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 : 744114 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 : ['2574366'] with mtr_portfolio_ids : ['20424999'] and first list_photo_ids : [] new path : /proc/744114/ Inside batchDatouExec : verbose : 0 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : mask_detect, crop_condition, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final, blur_detection, brightness, velours_tree, send_mail_cod, split_time_score over limit max, limiting to limit_max 40 list_input_json : [] origin We have 1 , BFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 9 ; length of list_pids : 9 ; length of list_args : 9 time to download the photos : 2.1460177898406982 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 Feb 11 02:30:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10332 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-02-11 02:30:34.366257: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-11 02:30:34.399049: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-11 02:30:34.401240: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f66d8000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-11 02:30:34.401292: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-11 02:30:34.406279: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-11 02:30:34.637650: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x13dcbbf0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-11 02:30:34.637701: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-11 02:30:34.639564: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-02-11 02:30:34.639977: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-11 02:30:34.643027: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-11 02:30:34.665060: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-11 02:30:34.665485: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-11 02:30:34.702163: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-11 02:30:34.708401: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-11 02:30:34.771521: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-11 02:30:34.773934: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-11 02:30:34.774413: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-11 02:30:34.775681: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-11 02:30:34.775710: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-11 02:30:34.775725: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-11 02:30:34.777989: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9570 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-02-11 02:30:35.160356: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-02-11 02:30:35.160472: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-11 02:30:35.160502: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-11 02:30:35.160525: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-11 02:30:35.160547: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-11 02:30:35.160569: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-11 02:30:35.160591: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-11 02:30:35.160615: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-11 02:30:35.162162: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-11 02:30:35.163545: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-02-11 02:30:35.163607: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-11 02:30:35.163638: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-11 02:30:35.163665: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-11 02:30:35.163695: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-11 02:30:35.163720: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-11 02:30:35.163744: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-11 02:30:35.163770: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-11 02:30:35.165305: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-11 02:30:35.165342: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-11 02:30:35.165354: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-11 02:30:35.165364: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-11 02:30:35.166957: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9570 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 : [] 2025-02-11 02:30:46.080068: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-11 02:30:46.247965: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23 /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23/mask_model.h5 size_local : 256009536 size in s3 : 256009536 create time local : 2021-08-09 09:43:22 create time in s3 : 2021-08-06 18:54:04 mask_model.h5 already exist and didn't need to update list_images length : 9 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 : 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 : 57 Detection mask done ! Trying to reset tf kernel 744678 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5043 tf kernel not reseted sub process len(results) : 9 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 9 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 : 10332 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 : 0.1053922176361084 nb_pixel_total : 118307 time to create 1 rle with old method : 0.1421506404876709 length of segment : 545 time for calcul the mask position with numpy : 0.0064144134521484375 nb_pixel_total : 15241 time to create 1 rle with old method : 0.01908731460571289 length of segment : 208 time for calcul the mask position with numpy : 0.019487380981445312 nb_pixel_total : 22921 time to create 1 rle with old method : 0.044060707092285156 length of segment : 182 time for calcul the mask position with numpy : 0.0030155181884765625 nb_pixel_total : 13001 time to create 1 rle with old method : 0.016970157623291016 length of segment : 166 time for calcul the mask position with numpy : 0.30788540840148926 nb_pixel_total : 240178 time to create 1 rle with new method : 0.02211475372314453 length of segment : 765 time for calcul the mask position with numpy : 0.017981290817260742 nb_pixel_total : 14464 time to create 1 rle with old method : 0.01979804039001465 length of segment : 220 time for calcul the mask position with numpy : 0.0905907154083252 nb_pixel_total : 50629 time to create 1 rle with old method : 0.05768895149230957 length of segment : 204 time for calcul the mask position with numpy : 0.13361144065856934 nb_pixel_total : 148539 time to create 1 rle with old method : 0.16762995719909668 length of segment : 757 time for calcul the mask position with numpy : 0.036319732666015625 nb_pixel_total : 38761 time to create 1 rle with old method : 0.04768729209899902 length of segment : 554 time for calcul the mask position with numpy : 0.0030303001403808594 nb_pixel_total : 7555 time to create 1 rle with old method : 0.009552955627441406 length of segment : 145 time for calcul the mask position with numpy : 0.009236574172973633 nb_pixel_total : 24688 time to create 1 rle with old method : 0.03339076042175293 length of segment : 181 time for calcul the mask position with numpy : 0.30600762367248535 nb_pixel_total : 280364 time to create 1 rle with new method : 0.028479576110839844 length of segment : 879 time for calcul the mask position with numpy : 0.02369403839111328 nb_pixel_total : 28368 time to create 1 rle with old method : 0.037279605865478516 length of segment : 288 time for calcul the mask position with numpy : 0.0031728744506835938 nb_pixel_total : 18186 time to create 1 rle with old method : 0.020319700241088867 length of segment : 158 time for calcul the mask position with numpy : 0.0069921016693115234 nb_pixel_total : 16590 time to create 1 rle with old method : 0.021239280700683594 length of segment : 138 time for calcul the mask position with numpy : 0.002145051956176758 nb_pixel_total : 7145 time to create 1 rle with old method : 0.008123159408569336 length of segment : 124 time for calcul the mask position with numpy : 0.0034630298614501953 nb_pixel_total : 13692 time to create 1 rle with old method : 0.017358064651489258 length of segment : 195 time for calcul the mask position with numpy : 0.00040984153747558594 nb_pixel_total : 9270 time to create 1 rle with old method : 0.011136293411254883 length of segment : 154 time for calcul the mask position with numpy : 0.0048716068267822266 nb_pixel_total : 6746 time to create 1 rle with old method : 0.008745908737182617 length of segment : 90 time for calcul the mask position with numpy : 0.010757923126220703 nb_pixel_total : 15355 time to create 1 rle with old method : 0.02292346954345703 length of segment : 122 time for calcul the mask position with numpy : 0.050379276275634766 nb_pixel_total : 56314 time to create 1 rle with old method : 0.06627392768859863 length of segment : 409 time for calcul the mask position with numpy : 0.04176735877990723 nb_pixel_total : 30851 time to create 1 rle with old method : 0.042992591857910156 length of segment : 385 time for calcul the mask position with numpy : 0.0044193267822265625 nb_pixel_total : 17703 time to create 1 rle with old method : 0.022231340408325195 length of segment : 208 time for calcul the mask position with numpy : 0.02054595947265625 nb_pixel_total : 22544 time to create 1 rle with old method : 0.028849363327026367 length of segment : 183 time for calcul the mask position with numpy : 0.013068675994873047 nb_pixel_total : 14724 time to create 1 rle with old method : 0.02100539207458496 length of segment : 138 time for calcul the mask position with numpy : 0.08321809768676758 nb_pixel_total : 51892 time to create 1 rle with old method : 0.06012773513793945 length of segment : 246 time for calcul the mask position with numpy : 0.003408670425415039 nb_pixel_total : 9662 time to create 1 rle with old method : 0.014373540878295898 length of segment : 156 time for calcul the mask position with numpy : 0.04532980918884277 nb_pixel_total : 47228 time to create 1 rle with old method : 0.05577731132507324 length of segment : 345 time for calcul the mask position with numpy : 0.007275581359863281 nb_pixel_total : 26101 time to create 1 rle with old method : 0.04912066459655762 length of segment : 155 time for calcul the mask position with numpy : 0.0022225379943847656 nb_pixel_total : 18337 time to create 1 rle with old method : 0.023318767547607422 length of segment : 337 time for calcul the mask position with numpy : 0.05360770225524902 nb_pixel_total : 133697 time to create 1 rle with old method : 0.23082542419433594 length of segment : 354 time for calcul the mask position with numpy : 0.040100812911987305 nb_pixel_total : 14162 time to create 1 rle with old method : 0.02657341957092285 length of segment : 186 time for calcul the mask position with numpy : 0.11307239532470703 nb_pixel_total : 168863 time to create 1 rle with new method : 0.012340068817138672 length of segment : 477 time for calcul the mask position with numpy : 0.01123952865600586 nb_pixel_total : 21959 time to create 1 rle with old method : 0.02632451057434082 length of segment : 183 time for calcul the mask position with numpy : 0.01777935028076172 nb_pixel_total : 44461 time to create 1 rle with old method : 0.052805185317993164 length of segment : 437 time for calcul the mask position with numpy : 0.0013709068298339844 nb_pixel_total : 3924 time to create 1 rle with old method : 0.00489497184753418 length of segment : 70 time for calcul the mask position with numpy : 0.003582477569580078 nb_pixel_total : 6967 time to create 1 rle with old method : 0.008494138717651367 length of segment : 149 time for calcul the mask position with numpy : 0.0019576549530029297 nb_pixel_total : 23058 time to create 1 rle with old method : 0.026866674423217773 length of segment : 392 time for calcul the mask position with numpy : 0.13190412521362305 nb_pixel_total : 66104 time to create 1 rle with old method : 0.09837937355041504 length of segment : 311 time for calcul the mask position with numpy : 0.10237598419189453 nb_pixel_total : 181557 time to create 1 rle with new method : 0.018094539642333984 length of segment : 562 time for calcul the mask position with numpy : 0.04489302635192871 nb_pixel_total : 55529 time to create 1 rle with old method : 0.06507420539855957 length of segment : 245 time for calcul the mask position with numpy : 0.004500389099121094 nb_pixel_total : 61922 time to create 1 rle with old method : 0.07126879692077637 length of segment : 371 time for calcul the mask position with numpy : 0.09525370597839355 nb_pixel_total : 101285 time to create 1 rle with old method : 0.11617708206176758 length of segment : 404 time for calcul the mask position with numpy : 0.0003414154052734375 nb_pixel_total : 3535 time to create 1 rle with old method : 0.004422426223754883 length of segment : 77 time for calcul the mask position with numpy : 0.01247262954711914 nb_pixel_total : 21339 time to create 1 rle with old method : 0.02417588233947754 length of segment : 229 time for calcul the mask position with numpy : 0.02974843978881836 nb_pixel_total : 34716 time to create 1 rle with old method : 0.03900551795959473 length of segment : 311 time for calcul the mask position with numpy : 0.0019240379333496094 nb_pixel_total : 31719 time to create 1 rle with old method : 0.04038572311401367 length of segment : 241 time for calcul the mask position with numpy : 0.0015461444854736328 nb_pixel_total : 21269 time to create 1 rle with old method : 0.024118423461914062 length of segment : 188 time for calcul the mask position with numpy : 0.002132892608642578 nb_pixel_total : 25938 time to create 1 rle with old method : 0.03026127815246582 length of segment : 217 time for calcul the mask position with numpy : 0.0010037422180175781 nb_pixel_total : 11995 time to create 1 rle with old method : 0.013887643814086914 length of segment : 174 time for calcul the mask position with numpy : 0.0014193058013916016 nb_pixel_total : 16342 time to create 1 rle with old method : 0.018390178680419922 length of segment : 197 time for calcul the mask position with numpy : 0.004436016082763672 nb_pixel_total : 43521 time to create 1 rle with old method : 0.04862570762634277 length of segment : 302 time for calcul the mask position with numpy : 0.0026247501373291016 nb_pixel_total : 41995 time to create 1 rle with old method : 0.050011634826660156 length of segment : 261 time for calcul the mask position with numpy : 0.0009007453918457031 nb_pixel_total : 13534 time to create 1 rle with old method : 0.015451908111572266 length of segment : 131 time for calcul the mask position with numpy : 0.0012652873992919922 nb_pixel_total : 12283 time to create 1 rle with old method : 0.014600515365600586 length of segment : 131 time for calcul the mask position with numpy : 0.0017108917236328125 nb_pixel_total : 23734 time to create 1 rle with old method : 0.02728724479675293 length of segment : 164 time for calcul the mask position with numpy : 0.001744985580444336 nb_pixel_total : 20371 time to create 1 rle with old method : 0.02390265464782715 length of segment : 142 time for calcul the mask position with numpy : 0.002551555633544922 nb_pixel_total : 26370 time to create 1 rle with old method : 0.029754161834716797 length of segment : 265 time for calcul the mask position with numpy : 0.0018460750579833984 nb_pixel_total : 25589 time to create 1 rle with old method : 0.02950286865234375 length of segment : 194 time for calcul the mask position with numpy : 0.0015289783477783203 nb_pixel_total : 23174 time to create 1 rle with old method : 0.027315378189086914 length of segment : 114 time for calcul the mask position with numpy : 0.003793478012084961 nb_pixel_total : 33519 time to create 1 rle with old method : 0.03935861587524414 length of segment : 212 time for calcul the mask position with numpy : 0.0016427040100097656 nb_pixel_total : 24079 time to create 1 rle with old method : 0.030363798141479492 length of segment : 232 time for calcul the mask position with numpy : 0.002228260040283203 nb_pixel_total : 27941 time to create 1 rle with old method : 0.03159475326538086 length of segment : 278 time for calcul the mask position with numpy : 0.001847982406616211 nb_pixel_total : 24712 time to create 1 rle with old method : 0.02895379066467285 length of segment : 175 time for calcul the mask position with numpy : 0.0008053779602050781 nb_pixel_total : 9687 time to create 1 rle with old method : 0.011480093002319336 length of segment : 114 time for calcul the mask position with numpy : 0.0006825923919677734 nb_pixel_total : 9372 time to create 1 rle with old method : 0.011352777481079102 length of segment : 91 time for calcul the mask position with numpy : 0.0014541149139404297 nb_pixel_total : 22889 time to create 1 rle with old method : 0.025807619094848633 length of segment : 242 time for calcul the mask position with numpy : 0.0022323131561279297 nb_pixel_total : 31107 time to create 1 rle with old method : 0.035822153091430664 length of segment : 198 time for calcul the mask position with numpy : 0.001890420913696289 nb_pixel_total : 25444 time to create 1 rle with old method : 0.029008150100708008 length of segment : 186 time for calcul the mask position with numpy : 0.0010528564453125 nb_pixel_total : 14152 time to create 1 rle with old method : 0.01720714569091797 length of segment : 141 time for calcul the mask position with numpy : 0.0022737979888916016 nb_pixel_total : 16885 time to create 1 rle with old method : 0.01946401596069336 length of segment : 161 time for calcul the mask position with numpy : 0.0010209083557128906 nb_pixel_total : 14792 time to create 1 rle with old method : 0.017388343811035156 length of segment : 167 time for calcul the mask position with numpy : 0.0006110668182373047 nb_pixel_total : 10045 time to create 1 rle with old method : 0.01173257827758789 length of segment : 102 time for calcul the mask position with numpy : 0.003216981887817383 nb_pixel_total : 24546 time to create 1 rle with old method : 0.028780698776245117 length of segment : 137 time for calcul the mask position with numpy : 0.0003101825714111328 nb_pixel_total : 2698 time to create 1 rle with old method : 0.003289461135864258 length of segment : 63 time for calcul the mask position with numpy : 0.005015373229980469 nb_pixel_total : 55533 time to create 1 rle with old method : 0.06547093391418457 length of segment : 350 time for calcul the mask position with numpy : 0.0029549598693847656 nb_pixel_total : 23816 time to create 1 rle with old method : 0.028674602508544922 length of segment : 244 time for calcul the mask position with numpy : 0.0006015300750732422 nb_pixel_total : 7439 time to create 1 rle with old method : 0.008802652359008789 length of segment : 111 time for calcul the mask position with numpy : 0.01217961311340332 nb_pixel_total : 65967 time to create 1 rle with old method : 0.07486391067504883 length of segment : 489 time for calcul the mask position with numpy : 0.0009829998016357422 nb_pixel_total : 17493 time to create 1 rle with old method : 0.02095937728881836 length of segment : 148 time for calcul the mask position with numpy : 0.002565622329711914 nb_pixel_total : 38409 time to create 1 rle with old method : 0.04308438301086426 length of segment : 224 time for calcul the mask position with numpy : 0.0013206005096435547 nb_pixel_total : 17640 time to create 1 rle with old method : 0.02118206024169922 length of segment : 233 time for calcul the mask position with numpy : 0.003078460693359375 nb_pixel_total : 28715 time to create 1 rle with old method : 0.03678107261657715 length of segment : 208 time for calcul the mask position with numpy : 0.0007283687591552734 nb_pixel_total : 8455 time to create 1 rle with old method : 0.00983572006225586 length of segment : 140 time for calcul the mask position with numpy : 0.002698659896850586 nb_pixel_total : 44075 time to create 1 rle with old method : 0.049742698669433594 length of segment : 232 time for calcul the mask position with numpy : 0.000576019287109375 nb_pixel_total : 6303 time to create 1 rle with old method : 0.007422685623168945 length of segment : 86 time for calcul the mask position with numpy : 0.0009067058563232422 nb_pixel_total : 12264 time to create 1 rle with old method : 0.01455378532409668 length of segment : 95 time for calcul the mask position with numpy : 0.001245737075805664 nb_pixel_total : 14463 time to create 1 rle with old method : 0.01646137237548828 length of segment : 138 time for calcul the mask position with numpy : 0.0009105205535888672 nb_pixel_total : 10397 time to create 1 rle with old method : 0.012149333953857422 length of segment : 160 time for calcul the mask position with numpy : 0.0004813671112060547 nb_pixel_total : 13912 time to create 1 rle with old method : 0.016309499740600586 length of segment : 195 time for calcul the mask position with numpy : 0.004309654235839844 nb_pixel_total : 63959 time to create 1 rle with old method : 0.07325339317321777 length of segment : 330 time for calcul the mask position with numpy : 0.002004861831665039 nb_pixel_total : 23228 time to create 1 rle with old method : 0.028279781341552734 length of segment : 226 time for calcul the mask position with numpy : 0.00424504280090332 nb_pixel_total : 58632 time to create 1 rle with old method : 0.06569480895996094 length of segment : 256 time for calcul the mask position with numpy : 0.00034546852111816406 nb_pixel_total : 5104 time to create 1 rle with old method : 0.006188631057739258 length of segment : 60 time for calcul the mask position with numpy : 0.0035715103149414062 nb_pixel_total : 41914 time to create 1 rle with old method : 0.04766583442687988 length of segment : 274 time for calcul the mask position with numpy : 0.0019099712371826172 nb_pixel_total : 31674 time to create 1 rle with old method : 0.03649282455444336 length of segment : 247 time for calcul the mask position with numpy : 0.0007677078247070312 nb_pixel_total : 11299 time to create 1 rle with old method : 0.01314687728881836 length of segment : 114 time for calcul the mask position with numpy : 0.002844572067260742 nb_pixel_total : 34637 time to create 1 rle with old method : 0.03974199295043945 length of segment : 247 time for calcul the mask position with numpy : 0.004158496856689453 nb_pixel_total : 54168 time to create 1 rle with old method : 0.07838821411132812 length of segment : 327 time for calcul the mask position with numpy : 0.0005292892456054688 nb_pixel_total : 6558 time to create 1 rle with old method : 0.007846832275390625 length of segment : 107 time for calcul the mask position with numpy : 0.001941680908203125 nb_pixel_total : 18249 time to create 1 rle with old method : 0.022725820541381836 length of segment : 133 time for calcul the mask position with numpy : 0.00023102760314941406 nb_pixel_total : 5064 time to create 1 rle with old method : 0.006616115570068359 length of segment : 67 time for calcul the mask position with numpy : 0.0003762245178222656 nb_pixel_total : 3997 time to create 1 rle with old method : 0.005055427551269531 length of segment : 57 time for calcul the mask position with numpy : 0.0008299350738525391 nb_pixel_total : 7028 time to create 1 rle with old method : 0.008577108383178711 length of segment : 126 time for calcul the mask position with numpy : 0.004758119583129883 nb_pixel_total : 62596 time to create 1 rle with old method : 0.07554817199707031 length of segment : 265 time for calcul the mask position with numpy : 0.0007965564727783203 nb_pixel_total : 12434 time to create 1 rle with old method : 0.015105724334716797 length of segment : 151 time for calcul the mask position with numpy : 0.002085447311401367 nb_pixel_total : 20350 time to create 1 rle with old method : 0.024690628051757812 length of segment : 233 time for calcul the mask position with numpy : 0.0006952285766601562 nb_pixel_total : 6015 time to create 1 rle with old method : 0.00865316390991211 length of segment : 107 time for calcul the mask position with numpy : 0.001062631607055664 nb_pixel_total : 12734 time to create 1 rle with old method : 0.014811038970947266 length of segment : 177 time for calcul the mask position with numpy : 0.001123666763305664 nb_pixel_total : 9894 time to create 1 rle with old method : 0.011615991592407227 length of segment : 148 time for calcul the mask position with numpy : 0.0013000965118408203 nb_pixel_total : 13648 time to create 1 rle with old method : 0.015650272369384766 length of segment : 173 time for calcul the mask position with numpy : 0.007456779479980469 nb_pixel_total : 109085 time to create 1 rle with old method : 0.12607407569885254 length of segment : 362 time for calcul the mask position with numpy : 0.0006830692291259766 nb_pixel_total : 16398 time to create 1 rle with old method : 0.020828723907470703 length of segment : 126 time for calcul the mask position with numpy : 0.0023751258850097656 nb_pixel_total : 32101 time to create 1 rle with old method : 0.037842750549316406 length of segment : 284 time for calcul the mask position with numpy : 0.0011823177337646484 nb_pixel_total : 15891 time to create 1 rle with old method : 0.0183560848236084 length of segment : 168 time for calcul the mask position with numpy : 0.0019402503967285156 nb_pixel_total : 20326 time to create 1 rle with old method : 0.024322032928466797 length of segment : 195 time for calcul the mask position with numpy : 0.0014102458953857422 nb_pixel_total : 16944 time to create 1 rle with old method : 0.02055954933166504 length of segment : 140 time for calcul the mask position with numpy : 0.0028629302978515625 nb_pixel_total : 38493 time to create 1 rle with old method : 0.04332685470581055 length of segment : 244 time for calcul the mask position with numpy : 0.0033571720123291016 nb_pixel_total : 46766 time to create 1 rle with old method : 0.05163121223449707 length of segment : 411 time for calcul the mask position with numpy : 0.0009872913360595703 nb_pixel_total : 12387 time to create 1 rle with old method : 0.015829086303710938 length of segment : 114 time for calcul the mask position with numpy : 0.0017168521881103516 nb_pixel_total : 16728 time to create 1 rle with old method : 0.020512104034423828 length of segment : 208 time for calcul the mask position with numpy : 0.0004699230194091797 nb_pixel_total : 4720 time to create 1 rle with old method : 0.005787849426269531 length of segment : 85 time for calcul the mask position with numpy : 0.0021746158599853516 nb_pixel_total : 22354 time to create 1 rle with old method : 0.026674985885620117 length of segment : 220 time for calcul the mask position with numpy : 0.0007367134094238281 nb_pixel_total : 10368 time to create 1 rle with old method : 0.011976242065429688 length of segment : 132 time for calcul the mask position with numpy : 0.0031969547271728516 nb_pixel_total : 42072 time to create 1 rle with old method : 0.04744577407836914 length of segment : 286 time for calcul the mask position with numpy : 0.005952596664428711 nb_pixel_total : 63813 time to create 1 rle with old method : 0.07596111297607422 length of segment : 365 time for calcul the mask position with numpy : 0.0008151531219482422 nb_pixel_total : 11337 time to create 1 rle with old method : 0.013637065887451172 length of segment : 112 time for calcul the mask position with numpy : 0.0002219676971435547 nb_pixel_total : 2256 time to create 1 rle with old method : 0.0026543140411376953 length of segment : 66 time for calcul the mask position with numpy : 0.0005323886871337891 nb_pixel_total : 6056 time to create 1 rle with old method : 0.00731968879699707 length of segment : 108 time for calcul the mask position with numpy : 0.0008480548858642578 nb_pixel_total : 17762 time to create 1 rle with old method : 0.020734786987304688 length of segment : 129 time for calcul the mask position with numpy : 0.001354217529296875 nb_pixel_total : 19624 time to create 1 rle with old method : 0.022376060485839844 length of segment : 169 time for calcul the mask position with numpy : 0.0010898113250732422 nb_pixel_total : 21698 time to create 1 rle with old method : 0.0250399112701416 length of segment : 178 time for calcul the mask position with numpy : 0.0007605552673339844 nb_pixel_total : 11972 time to create 1 rle with old method : 0.014226436614990234 length of segment : 147 time for calcul the mask position with numpy : 0.0009059906005859375 nb_pixel_total : 15264 time to create 1 rle with old method : 0.0179288387298584 length of segment : 173 time for calcul the mask position with numpy : 0.00014519691467285156 nb_pixel_total : 3862 time to create 1 rle with old method : 0.0047931671142578125 length of segment : 69 time for calcul the mask position with numpy : 0.0007264614105224609 nb_pixel_total : 15472 time to create 1 rle with old method : 0.01767444610595703 length of segment : 165 time for calcul the mask position with numpy : 0.0003993511199951172 nb_pixel_total : 10069 time to create 1 rle with old method : 0.011772632598876953 length of segment : 126 time for calcul the mask position with numpy : 0.003762483596801758 nb_pixel_total : 45584 time to create 1 rle with old method : 0.050765037536621094 length of segment : 371 time for calcul the mask position with numpy : 0.0018849372863769531 nb_pixel_total : 28615 time to create 1 rle with old method : 0.03236699104309082 length of segment : 212 time for calcul the mask position with numpy : 0.0011093616485595703 nb_pixel_total : 21533 time to create 1 rle with old method : 0.025211334228515625 length of segment : 194 time for calcul the mask position with numpy : 0.0018432140350341797 nb_pixel_total : 27248 time to create 1 rle with old method : 0.03302001953125 length of segment : 175 time for calcul the mask position with numpy : 0.0002834796905517578 nb_pixel_total : 2716 time to create 1 rle with old method : 0.0033998489379882812 length of segment : 41 time for calcul the mask position with numpy : 0.0001971721649169922 nb_pixel_total : 3102 time to create 1 rle with old method : 0.0037119388580322266 length of segment : 81 time for calcul the mask position with numpy : 0.004144191741943359 nb_pixel_total : 77739 time to create 1 rle with old method : 0.08595561981201172 length of segment : 487 time for calcul the mask position with numpy : 0.00032520294189453125 nb_pixel_total : 3101 time to create 1 rle with old method : 0.0037293434143066406 length of segment : 75 time for calcul the mask position with numpy : 0.0018436908721923828 nb_pixel_total : 20755 time to create 1 rle with old method : 0.02407050132751465 length of segment : 201 time for calcul the mask position with numpy : 0.0002574920654296875 nb_pixel_total : 2716 time to create 1 rle with old method : 0.003322124481201172 length of segment : 74 time for calcul the mask position with numpy : 0.0006108283996582031 nb_pixel_total : 6812 time to create 1 rle with old method : 0.007931947708129883 length of segment : 134 time for calcul the mask position with numpy : 0.0014271736145019531 nb_pixel_total : 26908 time to create 1 rle with old method : 0.030478715896606445 length of segment : 249 time for calcul the mask position with numpy : 0.0012469291687011719 nb_pixel_total : 18612 time to create 1 rle with old method : 0.02180194854736328 length of segment : 149 time for calcul the mask position with numpy : 0.001466512680053711 nb_pixel_total : 18350 time to create 1 rle with old method : 0.021306276321411133 length of segment : 142 time for calcul the mask position with numpy : 0.001531839370727539 nb_pixel_total : 13487 time to create 1 rle with old method : 0.01574397087097168 length of segment : 163 time for calcul the mask position with numpy : 0.0008709430694580078 nb_pixel_total : 13413 time to create 1 rle with old method : 0.015582084655761719 length of segment : 130 time for calcul the mask position with numpy : 0.002904176712036133 nb_pixel_total : 51367 time to create 1 rle with old method : 0.05818033218383789 length of segment : 250 time for calcul the mask position with numpy : 0.0011479854583740234 nb_pixel_total : 21334 time to create 1 rle with old method : 0.02444744110107422 length of segment : 158 time for calcul the mask position with numpy : 0.001062631607055664 nb_pixel_total : 16576 time to create 1 rle with old method : 0.019022226333618164 length of segment : 158 time for calcul the mask position with numpy : 0.0010180473327636719 nb_pixel_total : 11556 time to create 1 rle with old method : 0.013788938522338867 length of segment : 185 time for calcul the mask position with numpy : 0.0013222694396972656 nb_pixel_total : 13759 time to create 1 rle with old method : 0.01671314239501953 length of segment : 213 time for calcul the mask position with numpy : 0.0009639263153076172 nb_pixel_total : 17777 time to create 1 rle with old method : 0.020396709442138672 length of segment : 147 time for calcul the mask position with numpy : 0.002626657485961914 nb_pixel_total : 51803 time to create 1 rle with old method : 0.05944085121154785 length of segment : 280 time for calcul the mask position with numpy : 0.002977132797241211 nb_pixel_total : 43807 time to create 1 rle with old method : 0.04941606521606445 length of segment : 314 time for calcul the mask position with numpy : 0.0005071163177490234 nb_pixel_total : 3996 time to create 1 rle with old method : 0.004861116409301758 length of segment : 75 time for calcul the mask position with numpy : 0.0010333061218261719 nb_pixel_total : 34559 time to create 1 rle with old method : 0.03968620300292969 length of segment : 257 time for calcul the mask position with numpy : 0.0001723766326904297 nb_pixel_total : 3147 time to create 1 rle with old method : 0.0038657188415527344 length of segment : 56 time for calcul the mask position with numpy : 0.004836320877075195 nb_pixel_total : 59892 time to create 1 rle with old method : 0.06698226928710938 length of segment : 396 time for calcul the mask position with numpy : 0.0004734992980957031 nb_pixel_total : 12247 time to create 1 rle with old method : 0.014313697814941406 length of segment : 124 time for calcul the mask position with numpy : 0.0012614727020263672 nb_pixel_total : 15018 time to create 1 rle with old method : 0.017475605010986328 length of segment : 141 time for calcul the mask position with numpy : 0.0007936954498291016 nb_pixel_total : 8254 time to create 1 rle with old method : 0.010108232498168945 length of segment : 104 time for calcul the mask position with numpy : 0.0010294914245605469 nb_pixel_total : 9379 time to create 1 rle with old method : 0.011277437210083008 length of segment : 138 time for calcul the mask position with numpy : 0.002532482147216797 nb_pixel_total : 20300 time to create 1 rle with old method : 0.023229360580444336 length of segment : 258 time for calcul the mask position with numpy : 0.0020961761474609375 nb_pixel_total : 20139 time to create 1 rle with old method : 0.023587703704833984 length of segment : 206 time for calcul the mask position with numpy : 0.0011658668518066406 nb_pixel_total : 13615 time to create 1 rle with old method : 0.015971660614013672 length of segment : 146 time for calcul the mask position with numpy : 0.00041031837463378906 nb_pixel_total : 4418 time to create 1 rle with old method : 0.005407571792602539 length of segment : 83 time for calcul the mask position with numpy : 0.00115966796875 nb_pixel_total : 14731 time to create 1 rle with old method : 0.01692938804626465 length of segment : 157 time for calcul the mask position with numpy : 0.001024484634399414 nb_pixel_total : 13748 time to create 1 rle with old method : 0.015926122665405273 length of segment : 143 time for calcul the mask position with numpy : 0.001984119415283203 nb_pixel_total : 24817 time to create 1 rle with old method : 0.02868938446044922 length of segment : 186 time for calcul the mask position with numpy : 0.000989675521850586 nb_pixel_total : 11327 time to create 1 rle with old method : 0.013210773468017578 length of segment : 122 time for calcul the mask position with numpy : 0.0028235912322998047 nb_pixel_total : 39343 time to create 1 rle with old method : 0.0446627140045166 length of segment : 261 time for calcul the mask position with numpy : 0.00093841552734375 nb_pixel_total : 13373 time to create 1 rle with old method : 0.016182661056518555 length of segment : 172 time for calcul the mask position with numpy : 0.0023484230041503906 nb_pixel_total : 23319 time to create 1 rle with old method : 0.026833057403564453 length of segment : 329 time for calcul the mask position with numpy : 0.0014774799346923828 nb_pixel_total : 16831 time to create 1 rle with old method : 0.019177675247192383 length of segment : 168 time for calcul the mask position with numpy : 0.0016372203826904297 nb_pixel_total : 14724 time to create 1 rle with old method : 0.01730179786682129 length of segment : 169 time for calcul the mask position with numpy : 0.0011224746704101562 nb_pixel_total : 16650 time to create 1 rle with old method : 0.01949477195739746 length of segment : 203 time for calcul the mask position with numpy : 0.0016019344329833984 nb_pixel_total : 29399 time to create 1 rle with old method : 0.033310890197753906 length of segment : 211 time for calcul the mask position with numpy : 0.0030384063720703125 nb_pixel_total : 51502 time to create 1 rle with old method : 0.06572341918945312 length of segment : 211 time for calcul the mask position with numpy : 0.001065969467163086 nb_pixel_total : 6422 time to create 1 rle with old method : 0.010319948196411133 length of segment : 97 time for calcul the mask position with numpy : 0.003560781478881836 nb_pixel_total : 47632 time to create 1 rle with old method : 0.05348849296569824 length of segment : 292 time for calcul the mask position with numpy : 0.0010342597961425781 nb_pixel_total : 12787 time to create 1 rle with old method : 0.015636682510375977 length of segment : 122 time for calcul the mask position with numpy : 0.0015666484832763672 nb_pixel_total : 18733 time to create 1 rle with old method : 0.022005319595336914 length of segment : 185 time for calcul the mask position with numpy : 0.0010075569152832031 nb_pixel_total : 7076 time to create 1 rle with old method : 0.008553743362426758 length of segment : 123 time for calcul the mask position with numpy : 0.000827789306640625 nb_pixel_total : 9525 time to create 1 rle with old method : 0.011825799942016602 length of segment : 88 time for calcul the mask position with numpy : 0.007139921188354492 nb_pixel_total : 115272 time to create 1 rle with old method : 0.12816548347473145 length of segment : 288 time for calcul the mask position with numpy : 0.0013117790222167969 nb_pixel_total : 16061 time to create 1 rle with old method : 0.01847553253173828 length of segment : 161 time for calcul the mask position with numpy : 0.005570411682128906 nb_pixel_total : 88068 time to create 1 rle with