create 1 rle with old method : 0.043649911880493164 length of segment : 194 time for calcul the mask position with numpy : 0.06071114540100098 nb_pixel_total : 18218 time to create 1 rle with old method : 0.030705690383911133 length of segment : 151 time for calcul the mask position with numpy : 0.24933218955993652 nb_pixel_total : 84399 time to create 1 rle with old method : 0.11605691909790039 length of segment : 379 time for calcul the mask position with numpy : 0.14540791511535645 nb_pixel_total : 22223 time to create 1 rle with old method : 0.04240822792053223 length of segment : 255 time for calcul the mask position with numpy : 0.25850439071655273 nb_pixel_total : 78108 time to create 1 rle with old method : 0.09259581565856934 length of segment : 384 time for calcul the mask position with numpy : 0.002786397933959961 nb_pixel_total : 12178 time to create 1 rle with old method : 0.020807504653930664 length of segment : 152 time for calcul the mask position with numpy : 0.4220395088195801 nb_pixel_total : 102160 time to create 1 rle with old method : 0.13525629043579102 length of segment : 278 time for calcul the mask position with numpy : 0.04363393783569336 nb_pixel_total : 8090 time to create 1 rle with old method : 0.014768838882446289 length of segment : 130 time for calcul the mask position with numpy : 1.244473934173584 nb_pixel_total : 305496 time to create 1 rle with new method : 0.04240560531616211 length of segment : 647 time for calcul the mask position with numpy : 0.16051483154296875 nb_pixel_total : 34343 time to create 1 rle with old method : 0.04372358322143555 length of segment : 340 time for calcul the mask position with numpy : 0.06171154975891113 nb_pixel_total : 11370 time to create 1 rle with old method : 0.016422748565673828 length of segment : 200 time for calcul the mask position with numpy : 0.030152082443237305 nb_pixel_total : 9849 time to create 1 rle with old method : 0.01602029800415039 length of segment : 79 time for calcul the mask position with numpy : 0.14998316764831543 nb_pixel_total : 50008 time to create 1 rle with old method : 0.05970311164855957 length of segment : 397 time for calcul the mask position with numpy : 0.14214158058166504 nb_pixel_total : 19071 time to create 1 rle with old method : 0.032988786697387695 length of segment : 225 time for calcul the mask position with numpy : 0.06097221374511719 nb_pixel_total : 12722 time to create 1 rle with old method : 0.02102041244506836 length of segment : 117 time for calcul the mask position with numpy : 0.2681455612182617 nb_pixel_total : 80411 time to create 1 rle with old method : 0.12442660331726074 length of segment : 395 time for calcul the mask position with numpy : 1.646378755569458 nb_pixel_total : 470428 time to create 1 rle with new method : 0.028919458389282227 length of segment : 813 time for calcul the mask position with numpy : 0.3249855041503906 nb_pixel_total : 102345 time to create 1 rle with old method : 0.12766051292419434 length of segment : 428 time for calcul the mask position with numpy : 0.0009288787841796875 nb_pixel_total : 42689 time to create 1 rle with old method : 0.05122041702270508 length of segment : 249 time for calcul the mask position with numpy : 0.1790320873260498 nb_pixel_total : 59105 time to create 1 rle with old method : 0.07487201690673828 length of segment : 366 time for calcul the mask position with numpy : 0.037349700927734375 nb_pixel_total : 10151 time to create 1 rle with old method : 0.015752315521240234 length of segment : 144 time for calcul the mask position with numpy : 0.1636333465576172 nb_pixel_total : 24758 time to create 1 rle with old method : 0.03316950798034668 length of segment : 272 time for calcul the mask position with numpy : 0.3815288543701172 nb_pixel_total : 138590 time to create 1 rle with old method : 0.17642998695373535 length of segment : 296 time for calcul the mask position with numpy : 0.046825408935546875 nb_pixel_total : 14224 time to create 1 rle with old method : 0.06597542762756348 length of segment : 119 time for calcul the mask position with numpy : 0.010718822479248047 nb_pixel_total : 15306 time to create 1 rle with old method : 0.022531509399414062 length of segment : 115 time for calcul the mask position with numpy : 0.061691999435424805 nb_pixel_total : 14308 time to create 1 rle with old method : 0.021165132522583008 length of segment : 134 time for calcul the mask position with numpy : 0.08763718605041504 nb_pixel_total : 14113 time to create 1 rle with old method : 0.021723031997680664 length of segment : 222 time for calcul the mask position with numpy : 0.08420610427856445 nb_pixel_total : 79108 time to create 1 rle with old method : 0.09885358810424805 length of segment : 321 time for calcul the mask position with numpy : 0.039038896560668945 nb_pixel_total : 9334 time to create 1 rle with old method : 0.015417337417602539 length of segment : 112 time for calcul the mask position with numpy : 0.18041062355041504 nb_pixel_total : 52524 time to create 1 rle with old method : 0.09060192108154297 length of segment : 476 time for calcul the mask position with numpy : 0.028450965881347656 nb_pixel_total : 17162 time to create 1 rle with old method : 0.025398969650268555 length of segment : 179 time for calcul the mask position with numpy : 0.07112264633178711 nb_pixel_total : 21499 time to create 1 rle with old method : 0.03873181343078613 length of segment : 280 time for calcul the mask position with numpy : 0.02601909637451172 nb_pixel_total : 7659 time to create 1 rle with old method : 0.013570785522460938 length of segment : 105 time for calcul the mask position with numpy : 0.19306087493896484 nb_pixel_total : 45381 time to create 1 rle with old method : 0.0569615364074707 length of segment : 379 time for calcul the mask position with numpy : 0.22542381286621094 nb_pixel_total : 109957 time to create 1 rle with old method : 0.13774394989013672 length of segment : 418 time for calcul the mask position with numpy : 0.6123185157775879 nb_pixel_total : 15595 time to create 1 rle with old method : 0.023543596267700195 length of segment : 145 time for calcul the mask position with numpy : 0.03732752799987793 nb_pixel_total : 17458 time to create 1 rle with old method : 0.029895782470703125 length of segment : 118 time for calcul the mask position with numpy : 0.09995746612548828 nb_pixel_total : 27474 time to create 1 rle with old method : 0.035858869552612305 length of segment : 319 time for calcul the mask position with numpy : 0.06600475311279297 nb_pixel_total : 29266 time to create 1 rle with old method : 0.04121589660644531 length of segment : 327 time for calcul the mask position with numpy : 0.19091081619262695 nb_pixel_total : 77068 time to create 1 rle with old method : 0.10041451454162598 length of segment : 297 time for calcul the mask position with numpy : 0.015949010848999023 nb_pixel_total : 4991 time to create 1 rle with old method : 0.008573293685913086 length of segment : 227 time for calcul the mask position with numpy : 0.030837297439575195 nb_pixel_total : 61054 time to create 1 rle with old method : 0.07963895797729492 length of segment : 394 time for calcul the mask position with numpy : 0.050003767013549805 nb_pixel_total : 39897 time to create 1 rle with old method : 0.05096769332885742 length of segment : 139 time for calcul the mask position with numpy : 0.0007507801055908203 nb_pixel_total : 10983 time to create 1 rle with old method : 0.021919965744018555 length of segment : 98 time for calcul the mask position with numpy : 0.0017774105072021484 nb_pixel_total : 5806 time to create 1 rle with old method : 0.011495590209960938 length of segment : 84 time for calcul the mask position with numpy : 0.0019845962524414062 nb_pixel_total : 16143 time to create 1 rle with old method : 0.028838157653808594 length of segment : 150 time for calcul the mask position with numpy : 0.09737372398376465 nb_pixel_total : 25186 time to create 1 rle with old method : 0.03489184379577637 length of segment : 172 time for calcul the mask position with numpy : 0.23409366607666016 nb_pixel_total : 68024 time to create 1 rle with old method : 0.0980381965637207 length of segment : 299 time for calcul the mask position with numpy : 0.04905557632446289 nb_pixel_total : 12490 time to create 1 rle with old method : 0.02037835121154785 length of segment : 113 time for calcul the mask position with numpy : 0.05227851867675781 nb_pixel_total : 7787 time to create 1 rle with old method : 0.010917425155639648 length of segment : 115 time for calcul the mask position with numpy : 0.29805898666381836 nb_pixel_total : 82449 time to create 1 rle with old method : 0.10233473777770996 length of segment : 305 time for calcul the mask position with numpy : 0.04302644729614258 nb_pixel_total : 8869 time to create 1 rle with old method : 0.01602768898010254 length of segment : 96 time for calcul the mask position with numpy : 0.1751556396484375 nb_pixel_total : 110388 time to create 1 rle with old method : 0.13652253150939941 length of segment : 467 time for calcul the mask position with numpy : 0.38750290870666504 nb_pixel_total : 94479 time to create 1 rle with old method : 0.11411118507385254 length of segment : 461 time for calcul the mask position with numpy : 0.0492558479309082 nb_pixel_total : 18890 time to create 1 rle with old method : 0.02216482162475586 length of segment : 214 time for calcul the mask position with numpy : 0.03609776496887207 nb_pixel_total : 11687 time to create 1 rle with old method : 0.013944625854492188 length of segment : 117 time for calcul the mask position with numpy : 0.10228586196899414 nb_pixel_total : 30860 time to create 1 rle with old method : 0.036374807357788086 length of segment : 210 time for calcul the mask position with numpy : 0.15143203735351562 nb_pixel_total : 39070 time to create 1 rle with old method : 0.04655957221984863 length of segment : 169 time for calcul the mask position with numpy : 0.12696409225463867 nb_pixel_total : 28980 time to create 1 rle with old method : 0.03905057907104492 length of segment : 186 time for calcul the mask position with numpy : 0.10747432708740234 nb_pixel_total : 17844 time to create 1 rle with old method : 0.026266098022460938 length of segment : 249 time for calcul the mask position with numpy : 0.03295135498046875 nb_pixel_total : 10977 time to create 1 rle with old method : 0.016878843307495117 length of segment : 189 time for calcul the mask position with numpy : 0.026430606842041016 nb_pixel_total : 33945 time to create 1 rle with old method : 0.043495893478393555 length of segment : 457 time for calcul the mask position with numpy : 0.1909961700439453 nb_pixel_total : 104699 time to create 1 rle with old method : 0.1366417407989502 length of segment : 332 time for calcul the mask position with numpy : 0.049808502197265625 nb_pixel_total : 10391 time to create 1 rle with old method : 0.016258716583251953 length of segment : 153 time for calcul the mask position with numpy : 0.05957651138305664 nb_pixel_total : 10493 time to create 1 rle with old method : 0.014782190322875977 length of segment : 186 time for calcul the mask position with numpy : 0.020676136016845703 nb_pixel_total : 18443 time to create 1 rle with old method : 0.02612161636352539 length of segment : 247 time for calcul the mask position with numpy : 0.3412172794342041 nb_pixel_total : 104573 time to create 1 rle with old method : 0.12469911575317383 length of segment : 452 time for calcul the mask position with numpy : 0.040517568588256836 nb_pixel_total : 12224 time to create 1 rle with old method : 0.016431093215942383 length of segment : 112 time for calcul the mask position with numpy : 0.07176423072814941 nb_pixel_total : 32874 time to create 1 rle with old method : 0.045942068099975586 length of segment : 269 time for calcul the mask position with numpy : 0.07895445823669434 nb_pixel_total : 21027 time to create 1 rle with old method : 0.031223297119140625 length of segment : 249 time for calcul the mask position with numpy : 0.06593823432922363 nb_pixel_total : 25060 time to create 1 rle with old method : 0.03490638732910156 length of segment : 140 time for calcul the mask position with numpy : 0.07438874244689941 nb_pixel_total : 17050 time to create 1 rle with old method : 0.029252290725708008 length of segment : 188 time for calcul the mask position with numpy : 0.059552907943725586 nb_pixel_total : 11714 time to create 1 rle with old method : 0.016768932342529297 length of segment : 115 time for calcul the mask position with numpy : 2.878645658493042 nb_pixel_total : 1311950 time to create 1 rle with new method : 0.11229968070983887 length of segment : 1133 time for calcul the mask position with numpy : 0.06115388870239258 nb_pixel_total : 10954 time to create 1 rle with old method : 0.018400907516479492 length of segment : 95 time for calcul the mask position with numpy : 0.05892372131347656 nb_pixel_total : 8354 time to create 1 rle with old method : 0.012674331665039062 length of segment : 103 time for calcul the mask position with numpy : 0.11417961120605469 nb_pixel_total : 30586 time to create 1 rle with old method : 0.04235219955444336 length of segment : 236 time for calcul the mask position with numpy : 0.06721043586730957 nb_pixel_total : 23495 time to create 1 rle with old method : 0.03200650215148926 length of segment : 146 time for calcul the mask position with numpy : 0.057227373123168945 nb_pixel_total : 52205 time to create 1 rle with old method : 0.08544659614562988 length of segment : 309 time for calcul the mask position with numpy : 0.0503847599029541 nb_pixel_total : 20936 time to create 1 rle with old method : 0.03182506561279297 length of segment : 158 time for calcul the mask position with numpy : 0.018964290618896484 nb_pixel_total : 10082 time to create 1 rle with old method : 0.015411853790283203 length of segment : 127 time for calcul the mask position with numpy : 0.046387672424316406 nb_pixel_total : 14671 time to create 1 rle with old method : 0.02263951301574707 length of segment : 151 time for calcul the mask position with numpy : 0.0687100887298584 nb_pixel_total : 39451 time to create 1 rle with old method : 0.051540374755859375 length of segment : 268 time for calcul the mask position with numpy : 0.03845572471618652 nb_pixel_total : 21298 time to create 1 rle with old method : 0.031914472579956055 length of segment : 137 time for calcul the mask position with numpy : 0.00936746597290039 nb_pixel_total : 2944 time to create 1 rle with old method : 0.004751443862915039 length of segment : 38 time for calcul the mask position with numpy : 0.24336647987365723 nb_pixel_total : 98468 time to create 1 rle with old method : 0.12821650505065918 length of segment : 280 time for calcul the mask position with numpy : 0.2557992935180664 nb_pixel_total : 93931 time to create 1 rle with old method : 0.10959815979003906 length of segment : 470 time for calcul the mask position with numpy : 0.29792141914367676 nb_pixel_total : 34505 time to create 1 rle with old method : 0.04537487030029297 length of segment : 242 time for calcul the mask position with numpy : 0.15589237213134766 nb_pixel_total : 29688 time to create 1 rle with old method : 0.03827691078186035 length of segment : 140 time for calcul the mask position with numpy : 0.5400390625 nb_pixel_total : 73794 time to create 1 rle with old method : 0.09441566467285156 length of segment : 387 time for calcul the mask position with numpy : 0.0937814712524414 nb_pixel_total : 19530 time to create 1 rle with old method : 0.03245115280151367 length of segment : 240 time for calcul the mask position with numpy : 0.4140279293060303 nb_pixel_total : 132794 time to create 1 rle with old method : 0.17070770263671875 length of segment : 301 time for calcul the mask position with numpy : 0.2904069423675537 nb_pixel_total : 90817 time to create 1 rle with old method : 0.11415219306945801 length of segment : 327 time for calcul the mask position with numpy : 0.14712929725646973 nb_pixel_total : 81039 time to create 1 rle with old method : 0.11541080474853516 length of segment : 242 time for calcul the mask position with numpy : 0.05194520950317383 nb_pixel_total : 9344 time to create 1 rle with old method : 0.0163424015045166 length of segment : 129 time for calcul the mask position with numpy : 0.29654908180236816 nb_pixel_total : 83628 time to create 1 rle with old method : 0.1153261661529541 length of segment : 375 time for calcul the mask position with numpy : 0.08945155143737793 nb_pixel_total : 21300 time to create 1 rle with old method : 0.03507494926452637 length of segment : 173 time for calcul the mask position with numpy : 0.12971997261047363 nb_pixel_total : 34753 time to create 1 rle with old method : 0.049421072006225586 length of segment : 307 time for calcul the mask position with numpy : 0.017819643020629883 nb_pixel_total : 18506 time to create 1 rle with old method : 0.02681899070739746 length of segment : 144 time for calcul the mask position with numpy : 0.03818964958190918 nb_pixel_total : 7048 time to create 1 rle with old method : 0.011543035507202148 length of segment : 73 time for calcul the mask position with numpy : 0.04798626899719238 nb_pixel_total : 10806 time to create 1 rle with old method : 0.015055656433105469 length of segment : 105 time for calcul the mask position with numpy : 0.03188824653625488 nb_pixel_total : 11279 time to create 1 rle with old method : 0.019093751907348633 length of segment : 137 time for calcul the mask position with numpy : 0.09877538681030273 nb_pixel_total : 33026 time to create 1 rle with old method : 0.05741548538208008 length of segment : 248 time for calcul the mask position with numpy : 0.0551915168762207 nb_pixel_total : 20164 time to create 1 rle with old method : 0.026487350463867188 length of segment : 154 time for calcul the mask position with numpy : 0.024311304092407227 nb_pixel_total : 15889 time to create 1 rle with old method : 0.021630048751831055 length of segment : 95 time for calcul the mask position with numpy : 0.05885934829711914 nb_pixel_total : 9330 time to create 1 rle with old method : 0.018352508544921875 length of segment : 99 time for calcul the mask position with numpy : 0.023098230361938477 nb_pixel_total : 12808 time to create 1 rle with old method : 0.019209623336791992 length of segment : 102 time for calcul the mask position with numpy : 0.09186387062072754 nb_pixel_total : 19395 time to create 1 rle with old method : 0.030821800231933594 length of segment : 155 time for calcul the mask position with numpy : 0.036309003829956055 nb_pixel_total : 7680 time to create 1 rle with old method : 0.014192819595336914 length of segment : 92 time for calcul the mask position with numpy : 0.2875967025756836 nb_pixel_total : 75570 time to create 1 rle with old method : 0.09186649322509766 length of segment : 373 time for calcul the mask position with numpy : 0.2202749252319336 nb_pixel_total : 78093 time to create 1 rle with old method : 0.12012505531311035 length of segment : 361 time for calcul the mask position with numpy : 0.0036695003509521484 nb_pixel_total : 9183 time to create 1 rle with old method : 0.011950254440307617 length of segment : 95 time for calcul the mask position with numpy : 0.11544370651245117 nb_pixel_total : 12140 time to create 1 rle with old method : 0.019071340560913086 length of segment : 195 time for calcul the mask position with numpy : 0.13655853271484375 nb_pixel_total : 34958 time to create 1 rle with old method : 0.04393625259399414 length of segment : 353 time for calcul the mask position with numpy : 0.020123958587646484 nb_pixel_total : 8443 time to create 1 rle with old method : 0.014413833618164062 length of segment : 97 time for calcul the mask position with numpy : 0.048840999603271484 nb_pixel_total : 18858 time to create 1 rle with old method : 0.025257110595703125 length of segment : 231 time for calcul the mask position with numpy : 0.01573014259338379 nb_pixel_total : 12078 time to create 1 rle with old method : 0.018275976181030273 length of segment : 233 time for calcul the mask position with numpy : 0.1364762783050537 nb_pixel_total : 46438 time to create 1 rle with old method : 0.10444450378417969 length of segment : 436 time for calcul the mask position with numpy : 0.09010910987854004 nb_pixel_total : 46354 time to create 1 rle with old method : 0.05933523178100586 length of segment : 404 time for calcul the mask position with numpy : 0.0036652088165283203 nb_pixel_total : 7777 time to create 1 rle with old method : 0.011644363403320312 length of segment : 93 time for calcul the mask position with numpy : 0.22777223587036133 nb_pixel_total : 33690 time to create 1 rle with old method : 0.0527646541595459 length of segment : 465 time for calcul the mask position with numpy : 0.48967981338500977 nb_pixel_total : 122114 time to create 1 rle with old method : 0.21142363548278809 length of segment : 392 time for calcul the mask position with numpy : 0.3930962085723877 nb_pixel_total : 89603 time to create 1 rle with old method : 0.11137509346008301 length of segment : 331 time for calcul the mask position with numpy : 0.8880796432495117 nb_pixel_total : 264154 time to create 1 rle with new method : 0.03286552429199219 length of segment : 581 time for calcul the mask position with numpy : 0.04155588150024414 nb_pixel_total : 8576 time to create 1 rle with old method : 0.01448965072631836 length of segment : 117 time for calcul the mask position with numpy : 0.18293476104736328 nb_pixel_total : 43775 time to create 1 rle with old method : 0.05359768867492676 length of segment : 404 time for calcul the mask position with numpy : 0.14739537239074707 nb_pixel_total : 40128 time to create 1 rle with old method : 0.05335497856140137 length of segment : 272 time for calcul the mask position with numpy : 0.0635232925415039 nb_pixel_total : 23729 time to create 1 rle with old method : 0.031754493713378906 length of segment : 234 time for calcul the mask position with numpy : 0.06266665458679199 nb_pixel_total : 13652 time to create 1 rle with old method : 0.020687580108642578 length of segment : 144 time for calcul the mask position with numpy : 0.07500338554382324 nb_pixel_total : 15469 time to create 1 rle with old method : 0.02226734161376953 length of segment : 176 time for calcul the mask position with numpy : 0.07072257995605469 nb_pixel_total : 10842 time to create 1 rle with old method : 0.017901182174682617 length of segment : 167 time for calcul the mask position with numpy : 0.04438924789428711 nb_pixel_total : 11265 time to create 1 rle with old method : 0.015281915664672852 length of segment : 105 time for calcul the mask position with numpy : 0.09995746612548828 nb_pixel_total : 21770 time to create 1 rle with old method : 0.04264473915100098 length of segment : 166 time for calcul the mask position with numpy : 0.25853967666625977 nb_pixel_total : 60417 time to create 1 rle with old method : 0.08697199821472168 length of segment : 402 time for calcul the mask position with numpy : 0.34352707862854004 nb_pixel_total : 43892 time to create 1 rle with old method : 0.07790541648864746 length of segment : 259 time for calcul the mask position with numpy : 0.3158407211303711 nb_pixel_total : 197607 time to create 1 rle with new method : 0.017637252807617188 length of segment : 398 time for calcul the mask position with numpy : 0.13399648666381836 nb_pixel_total : 45612 time to create 1 rle with old method : 0.07432007789611816 length of segment : 244 time for calcul the mask position with numpy : 0.06193089485168457 nb_pixel_total : 13155 time to create 1 rle with old method : 0.022362947463989258 length of segment : 118 time for calcul the mask position with numpy : 0.33489441871643066 nb_pixel_total : 127339 time to create 1 rle with old method : 0.15583014488220215 length of segment : 498 time for calcul the mask position with numpy : 0.000606536865234375 nb_pixel_total : 23498 time to create 1 rle with old method : 0.027764081954956055 length of segment : 237 time for calcul the mask position with numpy : 0.3499162197113037 nb_pixel_total : 16124 time to create 1 rle with old method : 0.023920536041259766 length of segment : 152 time for calcul the mask position with numpy : 0.16773438453674316 nb_pixel_total : 16074 time to create 1 rle with old method : 0.02668929100036621 length of segment : 264 time for calcul the mask position with numpy : 0.150132417678833 nb_pixel_total : 6378 time to create 1 rle with old method : 0.014471054077148438 length of segment : 73 time for calcul the mask position with numpy : 0.3526425361633301 nb_pixel_total : 9545 time to create 1 rle with old method : 0.01189732551574707 length of segment : 123 time for calcul the mask position with numpy : 1.5623209476470947 nb_pixel_total : 144497 time to create 1 rle with old method : 0.18266534805297852 length of segment : 577 time for calcul the mask position with numpy : 0.5765469074249268 nb_pixel_total : 31512 time to create 1 rle with old method : 0.04207444190979004 length of segment : 352 time for calcul the mask position with numpy : 0.17250633239746094 nb_pixel_total : 17383 time to create 1 rle with old method : 0.025053739547729492 length of segment : 222 time for calcul the mask position with numpy : 0.07262420654296875 nb_pixel_total : 19094 time to create 1 rle with old method : 0.02544713020324707 length of segment : 249 time for calcul the mask position with numpy : 0.23140382766723633 nb_pixel_total : 23525 time to create 1 rle with old method : 0.03351306915283203 length of segment : 203 time for calcul the mask position with numpy : 0.08403205871582031 nb_pixel_total : 11727 time to create 1 rle with old method : 0.018700361251831055 length of segment : 171 time for calcul the mask position with numpy : 0.591782808303833 nb_pixel_total : 65413 time to create 1 rle with old method : 0.08312582969665527 length of segment : 210 time for calcul the mask position with numpy : 4.378077268600464 nb_pixel_total : 261728 time to create 1 rle with new method : 0.029842138290405273 length of segment : 816 time for calcul the mask position with numpy : 1.0294644832611084 nb_pixel_total : 102558 time to create 1 rle with old method : 0.12648916244506836 length of segment : 414 time for calcul the mask position with numpy : 0.09549880027770996 nb_pixel_total : 9475 time to create 1 rle with old method : 0.016211509704589844 length of segment : 105 time for calcul the mask position with numpy : 0.5389251708984375 nb_pixel_total : 30659 time to create 1 rle with old method : 0.039644718170166016 length of segment : 422 time for calcul the mask position with numpy : 0.32674384117126465 nb_pixel_total : 30362 time to create 1 rle with old method : 0.03967547416687012 length of segment : 212 time for calcul the mask position with numpy : 0.0605158805847168 nb_pixel_total : 16144 time to create 1 rle with old method : 0.023038387298583984 length of segment : 122 time for calcul the mask position with numpy : 0.30161333084106445 nb_pixel_total : 97975 time to create 1 rle with old method : 0.1193077564239502 length of segment : 330 time for calcul the mask position with numpy : 0.014319896697998047 nb_pixel_total : 9006 time to create 1 rle with old method : 0.015035629272460938 length of segment : 119 time for calcul the mask position with numpy : 0.08267879486083984 nb_pixel_total : 27800 time to create 1 rle with old method : 0.04450678825378418 length of segment : 170 time for calcul the mask position with numpy : 0.13743805885314941 nb_pixel_total : 43167 time to create 1 rle with old method : 0.05465865135192871 length of segment : 493 time for calcul the mask position with numpy : 0.2144303321838379 nb_pixel_total : 131977 time to create 1 rle with old method : 0.15646696090698242 length of segment : 334 time for calcul the mask position with numpy : 0.0006575584411621094 nb_pixel_total : 15215 time to create 1 rle with old method : 0.018010616302490234 length of segment : 222 time for calcul the mask position with numpy : 0.8423893451690674 nb_pixel_total : 346023 time to create 1 rle with new method : 0.03113102912902832 length of segment : 1146 time for calcul the mask position with numpy : 0.043520212173461914 nb_pixel_total : 16131 time to create 1 rle with old method : 0.04247879981994629 length of segment : 137 time for calcul the mask position with numpy : 0.09016299247741699 nb_pixel_total : 23477 time to create 1 rle with old method : 0.033672332763671875 length of segment : 260 time for calcul the mask position with numpy : 0.13377809524536133 nb_pixel_total : 29787 time to create 1 rle with old method : 0.03802180290222168 length of segment : 401 time for calcul the mask position with numpy : 0.15709948539733887 nb_pixel_total : 50839 time to create 1 rle with old method : 0.061223745346069336 length of segment : 299 time for calcul the mask position with numpy : 0.3610241413116455 nb_pixel_total : 121364 time to create 1 rle with old method : 0.14686942100524902 length of segment : 560 time for calcul the mask position with numpy : 0.36254215240478516 nb_pixel_total : 252684 time to create 1 rle with new method : 0.02107715606689453 length of segment : 846 time for calcul the mask position with numpy : 0.35156822204589844 nb_pixel_total : 238666 time to create 1 rle with new method : 0.018085241317749023 length of segment : 503 time for calcul the mask position with numpy : 0.08938121795654297 nb_pixel_total : 22515 time to create 1 rle with old method : 0.04021906852722168 length of segment : 147 time for calcul the mask position with numpy : 0.0006015300750732422 nb_pixel_total : 6598 time to create 1 rle with old method : 0.010724544525146484 length of segment : 82 time for calcul the mask position with numpy : 0.04180145263671875 nb_pixel_total : 75465 time to create 1 rle with old method : 0.0933234691619873 length of segment : 405 time for calcul the mask position with numpy : 0.11252403259277344 nb_pixel_total : 98793 time to create 1 rle with old method : 0.14545440673828125 length of segment : 491 time for calcul the mask position with numpy : 0.2667381763458252 nb_pixel_total : 11072 time to create 1 rle with old method : 0.01711750030517578 length of segment : 203 time for calcul the mask position with numpy : 0.01634049415588379 nb_pixel_total : 25449 time to create 1 rle with old method : 0.02982187271118164 length of segment : 315 time for calcul the mask position with numpy : 0.019126176834106445 nb_pixel_total : 8643 time to create 1 rle with old method : 0.0157468318939209 length of segment : 95 time for calcul the mask position with numpy : 0.010412931442260742 nb_pixel_total : 95792 time to create 1 rle with old method : 0.12456250190734863 length of segment : 520 time for calcul the mask position with numpy : 0.19127702713012695 nb_pixel_total : 107607 time to create 1 rle with old method : 0.13177895545959473 length of segment : 416 time for calcul the mask position with numpy : 0.02143073081970215 nb_pixel_total : 6373 time to create 1 rle with old method : 0.012072324752807617 length of segment : 90 time for calcul the mask position with numpy : 0.009704113006591797 nb_pixel_total : 14113 time to create 1 rle with old method : 0.020245790481567383 length of segment : 249 time for calcul the mask position with numpy : 0.026210308074951172 nb_pixel_total : 16664 time to create 1 rle with old method : 0.02143239974975586 length of segment : 255 time for calcul the mask position with numpy : 0.003963947296142578 nb_pixel_total : 17265 time to create 1 rle with old method : 0.020369768142700195 length of segment : 126 time for