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 : 2444690 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 : ['2711235'] with mtr_portfolio_ids : ['21930834'] and first list_photo_ids : [] new path : /proc/2444690/ 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 ofwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 16 ; length of list_pids : 16 ; length of list_args : 16 time to download the photos : 3.650261878967285 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 02:30:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 5304 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-01 02:30:34.882391: 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 02:30:34.898078: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-01 02:30:34.900236: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f54dc000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-01 02:30:34.900305: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-01 02:30:34.905689: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-01 02:30:35.631561: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x358b1160 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-01 02:30:35.631621: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-01 02:30:35.646682: 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 02:30:35.650552: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 02:30:35.654414: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 02:30:35.657373: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-01 02:30:35.657777: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-01 02:30:35.660626: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-01 02:30:35.662043: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-01 02:30:35.667974: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 02:30:35.669424: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-01 02:30:35.669523: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 02:30:35.688654: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-01 02:30:35.688701: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-01 02:30:35.688716: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-01 02:30:35.702028: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4701 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 02:30:36.305437: 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 02:30:36.305568: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 02:30:36.305592: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 02:30:36.305611: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-01 02:30:36.305630: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-01 02:30:36.305648: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-01 02:30:36.305666: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-01 02:30:36.305684: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 02:30:36.306844: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-01 02:30:36.308162: 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 02:30:36.308210: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 02:30:36.308232: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 02:30:36.308252: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-01 02:30:36.308272: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-01 02:30:36.308291: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-01 02:30:36.308310: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-01 02:30:36.308330: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 02:30:36.309444: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-01 02:30:36.309485: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-01 02:30:36.309496: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-01 02:30:36.309506: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-01 02:30:36.310684: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4701 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 02:30:51.226049: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 02:30:51.446882: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 02:30:53.072797: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.073369: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.60G (3865470464 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.073876: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.24G (3478923264 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.074352: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.92G (3131030784 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.074830: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.62G (2817927680 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.075377: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.36G (2536134912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.075889: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2282521344 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.075915: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-01 02:30:53.077144: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.077162: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-01 02:30:53.084156: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.084177: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-01 02:30:53.084686: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.084701: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-01 02:30:53.091186: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.091222: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-01 02:30:53.091734: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.091750: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-01 02:30:53.123066: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.123124: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-01 02:30:53.123638: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.123654: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-01 02:30:53.129471: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.129498: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-01 02:30:53.130013: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.130029: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-01 02:30:53.164210: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.164782: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.166627: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.167157: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.211385: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.211926: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.214086: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.214603: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.244800: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.245293: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.246957: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.247476: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.255713: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.256232: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.258151: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.258678: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.265939: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.266457: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.268323: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.269487: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.302681: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.303263: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.303790: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.304318: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.308956: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.309489: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.328723: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.329273: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.329799: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.330320: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.343577: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.344124: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.344647: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.345167: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.349609: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.350128: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.354669: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.355220: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.367108: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.367631: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.371687: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.372206: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.372718: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.373230: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.373914: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.374432: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.385213: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.385743: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.386276: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.386794: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.387340: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.387862: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.388381: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.388911: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.398080: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.398605: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.405011: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.405535: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.459660: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.459750: W tensorflow/core/kernels/gpu_utils.cc:49] Failed to allocate memory for convolution redzone checking; skipping this check. This is benign and only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once. 2025-04-01 02:30:53.460678: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.461611: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.468652: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.469318: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.469992: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.470644: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.478671: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.479210: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.508081: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.508769: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.509429: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.510066: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.514894: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.515881: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.516785: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.517683: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.523801: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.529746: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.530394: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.539433: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.539957: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.540479: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.540989: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.541509: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 02:30:53.542024: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 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 : 16 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 : 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 : 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 : 99 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 : 93 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 : 60 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 : 48 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 : 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 : 44 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 : 92 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 : 88 Detection mask done ! Trying to reset tf kernel 2446383 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 9621 tf kernel not reseted sub process len(results) : 16 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 16 len(list_Values) 0 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 : 10814 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 : 0.32259559631347656 nb_pixel_total : 121854 time to create 1 rle with old method : 0.13634800910949707 length of segment : 569 time for calcul the mask position with numpy : 0.28531384468078613 nb_pixel_total : 29008 time to create 1 rle with old method : 0.04829740524291992 length of segment : 287 time for calcul the mask position with numpy : 0.04343128204345703 nb_pixel_total : 7949 time to create 1 rle with old method : 0.018991470336914062 length of segment : 100 time for calcul the mask position with numpy : 0.3351631164550781 nb_pixel_total : 80214 time to create 1 rle with old method : 0.13557958602905273 length of segment : 360 time for calcul the mask position with numpy : 0.15937495231628418 nb_pixel_total : 60313 time to create 1 rle with old method : 0.07863211631774902 length of segment : 357 time for calcul the mask position with numpy : 0.14639568328857422 nb_pixel_total : 36846 time to create 1 rle with old method : 0.05131077766418457 length of segment : 270 time for calcul the mask position with numpy : 0.06262993812561035 nb_pixel_total : 28747 time to create 1 rle with old method : 0.04027724266052246 length of segment : 145 time for calcul the mask position with numpy : 0.02950739860534668 nb_pixel_total : 11910 time to create 1 rle with old method : 0.017540693283081055 length of segment : 151 time for calcul the mask position with numpy : 0.031196117401123047 nb_pixel_total : 6873 time to create 1 rle with old method : 0.01195836067199707 length of segment : 68 time for calcul the mask position with numpy : 0.10161447525024414 nb_pixel_total : 37649 time to create 1 rle with old method : 0.05358099937438965 length of segment : 196 time for calcul the mask position with numpy : 0.06723618507385254 nb_pixel_total : 11124 time to create 1 rle with old method : 0.019414663314819336 length of segment : 159 time for calcul the mask position with numpy : 0.17399883270263672 nb_pixel_total : 38846 time to create 1 rle with old method : 0.05208897590637207 length of segment : 313 time for calcul the mask position with numpy : 0.0778036117553711 nb_pixel_total : 18247 time to create 1 rle with old method : 0.03057551383972168 length of segment : 131 time for calcul the mask position with numpy : 0.052677154541015625 nb_pixel_total : 9379 time to create 1 rle with old method : 0.01525115966796875 length of segment : 108 time for calcul the mask position with numpy : 0.0409238338470459 nb_pixel_total : 9908 time to create 1 rle with old method : 0.018843650817871094 length of segment : 165 time for calcul the mask position with numpy : 0.1036374568939209 nb_pixel_total : 25921 time to create 1 rle with old method : 0.034383535385131836 length of segment : 174 time for calcul the mask position with numpy : 0.13320255279541016 nb_pixel_total : 37705 time to create 1 rle with old method : 0.04209184646606445 length of segment : 375 time for calcul the mask position with numpy : 0.0015537738800048828 nb_pixel_total : 17612 time to create 1 rle with old method : 0.019626140594482422 length of segment : 153 time for calcul the mask position with numpy : 0.030983686447143555 nb_pixel_total : 10715 time to create 1 rle with old method : 0.011895418167114258 length of segment : 106 time for calcul the mask position with numpy : 0.3143618106842041 nb_pixel_total : 154876 time to create 1 rle with new method : 0.013725996017456055 length of segment : 483 time for calcul the mask position with numpy : 0.07294940948486328 nb_pixel_total : 24320 time to create 1 rle with old method : 0.03219175338745117 length of segment : 219 time for calcul the mask position with numpy : 0.05172371864318848 nb_pixel_total : 27795 time to create 1 rle with old method : 0.036073923110961914 length of segment : 206 time for calcul the mask position with numpy : 0.17676234245300293 nb_pixel_total : 35768 time to create 1 rle with old method : 0.043651580810546875 length of segment : 363 time for calcul the mask position with numpy : 0.1332249641418457 nb_pixel_total : 19191 time to create 1 rle with old method : 0.025405168533325195 length of segment : 214 time for calcul the mask position with numpy : 0.10504341125488281 nb_pixel_total : 56274 time to create 1 rle with old method : 0.0634450912475586 length of segment : 477 time for calcul the mask position with numpy : 0.025315523147583008 nb_pixel_total : 7274 time to create 1 rle with old method : 0.01236724853515625 length of segment : 102 time for calcul the mask position with numpy : 0.0022013187408447266 nb_pixel_total : 8263 time to create 1 rle with old method : 0.010931730270385742 length of segment : 113 time for calcul the mask position with numpy : 0.07068371772766113 nb_pixel_total : 18516 time to create 1 rle with old method : 0.024883508682250977 length of segment : 154 time for calcul the mask position with numpy : 0.014124155044555664 nb_pixel_total : 11322 time to create 1 rle with old method : 0.017857789993286133 length of segment : 135 time for calcul the mask position with numpy : 0.06849884986877441 nb_pixel_total : 15926 time to create 1 rle with old method : 0.035857200622558594 length of segment : 133 time for calcul the mask position with numpy : 0.017813920974731445 nb_pixel_total : 14588 time to create 1 rle with old method : 0.02380228042602539 length of segment : 132 time for calcul the mask position with numpy : 0.0007851123809814453 nb_pixel_total : 18887 time to create 1 rle with old method : 0.021225929260253906 length of segment : 158 time for calcul the mask position with numpy : 0.016530990600585938 nb_pixel_total : 2451 time to create 1 rle with old method : 0.005333900451660156 length of segment : 52 time for calcul the mask position with numpy : 0.20647311210632324 nb_pixel_total : 58000 time to create 1 rle with old method : 0.06704235076904297 length of segment : 583 time for calcul the mask position with numpy : 0.0006253719329833984 nb_pixel_total : 23753 time to create 1 rle with old method : 0.026777982711791992 length of segment : 97 time for calcul the mask position with numpy : 0.09676051139831543 nb_pixel_total : 28130 time to create 1 rle with old method : 0.03436088562011719 length of segment : 201 time for calcul the mask position with numpy : 0.08405542373657227 nb_pixel_total : 14883 time to create 1 rle with old method : 0.020481586456298828 length of segment : 140 time for calcul the mask position with numpy : 0.00023627281188964844 nb_pixel_total : 8802 time to create 1 rle with old method : 0.010361671447753906 length of segment : 96 time for calcul the mask position with numpy : 0.11638402938842773 nb_pixel_total : 40657 time to create 1 rle with old method : 0.04935121536254883 length of segment : 274 time for calcul the mask position with numpy : 0.02340078353881836 nb_pixel_total : 13393 time to create 1 rle with old method : 0.01967000961303711 length of segment : 105 time for calcul the mask position with numpy : 0.058986663818359375 nb_pixel_total : 38714 time to create 1 rle with old method : 0.047905921936035156 length of segment : 282 time for calcul the mask position with numpy : 0.011115789413452148 nb_pixel_total : 27670 time to create 1 rle with old method : 0.03332853317260742 length of segment : 296 time for calcul the mask position with numpy : 0.019910812377929688 nb_pixel_total : 14458 time to create 1 rle with old method : 0.021242380142211914 length of segment : 122 time for calcul the mask position with numpy : 0.04346823692321777 nb_pixel_total : 25857 time to create 1 rle with old method : 0.04316210746765137 length of segment : 198 time for calcul the mask position with numpy : 0.007628202438354492 nb_pixel_total : 11000 time to create 1 rle with old method : 0.012318849563598633 length of segment : 157 time for calcul the mask position with numpy : 0.04380059242248535 nb_pixel_total : 16501 time to create 1 rle with old method : 0.018418073654174805 length of segment : 122 time for calcul the mask position with numpy : 0.05504035949707031 nb_pixel_total : 37122 time to create 1 rle with old method : 0.04608726501464844 length of segment : 212 time for calcul the mask position with numpy : 0.043509721755981445 nb_pixel_total : 16343 time to create 1 rle with old method : 0.022701740264892578 length of segment : 166 time for calcul the mask position with numpy : 0.25630664825439453 nb_pixel_total : 69991 time to create 1 rle with old method : 0.07694673538208008 length of segment : 353 time for calcul the mask position with numpy : 0.0767970085144043 nb_pixel_total : 14915 time to create 1 rle with old method : 0.020716428756713867 length of segment : 140 time for calcul the mask position with numpy : 0.07700467109680176 nb_pixel_total : 13386 time to create 1 rle with old method : 0.021375656127929688 length of segment : 173 time for calcul the mask position with numpy : 0.05621457099914551 nb_pixel_total : 11059 time to create 1 rle with old method : 0.017524242401123047 length of segment : 87 time for calcul the mask position with numpy : 0.05007290840148926 nb_pixel_total : 26830 time to create 1 rle with old method : 0.035439491271972656 length of segment : 230 time for calcul the mask position with numpy : 0.043877601623535156 nb_pixel_total : 10012 time to create 1 rle with old method : 0.015601396560668945 length of segment : 102 time for calcul the mask position with numpy : 0.03484678268432617 nb_pixel_total : 14002 time to create 1 rle with old method : 0.019989728927612305 length of segment : 118 time for calcul the mask position with numpy : 0.07253265380859375 nb_pixel_total : 12007 time to create 1 rle with old method : 0.01729869842529297 length of segment : 275 time for calcul the mask position with numpy : 0.2302405834197998 nb_pixel_total : 132814 time to create 1 rle with old method : 0.1487281322479248 length of segment : 359 time for calcul the mask position with numpy : 0.08338427543640137 nb_pixel_total : 23004 time to create 1 rle with old method : 0.029813051223754883 length of segment : 225 time for calcul the mask position with numpy : 0.06984639167785645 nb_pixel_total : 18626 time to create 1 rle with old method : 0.024625062942504883 length of segment : 145 time for calcul the mask position with numpy : 0.05478191375732422 nb_pixel_total : 28497 time to create 1 rle with old method : 0.03558969497680664 length of segment : 181 time for calcul the mask position with numpy : 0.04437136650085449 nb_pixel_total : 15428 time to create 1 rle with old method : 0.021706104278564453 length of segment : 132 time for calcul the mask position with numpy : 0.03665328025817871 nb_pixel_total : 17180 time to create 1 rle with old method : 0.027147531509399414 length of segment : 104 time for calcul the mask position with numpy : 0.061763763427734375 nb_pixel_total : 15354 time to create 1 rle with old method : 0.021793603897094727 length of segment : 129 time for calcul the mask position with numpy : 0.02352309226989746 nb_pixel_total : 7960 time to create 1 rle with old method : 0.012705326080322266 length of segment : 106 time for calcul the mask position with numpy : 0.12278270721435547 nb_pixel_total : 39203 time to create 1 rle with old method : 0.04866671562194824 length of segment : 246 time for calcul the mask position with numpy : 0.030436992645263672 nb_pixel_total : 10399 time to create 1 rle with old method : 0.014189720153808594 length of segment : 171 time for calcul the mask position with numpy : 0.19508647918701172 nb_pixel_total : 101105 time to create 1 rle with old method : 0.10981082916259766 length of segment : 405 time for calcul the mask position with numpy : 0.07962465286254883 nb_pixel_total : 17405 time to create 1 rle with old method : 0.02345132827758789 length of segment : 164 time for calcul the mask position with numpy : 0.08957338333129883 nb_pixel_total : 27375 time to create 1 rle with old method : 0.04545092582702637 length of segment : 204 time for calcul the mask position with numpy : 0.029292821884155273 nb_pixel_total : 7133 time to create 1 rle with old method : 0.01335597038269043 length of segment : 78 time for calcul the mask position with numpy : 0.0902090072631836 nb_pixel_total : 36464 time to create 1 rle with old method : 0.044898033142089844 length of segment : 165 time for calcul the mask position with numpy : 0.06700682640075684 nb_pixel_total : 14271 time to create 1 rle with old method : 0.019825458526611328 length of segment : 221 time for calcul the mask position with numpy : 0.08984875679016113 nb_pixel_total : 39217 time to create 1 rle with old method : 0.04444718360900879 length of segment : 267 time for calcul the mask position with numpy : 0.014744997024536133 nb_pixel_total : 4894 time to create 1 rle with old method : 0.008253335952758789 length of segment : 68 time for calcul the mask position with numpy : 0.05282449722290039 nb_pixel_total : 8935 time to create 1 rle with old method : 0.015840530395507812 length of segment : 132 time for calcul the mask position with numpy : 0.02768707275390625 nb_pixel_total : 12307 time to create 1 rle with old method : 0.014150142669677734 length of segment : 154 time for calcul the mask position with numpy : 0.19375014305114746 nb_pixel_total : 60469 time to create 1 rle with old method : 0.07222390174865723 length of segment : 373 time for calcul the mask position with numpy : 0.1005713939666748 nb_pixel_total : 23496 time to create 1 rle with old method : 0.03304004669189453 length of segment : 161 time for calcul the mask position with numpy : 0.1977689266204834 nb_pixel_total : 92040 time to create 1 rle with old method : 0.10922384262084961 length of segment : 405 time for calcul the mask position with numpy : 0.05831027030944824 nb_pixel_total : 13100 time to create 1 rle with old method : 0.01847362518310547 length of segment : 168 time for calcul the mask position with numpy : 0.07253670692443848 nb_pixel_total : 21847 time to create 1 rle with old method : 0.028626441955566406 length of segment : 97 time for calcul the mask position with numpy : 0.06422090530395508 nb_pixel_total : 22922 time to create 1 rle with old method : 0.030913114547729492 length of segment : 251 time for calcul the mask position with numpy : 0.02641773223876953 nb_pixel_total : 7589 time to create 1 rle with old method : 0.013289928436279297 length of segment : 78 time for calcul the mask position with numpy : 0.047629356384277344 nb_pixel_total : 14581 time to create 1 rle with old method : 0.02137446403503418 length of segment : 129 time for calcul the mask position with numpy : 0.05938243865966797 nb_pixel_total : 16987 time to create 1 rle with old method : 0.02367711067199707 length of segment : 173 time for calcul the mask position with numpy : 0.11451363563537598 nb_pixel_total : 12480 time to create 1 rle with old method : 0.01836109161376953 length of segment : 210 time for calcul the mask position with numpy : 0.015228033065795898 nb_pixel_total : 8237 time to create 1 rle with old method : 0.013737678527832031 length of segment : 123 time for calcul the mask position with numpy : 0.03520321846008301 nb_pixel_total : 5997 time to create 1 rle with old method : 0.012173891067504883 length of segment : 99 time for calcul the mask position with numpy : 0.020308971405029297 nb_pixel_total : 8408 time to create 1 rle with old method : 0.015199422836303711 length of segment : 105 time for calcul the mask position with numpy : 0.03626441955566406 nb_pixel_total : 6818 time to create 1 rle with old method : 0.01276707649230957 length of segment : 107 time for calcul the mask position with numpy : 0.07877707481384277 nb_pixel_total : 9496 time to create 1 rle with old method : 0.015371322631835938 length of segment : 129 time for calcul the mask position with numpy : 0.06306672096252441 nb_pixel_total : 33587 time to create 1 rle with old method : 0.041687726974487305 length of segment : 222 time for calcul the mask position with numpy : 0.04937100410461426 nb_pixel_total : 12200 time to create 1 rle with old method : 0.015466690063476562 length of segment : 178 time for calcul the mask position with numpy : 0.07040524482727051 nb_pixel_total : 29738 time to create 1 rle with old method : 0.03666353225708008 length of segment : 220 time for calcul the mask position with numpy : 0.02658390998840332 nb_pixel_total : 10349 time to create 1 rle with old method : 0.01618027687072754 length of segment : 134 time for calcul the mask position with numpy : 0.10349154472351074 nb_pixel_total : 34760 time to create 1 rle with old method : 0.04374384880065918 length of segment : 181 time for calcul the mask position with numpy : 0.07577276229858398 nb_pixel_total : 15410 time to create 1 rle with old method : 0.021382570266723633 length of segment : 172 time for calcul the mask position with numpy : 0.07303738594055176 nb_pixel_total : 28617 time to create 1 rle with old method : 0.036025047302246094 length of segment : 279 time for calcul the mask position with numpy : 0.06750845909118652 nb_pixel_total : 9381 time to create 1 rle with old method : 0.014027595520019531 length of segment : 180 time for calcul the mask position with numpy : 0.012175798416137695 nb_pixel_total : 9091 time to create 1 rle with old method : 0.013601541519165039 length of segment : 120 time for calcul the mask position with numpy : 0.02242565155029297 nb_pixel_total : 10488 time to create 1 rle with old method : 0.02313828468322754 length of segment : 89 time for calcul the mask position with numpy : 0.007101535797119141 nb_pixel_total : 6215 time to create 1 rle with old method : 0.007453441619873047 length of segment : 83 time for calcul the mask position with numpy : 0.020340442657470703 nb_pixel_total : 12381 time to create 1 rle with old method : 0.01776742935180664 length of segment : 196 time for calcul the mask position with numpy : 0.05018186569213867 nb_pixel_total : 16265 time to create 1 rle with old method : 0.02277088165283203 length of segment : 192 time for calcul the mask position with numpy : 0.11737632751464844 nb_pixel_total : 30491 time to create 1 rle with old method : 0.0367584228515625 length of segment : 352 time for calcul the mask position with numpy : 0.39746689796447754 nb_pixel_total : 65184 time to create 1 rle with old method : 0.08774876594543457 length of segment : 402 time for calcul the mask position with numpy : 0.009017705917358398 nb_pixel_total : 4605 time to create 1 rle with old method : 0.007231712341308594 length of segment : 84 time for calcul the mask position with numpy : 0.0062999725341796875 nb_pixel_total : 13611 time to create 1 rle with old method : 0.014753341674804688 length of segment : 165 time for calcul the mask position with numpy : 0.0009090900421142578 nb_pixel_total : 13732 time to create 1 rle with old method : 0.01493692398071289 length of segment : 251 time for calcul the mask position with numpy : 0.17925786972045898 nb_pixel_total : 82360 time to create 1 rle with old method : 0.09037470817565918 length of segment : 384 time for calcul the mask position with numpy : 0.00911855697631836 nb_pixel_total : 12911 time to create 1 rle with old method : 0.01738142967224121 length of segment : 192 time for calcul the mask position with numpy : 0.0008671283721923828 nb_pixel_total : 4402 time to create 1 rle with old method : 0.004875659942626953 length of segment : 106 time for calcul the mask position with numpy : 0.09953069686889648 nb_pixel_total : 22565 time to create 1 rle with old method : 0.031003475189208984 length of segment : 369 time for calcul the mask position with numpy : 0.0429685115814209 nb_pixel_total : 29302 time to create 1 rle with old method : 0.036896467208862305 length of segment : 235 time for calcul the mask position with numpy : 0.012852191925048828 nb_pixel_total : 6317 time to create 1 rle with old method : 0.011415958404541016 length of segment : 88 time for calcul the mask position with numpy : 0.13329529762268066 nb_pixel_total : 30839 time to create 1 rle with old method : 0.03865671157836914 length of segment : 247 time for calcul the mask position with numpy : 0.11613726615905762 nb_pixel_total : 27085 time to create 1 rle with old method : 0.04065847396850586 length of segment : 183 time for calcul the mask position with numpy : 0.16294479370117188 nb_pixel_total : 38089 time to create 1 rle with old method : 0.049507856369018555 length of segment : 315 time for calcul the mask position with numpy : 0.32779622077941895 nb_pixel_total : 93551 time to create 1 rle with old method : 0.10254621505737305 length of segment : 544 time for calcul the mask position with numpy : 0.30835390090942383 nb_pixel_total : 133153 time to create 1 rle with old method : 0.14621925354003906 length of segment : 400 time for calcul the mask position with numpy : 0.04399824142456055 nb_pixel_total : 8396 time to create 1 rle with old method : 0.013833045959472656 length of segment : 160 time for calcul the mask position with numpy : 0.007320880889892578 nb_pixel_total : 22101 time to create 1 rle with old method : 0.02596592903137207 length of segment : 407 time for calcul the mask position with numpy : 0.1279306411743164 nb_pixel_total : 41503 time to create 1 rle with old method : 0.04750394821166992 length of segment : 268 time for calcul the mask position with numpy : 0.07227516174316406 nb_pixel_total : 22748 time to create 1 rle with old method : 0.028020620346069336 length of segment : 170 time for calcul the mask position with numpy : 0.23450708389282227 nb_pixel_total : 56466 time to create 1 rle with old method : 0.06495451927185059 length of segment : 503 time for calcul the mask position with numpy : 0.03555727005004883 nb_pixel_total : 9595 time to create 1 rle with old method : 0.012875556945800781 length of segment : 98 time for calcul the mask position with numpy : 0.04671478271484375 nb_pixel_total : 10375 time to create 1 rle with old method : 0.011012554168701172 length of segment : 125 time for calcul the mask position with numpy : 0.043659210205078125 nb_pixel_total : 16967 time to create 1 rle with old method : 0.018421649932861328 length of segment : 126 time for calcul the mask position with numpy : 0.05684852600097656 nb_pixel_total : 16226 time to create 1 rle with old method : 0.017153263092041016 length of segment : 185 time for calcul the mask position with numpy : 0.09822511672973633 nb_pixel_total : 35479 time to create 1 rle with old method : 0.04686880111694336 length of segment : 295 time for calcul the mask position with numpy : 0.09992694854736328 nb_pixel_total : 57645 time to create 1 rle with old method : 0.06546854972839355 length of segment : 295 time for calcul the mask position with numpy : 0.027333974838256836 nb_pixel_total : 22400 time to create 1 rle with old method : 0.02884364128112793 length of segment : 171 time for calcul the mask position with numpy : 0.05456995964050293 nb_pixel_total : 43947 time to create 1 rle with old method : 0.05048823356628418 length of segment : 299 time for calcul the mask position with numpy : 0.1696326732635498 nb_pixel_total : 53905 time to create 1 rle with old method : 0.06361055374145508 length of segment : 340 time for calcul the mask position with numpy : 0.09069347381591797 nb_pixel_total : 15377 time to create 1 rle with old method : 0.019525766372680664 length of segment : 166 time for calcul the mask position with numpy : 0.018117666244506836 nb_pixel_total : 10014 time to create 1 rle with old method : 0.014204740524291992 length of segment : 264 time for calcul the mask position with numpy : 0.12463998794555664 nb_pixel_total : 55517 time to create 1 rle with old method : 0.07593536376953125 length of segment : 324 time for calcul the mask position with numpy : 0.019949674606323242 nb_pixel_total : 11967 time to create 1 rle with old method : 0.020247697830200195 length of segment : 138 time for calcul the mask position with numpy : 0.08532524108886719 nb_pixel_total : 20407 time to create 1 rle with old method : 0.028026342391967773 length of segment : 169 time for calcul the mask position with numpy : 0.06443262100219727 nb_pixel_total : 32946 time to create 1 rle with old method : 0.05332374572753906 length of segment : 302 time for calcul the mask position with numpy : 0.2588491439819336 nb_pixel_total : 67769 time to create 1 rle with old method : 0.08678245544433594 length of segment : 348 time for calcul the mask position with numpy : 0.23586153984069824 nb_pixel_total : 101772 time to create 1 rle with old method : 0.12858986854553223 length of segment : 415 time for calcul the mask position with numpy : 0.07629251480102539 nb_pixel_total : 16848 time to create 1 rle with old method : 0.024829387664794922 length of segment : 203 time for calcul the mask position with numpy : 0.05043840408325195 nb_pixel_total : 21916 time to create 1 rle with old method : 0.03234291076660156 length of segment : 430 time for calcul the mask position with numpy : 0.06237459182739258 nb_pixel_total : 19002 time to create 1 rle with old method : 0.025806188583374023 length of segment : 239 time for calcul the mask position with numpy : 0.08558034896850586 nb_pixel_total : 42053 time to create 1 rle with old method : 0.047878265380859375 length of segment : 213 time for calcul the mask position with numpy : 0.02237105369567871 nb_pixel_total : 9698 time to create 1 rle with old method : 0.015056371688842773 length of segment : 145 time for calcul the mask position with numpy : 0.030659914016723633 nb_pixel_total : 14859 time to create 1 rle with old method : 0.021454811096191406 length of segment : 124 time for calcul the mask position with numpy : 0.003844022750854492 nb_pixel_total : 15175 time to create 1 rle with old method : 0.019086360931396484 length of segment : 175 time for calcul the mask position with numpy : 0.022418975830078125 nb_pixel_total : 10901 time to create 1 rle with old method : 0.01729893684387207 length of segment : 236 time for calcul the mask position with numpy : 0.06769037246704102 nb_pixel_total : 24528 time to create 1 rle with old method : 0.02895975112915039 length of segment : 230 time for calcul the mask position with numpy : 0.1857624053955078 nb_pixel_total : 107295 time to create 1 rle with old method : 0.11684083938598633 length of segment : 660 time for calcul the mask position with numpy : 0.17616796493530273 nb_pixel_total : 67133 time to create 1 rle with old method : 0.0720832347869873 length of segment : 330 time for calcul the mask position with numpy : 0.009910106658935547 nb_pixel_total : 16389 time to create 1 rle with old method : 0.022325515747070312 length of segment : 200 time for calcul the mask position with numpy : 0.12593984603881836 nb_pixel_total : 30745 time to create 1 rle with old method : 0.03752326965332031 length of segment : 379 time for calcul the mask position with numpy : 0.08635354042053223 nb_pixel_total : 20470 time to create 1 rle with old method : 0.026237964630126953 length of segment : 216 time for calcul the mask position with numpy : 0.08709526062011719 nb_pixel_total : 21929 time to create 1 rle with old method : 0.028234481811523438 length of segment : 213 time for calcul the mask position with numpy : 0.0003058910369873047 nb_pixel_total : 11825 time to create 1 rle with old method : 0.013125419616699219 length of segment : 135 time for calcul the mask position with numpy : 0.010759353637695312 nb_pixel_total : 31634 time to create 1 rle with old method : 0.03954648971557617 length of segment : 313 time for calcul the mask position with numpy : 0.024524688720703125 nb_pixel_total : 5665 time to create 1 rle with old method : 0.010727882385253906 length of segment : 58 time for calcul the mask position with numpy : 0.03609323501586914 nb_pixel_total : 13121 time to create 1 rle with old method : 0.02108597755432129 length of segment : 151 time for calcul the mask position with numpy : 0.012867212295532227 nb_pixel_total : 26131 time to create 1 rle with old method : 0.03359675407409668 length of segment : 261 time for calcul the mask position with numpy : 0.05711102485656738 nb_pixel_total : 21736 time to create 1 rle with old method : 0.028828859329223633 length of segment : 104 time for calcul the mask position with numpy : 0.0012233257293701172 nb_pixel_total : 16945 time to create 1 rle with old method : 0.01845073699951172 length of segment : 150 time for calcul the mask position with numpy : 0.05511331558227539 nb_pixel_total : 14431 time to create 1 rle with old method : 0.019556760787963867 length of segment : 132 time for calcul the mask position with numpy : 0.0735173225402832 nb_pixel_total : 24762 time to create 1 rle with old method : 0.032488107681274414 length of segment : 225 time for calcul the mask position with numpy : 0.0003254413604736328 nb_pixel_total : 12385 time to create 1 rle with old method : 0.014235734939575195 length of segment : 134 time for calcul the mask position with numpy : 0.0025281906127929688 nb_pixel_total : 42827 time to create 1 rle with old method : 0.0484468936920166 length of segment : 277 time for calcul the mask position with numpy : 0.0015666484832763672 nb_pixel_total : 17963 time to create 1 rle with old method : 0.020370960235595703 length of segment : 257 time for calcul the mask position with numpy : 0.049745798110961914 nb_pixel_total : 8822 time to create 1 rle with old method : 0.01364755630493164 length of segment : 133 time for calcul the mask position with numpy : 0.09656500816345215 nb_pixel_total : 20293 time to create 1 rle with old method : 0.027534961700439453 length of segment : 198 time for calcul the mask position with numpy : 0.07039284706115723 nb_pixel_total : 43713 time to create 1 rle with old method : 0.05150341987609863 length of segment : 262 time for calcul the mask position with numpy : 0.008667230606079102 nb_pixel_total : 7219 time to create 1 rle with old method : 0.012747764587402344 length of segment : 97 time for calcul the mask position with numpy : 0.0870051383972168 nb_pixel_total : 34328 time to create 1 rle with old method : 0.04624032974243164 length of segment : 379 time for calcul the mask position with numpy : 0.03526902198791504 nb_pixel_total : 33944 time to create 1 rle with old method : 0.045107364654541016 length of segment : 267 time for calcul the mask position with numpy : 0.023798227310180664 nb_pixel_total : 10531 time to create 1 rle with old method : 0.024155139923095703 length of segment : 56 time for calcul the mask position with numpy : 0.169783353805542 nb_pixel_total : 111605 time to create 1 rle with old method : 0.13562583923339844 length of segment : 444 time for calcul the mask position with numpy : 0.010680675506591797 nb_pixel_total : 8006 time to create 1 rle with old method : 0.01857137680053711 length of segment : 121 time for calcul the mask position with numpy : 0.036383628845214844 nb_pixel_total : 5730 time to create 1 rle with old method : 0.013781547546386719 length of segment : 111 time for calcul the mask position with numpy : 0.002880096435546875 nb_pixel_total : 44066 time to create 1 rle with old method : 0.06352806091308594 length of segment : 259 time for calcul the mask position with numpy : 0.12351727485656738 nb_pixel_total : 53520 time to create 1 rle with old method : 0.0760953426361084 length of segment : 237 time for calcul the mask position with numpy : 0.00786590576171875 nb_pixel_total : 8123 time to create 1 rle with old method : 0.015719175338745117 length of segment : 182 time for calcul the mask position with numpy : 0.04224109649658203 nb_pixel_total : 9557 time to create 1 rle with old method : 0.017083168029785156 length of segment : 87 time for calcul the mask position with numpy : 0.05019497871398926 nb_pixel_total : 1060 time to create 1 rle with old method : 0.002846240997314453 length of segment : 67 time for calcul the mask position with numpy : 0.10854482650756836 nb_pixel_total : 75201 time to create 1 rle with old method : 0.0887606143951416 length of segment : 271 time for calcul the mask position with numpy : 0.15394377708435059 nb_pixel_total : 49189 time to create 1 rle with old method : 0.0631570816040039 length of segment : 374 time for calcul the mask position with numpy : 0.13060283660888672 nb_pixel_total : 41435 time to create 1 rle with old method : 0.047911643981933594 length of segment : 227 time for calcul the mask position with numpy : 0.08452105522155762 nb_pixel_total : 17238 time to create 1 rle with old method : 0.02295970916748047 length of segment : 148 time for calcul the mask position with numpy : 0.18930959701538086 nb_pixel_total : 40904 time to create 1 rle with old method : 0.048108577728271484 length of segment : 300 time for calcul the mask position with numpy : 0.1204526424407959 nb_pixel_total : 36787 time to create 1 rle with old method : 0.044896602630615234 length of segment : 307 time for calcul the mask position with numpy : 0.16280341148376465 nb_pixel_total : 60478 time to create 1 rle with old method : 0.06736993789672852 length of segment : 357 time for calcul the mask position with numpy : 0.2911531925201416 nb_pixel_total : 68365 time to create 1 rle with old method : 0.07815670967102051 length of segment : 426 time for calcul the mask position with numpy : 0.15049481391906738 nb_pixel_total : 52859 time to create 1 rle with old method : 0.061371803283691406 length of segment : 416 time for calcul the mask position with numpy : 0.10984921455383301 nb_pixel_total : 37932 time to create 1 rle with old method : 0.04669904708862305 length of segment : 195 time for calcul the mask position with numpy : 0.1892702579498291 nb_pixel_total : 36913 time to create 1 rle with old method : 0.04458045959472656 length of segment : 255 time for calcul the mask position with numpy : 0.049803972244262695 nb_pixel_total : 15156 time to create 1 rle with old method : 0.017600297927856445 length of segment : 123 time for calcul the mask position with numpy : 0.014846563339233398 nb_pixel_total : 20657 time to create 1 rle with old method : 0.023176908493041992 length of segment : 209 time for calcul the mask position with numpy : 0.08583235740661621 nb_pixel_total : 27432 time to create 1 rle with old method : 0.03513479232788086 length of segment : 229 time for calcul the mask position with numpy : 0.07840251922607422 