old method : 0.12170767784118652 length of segment : 364 time for calcul the mask position with numpy : 0.00045680999755859375 nb_pixel_total : 5928 time to create 1 rle with old method : 0.007115364074707031 length of segment : 79 time for calcul the mask position with numpy : 0.0008027553558349609 nb_pixel_total : 11687 time to create 1 rle with old method : 0.013679742813110352 length of segment : 100 time for calcul the mask position with numpy : 0.0016360282897949219 nb_pixel_total : 24775 time to create 1 rle with old method : 0.02848196029663086 length of segment : 135 time for calcul the mask position with numpy : 0.001481771469116211 nb_pixel_total : 23983 time to create 1 rle with old method : 0.027786970138549805 length of segment : 179 time for calcul the mask position with numpy : 0.0031118392944335938 nb_pixel_total : 54421 time to create 1 rle with old method : 0.06205558776855469 length of segment : 272 time for calcul the mask position with numpy : 0.0017566680908203125 nb_pixel_total : 31198 time to create 1 rle with old method : 0.034877777099609375 length of segment : 212 time for calcul the mask position with numpy : 0.00043511390686035156 nb_pixel_total : 5580 time to create 1 rle with old method : 0.0066776275634765625 length of segment : 69 time for calcul the mask position with numpy : 0.0005664825439453125 nb_pixel_total : 9691 time to create 1 rle with old method : 0.011435270309448242 length of segment : 146 time for calcul the mask position with numpy : 0.0017094612121582031 nb_pixel_total : 18832 time to create 1 rle with old method : 0.021831035614013672 length of segment : 324 time for calcul the mask position with numpy : 0.001306295394897461 nb_pixel_total : 16555 time to create 1 rle with old method : 0.019020795822143555 length of segment : 173 time for calcul the mask position with numpy : 0.0013706684112548828 nb_pixel_total : 13607 time to create 1 rle with old method : 0.016911745071411133 length of segment : 148 time for calcul the mask position with numpy : 0.0008101463317871094 nb_pixel_total : 12633 time to create 1 rle with old method : 0.014752388000488281 length of segment : 147 time for calcul the mask position with numpy : 0.0016584396362304688 nb_pixel_total : 23779 time to create 1 rle with old method : 0.026836872100830078 length of segment : 224 time for calcul the mask position with numpy : 0.00040531158447265625 nb_pixel_total : 6493 time to create 1 rle with old method : 0.007596731185913086 length of segment : 87 time for calcul the mask position with numpy : 0.0005338191986083984 nb_pixel_total : 4304 time to create 1 rle with old method : 0.005242347717285156 length of segment : 110 time for calcul the mask position with numpy : 0.0003197193145751953 nb_pixel_total : 5680 time to create 1 rle with old method : 0.006753444671630859 length of segment : 73 time for calcul the mask position with numpy : 0.00046253204345703125 nb_pixel_total : 1317 time to create 1 rle with old method : 0.0019299983978271484 length of segment : 59 time for calcul the mask position with numpy : 0.0003142356872558594 nb_pixel_total : 7570 time to create 1 rle with old method : 0.008974790573120117 length of segment : 98 time for calcul the mask position with numpy : 0.0012061595916748047 nb_pixel_total : 17593 time to create 1 rle with old method : 0.02067732810974121 length of segment : 210 time for calcul the mask position with numpy : 0.0003788471221923828 nb_pixel_total : 8199 time to create 1 rle with old method : 0.009820938110351562 length of segment : 103 time for calcul the mask position with numpy : 0.0016477108001708984 nb_pixel_total : 18547 time to create 1 rle with old method : 0.02209758758544922 length of segment : 178 time for calcul the mask position with numpy : 0.002254486083984375 nb_pixel_total : 23489 time to create 1 rle with old method : 0.027480363845825195 length of segment : 220 time for calcul the mask position with numpy : 0.0008785724639892578 nb_pixel_total : 14698 time to create 1 rle with old method : 0.01790022850036621 length of segment : 79 time for calcul the mask position with numpy : 0.0004818439483642578 nb_pixel_total : 6375 time to create 1 rle with old method : 0.0076792240142822266 length of segment : 101 time for calcul the mask position with numpy : 0.013443231582641602 nb_pixel_total : 175596 time to create 1 rle with new method : 0.016829729080200195 length of segment : 544 time for calcul the mask position with numpy : 0.010704755783081055 nb_pixel_total : 21126 time to create 1 rle with old method : 0.02562689781188965 length of segment : 265 time for calcul the mask position with numpy : 0.0003521442413330078 nb_pixel_total : 3083 time to create 1 rle with old method : 0.003833293914794922 length of segment : 57 time for calcul the mask position with numpy : 0.0010187625885009766 nb_pixel_total : 18856 time to create 1 rle with old method : 0.021628379821777344 length of segment : 182 time for calcul the mask position with numpy : 0.005750179290771484 nb_pixel_total : 26563 time to create 1 rle with old method : 0.031144142150878906 length of segment : 191 time for calcul the mask position with numpy : 0.002735614776611328 nb_pixel_total : 31665 time to create 1 rle with old method : 0.03595399856567383 length of segment : 263 time for calcul the mask position with numpy : 0.004297494888305664 nb_pixel_total : 63880 time to create 1 rle with old method : 0.0712132453918457 length of segment : 246 time for calcul the mask position with numpy : 0.0015900135040283203 nb_pixel_total : 21594 time to create 1 rle with old method : 0.025255680084228516 length of segment : 263 time for calcul the mask position with numpy : 0.0010042190551757812 nb_pixel_total : 14972 time to create 1 rle with old method : 0.018428564071655273 length of segment : 185 time for calcul the mask position with numpy : 0.001165151596069336 nb_pixel_total : 17740 time to create 1 rle with old method : 0.02064681053161621 length of segment : 122 time for calcul the mask position with numpy : 0.0009908676147460938 nb_pixel_total : 19473 time to create 1 rle with old method : 0.022875308990478516 length of segment : 179 time for calcul the mask position with numpy : 0.00013518333435058594 nb_pixel_total : 4613 time to create 1 rle with old method : 0.005383729934692383 length of segment : 121 time for calcul the mask position with numpy : 0.00015211105346679688 nb_pixel_total : 2248 time to create 1 rle with old method : 0.0029058456420898438 length of segment : 29 time for calcul the mask position with numpy : 0.00023245811462402344 nb_pixel_total : 4269 time to create 1 rle with old method : 0.005023002624511719 length of segment : 94 time for calcul the mask position with numpy : 0.0006775856018066406 nb_pixel_total : 14227 time to create 1 rle with old method : 0.016794919967651367 length of segment : 199 time for calcul the mask position with numpy : 0.0035665035247802734 nb_pixel_total : 58098 time to create 1 rle with old method : 0.06491613388061523 length of segment : 415 time for calcul the mask position with numpy : 0.0012052059173583984 nb_pixel_total : 16402 time to create 1 rle with old method : 0.018761157989501953 length of segment : 199 time for calcul the mask position with numpy : 0.0015931129455566406 nb_pixel_total : 23351 time to create 1 rle with old method : 0.02696061134338379 length of segment : 176 time for calcul the mask position with numpy : 0.0014791488647460938 nb_pixel_total : 23172 time to create 1 rle with old method : 0.02648472785949707 length of segment : 147 time for calcul the mask position with numpy : 0.0009207725524902344 nb_pixel_total : 14289 time to create 1 rle with old method : 0.016287565231323242 length of segment : 171 time for calcul the mask position with numpy : 0.0012845993041992188 nb_pixel_total : 13029 time to create 1 rle with old method : 0.01551961898803711 length of segment : 154 time for calcul the mask position with numpy : 0.018320083618164062 nb_pixel_total : 374789 time to create 1 rle with new method : 0.027628183364868164 length of segment : 922 time for calcul the mask position with numpy : 0.0010988712310791016 nb_pixel_total : 12283 time to create 1 rle with old method : 0.02052450180053711 length of segment : 176 time for calcul the mask position with numpy : 0.0010161399841308594 nb_pixel_total : 29338 time to create 1 rle with old method : 0.03336977958679199 length of segment : 201 time for calcul the mask position with numpy : 0.0014812946319580078 nb_pixel_total : 20232 time to create 1 rle with old method : 0.023169279098510742 length of segment : 113 time for calcul the mask position with numpy : 0.0008227825164794922 nb_pixel_total : 10166 time to create 1 rle with old method : 0.012339115142822266 length of segment : 121 time for calcul the mask position with numpy : 0.0010187625885009766 nb_pixel_total : 10644 time to create 1 rle with old method : 0.01235198974609375 length of segment : 187 time for calcul the mask position with numpy : 0.0009644031524658203 nb_pixel_total : 13945 time to create 1 rle with old method : 0.016435861587524414 length of segment : 118 time for calcul the mask position with numpy : 0.0017390251159667969 nb_pixel_total : 23652 time to create 1 rle with old method : 0.0298306941986084 length of segment : 199 time for calcul the mask position with numpy : 0.002918720245361328 nb_pixel_total : 40200 time to create 1 rle with old method : 0.04609990119934082 length of segment : 319 time for calcul the mask position with numpy : 0.0012362003326416016 nb_pixel_total : 17055 time to create 1 rle with old method : 0.01984429359436035 length of segment : 161 time for calcul the mask position with numpy : 