calcul the mask position with numpy : 0.27388763427734375 nb_pixel_total : 76773 time to create 1 rle with old method : 0.09315729141235352 length of segment : 603 time for calcul the mask position with numpy : 0.7053577899932861 nb_pixel_total : 168780 time to create 1 rle with new method : 0.02331709861755371 length of segment : 585 time for calcul the mask position with numpy : 0.43353843688964844 nb_pixel_total : 41517 time to create 1 rle with old method : 0.06799602508544922 length of segment : 338 time for calcul the mask position with numpy : 0.19850635528564453 nb_pixel_total : 35551 time to create 1 rle with old method : 0.05261492729187012 length of segment : 295 time for calcul the mask position with numpy : 0.11137247085571289 nb_pixel_total : 12568 time to create 1 rle with old method : 0.04058122634887695 length of segment : 101 time for calcul the mask position with numpy : 0.16807198524475098 nb_pixel_total : 26487 time to create 1 rle with old method : 0.04323124885559082 length of segment : 203 time for calcul the mask position with numpy : 0.1154477596282959 nb_pixel_total : 23419 time to create 1 rle with old method : 0.03803563117980957 length of segment : 186 time for calcul the mask position with numpy : 0.04100537300109863 nb_pixel_total : 7611 time to create 1 rle with old method : 0.012454032897949219 length of segment : 85 time for calcul the mask position with numpy : 0.13852500915527344 nb_pixel_total : 42070 time to create 1 rle with old method : 0.06386208534240723 length of segment : 311 time for calcul the mask position with numpy : 0.10143804550170898 nb_pixel_total : 21462 time to create 1 rle with old method : 0.029360532760620117 length of segment : 226 time for calcul the mask position with numpy : 0.2350170612335205 nb_pixel_total : 53588 time to create 1 rle with old method : 0.06688475608825684 length of segment : 308 time for calcul the mask position with numpy : 0.14349746704101562 nb_pixel_total : 38164 time to create 1 rle with old method : 0.05073356628417969 length of segment : 239 time for calcul the mask position with numpy : 0.08336591720581055 nb_pixel_total : 18036 time to create 1 rle with old method : 0.025603532791137695 length of segment : 93 time for calcul the mask position with numpy : 0.07802891731262207 nb_pixel_total : 11113 time to create 1 rle with old method : 0.018320083618164062 length of segment : 117 time for calcul the mask position with numpy : 0.013413190841674805 nb_pixel_total : 8420 time to create 1 rle with old method : 0.01509857177734375 length of segment : 92 time for calcul the mask position with numpy : 0.2332782745361328 nb_pixel_total : 60928 time to create 1 rle with old method : 0.08467984199523926 length of segment : 329 time for calcul the mask position with numpy : 0.4308488368988037 nb_pixel_total : 61321 time to create 1 rle with old method : 0.08126330375671387 length of segment : 601 time for calcul the mask position with numpy : 0.049456119537353516 nb_pixel_total : 11651 time to create 1 rle with old method : 0.017734766006469727 length of segment : 104 time for calcul the mask position with numpy : 0.07603049278259277 nb_pixel_total : 11853 time to create 1 rle with old method : 0.0191800594329834 length of segment : 173 time for calcul the mask position with numpy : 0.08985710144042969 nb_pixel_total : 19714 time to create 1 rle with old method : 0.031568288803100586 length of segment : 185 time for calcul the mask position with numpy : 0.04132485389709473 nb_pixel_total : 27130 time to create 1 rle with old method : 0.03795313835144043 length of segment : 203 time for calcul the mask position with numpy : 0.14128613471984863 nb_pixel_total : 35172 time to create 1 rle with old method : 0.044358253479003906 length of segment : 260 time for calcul the mask position with numpy : 0.04955887794494629 nb_pixel_total : 27199 time to create 1 rle with old method : 0.03842806816101074 length of segment : 174 time for calcul the mask position with numpy : 0.6997880935668945 nb_pixel_total : 27586 time to create 1 rle with old method : 0.037032127380371094 length of segment : 451 time for calcul the mask position with numpy : 0.12082815170288086 nb_pixel_total : 23862 time to create 1 rle with old method : 0.0337376594543457 length of segment : 187 time for calcul the mask position with numpy : 0.07571768760681152 nb_pixel_total : 16590 time to create 1 rle with old method : 0.026844501495361328 length of segment : 262 time for calcul the mask position with numpy : 0.18420767784118652 nb_pixel_total : 45438 time to create 1 rle with old method : 0.0615382194519043 length of segment : 479 time for calcul the mask position with numpy : 0.07099699974060059 nb_pixel_total : 17408 time to create 1 rle with old method : 0.3048672676086426 length of segment : 124 time for calcul the mask position with numpy : 0.04981493949890137 nb_pixel_total : 6733 time to create 1 rle with old method : 0.012246847152709961 length of segment : 97 time for calcul the mask position with numpy : 0.03600573539733887 nb_pixel_total : 11892 time to create 1 rle with old method : 0.018200397491455078 length of segment : 131 time for calcul the mask position with numpy : 0.05933380126953125 nb_pixel_total : 12417 time to create 1 rle with old method : 0.018908262252807617 length of segment : 209 time for calcul the mask position with numpy : 0.051874637603759766 nb_pixel_total : 12467 time to create 1 rle with old method : 0.02404022216796875 length of segment : 87 time for calcul the mask position with numpy : 0.14484024047851562 nb_pixel_total : 57570 time to create 1 rle with old method : 0.07317018508911133 length of segment : 328 time for calcul the mask position with numpy : 0.036637067794799805 nb_pixel_total : 5402 time to create 1 rle with old method : 0.009893417358398438 length of segment : 148 time for calcul the mask position with numpy : 0.23819804191589355 nb_pixel_total : 49758 time to create 1 rle with old method : 0.07846879959106445 length of segment : 278 time for calcul the mask position with numpy : 0.1504809856414795 nb_pixel_total : 38217 time to create 1 rle with old method : 0.053406715393066406 length of segment : 323 time for calcul the mask position with numpy : 0.060799598693847656 nb_pixel_total : 11667 time to create 1 rle with old method : 0.019815683364868164 length of segment : 150 time for calcul the mask position with numpy : 0.06852388381958008 nb_pixel_total : 19917 time to create 1 rle with old method : 0.03795266151428223 length of segment : 141 time for calcul the mask position with numpy : 0.09341120719909668 nb_pixel_total : 63595 time to create 1 rle with old method : 0.07566690444946289 length of segment : 313 time for calcul the mask position with numpy : 0.0494537353515625 nb_pixel_total : 8875 time to create 1 rle with old method : 0.014607667922973633 length of segment : 136 time for calcul the mask position with numpy : 0.16923880577087402 nb_pixel_total : 35320 time to create 1 rle with old method : 0.046646833419799805 length of segment : 277 time for calcul the mask position with numpy : 0.013168096542358398 nb_pixel_total : 2867 time to create 1 rle with old method : 0.0058917999267578125 length of segment : 96 time for calcul the mask position with numpy : 0.11299633979797363 nb_pixel_total : 35387 time to create 1 rle with old method : 0.04735279083251953 length of segment : 175 time for calcul the mask position with numpy : 0.3327775001525879 nb_pixel_total : 78493 time to create 1 rle with old method : 0.09380722045898438 length of segment : 392 time for calcul the mask position with numpy : 0.10463380813598633 nb_pixel_total : 17463 time to create 1 rle with old method : 0.02458786964416504 length of segment : 181 time for calcul the mask position with numpy : 0.17621850967407227 nb_pixel_total : 31500 time to create 1 rle with old method : 0.039418697357177734 length of segment : 285 time for calcul the mask position with numpy : 0.11468958854675293 nb_pixel_total : 24270 time to create 1 rle with old method : 0.03723740577697754 length of segment : 170 time for calcul the mask position with numpy : 0.0940694808959961 nb_pixel_total : 26119 time to create 1 rle with old method : 0.03008437156677246 length of segment : 200 time for calcul the mask position with numpy : 0.07116079330444336 nb_pixel_total : 18559 time to create 1 rle with old method : 0.021486282348632812 length of segment : 118 time for calcul the mask position with numpy : 0.08159589767456055 nb_pixel_total : 12413 time to create 1 rle with old method : 0.01915764808654785 length of segment : 239 time for calcul the mask position with numpy : 0.11867618560791016 nb_pixel_total : 17959 time to create 1 rle with old method : 0.02625751495361328 length of segment : 169 time for calcul the mask position with numpy : 0.4432380199432373 nb_pixel_total : 65302 time to create 1 rle with old method : 0.0780191421508789 length of segment : 486 time for calcul the mask position with numpy : 0.17802953720092773 nb_pixel_total : 25639 time to create 1 rle with old method : 0.03448605537414551 length of segment : 355 time for calcul the mask position with numpy : 0.13814067840576172 nb_pixel_total : 22245 time to create 1 rle with old method : 0.03200125694274902 length of segment : 239 time for calcul the mask position with numpy : 0.1934211254119873 nb_pixel_total : 66763 time to create 1 rle with old method : 0.07663154602050781 length of segment : 405 time for calcul the mask position with numpy : 0.15329861640930176 nb_pixel_total : 23812 time to create 1 rle with old method : 0.03155088424682617 length of segment : 273 time for calcul the mask position with numpy : 0.004912137985229492 nb_pixel_total : 3245 time to create 1 rle with old method : 0.005113124847412109 length of segment : 44 time for calcul the mask position with numpy : 0.05449080467224121 nb_pixel_total : 16094 time to create 1 rle with old method : 0.02074122428894043 length of segment : 268 time for calcul the mask position with numpy : 0.23560595512390137 nb_pixel_total : 38139 time to create 1 rle with old method : 0.05060696601867676 length of segment : 343 time for calcul the mask position with numpy : 0.0611722469329834 nb_pixel_total : 11672 time to create 1 rle with old method : 0.01634383201599121 length of segment : 94 time for calcul the mask position with numpy : 0.06916546821594238 nb_pixel_total : 17458 time to create 1 rle with old method : 0.027254343032836914 length of segment : 126 time for calcul the mask position with numpy : 0.07699227333068848 nb_pixel_total : 14849 time to create 1 rle with old method : 0.02384638786315918 length of segment : 262 time for calcul the mask position with numpy : 0.014268159866333008 nb_pixel_total : 2829 time to create 1 rle with old method : 0.0054814815521240234 length of segment : 97 time for calcul the mask position with numpy : 0.14298439025878906 nb_pixel_total : 31962 time to create 1 rle with old method : 0.04452371597290039 length of segment : 320 time for calcul the mask position with numpy : 0.031640052795410156 nb_pixel_total : 19787 time to create 1 rle with old method : 0.0290834903717041 length of segment : 214 time for calcul the mask position with numpy : 0.08765196800231934 nb_pixel_total : 20744 time to create 1 rle with old method : 0.037996768951416016 length of segment : 221 time for calcul the mask position with numpy : 0.06982874870300293 nb_pixel_total : 15062 time to create 1 rle with old method : 0.021649837493896484 length of segment : 141 time for calcul the mask position with numpy : 0.0593869686126709 nb_pixel_total : 14742 time to create 1 rle with old method : 0.021289348602294922 length of segment : 136 time for calcul the mask position with numpy : 0.28336310386657715 nb_pixel_total : 47917 time to create 1 rle with old method : 0.06327176094055176 length of segment : 359 time for calcul the mask position with numpy : 0.1022646427154541 nb_pixel_total : 17609 time to create 1 rle with old method : 0.024786949157714844 length of segment : 223 time for calcul the mask position with numpy : 0.05731654167175293 nb_pixel_total : 13228 time to create 1 rle with old method : 0.02017378807067871 length of segment : 132 time for calcul the mask position with numpy : 0.33339476585388184 nb_pixel_total : 75710 time to create 1 rle with old method : 0.09102296829223633 length of segment : 270 time for calcul the mask position with numpy : 0.12144994735717773 nb_pixel_total : 40691 time to create 1 rle with old method : 0.061531782150268555 length of segment : 309 time for calcul the mask position with numpy : 0.11621475219726562 nb_pixel_total : 31877 time to create 1 rle with old method : 0.04132962226867676 length of segment : 170 time for calcul the mask position with numpy : 0.03780794143676758 nb_pixel_total : 7230 time to create 1 rle with old method : 0.01493978500366211 length of segment : 122 time for calcul the mask position with numpy : 0.12276339530944824 nb_pixel_total : 34782 time to create 1 rle with old method : 0.04627847671508789 length of segment : 215 time for calcul the mask position with numpy : 0.009741067886352539 nb_pixel_total : 33501 time to create 1 rle with old method : 0.04593682289123535 length of segment : 341 time for calcul the mask position with numpy : 0.03362727165222168 nb_pixel_total : 15383 time to create 1 rle with old method : 0.022182226181030273 length of segment : 143 time for calcul the mask position with numpy : 0.047246694564819336 nb_pixel_total : 10790 time to create 1 rle with old method : 0.013143539428710938 length of segment : 158 time for calcul the mask position with numpy : 0.055761098861694336 nb_pixel_total : 13886 time to create 1 rle with old method : 0.0234982967376709 length of segment : 168 time for calcul the mask position with numpy : 0.4083859920501709 nb_pixel_total : 112263 time to create 1 rle with old method : 0.1411113739013672 length of segment : 762 time for calcul the mask position with numpy : 0.3638143539428711 nb_pixel_total : 72003 time to create 1 rle with old method : 0.11762619018554688 length of segment : 503 time for calcul the mask position with numpy : 0.07108283042907715 nb_pixel_total : 17456 time to create 1 rle with old method : 0.024392127990722656 length of segment : 136 time for calcul the mask position with numpy : 0.10350155830383301 nb_pixel_total : 18454 time to create 1 rle with old method : 0.028404712677001953 length of segment : 124 time for calcul the mask position with numpy : 0.006884574890136719 nb_pixel_total : 11173 time to create 1 rle with old method : 0.01592564582824707 length of segment : 279 time for calcul the mask position with numpy : 0.04254031181335449 nb_pixel_total : 14021 time to create 1 rle with old method : 0.02116560935974121 length of segment : 227 time for calcul the mask position with numpy : 0.13991641998291016 nb_pixel_total : 24857 time to create 1 rle with old method : 0.035263776779174805 length of segment : 215 time for calcul the mask position with numpy : 0.04386162757873535 nb_pixel_total : 22486 time to create 1 rle with old method : 0.031011343002319336 length of segment : 163 time for calcul the mask position with numpy : 0.013036489486694336 nb_pixel_total : 26006 time to create 1 rle with old method : 0.03610968589782715 length of segment : 195 time for calcul the mask position with numpy : 0.06215071678161621 nb_pixel_total : 10114 time to create 1 rle with old method : 0.01745891571044922 length of segment : 129 time for calcul the mask position with numpy : 0.06446123123168945 nb_pixel_total : 25163 time to create 1 rle with old method : 0.050486087799072266 length of segment : 210 time for calcul the mask position with numpy : 0.06214570999145508 nb_pixel_total : 42890 time to create 1 rle with old method : 0.057547569274902344 length of segment : 250 time for calcul the mask position with numpy : 0.002130270004272461 nb_pixel_total : 19450 time to create 1 rle with old method : 0.02308368682861328 length of segment : 207 time for calcul the mask position with numpy : 0.01608896255493164 nb_pixel_total : 14153 time to create 1 rle with old method : 0.027454853057861328 length of segment : 193 time for calcul the mask position with numpy : 0.27520108222961426 nb_pixel_total : 63420 time to create 1 rle with old method : 0.09639668464660645 length of segment : 443 time for calcul the mask position with numpy : 0.07202887535095215 nb_pixel_total : 19646 time to create 1 rle with old method : 0.02794337272644043 length of segment : 253 time for calcul the mask position with numpy : 0.07285261154174805 nb_pixel_total : 24684 time to create 1 rle with old method : 0.035033226013183594 length of segment : 283 time for calcul the mask position with numpy : 0.00017213821411132812 nb_pixel_total : 1714 time to create 1 rle with old method : 0.00222015380859375 length of segment : 71 time for calcul the mask position with numpy : 0.0862727165222168 nb_pixel_total : 33932 time to create 1 rle with old method : 0.049280405044555664 length of segment : 199 time for calcul the mask position with numpy : 0.17516183853149414 nb_pixel_total : 109820 time to create 1 rle with old method : 0.14614439010620117 length of segment : 471 time for calcul the mask position with numpy : 0.011119365692138672 nb_pixel_total : 25673 time to create 1 rle with old method : 0.036805152893066406 length of segment : 198 time for calcul the mask position with numpy : 0.7954542636871338 nb_pixel_total : 232598 time to create 1 rle with new method : 0.018743276596069336 length of segment : 502 time for calcul the mask position with numpy : 0.10496687889099121 nb_pixel_total : 20308 time to create 1 rle with old method : 0.026972293853759766 length of segment : 209 time for calcul the mask position with numpy : 0.03613471984863281 nb_pixel_total : 5711 time to create 1 rle with old method : 0.011568069458007812 length of segment : 62 time for calcul the mask position with numpy : 0.22946548461914062 nb_pixel_total : 32518 time to create 1 rle with old method : 0.043305397033691406 length of segment : 301 time for calcul the mask position with numpy : 0.42937636375427246 nb_pixel_total : 92642 time to create 1 rle with old method : 0.11219048500061035 length of segment : 485 time for calcul the mask position with numpy : 0.07938432693481445 nb_pixel_total : 15104 time to create 1 rle with old method : 0.021605491638183594 length of segment : 143 time for calcul the mask position with numpy : 0.03886127471923828 nb_pixel_total : 3474 time to create 1 rle with old method : 0.004396915435791016 length of segment : 71 time for calcul the mask position with numpy : 0.13474464416503906 nb_pixel_total : 25447 time to create 1 rle with old method : 0.0442044734954834 length of segment : 284 time for calcul the mask position with numpy : 0.12499237060546875 nb_pixel_total : 15386 time to create 1 rle with old method : 0.03230786323547363 length of segment : 146 time for calcul the mask position with numpy : 0.14099526405334473 nb_pixel_total : 15037 time to create 1 rle with old method : 0.02334117889404297 length of segment : 157 time for calcul the mask position with numpy : 0.5784904956817627 nb_pixel_total : 243467 time to create 1 rle with new method : 0.03511190414428711 length of segment : 808 time for calcul the mask position with numpy : 0.17599129676818848 nb_pixel_total : 32979 time to create 1 rle with old method : 0.04787611961364746 length of segment : 240 time for calcul the mask position with numpy : 0.14185833930969238 nb_pixel_total : 42381 time to create 1 rle with old method : 0.05840611457824707 length of segment : 197 time for calcul the mask position with numpy : 0.13064932823181152 nb_pixel_total : 29221 time to create 1 rle with old method : 0.04050564765930176 length of segment : 186 time for calcul the mask position with numpy : 0.05522012710571289 nb_pixel_total : 11249 time to create 1 rle with old method : 0.018465280532836914 length of segment : 127 time for calcul the mask position with numpy : 0.054407358169555664 nb_pixel_total : 7761 time to create 1 rle with old method : 0.01650238037109375 length of segment : 137 time for calcul the mask position with numpy : 0.14139699935913086 nb_pixel_total : 24848 time to create 1 rle with old method : 0.033477783203125 length of segment : 163 time for calcul the mask position with numpy : 0.0921487808227539 nb_pixel_total : 17704 time to create 1 rle with old method : 0.025568246841430664 length of segment : 167 time for calcul the mask position with numpy : 0.24810552597045898 nb_pixel_total : 57412 time to create 1 rle with old method : 0.08481359481811523 length of segment : 323 time for calcul the mask position with numpy : 0.017587900161743164 nb_pixel_total : 4183 time to create 1 rle with old method : 0.009193897247314453 length of segment : 52 time for calcul the mask position with numpy : 0.05620145797729492 nb_pixel_total : 19583 time to create 1 rle with old method : 0.02868819236755371 length of segment : 133 time for calcul the mask position with numpy : 0.09499192237854004 nb_pixel_total : 19016 time to create 1 rle with old method : 0.026494503021240234 length of segment : 309 time for calcul the mask position with numpy : 0.044277191162109375 nb_pixel_total : 9559 time to create 1 rle with old method : 0.015971899032592773 length of segment : 118 time for calcul the mask position with numpy : 0.03959202766418457 nb_pixel_total : 12404 time to create 1 rle with old method : 0.021505117416381836 length of segment : 144 time for calcul the mask position with numpy : 0.06682801246643066 nb_pixel_total : 16505 time to create 1 rle with old method : 0.021979331970214844 length of segment : 157 time for calcul the mask position with numpy : 0.0636911392211914 nb_pixel_total : 13921 time to create 1 rle with old method : 0.02216482162475586 length of segment : 164 time for calcul the mask position with numpy : 0.027517318725585938 nb_pixel_total : 8521 time to create 1 rle with old method : 0.013361692428588867 length of segment : 106 time for calcul the mask position with numpy : 0.07480764389038086 nb_pixel_total : 22891 time to create 1 rle with old method : 0.038384199142456055 length of segment : 156 time for calcul the mask position with numpy : 0.1027829647064209 nb_pixel_total : 21415 time to create 1 rle with old method : 0.03285789489746094 length of segment : 168 time for calcul the mask position with numpy : 0.06586313247680664 nb_pixel_total : 16967 time to create 1 rle with old method : 0.02904200553894043 length of segment : 105 time for calcul the mask position with numpy : 0.07010984420776367 nb_pixel_total : 11105 time to create 1 rle with old method : 0.019889354705810547 length of segment : 108 time for calcul the mask position with numpy : 0.0006480216979980469 nb_pixel_total : 15047 time to create 1 rle with old method : 0.019846677780151367 length of segment : 165 time for calcul the mask position with numpy : 0.12625622749328613 nb_pixel_total : 25451 time to create 1 rle with old method : 0.03652811050415039 length of segment : 215 time for calcul the mask position with numpy : 0.03712034225463867 nb_pixel_total : 19450 time to create 1 rle with old method : 0.03818941116333008 length of segment : 181 time for calcul the mask position with numpy : 0.0006353855133056641 nb_pixel_total : 27091 time to create 1 rle with old method : 0.03365635871887207 length of segment : 154 time for calcul the mask position with numpy : 0.035114288330078125 nb_pixel_total : 22021 time to create 1 rle with old method : 0.02750539779663086 length of segment : 164 time for calcul the mask position with numpy : 0.11204314231872559 nb_pixel_total : 28266 time to create 1 rle with old method : 0.037405967712402344 length of segment : 274 time for calcul the mask position with numpy : 0.013281106948852539 nb_pixel_total : 7443 time to create 1 rle with old method : 0.011386394500732422 length of segment : 114 time for calcul the mask position with numpy : 0.07576227188110352 nb_pixel_total : 21264 time to create 1 rle with old method : 0.027013540267944336 length of segment : 179 time for calcul the mask position with numpy : 0.04884839057922363 nb_pixel_total : 13910 time to create 1 rle with old method : 0.02115607261657715 length of segment : 135 time for calcul the mask position with numpy : 0.08226966857910156 nb_pixel_total : 20975 time to create 1 rle with old method : 0.029281139373779297 length of segment : 237 time for calcul the mask position with numpy : 0.11199760437011719 nb_pixel_total : 22185 time to create 1 rle with old method : 0.03064417839050293 length of segment : 229 time for calcul the mask position with numpy : 0.2840723991394043 nb_pixel_total : 75318 time to create 1 rle with old method : 0.09131288528442383 length of segment : 315 time for calcul the mask position with numpy : 0.06641125679016113 nb_pixel_total : 24632 time to create 1 rle with old method : 0.03988146781921387 length of segment : 159 time for calcul the mask position with numpy : 0.05230283737182617 nb_pixel_total : 9188 time to create 1 rle with old method : 0.011537790298461914 length of segment : 141 time for calcul the mask position with numpy : 0.0051975250244140625 nb_pixel_total : 8640 time to create 1 rle with old method : 0.010587453842163086 length of segment : 151 time for calcul the mask position with numpy : 0.0740058422088623 nb_pixel_total : 22712 time to create 1 rle with old method : 0.03920602798461914 length of segment : 124 time for calcul the mask position with numpy : 0.0858914852142334 nb_pixel_total : 18399 time to create 1 rle with old method : 0.03271675109863281 length of segment : 190 time for calcul the mask position with numpy : 0.005433559417724609 nb_pixel_total : 3541 time to create 1 rle with old method : 0.005521535873413086 length of segment : 48 time for calcul the mask position with numpy : 0.00034737586975097656 nb_pixel_total : 7880 time to create 1 rle with old method : 0.00957942008972168 length of segment : 154 time for calcul the mask position with numpy : 0.04937911033630371 nb_pixel_total : 22946 time to create 1 rle with old method : 0.031245708465576172 length of segment : 162 time for calcul the mask position with numpy : 0.020495891571044922 nb_pixel_total : 13541 time to create 1 rle with old method : 0.021882295608520508 length of segment : 128 time for calcul the mask position with numpy : 0.05362701416015625 nb_pixel_total : 13370 time to create 1 rle with old method : 0.01927471160888672 length of segment : 147 time for calcul the mask position with numpy : 0.011142492294311523 nb_pixel_total : 5266 time to create 1 rle with old method : 0.008094310760498047 length of segment : 97 time for calcul the mask position with numpy : 0.022982358932495117 nb_pixel_total : 13251 time to create 1 rle with old method : 0.020648956298828125 length of segment : 145 time for calcul the mask position with numpy : 0.13832902908325195 nb_pixel_total : 43486 time to create 1 rle with old method : 0.07033038139343262 length of segment : 237 time for calcul the mask position with numpy : 0.16504693031311035 nb_pixel_total : 41398 time to create 1 rle with old method : 0.0523378849029541 length of segment : 587 time for calcul the mask position with numpy : 0.013221502304077148 nb_pixel_total : 4203 time to create 1 rle with old method : 0.008474111557006836 length of segment : 130 time for calcul the mask position with numpy : 0.2503702640533447 nb_pixel_total : 55004 time to create 1 rle with old method : 0.09096264839172363 length of segment : 311 time for calcul the mask position with numpy : 0.06119132041931152 nb_pixel_total : 12981 time to create 1 rle with old method : 0.022526025772094727 length of segment : 126 time for calcul the mask position with numpy : 0.07219171524047852 nb_pixel_total : 14511 time to create 1 rle with old method : 0.022067546844482422 length of segment : 150 time for calcul the mask position with numpy : 0.056484222412109375 nb_pixel_total : 11386 time to create 1 rle with old method : 0.0167999267578125 length of segment : 175 time for calcul the mask position with numpy : 0.019616365432739258 nb_pixel_total : 4067 time to create 1 rle with old method : 0.006051778793334961 length of segment : 63 time for calcul the mask position with numpy : 0.06635546684265137 nb_pixel_total : 14491 time to create 1 rle with old method : 0.023221731185913086 length of segment : 118 time for calcul the mask position with numpy : 0.10268616676330566 nb_pixel_total : 22031 time to create 1 rle with old method : 0.029024362564086914 length of segment : 200 time for calcul the mask position with numpy : 0.14403700828552246 nb_pixel_total : 18276 time to create 1 rle with old method : 0.02422046661376953 length of segment : 189 time for calcul the mask position with numpy : 0.37834906578063965 nb_pixel_total : 93245 time to create 1 rle with old method : 0.11880993843078613 length of segment : 567 time for calcul the mask position with numpy : 0.03598642349243164 nb_pixel_total : 14099 time to create 1 rle with old method : 0.022489070892333984 length of segment : 145 time for calcul the mask position with numpy : 0.17565035820007324 nb_pixel_total : 60102 time to create 1 rle with old method : 0.07955026626586914 length of segment : 393 time for calcul the mask position with numpy : 0.09051084518432617 nb_pixel_total : 22550 time to create 1 rle with old method : 0.03132152557373047 length of segment : 190 time for calcul the mask position with numpy : 0.11481976509094238 nb_pixel_total : 42911 time to create 1 rle with old method : 0.07470560073852539 length of segment : 362 time for calcul the mask position with numpy : 0.20587539672851562 nb_pixel_total : 61094 time to create 1 rle with old method : 0.07592201232910156 length of segment : 494 time for calcul the mask position with numpy : 0.20209002494812012 nb_pixel_total : 53316 time to create 1 rle with old method : 0.06621050834655762 length of segment : 362 time for calcul the mask position with numpy : 0.10516571998596191 nb_pixel_total : 17517 time to create 1 rle with old method : 0.029001951217651367 length of segment : 196 time for calcul the mask position with numpy : 0.026972055435180664 nb_pixel_total : 12535 time to create 1 rle with old method : 0.020157337188720703 length of segment : 210 time for calcul the mask position with numpy : 0.07675457000732422 nb_pixel_total : 18183 time to create 1 rle with old method : 0.02764582633972168 length of segment : 182 time for calcul the mask position with numpy : 0.13115191459655762 nb_pixel_total : 36328 time to create 1 rle with old method : 0.04735970497131348 length of segment : 224 time for calcul the mask position with numpy : 0.020961761474609375 nb_pixel_total : 6759 time to create 1 rle with old method : 0.010972738265991211 length of segment : 140 time for calcul the mask position with numpy : 0.15811777114868164 nb_pixel_total : 22993 time to create 1 rle with old method : 0.04099774360656738 length of segment : 347 time for calcul the mask position with numpy : 0.08587455749511719 nb_pixel_total : 23902 time to create 1 rle with old method : 0.03403782844543457 length of segment : 146 time for calcul the mask position with numpy : 1.32466459274292 nb_pixel_total : 605764 time to create 1 rle with new method : 0.08854317665100098 length of segment : 966 time for calcul the mask position with numpy : 0.19348549842834473 nb_pixel_total : 57432 time to create 1 rle with old method : 0.0899667739868164 length of segment : 553 time for calcul the mask position with numpy : 0.05611395835876465 nb_pixel_total : 49283 time to