nb_pixel_total : 18242 time to create 1 rle with old method : 0.025681018829345703 length of segment : 175 time for calcul the mask position with numpy : 0.0261843204498291 nb_pixel_total : 13563 time to create 1 rle with old method : 0.02107715606689453 length of segment : 115 time for calcul the mask position with numpy : 0.2593846321105957 nb_pixel_total : 80942 time to create 1 rle with old method : 0.10059142112731934 length of segment : 349 time for calcul the mask position with numpy : 0.12045812606811523 nb_pixel_total : 30337 time to create 1 rle with old method : 0.037157297134399414 length of segment : 239 time for calcul the mask position with numpy : 0.04746651649475098 nb_pixel_total : 35372 time to create 1 rle with old method : 0.04557657241821289 length of segment : 201 time for calcul the mask position with numpy : 0.09428620338439941 nb_pixel_total : 33994 time to create 1 rle with old method : 0.043210744857788086 length of segment : 214 time for calcul the mask position with numpy : 0.1315317153930664 nb_pixel_total : 32640 time to create 1 rle with old method : 0.04120635986328125 length of segment : 279 time for calcul the mask position with numpy : 0.1995382308959961 nb_pixel_total : 46576 time to create 1 rle with old method : 0.0540461540222168 length of segment : 316 time for calcul the mask position with numpy : 0.06823563575744629 nb_pixel_total : 14107 time to create 1 rle with old method : 0.02187061309814453 length of segment : 185 time for calcul the mask position with numpy : 0.14089274406433105 nb_pixel_total : 54307 time to create 1 rle with old method : 0.06291079521179199 length of segment : 420 time for calcul the mask position with numpy : 0.014535188674926758 nb_pixel_total : 11659 time to create 1 rle with old method : 0.017801761627197266 length of segment : 120 time for calcul the mask position with numpy : 0.08760857582092285 nb_pixel_total : 45978 time to create 1 rle with old method : 0.05433058738708496 length of segment : 211 time for calcul the mask position with numpy : 0.014336585998535156 nb_pixel_total : 5661 time to create 1 rle with old method : 0.009809732437133789 length of segment : 85 time for calcul the mask position with numpy : 0.06145620346069336 nb_pixel_total : 15006 time to create 1 rle with old method : 0.022440433502197266 length of segment : 203 time for calcul the mask position with numpy : 0.039649009704589844 nb_pixel_total : 14356 time to create 1 rle with old method : 0.021402597427368164 length of segment : 171 time for calcul the mask position with numpy : 0.053255558013916016 nb_pixel_total : 8109 time to create 1 rle with old method : 0.012912511825561523 length of segment : 92 time for calcul the mask position with numpy : 0.058618783950805664 nb_pixel_total : 15646 time to create 1 rle with old method : 0.02231431007385254 length of segment : 115 time for calcul the mask position with numpy : 0.03566288948059082 nb_pixel_total : 8448 time to create 1 rle with old method : 0.014234066009521484 length of segment : 119 time for calcul the mask position with numpy : 0.026856660842895508 nb_pixel_total : 6910 time to create 1 rle with old method : 0.011476516723632812 length of segment : 70 time for calcul the mask position with numpy : 0.0352635383605957 nb_pixel_total : 10889 time to create 1 rle with old method : 0.016779184341430664 length of segment : 100 time for calcul the mask position with numpy : 0.05545401573181152 nb_pixel_total : 19441 time to create 1 rle with old method : 0.02553272247314453 length of segment : 132 time for calcul the mask position with numpy : 0.04905438423156738 nb_pixel_total : 15815 time to create 1 rle with old method : 0.023722410202026367 length of segment : 103 time for calcul the mask position with numpy : 0.0346834659576416 nb_pixel_total : 9341 time to create 1 rle with old method : 0.01426386833190918 length of segment : 117 time for calcul the mask position with numpy : 0.1602771282196045 nb_pixel_total : 25223 time to create 1 rle with old method : 0.032567501068115234 length of segment : 329 time for calcul the mask position with numpy : 0.017963409423828125 nb_pixel_total : 7157 time to create 1 rle with old method : 0.011971473693847656 length of segment : 81 time for calcul the mask position with numpy : 0.04389500617980957 nb_pixel_total : 8463 time to create 1 rle with old method : 0.014515399932861328 length of segment : 139 time for calcul the mask position with numpy : 0.0036220550537109375 nb_pixel_total : 22082 time to create 1 rle with old method : 0.025577306747436523 length of segment : 199 time for calcul the mask position with numpy : 0.06755661964416504 nb_pixel_total : 16401 time to create 1 rle with old method : 0.021875381469726562 length of segment : 206 time for calcul the mask position with numpy : 0.02918720245361328 nb_pixel_total : 17068 time to create 1 rle with old method : 0.023025035858154297 length of segment : 204 time for calcul the mask position with numpy : 0.0009233951568603516 nb_pixel_total : 14681 time to create 1 rle with old method : 0.015955448150634766 length of segment : 170 time for calcul the mask position with numpy : 0.030262470245361328 nb_pixel_total : 6342 time to create 1 rle with old method : 0.011967658996582031 length of segment : 74 time for calcul the mask position with numpy : 0.07875609397888184 nb_pixel_total : 49224 time to create 1 rle with old method : 0.05874800682067871 length of segment : 322 time for calcul the mask position with numpy : 0.009377002716064453 nb_pixel_total : 9967 time to create 1 rle with old method : 0.01413416862487793 length of segment : 99 time for calcul the mask position with numpy : 0.04420328140258789 nb_pixel_total : 16514 time to create 1 rle with old method : 0.0246584415435791 length of segment : 166 time for calcul the mask position with numpy : 0.015321969985961914 nb_pixel_total : 13342 time to create 1 rle with old method : 0.022508621215820312 length of segment : 127 time for calcul the mask position with numpy : 0.028311491012573242 nb_pixel_total : 26739 time to create 1 rle with old method : 0.037798404693603516 length of segment : 224 time for calcul the mask position with numpy : 0.08289337158203125 nb_pixel_total : 15654 time to create 1 rle with old method : 0.021434783935546875 length of segment : 176 time for calcul the mask position with numpy : 0.2184300422668457 nb_pixel_total : 69379 time to create 1 rle with old method : 0.10789752006530762 length of segment : 397 time for calcul the mask position with numpy : 0.07905149459838867 nb_pixel_total : 12254 time to create 1 rle with old method : 0.018873929977416992 length of segment : 141 time for calcul the mask position with numpy : 0.19601798057556152 nb_pixel_total : 55066 time to create 1 rle with old method : 0.062067270278930664 length of segment : 289 time for calcul the mask position with numpy : 0.12751483917236328 nb_pixel_total : 80415 time to create 1 rle with old method : 0.088897705078125 length of segment : 212 time for calcul the mask position with numpy : 0.21654915809631348 nb_pixel_total : 55771 time to create 1 rle with old method : 0.0649871826171875 length of segment : 435 time for calcul the mask position with numpy : 0.20135760307312012 nb_pixel_total : 93484 time to create 1 rle with old method : 0.10913944244384766 length of segment : 432 time for calcul the mask position with numpy : 0.049567461013793945 nb_pixel_total : 14411 time to create 1 rle with old method : 0.020481348037719727 length of segment : 90 time for calcul the mask position with numpy : 0.05209040641784668 nb_pixel_total : 14583 time to create 1 rle with old method : 0.02040243148803711 length of segment : 142 time for calcul the mask position with numpy : 0.053545236587524414 nb_pixel_total : 12911 time to create 1 rle with old method : 0.01916027069091797 length of segment : 167 time for calcul the mask position with numpy : 0.056324005126953125 nb_pixel_total : 11281 time to create 1 rle with old method : 0.01818990707397461 length of segment : 251 time for calcul the mask position with numpy : 0.23228859901428223 nb_pixel_total : 80224 time to create 1 rle with old method : 0.08844852447509766 length of segment : 327 time for calcul the mask position with numpy : 0.02851557731628418 nb_pixel_total : 8352 time to create 1 rle with old method : 0.014131546020507812 length of segment : 121 time for calcul the mask position with numpy : 0.13434815406799316 nb_pixel_total : 59185 time to create 1 rle with old method : 0.06821703910827637 length of segment : 341 time for calcul the mask position with numpy : 0.15419769287109375 nb_pixel_total : 114592 time to create 1 rle with old method : 0.12358307838439941 length of segment : 389 time for calcul the mask position with numpy : 0.014752388000488281 nb_pixel_total : 12098 time to create 1 rle with old method : 0.01976776123046875 length of segment : 105 time for calcul the mask position with numpy : 0.04896068572998047 nb_pixel_total : 7592 time to create 1 rle with old method : 0.012328624725341797 length of segment : 154 time for calcul the mask position with numpy : 0.06394267082214355 nb_pixel_total : 30373 time to create 1 rle with old method : 0.036962270736694336 length of segment : 135 time for calcul the mask position with numpy : 0.07767963409423828 nb_pixel_total : 25042 time to create 1 rle with old method : 0.029106855392456055 length of segment : 187 time for calcul the mask position with numpy : 0.01892399787902832 nb_pixel_total : 8704 time to create 1 rle with old method : 0.012379169464111328 length of segment : 86 time for calcul the mask position with numpy : 0.2394423484802246 nb_pixel_total : 58071 time to create 1 rle with old method : 0.07272052764892578 length of segment : 531 time for calcul the mask position with numpy : 0.0046651363372802734 nb_pixel_total : 3281 time to create 1 rle with old method : 0.0042383670806884766 length of segment : 66 time for calcul the mask position with numpy : 0.09688568115234375 nb_pixel_total : 58791 time to create 1 rle with old method : 0.08131599426269531 length of segment : 278 time for calcul the mask position with numpy : 0.03484940528869629 nb_pixel_total : 11292 time to create 1 rle with old method : 0.017456769943237305 length of segment : 185 time for calcul the mask position with numpy : 0.15079212188720703 nb_pixel_total : 47342 time to create 1 rle with old method : 0.055187225341796875 length of segment : 432 time for calcul the mask position with numpy : 0.10818958282470703 nb_pixel_total : 47342 time to create 1 rle with old method : 0.05877399444580078 length of segment : 293 time for calcul the mask position with numpy : 0.11416959762573242 nb_pixel_total : 41996 time to create 1 rle with old method : 0.05415797233581543 length of segment : 305 time for calcul the mask position with numpy : 0.03451871871948242 nb_pixel_total : 13336 time to create 1 rle with old method : 0.016991853713989258 length of segment : 153 time for calcul the mask position with numpy : 0.09047412872314453 nb_pixel_total : 19955 time to create 1 rle with old method : 0.02734088897705078 length of segment : 358 time for calcul the mask position with numpy : 0.004897117614746094 nb_pixel_total : 6839 time to create 1 rle with old method : 0.008832216262817383 length of segment : 86 time for calcul the mask position with numpy : 0.08184027671813965 nb_pixel_total : 39533 time to create 1 rle with old method : 0.04637861251831055 length of segment : 241 time for calcul the mask position with numpy : 0.10184550285339355 nb_pixel_total : 59740 time to create 1 rle with old method : 0.06942987442016602 length of segment : 457 time for calcul the mask position with numpy : 0.07770991325378418 nb_pixel_total : 20009 time to create 1 rle with old method : 0.025798320770263672 length of segment : 180 time for calcul the mask position with numpy : 0.02837657928466797 nb_pixel_total : 21240 time to create 1 rle with old method : 0.029408931732177734 length of segment : 203 time for calcul the mask position with numpy : 0.08658313751220703 nb_pixel_total : 32392 time to create 1 rle with old method : 0.038733482360839844 length of segment : 214 time for calcul the mask position with numpy : 0.0049021244049072266 nb_pixel_total : 20727 time to create 1 rle with old method : 0.02561783790588379 length of segment : 169 time for calcul the mask position with numpy : 0.060143470764160156 nb_pixel_total : 16940 time to create 1 rle with old method : 0.023053407669067383 length of segment : 142 time for calcul the mask position with numpy : 0.031123876571655273 nb_pixel_total : 36131 time to create 1 rle with old method : 0.04553651809692383 length of segment : 190 time for calcul the mask position with numpy : 0.17379164695739746 nb_pixel_total : 34632 time to create 1 rle with old method : 0.04375004768371582 length of segment : 191 time for calcul the mask position with numpy : 0.061676979064941406 nb_pixel_total : 24191 time to create 1 rle with old method : 0.03203129768371582 length of segment : 226 time for calcul the mask position with numpy : 0.016953706741333008 nb_pixel_total : 15312 time to create 1 rle with old method : 0.022144317626953125 length of segment : 147 time for calcul the mask position with numpy : 0.18228554725646973 nb_pixel_total : 43800 time to create 1 rle with old method : 0.06264567375183105 length of segment : 294 time for calcul the mask position with numpy : 0.09280228614807129 nb_pixel_total : 14748 time to create 1 rle with old method : 0.02012157440185547 length of segment : 219 time for calcul the mask position with numpy : 0.05517935752868652 nb_pixel_total : 13072 time to create 1 rle with old method : 0.018935680389404297 length of segment : 146 time for calcul the mask position with numpy : 0.0689089298248291 nb_pixel_total : 24614 time to create 1 rle with old method : 0.032639265060424805 length of segment : 188 time for calcul the mask position with numpy : 0.30254459381103516 nb_pixel_total : 156428 time to create 1 rle with new method : 0.019947052001953125 length of segment : 341 time for calcul the mask position with numpy : 0.03425788879394531 nb_pixel_total : 9525 time to create 1 rle with old method : 0.015414714813232422 length of segment : 83 time for calcul the mask position with numpy : 0.11286139488220215 nb_pixel_total : 36768 time to create 1 rle with old method : 0.05055356025695801 length of segment : 195 time for calcul the mask position with numpy : 0.16771459579467773 nb_pixel_total : 36364 time to create 1 rle with old method : 0.04848170280456543 length of segment : 406 time for calcul the mask position with numpy : 0.030138731002807617 nb_pixel_total : 2845 time to create 1 rle with old method : 0.006418704986572266 length of segment : 64 time for calcul the mask position with numpy : 0.07460546493530273 nb_pixel_total : 9057 time to create 1 rle with old method : 0.016087055206298828 length of segment : 98 time for calcul the mask position with numpy : 0.045047760009765625 nb_pixel_total : 7347 time to create 1 rle with old method : 0.013277053833007812 length of segment : 170 time for calcul the mask position with numpy : 0.10609078407287598 nb_pixel_total : 24616 time to create 1 rle with old method : 0.028526782989501953 length of segment : 192 time for calcul the mask position with numpy : 0.03884625434875488 nb_pixel_total : 9597 time to create 1 rle with old method : 0.01507115364074707 length of segment : 77 time for calcul the mask position with numpy : 0.07423973083496094 nb_pixel_total : 10672 time to create 1 rle with old method : 0.01545095443725586 length of segment : 246 time for calcul the mask position with numpy : 0.18576359748840332 nb_pixel_total : 71322 time to create 1 rle with old method : 0.08060073852539062 length of segment : 302 time for calcul the mask position with numpy : 0.43725109100341797 nb_pixel_total : 236201 time to create 1 rle with new method : 0.01600050926208496 length of segment : 704 time for calcul the mask position with numpy : 0.130906343460083 nb_pixel_total : 34464 time to create 1 rle with old method : 0.03801751136779785 length of segment : 250 time for calcul the mask position with numpy : 0.10404300689697266 nb_pixel_total : 25934 time to create 1 rle with old method : 0.03264164924621582 length of segment : 196 time for calcul the mask position with numpy : 0.08520245552062988 nb_pixel_total : 36838 time to create 1 rle with old method : 0.0451505184173584 length of segment : 414 time for calcul the mask position with numpy : 0.1574721336364746 nb_pixel_total : 48969 time to create 1 rle with old method : 0.05764198303222656 length of segment : 337 time for calcul the mask position with numpy : 0.20189976692199707 nb_pixel_total : 20230 time to create 1 rle with old method : 0.027109861373901367 length of segment : 302 time for calcul the mask position with numpy : 0.010234355926513672 nb_pixel_total : 4627 time to create 1 rle with old method : 0.008457183837890625 length of segment : 78 time for calcul the mask position with numpy : 0.0550684928894043 nb_pixel_total : 15458 time to create 1 rle with old method : 0.02288818359375 length of segment : 150 time for calcul the mask position with numpy : 0.007946252822875977 nb_pixel_total : 4611 time to create 1 rle with old method : 0.006684303283691406 length of segment : 70 time for calcul the mask position with numpy : 0.11892271041870117 nb_pixel_total : 24996 time to create 1 rle with old method : 0.03257489204406738 length of segment : 336 time for calcul the mask position with numpy : 0.0006911754608154297 nb_pixel_total : 6138 time to create 1 rle with old method : 0.006872415542602539 length of segment : 88 time for calcul the mask position with numpy : 0.008623600006103516 nb_pixel_total : 21936 time to create 1 rle with old method : 0.02557682991027832 length of segment : 152 time for calcul the mask position with numpy : 0.10321807861328125 nb_pixel_total : 8944 time to create 1 rle with old method : 0.013915538787841797 length of segment : 425 time for calcul the mask position with numpy : 0.06152701377868652 nb_pixel_total : 12922 time to create 1 rle with old method : 0.017838001251220703 length of segment : 112 time for calcul the mask position with numpy : 0.005108356475830078 nb_pixel_total : 8442 time to create 1 rle with old method : 0.011757135391235352 length of segment : 143 time for calcul the mask position with numpy : 0.06767702102661133 nb_pixel_total : 8873 time to create 1 rle with old method : 0.01365804672241211 length of segment : 135 time for calcul the mask position with numpy : 0.05502510070800781 nb_pixel_total : 9062 time to create 1 rle with old method : 0.01634693145751953 length of segment : 134 time for calcul the mask position with numpy : 0.1357741355895996 nb_pixel_total : 44032 time to create 1 rle with old method : 0.05873227119445801 length of segment : 185 time for calcul the mask position with numpy : 0.04060006141662598 nb_pixel_total : 8265 time to create 1 rle with old method : 0.01345682144165039 length of segment : 97 time for calcul the mask position with numpy : 0.08740425109863281 nb_pixel_total : 12950 time to create 1 rle with old method : 0.019039392471313477 length of segment : 105 time for calcul the mask position with numpy : 0.03154397010803223 nb_pixel_total : 6569 time to create 1 rle with old method : 0.013763189315795898 length of segment : 88 time for calcul the mask position with numpy : 0.07543516159057617 nb_pixel_total : 16038 time to create 1 rle with old method : 0.028362035751342773 length of segment : 150 time for calcul the mask position with numpy : 0.13314485549926758 nb_pixel_total : 22737 time to create 1 rle with old method : 0.028709888458251953 length of segment : 269 time for calcul the mask position with numpy : 0.08016180992126465 nb_pixel_total : 20637 time to create 1 rle with old method : 0.028380870819091797 length of segment : 228 time for calcul the mask position with numpy : 0.04464364051818848 nb_pixel_total : 18549 time to create 1 rle with old method : 0.023981094360351562 length of segment : 177 time for calcul the mask position with numpy : 0.007773637771606445 nb_pixel_total : 6790 time to create 1 rle with old method : 0.00947880744934082 length of segment : 125 time for calcul the mask position with numpy : 0.0010461807250976562 nb_pixel_total : 7319 time to create 1 rle with old method : 0.008191108703613281 length of segment : 122 time for calcul the mask position with numpy : 0.06142020225524902 nb_pixel_total : 13314 time to create 1 rle with old method : 0.020248889923095703 length of segment : 226 time for calcul the mask position with numpy : 0.03051614761352539 nb_pixel_total : 13457 time to create 1 rle with old method : 0.0213167667388916 length of segment : 137 time for calcul the mask position with numpy : 0.11407232284545898 nb_pixel_total : 43631 time to create 1 rle with old method : 0.050634145736694336 length of segment : 250 time for calcul the mask position with numpy : 0.0721273422241211 nb_pixel_total : 46638 time to create 1 rle with old method : 0.05341625213623047 length of segment : 298 time for calcul the mask position with numpy : 0.044114112854003906 nb_pixel_total : 27109 time to create 1 rle with old method : 0.03353452682495117 length of segment : 237 time for calcul the mask position with numpy : 0.08208870887756348 nb_pixel_total : 22813 time to create 1 rle with old method : 0.02607274055480957 length of segment : 332 time for calcul the mask position with numpy : 0.06421518325805664 nb_pixel_total : 25512 time to create 1 rle with old method : 0.027056455612182617 length of segment : 285 time for calcul the mask position with numpy : 0.0382838249206543 nb_pixel_total : 13176 time to create 1 rle with old method : 0.014535188674926758 length of segment : 204 time for calcul the mask position with numpy : 0.057337045669555664 nb_pixel_total : 17420 time to create 1 rle with old method : 0.024097204208374023 length of segment : 131 time for calcul the mask position with numpy : 0.06255912780761719 nb_pixel_total : 20155 time to create 1 rle with old method : 0.02710247039794922 length of segment : 178 time for calcul the mask position with numpy : 0.07119488716125488 nb_pixel_total : 26344 time to create 1 rle with old method : 0.04280996322631836 length of segment : 200 time for calcul the mask position with numpy : 0.012067794799804688 nb_pixel_total : 7217 time to create 1 rle with old method : 0.010251045227050781 length of segment : 94 time for calcul the mask position with numpy : 0.0013225078582763672 nb_pixel_total : 6463 time to create 1 rle with old method : 0.007244586944580078 length of segment : 104 time for calcul the mask position with numpy : 0.013641119003295898 nb_pixel_total : 5624 time to create 1 rle with old method : 0.009869098663330078 length of segment : 87 time for calcul the mask position with numpy : 0.23764777183532715 nb_pixel_total : 147745 time to create 1 rle with old method : 0.1663684844970703 length of segment : 398 time for calcul the mask position with numpy : 0.13491463661193848 nb_pixel_total : 38505 time to create 1 rle with old method : 0.054225921630859375 length of segment : 263 time for calcul the mask position with numpy : 0.07360100746154785 nb_pixel_total : 30023 time to create 1 rle with old method : 0.037183523178100586 length of segment : 207 time for calcul the mask position with numpy : 0.06664276123046875 nb_pixel_total : 22513 time to create 1 rle with old method : 0.030447959899902344 length of segment : 240 time for calcul the mask position with numpy : 0.46913957595825195 nb_pixel_total : 259019 time to create 1 rle with new method : 0.026335477828979492 length of segment : 698 time for calcul the mask position with numpy : 0.4769887924194336 nb_pixel_total : 254293 time to create 1 rle with new method : 0.03403663635253906 length of segment : 747 time for calcul the mask position with numpy : 0.22447800636291504 nb_pixel_total : 51066 time to create 1 rle with old method : 0.06621074676513672 length of segment : 541 time for calcul the mask position with numpy : 0.10124945640563965 nb_pixel_total : 29232 time to create 1 rle with old method : 0.036321163177490234 length of segment : 212 time for calcul the mask position with numpy : 0.024399995803833008 nb_pixel_total : 37371 time to create 1 rle with old method : 0.0460512638092041 length of segment : 269 time for calcul the mask position with numpy : 0.045890092849731445 nb_pixel_total : 28878 time to create 1 rle with old method : 0.03837418556213379 length of segment : 166 time for calcul the mask position with numpy : 0.0234832763671875 nb_pixel_total : 14253 time to create 1 rle with old method : 0.022336483001708984 length of segment : 106 time for calcul the mask position with numpy : 0.0722198486328125 nb_pixel_total : 16636 time to create 1 rle with old method : 0.025348901748657227 length of segment : 161 time for calcul the mask position with numpy : 0.032379865646362305 nb_pixel_total : 13167 time to create 1 rle with old method : 0.019511938095092773 length of segment : 154 time for calcul the mask position with numpy : 0.08526802062988281 nb_pixel_total : 50847 time to create 1 rle with old method : 0.08116841316223145 length of segment : 264 time for calcul the mask position with numpy : 0.12198281288146973 nb_pixel_total : 23758 time to create 1 rle with old method : 0.030802488327026367 length of segment : 243 time for calcul the mask position with numpy : 0.08436226844787598 nb_pixel_total : 12641 time to create 1 rle with old method : 0.01828932762145996 length of segment : 169 time for calcul the mask position with numpy : 0.011416435241699219 nb_pixel_total : 16959 time to create 1 rle with old method : 0.022409915924072266 length of segment : 141 time for calcul the mask position with numpy : 0.09624743461608887 nb_pixel_total : 34082 time to create 1 rle with old method : 0.04423856735229492 length of segment : 166 time for calcul the mask position with numpy : 0.0007197856903076172 nb_pixel_total : 27148 time to create 1 rle with old method : 0.030663490295410156 length of segment : 177 time for calcul the mask position with numpy : 0.06748032569885254 nb_pixel_total : 17850 time to create 1 rle with old method : 0.023894309997558594 length of segment : 121 time for calcul the mask position with numpy : 0.0938262939453125 nb_pixel_total : 77627 time to create 1 rle with old method : 0.08807992935180664 length of segment : 465 time for calcul the mask position with numpy : 0.0846414566040039 nb_pixel_total : 23800 time to create 1 rle with old method : 0.03079843521118164 length of segment : 238 time for calcul the mask position with numpy : 0.06253337860107422 nb_pixel_total : 25584 time to create 1 rle with old method : 0.02807760238647461 length of segment : 306 time for calcul the mask position with numpy : 0.049057722091674805 nb_pixel_total : 22776 time to create 1 rle with old method : 0.029743671417236328 length of segment : 171 time for calcul the mask position with numpy : 0.03978538513183594 nb_pixel_total : 19646 time to create 1 rle with old method : 0.026433706283569336 length of segment : 263 time for calcul the mask position with numpy : 0.08180928230285645 nb_pixel_total : 25947 time to create 1 rle with old method : 0.03463029861450195 length of segment : 192 time for calcul the mask position with numpy : 0.032295942306518555 nb_pixel_total : 7924 time to create 1 rle with old method : 0.013842344284057617 length of segment : 83 time for calcul the mask position with numpy : 0.06790757179260254 nb_pixel_total : 19920 time to create 1 rle with old method : 0.026980161666870117 length of segment : 202 time for calcul the mask position with numpy : 0.12572693824768066 nb_pixel_total : 48328 time to create 1 rle with old method : 0.05948019027709961 length of segment : 368 time for calcul the mask position with numpy : 0.03489804267883301 nb_pixel_total : 6012 time to create 1 rle with old method : 0.010238885879516602 length of segment : 109 time for calcul the mask position with numpy : 0.014587163925170898 nb_pixel_total : 7144 time to create 1 rle with old method : 0.01368093490600586 length of segment : 76 time for calcul the mask position with numpy : 0.05736279487609863 nb_pixel_total : 46766 time to create 1 rle with old method : 0.06298255920410156 length of segment : 393 time for calcul the mask position with numpy : 0.010739803314208984 nb_pixel_total : 15141 time to create 1 rle with old method : 0.022941112518310547 length of segment : 172 time for calcul the mask position with numpy : 0.3883242607116699 nb_pixel_total : 276811 time to create 1 rle with new method : 0.043373823165893555 length of segment : 692 time for calcul the mask position with numpy : 0.1505136489868164 nb_pixel_total : 120525 time to create 1 rle with old method : 0.16386723518371582 length of segment : 559 time for calcul the mask position with numpy : 0.0010535717010498047 nb_pixel_total : 5601 time to create 1 rle with old method : 0.0062792301177978516 length of segment : 100 time for calcul the mask position with numpy : 0.061003923416137695 nb_pixel_total : 49720 time to create 1 rle with old method : 0.05775094032287598 length of segment : 264 time for calcul the mask position with numpy : 0.0001704692840576172 nb_pixel_total : 4944 time to create 1 rle with old method : 0.005677938461303711 length of segment : 96 time for calcul the mask position with numpy : 0.03903627395629883 nb_pixel_total : 16438 time to create 1 rle with old method : 0.024381637573242188 length of segment : 122 time for calcul the mask position with numpy : 0.015681743621826172 nb_pixel_total : 9508 time to create 1 rle with old method : 0.0152435302734375 length of segment : 121 time for calcul the mask position with numpy : 0.08223915100097656 nb_pixel_total : 14568 time to create 1 rle with old method : 0.020170927047729492 length of segment : 201 time for calcul the mask position with numpy : 0.1321120262145996 nb_pixel_total : 12459 time to create 1 rle with old method : 0.020120620727539062 length of segment : 331 time for calcul the mask position with numpy : 0.03713631629943848 nb_pixel_total : 10043 time to create 1 rle with old method : 0.015588998794555664 length of segment : 127 time for calcul the mask position with numpy : 0.029960155487060547 nb_pixel_total : 20509 time to create 1 rle with old method : 0.027394533157348633 length of segment : 239 time for calcul the mask position with numpy : 0.04602622985839844 nb_pixel_total : 8459 time to create 1 rle with old method : 0.014055728912353516 length of segment : 98 time for calcul the mask position with numpy : 0.0843203067779541 nb_pixel_total : 22383 time to create 1 rle with old method : 0.037863969802856445 length of segment : 247 time for calcul the mask position with numpy : 0.017971277236938477 nb_pixel_total : 4056 time to create 1 rle with old method : 0.007911205291748047 length of segment : 68 time for calcul the mask position with numpy : 0.03282737731933594 nb_pixel_total : 11235 time to create 1 rle with old method : 0.017770767211914062 length of segment : 96 time for calcul the mask position with numpy : 0.0285031795501709 nb_pixel_total : 10412 time to create 1 rle with old method : 0.016731977462768555 length of segment : 132 time for calcul the mask position with numpy : 0.01790618896484375 nb_pixel_total : 164270 time to create 1 rle with new method : 0.01301884651184082 length of segment : 482 time for calcul the mask position with numpy : 0.0035049915313720703 nb_pixel_total : 34385 time to create 1 rle with old method : 0.03867506980895996 length of segment : 226 time for calcul the mask position with numpy : 0.006031036376953125 nb_pixel_total : 6747 time to create 1 rle with old method : 0.007638454437255859 length of segment : 102 time for calcul the mask position with numpy : 0.014067649841308594 nb_pixel_total : 38577 time to create 1 rle with old method : 0.04314446449279785 length of segment : 210 time for calcul the mask position with numpy : 0.0037643909454345703 nb_pixel_total : 32508 time to create 1 rle with old method : 0.03712320327758789 length of segment : 305 time for calcul the mask position with numpy : 0.010560274124145508 nb_pixel_total : 88139 time to create 1 rle with old method : 0.10378670692443848 length of segment : 333 time for calcul the mask position with numpy : 0.0003838539123535156 nb_pixel_total : 4660 time to create 1 rle with old method : 0.005385637283325195 length of segment : 102 time for calcul the mask position with numpy : 0.0034132003784179688 nb_pixel_total : 36501 time to create 1 rle with old method : 0.04364657402038574 length of segment : 243 time for calcul the mask position with numpy : 0.0017542839050292969 nb_pixel_total : 5364 time to create 1 rle with old method : 0.006247282028198242 length of segment : 116 time for calcul the mask position with numpy : 0.0015132427215576172 nb_pixel_total : 5785 time to create 1 rle with old method : 0.006730079650878906 length of segment : 90 time for calcul the mask position with numpy : 0.0033609867095947266 nb_pixel_total : 23681 time to create 1 rle with old method : 0.028615474700927734 length of segment : 144 time for calcul the mask position with numpy : 0.00116729736328125 nb_pixel_total : 9776 time to create 1 rle with old method : 0.011611461639404297 length of segment : 75 time for calcul the mask position with numpy : 0.0024178028106689453 nb_pixel_total : 7122 time to create 1 rle with old method : 0.008264780044555664 length of segment : 119 time for calcul the mask position with numpy : 0.003367185592651367 nb_pixel_total : 25043 time to create 1 rle with old method : 0.03106093406677246 length of segment : 114 time for calcul the mask position with numpy : 0.0007088184356689453 nb_pixel_total : 6472 time to create 1 rle with old method : 0.007876157760620117 length of segment : 106 time for calcul the mask position with numpy : 0.019106149673461914 nb_pixel_total : 114083 time to create 1 rle with old method : 0.12964153289794922 length of segment : 515 time for calcul the mask position with numpy : 0.002849102020263672 nb_pixel_total : 61492 time to create 1 rle with old method : 0.06675148010253906 length of segment : 312 time for calcul the mask position with numpy : 0.018184900283813477 nb_pixel_total : 156415 time to create 1 rle with new method : 0.013608455657958984 length of segment : 421 time for calcul the mask position with numpy : 0.0003845691680908203 nb_pixel_total : 4454 time to create 1 rle with old method : 0.00507044792175293 length of segment : 79 time for calcul the mask position with numpy : 0.005075693130493164 nb_pixel_total : 48538 time to create 1 rle with old method : 0.0525822639465332 length of segment : 421 time for calcul the mask position with numpy : 0.0017931461334228516 nb_pixel_total : 7695 time to create 1 rle with old method : 0.00851583480834961 length of segment : 220 time for calcul the mask position with numpy : 0.0020771026611328125 nb_pixel_total : 18282 time to create 1 rle with old method : 0.019944429397583008 length of segment : 279 time for calcul the mask position with numpy : 0.007558107376098633 nb_pixel_total : 83951 time to create 1 rle with old method : 0.08986973762512207 length of segment : 395 time for calcul the mask position with numpy : 0.003377676010131836 nb_pixel_total : 33226 time to create 1 rle with old method : 0.03692936897277832 length of segment : 182 time for calcul the mask position with numpy : 0.0230410099029541 nb_pixel_total : 79826 time to create 1 rle with old method : 0.09290361404418945 length of segment : 272 time for calcul the mask position with numpy : 0.0016312599182128906 nb_pixel_total : 28457 time to create 1 rle with old method : 0.030988454818725586 length of segment : 362 time for calcul the mask position with numpy : 0.015306472778320312 nb_pixel_total : 165092 time to create 1 rle with new method : 0.013072490692138672 length of segment : 529 time for calcul the mask position with numpy : 0.0014257431030273438 nb_pixel_total : 12745 time to create 1 rle with old method : 0.019936800003051758 length of segment : 159 time for calcul the mask position with numpy : 0.004754543304443359 nb_pixel_total : 21590 time to create 1 rle with old method : 0.03600001335144043 length of segment : 262 time for calcul the mask position with numpy : 0.0006930828094482422 nb_pixel_total : 5275 time to create 1 rle with old method : 0.006009340286254883 length of segment : 64 time for calcul the mask position with numpy : 0.0013937950134277344 nb_pixel_total : 11690 time to create 1 rle with old method : 0.013911008834838867 length of segment : 128 time for calcul the mask position with numpy : 0.00013971328735351562 nb_pixel_total : 2436 time to create 1 rle with old method : 0.0029883384704589844 length of segment : 70 time for calcul the mask position with numpy : 0.0022726058959960938 nb_pixel_total : 14158 time to create 1 rle with old method : 0.01731729507446289 length of segment : 163 time for calcul the mask position with numpy : 0.01131749153137207 nb_pixel_total : 19156 time to create 1 rle with old method : 0.027338504791259766 length of segment : 259 time for calcul the mask position with numpy : 0.0007674694061279297 nb_pixel_total : 13546 time to create 1 rle with old method : 0.015783071517944336 length of segment : 172 time for calcul the mask position with numpy : 0.002181529998779297 nb_pixel_total : 64441 time to create 1 rle with old method : 0.0721430778503418 length of segment : 347 time for calcul the mask position with numpy : 0.0035707950592041016 nb_pixel_total : 29264 time to create 1 rle with old method : 0.035958290100097656 length of segment : 221 time for calcul the mask position with numpy : 0.0033884048461914062 nb_pixel_total : 33147 time to create 1 rle with old method : 0.03828310966491699 length of segment : 222 time for calcul the mask position with numpy : 0.0025396347045898438 nb_pixel_total : 25707 time to create 1 rle with old method : 0.03209686279296875 length of segment : 206 time for calcul the mask position with numpy : 0.004454374313354492 nb_pixel_total : 23204 time to create 1 rle with old method : 0.028045177459716797 length of segment : 146 time for calcul the mask position with numpy : 0.0030982494354248047 nb_pixel_total : 12456 time to create 1 rle with old method : 0.015754222869873047 length of segment : 208 time for calcul the mask position with numpy : 0.009276151657104492 nb_pixel_total : 58162 time to create 1 rle with old method : 0.0667107105255127 length of segment : 131 time for calcul the mask position with numpy : 0.001287698745727539 nb_pixel_total : 9792 time to create 1 rle with old method : 0.01133584976196289 length of segment : 118 time for calcul the mask position with numpy : 0.00010776519775390625 nb_pixel_total : 2117 time to create 1 rle with old method : 0.002613544464111328 length of segment : 73 time for calcul the mask position with numpy : 0.005377292633056641 nb_pixel_total : 10902 time to create 1 rle with old method : 0.015461206436157227 length of segment : 188 time for calcul the mask position with numpy : 0.0014233589172363281 nb_pixel_total : 18023 time to create 1 rle with old method : 0.02022862434387207 length of segment : 145 time for calcul the mask position with numpy : 0.013025522232055664 nb_pixel_total : 81451 time to create 1 rle with old method : 0.09512829780578613 length of segment : 259 time for calcul the mask position with numpy : 0.0005762577056884766 nb_pixel_total : 4944 time to create 1 rle with old method : 0.005977153778076172 length of segment : 72 time for calcul the mask position with numpy : 0.006551265716552734 nb_pixel_total : 22475 time to create 1 rle with old method : 0.030241966247558594 length of segment : 121 time for calcul the mask position with numpy : 0.0004858970642089844 nb_pixel_total : 14754 time to create 1 rle with old method : 0.01706218719482422 length of segment : 153 time for calcul the mask position with numpy : 0.0007033348083496094 nb_pixel_total : 16784 time to create 1 rle with old method : 0.019358396530151367 length of segment : 113 time for calcul the mask position with numpy : 0.06772804260253906 nb_pixel_total : 12358 time to create 1 rle with old method : 0.01817011833190918 length of segment : 195 time for calcul the mask position with numpy : 0.009744405746459961 nb_pixel_total : 118015 time to create 1 rle with old method : 0.13865399360656738 length of segment : 456 time for calcul the mask position with numpy : 0.0003941059112548828 nb_pixel_total : 5311 time to create 1 rle with old method : 0.006198883056640625 length of segment : 116 time for calcul the mask position with numpy : 0.0301358699798584 nb_pixel_total : 338605 time to create 1 rle with new method : 0.022368431091308594 length of segment : 872 time for calcul the mask position with numpy : 0.002936840057373047 nb_pixel_total : 37766 time to create 1 rle with old method : 0.04192495346069336 length of segment : 287 time for calcul the mask position with numpy : 0.0010395050048828125 nb_pixel_total : 7759 time to create 1 rle with old method : 0.009281158447265625 length of segment : 118 time for calcul the mask position with numpy : 0.00533747673034668 nb_pixel_total : 89433 time to create 1 rle with old method : 0.1024465560913086 length of segment : 395 time for calcul the mask position with numpy : 0.0022563934326171875 nb_pixel_total : 21596 time to create 1 rle with old method : 0.02489328384399414 length of segment : 326 time for calcul the mask position with numpy : 0.011264801025390625 nb_pixel_total : 150538 time to create 1 rle with new method : 0.010936498641967773 length of segment : 553 time for calcul the mask position with numpy : 0.0022444725036621094 nb_pixel_total : 19910 time to create 1 rle with old method : 0.023717164993286133 length of segment : 179 time for calcul the mask position with numpy : 0.0019481182098388672 nb_pixel_total : 24586 time to create 1 rle with old method : 0.028629779815673828 length of segment : 114 time for calcul the mask position with numpy : 0.0017037391662597656 nb_pixel_total : 28990 time to create 1 rle with old method : 0.03316330909729004 length of segment : 208 time for calcul the mask position with numpy : 0.0014083385467529297 nb_pixel_total : 18644 time to create 1 rle with old method : 0.021123647689819336 length of segment : 326 time for calcul the mask position with numpy : 0.17046833038330078 nb_pixel_total : 65460 time to create 1 rle with old method : 0.10027766227722168 length of segment : 382 time for calcul the mask position with numpy : 0.0043642520904541016 nb_pixel_total : 93735 time to create 1 rle with old method : 0.11014628410339355 length of segment : 554 time for calcul the mask position with numpy : 0.0010616779327392578 nb_pixel_total : 16530 time to create 1 rle with old method : 0.0191190242767334 length of segment : 179 time for calcul the mask position with numpy : 0.12733888626098633 nb_pixel_total : 111867 time to create 1 rle with old method : 0.12360644340515137 length of segment : 464 time for calcul the mask position with numpy : 0.0011587142944335938 nb_pixel_total : 18441 time to create 1 rle with old method : 0.021282672882080078 length of segment : 214 time for calcul the mask position with numpy : 0.015635967254638672 nb_pixel_total : 224862 time to create 1 rle with new method : 0.028403282165527344 length of segment : 752 time for calcul the mask position with numpy : 0.0010941028594970703 nb_pixel_total : 24028 time to create 1 rle with old method : 0.02731180191040039 length of segment : 192 time for calcul the mask position with numpy : 0.030963897705078125 nb_pixel_total : 21736 time to create 1 rle with old method : 0.032312870025634766 length of segment : 181 time for calcul the mask position with numpy : 0.00590062141418457 nb_pixel_total : 21962 time to create 1 rle with old method : 0.028206825256347656 length of segment : 174 time for calcul the mask position with numpy : 0.0004227161407470703 nb_pixel_total : 6365 time to create 1 rle with old method : 0.007691621780395508 length of segment : 101 time for calcul the mask position with numpy : 0.00052642822265625 nb_pixel_total : 9744 time to create 1 rle with old method : 0.01138448715209961 length of segment : 130 time for calcul the mask position with numpy : 0.0007147789001464844 nb_pixel_total : 10212 time to create 1 rle with old method : 0.011861801147460938 length of segment : 133 time for calcul the mask position with numpy : 0.01263427734375 nb_pixel_total : 190614 time to create 1 rle with new method : 0.016011714935302734 length of segment : 561 time for calcul the mask position with numpy : 0.00799417495727539 nb_pixel_total : 21533 time to create 1 rle with old method : 0.02816176414489746 length of segment : 98 time for calcul the mask position with numpy : 0.0063288211822509766 nb_pixel_total : 65011 time to create 1 rle with old method : 0.07594656944274902 length of segment : 511 time for calcul the mask position with numpy : 0.0010249614715576172 nb_pixel_total : 9151 time to create 1 rle with old method : 0.012912273406982422 length of segment : 161 time for calcul the mask position with numpy : 0.008187532424926758 nb_pixel_total : 88998 time to create 1 rle with old method : 0.10376381874084473 length of segment : 253 time for calcul the mask position with numpy : 0.22841238975524902 nb_pixel_total : 147536 time to create 1 rle with old method : 0.17482566833496094 length of segment : 641 time for calcul the mask position with numpy : 0.10398650169372559 nb_pixel_total : 64768 time to create 1 rle with old method : 0.07613563537597656 length of segment : 373 time for calcul the mask position with numpy : 0.0002048015594482422 nb_pixel_total : 7744 time to create 1 rle with old method : 0.009119987487792969 length of segment : 73 time for calcul the mask position with numpy : 0.0013592243194580078 nb_pixel_total : 21646 time to create 1 rle with old method : 0.024373292922973633 length of segment : 149 time for calcul the mask position with numpy : 0.002279996871948242 nb_pixel_total : 46494 time to create 1 rle with old method : 0.05248880386352539 length of segment : 308 time for calcul the mask position with numpy : 0.0013341903686523438 nb_pixel_total : 28593 time to create 1 rle with old method : 0.032073020935058594 length of segment : 264 time for calcul the mask position with numpy : 0.011233091354370117 nb_pixel_total : 12968 time to create 1 rle with old method : 0.017893552780151367 length of segment : 118 time for calcul the mask position with numpy : 0.1310727596282959 nb_pixel_total : 310394 time to