0.002721548080444336 nb_pixel_total : 34658 time to create 1 rle with old method : 0.03954792022705078 length of segment : 256 time for calcul the mask position with numpy : 0.0006625652313232422 nb_pixel_total : 5354 time to create 1 rle with old method : 0.006585836410522461 length of segment : 180 time for calcul the mask position with numpy : 0.0008332729339599609 nb_pixel_total : 17623 time to create 1 rle with old method : 0.020862340927124023 length of segment : 166 time for calcul the mask position with numpy : 0.0009403228759765625 nb_pixel_total : 10612 time to create 1 rle with old method : 0.012202262878417969 length of segment : 143 time for calcul the mask position with numpy : 0.0006577968597412109 nb_pixel_total : 7640 time to create 1 rle with old method : 0.00938725471496582 length of segment : 137 time for calcul the mask position with numpy : 0.003461122512817383 nb_pixel_total : 29740 time to create 1 rle with old method : 0.033864498138427734 length of segment : 380 time for calcul the mask position with numpy : 0.001397848129272461 nb_pixel_total : 13259 time to create 1 rle with old method : 0.015650510787963867 length of segment : 246 time for calcul the mask position with numpy : 0.001963376998901367 nb_pixel_total : 20758 time to create 1 rle with old method : 0.02431511878967285 length of segment : 164 time for calcul the mask position with numpy : 0.0016644001007080078 nb_pixel_total : 13768 time to create 1 rle with old method : 0.016068220138549805 length of segment : 239 time for calcul the mask position with numpy : 0.002548694610595703 nb_pixel_total : 29048 time to create 1 rle with old method : 0.03861284255981445 length of segment : 131 time for calcul the mask position with numpy : 0.00040650367736816406 nb_pixel_total : 2050 time to create 1 rle with old method : 0.0031213760375976562 length of segment : 69 time for calcul the mask position with numpy : 0.0018832683563232422 nb_pixel_total : 22097 time to create 1 rle with old method : 0.03076958656311035 length of segment : 187 time for calcul the mask position with numpy : 0.0016448497772216797 nb_pixel_total : 23761 time to create 1 rle with old method : 0.026988744735717773 length of segment : 208 time for calcul the mask position with numpy : 0.0006449222564697266 nb_pixel_total : 9421 time to create 1 rle with old method : 0.011163473129272461 length of segment : 179 time for calcul the mask position with numpy : 0.0033257007598876953 nb_pixel_total : 41376 time to create 1 rle with old method : 0.0463566780090332 length of segment : 295 time for calcul the mask position with numpy : 0.0019974708557128906 nb_pixel_total : 30243 time to create 1 rle with old method : 0.03512406349182129 length of segment : 191 time for calcul the mask position with numpy : 0.0020494461059570312 nb_pixel_total : 41793 time to create 1 rle with old method : 0.04708433151245117 length of segment : 196 time for calcul the mask position with numpy : 0.0014095306396484375 nb_pixel_total : 21489 time to create 1 rle with old method : 0.024286270141601562 length of segment : 247 time for calcul the mask position with numpy : 0.000545501708984375 nb_pixel_total : 5808 time to create 1 rle with old method : 0.006750345230102539 length of segment : 95 time for calcul the mask position with numpy : 0.0017194747924804688 nb_pixel_total : 24707 time to create 1 rle with old method : 0.02822136878967285 length of segment : 174 time for calcul the mask position with numpy : 0.0018837451934814453 nb_pixel_total : 14872 time to create 1 rle with old method : 0.017716646194458008 length of segment : 179 time for calcul the mask position with numpy : 0.002765655517578125 nb_pixel_total : 30892 time to create 1 rle with old method : 0.03509211540222168 length of segment : 268 time for calcul the mask position with numpy : 0.0009279251098632812 nb_pixel_total : 12812 time to create 1 rle with old method : 0.01542806625366211 length of segment : 115 time for calcul the mask position with numpy : 0.0006399154663085938 nb_pixel_total : 10979 time to create 1 rle with old method : 0.012679100036621094 length of segment : 148 time for calcul the mask position with numpy : 0.001505136489868164 nb_pixel_total : 23648 time to create 1 rle with old method : 0.02698826789855957 length of segment : 217 time for calcul the mask position with numpy : 0.0018782615661621094 nb_pixel_total : 34907 time to create 1 rle with old method : 0.03977036476135254 length of segment : 317 time for calcul the mask position with numpy : 0.0036575794219970703 nb_pixel_total : 44108 time to create 1 rle with old method : 0.05050373077392578 length of segment : 290 time for calcul the mask position with numpy : 0.0004374980926513672 nb_pixel_total : 4351 time to create 1 rle with old method : 0.005112886428833008 length of segment : 138 time for calcul the mask position with numpy : 0.003692150115966797 nb_pixel_total : 64503 time to create 1 rle with old method : 0.07179379463195801 length of segment : 266 time for calcul the mask position with numpy : 0.0001552104949951172 nb_pixel_total : 2649 time to create 1 rle with old method : 0.003153562545776367 length of segment : 63 time for calcul the mask position with numpy : 0.0013120174407958984 nb_pixel_total : 12778 time to create 1 rle with old method : 0.014579296112060547 length of segment : 142 time for calcul the mask position with numpy : 0.0004363059997558594 nb_pixel_total : 6297 time to create 1 rle with old method : 0.007480621337890625 length of segment : 103 time for calcul the mask position with numpy : 0.0012807846069335938 nb_pixel_total : 21159 time to create 1 rle with old method : 0.023895263671875 length of segment : 184 time for calcul the mask position with numpy : 0.0020546913146972656 nb_pixel_total : 28767 time to create 1 rle with old method : 0.03460192680358887 length of segment : 304 time for calcul the mask position with numpy : 0.003238201141357422 nb_pixel_total : 29900 time to create 1 rle with old method : 0.03831911087036133 length of segment : 272 time for calcul the mask position with numpy : 0.0011322498321533203 nb_pixel_total : 11745 time to create 1 rle with old method : 0.013249874114990234 length of segment : 182 time for calcul the mask position with numpy : 0.0009560585021972656 nb_pixel_total : 12193 time to create 1 rle with old method : 0.014260292053222656 length of segment : 146 time for calcul the mask position with numpy : 0.0020499229431152344 nb_pixel_total : 26075 time to create 1 rle with old method : 0.029112815856933594 length of segment : 234 time for calcul the mask position with numpy : 0.0008413791656494141 nb_pixel_total : 5340 time to create 1 rle with old method : 0.0061376094818115234 length of segment : 129 time for calcul the mask position with numpy : 0.003833293914794922 nb_pixel_total : 41951 time to create 1 rle with old method : 0.04778766632080078 length of segment : 487 time for calcul the mask position with numpy : 0.0010230541229248047 nb_pixel_total : 16021 time to create 1 rle with old method : 0.018172502517700195 length of segment : 134 time for calcul the mask position with numpy : 0.00099945068359375 nb_pixel_total : 15017 time to create 1 rle with old method : 0.01737046241760254 length of segment : 213 time for calcul the mask position with numpy : 0.00046515464782714844 nb_pixel_total : 7619 time to create 1 rle with old method : 0.009006261825561523 length of segment : 58 time for calcul the mask position with numpy : 0.0004894733428955078 nb_pixel_total : 6418 time to create 1 rle with old method : 0.007696628570556641 length of segment : 63 time for calcul the mask position with numpy : 0.00571894645690918 nb_pixel_total : 91685 time to create 1 rle with old method : 0.10077571868896484 length of segment : 332 time for calcul the mask position with numpy : 0.00229644775390625 nb_pixel_total : 35922 time to create 1 rle with old method : 0.03975820541381836 length of segment : 341 time for calcul the mask position with numpy : 0.0022673606872558594 nb_pixel_total : 14848 time to create 1 rle with old method : 0.01711559295654297 length of segment : 204 time for calcul the mask position with numpy : 0.0009257793426513672 nb_pixel_total : 12433 time to create 1 rle with old method : 0.014101028442382812 length of segment : 154 time for calcul the mask position with numpy : 0.0003936290740966797 nb_pixel_total : 4688 time to create 1 rle with old method : 0.005456686019897461 length of segment : 123 time for calcul the mask position with numpy : 0.00036334991455078125 nb_pixel_total : 3927 time to create 1 rle with old method : 0.0047185420989990234 length of segment : 88 time for calcul the mask position with numpy : 0.0010213851928710938 nb_pixel_total : 10607 time to create 1 rle with old method : 0.012111186981201172 length of segment : 147 time for calcul the mask position with numpy : 0.0009350776672363281 nb_pixel_total : 10550 time to create 1 rle with old method : 0.012222051620483398 length of segment : 114 time for calcul the mask position with numpy : 0.0017743110656738281 nb_pixel_total : 19360 time to create 1 rle with old method : 0.0217437744140625 length of segment : 188 time for calcul the mask position with numpy : 0.0040569305419921875 nb_pixel_total : 67988 time to create 1 rle with old method : 0.07811188697814941 length of segment : 352 time for calcul the mask position with numpy : 0.000499725341796875 nb_pixel_total : 19073 time to create 1 rle with old method : 0.03493666648864746 length of segment : 188 time for calcul the mask position with numpy : 0.0015645027160644531 nb_pixel_total : 21811 time to create 1 rle with old method : 0.024399518966674805 length of segment : 303 