create 1 rle with old method : 0.06296992301940918 length of segment : 314 time for calcul the mask position with numpy : 0.18815827369689941 nb_pixel_total : 51523 time to create 1 rle with old method : 0.06681513786315918 length of segment : 247 time for calcul the mask position with numpy : 0.14632272720336914 nb_pixel_total : 79008 time to create 1 rle with old method : 0.09583830833435059 length of segment : 505 time for calcul the mask position with numpy : 0.03800702095031738 nb_pixel_total : 15363 time to create 1 rle with old method : 0.024738311767578125 length of segment : 265 time for calcul the mask position with numpy : 0.13535809516906738 nb_pixel_total : 63604 time to create 1 rle with old method : 0.1011040210723877 length of segment : 307 time for calcul the mask position with numpy : 0.08649277687072754 nb_pixel_total : 148235 time to create 1 rle with old method : 0.17431950569152832 length of segment : 646 time for calcul the mask position with numpy : 0.25815272331237793 nb_pixel_total : 137833 time to create 1 rle with old method : 0.17157912254333496 length of segment : 495 time for calcul the mask position with numpy : 0.08406591415405273 nb_pixel_total : 57541 time to create 1 rle with old method : 0.11790752410888672 length of segment : 281 time for calcul the mask position with numpy : 0.039504051208496094 nb_pixel_total : 11985 time to create 1 rle with old method : 0.019419431686401367 length of segment : 175 time for calcul the mask position with numpy : 0.43936586380004883 nb_pixel_total : 260242 time to create 1 rle with new method : 0.02490234375 length of segment : 611 time for calcul the mask position with numpy : 0.004767656326293945 nb_pixel_total : 5585 time to create 1 rle with old method : 0.0068666934967041016 length of segment : 99 time for calcul the mask position with numpy : 0.021909475326538086 nb_pixel_total : 6621 time to create 1 rle with old method : 0.011281251907348633 length of segment : 111 time for calcul the mask position with numpy : 0.06838250160217285 nb_pixel_total : 19460 time to create 1 rle with old method : 0.025621652603149414 length of segment : 144 time for calcul the mask position with numpy : 0.09029030799865723 nb_pixel_total : 65489 time to create 1 rle with old method : 0.08187389373779297 length of segment : 589 time for calcul the mask position with numpy : 0.08720612525939941 nb_pixel_total : 12877 time to create 1 rle with old method : 0.027463197708129883 length of segment : 197 time for calcul the mask position with numpy : 0.048735618591308594 nb_pixel_total : 31687 time to create 1 rle with old method : 0.051067352294921875 length of segment : 252 time spent for convertir_results : 179.69238328933716 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 1311 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 181224 save missing photos in datou_result : time spend for datou_step_exec : 634.0419011116028 time spend to save output : 15.533580780029297 total time spend for step 1 : 649.5754818916321 step2:crop_condition Tue Apr 1 01:51:24 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 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure Loading chi in step crop with photo_hashtag_type : 3594 Loading chi in step crop for list_pids : 19 ! batch 1 Loaded 1311 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : papier param for this class : {'min_score': 0.7} filtre for class : papier hashtag_id of this class : 492668766 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 882 About to insert : list_path_to_insert length 882 new photo from crops ! About to upload 882 photos upload in portfolio : 3736932 init cache_photo without model_param we have 882 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743465197_2177385 we have uploaded 882 photos in the portfolio 3736932 time of upload the photos Elapsed time : 366.77859568595886 we have finished the crop for the class : papier begin to crop the class : carton param for this class : {'min_score': 0.7} filtre for class : carton hashtag_id of this class : 492774966 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 237 About to insert : list_path_to_insert length 237 new photo from crops ! About to upload 237 photos upload in portfolio : 3736932 init cache_photo without model_param we have 237 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743465597_2177385 we have uploaded 237 photos in the portfolio 3736932 time of upload the photos Elapsed time : 131.08857035636902 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 6 About to insert : list_path_to_insert length 6 new photo from crops ! About to upload 6 photos upload in portfolio : 3736932 init cache_photo without model_param we have 6 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743465735_2177385 we have uploaded 6 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.3541836738586426 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 46 About to insert : list_path_to_insert length 46 new photo from crops ! About to upload 46 photos upload in portfolio : 3736932 init cache_photo without model_param we have 46 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743465755_2177385 we have uploaded 46 photos in the portfolio 3736932 time of upload the photos Elapsed time : 18.752889394760132 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 28 About to insert : list_path_to_insert length 28 new photo from crops ! About to upload 28 photos upload in portfolio : 3736932 init cache_photo without model_param we have 28 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743465784_2177385 we have uploaded 28 photos in the portfolio 3736932 time of upload the photos Elapsed time : 10.5549476146698 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 11 About to insert : list_path_to_insert length 11 new photo from crops ! About to upload 11 photos upload in portfolio : 3736932 init cache_photo without model_param we have 11 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743465802_2177385 we have uploaded 11 photos in the portfolio 3736932 time of upload the photos Elapsed time : 5.592422962188721 we have finished the crop for the class : pehd begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 10 About to insert : list_path_to_insert length 10 new photo from crops ! About to upload 10 photos upload in portfolio : 3736932 init cache_photo without model_param we have 10 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743465813_2177385 we have uploaded 10 photos in the portfolio 3736932 time of upload the photos Elapsed time : 4.07917857170105 we have finished the crop for the class : pet_fonce delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1349141845, 1349141766, 1349141762, 1349141757, 1349141752, 1349141681, 1348989197, 1348989194, 1348989174, 1348988829, 1348988791, 1348988744, 1348988511, 1348988362, 1348988329, 1348988271, 1348988219, 1348988180, 1348988140] Looping around the photos to save general results len do output : 1220 /1349165186Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165187Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165188Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165189Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165190Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165191Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165192Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165193Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165194Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165195Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165196Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165197Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165198Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165199Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165200Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165201Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165202Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165203Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165204Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165205Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165206Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165207Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165208Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165209Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165210Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165211Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165212Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165213Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165214Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165215Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165216Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165217Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165218Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165219Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165220Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165221Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165222Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165223Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165224Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165225Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165226Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165227Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165228Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165229Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165230Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165231Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165232Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165233Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165234Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165235Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165236Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165237Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165238Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165239Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165240Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165241Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165242Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165243Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165244Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165245Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165246Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165247Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165248Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165249Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165250Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165254Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165257Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165260Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165262Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165263Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165264Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165265Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165266Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165267Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165268Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165269Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165270Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165271Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165272Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165273Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165274Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165275Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165276Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165277Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165278Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165279Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165280Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165281Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165282Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165283Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165284Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165285Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165287Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165288Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165289Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165291Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165292Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165293Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165294Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165295Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165296Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165297Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165298Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165299Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165300Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165301Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165302Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349165303Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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retrieve data . /1349170118Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170121Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170122Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170124Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170127Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170128Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170130Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170132Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170133Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170135Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170136Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170138Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170139Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170141Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170143Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170144Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170145Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170146Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170150Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170151Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170155Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170159Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170161Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170163Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170168Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349170170Didn't retrieve data 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output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141845', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141766', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141762', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141757', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141752', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141681', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348989197', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348989194', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348989174', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988829', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988791', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988744', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988511', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988362', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988329', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988271', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988219', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988180', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988140', None, None, None, None, None, '2711132') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3679 time used for this insertion : 0.3539550304412842 save_final save missing photos in datou_result : time spend for datou_step_exec : 734.2649567127228 time spend to save output : 0.4817345142364502 total time spend for step 2 : 734.7466912269592 step3:rle_unique_nms_with_priority Tue Apr 1 02:03:39 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 1311 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 20 nb_hashtags : 3 time to prepare the origin masks : 8.007688760757446 time for calcul the mask position with numpy : 0.3767843246459961 nb_pixel_total : 5578913 time to create 1 rle with new method : 0.6258096694946289 time for calcul the mask position with numpy : 0.04468059539794922 nb_pixel_total : 13075 time to create 1 rle with old method : 0.015529155731201172 time for calcul the mask position with numpy : 0.04621529579162598 nb_pixel_total : 383603 time to create 1 rle with new method : 0.6576437950134277 time for calcul the mask position with numpy : 0.049063682556152344 nb_pixel_total : 7922 time to create 1 rle with old method : 0.011928558349609375 time for calcul the mask position with numpy : 0.04815196990966797 nb_pixel_total : 39969 time to create 1 rle with old method : 0.046288251876831055 time for calcul the mask position with numpy : 0.0472407341003418 nb_pixel_total : 162349 time to create 1 rle with new method : 0.6322741508483887 time for calcul the mask position with numpy : 0.03930807113647461 nb_pixel_total : 180 time to create 1 rle with old method : 0.0004780292510986328 time for calcul the mask position with numpy : 0.03837013244628906 nb_pixel_total : 30728 time to create 1 rle with old method : 0.03871560096740723 time for calcul the mask position with numpy : 0.05237603187561035 nb_pixel_total : 17701 time to create 1 rle with old method : 0.027078866958618164 time for calcul the mask position with numpy : 0.05771017074584961 nb_pixel_total : 24177 time to create 1 rle with old method : 0.03132963180541992 time for calcul the mask position with numpy : 0.03657984733581543 nb_pixel_total : 37676 time to create 1 rle with old method : 0.0444645881652832 time for calcul the mask position with numpy : 0.035651445388793945 nb_pixel_total : 57766 time to create 1 rle with old method : 0.0668330192565918 time for calcul the mask position with numpy : 0.036431074142456055 nb_pixel_total : 32654 time to create 1 rle with old method : 0.037944793701171875 time for calcul the mask position with numpy : 0.035100698471069336 nb_pixel_total : 41602 time to create 1 rle with old method : 0.04802370071411133 time for calcul the mask position with numpy : 0.025603055953979492 nb_pixel_total : 69601 time to create 1 rle with old method : 0.08190631866455078 time for calcul the mask position with numpy : 0.02404618263244629 nb_pixel_total : 37673 time to create 1 rle with old method : 0.04505205154418945 time for calcul the mask position with numpy : 0.02417135238647461 nb_pixel_total : 37378 time to create 1 rle with old method : 0.04804539680480957 time for calcul the mask position with numpy : 0.025995254516601562 nb_pixel_total : 18737 time to create 1 rle with old method : 0.023725271224975586 time for calcul the mask position with numpy : 0.028788328170776367 nb_pixel_total : 59699 time to create 1 rle with old method : 0.08638882637023926 time for calcul the mask position with numpy : 0.024976015090942383 nb_pixel_total : 313282 time to create 1 rle with new method : 0.608389139175415 time for calcul the mask position with numpy : 0.027435302734375 nb_pixel_total : 85555 time to create 1 rle with old method : 0.1023402214050293 create new chi : 4.542237281799316 time to delete rle : 0.017330408096313477 batch 1 Loaded 41 chid ids of type : 3594 ++++++++++++++++++++++++++Number RLEs to save : 14794 TO DO : save crop sub photo not yet done ! save time : 1.1369812488555908 nb_obj : 32 nb_hashtags : 4 time to prepare the origin masks : 4.380309820175171 time for calcul the mask position with numpy : 0.6162467002868652 nb_pixel_total : 6026735 time to create 1 rle with new method : 0.7655963897705078 time for calcul the mask position with numpy : 0.030529499053955078 nb_pixel_total : 12113 time to create 1 rle with old method : 0.014359474182128906 time for calcul the mask position with numpy : 0.030071735382080078 nb_pixel_total : 14678 time to create 1 rle with old method : 0.017589092254638672 time for calcul the mask position with numpy : 0.030430078506469727 nb_pixel_total : 20207 time to create 1 rle with old method : 0.023575544357299805 time for calcul the mask position with numpy : 0.03469657897949219 nb_pixel_total : 51854 time to create 1 rle with old method : 0.10152053833007812 time for calcul the mask position with numpy : 0.030373573303222656 nb_pixel_total : 29446 time to create 1 rle with old method : 0.03494381904602051 time for calcul the mask position with numpy : 0.032784461975097656 nb_pixel_total : 19515 time to create 1 rle with old method : 0.022851943969726562 time for calcul the mask position with numpy : 0.030901432037353516 nb_pixel_total : 7523 time to create 1 rle with old method : 0.009011030197143555 time for calcul the mask position with numpy : 0.030598878860473633 nb_pixel_total : 15440 time to create 1 rle with old method : 0.01840043067932129 time for calcul the mask position with numpy : 0.03104710578918457 nb_pixel_total : 15674 time to create 1 rle with old method : 0.018840551376342773 time for calcul the mask position with numpy : 0.02962017059326172 nb_pixel_total : 6043 time to create 1 rle with old method : 0.0071277618408203125 time for calcul the mask position with numpy : 0.03476595878601074 nb_pixel_total : 27972 time to create 1 rle with old method : 0.04559946060180664 time for calcul the mask position with numpy : 0.03822898864746094 nb_pixel_total : 204186 time to create 1 rle with new method : 0.6842546463012695 time for calcul the mask position with numpy : 0.031116485595703125 nb_pixel_total : 6916 time to create 1 rle with old method : 0.009543418884277344 time for calcul the mask position with numpy : 0.02983403205871582 nb_pixel_total : 19633 time to create 1 rle with old method : 0.022909879684448242 time for calcul the mask position with numpy : 0.029390811920166016 nb_pixel_total : 20026 time to create 1 rle with old method : 0.02358102798461914 time for calcul the mask position with numpy : 0.03046870231628418 nb_pixel_total : 81697 time to create 1 rle with old method : 0.09467005729675293 time for calcul the mask position with numpy : 0.048236846923828125 nb_pixel_total : 24912 time to create 1 rle with old method : 0.04048323631286621 time for calcul the mask position with numpy : 0.03234243392944336 nb_pixel_total : 30760 time to create 1 rle with old method : 0.035764217376708984 time for calcul the mask position with numpy : 0.030035018920898438 nb_pixel_total : 42951 time to create 1 rle with old method : 0.05003476142883301 time for calcul the mask position with numpy : 0.03029012680053711 nb_pixel_total : 39902 time to create 1 rle with old method : 0.04801297187805176 time for calcul the mask position with numpy : 0.03089761734008789 nb_pixel_total : 43821 time to create 1 rle with old method : 0.0545964241027832 time for calcul the mask position with numpy : 0.030365467071533203 nb_pixel_total : 17636 time to create 1 rle with old method : 0.02100849151611328 time for calcul the mask position with numpy : 0.02991771697998047 nb_pixel_total : 10776 time to create 1 rle with old method : 0.012781143188476562 time for calcul the mask position with numpy : 0.03030252456665039 nb_pixel_total : 19246 time to create 1 rle with old method : 0.022336483001708984 time for calcul the mask position with numpy : 0.031266212463378906 nb_pixel_total : 32287 time to create 1 rle with old method : 0.03892254829406738 time for calcul the mask position with numpy : 0.030531644821166992 nb_pixel_total : 61421 time to create 1 rle with old method : 0.07210636138916016 time for calcul the mask position with numpy : 0.029836654663085938 nb_pixel_total : 34590 time to create 1 rle with old method : 0.040468692779541016 time for calcul the mask position with numpy : 0.029552459716796875 nb_pixel_total : 20835 time to create 1 rle with old method : 0.024697303771972656 time for calcul the mask position with numpy : 0.049520254135131836 nb_pixel_total : 31541 time to create 1 rle with old method : 0.04266691207885742 time for calcul the mask position with numpy : 0.029884815216064453 nb_pixel_total : 23836 time to create 1 rle with old method : 0.028295040130615234 time for calcul the mask position with numpy : 0.030060529708862305 nb_pixel_total : 29009 time to create 1 rle with old method : 0.034447669982910156 time for calcul the mask position with numpy : 0.031167984008789062 nb_pixel_total : 7059 time to create 1 rle with old method : 0.008965253829956055 create new chi : 4.216122627258301 time to delete rle : 0.005482912063598633 batch 1 Loaded 65 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 16426 TO DO : save crop sub photo not yet done ! save time : 3.1071972846984863 nb_obj : 35 nb_hashtags : 4 time to prepare the origin masks : 5.002208471298218 time for calcul the mask position with numpy : 0.36956071853637695 nb_pixel_total : 4678769 time to create 1 rle with new method : 1.0240552425384521 time for calcul the mask position with numpy : 0.03868818283081055 nb_pixel_total : 30954 time to create 1 rle with old method : 0.03695487976074219 time for calcul the mask position with numpy : 0.030608415603637695 nb_pixel_total : 10363 time to create 1 rle with old method : 0.013203620910644531 time for calcul the mask position with numpy : 0.029961585998535156 nb_pixel_total : 13396 time to create 1 rle with old method : 0.015974998474121094 time for calcul the mask position with numpy : 0.05747866630554199 nb_pixel_total : 1190496 time to create 1 rle with new method : 0.9377412796020508 time for calcul the mask position with numpy : 0.03099989891052246 nb_pixel_total : 13642 time to create 1 rle with old method : 0.01611042022705078 time for calcul the mask position with numpy : 0.030066490173339844 nb_pixel_total : 14705 time to create 1 rle with old method : 0.017246007919311523 time for calcul the mask position with numpy : 0.030128002166748047 nb_pixel_total : 12226 time to create 1 rle with old method : 0.014562368392944336 time for calcul the mask position with numpy : 0.03053903579711914 nb_pixel_total : 34490 time to create 1 rle with old method : 0.0407254695892334 time for calcul the mask position with numpy : 0.031021833419799805 nb_pixel_total : 11846 time to create 1 rle with old method : 0.017209291458129883 time for calcul the mask position with numpy : 0.03936600685119629 nb_pixel_total : 19691 time to create 1 rle with old method : 0.03850197792053223 time for calcul the mask position with numpy : 0.03808999061584473 nb_pixel_total : 23132 time to create 1 rle with old method : 0.045069217681884766 time for calcul the mask position with numpy : 0.03809189796447754 nb_pixel_total : 102626 time to create 1 rle with old method : 0.1291193962097168 time for calcul the mask position with numpy : 0.03533053398132324 nb_pixel_total : 65731 time to create 1 rle with old method : 0.08260512351989746 time for calcul the mask position with numpy : 0.032505035400390625 nb_pixel_total : 59100 time to create 1 rle with old method : 0.07144546508789062 time for calcul the mask position with numpy : 0.030815839767456055 nb_pixel_total : 48295 time to create 1 rle with old method : 0.05746603012084961 time for calcul the mask position with numpy : 0.030983448028564453 nb_pixel_total : 45623 time to create 1 rle with old method : 0.05526852607727051 time for calcul the mask position with numpy : 0.03070855140686035 nb_pixel_total : 25460 time to create 1 rle with old method : 0.029780864715576172 time for calcul the mask position with numpy : 0.030136823654174805 nb_pixel_total : 19266 time to create 1 rle with old method : 0.024757862091064453 time for calcul the mask position with numpy : 0.03278088569641113 nb_pixel_total : 53805 time to create 1 rle with old method : 0.06536507606506348 time for calcul the mask position with numpy : 0.030559301376342773 nb_pixel_total : 9101 time to create 1 rle with old method : 0.010942459106445312 time for calcul the mask position with numpy : 0.032182931900024414 nb_pixel_total : 41205 time to create 1 rle with old method : 0.047800302505493164 time for calcul the mask position with numpy : 0.03727531433105469 nb_pixel_total : 77169 time to create 1 rle with old method : 0.10163760185241699 time for calcul the mask position with numpy : 0.03145337104797363 nb_pixel_total : 65624 time to create 1 rle with old method : 0.07901668548583984 time for calcul the mask position with numpy : 0.03407788276672363 nb_pixel_total : 12609 time to create 1 rle with old method : 0.01491999626159668 time for calcul the mask position with numpy : 0.030519962310791016 nb_pixel_total : 24039 time to create 1 rle with old method : 0.02810978889465332 time for calcul the mask position with numpy : 0.03607463836669922 nb_pixel_total : 10511 time to create 1 rle with old method : 0.012780904769897461 time for calcul the mask position with numpy : 0.035547733306884766 nb_pixel_total : 20041 time to create 1 rle with old method : 0.02397894859313965 time for calcul the mask position with numpy : 0.03109431266784668 nb_pixel_total : 3754 time to create 1 rle with old method : 0.004828691482543945 time for calcul the mask position with numpy : 0.031116724014282227 nb_pixel_total : 1863 time to create 1 rle with old method : 0.002332925796508789 time for calcul the mask position with numpy : 0.033593177795410156 nb_pixel_total : 73569 time to create 1 rle with old method : 0.08591008186340332 time for calcul the mask position with numpy : 0.03261685371398926 nb_pixel_total : 104694 time to create 1 rle with old method : 0.13350939750671387 time for calcul the mask position with numpy : 0.031273841857910156 nb_pixel_total : 12464 time to create 1 rle with old method : 0.014782190322875977 time for calcul the mask position with numpy : 0.03092217445373535 nb_pixel_total : 25359 time to create 1 rle with old method : 0.029976367950439453 time for calcul the mask position with numpy : 0.03131723403930664 nb_pixel_total : 47831 time to create 1 rle with old method : 0.05727434158325195 time for calcul the mask position with numpy : 0.030736207962036133 nb_pixel_total : 46791 time to create 1 rle with old method : 0.055092573165893555 create new chi : 5.0634987354278564 time to delete rle : 0.007745265960693359 batch 1 Loaded 72 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 19851 TO DO : save crop sub photo not yet done ! save time : 2.0735955238342285 nb_obj : 62 nb_hashtags : 4 time to prepare the origin masks : 4.925633668899536 time for calcul the mask position with numpy : 0.2120065689086914 nb_pixel_total : 5063022 time to create 1 rle with new method : 0.7642087936401367 time for calcul the mask position with numpy : 0.0330352783203125 nb_pixel_total : 18802 time to create 1 rle with old method : 0.023662090301513672 time for calcul the mask position with numpy : 0.032652854919433594 nb_pixel_total : 64063 time to create 1 rle with old method : 0.07729959487915039 time for calcul the mask position with numpy : 0.031028270721435547 nb_pixel_total : 4148 time to create 1 rle with old method : 0.004973411560058594 