create 1 rle with new method : 0.019160032272338867 length of segment : 848 time for calcul the mask position with numpy : 0.009612083435058594 nb_pixel_total : 240682 time to create 1 rle with new method : 0.012558937072753906 length of segment : 615 time for calcul the mask position with numpy : 0.00170135498046875 nb_pixel_total : 15244 time to create 1 rle with old method : 0.01753973960876465 length of segment : 108 time for calcul the mask position with numpy : 0.0004925727844238281 nb_pixel_total : 10904 time to create 1 rle with old method : 0.012457609176635742 length of segment : 141 time for calcul the mask position with numpy : 0.015050411224365234 nb_pixel_total : 288680 time to create 1 rle with new method : 0.02887415885925293 length of segment : 560 time for calcul the mask position with numpy : 0.0011115074157714844 nb_pixel_total : 17654 time to create 1 rle with old method : 0.019756078720092773 length of segment : 207 time for calcul the mask position with numpy : 0.00701451301574707 nb_pixel_total : 185809 time to create 1 rle with new method : 0.012421369552612305 length of segment : 745 time for calcul the mask position with numpy : 0.03482985496520996 nb_pixel_total : 60369 time to create 1 rle with old method : 0.06666994094848633 length of segment : 290 time for calcul the mask position with numpy : 0.006861209869384766 nb_pixel_total : 56367 time to create 1 rle with old method : 0.06712794303894043 length of segment : 232 time for calcul the mask position with numpy : 0.0008161067962646484 nb_pixel_total : 11453 time to create 1 rle with old method : 0.013088226318359375 length of segment : 126 time for calcul the mask position with numpy : 0.0017123222351074219 nb_pixel_total : 26563 time to create 1 rle with old method : 0.03029346466064453 length of segment : 224 time for calcul the mask position with numpy : 0.0009219646453857422 nb_pixel_total : 16523 time to create 1 rle with old method : 0.018829822540283203 length of segment : 138 time for calcul the mask position with numpy : 0.0022656917572021484 nb_pixel_total : 23875 time to create 1 rle with old method : 0.027973651885986328 length of segment : 128 time for calcul the mask position with numpy : 0.0017650127410888672 nb_pixel_total : 11495 time to create 1 rle with old method : 0.01325082778930664 length of segment : 126 time for calcul the mask position with numpy : 0.004726886749267578 nb_pixel_total : 41575 time to create 1 rle with old method : 0.04961800575256348 length of segment : 372 time for calcul the mask position with numpy : 0.0020198822021484375 nb_pixel_total : 22161 time to create 1 rle with old method : 0.02486729621887207 length of segment : 204 time for calcul the mask position with numpy : 0.0005021095275878906 nb_pixel_total : 8230 time to create 1 rle with old method : 0.010868072509765625 length of segment : 93 time for calcul the mask position with numpy : 0.0008449554443359375 nb_pixel_total : 11618 time to create 1 rle with old method : 0.013297557830810547 length of segment : 129 time for calcul the mask position with numpy : 0.00148773193359375 nb_pixel_total : 30145 time to create 1 rle with old method : 0.03406190872192383 length of segment : 178 time for calcul the mask position with numpy : 0.0024268627166748047 nb_pixel_total : 32761 time to create 1 rle with old method : 0.03697633743286133 length of segment : 267 time for calcul the mask position with numpy : 0.0010035037994384766 nb_pixel_total : 9714 time to create 1 rle with old method : 0.011229515075683594 length of segment : 144 time for calcul the mask position with numpy : 0.0009267330169677734 nb_pixel_total : 16146 time to create 1 rle with old method : 0.01836681365966797 length of segment : 166 time for calcul the mask position with numpy : 0.005072116851806641 nb_pixel_total : 50261 time to create 1 rle with old method : 0.05843305587768555 length of segment : 347 time for calcul the mask position with numpy : 0.0014040470123291016 nb_pixel_total : 7442 time to create 1 rle with old method : 0.008608102798461914 length of segment : 90 time for calcul the mask position with numpy : 0.0021903514862060547 nb_pixel_total : 24552 time to create 1 rle with old method : 0.027782440185546875 length of segment : 177 time for calcul the mask position with numpy : 0.0002865791320800781 nb_pixel_total : 5024 time to create 1 rle with old method : 0.00582122802734375 length of segment : 86 time for calcul the mask position with numpy : 0.0008857250213623047 nb_pixel_total : 13962 time to create 1 rle with old method : 0.016175270080566406 length of segment : 159 time for calcul the mask position with numpy : 0.00030803680419921875 nb_pixel_total : 4529 time to create 1 rle with old method : 0.005338430404663086 length of segment : 89 time for calcul the mask position with numpy : 0.0006475448608398438 nb_pixel_total : 18665 time to create 1 rle with old method : 0.02145838737487793 length of segment : 132 time for calcul the mask position with numpy : 0.0001850128173828125 nb_pixel_total : 4054 time to create 1 rle with old method : 0.0048444271087646484 length of segment : 75 time for calcul the mask position with numpy : 0.00613713264465332 nb_pixel_total : 59909 time to create 1 rle with old method : 0.09140729904174805 length of segment : 439 time for calcul the mask position with numpy : 0.0020949840545654297 nb_pixel_total : 25678 time to create 1 rle with old method : 0.0288541316986084 length of segment : 154 time for calcul the mask position with numpy : 0.0028934478759765625 nb_pixel_total : 17549 time to create 1 rle with old method : 0.019988298416137695 length of segment : 147 time for calcul the mask position with numpy : 0.004118442535400391 nb_pixel_total : 88715 time to create 1 rle with old method : 0.0991065502166748 length of segment : 497 time for calcul the mask position with numpy : 0.007081747055053711 nb_pixel_total : 53498 time to create 1 rle with old method : 0.06322264671325684 length of segment : 280 time for calcul the mask position with numpy : 0.0011949539184570312 nb_pixel_total : 11693 time to create 1 rle with old method : 0.013484716415405273 length of segment : 135 time for calcul the mask position with numpy : 0.006146669387817383 nb_pixel_total : 14173 time to create 1 rle with old method : 0.017761945724487305 length of segment : 111 time for calcul the mask position with numpy : 0.0013146400451660156 nb_pixel_total : 25513 time to create 1 rle with old method : 0.02903914451599121 length of segment : 164 time for calcul the mask position with numpy : 0.0005693435668945312 nb_pixel_total : 11873 time to create 1 rle with old method : 0.014097213745117188 length of segment : 91 time for calcul the mask position with numpy : 0.0025479793548583984 nb_pixel_total : 13419 time to create 1 rle with old method : 0.015980005264282227 length of segment : 198 time for calcul the mask position with numpy : 0.004615068435668945 nb_pixel_total : 34591 time to create 1 rle with old method : 0.04209423065185547 length of segment : 184 time for calcul the mask position with numpy : 0.002515554428100586 nb_pixel_total : 24948 time to create 1 rle with old method : 0.028039216995239258 length of segment : 387 time for calcul the mask position with numpy : 0.0074176788330078125 nb_pixel_total : 28969 time to create 1 rle with old method : 0.033705711364746094 length of segment : 304 time for calcul the mask position with numpy : 0.0015988349914550781 nb_pixel_total : 12180 time to create 1 rle with old method : 0.014384269714355469 length of segment : 162 time for calcul the mask position with numpy : 0.010057687759399414 nb_pixel_total : 24568 time to create 1 rle with old method : 0.031836509704589844 length of segment : 240 time for calcul the mask position with numpy : 0.0004048347473144531 nb_pixel_total : 4322 time to create 1 rle with old method : 0.0053408145904541016 length of segment : 67 time for calcul the mask position with numpy : 0.0005142688751220703 nb_pixel_total : 4637 time to create 1 rle with old method : 0.005563020706176758 length of segment : 81 time for calcul the mask position with numpy : 0.010427474975585938 nb_pixel_total : 64606 time to create 1 rle with old method : 0.07731795310974121 length of segment : 279 time for calcul the mask position with numpy : 0.0017864704132080078 nb_pixel_total : 15420 time to create 1 rle with old method : 0.01797938346862793 length of segment : 169 time for calcul the mask position with numpy : 0.0035543441772460938 nb_pixel_total : 17881 time to create 1 rle with old method : 0.02052140235900879 length of segment : 367 time for calcul the mask position with numpy : 0.0020537376403808594 nb_pixel_total : 25106 time to create 1 rle with old method : 0.03959488868713379 length of segment : 159 time for calcul the mask position with numpy : 0.0026803016662597656 nb_pixel_total : 31213 time to create 1 rle with old method : 0.03583335876464844 length of segment : 387 time for calcul the mask position with numpy : 0.0006241798400878906 nb_pixel_total : 6800 time to create 1 rle with old method : 0.007885932922363281 length of segment : 92 time for calcul the mask position with numpy : 0.0024690628051757812 nb_pixel_total : 34311 time to create 1 rle with old method : 0.03862142562866211 length of segment : 283 time for calcul the mask position with numpy : 0.00023746490478515625 nb_pixel_total : 5750 time to create 1 rle with old method : 0.006659507751464844 length of segment : 90 time for calcul the mask position with numpy : 0.0017292499542236328 nb_pixel_total : 20681 time to create 1 rle with old method : 0.023800134658813477 length of segment : 221 time for calcul the mask position with numpy : 0.0007429122924804688 nb_pixel_total : 6144 time to create 1 rle with old method : 0.007117748260498047 length of segment : 120 time for calcul the mask position with numpy : 0.0009958744049072266 nb_pixel_total : 15516 time to create 1 rle with old method : 0.017931699752807617 length of segment : 171 time for calcul the mask position with numpy : 0.007451057434082031 nb_pixel_total : 108317 time to create 1 rle with old method : 0.12282514572143555 length of segment : 302 time for calcul the mask position with numpy : 0.008585691452026367 nb_pixel_total : 53654 time to create 1 rle with old method : 0.06277847290039062 length of segment : 434 time for calcul the mask position with numpy : 0.00264739990234375 nb_pixel_total : 19234 time to create 1 rle with old method : 0.021918535232543945 length of segment : 236 time for calcul the mask position with numpy : 0.0033996105194091797 nb_pixel_total : 23923 time to create 1 rle with old method : 0.027063608169555664 length of segment : 180 time for calcul the mask position with numpy : 0.0076944828033447266 nb_pixel_total : 72103 time to create 1 rle with old method : 0.08323526382446289 length of segment : 319 time for calcul the mask position with numpy : 0.00015854835510253906 nb_pixel_total : 4946 time to create 1 rle with old method : 0.0057888031005859375 length of segment : 109 time for calcul the mask position with numpy : 0.0025064945220947266 nb_pixel_total : 30318 time to create 1 rle with old method : 0.03450775146484375 length of segment : 229 time for calcul the mask position with numpy : 0.0011060237884521484 nb_pixel_total : 17539 time to create 1 rle with old method : 0.01945781707763672 length of segment : 212 time for calcul the mask position with numpy : 0.004342317581176758 nb_pixel_total : 17834 time to create 1 rle with old method : 0.021430253982543945 length of segment : 186 time for calcul the mask position with numpy : 0.0009157657623291016 nb_pixel_total : 10655 time to create 1 rle with old method : 0.011761903762817383 length of segment : 114 time for calcul the mask position with numpy : 0.0005154609680175781 nb_pixel_total : 9747 time to create 1 rle with old method : 0.011176109313964844 length of segment : 96 time for calcul the mask position with numpy : 0.003803253173828125 nb_pixel_total : 45521 time to create 1 rle with old method : 0.050978660583496094 length of segment : 272 time for calcul the mask position with numpy : 0.0007534027099609375 nb_pixel_total : 7682 time to create 1 rle with old method : 0.008889913558959961 length of segment : 140 time for calcul the mask position with numpy : 0.0012977123260498047 nb_pixel_total : 11206 time to create 1 rle with old method : 0.012988805770874023 length of segment : 188 time for calcul the mask position with numpy : 0.0019199848175048828 nb_pixel_total : 18207 time to create 1 rle with old method : 0.021091461181640625 length of segment : 191 time for calcul the mask position with numpy : 0.0032684803009033203 nb_pixel_total : 37862 time to create 1 rle with old method : 0.04308366775512695 length of segment : 232 time for calcul the mask position with numpy : 0.0008533000946044922 nb_pixel_total : 16217 time to create 1 rle with old method : 0.01860213279724121 length of segment : 199 time for calcul the mask position with numpy : 0.0019168853759765625 nb_pixel_total : 27981 time to create 1 rle with old method : 0.031681060791015625 length of segment : 211 time for calcul the mask position with numpy : 0.0015287399291992188 nb_pixel_total : 46750 time to create 1 rle with old method : 0.05270791053771973 length of segment : 321 time for calcul the mask position with numpy : 0.01132059097290039 nb_pixel_total : 49004 time to create 1 rle with old method : 0.05716538429260254 length of segment : 255 time for calcul the mask position with numpy : 0.0008351802825927734 nb_pixel_total : 16838 time to create 1 rle with old method : 0.01945185661315918 length of segment : 192 time for calcul the mask position with numpy : 0.009984016418457031 nb_pixel_total : 42186 time to create 1 rle with old method : 0.0507054328918457 length of segment : 291 time for calcul the mask position with numpy : 0.002240419387817383 nb_pixel_total : 15206 time to create 1 rle with old method : 0.017697811126708984 length of segment : 134 time for calcul the mask position with numpy : 0.0033152103424072266 nb_pixel_total : 4955 time to create 1 rle with old method : 0.00599217414855957 length of segment : 86 time for calcul the mask position with numpy : 0.0014157295227050781 nb_pixel_total : 20792 time to create 1 rle with old method : 0.02374720573425293 length of segment : 143 time for calcul the mask position with numpy : 0.005954265594482422 nb_pixel_total : 112113 time to create 1 rle with old method : 0.1279134750366211 length of segment : 368 time for calcul the mask position with numpy : 0.003920793533325195 nb_pixel_total : 44732 time to create 1 rle with old method : 0.050734758377075195 length of segment : 388 time for calcul the mask position with numpy : 0.002652883529663086 nb_pixel_total : 31180 time to create 1 rle with old method : 0.03504776954650879 length of segment : 231 time for calcul the mask position with numpy : 0.0002315044403076172 nb_pixel_total : 2296 time to create 1 rle with old method : 0.0028116703033447266 length of segment : 50 time for calcul the mask position with numpy : 0.00692439079284668 nb_pixel_total : 59932 time to create 1 rle with old method : 0.06734251976013184 length of segment : 312 time for calcul the mask position with numpy : 0.0014886856079101562 nb_pixel_total : 18927 time to create 1 rle with old method : 0.021437883377075195 length of segment : 216 time for calcul the mask position with numpy : 0.0036361217498779297 nb_pixel_total : 35239 time to create 1 rle with old method : 0.04130268096923828 length of segment : 325 time for calcul the mask position with numpy : 0.0003077983856201172 nb_pixel_total : 5958 time to create 1 rle with old method : 0.00695037841796875 length of segment : 102 time for calcul the mask position with numpy : 0.00045299530029296875 nb_pixel_total : 7881 time to create 1 rle with old method : 0.00921773910522461 length of segment : 310 time for calcul the mask position with numpy : 0.0011250972747802734 nb_pixel_total : 14227 time to create 1 rle with old method : 0.018190622329711914 length of segment : 171 time for calcul the mask position with numpy : 0.004393577575683594 nb_pixel_total : 54567 time to create 1 rle with old method : 0.076812744140625 length of segment : 389 time for calcul the mask position with numpy : 0.0006003379821777344 nb_pixel_total : 4476 time to create 1 rle with old method : 0.005213022232055664 length of segment : 105 time for calcul the mask position with numpy : 0.0016641616821289062 nb_pixel_total : 18887 time to create 1 rle with old method : 0.021450281143188477 length of segment : 228 time for calcul the mask position with numpy : 0.002698659896850586 nb_pixel_total : 35918 time to create 1 rle with old method : 0.04002785682678223 length of segment : 392 time for calcul the mask position with numpy : 0.0008974075317382812 nb_pixel_total : 20488 time to create 1 rle with old method : 0.02342963218688965 length of segment : 198 time for calcul the mask position with numpy : 0.0009684562683105469 nb_pixel_total : 25517 time to create 1 rle with old method : 0.029680728912353516 length of segment : 221 time for calcul the mask position with numpy : 0.0009884834289550781 nb_pixel_total : 24893 time to create 1 rle with old method : 0.034818172454833984 length of segment : 203 time for calcul the mask position with numpy : 0.0006899833679199219 nb_pixel_total : 3372 time to create 1 rle with old method : 0.005763530731201172 length of segment : 130 time for calcul the mask position with numpy : 0.0007281303405761719 nb_pixel_total : 7859 time to create 1 rle with old method : 0.013199806213378906 length of segment : 95 time for calcul the mask position with numpy : 0.00015807151794433594 nb_pixel_total : 5248 time to create 1 rle with old method : 0.00630950927734375 length of segment : 94 time for calcul the mask position with numpy : 0.003325223922729492 nb_pixel_total : 44832 time to create 1 rle with old method : 0.05078744888305664 length of segment : 308 time for calcul the mask position with numpy : 0.00045418739318847656 nb_pixel_total : 5189 time to create 1 rle with old method : 0.006148815155029297 length of segment : 175 time for calcul the mask position with numpy : 0.007957220077514648 nb_pixel_total : 27692 time to create 1 rle with old method : 0.03130340576171875 length of segment : 236 time for calcul the mask position with numpy : 0.0020875930786132812 nb_pixel_total : 19157 time to create 1 rle with old method : 0.021761178970336914 length of segment : 187 time for calcul the mask position with numpy : 0.001127004623413086 nb_pixel_total : 9888 time to create 1 rle with old method : 0.011321783065795898 length of segment : 206 time for calcul the mask position with numpy : 0.0005369186401367188 nb_pixel_total : 8088 time to create 1 rle with old method : 0.00928950309753418 length of segment : 121 time for calcul the mask position with numpy : 0.0014095306396484375 nb_pixel_total : 26681 time to create 1 rle with old method : 0.0306241512298584 length of segment : 193 time for calcul the mask position with numpy : 0.0008442401885986328 nb_pixel_total : 11200 time to create 1 rle with old method : 0.012845993041992188 length of segment : 152 time for calcul the mask position with numpy : 0.0007498264312744141 nb_pixel_total : 13552 time to create 1 rle with old method : 0.015628814697265625 length of segment : 112 time for calcul the mask position with numpy : 0.0005300045013427734 nb_pixel_total : 8965 time to create 1 rle with old method : 0.010546445846557617 length of segment : 93 time for calcul the mask position with numpy : 0.0007505416870117188 nb_pixel_total : 8019 time to create 1 rle with old method : 0.009389877319335938 length of segment : 175 time for calcul the mask position with numpy : 0.0014388561248779297 nb_pixel_total : 24750 time to create 1 rle with old method : 0.02808547019958496 length of segment : 170 time for calcul the mask position with numpy : 0.002223491668701172 nb_pixel_total : 36638 time to create 1 rle with old method : 0.0437624454498291 length of segment : 231 time for calcul the mask position with numpy : 0.0038461685180664062 nb_pixel_total : 73017 time to create 1 rle with old method : 0.08168435096740723 length of segment : 366 time for calcul the mask position with numpy : 0.0005726814270019531 nb_pixel_total : 6023 time to create 1 rle with old method : 0.007119655609130859 length of segment : 94 time for calcul the mask position with numpy : 0.002416372299194336 nb_pixel_total : 43312 time to create 1 rle with old method : 0.04875922203063965 length of segment : 288 time for calcul the mask position with numpy : 0.0016932487487792969 nb_pixel_total : 33967 time to create 1 rle with old method : 0.038596391677856445 length of segment : 223 time for calcul the mask position with numpy : 0.0009000301361083984 nb_pixel_total : 17676 time to create 1 rle with old method : 0.020740985870361328 length of segment : 104 time for calcul the mask position with numpy : 0.0017359256744384766 nb_pixel_total : 27860 time to create 1 rle with old method : 0.031577110290527344 length of segment : 253 time for calcul the mask position with numpy : 0.0013475418090820312 nb_pixel_total : 20834 time to create 1 rle with old method : 0.02408623695373535 length of segment : 337 time for calcul the mask position with numpy : 0.0013797283172607422 nb_pixel_total : 15335 time to create 1 rle with old method : 0.017897605895996094 length of segment : 185 time for calcul the mask position with numpy : 0.0032510757446289062 nb_pixel_total : 32070 time to create 1 rle with old method : 0.03679037094116211 length of segment : 305 time for calcul the mask position with numpy : 0.00487828254699707 nb_pixel_total : 94512 time to create 1 rle with old method : 0.10588836669921875 length of segment : 597 time for calcul the mask position with numpy : 0.0007679462432861328 nb_pixel_total : 16912 time to create 1 rle with old method : 0.019061803817749023 length of segment : 204 time for calcul the mask position with numpy : 0.0007929801940917969 nb_pixel_total : 19348 time to create 1 rle with old method : 0.021932601928710938 length of segment : 231 time for calcul the mask position with numpy : 0.0005395412445068359 nb_pixel_total : 10345 time to create 1 rle with old method : 0.011922359466552734 length of segment : 182 time for calcul the mask position with numpy : 0.0003116130828857422 nb_pixel_total : 3670 time to create 1 rle with old method : 0.004458904266357422 length of segment : 69 time for calcul the mask position with numpy : 0.001199960708618164 nb_pixel_total : 23880 time to create 1 rle with old method : 0.027213096618652344 length of segment : 162 time for calcul the mask position with numpy : 0.0003681182861328125 nb_pixel_total : 5564 time to create 1 rle with old method : 0.006510496139526367 length of segment : 90 time for calcul the mask position with numpy : 0.002159595489501953 nb_pixel_total : 47133 time to create 1 rle with old method : 0.0541074275970459 length of segment : 278 time for calcul the mask position with numpy : 0.002293109893798828 nb_pixel_total : 30524 time to create 1 rle with old method : 0.03412604331970215 length of segment : 328 time for calcul the mask position with numpy : 0.0020837783813476562 nb_pixel_total : 35252 time to create 1 rle with old method : 0.039496660232543945 length of segment : 290 time for calcul the mask position with numpy : 0.0002899169921875 nb_pixel_total : 6209 time to create 1 rle with old method : 0.007122993469238281 length of segment : 92 time for calcul the mask position with numpy : 0.0004296302795410156 nb_pixel_total : 8985 time to create 1 rle with old method : 0.010489463806152344 length of segment : 89 time for calcul the mask position with numpy : 0.0004525184631347656 nb_pixel_total : 9685 time to create 1 rle with old method : 0.011290311813354492 length of segment : 92 time for calcul the mask position with numpy : 0.0010342597961425781 nb_pixel_total : 21232 time to create 1 rle with old method : 0.024447202682495117 length of segment : 181 time for calcul the mask position with numpy : 0.002086639404296875 nb_pixel_total : 44768 time to create 1 rle with old method : 0.04955172538757324 length of segment : 291 time for calcul the mask position with numpy : 0.0009737014770507812 nb_pixel_total : 20848 time to create 1 rle with old method : 0.02280712127685547 length of segment : 234 time for calcul the mask position with numpy : 0.0002522468566894531 nb_pixel_total : 7981 time to create 1 rle with old method : 0.009136676788330078 length of segment : 95 time for calcul the mask position with numpy : 0.0003075599670410156 nb_pixel_total : 5491 time to create 1 rle with old method : 0.006335258483886719 length of segment : 80 time for calcul the mask position with numpy : 0.0005807876586914062 nb_pixel_total : 12819 time to create 1 rle with old method : 0.014590263366699219 length of segment : 96 time for calcul the mask position with numpy : 0.001512765884399414 nb_pixel_total : 26079 time to create 1 rle with old method : 0.028678417205810547 length of segment : 335 time for calcul the mask position with numpy : 0.002152681350708008 nb_pixel_total : 57096 time to create 1 rle with old method : 0.06074833869934082 length of segment : 290 time for calcul the mask position with numpy : 0.002555370330810547 nb_pixel_total : 43723 time to create 1 rle with old method : 0.04805302619934082 length of segment : 284 time for calcul the mask position with numpy : 0.0007531642913818359 nb_pixel_total : 13959 time to create 1 rle with old method : 0.015466928482055664 length of segment : 209 time for calcul the mask position with numpy : 0.0008332729339599609 nb_pixel_total : 16606 time to create 1 rle with old method : 0.019100666046142578 length of segment : 179 time for calcul the mask position with numpy : 0.00024127960205078125 nb_pixel_total : 4560 time to create 1 rle with old method : 0.005477190017700195 length of segment : 64 time for calcul the mask position with numpy : 0.024018287658691406 nb_pixel_total : 440620 time to create 1 rle with new method : 0.041371822357177734 length of segment : 1020 time for calcul the mask position with numpy : 0.0010974407196044922 nb_pixel_total : 21159 time to create 1 rle with old method : 0.0245058536529541 length of segment : 161 time for calcul the mask position with numpy : 0.0005731582641601562 nb_pixel_total : 19495 time to create 1 rle with old method : 0.02254462242126465 length of segment : 161 time for calcul the mask position with numpy : 0.0019707679748535156 nb_pixel_total : 39215 time to create 1 rle with old method : 0.044591665267944336 length of segment : 284 time for calcul the mask position with numpy : 0.0031375885009765625 nb_pixel_total : 71822 time to create 1 rle with old method : 0.08028006553649902 length of segment : 411 time for calcul the mask position with numpy : 0.00078582763671875 nb_pixel_total : 9016 time to create 1 rle with old method : 0.010482311248779297 length of segment : 155 time for calcul the mask position with numpy : 0.0014190673828125 nb_pixel_total : 40755 time to create 1 rle with old method : 0.04557347297668457 length of segment : 255 time for calcul the mask position with numpy : 0.0010235309600830078 nb_pixel_total : 25537 time to create 1 rle with old method : 0.027664661407470703 length of segment : 224 time for calcul the mask position with numpy : 0.003534078598022461 nb_pixel_total : 37637 time to create 1 rle with old method : 0.04197096824645996 length of segment : 211 time for calcul the mask position with numpy : 0.0008819103240966797 nb_pixel_total : 11653 time to create 1 rle with old method : 0.013592243194580078 length of segment : 116 time for calcul the mask position with numpy : 0.002466440200805664 nb_pixel_total : 33783 time to create 1 rle with old method : 0.03659415245056152 length of segment : 323 time for calcul the mask position with numpy : 0.0019881725311279297 nb_pixel_total : 30558 time to create 1 rle with old method : 0.0333559513092041 length of segment : 206 time for calcul the mask position with numpy : 0.0010428428649902344 nb_pixel_total : 16155 time to create 1 rle with old method : 0.018352985382080078 length of segment : 135 time for calcul the mask position with numpy : 0.0006947517395019531 nb_pixel_total : 6499 time to create 1 rle with old method : 0.007528543472290039 length of segment : 109 time for calcul the mask position with numpy : 0.004906415939331055 nb_pixel_total : 62460 time to create 1 rle with old method : 0.06685686111450195 length of segment : 444 time for calcul the mask position with numpy : 0.004832029342651367 nb_pixel_total : 77023 time to create 1 rle with old method : 0.08542561531066895 length of segment : 410 time for calcul the mask position with numpy : 0.0011701583862304688 nb_pixel_total : 19738 time to create 1 rle with old method : 0.022368907928466797 length of segment : 135 time for calcul the mask position with numpy : 0.010424375534057617 nb_pixel_total : 128406 time to create 1 rle with old method : 0.16054964065551758 length of segment : 420 time for calcul the mask position with numpy : 0.0026383399963378906 nb_pixel_total : 43605 time to create 1 rle with old method : 0.04992198944091797 length of segment : 187 time for calcul the mask position with numpy : 0.0026404857635498047 nb_pixel_total : 14233 time to create 1 rle with old method : 0.016072511672973633 length of segment : 383 time for calcul the mask position with numpy : 0.0068585872650146484 nb_pixel_total : 94433 time to create 1 rle with old method : 0.10426044464111328 length of segment : 591 time for calcul the mask position with numpy : 0.0016829967498779297 nb_pixel_total : 28114 time to create 1 rle with old method : 0.03224515914916992 length of segment : 314 time for calcul the mask position with numpy : 0.010305404663085938 nb_pixel_total : 137560 time to create 1 rle with old method : 0.15428829193115234 length of segment : 521 time for calcul the mask position with numpy : 0.0006313323974609375 nb_pixel_total : 6888 time to create 1 rle with old method : 0.009122133255004883 length of segment : 57 time for calcul the mask position with numpy : 0.006700754165649414 nb_pixel_total : 63730 time to create 1 rle with old method : 0.07321453094482422 length of segment : 717 time for calcul the mask position with numpy : 0.0023958683013916016 nb_pixel_total : 26536 time to create 1 rle with old method : 0.03031444549560547 length of segment : 246 time for calcul the mask position with numpy : 0.00194549560546875 nb_pixel_total : 19481 time to create 1 rle with old method : 0.02288365364074707 length of segment : 230 time for calcul the mask position with numpy : 0.001758575439453125 nb_pixel_total : 31922 time to create 1 rle with old method : 0.03725481033325195 length of segment : 246 time for calcul the mask position with numpy : 0.0016925334930419922 nb_pixel_total : 31175 time to create 1 rle with old method : 0.03641676902770996 length of segment : 263 time for calcul the mask position with numpy : 0.0004496574401855469 nb_pixel_total : 11751 time to create 1 rle with old method : 0.013570070266723633 length of segment : 144 time for calcul the mask position with numpy : 0.005067110061645508 nb_pixel_total : 77172 time to create 1 rle with old method : 0.0871121883392334 length of segment : 402 time for calcul the mask position with numpy : 0.0009579658508300781 nb_pixel_total : 12690 time to create 1 rle with old method : 0.014925479888916016 length of segment : 126 time for calcul the mask position with numpy : 0.001922607421875 nb_pixel_total : 22293 time to create 1 rle with old method : 0.026160478591918945 length of segment : 178 time for calcul the mask position with numpy : 0.0004742145538330078 nb_pixel_total : 3022 time to create 1 rle with old method : 0.0038080215454101562 length of segment : 137 time for calcul the mask position with numpy : 0.00301361083984375 nb_pixel_total : 48391 time to create 1 rle with old method : 0.055722713470458984 length of segment : 253 time for calcul the mask position with numpy : 0.0013318061828613281 nb_pixel_total : 20469 time to create 1 rle with old method : 0.023542165756225586 length of segment : 171 time for calcul the mask position with numpy : 0.002597332000732422 nb_pixel_total : 21979 time to create 1 rle with old method : 0.025301694869995117 length of segment : 244 time for calcul the mask position with numpy : 0.0021142959594726562 nb_pixel_total : 28051 time to create 1 rle with old method : 0.03225898742675781 length of segment : 398 time for calcul the mask position with numpy : 0.0008285045623779297 nb_pixel_total : 13423 time to create 1 rle with old method : 0.015676021575927734 length of segment : 128 time for calcul the mask position with numpy : 0.0006413459777832031 nb_pixel_total : 3542 time to create 1 rle with old method : 0.004559040069580078 length of segment : 144 time for calcul the mask position with numpy : 0.0018951892852783203 nb_pixel_total : 37916 time to create 1 rle with old method : 0.041707754135131836 length of segment : 392 time for calcul the mask position with numpy : 0.0013425350189208984 nb_pixel_total : 29328 time to create 1 rle with old method : 0.03536367416381836 length of segment : 202 time for calcul the mask position with numpy : 0.0030927658081054688 nb_pixel_total : 66015 time to create 1 rle with old method : 0.0735628604888916 length of segment : 313 time for calcul the mask position with numpy : 0.00048089027404785156 nb_pixel_total : 5365 time to create 1 rle with old method : 0.005987405776977539 length of segment : 87 time for calcul the mask position with numpy : 0.0023217201232910156 nb_pixel_total : 37783 time to create 1 rle with old method : 0.043311357498168945 length of segment : 263 time for calcul the mask position with numpy : 0.00041031837463378906 nb_pixel_total : 6088 time to create 1 rle with old method : 0.007138729095458984 length of segment : 80 time for calcul the mask position with numpy : 0.004075765609741211 nb_pixel_total : 54422 time to create 1 rle with old method : 0.0601804256439209 length of segment : 507 time for calcul the mask position with numpy : 0.002603769302368164 nb_pixel_total : 34960 time to create 1 rle with old method : 0.03735160827636719 length of segment : 233 time for calcul the mask position with numpy : 0.00208282470703125 nb_pixel_total : 32919 time to create 1 rle with old method : 0.036679744720458984 length of segment : 294 time for calcul the mask position with numpy : 0.0018303394317626953 nb_pixel_total : 22890 time to create 1 rle with old method : 0.025473594665527344 length of segment : 183 time for calcul the mask position with numpy : 0.0002636909484863281 nb_pixel_total : 9906 time to create 1 rle with old method : 0.011240005493164062 length of segment : 122 time for calcul the mask position with numpy : 0.002728700637817383 nb_pixel_total : 36851 time to create 1 rle with old method : 0.04063725471496582 length of segment : 322 time for calcul the mask position with numpy : 0.00032591819763183594 nb_pixel_total : 11171 time to create 1 rle with old method : 0.012839317321777344 length of segment : 102 time for calcul the mask position with numpy : 0.004459857940673828 nb_pixel_total : 51958 time to create 1 rle with old method : 0.058035850524902344 length of segment : 473 time for calcul the mask position with numpy : 0.002209901809692383 nb_pixel_total : 43707 time to create 1 rle with old method : 0.048926353454589844 length of segment : 453 time for calcul the mask position with numpy : 0.0022683143615722656 nb_pixel_total : 28392 time to create 1 rle with old method : 0.0313265323638916 length of segment : 215 time for calcul the mask position with numpy : 0.0026769638061523438 nb_pixel_total : 72268 time to create 1 rle with old method : 0.07772111892700195 length of segment : 461 time for calcul the mask position with numpy : 0.003573894500732422 nb_pixel_total : 61669 time to create 1 rle with old method : 0.06886601448059082 length of segment : 317 time for calcul the mask position with numpy : 0.0010335445404052734 nb_pixel_total : 17317 time to create 1 rle with old method : 0.018854856491088867 length of segment : 189 time for calcul the mask position with numpy : 0.0003066062927246094 nb_pixel_total : 3362 time to create 1 rle with old method : 0.00377655029296875 length of segment : 87 time for calcul the mask position with numpy : 0.0007641315460205078 nb_pixel_total : 19178 time to create 1 rle with old method : 0.020567655563354492 length of segment : 236 time for calcul the mask position with numpy : 0.0008757114410400391 nb_pixel_total : 17134 time to create 1 rle with old method : 0.018675565719604492 length of segment : 185 time for calcul the mask position with numpy : 0.0018012523651123047 nb_pixel_total : 23741 time to create 1 rle with old method : 0.027100801467895508 length of segment : 219 time for calcul the mask position with numpy : 0.0029256343841552734 nb_pixel_total : 43726 time to create 1 rle with old method : 0.047779083251953125 length of segment : 254 time for calcul the mask position with numpy : 0.006852865219116211 nb_pixel_total : 88412 time to create 1 rle with old method : 0.09821438789367676 length of segment : 425 time for calcul the mask position with numpy : 0.004708290100097656 nb_pixel_total : 56110 time to create 1 rle with old method : 0.06105232238769531 length of segment : 403 time for calcul the mask position with numpy : 0.0032966136932373047 nb_pixel_total : 46359 time to create 1 rle with old method : 0.05118989944458008 length of segment : 317 time for calcul the mask position with numpy : 0.0020956993103027344 nb_pixel_total : 31493 time to create 1 rle with old method : 0.03470468521118164 length of segment : 267 time for calcul the mask position with numpy : 0.002594470977783203 nb_pixel_total : 33272 time to create 1 rle with old method : 0.03658652305603027 length of segment : 300 time for calcul the mask position with numpy : 0.001726388931274414 nb_pixel_total : 21681 time to create 1 rle with old method : 0.02440929412841797 length of segment : 193 time for calcul the mask position with numpy : 0.0007753372192382812 nb_pixel_total : 10325 time to create 1 rle with old method : 0.011864185333251953 length of segment : 145 time for calcul the mask position with numpy : 0.0006303787231445312 nb_pixel_total : 9074 time to create 1 rle with old method : 0.010580301284790039 length of segment : 76 time for calcul the mask position with numpy : 0.0012400150299072266 nb_pixel_total : 10240 time to create 1 rle with old method : 0.017105817794799805 length of segment : 209 time for calcul the mask position with numpy : 0.0010039806365966797 nb_pixel_total : 8636 time to create 1 rle with old method : 0.014551162719726562 length of segment : 106 time for calcul the mask position with numpy : 0.001008749008178711 nb_pixel_total : 21335 time to create 1 rle with old method : 0.02477288246154785 length of segment : 152 time for calcul the mask position with numpy : 0.003026723861694336 nb_pixel_total : 30194 time to create 1 rle with old method : 0.033487558364868164 length of segment : 233 time for calcul the mask position with numpy : 0.0028367042541503906 nb_pixel_total : 39182 time to create 1 rle with old method : 0.04253387451171875 length of segment : 273 time for calcul the mask position with numpy : 0.004249095916748047 nb_pixel_total : 64119 time to create 1 rle with old method : 0.06911349296569824 length of segment : 337 time for calcul the mask position with numpy : 0.005948543548583984 nb_pixel_total : 112459 time to create 1 rle with old method : 0.1217660903930664 length of segment : 380 time for calcul the mask position with numpy : 0.0015435218811035156 nb_pixel_total : 33537 time to create 1 rle with old method : 0.03945636749267578 length of segment : 218 time for calcul the mask position with numpy : 0.0016148090362548828 nb_pixel_total : 25306 time to create 1 rle with old method : 0.02918410301208496 length of segment : 209 time for calcul the mask position with numpy : 0.0011129379272460938 nb_pixel_total : 12461 time to create 1 rle with old method : 0.014438867568969727 length of segment : 184 time for calcul the mask position with numpy : 0.00045490264892578125 nb_pixel_total : 9229 time to create 1 rle with old method : 0.010976076126098633 length of segment : 135 time for calcul the mask position with numpy : 0.0021677017211914062 nb_pixel_total : 37372 time to create 1 rle with old method : 0.0422360897064209 length of segment : 308 time for calcul the mask position with numpy : 0.008595943450927734 nb_pixel_total : 137429 time to create 1 rle with old method : 0.17960429191589355 length of segment : 452 time for calcul the mask position with numpy : 0.0005791187286376953 nb_pixel_total : 13929 time to create 1 rle with old method : 0.015507936477661133 length of segment : 132 time for calcul the mask position with numpy : 0.0009107589721679688 nb_pixel_total : 26035 time to create 1 rle with old method : 0.02836298942565918 length of segment : 315 time for calcul the mask position with numpy : 0.0016949176788330078 nb_pixel_total : 24246 time to create 1 rle with old method : 0.026812076568603516 length of segment : 209 time for calcul the mask position with numpy : 0.0005064010620117188 nb_pixel_total : 8325 time to create 1 rle with old method : 0.00984811782836914 length of segment : 74 time for calcul the mask position with numpy : 0.0006804466247558594 nb_pixel_total : 9778 time to create 1 rle with old method : 0.011031150817871094 length of segment : 132 time for calcul the mask position with numpy : 0.0005939006805419922 nb_pixel_total : 7619 time to create 1 rle with old method : 0.008892059326171875 length of segment : 81 time for calcul the mask position with numpy : 0.001056671142578125 nb_pixel_total : 12668 time to create 1 rle with old method : 0.014318466186523438 length of segment : 142 time for calcul the mask position with numpy : 0.0009350776672363281 nb_pixel_total : 17523 time to create 1 rle with old method : 0.02036428451538086 length of segment : 121 time for calcul the mask position with numpy : 0.001905202865600586 nb_pixel_total : 33621 time to create 1 rle with old method : 0.037537574768066406 length of segment : 313 time for calcul the mask position with numpy : 0.0027899742126464844 nb_pixel_total : 35938 time to create 1 rle with old method : 0.03918004035949707 length of segment : 233 time for calcul the mask position with numpy : 0.0007348060607910156 nb_pixel_total : 15790 time to create 1 rle with old method : 0.01782369613647461 length of segment : 134 time for calcul the mask position with numpy : 0.0019071102142333984 nb_pixel_total : 28973 time to create 1 rle with old method : 0.03245067596435547 length of segment : 293 time for calcul the mask position with numpy : 0.0004551410675048828 nb_pixel_total : 7102 time to create 1 rle with old method : 0.00810098648071289 length of segment : 68 time for calcul the mask position with numpy : 0.00022721290588378906 nb_pixel_total : 6868 time to create 1 rle with old method : 0.007510185241699219 length of segment : 122 time for calcul the mask position with numpy : 0.001981019973754883 nb_pixel_total : 32647 time to create 1 rle with old method : 0.03571343421936035 length of segment : 247 time for calcul the mask position with numpy : 0.0015876293182373047 nb_pixel_total : 23388 time to create 1 rle with old method : 0.025399446487426758 length of segment : 199 time for calcul the mask position with numpy : 0.0028688907623291016 nb_pixel_total : 37108 time to create 1 rle with old method : 0.03925943374633789 length of segment : 240 time for calcul the mask position with numpy : 0.0014181137084960938 nb_pixel_total : 25613 time to create 1 rle with old method : 0.028516769409179688 length of segment : 282 time for calcul the mask position with numpy : 0.0009107589721679688 nb_pixel_total : 11727 time to create 1 rle with old method : 0.013171911239624023 length of segment : 157 time for calcul the mask position with numpy : 0.009169340133666992 nb_pixel_total : 26464 time to create 1 rle with old method : 0.03543663024902344 length of segment : 318 time for calcul the mask position with numpy : 0.005187034606933594 nb_pixel_total : 65821 time to create 1 rle with old method : 0.07122254371643066 length of segment : 446 time for calcul the mask position with numpy : 0.0008165836334228516 nb_pixel_total : 10110 time to create 1 rle with old method : 0.011362791061401367 length of segment : 119 time for calcul the mask position with numpy : 0.0010905265808105469 nb_pixel_total : 17739 time to create 1 rle with old method : 0.019713163375854492 length of segment : 154 time for calcul the mask position with numpy : 0.004514455795288086 nb_pixel_total : 26356 time to create 1 rle with old method : 0.028858661651611328 length of segment : 224 time for calcul the mask position with numpy : 0.0019350051879882812 nb_pixel_total : 39732 time to create 1 rle with old method : 0.04219555854797363 length of segment : 200 time for calcul the mask position with numpy : 0.003591299057006836 nb_pixel_total : 53265 time to create 1 rle with old method : 0.058785200119018555 length of segment : 367 time for calcul the mask position with numpy : 0.0007719993591308594 nb_pixel_total : 13402 time to create 1 rle with old method : 0.015001296997070312 length of segment : 155 time for calcul the mask position with numpy : 0.0014646053314208984 nb_pixel_total : 20153 time to create 1 rle with old method : 0.021831750869750977 length of segment : 161 time for calcul the mask position with numpy : 0.0014362335205078125 nb_pixel_total : 21548 time to create 1 rle with old method : 0.022901535034179688 length of segment : 213 time for calcul the mask position with numpy : 0.00034308433532714844 nb_pixel_total : 18573 time to create 1 rle with old method : 0.019693851470947266 length of segment : 179 time for calcul the mask position with numpy : 0.0009708404541015625 nb_pixel_total : 9493 time to create 1 rle with old method : 0.010500669479370117 length of segment : 206 time for calcul the mask position with numpy : 0.002005338668823242 nb_pixel_total : 32048 time to create 1 rle with old method : 0.03410768508911133 length of segment : 251 time for calcul the mask position with numpy : 0.003922939300537109 nb_pixel_total : 67090 time to create 1 rle with old method : 0.06970596313476562 length of segment : 342 time for calcul the mask position with numpy : 0.0016019344329833984 nb_pixel_total : 22752 time to create 1 rle with old method : 0.024010896682739258 length of segment : 260 time for calcul the