time for calcul the mask position with numpy : 0.0015392303466796875 nb_pixel_total : 24455 time to create 1 rle with old method : 0.029815196990966797 length of segment : 173 time for calcul the mask position with numpy : 0.004106283187866211 nb_pixel_total : 49027 time to create 1 rle with old method : 0.05828380584716797 length of segment : 363 time for calcul the mask position with numpy : 0.0011868476867675781 nb_pixel_total : 17113 time to create 1 rle with old method : 0.02197742462158203 length of segment : 116 time for calcul the mask position with numpy : 0.0005853176116943359 nb_pixel_total : 8495 time to create 1 rle with old method : 0.011949300765991211 length of segment : 75 time for calcul the mask position with numpy : 0.0010530948638916016 nb_pixel_total : 14654 time to create 1 rle with old method : 0.017087936401367188 length of segment : 98 time for calcul the mask position with numpy : 0.000766754150390625 nb_pixel_total : 7829 time to create 1 rle with old method : 0.009151697158813477 length of segment : 127 time for calcul the mask position with numpy : 0.0007596015930175781 nb_pixel_total : 10645 time to create 1 rle with old method : 0.01266789436340332 length of segment : 132 time for calcul the mask position with numpy : 0.002056598663330078 nb_pixel_total : 13465 time to create 1 rle with old method : 0.016439437866210938 length of segment : 275 time for calcul the mask position with numpy : 0.0010323524475097656 nb_pixel_total : 12613 time to create 1 rle with old method : 0.014487743377685547 length of segment : 157 time for calcul the mask position with numpy : 0.008260011672973633 nb_pixel_total : 127287 time to create 1 rle with old method : 0.14160394668579102 length of segment : 472 time for calcul the mask position with numpy : 0.0039098262786865234 nb_pixel_total : 46739 time to create 1 rle with old method : 0.05499434471130371 length of segment : 345 time for calcul the mask position with numpy : 0.0004885196685791016 nb_pixel_total : 8030 time to create 1 rle with old method : 0.009264469146728516 length of segment : 233 time for calcul the mask position with numpy : 0.0020551681518554688 nb_pixel_total : 29656 time to create 1 rle with old method : 0.03400301933288574 length of segment : 205 time for calcul the mask position with numpy : 0.0011429786682128906 nb_pixel_total : 11806 time to create 1 rle with old method : 0.014004945755004883 length of segment : 117 time for calcul the mask position with numpy : 0.000423431396484375 nb_pixel_total : 8567 time to create 1 rle with old method : 0.01028299331665039 length of segment : 86 time for calcul the mask position with numpy : 0.0006761550903320312 nb_pixel_total : 8620 time to create 1 rle with old method : 0.01020359992980957 length of segment : 114 time for calcul the mask position with numpy : 0.0008616447448730469 nb_pixel_total : 10323 time to create 1 rle with old method : 0.011928081512451172 length of segment : 123 time for calcul the mask position with numpy : 0.0005941390991210938 nb_pixel_total : 6892 time to create 1 rle with old method : 0.007873773574829102 length of segment : 160 time for calcul the mask position with numpy : 0.0006937980651855469 nb_pixel_total : 9408 time to create 1 rle with old method : 0.010646581649780273 length of segment : 169 time for calcul the mask position with numpy : 0.0031828880310058594 nb_pixel_total : 31520 time to create 1 rle with old method : 0.036098480224609375 length of segment : 262 time for calcul the mask position with numpy : 0.005685091018676758 nb_pixel_total : 83180 time to create 1 rle with old method : 0.09105896949768066 length of segment : 465 time for calcul the mask position with numpy : 0.004698753356933594 nb_pixel_total : 57290 time to create 1 rle with old method : 0.0646359920501709 length of segment : 316 time for calcul the mask position with numpy : 0.000736236572265625 nb_pixel_total : 6978 time to create 1 rle with old method : 0.008240938186645508 length of segment : 88 time for calcul the mask position with numpy : 0.0007081031799316406 nb_pixel_total : 10510 time to create 1 rle with old method : 0.012291669845581055 length of segment : 106 time for calcul the mask position with numpy : 0.0005292892456054688 nb_pixel_total : 7274 time to create 1 rle with old method : 0.008593320846557617 length of segment : 108 time for calcul the mask position with numpy : 0.0007455348968505859 nb_pixel_total : 11930 time to create 1 rle with old method : 0.01374053955078125 length of segment : 130 time for calcul the mask position with numpy : 0.003700733184814453 nb_pixel_total : 51430 time to create 1 rle with old method : 0.059602975845336914 length of segment : 225 time for calcul the mask position with numpy : 0.00058746337890625 nb_pixel_total : 9181 time to create 1 rle with old method : 0.010830879211425781 length of segment : 117 time for calcul the mask position with numpy : 0.003674030303955078 nb_pixel_total : 62189 time to create 1 rle with old method : 0.07003283500671387 length of segment : 258 time for calcul the mask position with numpy : 0.002424955368041992 nb_pixel_total : 26819 time to create 1 rle with old method : 0.030865192413330078 length of segment : 205 time for calcul the mask position with numpy : 0.0001480579376220703 nb_pixel_total : 4744 time to create 1 rle with old method : 0.0056455135345458984 length of segment : 83 time for calcul the mask position with numpy : 0.0026559829711914062 nb_pixel_total : 38319 time to create 1 rle with old method : 0.043084144592285156 length of segment : 389 time for calcul the mask position with numpy : 0.004007577896118164 nb_pixel_total : 37023 time to create 1 rle with old method : 0.06059575080871582 length of segment : 284 time for calcul the mask position with numpy : 0.0005033016204833984 nb_pixel_total : 11849 time to create 1 rle with old method : 0.013751506805419922 length of segment : 148 time for calcul the mask position with numpy : 0.0022389888763427734 nb_pixel_total : 20790 time to create 1 rle with old method : 0.027415752410888672 length of segment : 243 time for calcul the mask position with numpy : 0.0006706714630126953 nb_pixel_total : 7853 time to create 1 rle with old method : 0.013186931610107422 length of segment : 99 time for calcul the mask position with numpy : 0.0005145072937011719 nb_pixel_total : 4593 time to create 1 rle with old method : 0.005600929260253906 length of segment : 84 time for calcul the mask position with numpy : 0.00442957878112793 nb_pixel_total : 85508 time to create 1 rle with old method : 0.09440779685974121 length of segment : 235 time for calcul the mask position with numpy : 0.0014698505401611328 nb_pixel_total : 13080 time to create 1 rle with old method : 0.016736984252929688 length of segment : 164 time for calcul the mask position with numpy : 0.0007987022399902344 nb_pixel_total : 7694 time to create 1 rle with old method : 0.008988618850708008 length of segment : 90 time for calcul the mask position with numpy : 0.0005083084106445312 nb_pixel_total : 5462 time to create 1 rle with old method : 0.006482362747192383 length of segment : 87 time for calcul the mask position with numpy : 0.024359703063964844 nb_pixel_total : 145447 time to create 1 rle with old method : 0.1605679988861084 length of segment : 842 time for calcul the mask position with numpy : 0.0005095005035400391 nb_pixel_total : 6226 time to create 1 rle with old method : 0.007463693618774414 length of segment : 66 time for calcul the mask position with numpy : 0.0007777214050292969 nb_pixel_total : 8823 time to create 1 rle with old method : 0.010357379913330078 length of segment : 144 time for calcul the mask position with numpy : 0.0002601146697998047 nb_pixel_total : 2795 time to create 1 rle with old method : 0.0033416748046875 length of segment : 61 time for calcul the mask position with numpy : 0.0013370513916015625 nb_pixel_total : 29972 time to create 1 rle with old method : 0.03566861152648926 length of segment : 186 time for calcul the mask position with numpy : 0.009701728820800781 nb_pixel_total : 152692 time to create 1 rle with new method : 0.013400077819824219 length of segment : 276 time for calcul the mask position with numpy : 0.001129150390625 nb_pixel_total : 14428 time to create 1 rle with old method : 0.016692638397216797 length of segment : 188 time for calcul the mask position with numpy : 0.004399538040161133 nb_pixel_total : 103303 time to create 1 rle with old method : 0.11727643013000488 length of segment : 408 time for calcul the mask position with numpy : 0.0020074844360351562 nb_pixel_total : 19690 time to create 1 rle with old method : 0.02426910400390625 length of segment : 309 time for calcul the mask position with numpy : 0.0010535717010498047 nb_pixel_total : 13587 time to create 1 rle with old method : 0.015984535217285156 length of segment : 96 time for calcul the mask position with numpy : 0.0013124942779541016 nb_pixel_total : 19766 time to create 1 rle with old method : 0.02286219596862793 length of segment : 270 time for calcul the mask position with numpy : 0.0009648799896240234 nb_pixel_total : 8174 time to create 1 rle with old method : 0.009661197662353516 length of segment : 136 time for calcul the mask position with numpy : 0.0025081634521484375 nb_pixel_total : 33677 time to create 1 rle with old method : 0.03910040855407715 length of segment : 289 time for calcul the mask position with numpy : 0.0017905235290527344 nb_pixel_total : 36562 time to create 1 rle with old method : 0.04177689552307129 length of segment : 282 time for calcul the mask position with numpy : 0.004359245300292969 nb_pixel_total : 56495 time to create 1 rle with old method : 