time for calcul the mask position with numpy : 0.030928611755371094 nb_pixel_total : 56405 time to create 1 rle with old method : 0.07686090469360352 time for calcul the mask position with numpy : 0.03919029235839844 nb_pixel_total : 103564 time to create 1 rle with old method : 0.17107224464416504 time for calcul the mask position with numpy : 0.031410932540893555 nb_pixel_total : 2021 time to create 1 rle with old method : 0.0035867691040039062 time for calcul the mask position with numpy : 0.03564000129699707 nb_pixel_total : 16657 time to create 1 rle with old method : 0.024571895599365234 time for calcul the mask position with numpy : 0.032681941986083984 nb_pixel_total : 25503 time to create 1 rle with old method : 0.030451297760009766 time for calcul the mask position with numpy : 0.03516101837158203 nb_pixel_total : 39549 time to create 1 rle with old method : 0.0521388053894043 time for calcul the mask position with numpy : 0.05724167823791504 nb_pixel_total : 24786 time to create 1 rle with old method : 0.029214143753051758 time for calcul the mask position with numpy : 0.033026933670043945 nb_pixel_total : 26799 time to create 1 rle with old method : 0.03781890869140625 time for calcul the mask position with numpy : 0.03621411323547363 nb_pixel_total : 11328 time to create 1 rle with old method : 0.017363309860229492 time for calcul the mask position with numpy : 0.03795456886291504 nb_pixel_total : 81349 time to create 1 rle with old method : 0.1433398723602295 time for calcul the mask position with numpy : 0.03439497947692871 nb_pixel_total : 4557 time to create 1 rle with old method : 0.006086587905883789 time for calcul the mask position with numpy : 0.031271934509277344 nb_pixel_total : 26691 time to create 1 rle with old method : 0.03137063980102539 time for calcul the mask position with numpy : 0.03365612030029297 nb_pixel_total : 176663 time to create 1 rle with new method : 0.4427914619445801 time for calcul the mask position with numpy : 0.0382542610168457 nb_pixel_total : 9586 time to create 1 rle with old method : 0.01948261260986328 time for calcul the mask position with numpy : 0.03962278366088867 nb_pixel_total : 12583 time to create 1 rle with old method : 0.024834394454956055 time for calcul the mask position with numpy : 0.03999662399291992 nb_pixel_total : 163937 time to create 1 rle with new method : 0.5414230823516846 time for calcul the mask position with numpy : 0.0389862060546875 nb_pixel_total : 48156 time to create 1 rle with old method : 0.08224940299987793 time for calcul the mask position with numpy : 0.03998732566833496 nb_pixel_total : 31088 time to create 1 rle with old method : 0.06102275848388672 time for calcul the mask position with numpy : 0.03795790672302246 nb_pixel_total : 19255 time to create 1 rle with old method : 0.03754401206970215 time for calcul the mask position with numpy : 0.03833651542663574 nb_pixel_total : 12028 time to create 1 rle with old method : 0.023659229278564453 time for calcul the mask position with numpy : 0.03792905807495117 nb_pixel_total : 15405 time to create 1 rle with old method : 0.018341064453125 time for calcul the mask position with numpy : 0.03301811218261719 nb_pixel_total : 25064 time to create 1 rle with old method : 0.05371451377868652 time for calcul the mask position with numpy : 0.0522158145904541 nb_pixel_total : 78768 time to create 1 rle with old method : 0.10055685043334961 time for calcul the mask position with numpy : 0.030748605728149414 nb_pixel_total : 22199 time to create 1 rle with old method : 0.026457548141479492 time for calcul the mask position with numpy : 0.031404733657836914 nb_pixel_total : 20488 time to create 1 rle with old method : 0.03593087196350098 time for calcul the mask position with numpy : 0.037516117095947266 nb_pixel_total : 13957 time to create 1 rle with old method : 0.019860506057739258 time for calcul the mask position with numpy : 0.030725479125976562 nb_pixel_total : 27428 time to create 1 rle with old method : 0.03379344940185547 time for calcul the mask position with numpy : 0.03040909767150879 nb_pixel_total : 13161 time to create 1 rle with old method : 0.015453815460205078 time for calcul the mask position with numpy : 0.035939693450927734 nb_pixel_total : 63418 time to create 1 rle with old method : 0.09761285781860352 time for calcul the mask position with numpy : 0.04056596755981445 nb_pixel_total : 7268 time to create 1 rle with old method : 0.011435508728027344 time for calcul the mask position with numpy : 0.03745698928833008 nb_pixel_total : 46882 time to create 1 rle with old method : 0.05816078186035156 time for calcul the mask position with numpy : 0.030960798263549805 nb_pixel_total : 9280 time to create 1 rle with old method : 0.011236190795898438 time for calcul the mask position with numpy : 0.03434348106384277 nb_pixel_total : 42600 time to create 1 rle with old method : 0.0594639778137207 time for calcul the mask position with numpy : 0.0329594612121582 nb_pixel_total : 12247 time to create 1 rle with old method : 0.015703678131103516 time for calcul the mask position with numpy : 0.03335452079772949 nb_pixel_total : 32750 time to create 1 rle with old method : 0.04017996788024902 time for calcul the mask position with numpy : 0.03359365463256836 nb_pixel_total : 19806 time to create 1 rle with old method : 0.02691960334777832 time for calcul the mask position with numpy : 0.03159761428833008 nb_pixel_total : 9134 time to create 1 rle with old method : 0.012485980987548828 time for calcul the mask position with numpy : 0.03176760673522949 nb_pixel_total : 10473 time to create 1 rle with old method : 0.012463808059692383 time for calcul the mask position with numpy : 0.03264331817626953 nb_pixel_total : 11956 time to create 1 rle with old method : 0.015793800354003906 time for calcul the mask position with numpy : 0.034641265869140625 nb_pixel_total : 34835 time to create 1 rle with old method : 0.04141831398010254 time for calcul the mask position with numpy : 0.031577110290527344 nb_pixel_total : 13937 time to create 1 rle with old method : 0.02422809600830078 time for calcul the mask position with numpy : 0.03710603713989258 nb_pixel_total : 8955 time to create 1 rle with old method : 0.013146638870239258 time for calcul the mask position with numpy : 0.03608822822570801 nb_pixel_total : 41517 time to create 1 rle with old method : 0.0571897029876709 time for calcul the mask position with numpy : 0.03557276725769043 nb_pixel_total : 113759 time to create 1 rle with old method : 0.13710403442382812 time for calcul the mask position with numpy : 0.03278398513793945 nb_pixel_total : 17461 time to create 1 rle with old method : 0.021098852157592773 time for calcul the mask position with numpy : 0.03480648994445801 nb_pixel_total : 10806 time to create 1 rle with old method : 0.018359661102294922 time for calcul the mask position with numpy : 0.04131960868835449 nb_pixel_total : 10127 time to create 1 rle with old method : 0.01792120933532715 time for calcul the mask position with numpy : 0.04486489295959473 nb_pixel_total : 36374 time to create 1 rle with old method : 0.04504656791687012 time for calcul the mask position with numpy : 0.033216238021850586 nb_pixel_total : 7363 time to create 1 rle with old method : 0.01062774658203125 time for calcul the mask position with numpy : 0.03463602066040039 nb_pixel_total : 4389 time to create 1 rle with old method : 0.005462646484375 time for calcul the mask position with numpy : 0.035280466079711914 nb_pixel_total : 16759 time to create 1 rle with old method : 0.02087545394897461 time for calcul the mask position with numpy : 0.032775163650512695 nb_pixel_total : 8881 time to create 1 rle with old method : 0.015951156616210938 time for calcul the mask position with numpy : 0.03430485725402832 nb_pixel_total : 79603 time to create 1 rle with old method : 0.09530138969421387 time for calcul the mask position with numpy : 0.03359103202819824 nb_pixel_total : 43988 time to create 1 rle with old method : 0.053870201110839844 time for calcul the mask position with numpy : 0.030966997146606445 nb_pixel_total : 27945 time to create 1 rle with old method : 0.03274393081665039 time for calcul the mask position with numpy : 0.030866384506225586 nb_pixel_total : 6030 time to create 1 rle with old method : 0.007720947265625 time for calcul the mask position with numpy : 0.031223773956298828 nb_pixel_total : 33120 time to create 1 rle with old method : 0.039427757263183594 time for calcul the mask position with numpy : 0.03258085250854492 nb_pixel_total : 4951 time to create 1 rle with old method : 0.006021976470947266 time for calcul the mask position with numpy : 0.03334617614746094 nb_pixel_total : 4616 time to create 1 rle with old method : 0.005632877349853516 create new chi : 6.586550235748291 time to delete rle : 0.008090972900390625 batch 1 Loaded 125 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 28211 TO DO : save crop sub photo not yet done ! save time : 4.93698263168335 nb_obj : 51 nb_hashtags : 5 time to prepare the origin masks : 4.901762008666992 time for calcul the mask position with numpy : 0.6294143199920654 nb_pixel_total : 5461922 time to create 1 rle with new method : 1.3717145919799805 time for calcul the mask position with numpy : 0.031033754348754883 nb_pixel_total : 9911 time to create 1 rle with old method : 0.01593303680419922 time for calcul the mask position with numpy : 0.0385746955871582 nb_pixel_total : 8152 time to create 1 rle with old method : 0.014813899993896484 time for calcul the mask position with numpy : 0.03651928901672363 nb_pixel_total : 8050 time to create 1 rle with old method : 0.009962081909179688 time for calcul the mask position with numpy : 0.030250072479248047 nb_pixel_total : 13463 time to create 1 rle with old method : 0.016175270080566406 time for calcul the mask position with numpy : 0.03122425079345703 nb_pixel_total : 49986 time to create 1 rle with old method : 0.05949854850769043 time for calcul the mask position with numpy : 0.030655622482299805 nb_pixel_total : 30043 time to create 1 rle with old method : 0.035126447677612305 time for calcul the mask position with numpy : 0.030573606491088867 nb_pixel_total : 14754 time to create 1 rle with old method : 0.017363786697387695 time for calcul the mask position with numpy : 0.029869794845581055 nb_pixel_total : 9646 time to create 1 rle with old method : 0.01139068603515625 time for calcul the mask position with numpy : 0.031155824661254883 nb_pixel_total : 28508 time to create 1 rle with old method : 0.034697532653808594 time for calcul the mask position with numpy : 0.030150890350341797 nb_pixel_total : 22052 time to create 1 rle with old method : 0.02668452262878418 time for calcul the mask position with numpy : 0.029620885848999023 nb_pixel_total : 21605 time to create 1 rle with old method : 0.02701592445373535 time for calcul the mask position with numpy : 0.03157377243041992 nb_pixel_total : 52233 time to create 1 rle with old method : 0.06343674659729004 time for calcul the mask position with numpy : 0.0299835205078125 nb_pixel_total : 28078 time to create 1 rle with old method : 0.033883094787597656 time for calcul the mask position with numpy : 0.0321502685546875 nb_pixel_total : 5500 time to create 1 rle with old method : 0.009363174438476562 time for calcul the mask position with numpy : 0.035042524337768555 nb_pixel_total : 21032 time to create 1 rle with old method : 0.03078603744506836 time for calcul the mask position with numpy : 0.030852079391479492 nb_pixel_total : 24171 time to create 1 rle with old method : 0.02991318702697754 time for calcul the mask position with numpy : 0.03046131134033203 nb_pixel_total : 11518 time to create 1 rle with old method : 0.014653205871582031 time for calcul the mask position with numpy : 0.029444217681884766 nb_pixel_total : 7741 time to create 1 rle with old method : 0.009159088134765625 time for calcul the mask position with numpy : 0.030518531799316406 nb_pixel_total : 10257 time to create 1 rle with old method : 0.014853715896606445 time for calcul the mask position with numpy : 0.032524824142456055 nb_pixel_total : 10412 time to create 1 rle with old method : 0.012318134307861328 time for calcul the mask position with numpy : 0.03196215629577637 nb_pixel_total : 164828 time to create 1 rle with new method : 0.4526517391204834 time for calcul the mask position with numpy : 0.030220985412597656 nb_pixel_total : 26583 time to create 1 rle with old method : 0.03731203079223633 time for calcul the mask position with numpy : 0.030484437942504883 nb_pixel_total : 50616 time to create 1 rle with old method : 0.06035923957824707 time for calcul the mask position with numpy : 0.030621767044067383 nb_pixel_total : 8247 time to create 1 rle with old method : 0.013637065887451172 time for calcul the mask position with numpy : 0.04105067253112793 nb_pixel_total : 20833 time to create 1 rle with old method : 0.03049612045288086 time for calcul the mask position with numpy : 0.030576467514038086 nb_pixel_total : 4531 time to create 1 rle with old method : 0.005468130111694336 time for calcul the mask position with numpy : 0.031430721282958984 nb_pixel_total : 7390 time to create 1 rle with old method : 0.008829832077026367 time for calcul the mask position with numpy : 0.03246712684631348 nb_pixel_total : 15672 time to create 1 rle with old method : 0.0186312198638916 time for calcul the mask position with numpy : 0.030425310134887695 nb_pixel_total : 42974 time to create 1 rle with old method : 0.055765628814697266 time for calcul the mask position with numpy : 0.03164172172546387 nb_pixel_total : 80149 time to create 1 rle with old method : 0.09378647804260254 time for calcul the mask position with numpy : 0.031534671783447266 nb_pixel_total : 91873 time to create 1 rle with old method : 0.10755014419555664 time for calcul the mask position with numpy : 0.029981136322021484 nb_pixel_total : 5000 time to create 1 rle with old method : 0.006023883819580078 time for calcul the mask position with numpy : 0.030452728271484375 nb_pixel_total : 26756 time to create 1 rle with old method : 0.03266263008117676 time for calcul the mask position with numpy : 0.030650854110717773 nb_pixel_total : 70543 time to create 1 rle with old method : 0.10344266891479492 time for calcul the mask position with numpy : 0.040143489837646484 nb_pixel_total : 22195 time to create 1 rle with old method : 0.025910377502441406 time for calcul the mask position with numpy : 0.030196189880371094 nb_pixel_total : 7583 time to create 1 rle with old method : 0.009270668029785156 time for calcul the mask position with numpy : 0.030143260955810547 nb_pixel_total : 42368 time to create 1 rle with old method : 0.049553871154785156 time for calcul the mask position with numpy : 0.0332643985748291 nb_pixel_total : 180983 time to create 1 rle with new method : 1.166020393371582 time for calcul the mask position with numpy : 0.03295731544494629 nb_pixel_total : 10401 time to create 1 rle with old method : 0.01847386360168457 time for calcul the mask position with numpy : 0.030672788619995117 nb_pixel_total : 35028 time to create 1 rle with old method : 0.04346871376037598 time for calcul the mask position with numpy : 0.03634214401245117 nb_pixel_total : 28478 time to create 1 rle with old method : 0.03348708152770996 time for calcul the mask position with numpy : 0.029940366744995117 nb_pixel_total : 6150 time to create 1 rle with old method : 0.007729768753051758 time for calcul the mask position with numpy : 0.033315420150756836 nb_pixel_total : 58371 time to create 1 rle with old method : 0.06809878349304199 time for calcul the mask position with numpy : 0.03001117706298828 nb_pixel_total : 14974 time to create 1 rle with old method : 0.017710208892822266 time for calcul the mask position with numpy : 0.029950380325317383 nb_pixel_total : 18485 time to create 1 rle with old method : 0.026680946350097656 time for calcul the mask position with numpy : 0.030993938446044922 nb_pixel_total : 50529 time to create 1 rle with old method : 0.06171083450317383 time for calcul the mask position with numpy : 0.03126811981201172 nb_pixel_total : 74807 time to create 1 rle with old method : 0.08647799491882324 time for calcul the mask position with numpy : 0.02954840660095215 nb_pixel_total : 12200 time to create 1 rle with old method : 0.01432657241821289 time for calcul the mask position with numpy : 0.03150820732116699 nb_pixel_total : 4752 time to create 1 rle with old method : 0.005671977996826172 time for calcul the mask position with numpy : 0.030829429626464844 nb_pixel_total : 8977 time to create 1 rle with old method : 0.010478496551513672 time for calcul the mask position with numpy : 0.03043389320373535 nb_pixel_total : 8930 time to create 1 rle with old method : 0.01027822494506836 create new chi : 6.906821966171265 time to delete rle : 0.003679513931274414 batch 1 Loaded 103 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 22570 TO DO : save crop sub photo not yet done ! save time : 4.213856935501099 nb_obj : 55 nb_hashtags : 3 time to prepare the origin masks : 4.34519362449646 time for calcul the mask position with numpy : 0.5318691730499268 nb_pixel_total : 5821645 time to create 1 rle with new method : 0.6667113304138184 time for calcul the mask position with numpy : 0.031164884567260742 nb_pixel_total : 25478 time to create 1 rle with old method : 0.02958822250366211 time for calcul the mask position with numpy : 0.029840469360351562 nb_pixel_total : 18763 time to create 1 rle with old method : 0.02207326889038086 time for calcul the mask position with numpy : 0.029940128326416016 nb_pixel_total : 14468 time to create 1 rle with old method : 0.02097630500793457 time for calcul the mask position with numpy : 0.03156018257141113 nb_pixel_total : 9372 time to create 1 rle with old method : 0.01111149787902832 time for calcul the mask position with numpy : 0.03188133239746094 nb_pixel_total : 35854 time to create 1 rle with old method : 0.04363894462585449 time for calcul the mask position with numpy : 0.0300905704498291 nb_pixel_total : 29134 time to create 1 rle with old method : 0.0337672233581543 time for calcul the mask position with numpy : 0.029863357543945312 nb_pixel_total : 20413 time to create 1 rle with old method : 0.0238344669342041 time for calcul the mask position with numpy : 0.029744863510131836 nb_pixel_total : 16235 time to create 1 rle with old method : 0.05841350555419922 time for calcul the mask position with numpy : 0.03222179412841797 nb_pixel_total : 17062 time to create 1 rle with old method : 0.019958972930908203 time for calcul the mask position with numpy : 0.030548810958862305 nb_pixel_total : 30438 time to create 1 rle with old method : 0.03565621376037598 time for calcul the mask position with numpy : 0.03743720054626465 nb_pixel_total : 36603 time to create 1 rle with old method : 0.04233217239379883 time for calcul the mask position with numpy : 0.0326235294342041 nb_pixel_total : 45556 time to create 1 rle with old method : 0.0531773567199707 time for calcul the mask position with numpy : 0.030280113220214844 nb_pixel_total : 22544 time to create 1 rle with old method : 0.026747703552246094 time for calcul the mask position with numpy : 0.03035259246826172 nb_pixel_total : 5653 time to create 1 rle with old method : 0.0067348480224609375 time for calcul the mask position with numpy : 0.030626773834228516 nb_pixel_total : 20291 time to create 1 rle with old method : 0.024043560028076172 time for calcul the mask position with numpy : 0.030190229415893555 nb_pixel_total : 9225 time to create 1 rle with old method : 0.011058807373046875 time for calcul the mask position with numpy : 0.032148122787475586 nb_pixel_total : 14667 time to create 1 rle with old method : 0.017415761947631836 time for calcul the mask position with numpy : 0.03226327896118164 nb_pixel_total : 24596 time to create 1 rle with old method : 0.03093433380126953 time for calcul the mask position with numpy : 0.029569625854492188 nb_pixel_total : 15168 time to create 1 rle with old method : 0.018345355987548828 time for calcul the mask position with numpy : 0.03396964073181152 nb_pixel_total : 19567 time to create 1 rle with old method : 0.02296137809753418 time for calcul the mask position with numpy : 0.02980828285217285 nb_pixel_total : 9759 time to create 1 rle with old method : 0.011652708053588867 time for calcul the mask position with numpy : 0.029906749725341797 nb_pixel_total : 13535 time to create 1 rle with old method : 0.01616644859313965 time for calcul the mask position with numpy : 0.03061842918395996 nb_pixel_total : 6979 time to create 1 rle with old method : 0.008399724960327148 time for calcul the mask position with numpy : 0.031005382537841797 nb_pixel_total : 7870 time to create 1 rle with old method : 0.009394168853759766 time for calcul the mask position with numpy : 0.030522823333740234 nb_pixel_total : 17825 time to create 1 rle with old method : 0.02130866050720215 time for calcul the mask position with numpy : 0.033693790435791016 nb_pixel_total : 36402 time to create 1 rle with old method : 0.05408072471618652 time for calcul the mask position with numpy : 0.030666351318359375 nb_pixel_total : 19399 time to create 1 rle with old method : 0.02264714241027832 time for calcul the mask position with numpy : 0.031176090240478516 nb_pixel_total : 21526 time to create 1 rle with old method : 0.027388572692871094 time for calcul the mask position with numpy : 0.02967214584350586 nb_pixel_total : 24960 time to create 1 rle with old method : 0.02935004234313965 time for calcul the mask position with numpy : 0.02990555763244629 nb_pixel_total : 15276 time to create 1 rle with old method : 0.017980337142944336 time for calcul the mask position with numpy : 0.029917478561401367 nb_pixel_total : 14447 time to create 1 rle with old method : 0.016983509063720703 time for calcul the mask position with numpy : 0.029928922653198242 nb_pixel_total : 18189 time to create 1 rle with old method : 0.02139759063720703 time for calcul the mask position with numpy : 0.029665708541870117 nb_pixel_total : 14557 time to create 1 rle with old method : 0.01713395118713379 time for calcul the mask position with numpy : 0.031131505966186523 nb_pixel_total : 134837 time to create 1 rle with old method : 0.17978119850158691 time for calcul the mask position with numpy : 0.04147505760192871 nb_pixel_total : 7981 time to create 1 rle with old method : 0.015113115310668945 time for calcul the mask position with numpy : 0.04008626937866211 nb_pixel_total : 10972 time to create 1 rle with old method : 0.020778417587280273 time for calcul the mask position with numpy : 0.03837466239929199 nb_pixel_total : 7948 time to create 1 rle with old method : 0.018153667449951172 time for calcul the mask position with numpy : 0.030946969985961914 nb_pixel_total : 4471 time to create 1 rle with old method : 0.005341053009033203 time for calcul the mask position with numpy : 0.031017541885375977 nb_pixel_total : 48961 time to create 1 rle with old method : 0.05750751495361328 time for calcul the mask position with numpy : 0.02944183349609375 nb_pixel_total : 4317 time to create 1 rle with old method : 0.00530242919921875 time for calcul the mask position with numpy : 0.029825925827026367 nb_pixel_total : 36246 time to create 1 rle with old method : 0.043692588806152344 time for calcul the mask position with numpy : 0.03427839279174805 nb_pixel_total : 81185 time to create 1 rle with old method : 0.15149354934692383 time for calcul the mask position with numpy : 0.036253929138183594 nb_pixel_total : 7112 time to create 1 rle with old method : 0.01218271255493164 time for calcul the mask position with numpy : 0.0340571403503418 nb_pixel_total : 5698 time to create 1 rle with old method : 0.009293794631958008 time for calcul the mask position with numpy : 0.03466987609863281 nb_pixel_total : 13656 time to create 1 rle with old method : 0.02363109588623047 time for calcul the mask position with numpy : 0.03263044357299805 nb_pixel_total : 47908 time to create 1 rle with old method : 0.06389546394348145 time for calcul the mask position with numpy : 0.03126788139343262 nb_pixel_total : 75268 time to create 1 rle with old method : 0.08678483963012695 time for calcul the mask position with numpy : 0.030057430267333984 nb_pixel_total : 15581 time to create 1 rle with old method : 0.018170595169067383 time for calcul the mask position with numpy : 0.030419349670410156 nb_pixel_total : 8964 time to create 1 rle with old method : 0.010553359985351562 time for calcul the mask position with numpy : 0.02946949005126953 nb_pixel_total : 8732 time to create 1 rle with old method : 0.010399580001831055 time for calcul the mask position with numpy : 0.029860734939575195 nb_pixel_total : 21692 time to create 1 rle with old method : 0.03063201904296875 time for calcul the mask position with numpy : 0.03147149085998535 nb_pixel_total : 6688 time to create 1 rle with old method : 0.009704113006591797 time for calcul the mask position with numpy : 0.032753944396972656 nb_pixel_total : 4339 time to create 1 rle with old method : 0.005360603332519531 time for calcul the mask position with numpy : 0.031369686126708984 nb_pixel_total : 11708 time to create 1 rle with old method : 0.014286279678344727 time for calcul the mask position with numpy : 0.04612469673156738 nb_pixel_total : 12520 time to create 1 rle with old method : 0.018553733825683594 create new chi : 4.6437668800354 time to delete rle : 0.006020784378051758 batch 1 Loaded 111 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 22080 TO DO : save crop sub photo not yet done ! save time : 1.4862122535705566 nb_obj : 26 nb_hashtags : 5 time to prepare the origin masks : 5.235497951507568 time for calcul the mask position with numpy : 0.6506831645965576 nb_pixel_total : 4877297 time to create 1 rle with new method : 0.7228236198425293 time for calcul the mask position with numpy : 0.03380632400512695 nb_pixel_total : 7737 time to create 1 rle with old method : 0.01077723503112793 time for calcul the mask position with numpy : 0.02997875213623047 nb_pixel_total : 14964 time to create 1 rle with old method : 0.017452239990234375 time for calcul the mask position with numpy : 0.02948784828186035 nb_pixel_total : 2725 time to create 1 rle with old method : 0.004084587097167969 time for calcul the mask position with numpy : 0.031064748764038086 nb_pixel_total : 19987 time to create 1 rle with old method : 0.023361921310424805 time for calcul the mask position with numpy : 0.034680843353271484 nb_pixel_total : 129572 time to create 1 rle with old method : 0.15151286125183105 time for calcul the mask position with numpy : 0.029582500457763672 nb_pixel_total : 18297 time to create 1 rle with old method : 0.02110123634338379 time for calcul the mask position with numpy : 0.030018329620361328 nb_pixel_total : 35602 time to create 1 rle with old method : 0.042867183685302734 time for calcul the mask position with numpy : 0.030004024505615234 nb_pixel_total : 19378 time to create 1 rle with old method : 0.022966861724853516 time for calcul the mask position with numpy : 0.041381120681762695 nb_pixel_total : 727651 time to create 1 rle with new method : 0.6327474117279053 time for calcul the mask position with numpy : 0.03049778938293457 nb_pixel_total : 1536 time to create 1 rle with old method : 0.001990079879760742 time for calcul the mask position with numpy : 0.03169965744018555 nb_pixel_total : 101440 time to create 1 rle with old method : 0.1240236759185791 time for calcul the mask position with numpy : 0.03186750411987305 nb_pixel_total : 28530 time to create 1 rle with old method : 0.03434872627258301 time for calcul the mask position with numpy : 0.03518533706665039 nb_pixel_total : 177447 time to create 1 rle with new method : 0.9500434398651123 time for calcul the mask position with numpy : 0.034239768981933594 nb_pixel_total : 291631 time to create 1 rle with new method : 0.6411209106445312 time for calcul the mask position with numpy : 0.0319819450378418 nb_pixel_total : 29872 time to create 1 rle with old method : 0.036585092544555664 time for calcul the mask position with numpy : 0.034406423568725586 nb_pixel_total : 17396 time to create 1 rle with old method : 0.02585458755493164 time for calcul the mask position with numpy : 0.030467987060546875 nb_pixel_total : 19218 time to create 1 rle with old method : 0.027758121490478516 time for calcul the mask position with numpy : 0.030704975128173828 nb_pixel_total : 24067 time to create 1 rle with old method : 0.0279843807220459 time for calcul the mask position with numpy : 0.030238866806030273 nb_pixel_total : 64866 time to create 1 rle with old method : 0.07992744445800781 time for calcul the mask position with numpy : 0.032193899154663086 nb_pixel_total : 186322 time to create 1 rle with new method : 0.39748597145080566 time for calcul the mask position with numpy : 0.05369710922241211 nb_pixel_total : 68298 time to create 1 rle with old method : 0.11163067817687988 time for calcul the mask position with numpy : 0.0334019660949707 nb_pixel_total : 23608 time to create 1 rle with old method : 0.027370929718017578 time for calcul the mask position with numpy : 0.02939915657043457 nb_pixel_total : 13622 time to create 1 rle with old method : 0.016072750091552734 time for calcul the mask position with numpy : 0.03015422821044922 nb_pixel_total : 94456 time to create 1 rle with old method : 0.11564421653747559 time for calcul the mask position with numpy : 0.03058624267578125 nb_pixel_total : 9290 time to create 1 rle with old method : 0.01103973388671875 time for calcul the mask position with numpy : 0.030434370040893555 nb_pixel_total : 45431 time to create 1 rle with old method : 0.05434060096740723 create new chi : 6.014517307281494 time to delete rle : 0.0054547786712646484 batch 1 Loaded 55 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 19355 TO DO : save crop sub photo not yet done ! save time : 1.5547378063201904 nb_obj : 17 nb_hashtags : 5 time to prepare the origin masks : 4.018934726715088 time for calcul the mask position with numpy : 0.4397759437561035 nb_pixel_total : 6207050 time to create 1 rle with new method : 0.4576711654663086 time for calcul the mask position with numpy : 0.029378890991210938 nb_pixel_total : 7503 time to create 1 rle with old method : 0.00879359245300293 time for calcul the mask position with numpy : 0.030735015869140625 nb_pixel_total : 126201 time to create 1 rle with old method : 0.14580869674682617 time for calcul the mask position with numpy : 0.02982163429260254 nb_pixel_total : 53704 time to create 1 rle with old method : 0.06397151947021484 time for calcul the mask position with numpy : 0.032373905181884766 nb_pixel_total : 75938 time to create 1 rle with old method : 0.08820843696594238 time for calcul the mask position with numpy : 0.029820680618286133 nb_pixel_total : 44094 time to create 1 rle with old method : 0.05188393592834473 time for calcul the mask position with numpy : 0.032338619232177734 nb_pixel_total : 14630 time to create 1 rle with old method : 0.01703643798828125 time for calcul the mask position with numpy : 0.02949690818786621 nb_pixel_total : 25905 time to create 1 rle with old method : 0.03140902519226074 time for calcul the mask position with numpy : 0.029269933700561523 nb_pixel_total : 11137 time to create 1 rle with old method : 0.01670050621032715 time for calcul the mask position with numpy : 0.03157305717468262 nb_pixel_total : 3197 time to create 1 rle with old method : 0.003975391387939453 time for calcul the mask position with