mask position with numpy : 0.001363992691040039 nb_pixel_total : 24791 time to create 1 rle with old method : 0.026720762252807617 length of segment : 239 time for calcul the mask position with numpy : 0.00425410270690918 nb_pixel_total : 65302 time to create 1 rle with old method : 0.06889009475708008 length of segment : 258 time for calcul the mask position with numpy : 0.0018889904022216797 nb_pixel_total : 24075 time to create 1 rle with old method : 0.025974273681640625 length of segment : 379 time for calcul the mask position with numpy : 0.0005395412445068359 nb_pixel_total : 9556 time to create 1 rle with old method : 0.010457038879394531 length of segment : 79 time for calcul the mask position with numpy : 0.0012385845184326172 nb_pixel_total : 18035 time to create 1 rle with old method : 0.019410371780395508 length of segment : 166 time for calcul the mask position with numpy : 0.00080108642578125 nb_pixel_total : 9058 time to create 1 rle with old method : 0.009871244430541992 length of segment : 156 time for calcul the mask position with numpy : 0.0018682479858398438 nb_pixel_total : 29327 time to create 1 rle with old method : 0.03190159797668457 length of segment : 167 time for calcul the mask position with numpy : 0.005147218704223633 nb_pixel_total : 82688 time to create 1 rle with old method : 0.08815598487854004 length of segment : 309 time for calcul the mask position with numpy : 0.0025076866149902344 nb_pixel_total : 19989 time to create 1 rle with old method : 0.026015758514404297 length of segment : 141 time for calcul the mask position with numpy : 0.01114654541015625 nb_pixel_total : 112681 time to create 1 rle with old method : 0.12820959091186523 length of segment : 694 time for calcul the mask position with numpy : 0.0048639774322509766 nb_pixel_total : 54964 time to create 1 rle with old method : 0.06073904037475586 length of segment : 515 time for calcul the mask position with numpy : 0.003556489944458008 nb_pixel_total : 58517 time to create 1 rle with old method : 0.06441497802734375 length of segment : 318 time for calcul the mask position with numpy : 0.0015802383422851562 nb_pixel_total : 20136 time to create 1 rle with old method : 0.02219676971435547 length of segment : 167 time for calcul the mask position with numpy : 0.0020596981048583984 nb_pixel_total : 24691 time to create 1 rle with old method : 0.028213977813720703 length of segment : 367 time for calcul the mask position with numpy : 0.0006737709045410156 nb_pixel_total : 7610 time to create 1 rle with old method : 0.010132551193237305 length of segment : 169 time for calcul the mask position with numpy : 0.0003399848937988281 nb_pixel_total : 5521 time to create 1 rle with old method : 0.006655216217041016 length of segment : 55 time for calcul the mask position with numpy : 0.0042188167572021484 nb_pixel_total : 37743 time to create 1 rle with old method : 0.043500661849975586 length of segment : 405 time for calcul the mask position with numpy : 0.0059735774993896484 nb_pixel_total : 59840 time to create 1 rle with old method : 0.0700068473815918 length of segment : 380 time for calcul the mask position with numpy : 0.0014445781707763672 nb_pixel_total : 18056 time to create 1 rle with old method : 0.0206606388092041 length of segment : 129 time for calcul the mask position with numpy : 0.0006830692291259766 nb_pixel_total : 18708 time to create 1 rle with old method : 0.023755311965942383 length of segment : 185 time for calcul the mask position with numpy : 0.005457639694213867 nb_pixel_total : 76259 time to create 1 rle with old method : 0.08530807495117188 length of segment : 374 time for calcul the mask position with numpy : 0.0010669231414794922 nb_pixel_total : 19727 time to create 1 rle with old method : 0.02192997932434082 length of segment : 152 time for calcul the mask position with numpy : 0.0014028549194335938 nb_pixel_total : 15877 time to create 1 rle with old method : 0.018587589263916016 length of segment : 146 time for calcul the mask position with numpy : 0.00022101402282714844 nb_pixel_total : 8777 time to create 1 rle with old method : 0.010347604751586914 length of segment : 93 time for calcul the mask position with numpy : 0.002247333526611328 nb_pixel_total : 29043 time to create 1 rle with old method : 0.03431439399719238 length of segment : 239 time for calcul the mask position with numpy : 0.0006048679351806641 nb_pixel_total : 9822 time to create 1 rle with old method : 0.011336803436279297 length of segment : 97 time for calcul the mask position with numpy : 0.00110626220703125 nb_pixel_total : 19351 time to create 1 rle with old method : 0.02348494529724121 length of segment : 157 time for calcul the mask position with numpy : 0.0012829303741455078 nb_pixel_total : 26179 time to create 1 rle with old method : 0.028320789337158203 length of segment : 165 time for calcul the mask position with numpy : 0.0031888484954833984 nb_pixel_total : 68065 time to create 1 rle with old method : 0.0768430233001709 length of segment : 404 time for calcul the mask position with numpy : 0.002693653106689453 nb_pixel_total : 38930 time to create 1 rle with old method : 0.05120134353637695 length of segment : 266 time for calcul the mask position with numpy : 0.001314401626586914 nb_pixel_total : 19189 time to create 1 rle with old method : 0.02231287956237793 length of segment : 130 time for calcul the mask position with numpy : 0.0031201839447021484 nb_pixel_total : 42366 time to create 1 rle with old method : 0.05308938026428223 length of segment : 354 time for calcul the mask position with numpy : 0.0018639564514160156 nb_pixel_total : 24697 time to create 1 rle with old method : 0.029814720153808594 length of segment : 162 time for calcul the mask position with numpy : 0.0021309852600097656 nb_pixel_total : 33484 time to create 1 rle with old method : 0.038648128509521484 length of segment : 185 time for calcul the mask position with numpy : 0.002151012420654297 nb_pixel_total : 43546 time to create 1 rle with old method : 0.049417734146118164 length of segment : 281 time for calcul the mask position with numpy : 0.005324125289916992 nb_pixel_total : 71182 time to create 1 rle with old method : 0.08122777938842773 length of segment : 645 time for calcul the mask position with numpy : 0.0007176399230957031 nb_pixel_total : 12694 time to create 1 rle with old method : 0.014425277709960938 length of segment : 148 time for calcul the mask position with numpy : 0.0006480216979980469 nb_pixel_total : 9731 time to create 1 rle with old method : 0.01153254508972168 length of segment : 101 time for calcul the mask position with numpy : 0.002365589141845703 nb_pixel_total : 25709 time to create 1 rle with old method : 0.030077695846557617 length of segment : 184 time for calcul the mask position with numpy : 0.0010917186737060547 nb_pixel_total : 26628 time to create 1 rle with old method : 0.029999732971191406 length of segment : 250 time for calcul the mask position with numpy : 0.0022475719451904297 nb_pixel_total : 34751 time to create 1 rle with old method : 0.0401308536529541 length of segment : 222 time for calcul the mask position with numpy : 0.0008237361907958984 nb_pixel_total : 9497 time to create 1 rle with old method : 0.010971546173095703 length of segment : 158 time for calcul the mask position with numpy : 0.0006253719329833984 nb_pixel_total : 9462 time to create 1 rle with old method : 0.011271476745605469 length of segment : 88 time for calcul the mask position with numpy : 0.00794839859008789 nb_pixel_total : 97175 time to create 1 rle with old method : 0.10950541496276855 length of segment : 448 time for calcul the mask position with numpy : 0.0012383460998535156 nb_pixel_total : 24193 time to create 1 rle with old method : 0.027997970581054688 length of segment : 191 time spent for convertir_results : 84.40242671966553 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 782 chid ids of type : 3594 Number RLEs to save : 177578 save missing photos in datou_result : time spend for datou_step_exec : 447.90556478500366 time spend to save output : 14.842647314071655 total time spend for step 1 : 462.7482120990753 step2:crop_condition Tue Apr 1 02:38:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 16 ! batch 1 Loaded 782 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 ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 621 About to insert : list_path_to_insert length 621 new photo from crops ! About to upload 621 photos upload in portfolio : 3736932 init cache_photo without model_param we have 621 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743467988_2444690 we have uploaded 621 photos in the portfolio 3736932 time of upload the photos Elapsed time : 198.37888193130493 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 ! map_result returned by crop_photo_return_map_crop : length : 102 About to insert : list_path_to_insert length 102 new photo from crops ! About to upload 102 photos upload in portfolio : 3736932 init cache_photo without model_param we have 102 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743468211_2444690 we have uploaded 102 photos in the portfolio 3736932 time of upload the photos Elapsed time : 32.54871988296509 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 ! we have both polygon and rles Next one ! 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 : 8 About to insert : list_path_to_insert length 8 new photo from crops ! About to upload 8 photos upload in portfolio : 3736932 init cache_photo without model_param we have 8 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743468248_2444690 we have uploaded 8 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.839179039001465 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 32 About to insert : list_path_to_insert length 32 new photo from crops ! About to upload 32 photos upload in portfolio : 3736932 init cache_photo without model_param we have 32 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743468265_2444690 we have uploaded 32 photos in the portfolio 3736932 time of upload the photos Elapsed time : 11.863079309463501 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 ! map_result returned by crop_photo_return_map_crop : length : 7 About to insert : list_path_to_insert length 7 new photo from crops ! About to upload 7 photos upload in portfolio : 3736932 init cache_photo without model_param we have 7 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743468281_2444690 we have uploaded 7 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.707611083984375 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 ! map_result returned by crop_photo_return_map_crop : length : 5 About to insert : list_path_to_insert length 5 new photo from crops ! About to upload 5 photos upload in portfolio : 3736932 init cache_photo without model_param we have 5 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743468287_2444690 we have uploaded 5 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.3575890064239502 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 : 7 About to insert : list_path_to_insert length 7 new photo from crops ! About to upload 7 photos upload in portfolio : 3736932 init cache_photo without model_param we have 7 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743468294_2444690 we have uploaded 7 photos in the portfolio 3736932 time of upload the photos Elapsed time : 4.1526312828063965 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 Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1349157808, 1349157804, 1349157800, 1349157797, 1349157677, 1349157619, 1349157518, 1349157488, 1349012647, 1349012579, 1349012558, 1349012555, 1349012487, 1349012409, 1349012356, 1349012351] Looping around the photos to save general results len do output : 782 /1349179286Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179287Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179288Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179289Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179290Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179291Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179292Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179293Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179294Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179295Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179296Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179297Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179298Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179299Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179300Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179301Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179302Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179303Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179304Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179305Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179306Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179307Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179308Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179309Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179310Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179311Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179312Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179313Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179314Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179315Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179316Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179317Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179318Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179319Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179320Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179321Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179322Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179323Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179324Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179325Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179326Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179327Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179328Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179329Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179330Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179331Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179332Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179333Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179334Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179335Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179336Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179337Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179338Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179339Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179340Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179341Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179342Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179344Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179345Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179346Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179347Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179348Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179349Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179350Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179352Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179353Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179354Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179355Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179356Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179357Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179358Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179359Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179360Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179361Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179362Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179363Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179364Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179365Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179366Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179367Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179368Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179369Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179370Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179371Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179372Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179373Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179374Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179375Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179376Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179377Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179378Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179379Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179380Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179381Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179382Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179383Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179384Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179385Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179386Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179387Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179388Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179389Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179390Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179391Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179392Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179393Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179394Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179395Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179396Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179397Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179398Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179399Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179400Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179401Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179402Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179403Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179404Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179405Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179406Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179407Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179408Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179409Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179410Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179411Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179412Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179413Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179414Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179415Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179416Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179417Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179419Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179420Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179421Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179422Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179423Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179424Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179425Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179426Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179427Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179428Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179429Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179430Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179431Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179432Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179433Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179434Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179435Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179436Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179437Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179438Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179439Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179441Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179442Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179443Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179444Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179445Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179446Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179447Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179448Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179449Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179450Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179451Didn't 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/1349179464Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179465Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179466Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179467Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179468Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179469Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179470Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179471Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179472Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179473Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179474Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179475Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179476Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179477Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179478Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179479Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179480Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179481Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179482Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179483Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179484Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179485Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179486Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179487Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179488Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179489Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179490Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179491Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179493Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179494Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179495Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179496Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179497Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179498Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179499Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179500Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179501Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179502Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179503Didn't retrieve data 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data .Didn't retrieve data . /1349179557Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179558Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179559Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179560Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179561Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179562Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179563Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179564Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179565Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179566Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179567Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179568Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179569Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179570Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179571Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179572Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179573Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179574Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179575Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179576Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179577Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179579Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179580Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179581Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179582Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179583Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179584Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179585Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179586Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179587Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179588Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179589Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179590Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179591Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179592Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179593Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179594Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179595Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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retrieve data . /1349179609Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179610Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179611Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179612Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179613Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179614Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179615Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179616Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179617Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179618Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179619Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179621Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179622Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179623Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179624Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179625Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179626Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179627Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179628Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179629Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179630Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179631Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179632Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179633Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179634Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349179635Didn't retrieve data 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data .Didn't retrieve data . /1349180242Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180243Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180244Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180245Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180246Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180247Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157808', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157804', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157800', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157797', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157677', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157619', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157518', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157488', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012647', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012579', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012558', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012555', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012487', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012409', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012356', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012351', None, None, None, None, None, '2711235') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 2362 time used for this insertion : 0.12631750106811523 save_final save missing photos in datou_result : time spend for datou_step_exec : 404.4605917930603 time spend to save output : 0.45639610290527344 total time spend for step 2 : 404.9169878959656 step3:rle_unique_nms_with_priority Tue Apr 1 02:44:59 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 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 782 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 48 nb_hashtags : 6 time to prepare the origin masks : 3.962890386581421 time for calcul the mask position with numpy : 0.49497532844543457 nb_pixel_total : 5730235 time to create 1 rle with new method : 0.38808703422546387 time for calcul the mask position with numpy : 0.030055522918701172 nb_pixel_total : 2451 time to create 1 rle with old method : 0.0028438568115234375 time for calcul the mask position with numpy : 0.03035712242126465 nb_pixel_total : 29008 time to create 1 rle with old method : 0.033669233322143555 time for calcul the mask position with numpy : 0.02952265739440918 nb_pixel_total : 9908 time to create 1 rle with old method : 0.011127233505249023 time for calcul the mask position with numpy : 0.03158903121948242 nb_pixel_total : 18516 time to create 1 rle with old method : 0.020976781845092773 time for calcul the mask position with numpy : 0.02967238426208496 nb_pixel_total : 13393 time to create 1 rle with old method : 0.015468358993530273 time for calcul the mask position with numpy : 0.02955460548400879 nb_pixel_total : 12666 time to create 1 rle with old method : 0.014420509338378906 time for calcul the mask position with numpy : 0.029410362243652344 nb_pixel_total : 28130 time to create 1 rle with old method : 0.03235220909118652 time for calcul the mask position with numpy : 0.030649900436401367 nb_pixel_total : 16501 time to create 1 rle with old method : 0.026703357696533203 time for calcul the mask position with numpy : 0.032173871994018555 nb_pixel_total : 36429 time to create 1 rle with old method : 0.04044771194458008 time for calcul the mask position with numpy : 0.029064416885375977 nb_pixel_total : 11910 time to create 1 rle with old method : 0.013320446014404297 time for calcul the mask position with numpy : 0.029834985733032227 nb_pixel_total : 11322 time to create 1 rle with old method : 0.012984037399291992 time for calcul the mask position with numpy : 0.029963254928588867 nb_pixel_total : 40657 time to create 1 rle with old method : 0.04559683799743652 time for calcul the mask position with numpy : 0.0292971134185791 nb_pixel_total : 35768 time to create 1 rle with old method : 0.0396885871887207 time for calcul the mask position with numpy : 0.02936553955078125 nb_pixel_total : 80214 time to create 1 rle with old method : 0.08915042877197266 time for calcul the mask position with numpy : 0.02966618537902832 nb_pixel_total : 16343 time to create 1 rle with old method : 0.01862955093383789 time for calcul the mask position with numpy : 0.029324769973754883 nb_pixel_total : 60313 time to create 1 rle with old method : 0.06620121002197266 time for calcul the mask position with numpy : 0.02905750274658203 nb_pixel_total : 36846 time to create 1 rle with old method : 0.04125833511352539 time for calcul the mask position with numpy : 0.029160022735595703 nb_pixel_total : 28747 time to create 1 rle with old method : 0.03219461441040039 time for calcul the mask position with numpy : 0.02899622917175293 nb_pixel_total : 37122 time to create 1 rle with old method : 0.04187345504760742 time for calcul the mask position with numpy : 0.029309749603271484 nb_pixel_total : 19191 time to create 1 rle with old method : 0.021988630294799805 time for calcul the mask position with numpy : 0.029250621795654297 nb_pixel_total : 37705 time to create 1 rle with old method : 0.04256081581115723 time for calcul the mask position with numpy : 0.030014753341674805 nb_pixel_total : 121854 time to create 1 rle with old method : 0.14011788368225098 time for calcul the mask position with numpy : 0.03142070770263672 nb_pixel_total : 41920 time to create 1 rle with old method : 0.04753398895263672 time for calcul the mask position with numpy : 0.029370546340942383 nb_pixel_total : 17612 time to create 1 rle with old method : 0.020120620727539062 time for calcul the mask position with numpy : 0.029315471649169922 nb_pixel_total : 4758 time to create 1 rle with old method : 0.005641460418701172 time for calcul the mask position with numpy : 0.029356002807617188 nb_pixel_total : 12790 time to create 1 rle with old method : 0.014509201049804688 time for calcul the mask position with numpy : 0.02896428108215332 nb_pixel_total : 14588 time to create 1 rle with old method : 0.016312837600708008 time for calcul the mask position with numpy : 0.028958559036254883 nb_pixel_total : 18247 time to create 1 rle with old method : 0.020272493362426758 time for calcul the mask position with numpy : 0.028922319412231445 nb_pixel_total : 14883 time to create 1 rle with old method : 0.016541004180908203 time for calcul the mask position with numpy : 0.02895951271057129 nb_pixel_total : 24320 time to create 1 rle with old method : 0.02703714370727539 time for calcul the mask position with numpy : 0.0290067195892334 nb_pixel_total : 25857 time to create 1 rle with old method : 0.028853178024291992 time for calcul the mask position with numpy : 0.029125213623046875 nb_pixel_total : 8263 time to create 1 rle with old method : 0.00922250747680664 time for calcul the mask position with numpy : 0.029545307159423828 nb_pixel_total : 154876 time to create 1 rle with new method : 0.41153883934020996 time for calcul the mask position with numpy : 0.02911972999572754 nb_pixel_total : 5958 time to create 1 rle with old method : 0.006807804107666016 time for calcul the mask position with numpy : 0.029633522033691406 nb_pixel_total : 58000 time to create 1 rle with old method : 0.06778526306152344 time for calcul the mask position with numpy : 0.029779911041259766 nb_pixel_total : 11124 time to create 1 rle with old method : 0.01663374900817871 time for calcul the mask position with numpy : 0.02922224998474121 nb_pixel_total : 4686 time to create 1 rle with old method : 0.005349636077880859 time for calcul the mask position with numpy : 0.029226064682006836 nb_pixel_total : 27795 time to create 1 rle with old method : 0.0321202278137207 time for calcul the mask position with numpy : 0.031059980392456055 nb_pixel_total : 38846 time to create 1 rle with old method : 0.046358585357666016 time for calcul the mask position with numpy : 0.04036426544189453 nb_pixel_total : 10715 time to create 1 rle with old method : 0.01192784309387207 time for calcul the mask position with numpy : 0.028923511505126953 nb_pixel_total : 7274 time to create 1 rle with old method : 0.008170604705810547 time for calcul the mask position with numpy : 0.028806209564208984 nb_pixel_total : 25921 time to create 1 rle with old method : 0.028894662857055664 time for calcul the mask position with numpy : 0.028839588165283203 nb_pixel_total : 8802 time to create 1 rle with old method : 0.009717702865600586 time for calcul the mask position with numpy : 0.029748916625976562 nb_pixel_total : 9379 time to create 1 rle with old method : 0.015395641326904297 time for calcul the mask position with numpy : 0.03320813179016113 nb_pixel_total : 15926 time to create 1 rle with old method : 0.02469182014465332 time for calcul the mask position with numpy : 0.029106855392456055 nb_pixel_total : 37649 time to create 1 rle with old method : 0.041611671447753906 time for calcul the mask position with numpy : 0.028954744338989258 nb_pixel_total : 6873 time to create 1 rle with old method : 0.007712841033935547 time for calcul the mask position with numpy : 0.02894449234008789 nb_pixel_total : 7949 time to create 1 rle with old method : 0.00890207290649414 create new chi : 4.130695104598999 time to delete rle : 0.01965928077697754 batch 1 Loaded 97 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 21406 TO DO : save crop sub photo not yet done ! save time : 1.423370599746704 nb_obj : 67 nb_hashtags : 4 time to prepare the origin masks : 4.259745359420776 time for calcul the mask position with numpy : 0.20680499076843262 nb_pixel_total : 5482222 time to create 1 rle with new method : 0.35039353370666504 time for calcul the mask position with numpy : 0.029078960418701172 nb_pixel_total : 7133 time to create 1 rle with old method : 0.00812077522277832 time for calcul the mask position with numpy : 0.029302120208740234 nb_pixel_total : 4402 time to create 1 rle with old method : 0.00496220588684082 time for calcul the mask position with numpy : 0.02877211570739746 nb_pixel_total : 10792 time to create 1 rle with old method : 0.012057781219482422 time for calcul the mask position with numpy : 0.02916121482849121 nb_pixel_total : 132814 time to create 1 rle with old method : 0.1492784023284912 time for calcul the mask position with numpy : 0.028786897659301758 nb_pixel_total : 13100 time to create 1 rle with old method : 0.014490127563476562 time for calcul the mask position with numpy : 0.028979063034057617 nb_pixel_total : 28617 time to create 1 rle with old method : 0.032060861587524414 time for calcul the mask position with numpy : 0.028880834579467773 nb_pixel_total : 16987 time to create 1 rle with old method : 0.018906354904174805 time for calcul the mask position with numpy : 0.028813838958740234 nb_pixel_total : 12007 time to create 1 rle with old method : 0.013295412063598633 time for calcul the mask position with numpy : 0.029158830642700195 nb_pixel_total : 101105 time to create 1 rle with old method : 0.11441421508789062 time for calcul the mask position with numpy : 0.03535795211791992 nb_pixel_total : 18626 time to create 1 rle with old method : 0.020763397216796875 time for calcul the mask position with numpy : 0.02884221076965332 nb_pixel_total : 11751 time to create 1 rle with old method : 0.013099193572998047 time for calcul the mask position with numpy : 0.028882265090942383 nb_pixel_total : 13318 time to create 1 rle with old method : 0.014822721481323242 time for calcul the mask position with numpy : 0.029004812240600586 nb_pixel_total : 12911 time to create 1 rle with old method : 0.01436161994934082 time for calcul the mask position with numpy : 0.028978824615478516 nb_pixel_total : 12480 time to create 1 rle with old method : 0.014198541641235352 time for calcul the mask position with numpy : 0.029500722885131836 nb_pixel_total : 7589 time to create 1 rle with old method : 0.010422229766845703 time for calcul the mask position with numpy : 0.029705286026000977 nb_pixel_total : 27375 time to create 1 rle with old method : 0.03132271766662598 time for calcul the mask position with numpy : 0.02922844886779785 nb_pixel_total : 69991 time to create 1 rle with old method : 0.0783534049987793 time for calcul the mask position with numpy : 0.031946420669555664 nb_pixel_total : 22565 time to create 1 rle with old method : 0.025351285934448242 time for calcul the mask position with numpy : 0.028892040252685547 nb_pixel_total : 26830 time to create 1 rle with old method : 0.029952049255371094 time for calcul the mask position with numpy : 0.029040813446044922 nb_pixel_total : 28497 time to create 1 rle with old method : 0.03137040138244629 time for calcul the mask position with numpy : 0.028985261917114258 nb_pixel_total : 23004 time to create 1 rle with old method : 0.025708675384521484 time for calcul the mask position with numpy : 0.030167579650878906 nb_pixel_total : 92040 time to create 1 rle with old method : 0.15107440948486328 time for calcul the mask position with numpy : 0.028942346572875977 nb_pixel_total : 29302 time to create 1 rle with old method : 0.03261995315551758 time for calcul the mask position with numpy : 0.029311656951904297 nb_pixel_total : 7960 time to create 1 rle with old method : 0.008882284164428711 time for calcul the mask position with numpy : 0.029222488403320312 nb_pixel_total : 14915 time to create 1 rle with old method : 0.016937732696533203 time for calcul the mask position with numpy : 0.029488563537597656 nb_pixel_total : 39217 time to create 1 rle with old method : 0.04340672492980957 time for calcul the mask position with numpy : 0.02902388572692871 nb_pixel_total : 16118 time to create 1 rle with old method : 0.01810288429260254 time for calcul the mask position with numpy : 0.029384136199951172 nb_pixel_total : 14002 time to create 1 rle with old method : 0.01573014259338379 time for calcul the mask position with numpy : 0.028978824615478516 nb_pixel_total : 14581 time to create 1 rle with old method : 0.01612234115600586 time for calcul the mask position with numpy : 0.029026031494140625 nb_pixel_total : 10399 time to create 1 rle with old method : 0.014032602310180664 time for calcul the mask position with numpy : 0.030047178268432617 nb_pixel_total : 17405 time to create 1 rle with old method : 0.019278764724731445 time for calcul the mask position with numpy : 0.029044628143310547 nb_pixel_total : 11059 time to create 1 rle with old method : 0.012324810028076172 time for calcul the mask position with numpy : 0.0289919376373291 nb_pixel_total : 16265 time to create 1 rle with old method : 0.01814556121826172 time for calcul the mask position with numpy : 0.028853178024291992 nb_pixel_total : 39203 time to create 1 rle with old method : 0.04843711853027344 time for calcul the mask position with numpy : 0.033392906188964844 nb_pixel_total : 33587 time to create 1 rle with old method : 0.0469059944152832 time for calcul the mask position with numpy : 0.02907085418701172 nb_pixel_total : 15410 time to create 1 rle with old method : 0.01713275909423828 time for calcul the mask position with numpy : 0.028873205184936523 nb_pixel_total : 30491 time to create 1 rle with old method : 0.03409075736999512 time for calcul the mask position with numpy : 0.03151226043701172 nb_pixel_total : 23496 time to create 1 rle with old method : 0.02640056610107422 time for calcul the mask position with numpy : 0.02907729148864746 nb_pixel_total : 6215 time to create 1 rle with old method : 0.007012367248535156 time for calcul the mask position with numpy : 0.030887365341186523 nb_pixel_total : 34760 time to create 1 rle with old method : 0.044222116470336914 time for calcul the mask position with numpy : 0.028892993927001953 nb_pixel_total : 12200 time to create 1 rle with old method : 0.01378488540649414 time for calcul the mask position with numpy : 0.028717041015625 nb_pixel_total : 9496 time to create 1 rle with old method : 0.010733604431152344 time for calcul the mask position with numpy : 0.029052734375 nb_pixel_total : 65184 time to create 1 rle with old method : 0.07245707511901855 time for calcul the mask position with numpy : 0.028903961181640625 nb_pixel_total : 6317 time to create 1 rle with old method : 0.007109403610229492 time for calcul the mask position with numpy : 0.02896261215209961 nb_pixel_total : 13386 time to create 1 rle with old method : 0.014850616455078125 time for calcul the mask position with numpy : 0.03134751319885254 nb_pixel_total : 15428 time to create 1 rle with old method : 0.025485754013061523 time for calcul the mask position with numpy : 0.03234076499938965 nb_pixel_total : 10012 time to create 1 rle with old method : 0.011154651641845703 time for calcul the mask position with numpy : 0.028795719146728516 nb_pixel_total : 8408 time to create 1 rle with old method : 0.009343862533569336 time for calcul the mask position with numpy : 0.02916574478149414 nb_pixel_total : 34294 time to create 1 rle with old method : 0.038500070571899414 time for calcul the mask position with numpy : 0.029335737228393555 nb_pixel_total : 8237 time to create 1 rle with old method : 0.009200096130371094 time for calcul the mask position with numpy : 0.029018640518188477 nb_pixel_total : 15354 time to create 1 rle with old method : 0.017171859741210938 time for calcul the mask position with numpy : 0.028945207595825195 nb_pixel_total : 4894 time to create 1 rle with old method : 0.005514383316040039 time for calcul the mask position with numpy : 0.028814077377319336 nb_pixel_total : 14271 time to create 1 rle with old method : 0.015785694122314453 time for calcul the mask position with numpy : 0.028949260711669922 nb_pixel_total : 10167 time to create 1 rle with old method : 0.011289119720458984 time for calcul the mask position with numpy : 0.02898120880126953 nb_pixel_total : 5997 time to create 1 rle with old method : 0.006691932678222656 time for calcul the mask position with numpy : 0.03284573554992676 nb_pixel_total : 82011 time to create 1 rle with old method : 0.13488316535949707 time for calcul the mask position with numpy : 0.030896425247192383 nb_pixel_total : 6818 time to create 1 rle with old method : 0.007588386535644531 time for calcul the mask position with numpy : 0.029606342315673828 nb_pixel_total : 9381 time to create 1 rle with old method : 0.010404109954833984 time for calcul the mask position with numpy : 0.029093265533447266 nb_pixel_total : 60469 time to create 1 rle with old method : 0.06679701805114746 time for calcul the mask position with numpy : 0.028856515884399414 nb_pixel_total : 21847 time to create 1 rle with old method : 0.02434849739074707 time for calcul the mask position with numpy : 0.02905416488647461 nb_pixel_total : 4605 time to create 1 rle with old method : 0.005168437957763672 time for calcul the mask position with numpy : 0.028989553451538086 nb_pixel_total : 12307 time to create 1 rle with old method : 0.013694524765014648 time for calcul the mask position with numpy : 0.028804302215576172 nb_pixel_total : 22922 time to create 1 rle with old method : 0.02541828155517578 time for calcul the mask position with numpy : 0.028858423233032227 nb_pixel_total : 8935 time to create 1 rle with old method : 0.009891033172607422 time for calcul the mask position with numpy : 0.029160499572753906 nb_pixel_total : 9091 time to create 1 rle with old method : 0.011508941650390625 time for calcul the mask position with numpy : 0.028861284255981445 nb_pixel_total : 17180 time to create 1 rle with old method : 0.019211530685424805 time for calcul the mask position with numpy : 0.028911590576171875 nb_pixel_total : 10488 time to create 1 rle with old method : 0.011580944061279297 create new chi : 4.445592403411865 time to delete rle : 0.003612995147705078 batch 1 Loaded 135 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 27004 TO DO : save crop sub photo not yet done ! save time : 2.6843111515045166 nb_obj : 71 nb_hashtags : 2 time to prepare the origin masks : 5.068482875823975 time for calcul the mask position with numpy : 0.30089521408081055 nb_pixel_total : 4903488 time to create 1 rle with new method : 0.46932339668273926 time for calcul the mask position with numpy : 0.0291140079498291 nb_pixel_total : 7219 time to create 1 rle with old method : 0.008666276931762695 time for calcul the mask position with numpy : 0.03234076499938965 nb_pixel_total : 14859 time to create 1 rle with old method : 0.019922494888305664 time for calcul the mask position with numpy : 0.029457569122314453 nb_pixel_total : 9557 time to create 1 rle with old method : 0.01079869270324707 time for calcul the mask position with numpy : 0.02951669692993164 nb_pixel_total : 10901 time to create 1 rle with old method : 0.013881444931030273 time for calcul the mask position with numpy : 0.029358625411987305 nb_pixel_total : 5730 time to create 1 rle with old method : 0.006645679473876953 time for calcul the mask position with numpy : 0.032625436782836914 nb_pixel_total : 35479 time to create 1 rle with old method : 0.061353445053100586 time for calcul the mask position with numpy : 0.060045480728149414 nb_pixel_total : 14431 time to create 1 rle with old method : 0.029109477996826172 time for calcul the mask position with numpy : 0.030240774154663086 nb_pixel_total : 1060 time to create 1 rle with old method : 0.0014421939849853516 time for calcul the mask position with numpy : 0.03034687042236328 nb_pixel_total : 16822 time to create 1 rle with old method : 0.033899784088134766 time for calcul the mask position with numpy : 0.03901100158691406 nb_pixel_total : 93551 time to create 1 rle with old method : 0.11150026321411133 time for calcul the mask position with numpy : 0.030508756637573242 nb_pixel_total : 133153 time to create 1 rle with old method : 0.14951443672180176 time for calcul the mask position with numpy : 0.029688358306884766 nb_pixel_total : 15504 time to create 1 rle with old method : 0.017876625061035156 time for calcul the mask position with numpy : 0.03038334846496582 nb_pixel_total : 38089 time to create 1 rle with old method : 0.04322481155395508 time for calcul the mask position with numpy : 0.03020167350769043 nb_pixel_total : 30839 time to create 1 rle with old method : 0.0345454216003418 time for calcul the mask position with numpy : 0.030455589294433594 nb_pixel_total : 107295 time to create 1 rle with old method : 0.13983774185180664 time for calcul the mask position with numpy : 0.02924180030822754 nb_pixel_total : 10014 time to create 1 rle with old method : 0.01128840446472168 time for calcul the mask position with numpy : 0.03155922889709473 nb_pixel_total : 11967 time to create 1 rle with old method : 0.013408184051513672 time for calcul the mask position with numpy : 0.028909683227539062 nb_pixel_total : 12385 time to create 1 rle with old method : 0.013906002044677734 time for calcul the mask position with numpy : 0.028765201568603516 nb_pixel_total : 15175 time to create 1 rle with old method : 0.01696181297302246 time for calcul the mask position with numpy : 0.028868675231933594 nb_pixel_total : 22748 time to create 1 rle with old method : 0.02550649642944336 time for calcul the mask position with numpy : 0.028876066207885742 nb_pixel_total : 32946 time to create 1 rle with old method : 0.03644871711730957 time for calcul the mask position with numpy : 0.029192209243774414 nb_pixel_total : 8396 time to create 1 rle with old method : 0.00940251350402832 time for calcul the mask position with numpy : 0.029106855392456055 nb_pixel_total : 57645 time to create 1 rle with old method : 0.06389212608337402 time for calcul the mask position with numpy : 0.028701305389404297 nb_pixel_total : 8123 time to create 1 rle with old method : 0.009052276611328125 time for calcul the mask position with numpy : 0.02872610092163086 nb_pixel_total : 43947 time to create 1 rle with old method : 0.048189401626586914 time for calcul the mask position with numpy : 0.028745651245117188 nb_pixel_total : 26060 time to create 1 rle with old method : 0.028984546661376953 time for calcul the mask position with numpy : 0.02882695198059082 nb_pixel_total : 24528 time to create 1 rle with old method : 0.02727365493774414 time for calcul the mask position with numpy : 0.0287778377532959 nb_pixel_total : 49189 time to create 1 rle with old method : 0.054280757904052734 time for calcul the mask position with numpy : 0.028839826583862305 nb_pixel_total : 19002 time to create 1 rle with old method : 0.0210268497467041 time for calcul the mask position with numpy : 0.03173971176147461 nb_pixel_total : 20293 time to create 1 rle with old method : 0.03270888328552246 time for calcul the mask position with numpy : 0.032891273498535156 nb_pixel_total : 75201 time to create 1 rle with old method : 0.08350610733032227 time for calcul the mask position with numpy : 0.029180288314819336 nb_pixel_total : 15377 time to create 1 rle with old method : 0.01662755012512207 time for calcul the mask position with numpy : 0.028558969497680664 nb_pixel_total : 91349 time to create 1 rle with old method : 0.10030007362365723 time for calcul the mask position with numpy : 0.029616594314575195 nb_pixel_total : 28734 time to create 1 rle with old method : 0.03549790382385254 time for calcul the mask position with numpy : 0.03016948699951172 nb_pixel_total : 16226 time to create 1 rle with old method : 0.019102811813354492 time for calcul the mask position with numpy : 0.029401779174804688 nb_pixel_total : 42053 time to create 1 rle with old method : 0.05052351951599121 time for calcul the mask position with numpy : 0.03094482421875 nb_pixel_total : 33944 time to create 1 rle with old method : 0.04368901252746582 time for calcul the mask position with numpy : 0.029931306838989258 nb_pixel_total : 55517 time to create 1 rle with old method : 0.06375789642333984 time for calcul the mask position with numpy : 0.02966904640197754 nb_pixel_total : 19875 time to create 1 rle with old method : 0.02241039276123047 time for calcul the mask position with numpy : 0.02961874008178711 nb_pixel_total : 27085 time to create 1 rle with old method : 0.030471086502075195 time for calcul the mask position with numpy : 0.02946305274963379 nb_pixel_total : 53905 time to create 1 rle with old method : 0.06267046928405762 time for calcul the mask position with numpy : 0.02933335304260254 nb_pixel_total : 67133 time to create 1 rle with old method : 0.07477855682373047 time for calcul the mask position with numpy : 0.029555082321166992 nb_pixel_total : 43713 time to create 1 rle with old method : 0.04872941970825195 time for calcul the mask position with numpy : 0.02877664566040039 nb_pixel_total : 941 time to create 1 rle with old method : 0.0013854503631591797 time for calcul the mask position with numpy : 0.03085780143737793 nb_pixel_total : 5953 time to create 1 rle with old method : 0.006909847259521484 time for calcul the mask position with numpy : 0.029140472412109375 nb_pixel_total : 13121 time to create 1 rle with old method : 0.014619588851928711 time for calcul the mask position with numpy : 0.029009342193603516 nb_pixel_total : 24762 time to create 1 rle with old method : 0.02759075164794922 time for calcul the mask position with numpy : 0.02923583984375 nb_pixel_total : 14029 time to create 1 rle with old method : 0.015772104263305664 time for calcul the mask position with numpy : 0.028890371322631836 nb_pixel_total : 16848 time to create 1 rle with old method : 0.018687009811401367 time for calcul the mask position with numpy : 0.029032468795776367 nb_pixel_total : 9698 time to create 1 rle with old method : 0.010961294174194336 time for calcul the mask position with numpy : 0.02914738655090332 nb_pixel_total : 111605 time to create 1 rle with old method : 0.12306022644042969 time for calcul the mask position with numpy : 0.02879810333251953 nb_pixel_total : 10375 time to create 1 rle with old method : 0.011499166488647461 time for calcul the mask position with numpy : 0.028879642486572266 nb_pixel_total : 16967 time to create 1 rle with old method : 0.01889348030090332 time for calcul the mask position with numpy : 0.028741121292114258 nb_pixel_total : 8006 time to create 1 rle with old method : 0.009014368057250977 time for calcul the mask position with numpy : 0.028851032257080078 nb_pixel_total : 21929 time to create 1 rle with old method : 0.02438521385192871 time for calcul the mask position with numpy : 0.028978824615478516 nb_pixel_total : 19921 time to create 1 rle with old method : 0.02226710319519043 time for calcul the mask position with numpy : 0.028887510299682617 nb_pixel_total : 9595 time to create 1 rle with old method : 0.010752201080322266 time for calcul the mask position with numpy : 0.028992891311645508 nb_pixel_total : 20470 time to create 1 rle with old method : 0.022733688354492188 time for calcul the mask position with numpy : 0.02945685386657715 nb_pixel_total : 67769 time to create 1 rle with old method : 0.0751953125 time for calcul the mask position with numpy : 0.02881312370300293 nb_pixel_total : 8822 time to create 1 rle with old method : 0.009874820709228516 time for calcul the mask position with numpy : 0.028736591339111328 nb_pixel_total : 34328 time to create 1 rle with old method : 0.03826403617858887 time for calcul the mask position with numpy : 0.02901482582092285 nb_pixel_total : 16945 time to create 1 rle with old method : 0.01897120475769043 time for calcul the mask position with numpy : 0.029215574264526367 nb_pixel_total : 56466 time to create 1 rle with old method : 0.06300735473632812 time for calcul the mask position with numpy : 0.02892136573791504 nb_pixel_total : 21736 time to create 1 rle with old method : 0.02414536476135254 time for calcul the mask position with numpy : 0.0287322998046875 nb_pixel_total : 5083 time to create 1 rle with old method : 0.006014347076416016 time for calcul the mask position with numpy : 0.028678178787231445 nb_pixel_total : 30745 time to create 1 rle with old method : 0.03422427177429199 time for calcul the mask position with numpy : 0.028964996337890625 nb_pixel_total : 22400 time to create 1 rle with old method : 0.02491140365600586 time for calcul the mask position with numpy : 0.028868436813354492 nb_pixel_total : 5665 time to create 1 rle with old method : 0.006448268890380859 time for calcul the mask position with numpy : 0.029074907302856445 nb_pixel_total : 41503 time to create 1 rle with old method : 0.0461573600769043 time for calcul the mask position with numpy : 0.02936720848083496 nb_pixel_total : 53520 time to create 1 rle with old method : 0.05975961685180664 time for calcul the mask position with numpy : 0.028829097747802734 nb_pixel_total : 10531 time to create 1 rle with old method : 0.01184844970703125 create new chi : 5.44546914100647 time to delete rle : 0.00551152229309082 batch 1 Loaded 143 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 34610 TO DO : save crop sub photo not yet done ! save time : 2.4453206062316895 nb_obj : 49 nb_hashtags : 4 time to prepare the origin masks : 4.108379125595093 time for calcul the mask position with numpy : 1.7057290077209473 nb_pixel_total : 5809267 time to create 1 rle with new method : 0.8939893245697021 time for calcul the mask position with numpy : 0.028888702392578125 nb_pixel_total : 45978 time to create 1 rle with old method : 0.05069470405578613 time for calcul the mask position with numpy : 0.03475189208984375 nb_pixel_total : 6910 time to create 1 rle with old method : 0.010389566421508789 time for calcul the mask position with numpy : 0.032141923904418945 nb_pixel_total : 27432 time to create 1 rle with old method : 0.03812384605407715 time for calcul the mask position with numpy : 0.041042327880859375 nb_pixel_total : 30337 time to create 1 rle with old method : 0.04178667068481445 time for calcul the mask position with numpy : 0.029035091400146484 nb_pixel_total : 19441 time to create 1 rle with old method : 0.021663188934326172 time for calcul the mask position with numpy : 0.02903914451599121 nb_pixel_total : 41435 time to create 1 rle with old method : 0.04592180252075195 time for calcul the mask position with numpy : 0.028820037841796875 nb_pixel_total : 54307 time to create 1 rle with old method : 0.05998420715332031 time for calcul the mask position with numpy : 0.029035091400146484 nb_pixel_total : 60478 time to create 1 rle with old method : 0.06722140312194824 time for calcul the mask position with numpy : 0.029008150100708008 nb_pixel_total : 8109 time to create 1 rle with old method : 0.009136676788330078 time for calcul the mask position with numpy : 0.0320286750793457 nb_pixel_total : 7157 time to create 1 rle with old method : 0.008480072021484375 time for calcul the mask position with numpy : 0.030306100845336914 nb_pixel_total : 36913 time to create 1 rle with old method : 0.040715932846069336 time for calcul the mask position with numpy : 0.02906966209411621 nb_pixel_total : 52859 time to create 1 rle with old method : 0.06386566162109375 time for calcul the mask position with numpy : 0.02904343605041504 nb_pixel_total : 11659 time to create 1 rle with old method : 0.013020038604736328 time for calcul the mask position with numpy : 0.02906656265258789 nb_pixel_total : 13563 time to create 1 rle with old method : 0.0174863338470459 time for calcul the mask position with numpy : 0.03769850730895996 nb_pixel_total : 40904 time to create 1 rle with old method : 0.04518270492553711 time for calcul the mask position with numpy : 0.029068470001220703 nb_pixel_total : 15943 time to create 1 rle with old method : 0.017673730850219727 time for calcul the mask position with numpy : 0.02839827537536621 nb_pixel_total : 17068 time to create 1 rle with old method : 0.018827438354492188 time for calcul the mask position with numpy : 0.02874016761779785 nb_pixel_total : 15006 time to create 1 rle with old method : 0.016756057739257812 time for calcul the mask position with numpy : 0.028939008712768555 nb_pixel_total : 14681 time to create 1 rle with old method : 0.016330480575561523 time for calcul the mask position with numpy : 0.02919173240661621 nb_pixel_total : 36787 time to create 1 rle with old method : 0.040950775146484375 time for calcul the mask position with numpy : 0.029253005981445312 nb_pixel_total : 25223 time to create 1 rle with old method : 0.029665231704711914 time for calcul the mask position with numpy : 0.029773235321044922 nb_pixel_total : 14107 time to create 1 rle with old method : 0.025908946990966797 time for calcul the mask position with numpy : 0.03107428550720215 nb_pixel_total : 15654 time to create 1 rle with old method : 0.01731705665588379 time for calcul the mask position with numpy : 0.03641366958618164 nb_pixel_total : 32640 time to create 1 rle with old method : 0.036309242248535156 time for calcul the mask position with numpy : 0.030780792236328125 nb_pixel_total : 33994 time to create 1 rle with old method : 0.05456113815307617 time for calcul the mask position with numpy : 0.030526399612426758 nb_pixel_total : 18242 time to create 1 rle with old method : 0.020380496978759766 time for calcul the mask position with numpy : 0.02888655662536621 nb_pixel_total : 10889 time to create 1 rle with old method : 0.012067079544067383 time for calcul the mask position with numpy : 0.02891683578491211 nb_pixel_total : 37932 time to create 1 rle with old method : 0.04191875457763672 time for calcul the mask position with numpy : 0.02889275550842285 nb_pixel_total : 8448 time to create 1 rle with old method : 0.009382963180541992 time for calcul the mask position with numpy : 0.028978347778320312 nb_pixel_total : 6342 time to create 1 rle with old method : 0.007071495056152344 time for calcul the mask position with numpy : 0.029175758361816406 nb_pixel_total : 8463 time to create 1 rle with old method : 0.009457588195800781 time for calcul the mask position with numpy : 0.028882980346679688 nb_pixel_total : 16401 time to create 1 rle with old method : 0.01820826530456543 time for calcul the mask position with numpy : 0.028494834899902344 nb_pixel_total : 15815 time to create 1 rle with old method : 0.01742696762084961 time for calcul the mask position with numpy : 0.028591394424438477 nb_pixel_total : 17238 time to create 1 rle with old method : 0.019207477569580078 time for calcul the mask position with numpy : 0.03011918067932129 nb_pixel_total : 68365 time to create 1 rle with old method : 0.0975332260131836 time for calcul the mask position with numpy : 0.02956986427307129 nb_pixel_total : 80942 time to create 1 rle with old method : 0.09227490425109863 time for calcul the mask position with numpy : 0.02876114845275879 nb_pixel_total : 15156 time to create 1 rle with old method : 0.01646876335144043 time for calcul the mask position with numpy : 0.028244972229003906 nb_pixel_total : 26739 time to create 1 rle with old method : 0.028705358505249023 time for calcul the mask position with numpy : 0.028534889221191406 nb_pixel_total : 14356 time to create 1 rle with old method : 0.015839338302612305 time for calcul the mask position with numpy : 0.030940532684326172 nb_pixel_total : 15417 time to create 1 rle with old method : 0.02350640296936035 time for calcul the mask position with numpy : 0.03253817558288574 nb_pixel_total : 16514 time to create 1 rle with old method : 0.01970076560974121 time for calcul the mask position with numpy : 0.028939008712768555 nb_pixel_total : 13342 time to create 1 rle with old method : 0.014521360397338867 time for calcul the mask position with numpy : 0.02836894989013672 nb_pixel_total : 46576 time to create 1 rle with old method : 0.05077958106994629 time for calcul the mask position with numpy : 0.02927684783935547 nb_pixel_total : 49224 time to create 1 rle with old method : 0.05451011657714844 time for calcul the mask position with numpy : 0.028908252716064453 nb_pixel_total : 15646 time to create 1 rle with old method : 0.017457008361816406 time for calcul the mask position with numpy : 0.030308008193969727 nb_pixel_total : 9341 time to create 1 rle with old method : 0.010451555252075195 time for calcul the mask position with numpy : 0.028835773468017578 nb_pixel_total : 35372 time to create 1 rle with old method : 0.03929471969604492 time for calcul the mask position with numpy : 0.028890371322631836 nb_pixel_total : 9967 time to create 1 rle with old method : 0.011124134063720703 time for calcul the mask position with numpy : 0.028771638870239258 nb_pixel_total : 5661 time to create 1 rle with old method : 0.006357669830322266 create new chi : 5.573615074157715 time to delete rle : 0.002935171127319336 batch 1 Loaded 99 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 21673 TO DO : save crop sub photo not yet done ! save time : 2.3244247436523438 nb_obj : 40 nb_hashtags : 6 time to prepare the origin masks : 4.08689284324646 time for calcul the mask position with numpy : 0.6445472240447998 nb_pixel_total : 5655474 time to create 1 rle with new method : 1.1629879474639893 time for calcul the mask position with numpy : 0.029799222946166992 nb_pixel_total : 114592 time to create 1 rle with old method : 0.12836480140686035 time for calcul the mask position with numpy : 0.029465913772583008 nb_pixel_total : 30373 time to create 1 rle with old method : 0.03383922576904297 time for calcul the mask position with numpy : 0.029918909072875977 nb_pixel_total : 20727 time to create 1 rle with old method : 0.03409242630004883 time for calcul the mask position with numpy : 0.03366494178771973 nb_pixel_total : 80415 time to create 1 rle with old method : 0.0929708480834961 time for calcul the mask position with numpy : 0.029767274856567383 nb_pixel_total : 58791 time to create 1 rle with old method : 0.06483054161071777 time for calcul the mask position with numpy : 0.0293581485748291 nb_pixel_total : 34632 time to create 1 rle with old method : 0.03836512565612793 time for calcul the mask position with numpy : 0.029194355010986328 nb_pixel_total : 7592 time to create 1 rle with old method : 0.009114980697631836 time for calcul the mask position with numpy : 0.02963542938232422 nb_pixel_total : 11281 time to create 1 rle with old method : 0.012829065322875977 time for calcul the mask position with numpy : 0.03173685073852539 nb_pixel_total : 39533 time to create 1 rle with old method : 0.04815030097961426 time for calcul the mask position with numpy : 0.029630184173583984 nb_pixel_total : 41996 time to create 1 rle with old method : 0.04700922966003418 time for calcul the mask position with numpy : 0.029675722122192383 nb_pixel_total : 55771 time to create 1 rle with old method : 0.06156587600708008 time for calcul the mask position with numpy : 0.029378414154052734 nb_pixel_total : 16940 time to create 1 rle with old method : 0.018957138061523438 time for calcul the mask position with numpy : 0.02927708625793457 nb_pixel_total : 21240 time to create 1 rle with old method : 0.023783445358276367 time for calcul the mask position with numpy : 0.029164552688598633 nb_pixel_total : 11292 time to create 1 rle with old method : 0.012637138366699219 time for calcul the mask position with numpy : 0.029146671295166016 nb_pixel_total : 8704 time to create 1 rle with old method : 0.009901762008666992 time for calcul the mask position with numpy : 0.02932143211364746 nb_pixel_total : 12254 time to create 1 rle with old method : 0.01370382308959961 time for calcul the mask position with numpy : 0.029729127883911133 nb_pixel_total : 47342 time to create 1 rle with old method : 0.05286145210266113 time for calcul the mask position with numpy : 0.029769182205200195 nb_pixel_total : 19955 time to create 1 rle with old method : 0.025049924850463867 time for calcul the mask position with numpy : 0.030663251876831055 nb_pixel_total : 58071 time to create 1 rle with old method : 0.06512999534606934 time for calcul the mask position with numpy : 0.03021383285522461 nb_pixel_total : 80224 time to create 1 rle with old method : 0.08860445022583008 time for calcul the mask position with numpy : 0.02904367446899414 nb_pixel_total : 13336 time to create 1 rle with old method : 0.014919042587280273 time for calcul the mask position with numpy : 0.03016066551208496 nb_pixel_total : 15312 time to create 1 rle with old method : 0.017386674880981445 time for calcul the mask position with numpy : 0.02954411506652832 nb_pixel_total : 47342 time to create 1 rle with old method : 0.05255436897277832 time for calcul the mask position with numpy : 0.029579877853393555 nb_pixel_total : 59740 time to create 1 rle with old method : 0.0740969181060791 time for calcul the mask position with numpy : 0.04340791702270508 nb_pixel_total : 36131 time to create 1 rle with old method : 0.040047407150268555 time for calcul the mask position with numpy : 0.029145002365112305 nb_pixel_total : 12911 time to create 1 rle with old method : 0.014519929885864258 time for calcul the mask position with numpy : 0.02932906150817871 nb_pixel_total : 59185 time to create 1 rle with old method : 0.06621861457824707 time for calcul the mask position with numpy : 0.031964778900146484 nb_pixel_total : 93484 time to create 1 rle with old method : 0.14992666244506836 time for calcul the mask position with numpy : 0.0331883430480957 nb_pixel_total : 32392 time to create 1 rle with old method : 0.03579878807067871 time for calcul the mask position with numpy : 0.02916693687438965 nb_pixel_total : 8352 time to create 1 rle with old method : 0.009284257888793945 time for calcul the mask position with numpy : 0.031890869140625 nb_pixel_total : 69379 time to create 1 rle with old method : 0.07682061195373535 time for calcul the mask position with numpy : 0.02918076515197754 nb_pixel_total : 3265 time to create 1 rle with old method : 0.003721952438354492 time for calcul the mask position with numpy : 0.029516935348510742 nb_pixel_total : 24191 time to create 1 rle with old method : 0.02700519561767578 time for calcul the mask position with numpy : 0.029348373413085938 nb_pixel_total : 55066 time to create 1 rle with old method : 0.06117701530456543 time for calcul the mask position with numpy : 0.02941298484802246 nb_pixel_total : 20009 time to create 1 rle with old method : 0.022214174270629883 time for calcul the mask position with numpy : 0.02938389778137207 nb_pixel_total : 25042 time to create 1 rle with old method : 0.027864456176757812 time for calcul the mask position with numpy : 0.03542780876159668 nb_pixel_total : 12071 time to create 1 rle with old method : 0.014899253845214844 time for calcul the mask position with numpy : 0.032274484634399414 nb_pixel_total : 14583 time to create 1 rle with old method : 0.01639080047607422 time for calcul the mask position with numpy : 0.029386281967163086 nb_pixel_total : 6839 time to create 1 rle with old method : 0.007736682891845703 time for calcul the mask position with numpy : 0.029427766799926758 nb_pixel_total : 14411 time to create 1 rle with old method : 0.016302108764648438 create new chi : 4.695537328720093 time to delete rle : 0.003295421600341797 batch 1 Loaded 81 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 21026 TO DO : save crop sub photo not yet done ! save time : 1.5099825859069824 nb_obj : 58 nb_hashtags : 4 time to prepare the origin masks : 4.171390533447266 time for calcul the mask position with numpy : 0.40939879417419434 nb_pixel_total : 5429563 time to create 1 rle with new method : 0.325725793838501 time for calcul the mask position with numpy : 0.029026508331298828 nb_pixel_total : 6569 time to create 1 rle with old method : 0.00729060173034668 time for calcul the mask position with numpy : 0.02846217155456543 nb_pixel_total : 4611 time to create 1 rle with old method : 0.005037069320678711 time for calcul the mask position with numpy : 0.031766653060913086 nb_pixel_total : 236201 time to create 1 rle with new method : 0.2843947410583496 time for calcul the mask position with numpy : 0.029423236846923828 nb_pixel_total : 24614 time to create 1 rle with old method : 0.02757740020751953 time for calcul the mask position with numpy : 0.029662370681762695 nb_pixel_total : 36838 time to create 1 rle with old method : 0.04117250442504883 time for calcul the mask position with numpy : 0.0300748348236084 nb_pixel_total : 156428 time to create 1 rle with new method : 0.47519493103027344 time for calcul the mask position with numpy : 0.02927255630493164 nb_pixel_total : 36364 time to create 1 rle with old method : 0.040851593017578125 time for calcul the mask position with numpy : 0.030167818069458008 nb_pixel_total : 7217 time to create 1 rle with old method : 0.008229494094848633 time for calcul the mask position with numpy : 0.028870582580566406 nb_pixel_total : 7347 time to create 1 rle with old method : 0.008219242095947266 time for calcul the mask position with numpy : 0.02932906150817871 nb_pixel_total : 24616 time to create 1 rle with old method : 0.02731919288635254 time for calcul the mask position with numpy : 0.029888629913330078 nb_pixel_total : 22813 time to create 1 rle with old method : 0.025560617446899414 time for calcul the mask position with numpy : 0.03129458427429199 nb_pixel_total : 21936 time to create 1 rle with old method : 0.039414405822753906 time for calcul the mask position with numpy : 0.03505253791809082 nb_pixel_total : 24996 time to create 1 rle with old method : 0.03263354301452637 time for calcul the mask position with numpy : 0.0287628173828125 nb_pixel_total : 5857 time to create 1 rle with old method : 0.006615400314331055 time for calcul the mask position with numpy : 0.02871870994567871 nb_pixel_total : 20230 time to create 1 rle with old method : 0.02242279052734375 time for calcul the mask position with numpy : 0.029049158096313477 nb_pixel_total : 48969 time to create 1 rle with old method : 0.05459141731262207 time for calcul the mask position with numpy : 0.029501914978027344 nb_pixel_total : 26344 time to create 1 rle with old method : 0.030665874481201172 time for calcul the mask position with numpy : 0.030889272689819336 nb_pixel_total : 13072 time to create 1 rle with old method : 0.014630317687988281 time for calcul the mask position with numpy : 0.029164552688598633 nb_pixel_total : 8442 time to create 1 rle with old method : 0.009464502334594727 time for calcul the mask position with numpy : 0.02911829948425293 nb_pixel_total : 10672 time to create 1 rle with old method : 0.012203693389892578 time for calcul the mask position with numpy : 0.02904367446899414 nb_pixel_total : 2527 time to create 1 rle with old method : 0.0029897689819335938 time for calcul the mask position with numpy : 0.03196096420288086 nb_pixel_total : 147745 time to create 1 rle with old method : 0.1880934238433838 time for calcul the mask position with numpy : 0.03053879737854004 nb_pixel_total : 12950 time to create 1 rle with old method : 0.01450204849243164 time for calcul the mask position with numpy : 0.029036283493041992 nb_pixel_total : 8944 time to create 1 rle with old method : 0.010397911071777344 time for calcul the mask position with numpy : 0.029255151748657227 nb_pixel_total : 43631 time to create 1 rle with old method : 0.05262017250061035 time for calcul the mask position with numpy : 0.03159904479980469 nb_pixel_total : 20155 time to create 1 rle with old method : 0.030344247817993164 time for calcul the mask position with numpy : 0.03344321250915527 nb_pixel_total : 17420 time to create 1 rle with old method : 0.025769710540771484 time for calcul the mask position with numpy : 0.029440879821777344 nb_pixel_total : 25512 time to create 1 rle with old method : 0.03429102897644043 time for calcul the mask position with numpy : 0.029406309127807617 nb_pixel_total : 71322 time to create 1 rle with old method : 0.0811009407043457 time for calcul the mask position with numpy : 0.029580116271972656 nb_pixel_total : 36768 time to create 1 rle with old method : 0.04323458671569824 time for calcul the mask position with numpy : 0.02923440933227539 nb_pixel_total : 13457 time to create 1 rle with old method : 0.015018224716186523 time for calcul the mask position with numpy : 0.02947854995727539 nb_pixel_total : 22737 time to create 1 rle with old method : 0.02683424949645996 time for calcul the mask position with numpy : 0.029996395111083984 nb_pixel_total : 16038 time to create 1 rle with old method : 0.018088579177856445 time for calcul the mask position with numpy : 0.03212094306945801 nb_pixel_total : 9062 time to create 1 rle with old method : 0.014725446701049805 time for calcul the mask position with numpy : 0.033416748046875 nb_pixel_total : 15458 time to create 1 rle with old method : 0.020593643188476562 time for calcul the mask position with numpy : 0.029266357421875 nb_pixel_total : 13176 time to create 1 rle with old method : 0.01561880111694336 time for calcul the mask position with numpy : 0.029250144958496094 nb_pixel_total : 8873 time to create 1 rle with old method : 0.010163307189941406 time for calcul the mask position with numpy : 0.03018784523010254 nb_pixel_total : 43800 time to create 1 rle with old method : 0.050914764404296875 time for calcul the mask position with numpy : 0.029808759689331055 nb_pixel_total : 38505 time to create 1 rle with old method : 0.04354405403137207 time for calcul the mask position with numpy : 0.029288768768310547 nb_pixel_total : 8265 time to create 1 rle with old method : 0.009458303451538086 time for calcul the mask position with numpy : 0.029314279556274414 nb_pixel_total : 9057 time to create 1 rle with old method : 0.010546207427978516 time for calcul the mask position with numpy : 0.029443025588989258 nb_pixel_total : 20637 time to create 1 rle with old method : 0.023239612579345703 time for calcul the mask position with numpy : 0.029777050018310547 nb_pixel_total : 25934 time to create 1 rle with old method : 0.03184342384338379 time for calcul the mask position with numpy : 0.029230117797851562 nb_pixel_total : 18549 time to create 1 rle with old method : 0.02093791961669922 time for calcul the mask position with numpy : 0.03239631652832031 nb_pixel_total : 14748 time to create 1 rle with old method : 0.018359899520874023 time for calcul the mask position with numpy : 0.02967071533203125 nb_pixel_total : 34464 time to create 1 rle with old method : 0.03831982612609863 time for calcul the mask position with numpy : 0.03092193603515625 nb_pixel_total : 7319 time to create 1 rle with old method : 0.01053619384765625 time for calcul the mask position with numpy : 0.03142213821411133 nb_pixel_total : 33786 time to create 1 rle with old method : 0.03775501251220703 time for calcul the mask position with numpy : 0.02982807159423828 nb_pixel_total : 13314 time to create 1 rle with old method : 0.02348160743713379 time for calcul the mask position with numpy : 0.038701534271240234 nb_pixel_total : 12922 time to create 1 rle with old method : 0.014468669891357422 time for calcul the mask position with numpy : 0.029222965240478516 nb_pixel_total : 4596 time to create 1 rle with old method : 0.005185604095458984 time for calcul the mask position with numpy : 0.03225111961364746 nb_pixel_total : 2845 time to create 1 rle with old method : 0.005625247955322266 time for calcul the mask position with numpy : 0.029163599014282227 nb_pixel_total : 27109 time to create 1 rle with old method : 0.03016066551208496 time for calcul the mask position with numpy : 0.02951502799987793 nb_pixel_total : 44032 time to create 1 rle with old method : 0.048485517501831055 time for calcul the mask position with numpy : 0.02952098846435547 nb_pixel_total : 6138 time to create 1 rle with old method : 0.006864070892333984 time for calcul the mask position with numpy : 0.02958083152770996 nb_pixel_total : 9525 time to create 1 rle with old method : 0.013989448547363281 time for calcul the mask position with numpy : 0.029565811157226562 nb_pixel_total : 5624 time to create 1 rle with old method : 0.006534099578857422 time for calcul the mask position with numpy : 0.02930736541748047 nb_pixel_total : 9597 time to create 1 rle with old method : 0.010960102081298828 create new chi : 4.8055260181427 time to delete rle : 0.004170417785644531 batch 1 Loaded 117 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 25946 TO DO : save crop sub photo not yet done ! save time : 1.9227240085601807 nb_obj : 47 nb_hashtags : 5 time to prepare the origin masks : 4.393302917480469 time for calcul the mask position with numpy : 0.4646618366241455 nb_pixel_total : 5204067 time to create 1 rle with new method : 0.39751696586608887 time for calcul the mask position with numpy : 0.035150766372680664 nb_pixel_total : 14253 time to create 1 rle with old method : 0.023801326751708984 time for calcul the mask position with numpy : 0.03137087821960449 nb_pixel_total : 29232 time to create 1 rle with old method : 0.032614946365356445 time for calcul the mask position with numpy : 0.028934717178344727 nb_pixel_total : 10412 time to create 1 rle with old method : 0.011639595031738281 time for calcul the mask position with numpy : 0.02881932258605957 nb_pixel_total : 10043 time to create 1 rle with old method : 0.011225461959838867 time for calcul the mask position with numpy : 0.031236886978149414 nb_pixel_total : 259019 time to create 1 rle with new method : 0.5898208618164062 time for calcul the mask position with numpy : 0.029535293579101562 nb_pixel_total : 22383 time to create 1 rle with old method : 0.025104522705078125 time for calcul the mask position with numpy : 0.02955007553100586 nb_pixel_total : 15141 time to create 1 rle with old method : 0.01683497428894043 time for calcul the mask position with numpy : 0.02968597412109375 nb_pixel_total : 23758 time to create 1 rle with old method : 0.028488874435424805 time for calcul the mask position with numpy : 0.02995753288269043 nb_pixel_total : 34082 time to create 1 rle with old method : 0.03809356689453125 time for calcul the mask position with numpy : 0.029885292053222656 nb_pixel_total : 27148 time to create 1 rle with old method : 0.030681848526000977 time for calcul the mask position with numpy : 0.02963542938232422 nb_pixel_total : 14805 time to create 1 rle with old method : 0.016530990600585938 time for calcul the mask position with numpy : 0.029509305953979492 nb_pixel_total : 25584 time to create 1 rle with old method : 0.028616905212402344 time for calcul the mask position with numpy : 0.02917313575744629 nb_pixel_total : 5601 time to create 1 rle with old method : 0.006336212158203125 time for calcul the mask position with numpy : 0.029453277587890625 nb_pixel_total : 17850 time to create 1 rle with old method : 0.019781827926635742 time for calcul the mask position with numpy : 0.029376983642578125 nb_pixel_total : 48328 time to create 1 rle with old method : 0.05789947509765625 time for calcul the mask position with numpy : 0.02926468849182129 nb_pixel_total : 77588 time to create 1 rle with old method : 0.08525967597961426 time for calcul the mask position with numpy : 0.029048442840576172 nb_pixel_total : 37371 time to create 1 rle with old method : 0.043417930603027344 time for calcul the mask position with numpy : 0.029041767120361328 nb_pixel_total : 16636 time to create 1 rle with old method : 0.020806312561035156 time for calcul the mask position with numpy : 0.02944040298461914 nb_pixel_total : 12641 time to create 1 rle with old method : 0.014025449752807617 time for calcul the mask position with numpy : 0.029371261596679688 nb_pixel_total : 8459 time to create 1 rle with old method : 0.010563850402832031 time for calcul the mask position with numpy : 0.03134465217590332 nb_pixel_total : 6012 time to create 1 rle with old method : 0.009822845458984375 time for calcul the mask position with numpy : 0.031215667724609375 nb_pixel_total : 14568 time to create 1 rle with old method : 0.016571044921875 time for calcul the mask position with numpy : 0.02930760383605957 nb_pixel_total : 11235 time to create 1 rle with old method : 0.012674331665039062 time for calcul the mask position with numpy : 0.028858423233032227 nb_pixel_total : 4056 time to create 1 rle with old method : 0.004591703414916992 time for calcul the mask position with numpy : 0.02887582778930664 nb_pixel_total : 19920 time to create 1 rle with old method : 0.022057533264160156 time for calcul the mask position with numpy : 0.028975963592529297 nb_pixel_total : 12459 time to create 1 rle with old method : 0.014117717742919922 time for calcul the mask position with numpy : 0.033666372299194336 nb_pixel_total : 20509 time to create 1 rle with old method : 0.0255126953125 time for calcul the mask position with numpy : 0.03239035606384277 nb_pixel_total : 276811 time to create 1 rle with new method : 0.48169374465942383 time for calcul the mask position with numpy : 0.02944469451904297 nb_pixel_total : 2572 time to create 1 rle with old method : 0.0029332637786865234 time for calcul the mask position with numpy : 0.03099679946899414 nb_pixel_total : 254293 time to create 1 rle with new method : 0.42919063568115234 time for calcul the mask position with numpy : 0.03040909767150879 nb_pixel_total : 23152 time to create 1 rle with old method : 0.026259899139404297 time for calcul the mask position with numpy : 0.030030250549316406 nb_pixel_total : 13167 time to create 1 rle with old method : 0.015159368515014648 time for calcul the mask position with numpy : 0.03207039833068848 nb_pixel_total : 25947 time to create 1 rle with old method : 0.03399372100830078 time for calcul the mask position with numpy : 0.03058791160583496 nb_pixel_total : 51066 time to create 1 rle with old method : 0.07662105560302734 time for calcul the mask position with numpy : 0.03767514228820801 nb_pixel_total : 113542 time to create 1 rle with old method : 0.14998984336853027 time for calcul the mask position with numpy : 0.030167579650878906 nb_pixel_total : 16438 time to create 1 rle with old method : 0.018317222595214844 time for calcul the mask position with numpy : 0.02993011474609375 nb_pixel_total : 49720 time to create 1 rle with old method : 0.055350542068481445 time for calcul the mask position with numpy : 0.030191659927368164 nb_pixel_total : 7924 time to create 1 rle with old method : 0.009055614471435547 time for calcul the mask position with numpy : 0.02939009666442871 nb_pixel_total : 22776 time to create 1 rle with old method : 0.025234460830688477 time for calcul the mask position with numpy : 0.029942750930786133 nb_pixel_total : 22513 time to create 1 rle with old method : 0.025159120559692383 time for calcul the mask position with numpy : 0.030542373657226562 nb_pixel_total : 50847 time to create 1 rle with old method : 0.0563814640045166 time for calcul the mask position with numpy : 0.030175209045410156 nb_pixel_total : 23800 time to create 1 rle with old method : 0.029063940048217773 time for calcul the mask position with numpy : 0.030008554458618164 nb_pixel_total : 30023 time to create 1 rle with old method : 0.03347039222717285 time for calcul the mask position with numpy : 0.0294034481048584 nb_pixel_total : 28878 time to create 1 rle with old method : 0.032230377197265625 time for calcul the mask position with numpy : 0.03147006034851074 nb_pixel_total : 16959 time to create 1 rle with old method : 0.024141788482666016 time for calcul the mask position with numpy : 0.03375673294067383 nb_pixel_total : 9508 time to create 1 rle with old method : 0.015513896942138672 time for calcul the mask position with numpy : 0.03310370445251465 nb_pixel_total : 7144 time to create 1 rle with old method : 0.008126258850097656 create new chi : 5.176694869995117 time to delete rle : 0.006139278411865234 batch 1 Loaded 95 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 23960 TO DO : save crop sub photo not yet done ! save time : 1.6980633735656738 nb_obj : 64 nb_hashtags : 4 time to prepare the origin masks : 5.0782716274261475 time for calcul the mask position with numpy : 0.5881404876708984 nb_pixel_total : 4480208 time to create 1 rle with new method : 0.9154908657073975 time for calcul the mask position with numpy : 0.03498530387878418 nb_pixel_total : 314194 time to create 1 rle with new method : 0.4795095920562744 time for calcul the mask position with numpy : 0.03229260444641113 nb_pixel_total : 164270 time to create 1 rle with new method : 0.46651768684387207 time for calcul the mask position with numpy : 0.03086256980895996 nb_pixel_total : 25707 time to create 1 rle with old method : 0.02869892120361328 time for calcul the mask position with numpy : 0.030724525451660156 nb_pixel_total : 43502 time to create 1 rle with old method : 0.04939723014831543 time for calcul the mask position with numpy : 0.03087162971496582 nb_pixel_total : 36501 time to create 1 rle with old method : 0.04554152488708496 time for calcul the mask position with numpy : 0.033727169036865234 nb_pixel_total : 19910 time to create 1 rle with old method : 0.04159688949584961 time for calcul the mask position with numpy : 0.040354013442993164 nb_pixel_total : 12745 time to create 1 rle with old method : 0.014469623565673828 time for calcul the mask position with numpy : 0.030358076095581055 nb_pixel_total : 34385 time to create 1 rle with old method : 0.042085886001586914 time for calcul the mask position with numpy : 0.03170895576477051 nb_pixel_total : 114077 time to create 1 rle with old method : 0.12670540809631348 time for calcul the mask position with numpy : 0.029439210891723633 nb_pixel_total : 23681 time to create 1 rle with old method : 0.026404380798339844 time for calcul the mask position with numpy : 0.029617786407470703 nb_pixel_total : 21590 time to create 1 rle with old method : 0.024028778076171875 time for calcul the mask position with numpy : 0.04335498809814453 nb_pixel_total : 33226 time to create 1 rle with old method : 0.10434699058532715 time for calcul the mask position with numpy : 0.045595407485961914 nb_pixel_total : 5311 time to create 1 rle with old method : 0.024334192276000977 time for calcul the mask position with numpy : 0.08028578758239746 nb_pixel_total : 61492 time to create 1 rle with old method : 0.14815664291381836 time for calcul the mask position with numpy : 0.0299224853515625 nb_pixel_total : 18023 time to create 1 rle with old method : 0.020056962966918945 time for calcul the mask position with numpy : 0.030189037322998047 nb_pixel_total : 33147 time to create 1 rle with old method : 0.03744101524353027 time for calcul the mask position with numpy : 0.029846668243408203 nb_pixel_total : 7755 time to create 1 rle with old method : 0.008727312088012695 time for calcul the mask position with numpy : 0.031169414520263672 nb_pixel_total : 118015 time to create 1 rle with old method : 0.14429926872253418 time for calcul the mask position with numpy : 0.034175872802734375 nb_pixel_total : 24586 time to create 1 rle with old method : 0.03626513481140137 time for calcul the mask position with numpy : 0.03016066551208496 nb_pixel_total : 9127 time to create 1 rle with old method : 0.010273456573486328 time for calcul the mask position with numpy : 0.030549049377441406 nb_pixel_total : 16784 time to create 1 rle with old method : 0.018881559371948242 time for calcul the mask position with numpy : 0.030875444412231445 nb_pixel_total : 58843 time to create 1 rle with old method : 0.06530570983886719 time for calcul the mask position with numpy : 0.034147024154663086 nb_pixel_total : 156415 time to create 1 rle with new method : 0.705014705657959 time for calcul the mask position with numpy : 0.03521847724914551 nb_pixel_total : 150538 time to create 1 rle with new method : 0.43146610260009766 time for calcul the mask position with numpy : 0.030962228775024414 nb_pixel_total : 28990 time to create 1 rle with old method : 0.03269624710083008 time for calcul the mask position with numpy : 0.029758214950561523 nb_pixel_total : 32508 time to create 1 rle with old method : 0.036635637283325195 time for calcul the mask position with numpy : 0.029685020446777344 nb_pixel_total : 28457 time to create 1 rle with old