numpy : 0.030788421630859375 nb_pixel_total : 23166 time to create 1 rle with old method : 0.029444456100463867 time for calcul the mask position with numpy : 0.032820701599121094 nb_pixel_total : 61846 time to create 1 rle with old method : 0.07476449012756348 time for calcul the mask position with numpy : 0.029541969299316406 nb_pixel_total : 11870 time to create 1 rle with old method : 0.016777992248535156 time for calcul the mask position with numpy : 0.03305649757385254 nb_pixel_total : 284938 time to create 1 rle with new method : 0.5663332939147949 time for calcul the mask position with numpy : 0.030492067337036133 nb_pixel_total : 49590 time to create 1 rle with old method : 0.06045365333557129 time for calcul the mask position with numpy : 0.03001260757446289 nb_pixel_total : 27190 time to create 1 rle with old method : 0.03306412696838379 time for calcul the mask position with numpy : 0.029573678970336914 nb_pixel_total : 13323 time to create 1 rle with old method : 0.017121315002441406 time for calcul the mask position with numpy : 0.02945232391357422 nb_pixel_total : 8958 time to create 1 rle with old method : 0.010562419891357422 create new chi : 2.718446731567383 time to delete rle : 0.0019147396087646484 batch 1 Loaded 36 chid ids of type : 3594 +++++++++++++++++++++++++++++++Number RLEs to save : 11585 TO DO : save crop sub photo not yet done ! save time : 0.8316662311553955 nb_obj : 19 nb_hashtags : 4 time to prepare the origin masks : 5.773462772369385 time for calcul the mask position with numpy : 0.5301377773284912 nb_pixel_total : 4667343 time to create 1 rle with new method : 0.3598620891571045 time for calcul the mask position with numpy : 0.032891273498535156 nb_pixel_total : 3842 time to create 1 rle with old method : 0.006539106369018555 time for calcul the mask position with numpy : 0.0308990478515625 nb_pixel_total : 7940 time to create 1 rle with old method : 0.009377241134643555 time for calcul the mask position with numpy : 0.03236675262451172 nb_pixel_total : 129695 time to create 1 rle with old method : 0.16415023803710938 time for calcul the mask position with numpy : 0.03138589859008789 nb_pixel_total : 14540 time to create 1 rle with old method : 0.0169985294342041 time for calcul the mask position with numpy : 0.029699325561523438 nb_pixel_total : 6495 time to create 1 rle with old method : 0.007947683334350586 time for calcul the mask position with numpy : 0.032424211502075195 nb_pixel_total : 38089 time to create 1 rle with old method : 0.05004763603210449 time for calcul the mask position with numpy : 0.03578925132751465 nb_pixel_total : 117867 time to create 1 rle with old method : 0.16044211387634277 time for calcul the mask position with numpy : 0.04561877250671387 nb_pixel_total : 1062573 time to create 1 rle with new method : 0.6452841758728027 time for calcul the mask position with numpy : 0.030638456344604492 nb_pixel_total : 115781 time to create 1 rle with old method : 0.14788389205932617 time for calcul the mask position with numpy : 0.03437089920043945 nb_pixel_total : 187561 time to create 1 rle with new method : 0.6566948890686035 time for calcul the mask position with numpy : 0.06076836585998535 nb_pixel_total : 68700 time to create 1 rle with old method : 0.11327886581420898 time for calcul the mask position with numpy : 0.03004288673400879 nb_pixel_total : 29107 time to create 1 rle with old method : 0.0340421199798584 time for calcul the mask position with numpy : 0.029640913009643555 nb_pixel_total : 84308 time to create 1 rle with old method : 0.10562705993652344 time for calcul the mask position with numpy : 0.03583788871765137 nb_pixel_total : 66310 time to create 1 rle with old method : 0.07729339599609375 time for calcul the mask position with numpy : 0.030710935592651367 nb_pixel_total : 218221 time to create 1 rle with new method : 0.726189136505127 time for calcul the mask position with numpy : 0.03227496147155762 nb_pixel_total : 28655 time to create 1 rle with old method : 0.034336090087890625 time for calcul the mask position with numpy : 0.029971837997436523 nb_pixel_total : 69043 time to create 1 rle with old method : 0.08105897903442383 time for calcul the mask position with numpy : 0.03296256065368652 nb_pixel_total : 117432 time to create 1 rle with old method : 0.14136505126953125 time for calcul the mask position with numpy : 0.02970290184020996 nb_pixel_total : 16738 time to create 1 rle with old method : 0.02163076400756836 create new chi : 4.861982345581055 time to delete rle : 0.005963802337646484 batch 1 Loaded 41 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 18068 TO DO : save crop sub photo not yet done ! save time : 6.282072305679321 nb_obj : 42 nb_hashtags : 5 time to prepare the origin masks : 4.759809970855713 time for calcul the mask position with numpy : 0.69582200050354 nb_pixel_total : 5504479 time to create 1 rle with new method : 0.7560157775878906 time for calcul the mask position with numpy : 0.029593229293823242 nb_pixel_total : 8090 time to create 1 rle with old method : 0.009705066680908203 time for calcul the mask position with numpy : 0.03134870529174805 nb_pixel_total : 15298 time to create 1 rle with old method : 0.017853260040283203 time for calcul the mask position with numpy : 0.030241966247558594 nb_pixel_total : 19417 time to create 1 rle with old method : 0.03021097183227539 time for calcul the mask position with numpy : 0.03693199157714844 nb_pixel_total : 17066 time to create 1 rle with old method : 0.03133678436279297 time for calcul the mask position with numpy : 0.03839397430419922 nb_pixel_total : 49308 time to create 1 rle with old method : 0.17447590827941895 time for calcul the mask position with numpy : 0.03040480613708496 nb_pixel_total : 57777 time to create 1 rle with old method : 0.06913375854492188 time for calcul the mask position with numpy : 0.02911233901977539 nb_pixel_total : 17544 time to create 1 rle with old method : 0.020354032516479492 time for calcul the mask position with numpy : 0.029597997665405273 nb_pixel_total : 25156 time to create 1 rle with old method : 0.029192686080932617 time for calcul the mask position with numpy : 0.029137611389160156 nb_pixel_total : 35148 time to create 1 rle with old method : 0.04342842102050781 time for calcul the mask position with numpy : 0.031108617782592773 nb_pixel_total : 16813 time to create 1 rle with old method : 0.021426916122436523 time for calcul the mask position with numpy : 0.02965068817138672 nb_pixel_total : 15794 time to create 1 rle with old method : 0.025809526443481445 time for calcul the mask position with numpy : 0.03314828872680664 nb_pixel_total : 18173 time to create 1 rle with old method : 0.025365829467773438 time for calcul the mask position with numpy : 0.029165267944335938 nb_pixel_total : 9177 time to create 1 rle with old method : 0.01078343391418457 time for calcul the mask position with numpy : 0.029580354690551758 nb_pixel_total : 44680 time to create 1 rle with old method : 0.05176258087158203 time for calcul the mask position with numpy : 0.02939438819885254 nb_pixel_total : 21625 time to create 1 rle with old method : 0.025163888931274414 time for calcul the mask position with numpy : 0.03000354766845703 nb_pixel_total : 102160 time to create 1 rle with old method : 0.1191551685333252 time for calcul the mask position with numpy : 0.031060457229614258 nb_pixel_total : 78108 time to create 1 rle with old method : 0.09066224098205566 time for calcul the mask position with numpy : 0.02910304069519043 nb_pixel_total : 12178 time to create 1 rle with old method : 0.014342546463012695 time for calcul the mask position with numpy : 0.03155970573425293 nb_pixel_total : 22223 time to create 1 rle with old method : 0.025967836380004883 time for calcul the mask position with numpy : 0.031996965408325195 nb_pixel_total : 18218 time to create 1 rle with old method : 0.021349191665649414 time for calcul the mask position with numpy : 0.02955484390258789 nb_pixel_total : 21101 time to create 1 rle with old method : 0.02450728416442871 time for calcul the mask position with numpy : 0.029173851013183594 nb_pixel_total : 11370 time to create 1 rle with old method : 0.013315439224243164 time for calcul the mask position with numpy : 0.0295870304107666 nb_pixel_total : 84399 time to create 1 rle with old method : 0.10225224494934082 time for calcul the mask position with numpy : 0.03158283233642578 nb_pixel_total : 27507 time to create 1 rle with old method : 0.032163381576538086 time for calcul the mask position with numpy : 0.03281140327453613 nb_pixel_total : 24337 time to create 1 rle with old method : 0.028212308883666992 time for calcul the mask position with numpy : 0.029127120971679688 nb_pixel_total : 67982 time to create 1 rle with old method : 0.07879447937011719 time for calcul the mask position with numpy : 0.03005528450012207 nb_pixel_total : 38582 time to create 1 rle with old method : 0.0453181266784668 time for calcul the mask position with numpy : 0.030310630798339844 nb_pixel_total : 50008 time to create 1 rle with old method : 0.06601595878601074 time for calcul the mask position with numpy : 0.029325008392333984 nb_pixel_total : 36401 time to create 1 rle with old method : 0.04198288917541504 time for calcul the mask position with numpy : 0.03013753890991211 nb_pixel_total : 34343 time to create 1 rle with old method : 0.04378199577331543 time for calcul the mask position with numpy : 0.02962470054626465 nb_pixel_total : 10275 time to create 1 rle with old method : 0.012649059295654297 time for calcul the mask position with numpy : 0.030664920806884766 nb_pixel_total : 3100 time to create 1 rle with old method : 0.0037603378295898438 time for calcul the mask position with numpy : 0.030125141143798828 nb_pixel_total : 6725 time to create 1 rle with old method : 0.007901906967163086 time for calcul the mask position with numpy : 0.0340876579284668 nb_pixel_total : 305496 time to create 1 rle with new method : 0.9043583869934082 time for calcul the mask position with numpy : 0.030941009521484375 nb_pixel_total : 124532 time to create 1 rle with old method : 0.17158913612365723 time for calcul the mask position with numpy : 0.030025005340576172 nb_pixel_total : 1514 time to create 1 rle with old method : 0.0025539398193359375 time for calcul the mask position with numpy : 0.030040740966796875 nb_pixel_total : 41587 time to create 1 rle with old method : 0.04984140396118164 time for calcul the mask position with numpy : 0.029655933380126953 nb_pixel_total : 2563 time to create 1 rle with old method : 0.00313568115234375 time for calcul the mask position with numpy : 0.029957294464111328 nb_pixel_total : 9107 time to create 1 rle with old method : 0.010722160339355469 time for calcul the mask position with numpy : 0.02956986427307129 nb_pixel_total : 12848 time to create 1 rle with old method : 0.015507221221923828 time for calcul the mask position with numpy : 0.03152132034301758 nb_pixel_total : 18182 time to create 1 rle with old method : 0.03300738334655762 time for calcul the mask position with numpy : 0.03568601608276367 nb_pixel_total : 9849 time to create 1 rle with old method : 0.011513710021972656 create new chi : 5.381571054458618 time to delete rle : 0.005036592483520508 batch 1 Loaded 87 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 22610 TO DO : save crop sub photo not yet done ! save time : 2.043785333633423 nb_obj : 34 nb_hashtags : 6 time to prepare the origin masks : 5.457205772399902 time for calcul the mask position with numpy : 0.3002195358276367 nb_pixel_total : 5349833 time to create 1 rle with new method : 1.0132334232330322 time for calcul the mask position with numpy : 0.031009435653686523 nb_pixel_total : 4901 time to create 1 rle with old method : 0.0059757232666015625 time for calcul the mask position with numpy : 0.03602766990661621 nb_pixel_total : 470428 time to create 1 rle with new method : 1.0871095657348633 time for calcul the mask position with numpy : 0.029825448989868164 nb_pixel_total : 9334 time to create 1 rle with old method : 0.01125025749206543 time for calcul the mask position with numpy : 0.03211641311645508 nb_pixel_total : 59105 time to create 1 rle with old method : 0.07213664054870605 time for calcul the mask position with numpy : 0.031321048736572266 nb_pixel_total : 15595 time to create 1 rle with old method : 0.023231029510498047 time for calcul the mask position with numpy : 0.029738903045654297 nb_pixel_total : 10151 time to create 1 rle with old method : 0.011996030807495117 time for calcul the mask position with numpy : 0.02967357635498047 nb_pixel_total : 39897 time to create 1 rle with old method : 0.04739046096801758 time for calcul the mask position with numpy : 0.031477928161621094 nb_pixel_total : 79108 time to create 1 rle with old method : 0.09337830543518066 time for calcul the mask position with numpy : 0.029320716857910156 nb_pixel_total : 4991 time to create 1 rle with old method : 0.006064653396606445 time for calcul the mask position with numpy : 0.02939152717590332 nb_pixel_total : 24758 time to create 1 rle with old method : 0.02899909019470215 time for calcul the mask position with numpy : 0.030016183853149414 nb_pixel_total : 109957 time to create 1 rle with old method : 0.13608598709106445 time for calcul the mask position with numpy : 0.03025674819946289 nb_pixel_total : 42689 time to create 1 rle with old method : 0.04924416542053223 time for calcul the mask position with numpy : 0.03007960319519043 nb_pixel_total : 138590 time to create 1 rle with old method : 0.16097235679626465 time for calcul the mask position with numpy : 0.029754161834716797 nb_pixel_total : 80411 time to create 1 rle with old method : 0.13099026679992676 time for calcul the mask position with numpy : 0.029392004013061523 nb_pixel_total : 76996 time to create 1 rle with old method : 0.09102487564086914 time for calcul the mask position with numpy : 0.03027057647705078 nb_pixel_total : 21499 time to create 1 rle with old method : 0.024746417999267578 time for calcul the mask position with numpy : 0.02927255630493164 nb_pixel_total : 7003 time to create 1 rle with old method : 0.008324861526489258 time for calcul the mask position with numpy : 0.029686689376831055 nb_pixel_total : 10671 time to create 1 rle with old method : 0.012647867202758789 time for calcul the mask position with numpy : 0.029370546340942383 nb_pixel_total : 5806 time to create 1 rle with old method : 0.0068547725677490234 time for calcul the mask position with numpy : 0.02950429916381836 nb_pixel_total : 14113 time to create 1 rle with old method : 0.01658320426940918 time for calcul the mask position with numpy : 0.030610084533691406 nb_pixel_total : 102345 time to create 1 rle with old method : 0.1257643699645996 time for calcul the mask position with numpy : 0.030078649520874023 nb_pixel_total : 99083 time to create 1 rle with old method : 0.13000893592834473 time for calcul the mask position with numpy : 0.030867338180541992 nb_pixel_total : 27474 time to create 1 rle with old method : 0.032663583755493164 time for calcul the mask position with numpy : 0.032259464263916016 nb_pixel_total : 17458 time to create 1 rle with old method : 0.020283937454223633 time for calcul the mask position with numpy : 0.030387401580810547 nb_pixel_total : 14224 time to create 1 rle with old method : 0.019332408905029297 time for calcul the mask position with numpy : 0.030409574508666992 nb_pixel_total : 17162 time to create 1 rle with old method : 0.020117759704589844 time for calcul the mask position with numpy : 0.0299375057220459 nb_pixel_total : 52524 time to create 1 rle with old method : 0.06441640853881836 time for calcul the mask position with numpy : 0.03216242790222168 nb_pixel_total : 7659 time to create 1 rle with old method : 0.009352445602416992 time for calcul the mask position with numpy : 0.032350778579711914 nb_pixel_total : 15727 time to create 1 rle with old method : 0.01918506622314453 time for calcul the mask position with numpy : 0.04079627990722656 nb_pixel_total : 19071 time to create 1 rle with old method : 0.03099846839904785 time for calcul the mask position with numpy : 0.03186464309692383 nb_pixel_total : 45381 time to create 1 rle with old method : 0.055300235748291016 time for calcul the mask position with numpy : 0.03335213661193848 nb_pixel_total : 14308 time to create 1 rle with old method : 0.02177286148071289 time for calcul the mask position with numpy : 0.030207395553588867 nb_pixel_total : 29266 time to create 1 rle with old method : 0.03556704521179199 time for calcul the mask position with numpy : 0.029426097869873047 nb_pixel_total : 12722 time to create 1 rle with old method : 0.016016244888305664 create new chi : 5.061471939086914 time to delete rle : 0.003454923629760742 batch 1 Loaded 72 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 20015 TO DO : save crop sub photo not yet done ! save time : 1.46592378616333 nb_obj : 94 nb_hashtags : 4 time to prepare the origin masks : 21.274346113204956 time for calcul the mask position with numpy : 0.37293314933776855 nb_pixel_total : 3935742 time to create 1 rle with new method : 1.0231542587280273 time for calcul the mask position with numpy : 0.029228925704956055 nb_pixel_total : 12224 time to create 1 rle with old method : 0.014111757278442383 time for calcul the mask position with numpy : 0.030010461807250977 nb_pixel_total : 30860 time to create 1 rle with old method : 0.03613090515136719 time for calcul the mask position with numpy : 0.03385305404663086 nb_pixel_total : 435 time to create 1 rle with old method : 0.0008151531219482422 time for calcul the mask position with numpy : 0.0310056209564209 nb_pixel_total : 52205 time to create 1 rle with old method : 0.061380624771118164 time for calcul the mask position with numpy : 0.03013324737548828 nb_pixel_total : 2944 time to create 1 rle with old method : 0.0035839080810546875 time for calcul the mask position with numpy : 0.029997587203979492 nb_pixel_total : 864 time to create 1 rle with old method : 0.001155853271484375 time for calcul the mask position with numpy : 0.03051471710205078 nb_pixel_total : 5198 time to create 1 rle with old method : 0.006682872772216797 time for calcul the mask position with numpy : 0.029570817947387695 nb_pixel_total : 17202 time to create 1 rle with old method : 0.020214319229125977 time for calcul the mask position with numpy : 0.03017425537109375 nb_pixel_total : 82449 time to create 1 rle with old method : 0.0930023193359375 time for calcul the mask position with numpy : 0.02794194221496582 nb_pixel_total : 560 time to create 1 rle with old method : 0.0010225772857666016 time for calcul the mask position with numpy : 0.03105902671813965 nb_pixel_total : 17050 time to create 1 rle with old method : 0.019001245498657227 time for calcul the mask position with numpy : 0.029614925384521484 nb_pixel_total : 4953 time to create 1 rle with old method : 0.005944252014160156 time for calcul the mask position with numpy : 0.03054976463317871 nb_pixel_total : 95224 time to create 1 rle with old method : 0.10680651664733887 time for calcul the mask position with numpy : 0.030094146728515625 nb_pixel_total : 33945 time to create 1 rle with old method : 0.03963947296142578 time for calcul the mask position with numpy : 0.03071308135986328 nb_pixel_total : 1398 time to create 1 rle with old method : 0.0020139217376708984 time for calcul the mask position with numpy : 0.03136730194091797 nb_pixel_total : 3536 time to create 1 rle with old method : 0.005093097686767578 time for calcul the mask position with numpy : 0.030681848526000977 nb_pixel_total : 2394 time to create 1 rle with old method : 0.0031423568725585938 time for calcul the mask position with numpy : 0.031937599182128906 nb_pixel_total : 110388 time to create 1 rle with old method : 0.12752842903137207 time for calcul the mask position with numpy : 0.029328107833862305 nb_pixel_total : 464 time to create 1 rle with old method : 0.0008029937744140625 time for calcul the mask position with numpy : 0.030308008193969727 nb_pixel_total : 18890 time to create 1 rle with old method : 0.024613618850708008 time for calcul the mask position with numpy : 0.0304110050201416 nb_pixel_total : 25186 time to create 1 rle with old method : 0.02947688102722168 time for calcul the mask position with numpy : 0.030457735061645508 nb_pixel_total : 131 time to create 1 rle with old method : 0.0003914833068847656 time for calcul the mask position with numpy : 0.030796051025390625 nb_pixel_total : 13102 time to create 1 rle with old method : 0.015851974487304688 time for calcul the mask position with numpy : 0.02948141098022461 nb_pixel_total : 11968 time to create 1 rle with old method : 0.014068126678466797 time for calcul the mask position with numpy : 0.04508566856384277 nb_pixel_total : 1311950 time to create 1 rle with new method : 1.0614862442016602 time for calcul the mask position with numpy : 0.030878067016601562 nb_pixel_total : 68024 time to create 1 rle with old method : 0.0822300910949707 time for calcul the mask position with numpy : 0.02956390380859375 nb_pixel_total : 398 time to create 1 rle with old method : 0.0008835792541503906 time for calcul the mask position with numpy : 0.03071451187133789 nb_pixel_total : 65000 time to create 1 rle with old method : 0.07577943801879883 time for calcul the mask position with numpy : 0.028940677642822266 nb_pixel_total : 22804 time to create 1 rle with old method : 0.026328563690185547 time for calcul the mask position with numpy : 0.029673337936401367 nb_pixel_total : 27539 time to create 1 rle with old method : 0.03298640251159668 time for calcul the mask position with numpy : 0.030974388122558594 nb_pixel_total : 1338 time to create 1 rle with old method : 0.002437114715576172 time for calcul the mask position with numpy : 0.031198978424072266 nb_pixel_total : 10878 time to create 1 rle with old method : 0.012839794158935547 time for calcul the mask position with numpy : 0.029707670211791992 nb_pixel_total : 699 time to create 1 rle with old method : 0.0014696121215820312 time for calcul the mask position with numpy : 0.02960658073425293 nb_pixel_total : 20774 time to create 1 rle with old method : 0.024564027786254883 time for calcul the mask position with numpy : 0.029673099517822266 nb_pixel_total : 5469 time to create 1 rle with old method : 0.006723642349243164 time for calcul the mask position with numpy : 0.030083656311035156 nb_pixel_total : 10728 time to create 1 rle with old method : 0.017529726028442383 time for calcul the mask position with numpy : 0.03272652626037598 nb_pixel_total : 238 time to create 1 rle with old method : 0.0005109310150146484 time for calcul the mask position with numpy : 0.029984235763549805 nb_pixel_total : 7 time to create 1 rle with old method : 6.914138793945312e-05 time for calcul the mask position with numpy : 0.030120849609375 nb_pixel_total : 3018 time to create 1 rle with old method : 0.003953695297241211 time for calcul the mask position with numpy : 0.02983403205871582 nb_pixel_total : 39070 time to create 1 rle with old method : 0.0455014705657959 time for calcul the mask position with numpy : 0.029564619064331055 nb_pixel_total : 1986 time to create 1 rle with old method : 0.002699613571166992 time for calcul the mask position with numpy : 0.030017852783203125 nb_pixel_total : 20936 time to create 1 rle with old method : 0.024847030639648438 time for calcul the mask position with numpy : 0.02958846092224121 nb_pixel_total : 669 time to create 1 rle with old method : 0.0010485649108886719 time for calcul the mask position with numpy : 0.0312192440032959 nb_pixel_total : 4021 time to create 1 rle with old method : 0.0047490596771240234 time for calcul the mask position with numpy : 0.029977083206176758 nb_pixel_total : 40569 time to create 1 rle with old method : 0.04737138748168945 time for calcul the mask position with numpy : 0.03047800064086914 nb_pixel_total : 291 time to create 1 rle with old method : 0.0005946159362792969 time for calcul the mask position with numpy : 0.03000497817993164 nb_pixel_total : 2083 time to create 1 rle with old method : 0.002870798110961914 time for calcul the mask position with numpy : 0.03040766716003418 nb_pixel_total : 27099 time to create 1 rle with old method : 0.03343701362609863 time for calcul the mask position with numpy : 0.03136324882507324 nb_pixel_total : 63603 time to create 1 rle with old method : 0.07720232009887695 time for calcul the mask position with numpy : 0.030221939086914062 nb_pixel_total : 25060 time to create 1 rle with old method : 0.029270172119140625 time for calcul the mask position with numpy : 0.029581308364868164 nb_pixel_total : 1681 time to create 1 rle with old method : 0.002176523208618164 time for calcul the mask position with numpy : 0.02997279167175293 nb_pixel_total : 53239 time to create 1 rle with old method : 0.06307005882263184 time for calcul the mask position with numpy : 0.029785633087158203 nb_pixel_total : 12406 time to create 1 rle with old method : 0.01466226577758789 time for calcul the mask position with numpy : 0.029532909393310547 nb_pixel_total : 23495 time to create 1 rle with old method : 0.028887510299682617 time for calcul the mask position with numpy : 0.033632516860961914 nb_pixel_total : 60546 time to create 1 rle with old method : 0.0914156436920166 time for calcul the mask position with numpy : 0.03397679328918457 nb_pixel_total : 1197 time to create 1 rle with old method : 0.0016131401062011719 time for calcul the mask position with numpy : 0.03683900833129883 nb_pixel_total : 7716 time to create 1 rle with old method : 0.009058952331542969 time for calcul the mask position with numpy : 0.02956223487854004 nb_pixel_total : 5830 time to create 1 rle with old method : 0.006943941116333008 time for calcul the mask position with numpy : 0.02948594093322754 nb_pixel_total : 258 time to create 1 rle with old method : 0.0004584789276123047 time for calcul the mask position with numpy : 0.029307842254638672 nb_pixel_total : 11687 time to create 1 rle with old method : 0.01360630989074707 time for calcul the mask position with numpy : 0.029574871063232422 nb_pixel_total : 1829 time to create 1 rle with old method : 0.002504587173461914 time for calcul the mask position with numpy : 0.03659319877624512 nb_pixel_total : 602 time to create 1 rle with old method : 0.0014078617095947266 time for calcul the mask position with numpy : 0.03639960289001465 nb_pixel_total : 1547 time to create 1 rle with old method : 0.0029251575469970703 time for calcul the mask position with numpy : 0.037749528884887695 nb_pixel_total : 11714 time to create 1 rle with old method : 0.020734786987304688 time for calcul the mask position with numpy : 0.029870986938476562 nb_pixel_total : 8525 time to create 1 rle with old method : 0.014002561569213867 time for calcul the mask position with numpy : 0.03624105453491211 nb_pixel_total : 1408 time to create 1 rle with old method : 0.0026464462280273438 time for calcul the mask position with numpy : 0.03990530967712402 nb_pixel_total : 107 time to create 1 rle with old method : 0.0005502700805664062 time for calcul the mask position with numpy : 0.03054666519165039 nb_pixel_total : 7069 time to create 1 rle with old method : 0.008640289306640625 time for calcul the mask position with numpy : 0.030467748641967773 nb_pixel_total : 23466 time to create 1 rle with old method : 0.02766275405883789 time for calcul the mask position with numpy : 0.033002376556396484 nb_pixel_total : 22937 time to create 1 rle with old method : 0.03734159469604492 time for calcul the mask position with numpy : 0.031228303909301758 nb_pixel_total : 1762 time to create 1 rle with old method : 0.002338409423828125 time for calcul the mask position with numpy : 0.029900550842285156 nb_pixel_total : 6052 time to create 1 rle with old method : 0.0071201324462890625 time for calcul the mask position with numpy : 0.03029012680053711 nb_pixel_total : 3041 time to create 1 rle with old method : 0.004627704620361328 time for calcul the mask position with numpy : 0.032100677490234375 nb_pixel_total : 549 time to create 1 rle with old method : 0.000728607177734375 time for calcul the mask position with numpy : 0.028766870498657227 nb_pixel_total : 18343 time to create 1 rle with old method : 0.021093130111694336 time for calcul the mask position with numpy : 0.0306093692779541 nb_pixel_total : 104573 time to create 1 rle with old method : 0.12293362617492676 time for calcul the mask position with numpy : 0.029549121856689453 nb_pixel_total : 13709 time to create 1 rle with old method : 0.016341209411621094 time for calcul the mask position with numpy : 0.029353618621826172 nb_pixel_total : 1308 time to create 1 rle with old method : 0.001795053482055664 time for calcul the mask position with numpy : 0.030643224716186523 nb_pixel_total : 9128 time to create 1 rle with old method : 0.010795354843139648 time for calcul the mask position with numpy : 0.029628753662109375 nb_pixel_total : 15811 time to create 1 rle with old method : 0.01861095428466797 time for calcul the mask position with numpy : 0.02975153923034668 nb_pixel_total : 962 time to create 1 rle with old method : 0.0016825199127197266 time for calcul the mask position with numpy : 0.030558347702026367 nb_pixel_total : 22734 time to create 1 rle with old method : 0.026185274124145508 time for calcul the mask position with numpy : 0.02942490577697754 nb_pixel_total : 98468 time to create 1 rle with old method : 0.11235761642456055 time for calcul the mask position with numpy : 0.030269861221313477 nb_pixel_total : 32874 time to create 1 rle with old method : 0.03768491744995117 time for calcul the mask position with numpy : 0.02934432029724121 nb_pixel_total : 104699 time to create 1 rle with old method : 0.1184389591217041 time for calcul the mask position with numpy : 0.02862858772277832 nb_pixel_total : 8899 time to create 1 rle with old method : 0.01068568229675293 time for calcul the mask position with numpy : 0.028366804122924805 nb_pixel_total : 17844 time to create 1 rle with old method : 0.02097797393798828 time for calcul the mask position with numpy : 0.029362916946411133 nb_pixel_total : 3416 time to create 1 rle with old method : 0.004068136215209961 time for calcul the mask position with numpy : 0.028657913208007812 nb_pixel_total : 10977 time to create 1 rle with old method : 0.012896060943603516 time for calcul the mask position with numpy : 0.0287322998046875 nb_pixel_total : 30586 time to create 1 rle with old method : 0.0375673770904541 time for calcul the mask position with numpy : 0.02879166603088379 nb_pixel_total : 13615 time to create 1 rle with old method : 0.016479969024658203 time for calcul the mask position with numpy : 0.02886033058166504 nb_pixel_total : 12490 time to create 1 rle with old method : 0.014934778213500977 time for calcul the mask position with numpy : 0.029361724853515625 nb_pixel_total : 60 time to create 1 rle with old method : 0.0007314682006835938 time for calcul the mask position with numpy : 0.02985978126525879 nb_pixel_total : 7518 time to create 1 rle with old method : 0.009838104248046875 create new chi : 7.623945713043213 time to delete rle : 0.011316299438476562 batch 1 Loaded 232 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 36163 TO DO : save crop sub photo not yet done ! save time : 10.851900577545166 nb_obj : 34 nb_hashtags : 3 time to prepare the origin masks : 4.108865737915039 time for calcul the mask position with numpy : 0.6390330791473389 nb_pixel_total : 5835983 time to create 1 rle with new method : 0.8067951202392578 time for calcul the mask position with numpy : 0.030787229537963867 nb_pixel_total : 41575 time to create 1 rle with old method : 0.04876399040222168 time for calcul the mask position with numpy : 0.03445076942443848 nb_pixel_total : 132794 time to create 1 rle with old method : 0.17789411544799805 time for calcul the mask position with numpy : 0.030731678009033203 nb_pixel_total : 18506 time to create 1 rle with old method : 0.0232088565826416 time for calcul the mask position with numpy : 0.030589580535888672 nb_pixel_total : 29688 time to create 1 rle with old method : 0.039237022399902344 time for calcul the mask position with numpy : 0.033384084701538086 nb_pixel_total : 19530 time to create 1 rle with old method : 0.033083438873291016 time for calcul the mask position with numpy : 0.030134201049804688 nb_pixel_total : 75540 time to create 1 rle with old method : 0.09461021423339844 time for calcul the mask position with numpy : 0.03289175033569336 nb_pixel_total : 90817 time to create 1 rle with old method : 0.10649514198303223 time for calcul the mask position with numpy : 0.0358884334564209 nb_pixel_total : 81039 time to create 1 rle with old method : 0.09427022933959961 time for calcul the mask position with numpy : 0.030181884765625 nb_pixel_total : 72948 time to create 1 rle with old method : 0.0840911865234375 time for calcul the mask position with numpy : 0.02917933464050293 nb_pixel_total : 9344 time to create 1 rle with old method : 0.01098775863647461 time for calcul the mask position with numpy : 0.03138017654418945 nb_pixel_total : 33026 time to create 1 rle with old method : 0.053430795669555664 time for calcul the mask position with numpy : 0.0316472053527832 nb_pixel_total : 8507 time to create 1 rle with old method : 0.010072469711303711 time for calcul the mask position with numpy : 0.02994847297668457 nb_pixel_total : 15889 time to create 1 rle with old method : 0.018516063690185547 time for calcul the mask position with numpy : 0.029778242111206055 nb_pixel_total : 34753 time to create 1 rle with old method : 0.04027581214904785 time for calcul the mask position with numpy : 0.029875755310058594 nb_pixel_total : 7048 time to create 1 rle with old method : 0.00822138786315918 time for calcul the mask position with numpy : 0.029259920120239258 nb_pixel_total : 20164 time to create 1 rle with old method : 0.023346424102783203 time for calcul the mask position with numpy : 0.029690265655517578 nb_pixel_total : 34505 time to create 1 rle with old method : 0.04601335525512695 time for calcul the mask position with numpy : 0.033673763275146484 nb_pixel_total : 10806 time to create 1 rle with old method : 0.01796722412109375 time for calcul the mask position with numpy : 0.05257749557495117 nb_pixel_total : 7680 time to create 1 rle with old method : 0.009090900421142578 time for calcul the mask position with numpy : 0.0318300724029541 nb_pixel_total : 9330 time to create 1 rle with old method : 0.011568307876586914 time for calcul the mask position with numpy : 0.03172898292541504 nb_pixel_total : 18858 time to create 1 rle with old method : 0.022047758102416992 time for calcul the mask position with numpy : 0.02987813949584961 nb_pixel_total : 12140 time to create 1 rle with old method : 0.0143280029296875 time for calcul the mask position with numpy : 0.02996516227722168 nb_pixel_total : 46438 time to create 1 rle with old method : 0.05798792839050293 time for calcul the mask position with numpy : 0.030207157135009766 nb_pixel_total : 21300 time to create 1 rle with old method : 0.026558637619018555 time for calcul the mask position with numpy : 0.0314183235168457 nb_pixel_total : 78093 time to create 1 rle with old method : 0.0940561294555664 time for calcul the mask position with numpy : 0.05823326110839844 nb_pixel_total : 8443 time to create 1 rle with old method : 0.02094554901123047 time for calcul the mask position with numpy : 0.040780067443847656 nb_pixel_total : 93931 time to create 1 rle with old method : 0.13128352165222168 time for calcul the mask position with numpy : 0.030065059661865234 nb_pixel_total : 19395 time to create 1 rle with old method : 0.022275924682617188 time for calcul the mask position with numpy : 0.030444622039794922 nb_pixel_total : 12078 time to create 1 rle with old method : 0.014124393463134766 time for calcul the mask position with numpy : 0.02977299690246582 nb_pixel_total : 83628 time to create 1 rle with old method : 0.09978175163269043 time for calcul the mask position with numpy : 0.030830860137939453 nb_pixel_total : 34958 time to create 1 rle with old method : 0.04109525680541992 time for calcul the mask position with numpy : 0.02975940704345703 nb_pixel_total : 7777 time to create 1 rle with old method : 0.011220932006835938 time for calcul the mask position with numpy : 0.03637099266052246 nb_pixel_total : 10921 time to create 1 rle with old method : 0.020434856414794922 time for calcul the mask position with numpy : 0.03438591957092285 nb_pixel_total : 12808 time to create 1 rle with old method : 0.015030860900878906 create new chi : 4.164579153060913 time to delete rle : 0.0031876564025878906 batch 1 Loaded 70 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 17080 TO DO : save crop sub photo not yet done ! save time : 1.3870084285736084 nb_obj : 30 nb_hashtags : 3 time to prepare the origin masks : 4.362985372543335 time for calcul the mask position with numpy : 0.5040490627288818 nb_pixel_total : 5547817 time to create 1 rle with new method : 0.8131892681121826 time for calcul the mask position with numpy : 0.03300666809082031 nb_pixel_total : 264154 time to create 1 rle with new method : 0.617039680480957 time for calcul the mask position with numpy : 0.030033349990844727 nb_pixel_total : 197607 time to create 1 rle with new method : 0.557950496673584 time for calcul the mask position with numpy : 0.032172441482543945 nb_pixel_total : 23498 time to create 1 rle with old method : 0.030719280242919922 time for calcul the mask position with numpy : 0.02934408187866211 nb_pixel_total : 127339 time to create 1 rle with old method : 0.19704985618591309 time for calcul the mask position with numpy : 0.029099702835083008 nb_pixel_total : 19094 time to create 1 rle with old method : 0.022059202194213867 time for calcul the mask position with numpy : 0.0293118953704834 nb_pixel_total : 60417 time to create 1 rle with old method : 0.06995630264282227 time for calcul the mask position with numpy : 0.02909111976623535 nb_pixel_total : 43892 time to create 1 rle with old method : 0.05070328712463379 time for calcul the mask position with numpy : 0.029171228408813477 nb_pixel_total : 122114 time to create 1 rle with old method : 0.14089250564575195 time for calcul the mask position with numpy : 0.02877497673034668 nb_pixel_total : 11727 time to create 1 rle with old method : 0.013825654983520508 time for calcul the mask position with numpy : 0.028814315795898438 nb_pixel_total : 40128 time to create 1 rle with old method : 0.04640483856201172 time for calcul the mask position with numpy : 0.028820514678955078 nb_pixel_total : 23525 time to create 1 rle with old method : 0.027456283569335938 time for calcul the mask position with numpy : 0.031256675720214844 nb_pixel_total : 89603 time to create 1 rle with old method : 0.10475444793701172 time for calcul the mask position with numpy : 0.028900623321533203 nb_pixel_total : 13660 time to create 1 rle with old method : 0.015724897384643555 time for calcul the mask position with numpy : 0.028962135314941406 nb_pixel_total : 6378 time to create 1 rle with old method : 0.007367610931396484 time for calcul the mask position with numpy : 0.028943300247192383 nb_pixel_total : 16074 time to create 1 rle with old method : 0.01874089241027832 time for calcul the mask position with numpy : 0.029656410217285156 nb_pixel_total : 9545 time to create 1 rle with old method : 0.011246442794799805 time for calcul the mask position with numpy : 0.029262781143188477 nb_pixel_total : 13652 time to create 1 rle with old method : 0.01603221893310547 time for calcul the mask position with numpy : 0.029219627380371094 nb_pixel_total : 43775 time to create 1 rle with old method : 0.050812721252441406 time for calcul the mask position with numpy : 0.029718399047851562 nb_pixel_total : 144497 time to create 1 rle with old method : 0.20357775688171387 time for calcul the mask position with numpy : 0.02938675880432129 nb_pixel_total : 33690 time to create 1 rle with old method : 0.039485931396484375 time for calcul the mask position with numpy : 0.0291750431060791 nb_pixel_total : 23729 time to create 1 rle with old method : 0.029045581817626953 time for calcul the mask position with numpy : 0.029680967330932617 nb_pixel_total : 45612 time to create 1 rle with old method : 0.05291485786437988 time for calcul the mask position with numpy : 0.029285192489624023 nb_pixel_total : 8576 time to create 1 rle with old method : 0.010066747665405273 time for calcul the mask position with numpy : 0.029483556747436523 nb_pixel_total : 31512 time to create 1 rle with old method : 0.037188053131103516 time for calcul the mask position with numpy : 0.029321908950805664 nb_pixel_total : 16124 time to create 1 rle with old method : 0.019999980926513672 time for calcul the mask position with numpy : 0.029186487197875977 nb_pixel_total : 13155 time to create 1 rle with old method : 0.015412092208862305 time for calcul the mask position with numpy : 0.029293060302734375 nb_pixel_total : 15469 time to create 1 rle with old method : 0.017990827560424805 time for calcul the mask position with numpy : 0.029270410537719727 nb_pixel_total : 11265 time to create 1 rle with old method : 0.013118267059326172 time for calcul the mask position with numpy : 0.02921009063720703 nb_pixel_total : 10842 time to create 1 rle with old method : 0.012707233428955078 time for calcul the mask position with numpy : 0.02925395965576172 nb_pixel_total : 21770 time to create 1 rle with old method : 0.02526998519897461 create new chi : 4.772599935531616 time to delete rle : 0.0029015541076660156 batch 1 Loaded 61 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 18226 TO DO : save crop sub photo not yet done ! save time : 2.8552913665771484 nb_obj : 36 nb_hashtags : 2 time to prepare the origin masks : 5.784074544906616 time for calcul the mask position with numpy : 0.47886085510253906 nb_pixel_total : 4651312 time to create 1 rle with new method : 0.6410205364227295 time for calcul the mask position with numpy : 0.034308671951293945 nb_pixel_total : 75465 time to create 1 rle with old method : 0.0892026424407959 time for calcul the mask position with numpy : 0.029523849487304688 nb_pixel_total : 25449 time to create 1 rle with old method : 0.029569387435913086 time for calcul the mask position with numpy : 0.02930164337158203 nb_pixel_total : 17265 time to create 1 rle with old method : 0.020267963409423828 time for calcul the mask position with numpy : 0.04324626922607422 nb_pixel_total : 261728 time to create 1 rle with new method : 0.7295174598693848 time for calcul the mask position with numpy : 0.030429363250732422 nb_pixel_total : 121364 time to create 1 rle with old method : 0.14128684997558594 time for calcul the mask position with numpy : 0.030015945434570312 nb_pixel_total : 14113 time to create 1 rle with old method : 0.016617774963378906 time for calcul the mask position with numpy : 0.02929854393005371 nb_pixel_total : 9443 time to create 1 rle with old method : 0.011479854583740234 time for calcul the mask position with numpy : 0.029938459396362305 nb_pixel_total : 27800 time to create 1 rle with old method : 0.03526592254638672 time for calcul the mask position with numpy : 0.029356002807617188 nb_pixel_total : 6373 time to create 1 rle with old method : 0.007599592208862305 time for calcul the mask position with numpy : 0.03239727020263672 nb_pixel_total : 346023 time to create 1 rle with new method : 0.6774296760559082 time for calcul the mask position with numpy : 0.029717206954956055 nb_pixel_total : 6598 time to create 1 rle with old method : 0.007814168930053711 time for calcul the mask position with numpy : 0.029479265213012695 nb_pixel_total : 30362 time to create 1 rle with old method : 0.035100460052490234 time for calcul the mask position with numpy : 0.030520915985107422 nb_pixel_total : 238666 time to create 1 rle with new method : 0.5210556983947754 time for calcul the mask position with numpy : 0.029490232467651367 nb_pixel_total : 107607 time to create 1 rle with old method : 0.12531685829162598 time for calcul the mask position with numpy : 0.0312042236328125 nb_pixel_total : 50839 time to create 1 rle with old method : 0.09509706497192383 time for calcul the mask position with numpy : 0.030910015106201172 nb_pixel_total : 76773 time to create 1 rle with old method : 0.08917355537414551 time for calcul the mask position with numpy : 0.02938985824584961 nb_pixel_total : 4071 time to create 1 rle with old method : 0.005161762237548828 time for calcul the mask position with numpy : 0.02950572967529297 nb_pixel_total : 98793 time to create 1 rle with old method : 0.11197853088378906 time for calcul the mask position with numpy : 0.028688430786132812 nb_pixel_total : 16144 time to create 1 rle with old method : 0.018455028533935547 time for calcul the mask position with numpy : 0.030272722244262695 nb_pixel_total : 102558 time to create 1 rle with old method : 0.11970973014831543 time for calcul the mask position with numpy : 0.029403209686279297 nb_pixel_total : 9475 time to create 1 rle with old method : 0.011168241500854492 time for calcul the mask position with numpy : 0.029329776763916016 nb_pixel_total : 16131 time to create 1 rle with old method : 0.018976211547851562 time for calcul the mask position with numpy : 0.03223681449890137 nb_pixel_total : 252684 time to create 1 rle with new method : 0.8481841087341309 time for calcul the mask position with numpy : 0.029296875 nb_pixel_total : 22515 time to create 1 rle with old method : 0.026417016983032227 time for calcul the mask position with numpy : 0.029456377029418945 nb_pixel_total : 1930 time to create 1 rle with old method : 0.0025162696838378906 time for calcul the mask position with numpy : 0.029314041137695312 nb_pixel_total : 43106 time to create 1 rle with old method : 0.05061531066894531 time for calcul the mask position with numpy : 0.02955031394958496 nb_pixel_total : 23477 time to create 1 rle with old method : 0.027246713638305664 time for calcul the mask position with numpy : 0.0312044620513916 nb_pixel_total : 16243 time to create 1 rle with old method : 0.024932146072387695 time for calcul the mask position with numpy : 0.03645515441894531 nb_pixel_total : 29787 time to create 1 rle with old method : 0.04103446006774902 time for calcul the mask position with numpy : 0.03257918357849121 nb_pixel_total : 30659 time to create 1 rle with old method : 0.038994550704956055 time for calcul the mask position with numpy : 0.032103776931762695 nb_pixel_total : 131977 time to create 1 rle with old method : 0.15219926834106445 time for calcul the mask position with numpy : 0.029308795928955078 nb_pixel_total : 9006 time to create 1 rle with old method : 0.01045083999633789 time for calcul the mask position with numpy : 0.02980327606201172 nb_pixel_total : 97975 time to create 1 rle with old method : 0.11491274833679199 time for calcul the mask position with numpy : 0.029192209243774414 nb_pixel_total : 2473 time to create 1 rle with old method : 0.0030126571655273438 time for calcul the mask position with numpy : 0.029796123504638672 nb_pixel_total : 65413 time to create 1 rle with old method : 0.07746386528015137 time for calcul the mask position with numpy : 0.029142379760742188 nb_pixel_total : 8643 time to create 1 rle with old method : 0.010133743286132812 create new chi : 6.726786136627197 time to delete rle : 0.004496335983276367 batch 1 Loaded 74 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 25756 TO DO : save crop sub photo not yet done ! save time : 5.483908176422119 nb_obj : 43 nb_hashtags : 3 time to prepare the origin masks : 4.767906904220581 time for calcul the mask position with numpy : 0.6484994888305664 nb_pixel_total : 5785662 time to create 1 rle with new method : 1.1353843212127686 time for calcul the mask position with numpy : 0.029479026794433594 nb_pixel_total : 11113 time to create 1 rle with old method : 0.013153314590454102 time for calcul the mask position with numpy : 0.02973318099975586 nb_pixel_total : 27019 time to create 1 rle with old method : 0.03154706954956055 time for calcul the mask position with numpy : 0.029826879501342773 nb_pixel_total : 17408 time to create 1 rle with old method : 0.02030634880065918 time for calcul the mask position with numpy : 0.02958202362060547 nb_pixel_total : 6733 time to create 1 rle with old method : 0.00804758071899414 time for calcul the mask position with numpy : 0.029650211334228516 nb_pixel_total : 35320 time to create 1 rle with old method : 0.045667409896850586 time for calcul the mask position with numpy : 0.029241085052490234 nb_pixel_total : 61321 time to create 1 rle with old method : 0.07215452194213867 time for calcul the mask position with numpy : 0.029285907745361328 nb_pixel_total : 11853 time to create 1 rle with old method : 0.014047622680664062 time for calcul the mask position with numpy : 0.029401540756225586 nb_pixel_total : 45438 time to create 1 rle with old method : 0.05340862274169922 time for calcul the mask position with numpy : 0.029165029525756836 nb_pixel_total : 9657 time to create 1 rle with old method : 0.011368513107299805 time for calcul the mask position with numpy : 0.029404401779174805 nb_pixel_total : 38164 time to create 1 rle with old method : 0.04399514198303223 time for calcul the mask position with numpy : 0.02962017059326172 nb_pixel_total : 49758 time to create 1 rle with old method : 0.058841705322265625 time for calcul the mask position with numpy : 0.02925395965576172 nb_pixel_total : 12568 time to create 1 rle with old method : 0.017671823501586914 time for calcul the mask position with numpy : 0.03139805793762207 nb_pixel_total : 11651 time to create 1 rle with old method : 0.01849961280822754 time for calcul the mask position with numpy : 0.03348374366760254 nb_pixel_total : 8420 time to create 1 rle with old method : 0.013755321502685547 time for calcul the mask position with numpy : 0.04891157150268555 nb_pixel_total : 5402 time to create 1 rle with old method : 0.006388664245605469 time for calcul the mask position with numpy : 0.02996540069580078 nb_pixel_total : 41517 time to create 1 rle with old method : 0.04842948913574219 time for calcul the mask position with numpy : 0.0294950008392334 nb_pixel_total : 27309 time to create 1 rle with old method : 0.03180265426635742 time for calcul the mask position with numpy : 0.02889537811279297 nb_pixel_total : 479 time to create 1 rle with old method : 0.0008223056793212891 time for calcul the mask position with numpy : 0.029033660888671875 nb_pixel_total : 23419 time to create 1 rle with old method : 0.027558565139770508 time for calcul the mask position with numpy : 0.029113054275512695 nb_pixel_total : 38217 time to create 1 rle with old method : 0.04562067985534668 time for calcul the mask position with numpy : 0.03278088569641113 nb_pixel_total : 21462 time to create 1 rle with old method : 0.02633833885192871 time for calcul the mask position with numpy : 0.0337681770324707 nb_pixel_total : 7611 time to create 1 rle with old method : 0.009041547775268555 time for calcul the mask position with numpy : 0.03054666519165039 nb_pixel_total : 53588 time to create 1 rle with old method : 0.06493616104125977 time for calcul the mask position with numpy : 0.030796051025390625 nb_pixel_total : 2867 time to create 1 rle with old method : 0.0034189224243164062 time for calcul the mask position with numpy : 0.029662370681762695 nb_pixel_total : 23862 time to create 1 rle with old method : 0.03855443000793457 time for calcul the mask position with numpy : 0.03381204605102539 nb_pixel_total : 35387 time to create 1 rle with old method : 0.0411376953125 time for calcul the mask position with numpy : 0.029280662536621094 nb_pixel_total : 12417 time to create 1 rle with old method : 0.01457834243774414 time for calcul the mask position with numpy : 0.029671430587768555 nb_pixel_total : 57570 time to create 1 rle with old method : 0.06679415702819824 time for calcul the mask position with numpy : 0.03124690055847168 nb_pixel_total : 63595 time to create 1 rle with old method : 0.0771939754486084 time for calcul the mask position with numpy : 0.03370976448059082 nb_pixel_total : 26487 time to create 1 rle with old method : 0.0417940616607666 time for calcul the mask position with numpy : 0.02944016456604004 nb_pixel_total : 42070 time to create 1 rle with old method : 0.04890584945678711 time for calcul the mask position with numpy : 0.03167605400085449 nb_pixel_total : 35551 time to create 1 rle with old method : 0.044286489486694336 time for calcul the mask position with numpy : 0.029828548431396484 nb_pixel_total : 35172 time to create 1 rle with old method : 0.04308128356933594 time for calcul the mask position with numpy : 0.02948760986328125 nb_pixel_total : 16590 time to create 1 rle with old method : 0.019977807998657227 time for calcul the mask position with numpy : 0.03368425369262695 nb_pixel_total : 8875 time to create 1 rle with old method : 0.013941287994384766 time for calcul the mask position with numpy : 0.03628969192504883 nb_pixel_total : 60928 time to create 1 rle with old method : 0.07553672790527344 time for calcul the mask position with numpy : 0.032044410705566406 nb_pixel_total : 27199 time to create 1 rle with old method : 0.035344839096069336 time for calcul the mask position with numpy : 0.03332042694091797 nb_pixel_total : 18036 time to create 1 rle with old method : 0.030017852783203125 time for calcul the mask position with numpy : 0.03156328201293945 nb_pixel_total : 168780 time to create 1 rle with new method : 1.1375365257263184 time for calcul the mask position with numpy : 0.029513120651245117 nb_pixel_total : 19917 time to create 1 rle with old method : 0.02355194091796875 time for calcul the mask position with numpy : 0.029513120651245117 nb_pixel_total : 19714 time to create 1 rle with old method : 0.02303481101989746 time for calcul the mask position with numpy : 0.02971196174621582 nb_pixel_total : 11667 time to create 1 rle with old method : 0.013649463653564453 time for calcul the mask position with numpy : 0.04044818878173828 nb_pixel_total : 12467 time to create 1 rle with old method : 0.020540952682495117 create new chi : 5.694636106491089 time to delete rle : 0.006543636322021484 batch 1 Loaded 93 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 21505 TO DO : save crop sub photo not yet done ! save time : 3.807560920715332 nb_obj : 59 nb_hashtags : 5 time to prepare the origin masks : 5.403556823730469 time for calcul the mask position with numpy : 0.7978520393371582 nb_pixel_total : 5436622 time to create 1 rle with new method : 0.8021438121795654 time for calcul the mask position with numpy : 0.031496524810791016 nb_pixel_total : 31962 time to create 1 rle with old method : 0.045670509338378906 time for calcul the mask position with numpy : 0.04249119758605957 nb_pixel_total : 24270 time to create 1 rle with old method : 0.03414344787597656 time for calcul the mask position with numpy : 0.03085803985595703 nb_pixel_total : 3996 time to create 1 rle with old method : 0.004908323287963867 time for calcul the mask position with numpy : 0.03289961814880371 nb_pixel_total : 34782 time to create 1 rle with old method : 0.04187917709350586 time for calcul the mask position with numpy : 0.02967524528503418 nb_pixel_total : 22486 time to create 1 rle with old method : 0.027698516845703125 time for calcul the mask position with numpy : 0.02948307991027832 nb_pixel_total : 19450 time to create 1 rle with old method : 0.023129701614379883 time for calcul the mask position with numpy : 0.0333104133605957 nb_pixel_total : 25163 time to create 1 rle with old method : 0.039528608322143555 time for calcul the mask position with numpy : 0.034715890884399414 nb_pixel_total : 66762 time to create 1 rle with old method : 0.0782308578491211 time for calcul the mask position with numpy : 0.029614686965942383 nb_pixel_total : 13886 time to create 1 rle with old method : 0.018753528594970703 time for calcul the mask position with numpy : 0.03277850151062012 nb_pixel_total : 66483 time to create 1 rle with old method : 0.08214426040649414 time for calcul the mask position with numpy : 0.02938675880432129 nb_pixel_total : 253 time to create 1 rle with old method : 0.0005507469177246094 time for calcul the mask position with numpy : 0.02969074249267578 nb_pixel_total : 24537 time to create 1 rle with old method : 0.028590917587280273 time for calcul the mask position with numpy : 0.02987504005432129 nb_pixel_total : 17609 time to create 1 rle with old method : 0.021245956420898438 time for calcul the mask position with numpy : 0.029540538787841797 nb_pixel_total : 19533 time to create 1 rle with old method : 0.023076772689819336 time for calcul the mask position with numpy : 0.02971506118774414 nb_pixel_total : 38139 time to create 1 rle with old method : 0.04548001289367676 time for calcul the mask position with numpy : 0.030229806900024414 nb_pixel_total : 75710 time to create 1 rle with old method : 0.08793306350708008 time for calcul the mask position with numpy : 0.02971506118774414 nb_pixel_total : 65302 time to create 1 rle with old method : 0.07590436935424805 time for calcul the mask position with numpy : 0.033004045486450195 nb_pixel_total : 25639 time to create 1 rle with old method : 0.03614664077758789 time for calcul the mask position with numpy : 0.030964136123657227 nb_pixel_total : 13228 time to create 1 rle with old method : 0.02313852310180664 time for calcul the mask position with numpy : 0.029560089111328125 nb_pixel_total : 40691 time to create 1 rle with old method : 0.04761385917663574 time for calcul the mask position with numpy : 0.029375553131103516 nb_pixel_total : 25673 time to create 1 rle with old method : 0.03010082244873047 time for calcul the mask position with numpy : 0.029209613800048828 nb_pixel_total : 25958 time to create 1 rle with old method : 0.03592276573181152 time for calcul the mask position with numpy : 0.035272836685180664 nb_pixel_total : 42890 time to create 1 rle with old method : 0.05094003677368164 time for calcul the mask position with numpy : 0.02960658073425293 nb_pixel_total : 14849 time to create 1 rle with old method : 0.018820524215698242 time for calcul the mask position with numpy : 0.03893470764160156 nb_pixel_total : 17959 time to create 1 rle with old method : 0.031890869140625 time for calcul the mask position with numpy : 0.034200429916381836 nb_pixel_total : 10114 time to create 1 rle with old method : 0.011766433715820312 time for calcul the mask position with numpy : 0.03123617172241211 nb_pixel_total : 78493 time to create 1 rle with old method : 0.09442377090454102 time for calcul the mask position with numpy : 0.02938532829284668 nb_pixel_total : 15383 time to create 1 rle with old method : 0.01798725128173828 time for calcul the mask position with numpy : 0.02942967414855957 nb_pixel_total : 7230 time to create 1 rle with old method : 0.008455038070678711 time for calcul the mask position with numpy : 0.029391765594482422 nb_pixel_total : 16094 time to create 1 rle with old method : 0.018940448760986328 time for calcul the mask position with numpy : 0.03083348274230957 nb_pixel_total : 22245 time to create 1 rle with old method : 0.02841043472290039 time for calcul the mask position with numpy : 0.029700517654418945 nb_pixel_total : 47917 time to create 1 rle with old method : 0.057259559631347656 time for calcul the mask position with numpy : 0.030620098114013672 nb_pixel_total : 24857 time to create 1 rle with old method : 0.02889251708984375 time for calcul the mask position with numpy : 0.02989983558654785 nb_pixel_total : 19646 time to create 1 rle with old method : 0.023240089416503906 time for calcul the mask position with numpy : 0.03097057342529297 nb_pixel_total : 14021 time to create 1 rle with old method : 0.017736434936523438 time for calcul the mask position with numpy : 0.0298922061920166 nb_pixel_total : 31877 time to create 1 rle with old method : 0.03748512268066406 time for calcul the mask position with numpy : 0.02992081642150879 nb_pixel_total : 17463 time to create 1 rle with old method : 0.03109264373779297 time for calcul the mask position with numpy : 0.03575921058654785 nb_pixel_total : 18454 time to create 1 rle with old method : 0.030986309051513672 time for calcul the mask position with numpy : 0.03082442283630371 nb_pixel_total : 63420 time to create 1 rle with old method : 0.0876457691192627 time for calcul the mask position with numpy : 0.030454397201538086 nb_pixel_total : 12413 time to create 1 rle with old method : 0.014360904693603516 time for calcul the mask position with numpy : 0.034512996673583984 nb_pixel_total : 109820 time to create 1 rle with old method : 0.1577892303466797 time for calcul the mask position with numpy : 0.02989363670349121 nb_pixel_total : 33932 time to create 1 rle with old method : 0.040721893310546875 time for calcul the mask position with numpy : 0.0295565128326416 nb_pixel_total : 72003 time to create 1 rle with old method : 0.08441805839538574 time for calcul the mask position with numpy : 0.029410362243652344 nb_pixel_total : 20744 time to create 1 rle with old method : 0.024341583251953125 time for calcul the mask position with numpy : 0.029422521591186523 nb_pixel_total : 14742 time to create 1 rle with old method : 0.017165660858154297 time for calcul the mask position with numpy : 0.02944207191467285 nb_pixel_total : 10790 time to create 1 rle with old method : 0.012940645217895508 time for calcul the mask position with numpy : 0.031238555908203125 nb_pixel_total : 11672 time to create 1 rle with old method : 0.013663768768310547 time for calcul the mask position with numpy : 0.029307842254638672 nb_pixel_total : 17458 time to create 1 rle with old method : 0.021080732345581055 time for calcul the mask position with numpy : 0.029549121856689453 nb_pixel_total : 31500 time to create 1 rle with old method : 0.04067254066467285 time for calcul the mask position with numpy : 0.03046107292175293 nb_pixel_total : 14153 time to create 1 rle with old method : 0.016798973083496094 time for calcul the mask position with numpy : 0.03166389465332031 nb_pixel_total : 3245 time to create 1 rle with old method : 0.004838705062866211 time for calcul the mask position with numpy : 0.03551340103149414 nb_pixel_total : 23812 time to create 1 rle with old method : 0.028268814086914062 time for calcul the mask position with numpy : 0.03472733497619629 nb_pixel_total : 26119 time to create 1 rle with old method : 0.030989408493041992 time for calcul the mask position with numpy : 0.030883073806762695 nb_pixel_total : 17456 time to create 1 rle with old method : 0.02395915985107422 time for calcul the mask position with numpy : 0.029935598373413086 nb_pixel_total : 11173 time to create 1 rle with old method : 0.013341188430786133 time for calcul the mask position with numpy : 0.03266096115112305 nb_pixel_total : 1712 time to create 1 rle with old method : 0.002054452896118164 time for calcul the mask position with numpy : 0.029236316680908203 nb_pixel_total : 2829 time to create 1 rle with old method : 0.003390789031982422 time for calcul the mask position