method : 0.03205251693725586 time for calcul the mask position with numpy : 0.03056049346923828 nb_pixel_total : 165092 time to create 1 rle with new method : 0.5438416004180908 time for calcul the mask position with numpy : 0.030612945556640625 nb_pixel_total : 18644 time to create 1 rle with old method : 0.020905017852783203 time for calcul the mask position with numpy : 0.03122735023498535 nb_pixel_total : 21596 time to create 1 rle with old method : 0.02511143684387207 time for calcul the mask position with numpy : 0.03225517272949219 nb_pixel_total : 37766 time to create 1 rle with old method : 0.042569637298583984 time for calcul the mask position with numpy : 0.03021240234375 nb_pixel_total : 11690 time to create 1 rle with old method : 0.013078689575195312 time for calcul the mask position with numpy : 0.029997587203979492 nb_pixel_total : 18282 time to create 1 rle with old method : 0.02219533920288086 time for calcul the mask position with numpy : 0.03050851821899414 nb_pixel_total : 81451 time to create 1 rle with old method : 0.09241867065429688 time for calcul the mask position with numpy : 0.03033447265625 nb_pixel_total : 58162 time to create 1 rle with old method : 0.06431841850280762 time for calcul the mask position with numpy : 0.029839277267456055 nb_pixel_total : 11366 time to create 1 rle with old method : 0.013444662094116211 time for calcul the mask position with numpy : 0.034005165100097656 nb_pixel_total : 14207 time to create 1 rle with old method : 0.016025304794311523 time for calcul the mask position with numpy : 0.030562639236450195 nb_pixel_total : 88139 time to create 1 rle with old method : 0.10161280632019043 time for calcul the mask position with numpy : 0.03053736686706543 nb_pixel_total : 83951 time to create 1 rle with old method : 0.10096931457519531 time for calcul the mask position with numpy : 0.029497146606445312 nb_pixel_total : 29264 time to create 1 rle with old method : 0.034781694412231445 time for calcul the mask position with numpy : 0.03018355369567871 nb_pixel_total : 14158 time to create 1 rle with old method : 0.015899658203125 time for calcul the mask position with numpy : 0.029308557510375977 nb_pixel_total : 7695 time to create 1 rle with old method : 0.008937835693359375 time for calcul the mask position with numpy : 0.029703855514526367 nb_pixel_total : 19156 time to create 1 rle with old method : 0.021470069885253906 time for calcul the mask position with numpy : 0.029727458953857422 nb_pixel_total : 4660 time to create 1 rle with old method : 0.005228281021118164 time for calcul the mask position with numpy : 0.02974867820739746 nb_pixel_total : 4131 time to create 1 rle with old method : 0.0047876834869384766 time for calcul the mask position with numpy : 0.03232836723327637 nb_pixel_total : 4780 time to create 1 rle with old method : 0.005695343017578125 time for calcul the mask position with numpy : 0.04390668869018555 nb_pixel_total : 10961 time to create 1 rle with old method : 0.03838706016540527 time for calcul the mask position with numpy : 0.03985261917114258 nb_pixel_total : 5364 time to create 1 rle with old method : 0.0061473846435546875 time for calcul the mask position with numpy : 0.029738903045654297 nb_pixel_total : 2436 time to create 1 rle with old method : 0.003205537796020508 time for calcul the mask position with numpy : 0.030554771423339844 nb_pixel_total : 38577 time to create 1 rle with old method : 0.04881095886230469 time for calcul the mask position with numpy : 0.030054092407226562 nb_pixel_total : 14410 time to create 1 rle with old method : 0.01682901382446289 time for calcul the mask position with numpy : 0.030071735382080078 nb_pixel_total : 79826 time to create 1 rle with old method : 0.09527993202209473 time for calcul the mask position with numpy : 0.029773473739624023 nb_pixel_total : 7122 time to create 1 rle with old method : 0.008044242858886719 time for calcul the mask position with numpy : 0.029512882232666016 nb_pixel_total : 10902 time to create 1 rle with old method : 0.012359142303466797 time for calcul the mask position with numpy : 0.03030562400817871 nb_pixel_total : 6747 time to create 1 rle with old method : 0.00816655158996582 time for calcul the mask position with numpy : 0.031946659088134766 nb_pixel_total : 22475 time to create 1 rle with old method : 0.02569580078125 time for calcul the mask position with numpy : 0.029360532760620117 nb_pixel_total : 9209 time to create 1 rle with old method : 0.010414361953735352 time for calcul the mask position with numpy : 0.029120922088623047 nb_pixel_total : 5275 time to create 1 rle with old method : 0.0060160160064697266 time for calcul the mask position with numpy : 0.028838157653808594 nb_pixel_total : 497 time to create 1 rle with old method : 0.0006151199340820312 time for calcul the mask position with numpy : 0.02914738655090332 nb_pixel_total : 23204 time to create 1 rle with old method : 0.026340961456298828 time for calcul the mask position with numpy : 0.02927374839782715 nb_pixel_total : 25043 time to create 1 rle with old method : 0.02822399139404297 time for calcul the mask position with numpy : 0.029349565505981445 nb_pixel_total : 5785 time to create 1 rle with old method : 0.009469270706176758 time for calcul the mask position with numpy : 0.03030085563659668 nb_pixel_total : 4454 time to create 1 rle with old method : 0.0051844120025634766 time for calcul the mask position with numpy : 0.029252290725708008 nb_pixel_total : 9776 time to create 1 rle with old method : 0.011154890060424805 create new chi : 8.511220693588257 time to delete rle : 0.007501840591430664 batch 1 Loaded 129 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 30351 TO DO : save crop sub photo not yet done ! save time : 2.1350369453430176 nb_obj : 34 nb_hashtags : 5 time to prepare the origin masks : 4.795895338058472 time for calcul the mask position with numpy : 0.32695698738098145 nb_pixel_total : 4524848 time to create 1 rle with new method : 0.6796700954437256 time for calcul the mask position with numpy : 0.030264854431152344 nb_pixel_total : 21962 time to create 1 rle with old method : 0.024240493774414062 time for calcul the mask position with numpy : 0.030215978622436523 nb_pixel_total : 56367 time to create 1 rle with old method : 0.06257820129394531 time for calcul the mask position with numpy : 0.032444000244140625 nb_pixel_total : 288680 time to create 1 rle with new method : 0.49083447456359863 time for calcul the mask position with numpy : 0.029523134231567383 nb_pixel_total : 7744 time to create 1 rle with old method : 0.008840084075927734 time for calcul the mask position with numpy : 0.02987837791442871 nb_pixel_total : 88998 time to create 1 rle with old method : 0.09900856018066406 time for calcul the mask position with numpy : 0.038080453872680664 nb_pixel_total : 240682 time to create 1 rle with new method : 0.5630216598510742 time for calcul the mask position with numpy : 0.029312849044799805 nb_pixel_total : 28593 time to create 1 rle with old method : 0.031282663345336914 time for calcul the mask position with numpy : 0.03239631652832031 nb_pixel_total : 10904 time to create 1 rle with old method : 0.012299060821533203 time for calcul the mask position with numpy : 0.02961874008178711 nb_pixel_total : 9744 time to create 1 rle with old method : 0.010918378829956055 time for calcul the mask position with numpy : 0.02959442138671875 nb_pixel_total : 21646 time to create 1 rle with old method : 0.0242764949798584 time for calcul the mask position with numpy : 0.029208898544311523 nb_pixel_total : 18441 time to create 1 rle with old method : 0.020427227020263672 time for calcul the mask position with numpy : 0.02990102767944336 nb_pixel_total : 185809 time to create 1 rle with new method : 0.36479640007019043 time for calcul the mask position with numpy : 0.029973268508911133 nb_pixel_total : 17654 time to create 1 rle with old method : 0.019611358642578125 time for calcul the mask position with numpy : 0.03258323669433594 nb_pixel_total : 93735 time to create 1 rle with old method : 0.10443830490112305 time for calcul the mask position with numpy : 0.029625654220581055 nb_pixel_total : 224862 time to create 1 rle with new method : 0.5756785869598389 time for calcul the mask position with numpy : 0.029033660888671875 nb_pixel_total : 6365 time to create 1 rle with old method : 0.007109642028808594 time for calcul the mask position with numpy : 0.029004573822021484 nb_pixel_total : 10212 time to create 1 rle with old method : 0.0114593505859375 time for calcul the mask position with numpy : 0.029004812240600586 nb_pixel_total : 9151 time to create 1 rle with old method : 0.010245800018310547 time for calcul the mask position with numpy : 0.030796289443969727 nb_pixel_total : 190614 time to create 1 rle with new method : 0.9220108985900879 time for calcul the mask position with numpy : 0.030519485473632812 nb_pixel_total : 308232 time to create 1 rle with new method : 0.6020138263702393 time for calcul the mask position with numpy : 0.029247760772705078 nb_pixel_total : 46494 time to create 1 rle with old method : 0.051664113998413086 time for calcul the mask position with numpy : 0.029295921325683594 nb_pixel_total : 24028 time to create 1 rle with old method : 0.027196168899536133 time for calcul the mask position with numpy : 0.029641389846801758 nb_pixel_total : 65011 time to create 1 rle with old method : 0.07285904884338379 time for calcul the mask position with numpy : 0.02967071533203125 nb_pixel_total : 16530 time to create 1 rle with old method : 0.01879286766052246 time for calcul the mask position with numpy : 0.02965378761291504 nb_pixel_total : 15244 time to create 1 rle with old method : 0.016791105270385742 time for calcul the mask position with numpy : 0.03062272071838379 nb_pixel_total : 147536 time to create 1 rle with old method : 0.16649079322814941 time for calcul the mask position with numpy : 0.029472827911376953 nb_pixel_total : 11453 time to create 1 rle with old method : 0.012888908386230469 time for calcul the mask position with numpy : 0.03249645233154297 nb_pixel_total : 111867 time to create 1 rle with old method : 0.14562535285949707 time for calcul the mask position with numpy : 0.03044605255126953 nb_pixel_total : 21736 time to create 1 rle with old method : 0.024332046508789062 time for calcul the mask position with numpy : 0.030358076095581055 nb_pixel_total : 65460 time to create 1 rle with old method : 0.07282257080078125 time for calcul the mask position with numpy : 0.029766559600830078 nb_pixel_total : 60369 time to create 1 rle with old method : 0.06721091270446777 time for calcul the mask position with numpy : 0.030409574508666992 nb_pixel_total : 64768 time to create 1 rle with old method : 0.07347249984741211 time for calcul the mask position with numpy : 0.02960491180419922 nb_pixel_total : 12968 time to create 1 rle with old method : 0.014527559280395508 time for calcul the mask position with numpy : 0.029969215393066406 nb_pixel_total : 21533 time to create 1 rle with old method : 0.024601221084594727 create new chi : 6.996536731719971 time to delete rle : 0.007027149200439453 batch 1 Loaded 69 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 23724 TO DO : save crop sub photo not yet done ! save time : 3.9200599193573 nb_obj : 30 nb_hashtags : 3 time to prepare the origin masks : 3.4951682090759277 time for calcul the mask position with numpy : 0.9445140361785889 nb_pixel_total : 6356008 time to create 1 rle with new method : 0.6422309875488281 time for calcul the mask position with numpy : 0.030771732330322266 nb_pixel_total : 4529 time to create 1 rle with old method : 0.005279541015625 time for calcul the mask position with numpy : 0.030600547790527344 nb_pixel_total : 16523 time to create 1 rle with old method : 0.01838850975036621 time for calcul the mask position with numpy : 0.029466867446899414 nb_pixel_total : 13962 time to create 1 rle with old method : 0.015796184539794922 time for calcul the mask position with numpy : 0.029886960983276367 nb_pixel_total : 30145 time to create 1 rle with old method : 0.03341841697692871 time for calcul the mask position with numpy : 0.029592037200927734 nb_pixel_total : 16146 time to create 1 rle with old method : 0.01842522621154785 time for calcul the mask position with numpy : 0.030032873153686523 nb_pixel_total : 11693 time to create 1 rle with old method : 0.013316869735717773 time for calcul the mask position with numpy : 0.02974224090576172 nb_pixel_total : 41575 time to create 1 rle with old method : 0.04616713523864746 time for calcul the mask position with numpy : 0.029402971267700195 nb_pixel_total : 50246 time to create 1 rle with old method : 0.055841922760009766 time for calcul the mask position with numpy : 0.02918529510498047 nb_pixel_total : 7442 time to create 1 rle with old method : 0.008363723754882812 time for calcul the mask position with numpy : 0.0290682315826416 nb_pixel_total : 11495 time to create 1 rle with old method : 0.012714147567749023 time for calcul the mask position with numpy : 0.029440641403198242 nb_pixel_total : 23875 time to create 1 rle with old method : 0.02653670310974121 time for calcul the mask position with numpy : 0.02939438819885254 nb_pixel_total : 24552 time to create 1 rle with old method : 0.027281999588012695 time for calcul the mask position with numpy : 0.02901458740234375 nb_pixel_total : 4054 time to create 1 rle with old method : 0.004626035690307617 time for calcul the mask position with numpy : 0.02921772003173828 nb_pixel_total : 25513 time to create 1 rle with old method : 0.02837347984313965 time for calcul the mask position with numpy : 0.029093027114868164 nb_pixel_total : 8230 time to create 1 rle with old method : 0.009221553802490234 time for calcul the mask position with numpy : 0.029110193252563477 nb_pixel_total : 9714 time to create 1 rle with old method : 0.011016845703125 time for calcul the mask position with numpy : 0.029472827911376953 nb_pixel_total : 32761 time to create 1 rle with old method : 0.03631472587585449 time for calcul the mask position with numpy : 0.029581785202026367 nb_pixel_total : 52841 time to create 1 rle with old method : 0.058969736099243164 time for calcul the mask position with numpy : 0.030035972595214844 nb_pixel_total : 88715 time to create 1 rle with old method : 0.0980839729309082 time for calcul the mask position with numpy : 0.02952098846435547 nb_pixel_total : 18665 time to create 1 rle with old method : 0.02071356773376465 time for calcul the mask position with numpy : 0.029205322265625 nb_pixel_total : 11618 time to create 1 rle with old method : 0.013008832931518555 time for calcul the mask position with numpy : 0.02932143211364746 nb_pixel_total : 22161 time to create 1 rle with old method : 0.0248720645904541 time for calcul the mask position with numpy : 0.029435396194458008 nb_pixel_total : 26563 time to create 1 rle with old method : 0.02953934669494629 time for calcul the mask position with numpy : 0.030048847198486328 nb_pixel_total : 5024 time to create 1 rle with old method : 0.005688905715942383 time for calcul the mask position with numpy : 0.029612302780151367 nb_pixel_total : 17549 time to create 1 rle with old method : 0.020302295684814453 time for calcul the mask position with numpy : 0.029717683792114258 nb_pixel_total : 13419 time to create 1 rle with old method : 0.01526331901550293 time for calcul the mask position with numpy : 0.029537677764892578 nb_pixel_total : 25678 time to create 1 rle with old method : 0.028604745864868164 time for calcul the mask position with numpy : 0.030130386352539062 nb_pixel_total : 53498 time to create 1 rle with old method : 0.059555768966674805 time for calcul the mask position with numpy : 0.02941608428955078 nb_pixel_total : 11873 time to create 1 rle with old method : 0.013286828994750977 time for calcul the mask position with numpy : 0.029267549514770508 nb_pixel_total : 14173 time to create 1 rle with old method : 0.015940427780151367 create new chi : 3.2995855808258057 time to delete rle : 0.0030019283294677734 batch 1 Loaded 61 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 13104 TO DO : save crop sub photo not yet done ! save time : 2.7404487133026123 nb_obj : 68 nb_hashtags : 4 time to prepare the origin masks : 4.203489065170288 time for calcul the mask position with numpy : 0.4000246524810791 nb_pixel_total : 5346784 time to create 1 rle with new method : 1.1993179321289062 time for calcul the mask position with numpy : 0.02922987937927246 nb_pixel_total : 17834 time to create 1 rle with old method : 0.019907474517822266 time for calcul the mask position with numpy : 0.029445886611938477 nb_pixel_total : 4955 time to create 1 rle with old method : 0.005623817443847656 time for calcul the mask position with numpy : 0.030078649520874023 nb_pixel_total : 59932 time to create 1 rle with old method : 0.06804895401000977 time for calcul the mask position with numpy : 0.030417203903198242 nb_pixel_total : 28969 time to create 1 rle with old method : 0.032732248306274414 time for calcul the mask position with numpy : 0.034027814865112305 nb_pixel_total : 108317 time to create 1 rle with old method : 0.12074542045593262 time for calcul the mask position with numpy : 0.029786109924316406 nb_pixel_total : 4910 time to create 1 rle with old method : 0.005633354187011719 time for calcul the mask position with numpy : 0.029897689819335938 nb_pixel_total : 6800 time to create 1 rle with old method : 0.007670402526855469 time for calcul the mask position with numpy : 0.030102014541625977 nb_pixel_total : 5038 time to create 1 rle with old method : 0.00571751594543457 time for calcul the mask position with numpy : 0.02967357635498047 nb_pixel_total : 7682 time to create 1 rle with old method : 0.00866079330444336 time for calcul the mask position with numpy : 0.02948451042175293 nb_pixel_total : 4476 time to create 1 rle with old method : 0.005083322525024414 time for calcul the mask position with numpy : 0.029623746871948242 nb_pixel_total : 9888 time to create 1 rle with old method : 0.011224031448364258 time for calcul the mask position with numpy : 0.02957916259765625 nb_pixel_total : 15420 time to create 1 rle with old method : 0.017322540283203125 time for calcul the mask position with numpy : 0.029460668563842773 nb_pixel_total : 15206 time to create 1 rle with old method : 0.017056703567504883 time for calcul the mask position with numpy : 0.029420852661132812 nb_pixel_total : 35918 time to create 1 rle with old method : 0.0405278205871582 time for calcul the mask position with numpy : 0.02915477752685547 nb_pixel_total : 5189 time to create 1 rle with old method : 0.005947113037109375 time for calcul the mask position with numpy : 0.029106616973876953 nb_pixel_total : 14227 time to create 1 rle with old method : 0.016048669815063477 time for calcul the mask position with numpy : 0.029147863388061523 nb_pixel_total : 13009 time to create 1 rle with old method : 0.01463007926940918 time for calcul the mask position with numpy : 0.029285430908203125 nb_pixel_total : 45521 time to create 1 rle with old method : 0.05043196678161621 time for calcul the mask position with numpy : 0.029146432876586914 nb_pixel_total : 19234 time to create 1 rle with old method : 0.02146744728088379 time for calcul the mask position with numpy : 0.02955007553100586 nb_pixel_total : 96902 time to create 1 rle with old method : 0.10702848434448242 time for calcul the mask position with numpy : 0.02923583984375 nb_pixel_total : 9747 time to create 1 rle with old method : 0.010930776596069336 time for calcul the mask position with numpy : 0.02919793128967285 nb_pixel_total : 18887 time to create 1 rle with old method : 0.021055936813354492 time for calcul the mask position with numpy : 0.029295682907104492 nb_pixel_total : 25106 time to create 1 rle with old method : 0.028078317642211914 time for calcul the mask position with numpy : 0.029088735580444336 nb_pixel_total : 3372 time to create 1 rle with old method : 0.0038301944732666016 time for calcul the mask position with numpy : 0.029269933700561523 nb_pixel_total : 10655 time to create 1 rle with old method : 0.011909723281860352 time for calcul the mask position with numpy : 0.02904534339904785 nb_pixel_total : 20681 time to create 1 rle with old method : 0.023204326629638672 time for calcul the mask position with numpy : 0.029361963272094727 nb_pixel_total : 4322 time to create 1 rle with old method : 0.004863262176513672 time for calcul the mask position with numpy : 0.028883934020996094 nb_pixel_total : 34311 time to create 1 rle with old method : 0.03805065155029297 time for calcul the mask position with numpy : 0.029193878173828125 nb_pixel_total : 17881 time to create 1 rle with old method : 0.020189285278320312 time for calcul the mask position with numpy : 0.02914118766784668 nb_pixel_total : 11206 time to create 1 rle with old method : 0.012675285339355469 time for calcul the mask position with numpy : 0.02930426597595215 nb_pixel_total : 12180 time to create 1 rle with old method : 0.013688087463378906 time for calcul the mask position with numpy : 0.030285358428955078 nb_pixel_total : 44832 time to create 1 rle with old method : 0.05331587791442871 time for calcul the mask position with numpy : 0.02979254722595215 nb_pixel_total : 24893 time to create 1 rle with old method : 0.02762460708618164 time for calcul the mask position with numpy : 0.029413461685180664 nb_pixel_total : 1290 time to create 1 rle with old method : 0.0015752315521240234 time for calcul the mask position with numpy : 0.029198169708251953 nb_pixel_total : 16227 time to create 1 rle with old method : 0.01828622817993164 time for calcul the mask position with numpy : 0.029160737991333008 nb_pixel_total : 30054 time to create 1 rle with old method : 0.03396892547607422 time for calcul the mask position with numpy : 0.02950572967529297 nb_pixel_total : 30318 time to create 1 rle with old method : 0.03386092185974121 time for calcul the mask position with numpy : 0.02921271324157715 nb_pixel_total : 5750 time to create 1 rle with old method : 0.0065419673919677734 time for calcul the mask position with numpy : 0.029325008392333984 nb_pixel_total : 35239 time to create 1 rle with old method : 0.039484500885009766 time for calcul the mask position with numpy : 0.02922368049621582 nb_pixel_total : 7859 time to create 1 rle with old method : 0.00885152816772461 time for calcul the mask position with numpy : 0.029610633850097656 nb_pixel_total : 31180 time to create 1 rle with old method : 0.0349125862121582 time for calcul the mask position with numpy : 0.0298001766204834 nb_pixel_total : 18207 time to create 1 rle with old method : 0.020367145538330078 time for calcul the mask position with numpy : 0.029290437698364258 nb_pixel_total : 24948 time to create 1 rle with old method : 0.027860403060913086 time for calcul the mask position with numpy : 0.029076099395751953 nb_pixel_total : 27981 time to create 1 rle with old method : 0.030875444412231445 time for calcul the mask position with numpy : 0.0291445255279541 nb_pixel_total : 17480 time to create 1 rle with old method : 0.019706010818481445 time for calcul the mask position with numpy : 0.029332637786865234 nb_pixel_total : 23923 time to create 1 rle with old method : 0.026612043380737305 time for calcul the mask position with numpy : 0.029310226440429688 nb_pixel_total : 31213 time to create 1 rle with old method : 0.03524351119995117 time for calcul the mask position with numpy : 0.029250144958496094 nb_pixel_total : 19157 time to create 1 rle with old method : 0.021596431732177734 time for calcul the mask position with numpy : 0.029197216033935547 nb_pixel_total : 18927 time to create 1 rle with old method : 0.02117156982421875 time for calcul the mask position with numpy : 0.029122114181518555 nb_pixel_total : 5248 time to create 1 rle with old method : 0.005974531173706055 time for calcul the mask position with numpy : 0.029233932495117188 nb_pixel_total : 44732 time to create 1 rle with old method : 0.05009818077087402 time for calcul the mask position with numpy : 0.030077695846557617 nb_pixel_total : 54567 time to create 1 rle with old method : 0.08512663841247559 time for calcul the mask position with numpy : 0.02923297882080078 nb_pixel_total : 20792 time to create 1 rle with old method : 0.02428436279296875 time for calcul the mask position with numpy : 0.029337644577026367 nb_pixel_total : 20488 time to create 1 rle with old method : 0.022953510284423828 time for calcul the mask position with numpy : 0.02947998046875 nb_pixel_total : 37862 time to create 1 rle with old method : 0.041994571685791016 time for calcul the mask position with numpy : 0.029488086700439453 nb_pixel_total : 42186 time to create 1 rle with old method : 0.047181129455566406 time for calcul the mask position with numpy : 0.029381990432739258 nb_pixel_total : 6144 time to create 1 rle with old method : 0.006975650787353516 time for calcul the mask position with numpy : 0.029503345489501953 nb_pixel_total : 49004 time to create 1 rle with old method : 0.05864119529724121 time for calcul the mask position with numpy : 0.029345989227294922 nb_pixel_total : 24568 time to create 1 rle with old method : 0.027513504028320312 time for calcul the mask position with numpy : 0.029286861419677734 nb_pixel_total : 25517 time to create 1 rle with old method : 0.029206037521362305 time for calcul the mask position with numpy : 0.029596805572509766 nb_pixel_total : 53654 time to create 1 rle with old method : 0.05955219268798828 time for calcul the mask position with numpy : 0.029238462448120117 nb_pixel_total : 2296 time to create 1 rle with old method : 0.002637624740600586 time for calcul the mask position with numpy : 0.02940654754638672 nb_pixel_total : 15516 time to create 1 rle with old method : 0.017438173294067383 time for calcul the mask position with numpy : 0.029294490814208984 nb_pixel_total : 27692 time to create 1 rle with old method : 0.03472113609313965 time for calcul the mask position with numpy : 0.029217004776000977 nb_pixel_total : 34591 time to create 1 rle with old method : 0.03864145278930664 time for calcul the mask position with numpy : 0.030757427215576172 nb_pixel_total : 72103 time to create 1 rle with old method : 0.08047842979431152 time for calcul the mask position with numpy : 0.029567480087280273 nb_pixel_total : 64606 time to create 1 rle with old method : 0.07179617881774902 time for calcul the mask position with numpy : 0.029306411743164062 nb_pixel_total : 4637 time to create 1 rle with old method : 0.00529026985168457 create new chi : 5.58990740776062 time to delete rle : 0.010064840316772461 batch 1 Loaded 137 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 30450 TO DO : save crop sub photo not yet done ! save time : 2.808842420578003 nb_obj : 29 nb_hashtags : 3 time to prepare the origin masks : 3.4694302082061768 time for calcul the mask position with numpy : 0.36720848083496094 nb_pixel_total : 6339919 time to create 1 rle with new method : 1.1872103214263916 time for calcul the mask position with numpy : 0.029451847076416016 nb_pixel_total : 8985 time to create 1 rle with old method : 0.009775876998901367 time for calcul the mask position with numpy : 0.029530048370361328 nb_pixel_total : 10345 time to create 1 rle with old method : 0.011507511138916016 time for calcul the mask position with numpy : 0.02987837791442871 nb_pixel_total : 33967 time to create 1 rle with old method : 0.03725576400756836 time for calcul the mask position with numpy : 0.029755830764770508 nb_pixel_total : 19348 time to create 1 rle with old method : 0.021127700805664062 time for calcul the mask position with numpy : 0.03047633171081543 nb_pixel_total : 94512 time to create 1 rle with old method : 0.10280418395996094 time for calcul the mask position with numpy : 0.029720306396484375 nb_pixel_total : 8965 time to create 1 rle with old method : 0.012130498886108398 time for calcul the mask position with numpy : 0.02933025360107422 nb_pixel_total : 20834 time to create 1 rle with old method : 0.022325515747070312 time for calcul the mask position with numpy : 0.02917027473449707 nb_pixel_total : 11200 time to create 1 rle with old method : 0.012115001678466797 time for calcul the mask position with numpy : 0.029882192611694336 nb_pixel_total : 47133 time to create 1 rle with old method : 0.05274081230163574 time for calcul the mask position with numpy : 0.029854536056518555 nb_pixel_total : 73017 time to create 1 rle with old method : 0.08025979995727539 time for calcul the mask position with numpy : 0.029790639877319336 nb_pixel_total : 23880 time to create 1 rle with old method : 0.02669548988342285 time for calcul the mask position with numpy : 0.029457569122314453 nb_pixel_total : 43312 time to create 1 rle with old method : 0.04836559295654297 time for calcul the mask position with numpy : 0.029535770416259766 nb_pixel_total : 6209 time to create 1 rle with old method : 0.0072705745697021484 time for calcul the mask position with numpy : 0.02984452247619629 nb_pixel_total : 27860 time to create 1 rle with old method : 0.03365635871887207 time for calcul the mask position with numpy : 0.02970099449157715 nb_pixel_total : 13552 time to create 1 rle with old method : 0.014784097671508789 time for calcul the mask position with numpy : 0.029202938079833984 nb_pixel_total : 8019 time to create 1 rle with old method : 0.009153366088867188 time for calcul the mask position with numpy : 0.02924656867980957 nb_pixel_total : 26681 time to create 1 rle with old method : 0.02952742576599121 time for calcul the mask position with numpy : 0.02941131591796875 nb_pixel_total : 30524 time to create 1 rle with old method : 0.03396749496459961 time for calcul the mask position with numpy : 0.028582096099853516 nb_pixel_total : 3670 time to create 1 rle with old method : 0.004172801971435547 time for calcul the mask position with numpy : 0.02923727035522461 nb_pixel_total : 36638 time to create 1 rle with old method : 0.03994393348693848 time for calcul the mask position with numpy : 0.028766155242919922 nb_pixel_total : 32070 time to create 1 rle with old method : 0.03511762619018555 time for calcul the mask position with numpy : 0.028037071228027344 nb_pixel_total : 6023 time to create 1 rle with old method : 0.006882905960083008 time for calcul the mask position with numpy : 0.028201580047607422 nb_pixel_total : 5564 time to create 1 rle with old method : 0.006375551223754883 time for calcul the mask position with numpy : 0.029270648956298828 nb_pixel_total : 35252 time to create 1 rle with old method : 0.04161405563354492 time for calcul the mask position with numpy : 0.029625892639160156 nb_pixel_total : 15335 time to create 1 rle with old method : 0.017292499542236328 time for calcul the mask position with numpy : 0.029210329055786133 nb_pixel_total : 8088 time to create 1 rle with old method : 0.00907754898071289 time for calcul the mask position with numpy : 0.02910757064819336 nb_pixel_total : 16912 time to create 1 rle with old method : 0.019051551818847656 time for calcul the mask position with numpy : 0.02921915054321289 nb_pixel_total : 24750 time to create 1 rle with old method : 0.02739262580871582 time for calcul the mask position with numpy : 0.02925419807434082 nb_pixel_total : 17676 time to create 1 rle with old method : 0.01946544647216797 create new chi : 3.2398557662963867 time to delete rle : 0.004808187484741211 batch 1 Loaded 59 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++Number RLEs to save : 14188 TO DO : save crop sub photo not yet done ! save time : 2.3891239166259766 nb_obj : 21 nb_hashtags : 3 time to prepare the origin masks : 7.237743377685547 time for calcul the mask position with numpy : 0.8139641284942627 nb_pixel_total : 6146076 time to create 1 rle with new method : 0.6624758243560791 time for calcul the mask position with numpy : 0.046552419662475586 nb_pixel_total : 25537 time to create 1 rle with old method : 0.02964186668395996 time for calcul the mask position with numpy : 0.028392314910888672 nb_pixel_total : 39734 time to create 1 rle with old method : 0.043560028076171875 time for calcul the mask position with numpy : 0.02970290184020996 nb_pixel_total : 9016 time to create 1 rle with old method : 0.009923458099365234 time for calcul the mask position with numpy : 0.027625322341918945 nb_pixel_total : 71822 time to create 1 rle with old method : 0.07873129844665527 time for calcul the mask position with numpy : 0.0316462516784668 nb_pixel_total : 39215 time to create 1 rle with old method : 0.042363643646240234 time for calcul the mask position with numpy : 0.0303342342376709 nb_pixel_total : 19495 time to create 1 rle with old method : 0.02602529525756836 time for calcul the mask position with numpy : 0.03016042709350586 nb_pixel_total : 21159 time to create 1 rle with old method : 0.022233247756958008 time for calcul the mask position with numpy : 0.03383493423461914 nb_pixel_total : 399810 time to create 1 rle with new method : 0.43612003326416016 time for calcul the mask position with numpy : 0.02678370475769043 nb_pixel_total : 4560 time to create 1 rle with old method : 0.005173444747924805 time for calcul the mask position with numpy : 0.02605295181274414 nb_pixel_total : 16606 time to create 1 rle with old method : 0.01865696907043457 time for calcul the mask position with numpy : 0.024812698364257812 nb_pixel_total : 13959 time to create 1 rle with old method : 0.017203569412231445 time for calcul the mask position with numpy : 0.02373981475830078 nb_pixel_total : 43723 time to create 1 rle with old method : 0.0478363037109375 time for calcul the mask position with numpy : 0.023458480834960938 nb_pixel_total : 57096 time to create 1 rle with old method : 0.06332182884216309 time for calcul the mask position with numpy : 0.025713682174682617 nb_pixel_total : 26079 time to create 1 rle with old method : 0.03331804275512695 time for calcul the mask position with numpy : 0.04548072814941406 nb_pixel_total : 12819 time to create 1 rle with old method : 0.021679401397705078 time for calcul the mask position with numpy : 0.03957819938659668 nb_pixel_total : 5491 time to create 1 rle with old method : 0.006020784378051758 time for calcul the mask position with numpy : 0.033571481704711914 nb_pixel_total : 1510 time to create 1 rle with old method : 0.0017142295837402344 time for calcul the mask position with numpy : 0.02549123764038086 nb_pixel_total : 20848 time to create 1 rle with old method : 0.02334880828857422 time for calcul the mask position with numpy : 0.02205801010131836 nb_pixel_total : 44768 time to create 1 rle with old method : 0.050174713134765625 time for calcul the mask position with numpy : 0.02351069450378418 nb_pixel_total : 21232 time to create 1 rle with old method : 0.024817228317260742 time for calcul the mask position with numpy : 0.02201700210571289 nb_pixel_total : 9685 time to create 1 rle with old method : 0.010892391204833984 create new chi : 3.1807754039764404 time to delete rle : 0.001847982406616211 batch 1 Loaded 43 chid ids of type : 3594 +++++++++++++++++++++++++Number RLEs to save : 12184 TO DO : save crop sub photo not yet done ! save time : 1.0306520462036133 nb_obj : 54 nb_hashtags : 5 time to prepare the origin masks : 4.465372085571289 time for calcul the mask position with numpy : 0.78322434425354 nb_pixel_total : 5180410 time to create 1 rle with new method : 0.9488768577575684 time for calcul the mask position with numpy : 0.029240846633911133 nb_pixel_total : 5365 time to create 1 rle with old method : 0.006012678146362305 time for calcul the mask position with numpy : 0.02877330780029297 nb_pixel_total : 3022 time to create 1 rle with old method : 0.0035881996154785156 time for calcul the mask position with numpy : 0.02926492691040039 nb_pixel_total : 28051 time to create 1 rle with old method : 0.03125953674316406 time for calcul the mask position with numpy : 0.029732227325439453 nb_pixel_total : 11751 time to create 1 rle with old method : 0.013284683227539062 time for calcul the mask position with numpy : 0.02967071533203125 nb_pixel_total : 66015 time to create 1 rle with old method : 0.07183241844177246 time for calcul the mask position with numpy : 0.02945852279663086 nb_pixel_total : 19481 time to create 1 rle with old method : 0.022261619567871094 time for calcul the mask position with numpy : 0.02896714210510254 nb_pixel_total : 6088 time to create 1 rle with old method : 0.006704568862915039 time for calcul the mask position with numpy : 0.028885364532470703 nb_pixel_total : 54422 time to create 1 rle with old method : 0.05898618698120117 time for calcul the mask position with numpy : 0.028589725494384766 nb_pixel_total : 16155 time to create 1 rle with old method : 0.018358945846557617 time for calcul the mask position with numpy : 0.030287742614746094 nb_pixel_total : 37637 time to create 1 rle with old method : 0.04244065284729004 time for calcul the mask position with numpy : 0.02988409996032715 nb_pixel_total : 34960 time to create 1 rle with old method : 0.03942561149597168 time for calcul the mask position with numpy : 0.030371904373168945 nb_pixel_total : 3516 time to create 1 rle with old method : 0.004267215728759766 time for calcul the mask position with numpy : 0.030515432357788086 nb_pixel_total : 8839 time to create 1 rle with old method : 0.010214090347290039 time for calcul the mask position with numpy : 0.029433488845825195 nb_pixel_total : 22293 time to create 1 rle with old method : 0.023453235626220703 time for calcul the mask position with numpy : 0.028172016143798828 nb_pixel_total : 33783 time to create 1 rle with old method : 0.036887407302856445 time for calcul the mask position with numpy : 0.029196977615356445 nb_pixel_total : 10670 time to create 1 rle with old method : 0.012108564376831055 time for calcul the mask position with numpy : 0.02929234504699707 nb_pixel_total : 62460 time to create 1 rle with old method : 0.0675361156463623 time for calcul the mask position with numpy : 0.027950048446655273 nb_pixel_total : 59537 time to create 1 rle with old method : 0.06425976753234863 time for calcul the mask position with numpy : 0.028101205825805664 nb_pixel_total : 27873 time to create 1 rle with old method : 0.03031301498413086 time for calcul the mask position with numpy : 0.02880716323852539 nb_pixel_total : 137560 time to create 1 rle with old method : 0.15363240242004395 time for calcul the mask position with numpy : 0.02837061882019043 nb_pixel_total : 31922 time to create 1 rle with old method : 0.03547334671020508 time for calcul the mask position with numpy : 0.029274940490722656 nb_pixel_total : 32919 time to create 1 rle with old method : 0.036866188049316406 time for calcul the mask position with numpy : 0.030895233154296875 nb_pixel_total : 43605 time to create 1 rle with old method : 0.04853940010070801 time for calcul the mask position with numpy : 0.029419660568237305 nb_pixel_total : 28392 time to create 1 rle with old method : 0.031429290771484375 time for calcul the mask position with numpy : 0.02833843231201172 nb_pixel_total : 26536 time to create 1 rle with old method : 0.029271364212036133 time for calcul the mask position with numpy : 0.028488874435424805 nb_pixel_total : 43707 time to create 1 rle with old method : 0.04776501655578613 time for calcul the mask position with numpy : 0.028020381927490234 nb_pixel_total : 13423 time to create 1 rle with old method : 0.015118598937988281 time for calcul the mask position with numpy : 0.030630111694335938 nb_pixel_total : 31175 time to create 1 rle with old method : 0.03428912162780762 time for calcul the mask position with numpy : 0.030164480209350586 nb_pixel_total : 6888 time to create 1 rle with old method : 0.0076656341552734375 time for calcul the mask position with numpy : 0.028733253479003906 nb_pixel_total : 48391 time to create 1 rle with old method : 0.05331730842590332 time for calcul the mask position with numpy : 0.028926849365234375 nb_pixel_total : 94433 time to create 1 rle with old method : 0.10389518737792969 time for calcul the mask position with numpy : 0.028284311294555664 nb_pixel_total : 17134 time to create 1 rle with old method : 0.01886606216430664 time for calcul the mask position with numpy : 0.029065608978271484 nb_pixel_total : 63730 time to create 1 rle with old method : 0.06939888000488281 time for calcul the mask position with numpy : 0.028930187225341797 nb_pixel_total : 128406 time to create 1 rle with old method : 0.1396803855895996 time for calcul the mask position with numpy : 0.02762913703918457 nb_pixel_total : 12690 time to create 1 rle with old method : 0.014170169830322266 time for calcul the mask position with numpy : 0.028775691986083984 nb_pixel_total : 77172 time to create 1 rle with old method : 0.08446431159973145 time for calcul the mask position with numpy : 0.028752565383911133 nb_pixel_total : 14233 time to create 1 rle with old method : 0.016059398651123047 time for calcul the mask position with numpy : 0.02929091453552246 nb_pixel_total : 61669 time to create 1 rle with old method : 0.0696260929107666 time for calcul the mask position with numpy : 0.028983354568481445 nb_pixel_total : 36851 time to create 1 rle with old method : 0.04154777526855469 time for calcul the mask position with numpy : 0.029353857040405273 nb_pixel_total : 51958 time to create 1 rle with old method : 0.057970285415649414 time for calcul the mask position with numpy : 0.028946876525878906 nb_pixel_total : 19178 time to create 1 rle with old method : 0.026696205139160156 time for calcul the mask position with numpy : 0.030129194259643555 nb_pixel_total : 30558 time to create 1 rle with old method : 0.03384661674499512 time for calcul the mask position with numpy : 0.031264305114746094 nb_pixel_total : 21979 time to create 1 rle with old method : 0.03660988807678223 time for calcul the mask position with numpy : 0.03471517562866211 nb_pixel_total : 3362 time to create 1 rle with old method : 0.00384521484375 time for calcul the mask position with numpy : 0.029350996017456055 nb_pixel_total : 77023 time to create 1 rle with old method : 0.08779191970825195 time for calcul the mask position with numpy : 0.029035091400146484 nb_pixel_total : 20469 time to create 1 rle with old method : 0.022929906845092773 time for calcul the mask position with numpy : 0.029179096221923828 nb_pixel_total : 22890 time to create 1 rle with old method : 0.02807450294494629 time for calcul the mask position with numpy : 0.03391289710998535 nb_pixel_total : 37916 time to create 1 rle with old method : 0.045413970947265625 time for calcul the mask position with numpy : 0.028589963912963867 nb_pixel_total : 19738 time to create 1 rle with old method : 0.02337646484375 time for calcul the mask position with numpy : 0.03561115264892578 nb_pixel_total : 37208 time to create 1 rle with old method : 0.05291581153869629 time for calcul the mask position with numpy : 0.030641794204711914 nb_pixel_total : 17317 time to create 1 rle with old method : 0.019062280654907227 time for calcul the mask position with numpy : 0.028568267822265625 nb_pixel_total : 29328 time to create 1 rle with old method : 0.032308101654052734 time for calcul the mask position with numpy : 0.02877044677734375 nb_pixel_total : 6499 time to create 1 rle with old method : 0.00720524787902832 time for calcul the mask position with numpy : 0.028423547744750977 nb_pixel_total : 11653 time to create 1 rle with old method : 0.01700568199157715 create new chi : 5.4759931564331055 time to delete rle : 0.008708477020263672 batch 1 Loaded 109 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 30046 TO DO : save crop sub photo not yet done ! save time : 3.700188636779785 nb_obj : 55 nb_hashtags : 2 time to prepare the origin masks : 4.2849695682525635 time for calcul the mask position with numpy : 0.49460792541503906 nb_pixel_total : 5466372 time to create 1 rle with new method : 0.819199800491333 time for calcul the mask position with numpy : 0.02924633026123047 nb_pixel_total : 9074 time to create 1 rle with old method : 0.010194063186645508 time for calcul the mask position with numpy : 0.029081344604492188 nb_pixel_total : 9493 time to create 1 rle with old method : 0.010708093643188477 time for calcul the mask position with numpy : 0.0295102596282959 nb_pixel_total : 21382 time to create 1 rle with old method : 0.02900242805480957 time for calcul the mask position with numpy : 0.029456615447998047 nb_pixel_total : 12461 time to create 1 rle with old method : 0.013851165771484375 time for calcul the mask position with numpy : 0.029471158981323242 nb_pixel_total : 17739 time to create 1 rle with old method : 0.02035355567932129 time for calcul the mask position with numpy : 0.031044721603393555 nb_pixel_total : 65821 time to create 1 rle with old method : 0.07232522964477539 time for calcul the mask position with numpy : 0.02776503562927246 nb_pixel_total : 23388 time to create 1 rle with old method : 0.0240628719329834 time for calcul the mask position with numpy : 0.028208017349243164 nb_pixel_total : 9778 time to create 1 rle with old method : 0.010640382766723633 time for calcul the mask position with numpy : 0.028708219528198242 nb_pixel_total : 39182 time to create 1 rle with old method : 0.04303550720214844 time for calcul the mask position with numpy : 0.02762913703918457 nb_pixel_total : 11727 time to create 1 rle with old method : 0.0123748779296875 time for calcul the mask position with numpy : 0.02740645408630371 nb_pixel_total : 13402 time to create 1 rle with old method : 0.014805078506469727 time for calcul the mask position with numpy : 0.029072046279907227 nb_pixel_total : 1762 time to create 1 rle with old method : 0.002358675003051758 time for calcul the mask position with numpy : 0.02911829948425293 nb_pixel_total : 46359 time to create 1 rle with old method : 0.051285505294799805 time for calcul the mask position with numpy : 0.030400514602661133 nb_pixel_total : 21335 time to create 1 rle with old method : 0.024083375930786133 time for calcul the mask position with numpy : 0.029804229736328125 nb_pixel_total : 10110 time to create 1 rle with old method : 0.012483835220336914 time for calcul the mask position with numpy : 0.0325314998626709 nb_pixel_total : 56110 time to create 1 rle with old method : 0.06249737739562988 time for calcul the mask position with numpy : 0.03091716766357422 nb_pixel_total : 24246 time to create 1 rle with old method : 0.026883840560913086 time for calcul the mask position with numpy : 0.028413057327270508 nb_pixel_total : 35938 time to create 1 rle with old method : 0.04463839530944824 time for calcul the mask position with numpy : 0.03209376335144043 