with numpy : 0.031294822692871094 nb_pixel_total : 15062 time to create 1 rle with old method : 0.017709970474243164 time for calcul the mask position with numpy : 0.029717683792114258 nb_pixel_total : 18559 time to create 1 rle with old method : 0.021885395050048828 create new chi : 5.537864446640015 time to delete rle : 0.007636070251464844 batch 1 Loaded 122 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 29286 TO DO : save crop sub photo not yet done ! save time : 21.433435916900635 nb_obj : 59 nb_hashtags : 3 time to prepare the origin masks : 4.791995286941528 time for calcul the mask position with numpy : 0.7742776870727539 nb_pixel_total : 5433429 time to create 1 rle with new method : 0.9996874332427979 time for calcul the mask position with numpy : 0.02938985824584961 nb_pixel_total : 20308 time to create 1 rle with old method : 0.023791790008544922 time for calcul the mask position with numpy : 0.03072500228881836 nb_pixel_total : 42381 time to create 1 rle with old method : 0.06819868087768555 time for calcul the mask position with numpy : 0.029292821884155273 nb_pixel_total : 92642 time to create 1 rle with old method : 0.10551714897155762 time for calcul the mask position with numpy : 0.02950119972229004 nb_pixel_total : 241111 time to create 1 rle with new method : 0.8347814083099365 time for calcul the mask position with numpy : 0.029223918914794922 nb_pixel_total : 12404 time to create 1 rle with old method : 0.014708280563354492 time for calcul the mask position with numpy : 0.02946949005126953 nb_pixel_total : 13910 time to create 1 rle with old method : 0.016397953033447266 time for calcul the mask position with numpy : 0.02931499481201172 nb_pixel_total : 15386 time to create 1 rle with old method : 0.017588376998901367 time for calcul the mask position with numpy : 0.02873826026916504 nb_pixel_total : 13541 time to create 1 rle with old method : 0.01584911346435547 time for calcul the mask position with numpy : 0.027973413467407227 nb_pixel_total : 19450 time to create 1 rle with old method : 0.02188706398010254 time for calcul the mask position with numpy : 0.028032541275024414 nb_pixel_total : 25451 time to create 1 rle with old method : 0.031348228454589844 time for calcul the mask position with numpy : 0.034780263900756836 nb_pixel_total : 41398 time to create 1 rle with old method : 0.09225106239318848 time for calcul the mask position with numpy : 0.03433871269226074 nb_pixel_total : 18399 time to create 1 rle with old method : 0.021609783172607422 time for calcul the mask position with numpy : 0.030625343322753906 nb_pixel_total : 57412 time to create 1 rle with old method : 0.06821608543395996 time for calcul the mask position with numpy : 0.029304981231689453 nb_pixel_total : 28266 time to create 1 rle with old method : 0.032729387283325195 time for calcul the mask position with numpy : 0.029311656951904297 nb_pixel_total : 32518 time to create 1 rle with old method : 0.03785252571105957 time for calcul the mask position with numpy : 0.03083515167236328 nb_pixel_total : 232598 time to create 1 rle with new method : 0.8656373023986816 time for calcul the mask position with numpy : 0.03129935264587402 nb_pixel_total : 22185 time to create 1 rle with old method : 0.027990341186523438 time for calcul the mask position with numpy : 0.029676198959350586 nb_pixel_total : 43486 time to create 1 rle with old method : 0.05241107940673828 time for calcul the mask position with numpy : 0.029811859130859375 nb_pixel_total : 15047 time to create 1 rle with old method : 0.017499208450317383 time for calcul the mask position with numpy : 0.029362201690673828 nb_pixel_total : 25447 time to create 1 rle with old method : 0.029580116271972656 time for calcul the mask position with numpy : 0.029739856719970703 nb_pixel_total : 15037 time to create 1 rle with old method : 0.018833398818969727 time for calcul the mask position with numpy : 0.037390708923339844 nb_pixel_total : 74951 time to create 1 rle with old method : 0.09468579292297363 time for calcul the mask position with numpy : 0.031365156173706055 nb_pixel_total : 569 time to create 1 rle with old method : 0.0010371208190917969 time for calcul the mask position with numpy : 0.030174732208251953 nb_pixel_total : 2766 time to create 1 rle with old method : 0.0034942626953125 time for calcul the mask position with numpy : 0.030260562896728516 nb_pixel_total : 9188 time to create 1 rle with old method : 0.010879993438720703 time for calcul the mask position with numpy : 0.030445575714111328 nb_pixel_total : 7761 time to create 1 rle with old method : 0.009334087371826172 time for calcul the mask position with numpy : 0.02991008758544922 nb_pixel_total : 5266 time to create 1 rle with old method : 0.0063076019287109375 time for calcul the mask position with numpy : 0.03032827377319336 nb_pixel_total : 13921 time to create 1 rle with old method : 0.016787052154541016 time for calcul the mask position with numpy : 0.03018474578857422 nb_pixel_total : 12181 time to create 1 rle with old method : 0.01820850372314453 time for calcul the mask position with numpy : 0.03006768226623535 nb_pixel_total : 22891 time to create 1 rle with old method : 0.028208494186401367 time for calcul the mask position with numpy : 0.030153989791870117 nb_pixel_total : 21415 time to create 1 rle with old method : 0.02864217758178711 time for calcul the mask position with numpy : 0.03969836235046387 nb_pixel_total : 21264 time to create 1 rle with old method : 0.027138948440551758 time for calcul the mask position with numpy : 0.03089427947998047 nb_pixel_total : 32979 time to create 1 rle with old method : 0.0401613712310791 time for calcul the mask position with numpy : 0.03154897689819336 nb_pixel_total : 22946 time to create 1 rle with old method : 0.026841163635253906 time for calcul the mask position with numpy : 0.029699087142944336 nb_pixel_total : 11249 time to create 1 rle with old method : 0.013256072998046875 time for calcul the mask position with numpy : 0.03078746795654297 nb_pixel_total : 11105 time to create 1 rle with old method : 0.017879247665405273 time for calcul the mask position with numpy : 0.036598920822143555 nb_pixel_total : 8640 time to create 1 rle with old method : 0.010396957397460938 time for calcul the mask position with numpy : 0.029692888259887695 nb_pixel_total : 21780 time to create 1 rle with old method : 0.025585651397705078 time for calcul the mask position with numpy : 0.0341792106628418 nb_pixel_total : 19583 time to create 1 rle with old method : 0.023859500885009766 time for calcul the mask position with numpy : 0.03100872039794922 nb_pixel_total : 17704 time to create 1 rle with old method : 0.027736425399780273 time for calcul the mask position with numpy : 0.036407470703125 nb_pixel_total : 19016 time to create 1 rle with old method : 0.022243976593017578 time for calcul the mask position with numpy : 0.02950906753540039 nb_pixel_total : 541 time to create 1 rle with old method : 0.0007574558258056641 time for calcul the mask position with numpy : 0.029542922973632812 nb_pixel_total : 29221 time to create 1 rle with old method : 0.034203529357910156 time for calcul the mask position with numpy : 0.029688119888305664 nb_pixel_total : 4203 time to create 1 rle with old method : 0.005074977874755859 time for calcul the mask position with numpy : 0.02931380271911621 nb_pixel_total : 4183 time to create 1 rle with old method : 0.009221076965332031 time for calcul the mask position with numpy : 0.030029773712158203 nb_pixel_total : 13370 time to create 1 rle with old method : 0.0226747989654541 time for calcul the mask position with numpy : 0.033493995666503906 nb_pixel_total : 16967 time to create 1 rle with old method : 0.027800321578979492 time for calcul the mask position with numpy : 0.05762887001037598 nb_pixel_total : 15104 time to create 1 rle with old method : 0.025341272354125977 time for calcul the mask position with numpy : 0.04124140739440918 nb_pixel_total : 8521 time to create 1 rle with old method : 0.013877630233764648 time for calcul the mask position with numpy : 0.02979755401611328 nb_pixel_total : 9559 time to create 1 rle with old method : 0.011908769607543945 time for calcul the mask position with numpy : 0.032891273498535156 nb_pixel_total : 5711 time to create 1 rle with old method : 0.006890296936035156 time for calcul the mask position with numpy : 0.029922008514404297 nb_pixel_total : 7443 time to create 1 rle with old method : 0.008878469467163086 time for calcul the mask position with numpy : 0.029636144638061523 nb_pixel_total : 24848 time to create 1 rle with old method : 0.028777122497558594 time for calcul the mask position with numpy : 0.029313325881958008 nb_pixel_total : 3407 time to create 1 rle with old method : 0.004048585891723633 time for calcul the mask position with numpy : 0.029645919799804688 nb_pixel_total : 20975 time to create 1 rle with old method : 0.0245363712310791 time for calcul the mask position with numpy : 0.03233838081359863 nb_pixel_total : 16505 time to create 1 rle with old method : 0.019136905670166016 time for calcul the mask position with numpy : 0.02983236312866211 nb_pixel_total : 24632 time to create 1 rle with old method : 0.028826475143432617 time for calcul the mask position with numpy : 0.032201528549194336 nb_pixel_total : 22712 time to create 1 rle with old method : 0.0294649600982666 time for calcul the mask position with numpy : 0.030443668365478516 nb_pixel_total : 3357 time to create 1 rle with old method : 0.003922939300537109 create new chi : 7.039184093475342 time to delete rle : 0.004300355911254883 batch 1 Loaded 120 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 24593 TO DO : save crop sub photo not yet done ! save time : 4.614035367965698 nb_obj : 54 nb_hashtags : 4 time to prepare the origin masks : 8.991519212722778 time for calcul the mask position with numpy : 0.3462657928466797 nb_pixel_total : 4742719 time to create 1 rle with new method : 0.6487057209014893 time for calcul the mask position with numpy : 0.029480695724487305 nb_pixel_total : 19460 time to create 1 rle with old method : 0.02202606201171875 time for calcul the mask position with numpy : 0.02794790267944336 nb_pixel_total : 12981 time to create 1 rle with old method : 0.01504373550415039 time for calcul the mask position with numpy : 0.02881789207458496 nb_pixel_total : 12535 time to create 1 rle with old method : 0.014959573745727539 time for calcul the mask position with numpy : 0.030910968780517578 nb_pixel_total : 18276 time to create 1 rle with old method : 0.029799699783325195 time for calcul the mask position with numpy : 0.032999515533447266 nb_pixel_total : 17517 time to create 1 rle with old method : 0.021077394485473633 time for calcul the mask position with numpy : 0.030203580856323242 nb_pixel_total : 137833 time to create 1 rle with old method : 0.15622401237487793 time for calcul the mask position with numpy : 0.02840113639831543 nb_pixel_total : 63596 time to create 1 rle with old method : 0.07282161712646484 time for calcul the mask position with numpy : 0.027420520782470703 nb_pixel_total : 23902 time to create 1 rle with old method : 0.027277708053588867 time for calcul the mask position with numpy : 0.03345179557800293 nb_pixel_total : 603822 time to create 1 rle with new method : 0.6548786163330078 time for calcul the mask position with numpy : 0.02923417091369629 nb_pixel_total : 803 time to create 1 rle with old method : 0.0022046566009521484 time for calcul the mask position with numpy : 0.030228137969970703 nb_pixel_total : 74016 time to create 1 rle with old method : 0.09002876281738281 time for calcul the mask position with numpy : 0.030152082443237305 nb_pixel_total : 93245 time to create 1 rle with old method : 0.11424469947814941 time for calcul the mask position with numpy : 0.028529644012451172 nb_pixel_total : 79008 time to create 1 rle with old method : 0.09042906761169434 time for calcul the mask position with numpy : 0.029482126235961914 nb_pixel_total : 22993 time to create 1 rle with old method : 0.026819944381713867 time for calcul the mask position with numpy : 0.029667139053344727 nb_pixel_total : 36328 time to create 1 rle with old method : 0.04335498809814453 time for calcul the mask position with numpy : 0.029427766799926758 nb_pixel_total : 49283 time to create 1 rle with old method : 0.05722212791442871 time for calcul the mask position with numpy : 0.03201150894165039 nb_pixel_total : 60102 time to create 1 rle with old method : 0.06996870040893555 time for calcul the mask position with numpy : 0.029546737670898438 nb_pixel_total : 55004 time to create 1 rle with old method : 0.06311559677124023 time for calcul the mask position with numpy : 0.029918432235717773 nb_pixel_total : 31687 time to create 1 rle with old method : 0.037024497985839844 time for calcul the mask position with numpy : 0.029016733169555664 nb_pixel_total : 4067 time to create 1 rle with old method : 0.004790067672729492 time for calcul the mask position with numpy : 0.02909564971923828 nb_pixel_total : 57086 time to create 1 rle with old method : 0.0665433406829834 time for calcul the mask position with numpy : 0.03244328498840332 nb_pixel_total : 2084 time to create 1 rle with old method : 0.003120899200439453 time for calcul the mask position with numpy : 0.03197908401489258 nb_pixel_total : 262356 time to create 1 rle with new method : 0.942110538482666 time for calcul the mask position with numpy : 0.028475046157836914 nb_pixel_total : 22550 time to create 1 rle with old method : 0.029011011123657227 time for calcul the mask position with numpy : 0.02904486656188965 nb_pixel_total : 11386 time to create 1 rle with old method : 0.013564348220825195 time for calcul the mask position with numpy : 0.029155254364013672 nb_pixel_total : 6759 time to create 1 rle with old method : 0.007843255996704102 time for calcul the mask position with numpy : 0.032302141189575195 nb_pixel_total : 37 time to create 1 rle with old method : 0.00014162063598632812 time for calcul the mask position with numpy : 0.02927374839782715 nb_pixel_total : 316 time to create 1 rle with old method : 0.0006582736968994141 time for calcul the mask position with numpy : 0.029554128646850586 nb_pixel_total : 51730 time to create 1 rle with old method : 0.06078362464904785 time for calcul the mask position with numpy : 0.03060626983642578 nb_pixel_total : 42911 time to create 1 rle with old method : 0.05623984336853027 time for calcul the mask position with numpy : 0.02983570098876953 nb_pixel_total : 11985 time to create 1 rle with old method : 0.014099359512329102 time for calcul the mask position with numpy : 0.029584169387817383 nb_pixel_total : 14511 time to create 1 rle with old method : 0.017178058624267578 time for calcul the mask position with numpy : 0.02930593490600586 nb_pixel_total : 6607 time to create 1 rle with old method : 0.007803916931152344 time for calcul the mask position with numpy : 0.03015756607055664 nb_pixel_total : 69 time to create 1 rle with old method : 0.00020265579223632812 time for calcul the mask position with numpy : 0.0296630859375 nb_pixel_total : 23 time to create 1 rle with old method : 0.00017642974853515625 time for calcul the mask position with numpy : 0.03009629249572754 nb_pixel_total : 14491 time to create 1 rle with old method : 0.01846599578857422 time for calcul the mask position with numpy : 0.03377962112426758 nb_pixel_total : 286 time to create 1 rle with old method : 0.0007269382476806641 time for calcul the mask position with numpy : 0.032941341400146484 nb_pixel_total : 17992 time to create 1 rle with old method : 0.02717423439025879 time for calcul the mask position with numpy : 0.02973175048828125 nb_pixel_total : 63660 time to create 1 rle with old method : 0.07907485961914062 time for calcul the mask position with numpy : 0.029506683349609375 nb_pixel_total : 597 time to create 1 rle with old method : 0.0010352134704589844 time for calcul the mask position with numpy : 0.030669689178466797 nb_pixel_total : 53316 time to create 1 rle with old method : 0.06682658195495605 time for calcul the mask position with numpy : 0.02972126007080078 nb_pixel_total : 221 time to create 1 rle with old method : 0.0004639625549316406 time for calcul the mask position with numpy : 0.031148195266723633 nb_pixel_total : 21993 time to create 1 rle with old method : 0.025689363479614258 time for calcul the mask position with numpy : 0.029579877853393555 nb_pixel_total : 12090 time to create 1 rle with old method : 0.014183521270751953 time for calcul the mask position with numpy : 0.029554367065429688 nb_pixel_total : 61094 time to create 1 rle with old method : 0.07494354248046875 time for calcul the mask position with numpy : 0.03302407264709473 nb_pixel_total : 57432 time to create 1 rle with old method : 0.08447098731994629 time for calcul the mask position with numpy : 0.03364920616149902 nb_pixel_total : 6041 time to create 1 rle with old method : 0.01040029525756836 time for calcul the mask position with numpy : 0.03389883041381836 nb_pixel_total : 58830 time to create 1 rle with old method : 0.06845641136169434 time for calcul the mask position with numpy : 0.02894902229309082 nb_pixel_total : 777 time to create 1 rle with old method : 0.0012292861938476562 time for calcul the mask position with numpy : 0.02841663360595703 nb_pixel_total : 14099 time to create 1 rle with old method : 0.01638340950012207 time for calcul the mask position with numpy : 0.02928757667541504 nb_pixel_total : 15309 time to create 1 rle with old method : 0.017804861068725586 time for calcul the mask position with numpy : 0.028538942337036133 nb_pixel_total : 676 time to create 1 rle with old method : 0.001016855239868164 time for calcul the mask position with numpy : 0.028706789016723633 nb_pixel_total : 1664 time to create 1 rle with old method : 0.002032756805419922 time for calcul the mask position with numpy : 0.0284268856048584 nb_pixel_total : 85 time to create 1 rle with old method : 0.00017881393432617188 create new chi : 6.059128522872925 time to delete rle : 0.004628658294677734 batch 1 Loaded 124 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 28111 TO DO : save crop sub photo not yet done ! save time : 2.62713360786438 map_output_result : {1349141845: (0.0, 'Should be the crop_list due to order', 0), 1349141766: (0.0, 'Should be the crop_list due to order', 0), 1349141762: (0.0, 'Should be the crop_list due to order', 0), 1349141757: (0.0, 'Should be the crop_list due to order', 0), 1349141752: (0.0, 'Should be the crop_list due to order', 0), 1349141681: (0.0, 'Should be the crop_list due to order', 0), 1348989197: (0.0, 'Should be the crop_list due to order', 0), 1348989194: (0.0, 'Should be the crop_list due to order', 0), 1348989174: (0.0, 'Should be the crop_list due to order', 0), 1348988829: (0.0, 'Should be the crop_list due to order', 0), 1348988791: (0.0, 'Should be the crop_list due to order', 0), 1348988744: (0.0, 'Should be the crop_list due to order', 0), 1348988511: (0.0, 'Should be the crop_list due to order', 0), 1348988362: (0.0, 'Should be the crop_list due to order', 0), 1348988329: (0.0, 'Should be the crop_list due to order', 0), 1348988271: (0.0, 'Should be the crop_list due to order', 0), 1348988219: (0.0, 'Should be the crop_list due to order', 0), 1348988180: (0.0, 'Should be the crop_list due to order', 0), 1348988140: (0.0, 'Should be the crop_list due to order', 0)} End step rle-unique-nms Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : rle_unique_nms_with_priority we use saveGeneral [1349141845, 1349141766, 1349141762, 1349141757, 1349141752, 1349141681, 1348989197, 1348989194, 1348989174, 1348988829, 1348988791, 1348988744, 1348988511, 1348988362, 1348988329, 1348988271, 1348988219, 1348988180, 1348988140] Looping around the photos to save general results len do output : 19 /1349141845.Didn't retrieve data . /1349141766.Didn't retrieve data . /1349141762.Didn't retrieve data . /1349141757.Didn't retrieve data . /1349141752.Didn't retrieve data . /1349141681.Didn't retrieve data . /1348989197.Didn't retrieve data . /1348989194.Didn't retrieve data . /1348989174.Didn't retrieve data . /1348988829.Didn't retrieve data . /1348988791.Didn't retrieve data . /1348988744.Didn't retrieve data . /1348988511.Didn't retrieve data . /1348988362.Didn't retrieve data . /1348988329.Didn't retrieve data . /1348988271.Didn't retrieve data . /1348988219.Didn't retrieve data . /1348988180.Didn't retrieve data . /1348988140.Didn't retrieve data . before output type Used above Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141845', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141766', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141762', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141757', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141752', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141681', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348989197', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348989194', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348989174', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988829', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988791', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988744', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988511', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988362', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988329', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988271', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988219', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988180', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988140', None, None, None, None, None, '2711132') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 57 time used for this insertion : 0.02460455894470215 save_final save missing photos in datou_result : time spend for datou_step_exec : 308.0052692890167 time spend to save output : 0.02989792823791504 total time spend for step 3 : 308.03516721725464 step4:ventilate_hashtags_in_portfolio Tue Apr 1 02:08:47 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 ! 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 : 21929800 get user id for portfolio 21929800 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`=21929800 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','pehd','background','environnement','pet_fonce','mal_croppe','flou','autre','carton','papier','metal')) AND mptpi.`min_score`=0.5 To do To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21929800 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','pehd','background','environnement','pet_fonce','mal_croppe','flou','autre','carton','papier','metal')) AND mptpi.`min_score`=0.5 To do Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21929800 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','pehd','background','environnement','pet_fonce','mal_croppe','flou','autre','carton','papier','metal')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/21930687,21930688,21930689,21930690,21930691,21930692,21930693,21930694,21930695,21930696,21930697?tags=pet_clair,pehd,background,environnement,pet_fonce,mal_croppe,flou,autre,carton,papier,metal Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1349141845, 1349141766, 1349141762, 1349141757, 1349141752, 1349141681, 1348989197, 1348989194, 1348989174, 1348988829, 1348988791, 1348988744, 1348988511, 1348988362, 1348988329, 1348988271, 1348988219, 1348988180, 1348988140] Looping around the photos to save general results len do output : 1 /21929800. before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141845', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141766', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141762', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141757', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141752', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141681', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348989197', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348989194', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348989174', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988829', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988791', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988744', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988511', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988362', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988329', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988271', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988219', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988180', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988140', None, None, None, None, None, '2711132') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 20 time used for this insertion : 0.01894235610961914 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.045053720474243 time spend to save output : 0.019591331481933594 total time spend for step 4 : 2.0646450519561768 step5:final Tue Apr 1 02:08:49 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 : {1349141845: ('0.24895207748060239',), 1349141766: ('0.24895207748060239',), 1349141762: ('0.24895207748060239',), 1349141757: ('0.24895207748060239',), 1349141752: ('0.24895207748060239',), 1349141681: ('0.24895207748060239',), 1348989197: ('0.24895207748060239',), 1348989194: ('0.24895207748060239',), 1348989174: ('0.24895207748060239',), 1348988829: ('0.24895207748060239',), 1348988791: ('0.24895207748060239',), 1348988744: ('0.24895207748060239',), 1348988511: ('0.24895207748060239',), 1348988362: ('0.24895207748060239',), 1348988329: ('0.24895207748060239',), 1348988271: ('0.24895207748060239',), 1348988219: ('0.24895207748060239',), 1348988180: ('0.24895207748060239',), 1348988140: ('0.24895207748060239',)} new output for save of step final : {1349141845: ('0.24895207748060239',), 1349141766: ('0.24895207748060239',), 1349141762: ('0.24895207748060239',), 1349141757: ('0.24895207748060239',), 1349141752: ('0.24895207748060239',), 1349141681: ('0.24895207748060239',), 1348989197: ('0.24895207748060239',), 1348989194: ('0.24895207748060239',), 1348989174: ('0.24895207748060239',), 1348988829: ('0.24895207748060239',), 1348988791: ('0.24895207748060239',), 1348988744: ('0.24895207748060239',), 1348988511: ('0.24895207748060239',), 1348988362: ('0.24895207748060239',), 1348988329: ('0.24895207748060239',), 1348988271: ('0.24895207748060239',), 1348988219: ('0.24895207748060239',), 1348988180: ('0.24895207748060239',), 1348988140: ('0.24895207748060239',)} [1349141845, 1349141766, 1349141762, 1349141757, 1349141752, 1349141681, 1348989197, 1348989194, 1348989174, 1348988829, 1348988791, 1348988744, 1348988511, 1348988362, 1348988329, 1348988271, 1348988219, 1348988180, 1348988140] Looping around the photos to save general results len do output : 19 /1349141845.Didn't retrieve data . /1349141766.Didn't retrieve data . /1349141762.Didn't retrieve data . /1349141757.Didn't retrieve data . /1349141752.Didn't retrieve data . /1349141681.Didn't retrieve data . /1348989197.Didn't retrieve data . /1348989194.Didn't retrieve data . /1348989174.Didn't retrieve data . /1348988829.Didn't retrieve data . /1348988791.Didn't retrieve data . /1348988744.Didn't retrieve data . /1348988511.Didn't retrieve data . /1348988362.Didn't retrieve data . /1348988329.Didn't retrieve data . /1348988271.Didn't retrieve data . /1348988219.Didn't retrieve data . /1348988180.Didn't retrieve data . /1348988140.Didn't retrieve data . before output type Used above Used above Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141845', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141766', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141762', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141757', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141752', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141681', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348989197', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348989194', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348989174', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988829', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988791', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988744', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988511', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988362', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988329', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988271', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988219', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988180', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988140', None, None, None, None, None, '2711132') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 57 time used for this insertion : 0.02063274383544922 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.12320494651794434 time spend to save output : 0.021648406982421875 total time spend for step 5 : 0.1448533535003662 step6:blur_detection Tue Apr 1 02:08:49 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection methode: ratio et variance treat image : temp/1743464430_2177385_1349141845_ce2156e75d4e8765fada799ab02cb6f4.jpg resize: (2160, 3264) 1349141845 -7.24955762508241 treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee.jpg resize: (2160, 3264) 1349141766 -4.312539289775187 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d.jpg resize: (2160, 3264) 1349141762 -4.204484701748642 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9.jpg resize: (2160, 3264) 1349141757 -5.071147621059683 treat image : temp/1743464430_2177385_1349141752_e1fb4b05ce5be48b02c4e7da003fb4c3.jpg resize: (2160, 3264) 1349141752 -4.097351521015433 treat image : temp/1743464430_2177385_1349141681_aae0c5bc94319153fe8f609732340725.jpg resize: (2160, 3264) 1349141681 -5.014450881727802 treat image : temp/1743464430_2177385_1348989197_5149d0c836d85b7dc04e09a7c3729add.jpg resize: (2160, 3264) 1348989197 -5.658503225070488 treat image : temp/1743464430_2177385_1348989194_a2cced1ca12564ce8b404b3305346fe0.jpg resize: (2160, 3264) 1348989194 -3.2718669839523797 treat image : temp/1743464430_2177385_1348989174_8503bb899749ccc77ffa8a006fa766d5.jpg resize: (2160, 3264) 1348989174 -4.96328948542971 treat image : temp/1743464430_2177385_1348988829_148c43da2bdd74f393295997e011c42b.jpg resize: (2160, 3264) 1348988829 -4.464965460289582 treat image : temp/1743464430_2177385_1348988791_c5ae12364b2da1f4f99a80ce5dd0afc3.jpg resize: (2160, 3264) 1348988791 -4.9353417506355886 treat image : temp/1743464430_2177385_1348988744_6a0d5da77a5a898617018ca40261358e.jpg resize: (2160, 3264) 1348988744 -6.439894001788027 treat image : temp/1743464430_2177385_1348988511_6122c05c9b24b584e9af1ec4d3753995.jpg resize: (2160, 3264) 1348988511 -5.747034324943433 treat image : temp/1743464430_2177385_1348988362_11c89b2ad620bcea7f2856028fe6f08c.jpg resize: (2160, 3264) 1348988362 -5.5242211044887926 treat image : temp/1743464430_2177385_1348988329_6fdea75e1c46da5b872821342d638084.jpg resize: (2160, 3264) 1348988329 -1.746691917102628 treat image : temp/1743464430_2177385_1348988271_36481ff51cd299d4b964ec4860a49ae2.jpg resize: (2160, 3264) 1348988271 -4.470621376666107 treat image : temp/1743464430_2177385_1348988219_c791a51274bbc721d17bea456bce68e6.jpg resize: (2160, 3264) 1348988219 -5.578856964409478 treat image : temp/1743464430_2177385_1348988180_25ae5c9e0b3f871ebfc1a8bff144b63c.jpg resize: (2160, 3264) 1348988180 -4.780294844308383 treat image : temp/1743464430_2177385_1348988140_abae34bfd97e8fbbda78e43d9c44e7cc.jpg resize: (2160, 3264) 1348988140 -5.037884762860154 treat image : temp/1743464430_2177385_1349141845_ce2156e75d4e8765fada799ab02cb6f4_rle_crop_3742077068_0.png resize: (172, 310) 1349165186 -3.7642147044838383 treat image : temp/1743464430_2177385_1349141845_ce2156e75d4e8765fada799ab02cb6f4_rle_crop_3742077059_0.png resize: (343, 210) 1349165187 -3.245524114043479 treat image : temp/1743464430_2177385_1349141845_ce2156e75d4e8765fada799ab02cb6f4_rle_crop_3742077055_0.png resize: (340, 357) 1349165188 -3.8410474270179082 treat image : temp/1743464430_2177385_1349141845_ce2156e75d4e8765fada799ab02cb6f4_rle_crop_3742077061_0.png resize: (300, 358) 1349165189 -3.8049063554854787 treat image : temp/1743464430_2177385_1349141845_ce2156e75d4e8765fada799ab02cb6f4_rle_crop_3742077060_0.png resize: (169, 362) 1349165190 -4.149730865414078 treat image : temp/1743464430_2177385_1349141845_ce2156e75d4e8765fada799ab02cb6f4_rle_crop_3742077074_0.png resize: (120, 139) 1349165191 -3.5462708226515414 treat image : temp/1743464430_2177385_1349141845_ce2156e75d4e8765fada799ab02cb6f4_rle_crop_3742077073_0.png resize: (751, 859) 1349165192 -5.593334050178419 treat image : temp/1743464430_2177385_1349141845_ce2156e75d4e8765fada799ab02cb6f4_rle_crop_3742077065_0.png resize: (344, 237) 1349165193 -4.016133029987798 treat image : temp/1743464430_2177385_1349141845_ce2156e75d4e8765fada799ab02cb6f4_rle_crop_3742077066_0.png resize: (195, 164) 1349165194 -5.074273563903201 treat image : temp/1743464430_2177385_1349141845_ce2156e75d4e8765fada799ab02cb6f4_rle_crop_3742077063_0.png resize: (226, 272) 1349165195 -4.26210183024744 treat image : temp/1743464430_2177385_1349141845_ce2156e75d4e8765fada799ab02cb6f4_rle_crop_3742077062_0.png resize: (300, 194) 1349165196 -5.45743122533339 treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee_rle_crop_3742077085_0.png resize: (566, 848) 1349165197 -1.6632660945444049 treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee_rle_crop_3742077100_0.png resize: (197, 260) 1349165198 -1.9544016965883297 treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee_rle_crop_3742077087_0.png resize: (190, 201) 1349165199 -2.107860696163009 treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee_rle_crop_3742077103_0.png resize: (116, 165) 1349165200 -2.85215635232443 treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee_rle_crop_3742077098_0.png resize: (99, 174) 1349165201 -0.7924046355985832 treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee_rle_crop_3742077096_0.png resize: (118, 108) 1349165202 -2.197864388121123 treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee_rle_crop_3742077077_0.png resize: (314, 165) 1349165203 -3.708251069328832 treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee_rle_crop_3742077080_0.png resize: (210, 230) 1349165204 -3.36444171432318 treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee_rle_crop_3742077106_0.png resize: (143, 285) 1349165205 -3.664984011071617 treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee_rle_crop_3742077097_0.png resize: (118, 254) 1349165206 -3.561071219154176 treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee_rle_crop_3742077090_0.png resize: (120, 65) 1349165207 -1.6758464739287557 treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee_rle_crop_3742077105_0.png resize: (91, 186) 1349165208 -3.138170251728998 treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee_rle_crop_3742077101_0.png resize: (86, 224) 1349165209 -4.384676174348694 treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee_rle_crop_3742077084_0.png resize: (116, 179) 1349165210 -1.9731155549967385 treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee_rle_crop_3742077081_0.png resize: (171, 206) 1349165211 -1.7553067269104603 treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee_rle_crop_3742077093_0.png resize: (175, 154) 1349165212 -3.3831309303533685 treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee_rle_crop_3742077083_0.png resize: (358, 247) 1349165213 -2.434319893603726 treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee_rle_crop_3742077102_0.png resize: (165, 254) 1349165214 -2.7402855538587523 treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee_rle_crop_3742077104_0.png resize: (225, 140) 1349165215 -2.3968556470986773 treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee_rle_crop_3742077092_0.png resize: (82, 181) 1349165216 -1.573137288357542 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077128_0.png resize: (410, 308) 1349165217 -3.0433720866267278 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077134_0.png resize: (229, 178) 1349165218 -3.4526427110874343 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077119_0.png resize: (400, 275) 1349165219 -3.128186545567978 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077115_0.png resize: (96, 238) 1349165220 -2.5403025008883295 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077122_0.png resize: (307, 485) 1349165221 -1.7224727378398184 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077129_0.png resize: (107, 116) 1349165222 -1.8979917642384059 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077132_0.png resize: (90, 222) 1349165223 -0.3892915428762509 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077135_0.png resize: (221, 207) 1349165224 -0.8046790532167595 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077111_0.png resize: (279, 244) 1349165225 0.24377037549283506 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077110_0.png resize: (257, 306) 1349165226 -2.139675706391685 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077125_0.png resize: (276, 152) 1349165227 -2.1642934373053113 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077109_0.png resize: (318, 203) 1349165228 -1.0413530603263623 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077116_0.png resize: (1214, 1256) 1349165229 -4.2139049893625975 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077113_0.png resize: (263, 492) 1349165230 -1.0494756536626275 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077121_0.png resize: (254, 370) 1349165231 -2.5863528185736033 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077107_0.png resize: (118, 137) 1349165232 -1.815371616640143 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077120_0.png resize: (88, 57) 1349165233 0.15350496868969082 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077136_0.png resize: (66, 36) 1349165234 -1.4721484865730892 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077133_0.png resize: (178, 461) 1349165235 -1.7120895764247783 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077130_0.png resize: (88, 271) 1349165236 -3.0553591979496124 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077140_0.png resize: (194, 260) 1349165237 -2.697464158719267 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077127_0.png resize: (207, 168) 1349165238 -3.5363766688314557 treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d_rle_crop_3742077117_0.png resize: (118, 135) 1349165239 -3.0892860907776107 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077157_0.png resize: (255, 125) 1349165240 -0.9985376430898962 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077183_0.png resize: (139, 458) 1349165241 -1.8294763558355183 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077200_0.png resize: (202, 205) 1349165242 -2.3105019063484646 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077166_0.png resize: (313, 294) 1349165243 -2.6805067588897864 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077192_0.png resize: (131, 218) 1349165244 -2.5973430563271784 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077185_0.png resize: (271, 185) 1349165245 -2.442399415931662 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077164_0.png resize: (163, 219) 1349165246 -2.893535388903881 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077190_0.png resize: (190, 159) 1349165247 -1.9106066274102527 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077184_0.png resize: (218, 182) 1349165248 -1.2340514653665926 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077189_0.png resize: (235, 240) 1349165249 -1.8347538062277229 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077149_0.png resize: (107, 93) 1349165250 -1.4143307771416176 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077158_0.png resize: (213, 163) 1349165254 -2.0123597428901667 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077148_0.png resize: (85, 133) 1349165257 -0.3800790784892425 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077147_0.png resize: (140, 94) 1349165260 -1.2672032209174202 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077146_0.png resize: (96, 118) 1349165262 0.1415778995167866 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077159_0.png resize: (170, 166) 1349165263 -2.9825652540239167 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077155_0.png resize: (183, 85) 1349165264 -1.7128205233084604 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077165_0.png resize: (107, 160) 1349165265 -3.297236874668514 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077167_0.png resize: (183, 116) 1349165266 -2.442252411679697 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077177_0.png resize: (166, 112) 1349165267 -3.25155727668129 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077175_0.png resize: (133, 180) 1349165268 -2.2416067373812694 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077181_0.png resize: (141, 147) 1349165269 -1.0854070167370569 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077152_0.png resize: (187, 243) 1349165270 -3.294218346151865 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077161_0.png resize: (292, 257) 1349165271 -2.7068613375274273 treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9_rle_crop_3742077145_0.png resize: (232, 471) 1349165272 -3.111146936909465 treat image : 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temp/1743464430_2177385_1349141681_aae0c5bc94319153fe8f609732340725_rle_crop_3742077309_0.png resize: (203, 146) 1349171093 -2.8283583637428316 treat image : temp/1743464430_2177385_1348988829_148c43da2bdd74f393295997e011c42b_bib_crop_3741019207_0.jpg resize: (174, 67) 1349171098 -1.8083701218683905 treat image : temp/1743464430_2177385_1348988829_148c43da2bdd74f393295997e011c42b_rle_crop_3741010975_0.png resize: (175, 68) 1349171099 -3.186637014520837 treat image : temp/1743464430_2177385_1348988791_c5ae12364b2da1f4f99a80ce5dd0afc3_rle_crop_3741011039_0.png resize: (137, 355) 1349171100 -4.5106824876622325 treat image : temp/1743464430_2177385_1348988791_c5ae12364b2da1f4f99a80ce5dd0afc3_bib_crop_3741019265_0.jpg resize: (136, 354) 1349171106 -4.3998244225750875 treat image : temp/1743464430_2177385_1348988180_25ae5c9e0b3f871ebfc1a8bff144b63c_bib_crop_3741020025_0.jpg resize: (125, 72) 1349171107 -1.0050181357640318 treat image : temp/1743464430_2177385_1348988180_25ae5c9e0b3f871ebfc1a8bff144b63c_rle_crop_3741011348_0.png resize: (144, 73) 1349171112 -3.235607750135487 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 : 1239 time used for this insertion : 0.10414814949035645 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1239 time used for this insertion : 0.35765981674194336 save missing photos in datou_result : time spend for datou_step_exec : 102.12137842178345 time spend to save output : 0.48851799964904785 total time spend for step 6 : 102.6098964214325 step7:brightness Tue Apr 1 02:10:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness treat image : temp/1743464430_2177385_1349141845_ce2156e75d4e8765fada799ab02cb6f4.jpg treat image : temp/1743464430_2177385_1349141766_1996d9867cea814c21e18c520bdb51ee.jpg treat image : temp/1743464430_2177385_1349141762_35d79392b6553e78bb79528becf26e7d.jpg treat image : temp/1743464430_2177385_1349141757_19bb0fad370f0b1c1c0e2852140996f9.jpg treat image : temp/1743464430_2177385_1349141752_e1fb4b05ce5be48b02c4e7da003fb4c3.jpg treat image : temp/1743464430_2177385_1349141681_aae0c5bc94319153fe8f609732340725.jpg treat image : temp/1743464430_2177385_1348989197_5149d0c836d85b7dc04e09a7c3729add.jpg treat image : temp/1743464430_2177385_1348989194_a2cced1ca12564ce8b404b3305346fe0.jpg treat image : temp/1743464430_2177385_1348989174_8503bb899749ccc77ffa8a006fa766d5.jpg treat image : temp/1743464430_2177385_1348988829_148c43da2bdd74f393295997e011c42b.jpg treat image : 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temp/1743464430_2177385_1348988791_c5ae12364b2da1f4f99a80ce5dd0afc3_bib_crop_3741019265_0.jpg treat image : temp/1743464430_2177385_1348988180_25ae5c9e0b3f871ebfc1a8bff144b63c_bib_crop_3741020025_0.jpg treat image : temp/1743464430_2177385_1348988180_25ae5c9e0b3f871ebfc1a8bff144b63c_rle_crop_3741011348_0.png Inside saveOutput : final : False verbose : 0 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1239 time used for this insertion : 0.31092333793640137 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1239 time used for this insertion : 0.2568197250366211 save missing photos in datou_result : time spend for datou_step_exec : 28.926520824432373 time spend to save output : 0.5793750286102295 total time spend for step 7 : 29.505895853042603 step8:velours_tree Tue Apr 1 02:11:01 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 : 2.353780746459961 time spend to save output : 9.584426879882812e-05 total time spend for step 8 : 2.3538765907287598 step9:send_mail_cod Tue Apr 1 02:11:03 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 2 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 3 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure dans la step send mail cod work_area: /home/admin/workarea/git/Velours/python in order to get the selector url, please entre the license of selector results_Auto_P21929800_01-04-2025_02_11_03.pdf 21930687 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 .imagette219306871743466263 21930688 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 .imagette219306881743466265 21930689 imagette219306891743466266 21930691 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 .imagette219306911743466266 21930692 imagette219306921743466266 21930693 imagette219306931743466266 21930694 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 .imagette219306941743466266 21930695 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 .imagette219306951743466268 21930696 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 .imagette219306961743466270 21930697 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 .imagette219306971743466273 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=21929800 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/21930687,21930688,21930689,21930690,21930691,21930692,21930693,21930694,21930695,21930696,21930697?tags=pet_clair,pehd,background,environnement,pet_fonce,mal_croppe,flou,autre,carton,papier,metal args[1349141845] : ((1349141845, -7.24955762508241, 492609224), (1349141845, 0.1763153321842616, 2107752395), '0.24895207748060239') We are sending mail with results at report@fotonower.com args[1349141766] : ((1349141766, -4.312539289775187, 492609224), (1349141766, -0.29235745344458247, 496442774), '0.24895207748060239') We are sending mail with results at report@fotonower.com args[1349141762] : ((1349141762, -4.204484701748642, 492609224), (1349141762, 0.21625161710736535, 2107752395), '0.24895207748060239') We are sending mail with results at report@fotonower.com args[1349141757] : ((1349141757, -5.071147621059683, 492609224), (1349141757, -0.16229930965815403, 496442774), '0.24895207748060239') We are sending mail with results at report@fotonower.com args[1349141752] : ((1349141752, -4.097351521015433, 492609224), (1349141752, -0.1605151993078668, 496442774), '0.24895207748060239') We are sending mail with results at report@fotonower.com args[1349141681] : ((1349141681, -5.014450881727802, 492609224), (1349141681, -0.10605940221990143, 496442774), '0.24895207748060239') We are sending mail with results at report@fotonower.com args[1348989197] : ((1348989197, -5.658503225070488, 492609224), (1348989197, 0.1698964276472357, 2107752395), '0.24895207748060239') We are sending mail with results at report@fotonower.com args[1348989194] : ((1348989194, -3.2718669839523797, 492609224), (1348989194, -0.35519854425189956, 496442774), '0.24895207748060239') We are sending mail with results at report@fotonower.com args[1348989174] : ((1348989174, -4.96328948542971, 492609224), (1348989174, -0.550924507641314, 501862349), '0.24895207748060239') We are sending mail with results at report@fotonower.com args[1348988829] : ((1348988829, -4.464965460289582, 492609224), (1348988829, -0.022516919204273438, 2107752395), '0.24895207748060239') We are sending mail with results at report@fotonower.com args[1348988791] : ((1348988791, -4.9353417506355886, 492609224), (1348988791, -0.20107673234265092, 496442774), '0.24895207748060239') We are sending mail with results at report@fotonower.com args[1348988744] : ((1348988744, -6.439894001788027, 492609224), (1348988744, -0.15062252140092738, 496442774), '0.24895207748060239') We are sending mail with results at report@fotonower.com args[1348988511] : ((1348988511, -5.747034324943433, 492609224), (1348988511, -0.17776028077551068, 496442774), '0.24895207748060239') We are sending mail with results at report@fotonower.com args[1348988362] : ((1348988362, -5.5242211044887926, 492609224), (1348988362, -0.26111015548651817, 496442774), '0.24895207748060239') We are sending mail with results at report@fotonower.com args[1348988329] : ((1348988329, -1.746691917102628, 492688767), (1348988329, -0.35024654872547023, 496442774), '0.24895207748060239') We are sending mail with results at report@fotonower.com args[1348988271] : ((1348988271, -4.470621376666107, 492609224), (1348988271, -0.06207983720500896, 2107752395), '0.24895207748060239') We are sending mail with results at report@fotonower.com args[1348988219] : ((1348988219, -5.578856964409478, 492609224), (1348988219, -0.055978588886298405, 2107752395), '0.24895207748060239') We are sending mail with results at report@fotonower.com args[1348988180] : ((1348988180, -4.780294844308383, 492609224), (1348988180, -0.12920786668533332, 496442774), '0.24895207748060239') We are sending mail with results at report@fotonower.com args[1348988140] : ((1348988140, -5.037884762860154, 492609224), (1348988140, 0.022407762878759285, 2107752395), '0.24895207748060239') We are sending mail with results at report@fotonower.com refus_total : 0.24895207748060239 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=21929800 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1349141845,1348989197,1348989194,1348989174,1348988744,1348988180,1349141757,1349141752,1348988791,1348988362,1348988329,1348988829,1348988511,1349141681,1348988271,1348988219,1349141762,1349141766,1348988140) Found this number of photos: 19 begin to download photo : 1349141845 begin to download photo : 1348988744 begin to download photo : 1348988791 begin to download photo : 1348988511 begin to download photo : 1349141762 download finish for photo 1349141762 begin to download photo : 1349141766 download finish for photo 1348988511 begin to download photo : 1349141681 download finish for photo 1349141845 begin to download photo : 1348989197 download finish for photo 1348988744 begin to download photo : 1348988180 download finish for photo 1348988791 begin to download photo : 1348988362 download finish for photo 1349141766 begin to download photo : 1348988140 download finish for photo 1348989197 begin to download photo : 1348989194 download finish for photo 1348988180 begin to download photo : 1349141757 download finish for photo 1349141681 begin to download photo : 1348988271 download finish for photo 1348988362 begin to download photo : 1348988329 download finish for photo 1348989194 begin to download photo : 1348989174 download finish for photo 1348988140 download finish for photo 1348988271 begin to download photo : 1348988219 download finish for photo 1349141757 begin to download photo : 1349141752 download finish for photo 1348988329 begin to download photo : 1348988829 download finish for photo 1348988219 download finish for photo 1348989174 download finish for photo 1349141752 download finish for photo 1348988829 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929800_01-04-2025_02_11_03.pdf results_Auto_P21929800_01-04-2025_02_11_03.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929800_01-04-2025_02_11_03.pdf start insert file to database insert into MTRUser.mtr_files (mtd_id,mtr_portfolio_id,text,url,format,tags,file_size,value) values ('3318','21929800','results_Auto_P21929800_01-04-2025_02_11_03.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929800_01-04-2025_02_11_03.pdf','pdf','','1.28','0.24895207748060239') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/21929800

https://www.fotonower.com/image?json=false&list_photos_id=1349141845
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349141766
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349141762
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349141757
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349141752
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349141681
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348989197
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348989194
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348989174
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348988829
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348988791
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348988744
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348988511
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348988362
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348988329
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348988271
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348988219
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348988180
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348988140
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 24.90%
Veuillez trouver les photos des contaminants.

exemples de contaminants: pet_clair: https://www.fotonower.com/view/21930687?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/21930688?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/21930691?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/21930694?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/21930695?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/21930696?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/21930697?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929800_01-04-2025_02_11_03.pdf.

Lien vers velours :https://www.fotonower.com/velours/21930687,21930688,21930689,21930690,21930691,21930692,21930693,21930694,21930695,21930696,21930697?tags=pet_clair,pehd,background,environnement,pet_fonce,mal_croppe,flou,autre,carton,papier,metal.


L'équipe Fotonower 202 b'' Server: nginx Date: Tue, 01 Apr 2025 00:11:18 GMT Content-Length: 0 Connection: close X-Message-Id: EvKIwmGvTSWg1vADhDVhWQ Access-Control-Allow-Origin: https://sendgrid.api-docs.io Access-Control-Allow-Methods: POST Access-Control-Allow-Headers: Authorization, Content-Type, On-behalf-of, x-sg-elas-acl Access-Control-Max-Age: 600 X-No-CORS-Reason: https://sendgrid.com/docs/Classroom/Basics/API/cors.html Strict-Transport-Security: max-age=31536000; includeSubDomains Content-Security-Policy: frame-ancestors 'none' Cache-Control: no-cache X-Content-Type-Options: no-sniff Referrer-Policy: strict-origin-when-cross-origin Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : send_mail_cod we use saveGeneral [1349141845, 1349141766, 1349141762, 1349141757, 1349141752, 1349141681, 1348989197, 1348989194, 1348989174, 1348988829, 1348988791, 1348988744, 1348988511, 1348988362, 1348988329, 1348988271, 1348988219, 1348988180, 1348988140] Looping around the photos to save general results len do output : 0 before output type Used above Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141845', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141766', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141762', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141757', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141752', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141681', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348989197', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348989194', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348989174', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988829', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988791', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988744', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988511', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988362', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988329', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988271', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988219', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988180', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988140', None, None, None, None, None, '2711132') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 19 time used for this insertion : 0.08125448226928711 save_final save missing photos in datou_result : time spend for datou_step_exec : 14.967289447784424 time spend to save output : 0.08176445960998535 total time spend for step 9 : 15.04905390739441 step10:split_time_score Tue Apr 1 02:11:18 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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'}] (('12', 19),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 31032025 21929800 Nombre de photos uploadées : 19 / 23040 (0%) 31032025 21929800 Nombre de photos taguées (types de déchets): 0 / 19 (0%) 31032025 21929800 Nombre de photos taguées (volume) : 0 / 19 (0%) elapsed_time : load_data_split_time_score 3.814697265625e-06 elapsed_time : order_list_meta_photo_and_scores 8.344650268554688e-06 ??????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.000667572021484375 elapsed_time : insert_dashboard_record_day_entry 0.02789592742919922 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 : 0.14630428184005087 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21925661_31-03-2025_22_59_57.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21925661 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`=21925661 AND mptpi.`type`=3594 To do Qualite : 0.14145216719895026 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21925662_31-03-2025_22_51_38.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21925662 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`=21925662 AND mptpi.`type`=3594 To do Qualite : 0.0939584877250109 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21905169_31-03-2025_11_54_31.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21905169 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`=21905169 AND mptpi.`type`=3726 To do Qualite : 0.24895207748060239 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929800_01-04-2025_02_11_03.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21929800 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`=21929800 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21929818 order by id desc limit 1 Qualite : 0.10137259221816262 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21911519_31-03-2025_15_32_52.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21911519 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`=21911519 AND mptpi.`type`=3726 To do Qualite : 0.17051974471791034 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21913880_31-03-2025_16_28_37.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21913880 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`=21913880 AND mptpi.`type`=3594 To do Qualite : 0.2305784432354592 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929822_01-04-2025_01_34_28.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21929822 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`=21929822 AND mptpi.`type`=3594 To do Qualite : 0.06655992376993317 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929825_01-04-2025_01_30_31.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21929825 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`=21929825 AND mptpi.`type`=3726 To do Qualite : 0.22924124322132222 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21926965_31-03-2025_23_29_21.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21926965 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`=21926965 AND mptpi.`type`=3594 To do Qualite : 0.2189041275706411 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21925669_31-03-2025_22_45_27.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21925669 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`=21925669 AND mptpi.`type`=3594 To do Qualite : 0.18545522031874095 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21925670_31-03-2025_22_36_34.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21925670 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`=21925670 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'31032025': {'nb_upload': 19, '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 [1349141845, 1349141766, 1349141762, 1349141757, 1349141752, 1349141681, 1348989197, 1348989194, 1348989174, 1348988829, 1348988791, 1348988744, 1348988511, 1348988362, 1348988329, 1348988271, 1348988219, 1348988180, 1348988140] Looping around the photos to save general results len do output : 1 /21929800Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141845', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141766', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141762', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141757', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141752', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1349141681', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348989197', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348989194', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348989174', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988829', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988791', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988744', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988511', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988362', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988329', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988271', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988219', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988180', None, None, None, None, None, '2711132') ('3318', None, None, None, None, None, None, None, '2711132') ('3318', '21929800', '1348988140', None, None, None, None, None, '2711132') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 20 time used for this insertion : 0.01950359344482422 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.6669175624847412 time spend to save output : 0.019772768020629883 total time spend for step 10 : 1.686690330505371 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 19 set_done_treatment 664.52user 331.49system 30:55.22elapsed 53%CPU (0avgtext+0avgdata 6211020maxresident)k 40200504inputs+491144outputs (1366218major+52427367minor)pagefaults 0swaps