nb_pixel_total : 15790 time to create 1 rle with old method : 0.017746925354003906 time for calcul the mask position with numpy : 0.02949380874633789 nb_pixel_total : 37372 time to create 1 rle with old method : 0.04186367988586426 time for calcul the mask position with numpy : 0.028884172439575195 nb_pixel_total : 8636 time to create 1 rle with old method : 0.009649038314819336 time for calcul the mask position with numpy : 0.028778076171875 nb_pixel_total : 2807 time to create 1 rle with old method : 0.003145456314086914 time for calcul the mask position with numpy : 0.02913665771484375 nb_pixel_total : 112459 time to create 1 rle with old method : 0.13301372528076172 time for calcul the mask position with numpy : 0.028179168701171875 nb_pixel_total : 88412 time to create 1 rle with old method : 0.09277749061584473 time for calcul the mask position with numpy : 0.028267621994018555 nb_pixel_total : 30194 time to create 1 rle with old method : 0.03231239318847656 time for calcul the mask position with numpy : 0.027593612670898438 nb_pixel_total : 37108 time to create 1 rle with old method : 0.039792537689208984 time for calcul the mask position with numpy : 0.029187679290771484 nb_pixel_total : 23741 time to create 1 rle with old method : 0.027053356170654297 time for calcul the mask position with numpy : 0.029647111892700195 nb_pixel_total : 21548 time to create 1 rle with old method : 0.026950836181640625 time for calcul the mask position with numpy : 0.03030705451965332 nb_pixel_total : 31799 time to create 1 rle with old method : 0.047606706619262695 time for calcul the mask position with numpy : 0.032570838928222656 nb_pixel_total : 32647 time to create 1 rle with old method : 0.03750896453857422 time for calcul the mask position with numpy : 0.03309512138366699 nb_pixel_total : 33537 time to create 1 rle with old method : 0.04538917541503906 time for calcul the mask position with numpy : 0.031111955642700195 nb_pixel_total : 7619 time to create 1 rle with old method : 0.008504867553710938 time for calcul the mask position with numpy : 0.029203414916992188 nb_pixel_total : 64119 time to create 1 rle with old method : 0.07104063034057617 time for calcul the mask position with numpy : 0.029015064239501953 nb_pixel_total : 7102 time to create 1 rle with old method : 0.007962465286254883 time for calcul the mask position with numpy : 0.02892160415649414 nb_pixel_total : 53265 time to create 1 rle with old method : 0.059392690658569336 time for calcul the mask position with numpy : 0.029378414154052734 nb_pixel_total : 43726 time to create 1 rle with old method : 0.048696041107177734 time for calcul the mask position with numpy : 0.02928614616394043 nb_pixel_total : 25306 time to create 1 rle with old method : 0.02800750732421875 time for calcul the mask position with numpy : 0.02909708023071289 nb_pixel_total : 8325 time to create 1 rle with old method : 0.009374141693115234 time for calcul the mask position with numpy : 0.029852867126464844 nb_pixel_total : 137429 time to create 1 rle with old method : 0.15173816680908203 time for calcul the mask position with numpy : 0.029376983642578125 nb_pixel_total : 16300 time to create 1 rle with old method : 0.018215656280517578 time for calcul the mask position with numpy : 0.02924799919128418 nb_pixel_total : 39732 time to create 1 rle with old method : 0.042542219161987305 time for calcul the mask position with numpy : 0.028961658477783203 nb_pixel_total : 20153 time to create 1 rle with old method : 0.022188425064086914 time for calcul the mask position with numpy : 0.0291445255279541 nb_pixel_total : 13929 time to create 1 rle with old method : 0.015158653259277344 time for calcul the mask position with numpy : 0.028612613677978516 nb_pixel_total : 28973 time to create 1 rle with old method : 0.031134843826293945 time for calcul the mask position with numpy : 0.029301166534423828 nb_pixel_total : 31493 time to create 1 rle with old method : 0.03432416915893555 time for calcul the mask position with numpy : 0.02903461456298828 nb_pixel_total : 10325 time to create 1 rle with old method : 0.01191258430480957 time for calcul the mask position with numpy : 0.0334477424621582 nb_pixel_total : 6868 time to create 1 rle with old method : 0.00806879997253418 time for calcul the mask position with numpy : 0.029651403427124023 nb_pixel_total : 10240 time to create 1 rle with old method : 0.011570215225219727 time for calcul the mask position with numpy : 0.03146791458129883 nb_pixel_total : 33272 time to create 1 rle with old method : 0.051820993423461914 time for calcul the mask position with numpy : 0.03019261360168457 nb_pixel_total : 17523 time to create 1 rle with old method : 0.019759654998779297 time for calcul the mask position with numpy : 0.030841350555419922 nb_pixel_total : 33621 time to create 1 rle with old method : 0.04233503341674805 time for calcul the mask position with numpy : 0.03460073471069336 nb_pixel_total : 25613 time to create 1 rle with old method : 0.0424962043762207 time for calcul the mask position with numpy : 0.031227827072143555 nb_pixel_total : 12668 time to create 1 rle with old method : 0.014391660690307617 time for calcul the mask position with numpy : 0.030166149139404297 nb_pixel_total : 9229 time to create 1 rle with old method : 0.010401725769042969 time for calcul the mask position with numpy : 0.0299227237701416 nb_pixel_total : 21681 time to create 1 rle with old method : 0.023550748825073242 create new chi : 4.816333293914795 time to delete rle : 0.006815910339355469 batch 1 Loaded 111 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 25749 TO DO : save crop sub photo not yet done ! save time : 3.028808832168579 nb_obj : 47 nb_hashtags : 5 time to prepare the origin masks : 4.604228496551514 time for calcul the mask position with numpy : 0.6191084384918213 nb_pixel_total : 5465336 time to create 1 rle with new method : 0.6501257419586182 time for calcul the mask position with numpy : 0.03023505210876465 nb_pixel_total : 19351 time to create 1 rle with old method : 0.02156352996826172 time for calcul the mask position with numpy : 0.03032207489013672 nb_pixel_total : 18056 time to create 1 rle with old method : 0.020150184631347656 time for calcul the mask position with numpy : 0.03036355972290039 nb_pixel_total : 43514 time to create 1 rle with old method : 0.04878807067871094 time for calcul the mask position with numpy : 0.037763118743896484 nb_pixel_total : 33484 time to create 1 rle with old method : 0.046442508697509766 time for calcul the mask position with numpy : 0.034684181213378906 nb_pixel_total : 22752 time to create 1 rle with old method : 0.0254518985748291 time for calcul the mask position with numpy : 0.030450820922851562 nb_pixel_total : 15877 time to create 1 rle with old method : 0.01789689064025879 time for calcul the mask position with numpy : 0.033423662185668945 nb_pixel_total : 20136 time to create 1 rle with old method : 0.03075551986694336 time for calcul the mask position with numpy : 0.03864431381225586 nb_pixel_total : 19989 time to create 1 rle with old method : 0.03018808364868164 time for calcul the mask position with numpy : 0.03606867790222168 nb_pixel_total : 19189 time to create 1 rle with old method : 0.028689146041870117 time for calcul the mask position with numpy : 0.032794952392578125 nb_pixel_total : 9462 time to create 1 rle with old method : 0.010605096817016602 time for calcul the mask position with numpy : 0.030748605728149414 nb_pixel_total : 24791 time to create 1 rle with old method : 0.028116703033447266 time for calcul the mask position with numpy : 0.030222177505493164 nb_pixel_total : 59840 time to create 1 rle with old method : 0.06833839416503906 time for calcul the mask position with numpy : 0.03136754035949707 nb_pixel_total : 8777 time to create 1 rle with old method : 0.010003089904785156 time for calcul the mask position with numpy : 0.029880046844482422 nb_pixel_total : 24193 time to create 1 rle with old method : 0.027479171752929688 time for calcul the mask position with numpy : 0.029781579971313477 nb_pixel_total : 9822 time to create 1 rle with old method : 0.011373281478881836 time for calcul the mask position with numpy : 0.03539156913757324 nb_pixel_total : 34751 time to create 1 rle with old method : 0.03989362716674805 time for calcul the mask position with numpy : 0.029851675033569336 nb_pixel_total : 24691 time to create 1 rle with old method : 0.028285503387451172 time for calcul the mask position with numpy : 0.030491352081298828 nb_pixel_total : 112681 time to create 1 rle with old method : 0.1253206729888916 time for calcul the mask position with numpy : 0.030422687530517578 nb_pixel_total : 24075 time to create 1 rle with old method : 0.02730703353881836 time for calcul the mask position with numpy : 0.030478954315185547 nb_pixel_total : 29043 time to create 1 rle with old method : 0.03270316123962402 time for calcul the mask position with numpy : 0.030540943145751953 nb_pixel_total : 38930 time to create 1 rle with old method : 0.04373502731323242 time for calcul the mask position with numpy : 0.030630826950073242 nb_pixel_total : 58517 time to create 1 rle with old method : 0.06624770164489746 time for calcul the mask position with numpy : 0.030846118927001953 nb_pixel_total : 67090 time to create 1 rle with old method : 0.07659602165222168 time for calcul the mask position with numpy : 0.028464078903198242 nb_pixel_total : 26628 time to create 1 rle with old method : 0.031316518783569336 time for calcul the mask position with numpy : 0.031128406524658203 nb_pixel_total : 94740 time to create 1 rle with old method : 0.10434746742248535 time for calcul the mask position with numpy : 0.028614044189453125 nb_pixel_total : 18708 time to create 1 rle with old method : 0.02067875862121582 time for calcul the mask position with numpy : 0.02868819236755371 nb_pixel_total : 9731 time to create 1 rle with old method : 0.010954618453979492 time for calcul the mask position with numpy : 0.028743743896484375 nb_pixel_total : 12694 time to create 1 rle with old method : 0.014129161834716797 time for calcul the mask position with numpy : 0.028882741928100586 nb_pixel_total : 71182 time to create 1 rle with old method : 0.0780184268951416 time for calcul the mask position with numpy : 0.028682231903076172 nb_pixel_total : 76259 time to create 1 rle with old method : 0.08379697799682617 time for calcul the mask position with numpy : 0.029005050659179688 nb_pixel_total : 65302 time to create 1 rle with old method : 0.07213401794433594 time for calcul the mask position with numpy : 0.02898716926574707 nb_pixel_total : 9404 time to create 1 rle with old method : 0.010469198226928711 time for calcul the mask position with numpy : 0.028746604919433594 nb_pixel_total : 9058 time to create 1 rle with old method : 0.010094404220581055 time for calcul the mask position with numpy : 0.028705596923828125 nb_pixel_total : 19727 time to create 1 rle with old method : 0.021439313888549805 time for calcul the mask position with numpy : 0.028307437896728516 nb_pixel_total : 54964 time to create 1 rle with old method : 0.0607762336730957 time for calcul the mask position with numpy : 0.029145240783691406 nb_pixel_total : 68065 time to create 1 rle with old method : 0.07581949234008789 time for calcul the mask position with numpy : 0.029115676879882812 nb_pixel_total : 37743 time to create 1 rle with old method : 0.04155135154724121 time for calcul the mask position with numpy : 0.029021024703979492 nb_pixel_total : 24697 time to create 1 rle with old method : 0.029067039489746094 time for calcul the mask position with numpy : 0.028407573699951172 nb_pixel_total : 29327 time to create 1 rle with old method : 0.03226041793823242 time for calcul the mask position with numpy : 0.029375076293945312 nb_pixel_total : 7610 time to create 1 rle with old method : 0.008815526962280273 time for calcul the mask position with numpy : 0.02943253517150879 nb_pixel_total : 42366 time to create 1 rle with old method : 0.04845857620239258 time for calcul the mask position with numpy : 0.028992652893066406 nb_pixel_total : 82688 time to create 1 rle with old method : 0.08998823165893555 time for calcul the mask position with numpy : 0.02707052230834961 nb_pixel_total : 26179 time to create 1 rle with old method : 0.02831292152404785 time for calcul the mask position with numpy : 0.031572818756103516 nb_pixel_total : 25709 time to create 1 rle with old method : 0.04208874702453613 time for calcul the mask position with numpy : 0.030896663665771484 nb_pixel_total : 18035 time to create 1 rle with old method : 0.01992487907409668 time for calcul the mask position with numpy : 0.02885580062866211 nb_pixel_total : 9556 time to create 1 rle with old method : 0.010713577270507812 time for calcul the mask position with numpy : 0.028461933135986328 nb_pixel_total : 5521 time to create 1 rle with old method : 0.006110429763793945 create new chi : 4.591821670532227 time to delete rle : 0.003904581069946289 batch 1 Loaded 95 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 25137 TO DO : save crop sub photo not yet done ! save time : 2.0492923259735107 map_output_result : {1349157808: (0.0, 'Should be the crop_list due to order', 0), 1349157804: (0.0, 'Should be the crop_list due to order', 0), 1349157800: (0.0, 'Should be the crop_list due to order', 0), 1349157797: (0.0, 'Should be the crop_list due to order', 0), 1349157677: (0.0, 'Should be the crop_list due to order', 0), 1349157619: (0.0, 'Should be the crop_list due to order', 0), 1349157518: (0.0, 'Should be the crop_list due to order', 0), 1349157488: (0.0, 'Should be the crop_list due to order', 0), 1349012647: (0.0, 'Should be the crop_list due to order', 0), 1349012579: (0.0, 'Should be the crop_list due to order', 0), 1349012558: (0.0, 'Should be the crop_list due to order', 0), 1349012555: (0.0, 'Should be the crop_list due to order', 0), 1349012487: (0.0, 'Should be the crop_list due to order', 0), 1349012409: (0.0, 'Should be the crop_list due to order', 0), 1349012356: (0.0, 'Should be the crop_list due to order', 0), 1349012351: (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 [1349157808, 1349157804, 1349157800, 1349157797, 1349157677, 1349157619, 1349157518, 1349157488, 1349012647, 1349012579, 1349012558, 1349012555, 1349012487, 1349012409, 1349012356, 1349012351] Looping around the photos to save general results len do output : 16 /1349157808.Didn't retrieve data . /1349157804.Didn't retrieve data . /1349157800.Didn't retrieve data . /1349157797.Didn't retrieve data . /1349157677.Didn't retrieve data . /1349157619.Didn't retrieve data . /1349157518.Didn't retrieve data . /1349157488.Didn't retrieve data . /1349012647.Didn't retrieve data . /1349012579.Didn't retrieve data . /1349012558.Didn't retrieve data . /1349012555.Didn't retrieve data . /1349012487.Didn't retrieve data . /1349012409.Didn't retrieve data . /1349012356.Didn't retrieve data . /1349012351.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, '2711235') ('3318', '21930834', '1349157808', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157804', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157800', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157797', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157677', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157619', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157518', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157488', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012647', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012579', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012558', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012555', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012487', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012409', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012356', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012351', None, None, None, None, None, '2711235') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 48 time used for this insertion : 0.016129732131958008 save_final save missing photos in datou_result : time spend for datou_step_exec : 192.50648021697998 time spend to save output : 0.0266420841217041 total time spend for step 3 : 192.53312230110168 step4:ventilate_hashtags_in_portfolio Tue Apr 1 02:48:11 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 21930834 get user id for portfolio 21930834 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`=21930834 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pehd','flou','papier','environnement','mal_croppe','autre','carton','pet_clair','metal','pet_fonce','background')) 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`=21930834 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pehd','flou','papier','environnement','mal_croppe','autre','carton','pet_clair','metal','pet_fonce','background')) AND mptpi.`min_score`=0.5 To do To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21930834 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pehd','flou','papier','environnement','mal_croppe','autre','carton','pet_clair','metal','pet_fonce','background')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/21931486,21931487,21931488,21931489,21931490,21931491,21931492,21931493,21931494,21931495,21931496?tags=pehd,flou,papier,environnement,mal_croppe,autre,carton,pet_clair,metal,pet_fonce,background Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1349157808, 1349157804, 1349157800, 1349157797, 1349157677, 1349157619, 1349157518, 1349157488, 1349012647, 1349012579, 1349012558, 1349012555, 1349012487, 1349012409, 1349012356, 1349012351] Looping around the photos to save general results len do output : 1 /21930834. 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, '2711235') ('3318', '21930834', '1349157808', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157804', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157800', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157797', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157677', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157619', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157518', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157488', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012647', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012579', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012558', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012555', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012487', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012409', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012356', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012351', None, None, None, None, None, '2711235') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 17 time used for this insertion : 0.017585039138793945 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.2999954223632812 time spend to save output : 0.01800060272216797 total time spend for step 4 : 1.3179960250854492 step5:final Tue Apr 1 02:48:13 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 : {1349157808: ('0.22413743184629167',), 1349157804: ('0.22413743184629167',), 1349157800: ('0.22413743184629167',), 1349157797: ('0.22413743184629167',), 1349157677: ('0.22413743184629167',), 1349157619: ('0.22413743184629167',), 1349157518: ('0.22413743184629167',), 1349157488: ('0.22413743184629167',), 1349012647: ('0.22413743184629167',), 1349012579: ('0.22413743184629167',), 1349012558: ('0.22413743184629167',), 1349012555: ('0.22413743184629167',), 1349012487: ('0.22413743184629167',), 1349012409: ('0.22413743184629167',), 1349012356: ('0.22413743184629167',), 1349012351: ('0.22413743184629167',)} new output for save of step final : {1349157808: ('0.22413743184629167',), 1349157804: ('0.22413743184629167',), 1349157800: ('0.22413743184629167',), 1349157797: ('0.22413743184629167',), 1349157677: ('0.22413743184629167',), 1349157619: ('0.22413743184629167',), 1349157518: ('0.22413743184629167',), 1349157488: ('0.22413743184629167',), 1349012647: ('0.22413743184629167',), 1349012579: ('0.22413743184629167',), 1349012558: ('0.22413743184629167',), 1349012555: ('0.22413743184629167',), 1349012487: ('0.22413743184629167',), 1349012409: ('0.22413743184629167',), 1349012356: ('0.22413743184629167',), 1349012351: ('0.22413743184629167',)} [1349157808, 1349157804, 1349157800, 1349157797, 1349157677, 1349157619, 1349157518, 1349157488, 1349012647, 1349012579, 1349012558, 1349012555, 1349012487, 1349012409, 1349012356, 1349012351] Looping around the photos to save general results len do output : 16 /1349157808.Didn't retrieve data . /1349157804.Didn't retrieve data . /1349157800.Didn't retrieve data . /1349157797.Didn't retrieve data . /1349157677.Didn't retrieve data . /1349157619.Didn't retrieve data . /1349157518.Didn't retrieve data . /1349157488.Didn't retrieve data . /1349012647.Didn't retrieve data . /1349012579.Didn't retrieve data . /1349012558.Didn't retrieve data . /1349012555.Didn't retrieve data . /1349012487.Didn't retrieve data . /1349012409.Didn't retrieve data . /1349012356.Didn't retrieve data . /1349012351.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, '2711235') ('3318', '21930834', '1349157808', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157804', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157800', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157797', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157677', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157619', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157518', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157488', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012647', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012579', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012558', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012555', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012487', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012409', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012356', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012351', None, None, None, None, None, '2711235') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 48 time used for this insertion : 0.09567952156066895 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.22808122634887695 time spend to save output : 0.09931063652038574 total time spend for step 5 : 0.3273918628692627 step6:blur_detection Tue Apr 1 02:48:13 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e.jpg resize: (2160, 3264) 1349157808 -4.69705438042978 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47.jpg resize: (2160, 3264) 1349157804 -4.319816134674197 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b.jpg resize: (2160, 3264) 1349157800 -4.089615144478795 treat image : temp/1743467428_2444690_1349157797_4866fba02d00bbca5bcabb52743389df.jpg resize: (2160, 3264) 1349157797 -4.024990494423274 treat image : temp/1743467428_2444690_1349157677_fbb5e97de63dd226feffdf22501fed9a.jpg resize: (2160, 3264) 1349157677 -3.3758259597737066 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62.jpg resize: (2160, 3264) 1349157619 -5.751099315535441 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce.jpg resize: (2160, 3264) 1349157518 -4.460140428146166 treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d.jpg resize: (2160, 3264) 1349157488 -4.815111678886501 treat image : temp/1743467428_2444690_1349012647_f9779bbb754ac99a11043b575d6acb24.jpg resize: (2160, 3264) 1349012647 -3.6407625372070886 treat image : temp/1743467428_2444690_1349012579_3a25094eda35ea087119d8b41728042b.jpg resize: (2160, 3264) 1349012579 -3.472596526805101 treat image : temp/1743467428_2444690_1349012558_eb09958bc9444f29da4245e51b9cbb5d.jpg resize: (2160, 3264) 1349012558 -4.519187374035535 treat image : temp/1743467428_2444690_1349012555_b56fff58c907323845293e3e1d4bf0ce.jpg resize: (2160, 3264) 1349012555 -3.5775098253972555 treat image : temp/1743467428_2444690_1349012487_887ad76ad3e3b6ed97b56bb5e6076744.jpg resize: (2160, 3264) 1349012487 -3.3461741870340878 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8.jpg resize: (2160, 3264) 1349012409 -5.7034803572006725 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765.jpg resize: (2160, 3264) 1349012356 -4.396704748368035 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2.jpg resize: (2160, 3264) 1349012351 -5.460273956767052 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151465_0.png resize: (562, 306) 1349179286 -1.2373316649295183 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151503_0.png resize: (243, 268) 1349179287 -1.715758797772365 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151482_0.png resize: (140, 174) 1349179288 -2.4438941972201587 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151506_0.png resize: (237, 337) 1349179289 -2.824681326501111 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151471_0.png resize: (143, 309) 1349179290 -2.641999069723312 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151479_0.png resize: (149, 128) 1349179291 -2.4032170097671837 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151495_0.png resize: (131, 166) 1349179292 -2.2194347416572056 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151485_0.png resize: (218, 170) 1349179293 -1.525951557184402 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151466_0.png resize: (273, 205) 1349179294 -2.528048521202187 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151494_0.png resize: (133, 155) 1349179295 -1.6151497063955365 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151483_0.png resize: (105, 124) 1349179296 -0.1120532954905955 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151475_0.png resize: (137, 145) 1349179297 -2.623226793518589 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151499_0.png resize: (80, 391) 1349179298 -3.0136497802947293 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151468_0.png resize: (360, 425) 1349179299 -3.839325791146278 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151489_0.png resize: (285, 511) 1349179300 -3.3137668019485016 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151505_0.png resize: (228, 321) 1349179301 -3.12660572700126 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151498_0.png resize: (407, 243) 1349179302 -3.129140937723622 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151493_0.png resize: (114, 133) 1349179303 -2.323313647803076 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151469_0.png resize: (299, 299) 1349179304 -3.3880125355948656 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151477_0.png resize: (128, 212) 1349179305 -2.84429460605657 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151472_0.png resize: (150, 107) 1349179306 -1.827144574864629 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151486_0.png resize: (199, 233) 1349179307 -2.3899623349813544 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151492_0.png resize: (154, 217) 1349179308 -2.821394380988656 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151511_0.png resize: (192, 282) 1349179309 -3.107077893831027 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151504_0.png resize: (93, 180) 1349179310 -1.8749939079605518 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151470_0.png resize: (255, 252) 1349179311 -2.5569255988982724 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151490_0.png resize: (84, 129) 1349179312 -2.776595171745316 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151481_0.png resize: (369, 142) 1349179313 -3.043243871456397 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151473_0.png resize: (68, 139) 1349179314 -1.7462929066088007 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151509_0.png resize: (153, 151) 1349179315 -3.420424262570189 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151484_0.png resize: (370, 692) 1349179316 -3.7477013892309845 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151474_0.png resize: (196, 256) 1349179317 -3.009028913352695 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151491_0.png resize: (96, 139) 1349179318 -3.8484146937071144 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151512_0.png resize: (153, 143) 1349179319 -3.175472060692451 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151502_0.png resize: (96, 124) 1349179320 -2.031203093338606 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151467_0.png resize: (100, 154) 1349179321 -2.3831521594132328 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151487_0.png resize: (310, 262) 1349179322 -2.1531841988149107 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151496_0.png resize: (123, 240) 1349179323 -2.7941527969163977 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151488_0.png resize: (203, 295) 1349179324 -2.9501883246512604 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151480_0.png resize: (174, 227) 1349179325 -1.6119825782756796 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151513_0.png resize: (353, 308) 1349179326 -1.6766196166379472 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151574_0.png resize: (295, 415) 1349179327 -2.3041008981901037 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151562_0.png resize: (265, 250) 1349179328 -1.9900734973140075 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151532_0.png resize: (157, 165) 1349179329 -2.3077014283501773 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151546_0.png resize: (219, 142) 1349179330 -2.9776827563227792 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151573_0.png resize: (175, 126) 1349179331 -2.624536788824457 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151518_0.png resize: (96, 132) 1349179332 -0.4205801202350531 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151555_0.png resize: (102, 159) 1349179333 -2.9171497233344517 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151579_0.png resize: (79, 126) 1349179334 -2.399861822803229 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151559_0.png resize: (125, 145) 1349179335 -2.947820383629284 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151538_0.png resize: (62, 98) 1349179336 0.37946261074212956 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151539_0.png resize: (132, 97) 1349179337 -1.6753153536568703 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151517_0.png resize: (228, 160) 1349179338 -0.7964858049122037 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151575_0.png resize: (191, 87) 1349179339 -1.5617897937840093 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151564_0.png resize: (105, 124) 1349179340 -2.1864368300788906 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151525_0.png resize: (131, 163) 1349179341 -1.9985823852720757 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151549_0.png resize: (143, 165) 1349179342 -1.34496635906229 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151571_0.png resize: (84, 73) 1349179344 -1.0702828423608013 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151552_0.png resize: (91, 91) 1349179345 -1.5665881575029819 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151540_0.png resize: (154, 121) 1349179346 -2.4638338196486953 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151563_0.png resize: (179, 78) 1349179347 -2.4037328408124083 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151558_0.png resize: (202, 264) 1349179348 -2.5006658127728483 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151547_0.png resize: (78, 157) 1349179349 -2.846610525307646 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151567_0.png resize: (196, 86) 1349179350 -1.5162070316491676 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151523_0.png resize: (133, 218) 1349179352 -1.6468418186967275 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151519_0.png resize: (116, 190) 1349179353 -2.592757822158173 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151569_0.png resize: (275, 212) 1349179354 -3.213058288693551 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151553_0.png resize: (105, 117) 1349179355 -1.6818764643288509 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151528_0.png resize: (106, 96) 1349179356 -0.41025532212035787 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151514_0.png resize: (134, 172) 1349179357 -2.2715681875594296 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151545_0.png resize: (87, 363) 1349179358 -3.840782512255684 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151515_0.png resize: (173, 105) 1349179359 -2.2148724455131634 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151536_0.png resize: (221, 113) 1349179360 -1.687830423190245 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151578_0.png resize: (223, 209) 1349179361 -2.7400335460165137 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151520_0.png resize: (275, 68) 1349179362 -2.4588602094530003 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151551_0.png resize: (118, 88) 1349179363 -0.7193498486256451 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151543_0.png resize: (403, 378) 1349179364 -2.606067934136082 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151535_0.png resize: (124, 462) 1349179365 -3.6687822259701237 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151527_0.png resize: (123, 175) 1349179366 -2.5169093124532176 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151533_0.png resize: (183, 234) 1349179367 -2.557038968145641 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151522_0.png resize: (171, 206) 1349179368 -1.8929531215622446 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151537_0.png resize: (260, 240) 1349179369 -2.875124704597946 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151544_0.png resize: (168, 141) 1349179370 -2.349112938845596 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151529_0.png resize: (246, 235) 1349179371 -2.3056845647543995 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151541_0.png resize: (362, 278) 1349179372 -1.9861508935728294 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151550_0.png resize: (192, 126) 1349179373 -3.2803446847260624 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151530_0.png resize: (171, 126) 1349179374 -2.3137897684931086 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151565_0.png resize: (87, 151) 1349179375 -1.4535480087953385 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151516_0.png resize: (87, 181) 1349179376 -1.12980750580952 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151521_0.png resize: (354, 572) 1349179377 -2.2585021330821458 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151576_0.png resize: (101, 73) 1349179378 -2.182910764527559 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151548_0.png resize: (129, 155) 1349179379 -2.159416174845827 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151570_0.png resize: (401, 477) 1349179380 -2.0862848838512065 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151572_0.png resize: (165, 168) 1349179381 -2.1959686755811423 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151577_0.png resize: (368, 79) 1349179382 -3.9336552053774905 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151554_0.png resize: (106, 97) 1349179383 -1.3323071813108163 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151526_0.png resize: (100, 217) 1349179384 -1.5946958065138388 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151620_0.png resize: (216, 114) 1349179385 -0.9211307866631071 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151597_0.png resize: (294, 199) 1349179386 -2.25874799576421 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151591_0.png resize: (124, 121) 1349179387 -2.4412057521317942 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151614_0.png resize: (148, 131) 1349179388 -2.5609157139541554 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151583_0.png resize: (470, 333) 1349179389 -1.9849582594968982 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151592_0.png resize: (122, 192) 1349179390 -1.8296337270967995 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151603_0.png resize: (162, 273) 1349179391 -3.204348895103856 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151645_0.png resize: (198, 491) 1349179392 -2.762097109412571 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151627_0.png resize: (104, 283) 1349179393 -2.4486804680895653 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151593_0.png resize: (156, 189) 1349179394 -3.5055735995953863 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151616_0.png resize: (590, 327) 1349179395 -3.3230861578076047 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151633_0.png resize: (254, 199) 1349179396 -2.7259938353952595 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151632_0.png resize: (220, 336) 1349179397 -2.9230302396039742 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151642_0.png resize: (121, 89) 1349179398 -1.0923234604795526 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151590_0.png resize: (94, 161) 1349179399 -1.7580929415365294 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151624_0.png resize: (56, 130) 1349179400 -0.8206841447410171 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151631_0.png resize: (134, 144) 1349179401 -3.3155816441747596 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151626_0.png resize: (207, 244) 1349179402 -2.2959721407215636 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151608_0.png resize: (224, 309) 1349179403 -2.676255762497902 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151622_0.png resize: (135, 151) 1349179404 -1.959370072548748 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151594_0.png resize: (228, 269) 1349179405 -2.4812184715700285 treat image : 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temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151743_0.png resize: (187, 190) 1349179537 -4.064269179039236 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151794_0.png resize: (101, 86) 1349179538 -1.9627894195603137 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151764_0.png resize: (302, 118) 1349179539 -3.2494593261203684 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151777_0.png resize: (219, 240) 1349179540 -2.509311643695901 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151768_0.png resize: (110, 153) 1349179541 -1.8117056435868355 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151784_0.png resize: (239, 234) 1349179542 -4.344033209301464 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151774_0.png resize: (98, 176) 1349179543 -3.539502057512901 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151786_0.png resize: (213, 220) 1349179544 -3.0513970980271696 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151780_0.png resize: (122, 110) 1349179545 -2.721870232094216 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151760_0.png resize: (277, 171) 1349179546 -3.362804554662018 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151778_0.png resize: (225, 148) 1349179547 -3.7135162620919218 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151791_0.png resize: (178, 155) 1349179548 -2.0372325639394275 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151741_0.png resize: (218, 112) 1349179549 -2.88106510447029 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151746_0.png resize: (195, 303) 1349179550 -3.585726606979576 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151792_0.png resize: (194, 310) 1349179551 -3.0810504981252147 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151751_0.png resize: (188, 196) 1349179552 -3.1803833191987283 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151758_0.png resize: (369, 225) 1349179553 -1.8722261157505806 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151789_0.png resize: (151, 154) 1349179554 -3.1272371667889756 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151749_0.png resize: (98, 157) 1349179555 -3.633018343018558 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151756_0.png resize: (215, 233) 1349179556 -2.4607613939080974 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151788_0.png resize: (284, 122) 1349179557 -3.8018300792682087 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151747_0.png resize: (405, 122) 1349179558 -4.675485764497251 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151759_0.png resize: (255, 324) 1349179559 -4.469176898625992 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151771_0.png resize: (133, 108) 1349179560 -2.8208101711905944 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151752_0.png resize: (71, 166) 1349179561 -2.9681186436430034 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151765_0.png resize: (88, 96) 1349179562 -2.328253799199285 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151745_0.png resize: (73, 175) 1349179563 -2.6291631338677 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151787_0.png resize: (251, 178) 1349179564 -4.077986974648816 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151754_0.png resize: (283, 339) 1349179565 -3.6808837556176868 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151797_0.png resize: (182, 327) 1349179566 -3.4034221540638114 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151762_0.png resize: (148, 139) 1349179567 -2.5933553803854434 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151782_0.png resize: (212, 86) 1349179568 -3.5139257501287546 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151841_0.png resize: (247, 106) 1349179569 -0.04782813239522448 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151827_0.png resize: (266, 263) 1349179570 -3.058239800838196 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151830_0.png resize: (559, 442) 1349179571 -2.5377714749129647 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151798_0.png resize: (181, 256) 1349179572 -2.2944069269544625 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151805_0.png resize: (164, 247) 1349179573 -1.409767904028978 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151822_0.png resize: (83, 165) 1349179574 -2.2662775713717433 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151824_0.png resize: (343, 223) 1349179575 -2.6529259233218174 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151820_0.png resize: (162, 236) 1349179576 -3.1466372319117104 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151818_0.png resize: (298, 114) 1349179577 -0.7103748535633427 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151838_0.png resize: (123, 153) 1349179579 -1.2796348513381726 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151816_0.png resize: (388, 314) 1349179580 -3.70687910041169 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151831_0.png resize: (90, 99) 1349179581 -3.7716175672434593 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151828_0.png resize: (172, 111) 1349179582 -1.1806917079724513 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151814_0.png resize: (177, 272) 1349179583 -2.3016681123961504 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151839_0.png resize: (239, 133) 1349179584 -2.706109681734566 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151804_0.png resize: (242, 298) 1349179585 -2.639480900268563 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151803_0.png resize: (210, 184) 1349179586 -2.5987823838621082 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151826_0.png resize: (76, 123) 1349179587 -1.7552068542852137 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151840_0.png resize: (98, 126) 1349179588 -0.6952407291623443 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151799_0.png resize: (240, 188) 1349179589 -1.8424288439158325 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151843_0.png resize: (95, 142) 1349179590 2.735956681832929 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151825_0.png resize: (109, 88) 1349179591 -1.6558893515037991 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151835_0.png resize: (117, 119) 1349179592 -2.795877448838881 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151836_0.png resize: (189, 152) 1349179593 -1.6438080713554395 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151834_0.png resize: (118, 179) 1349179594 -2.0467713081284806 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151832_0.png resize: (249, 326) 1349179595 -2.856626039464363 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151837_0.png resize: (169, 247) 1349179596 -3.5678415001440413 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151808_0.png resize: (148, 138) 1349179597 -1.7537636306761872 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151811_0.png resize: (166, 114) 1349179598 -2.236042656309658 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151802_0.png resize: (537, 182) 1349179599 -2.9354521190427003 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151842_0.png resize: (68, 74) 1349179600 -0.06914323272051465 treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d_rle_crop_3742151893_0.png resize: (111, 376) 1349179601 -3.1537788077146924 treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d_rle_crop_3742151875_0.png resize: (128, 144) 1349179602 -2.3175682751221203 treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d_rle_crop_3742151872_0.png resize: (153, 183) 1349179603 -2.7893385915685167 treat image : 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temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152112_0.png resize: (144, 106) 1349179820 -4.11779824082186 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152137_0.png resize: (306, 304) 1349179821 -4.288639704708873 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152093_0.png resize: (323, 162) 1349179822 -2.2006766701760574 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152104_0.png resize: (284, 167) 1349179823 -2.37924559434626 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152134_0.png resize: (280, 272) 1349179824 -3.766671258299074 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152100_0.png resize: (375, 524) 1349179825 -5.218077921969412 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152109_0.png resize: (230, 155) 1349179826 -2.7747461836571787 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152132_0.png resize: (156, 219) 1349179827 -2.570816987136074 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152101_0.png resize: (184, 328) 1349179828 -0.8301720761119805 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152122_0.png resize: (188, 178) 1349179829 -2.021342445564824 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152115_0.png resize: (178, 196) 1349179831 -4.8861407011489675 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152133_0.png resize: (122, 108) 1349179832 -2.3528744108644473 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152129_0.png resize: (379, 325) 1349179833 -3.2784347540934395 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152095_0.png resize: (135, 179) 1349179834 -0.4977284270093848 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152092_0.png resize: (113, 157) 1349179835 -2.0504319191002485 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152131_0.png resize: (294, 224) 1349179836 -2.630384562454743 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152119_0.png resize: (242, 215) 1349179837 -3.3535451444781743 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152099_0.png resize: (135, 203) 1349179838 -3.505263464749068 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152135_0.png resize: (101, 172) 1349179839 -2.108368513676901 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152096_0.png resize: (105, 147) 1349179840 -2.6580237439515666 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152127_0.png resize: (164, 381) 1349179841 -3.366132180558462 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152125_0.png resize: (293, 289) 1349179842 -4.2181719431791045 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152118_0.png resize: (169, 162) 1349179843 -1.7020577581299539 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152120_0.png resize: (295, 144) 1349179844 -3.4732150993278283 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152185_0.png resize: (197, 164) 1349179845 -2.594794873253893 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152189_0.png resize: (109, 134) 1349179846 -2.2032370727633666 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152145_0.png resize: (219, 189) 1349179847 -1.8332337543657828 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152161_0.png resize: (378, 416) 1349179848 -2.646557824533561 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152151_0.png resize: (269, 218) 1349179849 -2.815969323316321 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152162_0.png resize: (203, 194) 1349179850 -0.6940845577291376 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152157_0.png resize: (152, 192) 1349179851 -1.0286927037068003 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152165_0.png resize: (135, 101) 1349179852 -1.8460235159129437 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152197_0.png resize: (179, 131) 1349179853 -1.2176190285373605 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152148_0.png resize: (386, 290) 1349179854 -2.5296678494206195 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152152_0.png resize: (193, 212) 1349179855 -2.3384421595115574 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152160_0.png resize: (305, 329) 1349179856 -3.3085853728188717 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152153_0.png resize: (140, 107) 1349179857 -3.3869202600359536 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152198_0.png resize: (141, 142) 1349179858 -1.5793143201890374 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152168_0.png resize: (132, 154) 1349179859 -4.85760493383072 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152181_0.png resize: (120, 80) 1349179860 -3.726656405165693 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152179_0.png resize: (268, 160) 1349179861 -3.918320743128388 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152163_0.png resize: (207, 191) 1349179863 -1.9914829950966353 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152147_0.png resize: (425, 398) 1349179864 -1.5166816052295236 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152182_0.png resize: (225, 241) 1349179865 -2.0393832710489863 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152176_0.png resize: (244, 208) 1349179866 -3.501478265611942 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152167_0.png resize: (366, 575) 1349179867 -3.6554383401971244 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152194_0.png resize: (155, 199) 1349179868 -2.5270905959503582 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152187_0.png resize: (288, 474) 1349179869 0.12894783793938366 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152183_0.png resize: (199, 164) 1349179870 -1.7089659355877165 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152186_0.png resize: (138, 144) 1349179871 -1.4705303580251838 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152149_0.png resize: (315, 230) 1349179872 -1.04074543703573 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152169_0.png resize: (271, 167) 1349179873 -2.528208127983249 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152174_0.png resize: (142, 138) 1349179875 -2.593571738916649 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152177_0.png resize: (224, 258) 1349179876 -3.516871464708438 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152188_0.png resize: (428, 342) 1349179877 -2.4917387997529055 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152184_0.png resize: (197, 264) 1349179878 -2.2098384329671656 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152191_0.png resize: (224, 229) 1349179879 1.289106372434127 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152159_0.png resize: (259, 239) 1349179880 -3.067578610989818 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152196_0.png resize: (191, 183) 1349179881 -2.820317962950523 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152199_0.png resize: (222, 251) 1349179882 -1.3815135809976073 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152192_0.png resize: (198, 283) 1349179883 -3.784887209608156 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152171_0.png resize: (72, 132) 1349179884 -1.4643092075732314 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152150_0.png resize: (246, 161) 1349179885 -2.1030502793246497 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152166_0.png resize: (266, 198) 1349179886 -4.1545088840299425 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152180_0.png resize: (66, 137) 1349179887 -1.1535885280635945 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152164_0.png resize: (184, 114) 1349179888 0.7976265256942625 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152178_0.png resize: (131, 164) 1349179889 -1.6483046941762596 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152173_0.png resize: (81, 130) 1349179890 -1.0935022003357115 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152158_0.png resize: (232, 290) 1349179891 -1.365182004074601 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152155_0.png resize: (131, 126) 1349179892 -3.2112043861334896 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152156_0.png resize: (106, 111) 1349179893 -1.3637441232804626 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152172_0.png resize: (132, 90) 1349179894 -1.752119497777314 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152241_0.png resize: (243, 156) 1349179895 -1.4922592483953054 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152237_0.png resize: (457, 237) 1349179896 -3.181353134631557 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152213_0.png resize: (255, 356) 1349179897 -3.1059229852262042 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152206_0.png resize: (165, 154) 1349179898 -2.3526751128358367 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152238_0.png resize: (146, 108) 1349179899 -0.8486299590459983 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152204_0.png resize: (224, 233) 1349179900 -2.993134000634934 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152223_0.png resize: (152, 167) 1349179901 -0.7879642787807214 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152215_0.png resize: (222, 280) 1349179903 -2.276888053916842 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152200_0.png resize: (342, 295) 1349179904 -1.3852618774655718 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152240_0.png resize: (175, 255) 1349179906 -3.3603458377649553 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152236_0.png resize: (174, 392) 1349179907 -2.539210268314915 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152202_0.png resize: (236, 133) 1349179908 -1.5779295056569411 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152233_0.png resize: (288, 295) 1349179910 -3.6805037468523696 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152220_0.png resize: (128, 227) 1349179911 -0.991304237790924 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152232_0.png resize: (102, 265) 1349179912 -4.003397388647532 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152242_0.png resize: (173, 330) 1349179913 -4.089377040100675 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152218_0.png resize: (312, 333) 1349179914 -3.002057510270266 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152209_0.png resize: (232, 540) 1349179915 -3.7567705721077687 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152224_0.png resize: (145, 197) 1349179916 -1.7353356660232664 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152211_0.png resize: (577, 408) 1349179917 -2.7375310678204356 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152228_0.png resize: (157, 191) 1349179918 -3.2991051027748757 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152214_0.png resize: (158, 188) 1349179919 -3.8773330278845357 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152201_0.png resize: (237, 131) 1349179920 -2.939313407618485 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152207_0.png resize: (156, 119) 1349179921 -3.587012636354037 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152217_0.png resize: (55, 124) 1349179922 -1.6522099241626576 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152243_0.png resize: (148, 92) 1349179923 -2.9078874908414454 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152210_0.png resize: (141, 227) 1349179924 -4.122967373748271 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152219_0.png resize: (377, 404) 1349179925 -2.477839935587384 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152225_0.png resize: (92, 124) 1349179926 -3.4973351453529338 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152205_0.png resize: (78, 156) 1349179927 -1.6211236236197046 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152208_0.png resize: (167, 252) 1349179928 -2.0481968023604558 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152222_0.png resize: (323, 399) 1349179929 -4.4985304472304435 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152212_0.png resize: (326, 410) 1349179930 -4.154833031459473 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152226_0.png resize: (236, 208) 1349179931 -2.6801943052748642 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152216_0.png resize: (169, 64) 1349179932 -1.5412504797254465 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151476_0.png resize: (313, 192) 1349179954 -2.5200605539549024 treat image : 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temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151557_0.png resize: (178, 104) 1349179962 -2.2831924949192532 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151524_0.png resize: (179, 269) 1349179963 -1.5091634236167533 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151568_0.png resize: (126, 203) 1349179964 -4.096725485924468 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151649_0.png resize: (262, 374) 1349179965 -2.322425154193377 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151637_0.png resize: (95, 100) 1349179966 -2.566794650543319 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151612_0.png resize: (117, 166) 1349179967 -1.3597757880743588 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151588_0.png resize: (170, 187) 1349179968 -2.655897024904661 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151639_0.png resize: (234, 244) 1349179969 -2.0655474745665763 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151647_0.png resize: (81, 164) 1349179970 -1.7928408630564343 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151613_0.png resize: (174, 169) 1349179971 -1.3512485496123716 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151619_0.png resize: (298, 325) 1349179972 -2.5300056095133083 treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b_rle_crop_3742151636_0.png resize: (262, 340) 1349179973 -1.7559981764397892 treat image : temp/1743467428_2444690_1349157797_4866fba02d00bbca5bcabb52743389df_rle_crop_3742151671_0.png resize: (172, 134) 1349179975 -2.161991710753698 treat image : temp/1743467428_2444690_1349157797_4866fba02d00bbca5bcabb52743389df_rle_crop_3742151669_0.png resize: (205, 315) 1349179976 -3.0838686815077883 treat image : temp/1743467428_2444690_1349157797_4866fba02d00bbca5bcabb52743389df_rle_crop_3742151663_0.png resize: (134, 284) 1349179977 -3.2820870502483093 treat image : temp/1743467428_2444690_1349157797_4866fba02d00bbca5bcabb52743389df_rle_crop_3742151680_0.png resize: (118, 98) 1349179978 -0.5269281544486095 treat image : temp/1743467428_2444690_1349157797_4866fba02d00bbca5bcabb52743389df_rle_crop_3742151668_0.png resize: (213, 222) 1349179979 -2.6858396148030454 treat image : temp/1743467428_2444690_1349157797_4866fba02d00bbca5bcabb52743389df_rle_crop_3742151699_0.png resize: (176, 191) 1349179980 -3.1206141801527103 treat image : temp/1743467428_2444690_1349157797_4866fba02d00bbca5bcabb52743389df_rle_crop_3742151688_0.png resize: (141, 121) 1349179981 -3.737028364956214 treat image : temp/1743467428_2444690_1349157677_fbb5e97de63dd226feffdf22501fed9a_rle_crop_3742151734_0.png resize: (158, 200) 1349179982 -2.8197293797532907 treat image : temp/1743467428_2444690_1349157677_fbb5e97de63dd226feffdf22501fed9a_rle_crop_3742151705_0.png resize: (309, 629) 1349179983 -2.197743831178069 treat image : temp/1743467428_2444690_1349157677_fbb5e97de63dd226feffdf22501fed9a_rle_crop_3742151736_0.png resize: (189, 280) 1349179984 -2.599385630838384 treat image : temp/1743467428_2444690_1349157677_fbb5e97de63dd226feffdf22501fed9a_rle_crop_3742151729_0.png resize: (241, 233) 1349179985 -1.8364016717743241 treat image : temp/1743467428_2444690_1349157677_fbb5e97de63dd226feffdf22501fed9a_rle_crop_3742151716_0.png resize: (132, 387) 1349179986 -1.898693454213583 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151796_0.png resize: (398, 442) 1349179987 -1.1936022724352304 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151763_0.png resize: (67, 104) 1349179988 -3.581535829068376 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151770_0.png resize: (117, 145) 1349179989 -3.457880044245418 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151821_0.png resize: (178, 232) 1349179990 -2.085227768559631 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151812_0.png resize: (141, 165) 1349179991 -1.9728806912408687 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151844_0.png resize: (107, 140) 1349179992 -2.884686547109155 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151801_0.png resize: (741, 604) 1349179993 -1.8616532164281352 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151800_0.png resize: (681, 674) 1349179994 -1.6682424609038244 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151817_0.png resize: (285, 207) 1349179995 -2.535307229999394 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151823_0.png resize: (161, 237) 1349179996 -4.6088611584691845 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151833_0.png resize: (95, 80) 1349179997 -4.019687576591563 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151819_0.png resize: (171, 159) 1349179998 -3.104783394697311 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151815_0.png resize: (120, 206) 1349179999 -2.4389716681030698 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151809_0.png resize: (261, 316) 1349180000 -3.358682184143285 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151810_0.png resize: (243, 139) 1349180001 -1.478535944621322 treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d_rle_crop_3742151897_0.png resize: (456, 405) 1349180002 -2.040260034480536 treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d_rle_crop_3742151845_0.png resize: (446, 559) 1349180003 -2.094345999919103 treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d_rle_crop_3742151871_0.png resize: (527, 453) 1349180004 -3.2675719397792977 treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d_rle_crop_3742151850_0.png resize: (309, 378) 1349180005 -2.29401268432439 treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d_rle_crop_3742151882_0.png resize: (216, 210) 1349180006 -1.5215779920349481 treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d_rle_crop_3742151879_0.png resize: (131, 177) 1349180007 -2.791617029808016 treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d_rle_crop_3742151889_0.png resize: (150, 119) 1349180008 -4.5975213007506985 treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d_rle_crop_3742151868_0.png resize: (174, 263) 1349180009 -3.629007448185485 treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d_rle_crop_3742151907_0.png resize: (166, 240) 1349180010 -4.077242880380118 treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d_rle_crop_3742151906_0.png resize: (113, 270) 1349180011 -0.40493206337939186 treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d_rle_crop_3742151846_0.png resize: (193, 275) 1349180012 -2.8181785026880393 treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d_rle_crop_3742151864_0.png resize: (359, 313) 1349180013 -1.993549154089071 treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d_rle_crop_3742151905_0.png resize: (178, 154) 1349180014 -4.0395221790489 treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d_rle_crop_3742151898_0.png resize: (88, 90) 1349180015 -3.422867075489042 treat image : temp/1743467428_2444690_1349012647_f9779bbb754ac99a11043b575d6acb24_rle_crop_3742151939_0.png resize: (600, 497) 1349180016 -1.9266122497441942 treat image : temp/1743467428_2444690_1349012647_f9779bbb754ac99a11043b575d6acb24_rle_crop_3742151936_0.png resize: (141, 111) 1349180017 -1.3544072455845928 treat image : temp/1743467428_2444690_1349012647_f9779bbb754ac99a11043b575d6acb24_rle_crop_3742151919_0.png resize: (130, 102) 1349180018 -0.9065617509293428 treat image : temp/1743467428_2444690_1349012647_f9779bbb754ac99a11043b575d6acb24_rle_crop_3742151913_0.png resize: (214, 114) 1349180019 -1.07076145653062 treat image : temp/1743467428_2444690_1349012647_f9779bbb754ac99a11043b575d6acb24_rle_crop_3742151911_0.png resize: (179, 154) 1349180020 -1.562285557927824 treat image : temp/1743467428_2444690_1349012647_f9779bbb754ac99a11043b575d6acb24_rle_crop_3742151909_0.png resize: (374, 283) 1349180021 -2.252504262601621 treat image : temp/1743467428_2444690_1349012647_f9779bbb754ac99a11043b575d6acb24_rle_crop_3742151914_0.png resize: (752, 632) 1349180022 -3.223956746204531 treat image : temp/1743467428_2444690_1349012647_f9779bbb754ac99a11043b575d6acb24_rle_crop_3742151927_0.png resize: (373, 358) 1349180023 -1.909934733861314 treat image : temp/1743467428_2444690_1349012647_f9779bbb754ac99a11043b575d6acb24_rle_crop_3742151940_0.png resize: (288, 330) 1349180024 -3.9039897617298496 treat image : temp/1743467428_2444690_1349012647_f9779bbb754ac99a11043b575d6acb24_rle_crop_3742151922_0.png resize: (95, 315) 1349180025 -1.8146672092276925 treat image : temp/1743467428_2444690_1349012647_f9779bbb754ac99a11043b575d6acb24_rle_crop_3742151926_0.png resize: (540, 707) 1349180026 -2.563081983161243 treat image : temp/1743467428_2444690_1349012579_3a25094eda35ea087119d8b41728042b_rle_crop_3742151943_0.png resize: (199, 224) 1349180027 -2.2054045118930454 treat image : temp/1743467428_2444690_1349012579_3a25094eda35ea087119d8b41728042b_rle_crop_3742151945_0.png resize: (128, 232) 1349180028 -1.00163625161323 treat image : temp/1743467428_2444690_1349012579_3a25094eda35ea087119d8b41728042b_rle_crop_3742151946_0.png resize: (126, 126) 1349180029 -2.0879626483338223 treat image : temp/1743467428_2444690_1349012579_3a25094eda35ea087119d8b41728042b_rle_crop_3742151965_0.png resize: (147, 185) 1349180030 -2.3588860089045873 treat image : temp/1743467428_2444690_1349012579_3a25094eda35ea087119d8b41728042b_rle_crop_3742151953_0.png resize: (123, 165) 1349180031 -3.4354924049037394 treat image : temp/1743467428_2444690_1349012558_eb09958bc9444f29da4245e51b9cbb5d_rle_crop_3742152021_0.png resize: (216, 145) 1349180032 -2.5778681595938227 treat image : temp/1743467428_2444690_1349012558_eb09958bc9444f29da4245e51b9cbb5d_rle_crop_3742152026_0.png resize: (365, 238) 1349180033 -3.0471689940532274 treat image : temp/1743467428_2444690_1349012558_eb09958bc9444f29da4245e51b9cbb5d_rle_crop_3742152000_0.png resize: (114, 181) 1349180034 -2.84823969883338 treat image : temp/1743467428_2444690_1349012558_eb09958bc9444f29da4245e51b9cbb5d_rle_crop_3742152022_0.png resize: (325, 178) 1349180035 -1.9851607367210138 treat image : temp/1743467428_2444690_1349012558_eb09958bc9444f29da4245e51b9cbb5d_rle_crop_3742152027_0.png resize: (105, 84) 1349180036 -2.2084419690750803 treat image : temp/1743467428_2444690_1349012555_b56fff58c907323845293e3e1d4bf0ce_rle_crop_3742152048_0.png resize: (227, 258) 1349180037 -2.9664277465390745 treat image : temp/1743467428_2444690_1349012555_b56fff58c907323845293e3e1d4bf0ce_rle_crop_3742152049_0.png resize: (365, 291) 1349180038 -2.5191722767955786 treat image : temp/1743467428_2444690_1349012487_887ad76ad3e3b6ed97b56bb5e6076744_rle_crop_3742152081_0.png resize: (179, 147) 1349180039 -1.7352322722606408 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152130_0.png resize: (215, 266) 1349180040 -4.268059720288176 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152126_0.png resize: (87, 77) 1349180041 -1.182393251531408 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152141_0.png resize: (184, 147) 1349180042 -2.0183540800737085 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152091_0.png resize: (197, 342) 1349180043 -2.9158768389233254 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152116_0.png resize: (86, 96) 1349180044 -4.565673162339187 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152094_0.png resize: (206, 241) 1349180045 -2.3630134471941946 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152146_0.png resize: (254, 249) 1349180046 -1.65847234403121 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152193_0.png resize: (293, 333) 1349180047 -3.0319164459718984 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152170_0.png resize: (208, 167) 1349180048 -1.7112990865606887 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152175_0.png resize: (116, 267) 1349180049 -3.598523226938341 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152190_0.png resize: (153, 187) 1349180050 -2.7771271775873743 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152154_0.png resize: (75, 145) 1349180051 0.49092642114396545 treat image : temp/1743467428_2444690_1349012356_cfe494470bffaf0010546e3620de6765_rle_crop_3742152195_0.png resize: (145, 243) 1349180052 -3.648377388960732 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152244_0.png resize: (88, 143) 1349180054 -1.5698009411120963 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152234_0.png resize: (157, 222) 1349180055 -2.9747567613841874 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152239_0.png resize: (92, 183) 1349180056 -1.9420427913370288 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152246_0.png resize: (134, 301) 1349180057 -3.6061942588380496 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152221_0.png resize: (185, 176) 1349180058 -3.0770398599609754 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151497_0.png resize: (52, 57) 1349180060 -1.72768960869452 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151561_0.png resize: (114, 192) 1349180061 -3.9744514925444743 treat image : temp/1743467428_2444690_1349157797_4866fba02d00bbca5bcabb52743389df_rle_crop_3742151681_0.png resize: (67, 146) 1349180062 -2.32778804424123 treat image : temp/1743467428_2444690_1349157677_fbb5e97de63dd226feffdf22501fed9a_rle_crop_3742151733_0.png resize: (184, 269) 1349180063 -2.7040968440373945 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151807_0.png resize: (157, 153) 1349180064 -2.6831974297992858 treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151806_0.png resize: (106, 190) 1349180065 -3.885602036114681 treat image : temp/1743467428_2444690_1349012647_f9779bbb754ac99a11043b575d6acb24_rle_crop_3742151929_0.png resize: (140, 226) 1349180066 -2.732502237519336 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152128_0.png resize: (76, 153) 1349180067 -3.5935614257472137 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151500_0.png resize: (161, 283) 1349180187 -0.9472450313723618 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151507_0.png resize: (119, 151) 1349180188 -1.233191739880861 treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151508_0.png resize: (193, 185) 1349180189 -3.114231204534172 treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47_rle_crop_3742151531_0.png resize: (405, 322) 1349180190 -4.442622213270422 treat image : temp/1743467428_2444690_1349157677_fbb5e97de63dd226feffdf22501fed9a_rle_crop_3742151737_0.png resize: (188, 271) 1349180191 -1.9879119381521742 treat image : temp/1743467428_2444690_1349157677_fbb5e97de63dd226feffdf22501fed9a_rle_crop_3742151702_0.png resize: (283, 297) 1349180192 -2.7545466741725373 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151755_0.png resize: (641, 482) 1349180193 -1.8633726143946807 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151744_0.png resize: (321, 798) 1349180194 -3.1394019062098746 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151775_0.png resize: (88, 99) 1349180195 -4.403709175761685 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151766_0.png resize: (151, 183) 1349180196 -2.903930303775613 treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62_rle_crop_3742151779_0.png resize: (177, 139) 1349180197 -2.788815523250684 treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d_rle_crop_3742151891_0.png resize: (227, 458) 1349180198 -4.248422301070872 treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d_rle_crop_3742151899_0.png resize: (610, 747) 1349180199 -3.012878830609206 treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d_rle_crop_3742151869_0.png resize: (257, 402) 1349180200 -4.64751152008854 treat image : temp/1743467428_2444690_1349012647_f9779bbb754ac99a11043b575d6acb24_rle_crop_3742151941_0.png resize: (225, 421) 1349180201 -1.619292060200043 treat image : temp/1743467428_2444690_1349012647_f9779bbb754ac99a11043b575d6acb24_rle_crop_3742151934_0.png resize: (561, 487) 1349180202 -2.690986647197513 treat image : temp/1743467428_2444690_1349012647_f9779bbb754ac99a11043b575d6acb24_rle_crop_3742151937_0.png resize: (559, 627) 1349180203 -2.731201665457363 treat image : temp/1743467428_2444690_1349012647_f9779bbb754ac99a11043b575d6acb24_rle_crop_3742151910_0.png resize: (554, 218) 1349180204 -3.1838877760762596 treat image : temp/1743467428_2444690_1349012647_f9779bbb754ac99a11043b575d6acb24_rle_crop_3742151923_0.png resize: (478, 166) 1349180205 -3.105273294485237 treat image : temp/1743467428_2444690_1349012579_3a25094eda35ea087119d8b41728042b_rle_crop_3742151955_0.png resize: (310, 271) 1349180206 -3.0240217857459863 treat image : temp/1743467428_2444690_1349012558_eb09958bc9444f29da4245e51b9cbb5d_rle_crop_3742151995_0.png resize: (311, 313) 1349180207 -1.9827509248409976 treat image : temp/1743467428_2444690_1349012558_eb09958bc9444f29da4245e51b9cbb5d_rle_crop_3742152006_0.png resize: (214, 252) 1349180208 -3.623960631050147 treat image : temp/1743467428_2444690_1349012558_eb09958bc9444f29da4245e51b9cbb5d_rle_crop_3742152009_0.png resize: (307, 197) 1349180209 -4.04709695121477 treat image : temp/1743467428_2444690_1349012558_eb09958bc9444f29da4245e51b9cbb5d_rle_crop_3742152029_0.png resize: (336, 146) 1349180210 -3.5272155710772113 treat image : temp/1743467428_2444690_1349012487_887ad76ad3e3b6ed97b56bb5e6076744_rle_crop_3742152077_0.png resize: (327, 130) 1349180211 -3.062760342140194 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152139_0.png resize: (304, 334) 1349180212 -4.673529960777955 treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152121_0.png resize: (125, 141) 1349180213 -1.3463991839494631 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152203_0.png resize: (257, 365) 1349180214 -2.5809678450736246 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152245_0.png resize: (375, 518) 1349180215 -2.425000826153553 treat image : 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temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151829_0.png resize: (689, 635) 1349180244 -2.9556732655319378 treat image : temp/1743467428_2444690_1349012558_eb09958bc9444f29da4245e51b9cbb5d_rle_crop_3742151994_0.png resize: (179, 204) 1349180245 -3.588536668459548 treat image : temp/1743467428_2444690_1349012558_eb09958bc9444f29da4245e51b9cbb5d_rle_crop_3742152040_0.png resize: (154, 101) 1349180246 -3.974010944288557 treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152229_0.png resize: (165, 207) 1349180247 -2.955468189008 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 : 798 time used for this insertion : 0.06519317626953125 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 798 time used for this insertion : 0.17957615852355957 save missing photos in datou_result : time spend for datou_step_exec : 73.29102325439453 time spend to save output : 0.2584376335144043 total time spend for step 6 : 73.54946088790894 step7:brightness Tue Apr 1 02:49:27 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e.jpg treat image : temp/1743467428_2444690_1349157804_f369b3296050cc3d798c3fbe86262b47.jpg treat image : temp/1743467428_2444690_1349157800_766507b0f0bc9dd935cb65a68b674e5b.jpg treat image : temp/1743467428_2444690_1349157797_4866fba02d00bbca5bcabb52743389df.jpg treat image : temp/1743467428_2444690_1349157677_fbb5e97de63dd226feffdf22501fed9a.jpg treat image : temp/1743467428_2444690_1349157619_8cbbcd2f37668d20295cda3769262f62.jpg treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce.jpg treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d.jpg treat image : temp/1743467428_2444690_1349012647_f9779bbb754ac99a11043b575d6acb24.jpg treat image : temp/1743467428_2444690_1349012579_3a25094eda35ea087119d8b41728042b.jpg treat image : 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temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152227_0.png treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151478_0.png treat image : temp/1743467428_2444690_1349157677_fbb5e97de63dd226feffdf22501fed9a_rle_crop_3742151732_0.png treat image : temp/1743467428_2444690_1349157488_fe3b82d711f46d0df944612dc023fe2d_rle_crop_3742151890_0.png treat image : temp/1743467428_2444690_1349012555_b56fff58c907323845293e3e1d4bf0ce_rle_crop_3742152064_0.png treat image : temp/1743467428_2444690_1349012409_0cf305b978a23d3d1d05eaa0021956d8_rle_crop_3742152138_0.png treat image : temp/1743467428_2444690_1349157808_dda2c0ff3cf09d90affa217163aa913e_rle_crop_3742151510_0.png treat image : temp/1743467428_2444690_1349157797_4866fba02d00bbca5bcabb52743389df_rle_crop_3742151698_0.png treat image : temp/1743467428_2444690_1349157797_4866fba02d00bbca5bcabb52743389df_rle_crop_3742151658_0.png treat image : temp/1743467428_2444690_1349157518_25b265d83617b1feb35ad366abacb0ce_rle_crop_3742151829_0.png treat image : temp/1743467428_2444690_1349012558_eb09958bc9444f29da4245e51b9cbb5d_rle_crop_3742151994_0.png treat image : temp/1743467428_2444690_1349012558_eb09958bc9444f29da4245e51b9cbb5d_rle_crop_3742152040_0.png treat image : temp/1743467428_2444690_1349012351_cec8186e47dba91304cfa60bfc5ef5d2_rle_crop_3742152229_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 : 798 time used for this insertion : 0.058069467544555664 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 798 time used for this insertion : 0.1756601333618164 save missing photos in datou_result : time spend for datou_step_exec : 20.594010591506958 time spend to save output : 0.24248695373535156 total time spend for step 7 : 20.83649754524231 step8:velours_tree Tue Apr 1 02:49:47 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 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 : 1.066939353942871 time spend to save output : 6.29425048828125e-05 total time spend for step 8 : 1.067002296447754 step9:send_mail_cod Tue Apr 1 02:49:49 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 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_P21930834_01-04-2025_02_49_49.pdf 21931486 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette219314861743468589 21931487 imagette219314871743468589 21931488 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 .imagette219314881743468589 21931490 imagette219314901743468591 21931491 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 .imagette219314911743468591 21931492 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 .imagette219314921743468591 21931493 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 .imagette219314931743468593 21931494 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 .imagette219314941743468594 21931495 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 .imagette219314951743468595 21931496 imagette219314961743468595 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=21930834 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/21931486,21931487,21931488,21931489,21931490,21931491,21931492,21931493,21931494,21931495,21931496?tags=pehd,flou,papier,environnement,mal_croppe,autre,carton,pet_clair,metal,pet_fonce,background args[1349157808] : ((1349157808, -4.69705438042978, 492609224), (1349157808, -0.05215657385269119, 2107752395), '0.22413743184629167') We are sending mail with results at report@fotonower.com args[1349157804] : ((1349157804, -4.319816134674197, 492609224), (1349157804, -0.10839885418679238, 496442774), '0.22413743184629167') We are sending mail with results at report@fotonower.com args[1349157800] : ((1349157800, -4.089615144478795, 492609224), (1349157800, -0.040342274598425834, 2107752395), '0.22413743184629167') We are sending mail with results at report@fotonower.com args[1349157797] : ((1349157797, -4.024990494423274, 492609224), (1349157797, -0.10195199349493064, 496442774), '0.22413743184629167') We are sending mail with results at report@fotonower.com args[1349157677] : ((1349157677, -3.3758259597737066, 492609224), (1349157677, -0.16371684020597033, 496442774), '0.22413743184629167') We are sending mail with results at report@fotonower.com args[1349157619] : ((1349157619, -5.751099315535441, 492609224), (1349157619, 0.004917789224691323, 2107752395), '0.22413743184629167') We are sending mail with results at report@fotonower.com args[1349157518] : ((1349157518, -4.460140428146166, 492609224), (1349157518, -0.14110508833273294, 496442774), '0.22413743184629167') We are sending mail with results at report@fotonower.com args[1349157488] : ((1349157488, -4.815111678886501, 492609224), (1349157488, -0.3711844358021925, 496442774), '0.22413743184629167') We are sending mail with results at report@fotonower.com args[1349012647] : ((1349012647, -3.6407625372070886, 492609224), (1349012647, -0.1691605205062516, 496442774), '0.22413743184629167') We are sending mail with results at report@fotonower.com args[1349012579] : ((1349012579, -3.472596526805101, 492609224), (1349012579, -0.19305119998041917, 496442774), '0.22413743184629167') We are sending mail with results at report@fotonower.com args[1349012558] : ((1349012558, -4.519187374035535, 492609224), (1349012558, -0.1705233501381428, 496442774), '0.22413743184629167') We are sending mail with results at report@fotonower.com args[1349012555] : ((1349012555, -3.5775098253972555, 492609224), (1349012555, -0.3379922442805209, 496442774), '0.22413743184629167') We are sending mail with results at report@fotonower.com args[1349012487] : ((1349012487, -3.3461741870340878, 492609224), (1349012487, 0.14920980170504167, 2107752395), '0.22413743184629167') We are sending mail with results at report@fotonower.com args[1349012409] : ((1349012409, -5.7034803572006725, 492609224), (1349012409, -0.0609963194743876, 2107752395), '0.22413743184629167') We are sending mail with results at report@fotonower.com args[1349012356] : ((1349012356, -4.396704748368035, 492609224), (1349012356, 0.05303656633110135, 2107752395), '0.22413743184629167') We are sending mail with results at report@fotonower.com args[1349012351] : ((1349012351, -5.460273956767052, 492609224), (1349012351, -0.025627383759818337, 2107752395), '0.22413743184629167') We are sending mail with results at report@fotonower.com refus_total : 0.22413743184629167 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=21930834 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1349012647,1349157488,1349157619,1349012351,1349012356,1349012409,1349012558,1349157518,1349157677,1349157800,1349157804,1349012487,1349012555,1349012579,1349157797,1349157808) Found this number of photos: 16 begin to download photo : 1349012647 begin to download photo : 1349012356 begin to download photo : 1349157677 begin to download photo : 1349012555 download finish for photo 1349157677 begin to download photo : 1349157800 download finish for photo 1349012647 begin to download photo : 1349157488 download finish for photo 1349012555 begin to download photo : 1349012579 download finish for photo 1349012356 begin to download photo : 1349012409 download finish for photo 1349157488 begin to download photo : 1349157619 download finish for photo 1349157800 begin to download photo : 1349157804 download finish for photo 1349012579 begin to download photo : 1349157797 download finish for photo 1349157804 begin to download photo : 1349012487 download finish for photo 1349012409 begin to download photo : 1349012558 download finish for photo 1349157797 begin to download photo : 1349157808 download finish for photo 1349157619 begin to download photo : 1349012351 download finish for photo 1349012487 download finish for photo 1349012558 begin to download photo : 1349157518 download finish for photo 1349157808 download finish for photo 1349012351 download finish for photo 1349157518 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21930834_01-04-2025_02_49_49.pdf results_Auto_P21930834_01-04-2025_02_49_49.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21930834_01-04-2025_02_49_49.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','21930834','results_Auto_P21930834_01-04-2025_02_49_49.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21930834_01-04-2025_02_49_49.pdf','pdf','','1.04','0.22413743184629167') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/21930834

https://www.fotonower.com/image?json=false&list_photos_id=1349157808
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349157804
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349157800
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349157797
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349157677
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349157619
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349157518
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349157488
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349012647
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349012579
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349012558
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349012555
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349012487
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349012409
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349012356
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349012351
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 22.41%
Veuillez trouver les photos des contaminants.

exemples de contaminants: pehd: https://www.fotonower.com/view/21931486?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/21931488?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/21931491?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/21931492?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/21931493?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/21931494?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/21931495?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21930834_01-04-2025_02_49_49.pdf.

Lien vers velours :https://www.fotonower.com/velours/21931486,21931487,21931488,21931489,21931490,21931491,21931492,21931493,21931494,21931495,21931496?tags=pehd,flou,papier,environnement,mal_croppe,autre,carton,pet_clair,metal,pet_fonce,background.


L'équipe Fotonower 202 b'' Server: nginx Date: Tue, 01 Apr 2025 00:50:01 GMT Content-Length: 0 Connection: close X-Message-Id: ER83XXhbS2yAxqwUELUNlA 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 [1349157808, 1349157804, 1349157800, 1349157797, 1349157677, 1349157619, 1349157518, 1349157488, 1349012647, 1349012579, 1349012558, 1349012555, 1349012487, 1349012409, 1349012356, 1349012351] 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, '2711235') ('3318', '21930834', '1349157808', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157804', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157800', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157797', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157677', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157619', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157518', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157488', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012647', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012579', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012558', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012555', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012487', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012409', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012356', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012351', None, None, None, None, None, '2711235') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 16 time used for this insertion : 0.01773691177368164 save_final save missing photos in datou_result : time spend for datou_step_exec : 12.820466995239258 time spend to save output : 0.0246737003326416 total time spend for step 9 : 12.8451406955719 step10:split_time_score Tue Apr 1 02:50:01 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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'}] (('14', 16),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 31032025 21930834 Nombre de photos uploadées : 16 / 23040 (0%) 31032025 21930834 Nombre de photos taguées (types de déchets): 0 / 16 (0%) 31032025 21930834 Nombre de photos taguées (volume) : 0 / 16 (0%) elapsed_time : load_data_split_time_score 2.86102294921875e-06 elapsed_time : order_list_meta_photo_and_scores 9.298324584960938e-06 ???????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0013294219970703125 elapsed_time : insert_dashboard_record_day_entry 0.0227353572845459 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 : 0.083542372459225 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21930826_01-04-2025_02_21_48.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21930826 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`=21930826 AND mptpi.`type`=3726 To do Qualite : 0.22413743184629167 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21930834_01-04-2025_02_49_49.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21930834 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`=21930834 AND mptpi.`type`=3594 To do Qualite : 0.18642559428022043 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21930836_01-04-2025_02_34_33.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21930836 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`=21930836 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': 16, '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 [1349157808, 1349157804, 1349157800, 1349157797, 1349157677, 1349157619, 1349157518, 1349157488, 1349012647, 1349012579, 1349012558, 1349012555, 1349012487, 1349012409, 1349012356, 1349012351] Looping around the photos to save general results len do output : 1 /21930834Didn'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, '2711235') ('3318', '21930834', '1349157808', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157804', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157800', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157797', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157677', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157619', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157518', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349157488', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012647', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012579', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012558', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012555', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012487', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012409', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012356', None, None, None, None, None, '2711235') ('3318', None, None, None, None, None, None, None, '2711235') ('3318', '21930834', '1349012351', None, None, None, None, None, '2711235') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 17 time used for this insertion : 0.017426729202270508 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.9026472568511963 time spend to save output : 0.01778554916381836 total time spend for step 10 : 1.9204328060150146 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 16 set_done_treatment 501.90user 249.59system 19:46.37elapsed 63%CPU (0avgtext+0avgdata 7657904maxresident)k 32364992inputs+333424outputs (981863major+50190039minor)pagefaults 0swaps