0.06367301940917969 length of segment : 363 time for calcul the mask position with numpy : 0.001232147216796875 nb_pixel_total : 10384 time to create 1 rle with old method : 0.012472867965698242 length of segment : 167 time for calcul the mask position with numpy : 0.0006937980651855469 nb_pixel_total : 14427 time to create 1 rle with old method : 0.01656055450439453 length of segment : 146 time for calcul the mask position with numpy : 0.0016205310821533203 nb_pixel_total : 30087 time to create 1 rle with old method : 0.034255027770996094 length of segment : 267 time for calcul the mask position with numpy : 0.0034513473510742188 nb_pixel_total : 46265 time to create 1 rle with old method : 0.052904367446899414 length of segment : 253 time for calcul the mask position with numpy : 0.0012156963348388672 nb_pixel_total : 19216 time to create 1 rle with old method : 0.02208232879638672 length of segment : 212 time for calcul the mask position with numpy : 0.0003864765167236328 nb_pixel_total : 7325 time to create 1 rle with old method : 0.008488178253173828 length of segment : 112 time for calcul the mask position with numpy : 0.0019555091857910156 nb_pixel_total : 34858 time to create 1 rle with old method : 0.03928565979003906 length of segment : 222 time for calcul the mask position with numpy : 0.002513408660888672 nb_pixel_total : 37411 time to create 1 rle with old method : 0.04240298271179199 length of segment : 257 time for calcul the mask position with numpy : 0.0004839897155761719 nb_pixel_total : 5257 time to create 1 rle with old method : 0.006234884262084961 length of segment : 94 time for calcul the mask position with numpy : 0.0019326210021972656 nb_pixel_total : 26273 time to create 1 rle with old method : 0.030346155166625977 length of segment : 183 time for calcul the mask position with numpy : 0.0006272792816162109 nb_pixel_total : 9462 time to create 1 rle with old method : 0.011228322982788086 length of segment : 116 time for calcul the mask position with numpy : 0.0006303787231445312 nb_pixel_total : 7155 time to create 1 rle with old method : 0.008573055267333984 length of segment : 121 time for calcul the mask position with numpy : 0.0007550716400146484 nb_pixel_total : 13576 time to create 1 rle with old method : 0.015372037887573242 length of segment : 157 time for calcul the mask position with numpy : 0.0009529590606689453 nb_pixel_total : 13228 time to create 1 rle with old method : 0.015316963195800781 length of segment : 167 time for calcul the mask position with numpy : 0.0014467239379882812 nb_pixel_total : 17550 time to create 1 rle with old method : 0.01992011070251465 length of segment : 259 time for calcul the mask position with numpy : 0.0023345947265625 nb_pixel_total : 25353 time to create 1 rle with old method : 0.029772043228149414 length of segment : 210 time for calcul the mask position with numpy : 0.0016932487487792969 nb_pixel_total : 21285 time to create 1 rle with old method : 0.023973703384399414 length of segment : 216 time for calcul the mask position with numpy : 0.0016164779663085938 nb_pixel_total : 19977 time to create 1 rle with old method : 0.023110389709472656 length of segment : 156 time for calcul the mask position with numpy : 0.0015707015991210938 nb_pixel_total : 20977 time to create 1 rle with old method : 0.023908615112304688 length of segment : 233 time for calcul the mask position with numpy : 0.0007412433624267578 nb_pixel_total : 12573 time to create 1 rle with old method : 0.014363288879394531 length of segment : 131 time for calcul the mask position with numpy : 0.001581430435180664 nb_pixel_total : 23581 time to create 1 rle with old method : 0.027748584747314453 length of segment : 143 time for calcul the mask position with numpy : 0.00045299530029296875 nb_pixel_total : 5543 time to create 1 rle with old method : 0.006582498550415039 length of segment : 77 time for calcul the mask position with numpy : 0.0008187294006347656 nb_pixel_total : 9855 time to create 1 rle with old method : 0.011252641677856445 length of segment : 140 time for calcul the mask position with numpy : 0.00036144256591796875 nb_pixel_total : 4194 time to create 1 rle with old method : 0.005118370056152344 length of segment : 88 time for calcul the mask position with numpy : 0.001491546630859375 nb_pixel_total : 10434 time to create 1 rle with old method : 0.012026786804199219 length of segment : 372 time for calcul the mask position with numpy : 0.003212451934814453 nb_pixel_total : 32077 time to create 1 rle with old method : 0.03697681427001953 length of segment : 184 time for calcul the mask position with numpy : 0.0018033981323242188 nb_pixel_total : 21919 time to create 1 rle with old method : 0.02538323402404785 length of segment : 132 time for calcul the mask position with numpy : 0.002203226089477539 nb_pixel_total : 26066 time to create 1 rle with old method : 0.03264927864074707 length of segment : 334 time for calcul the mask position with numpy : 0.0006856918334960938 nb_pixel_total : 10668 time to create 1 rle with old method : 0.012768030166625977 length of segment : 114 time for calcul the mask position with numpy : 0.0016469955444335938 nb_pixel_total : 26808 time to create 1 rle with old method : 0.03117513656616211 length of segment : 166 time for calcul the mask position with numpy : 0.00047206878662109375 nb_pixel_total : 4233 time to create 1 rle with old method : 0.0050122737884521484 length of segment : 66 time for calcul the mask position with numpy : 0.0011284351348876953 nb_pixel_total : 20146 time to create 1 rle with old method : 0.02291083335876465 length of segment : 156 time for calcul the mask position with numpy : 0.00070953369140625 nb_pixel_total : 7450 time to create 1 rle with old method : 0.008593320846557617 length of segment : 165 time for calcul the mask position with numpy : 0.002956867218017578 nb_pixel_total : 36773 time to create 1 rle with old method : 0.04202914237976074 length of segment : 313 time for calcul the mask position with numpy : 0.0008838176727294922 nb_pixel_total : 12513 time to create 1 rle with old method : 0.014664173126220703 length of segment : 208 time for calcul the mask position with numpy : 0.0012035369873046875 nb_pixel_total : 6650 time to create 1 rle with old method : 0.007857561111450195 length of segment : 160 time for calcul the mask position with numpy : 0.003392457962036133 nb_pixel_total : 52396 time to create 1 rle with old method : 0.05861496925354004 length of segment : 345 time for calcul the mask position with numpy : 0.001550912857055664 nb_pixel_total : 23397 time to create 1 rle with old method : 0.026999711990356445 length of segment : 178 time for calcul the mask position with numpy : 0.0007250308990478516 nb_pixel_total : 6776 time to create 1 rle with old method : 0.008148193359375 length of segment : 109 time for calcul the mask position with numpy : 0.0008840560913085938 nb_pixel_total : 10056 time to create 1 rle with old method : 0.012368202209472656 length of segment : 116 time for calcul the mask position with numpy : 0.0008509159088134766 nb_pixel_total : 10646 time to create 1 rle with old method : 0.01259469985961914 length of segment : 178 time for calcul the mask position with numpy : 0.0005588531494140625 nb_pixel_total : 4916 time to create 1 rle with old method : 0.006201028823852539 length of segment : 105 time for calcul the mask position with numpy : 0.0005280971527099609 nb_pixel_total : 11832 time to create 1 rle with old method : 0.013760089874267578 length of segment : 149 time for calcul the mask position with numpy : 0.008545875549316406 nb_pixel_total : 135901 time to create 1 rle with old method : 0.1516108512878418 length of segment : 411 time for calcul the mask position with numpy : 0.0007228851318359375 nb_pixel_total : 9832 time to create 1 rle with old method : 0.011789560317993164 length of segment : 112 time for calcul the mask position with numpy : 0.0005655288696289062 nb_pixel_total : 7566 time to create 1 rle with old method : 0.009279251098632812 length of segment : 54 time for calcul the mask position with numpy : 0.0007779598236083984 nb_pixel_total : 10917 time to create 1 rle with old method : 0.013044357299804688 length of segment : 158 time for calcul the mask position with numpy : 0.0006847381591796875 nb_pixel_total : 7830 time to create 1 rle with old method : 0.009271860122680664 length of segment : 116 time for calcul the mask position with numpy : 0.0014238357543945312 nb_pixel_total : 17511 time to create 1 rle with old method : 0.020473003387451172 length of segment : 180 time for calcul the mask position with numpy : 0.0024940967559814453 nb_pixel_total : 30300 time to create 1 rle with old method : 0.04957914352416992 length of segment : 192 time for calcul the mask position with numpy : 0.000965118408203125 nb_pixel_total : 12272 time to create 1 rle with old method : 0.020440101623535156 length of segment : 127 time for calcul the mask position with numpy : 0.0005011558532714844 nb_pixel_total : 4774 time to create 1 rle with old method : 0.008028030395507812 length of segment : 102 time for calcul the mask position with numpy : 0.0006344318389892578 nb_pixel_total : 12245 time to create 1 rle with old method : 0.014269351959228516 length of segment : 110 time for calcul the mask position with numpy : 0.0007123947143554688 nb_pixel_total : 9869 time to create 1 rle with old method : 0.011666536331176758 length of segment : 112 time for calcul the mask position with numpy : 0.0013206005096435547 nb_pixel_total : 13561 time to create 1 rle with old method : 0.016288042068481445 length of segment : 139 time for calcul the mask position with numpy : 0.0020568370819091797 nb_pixel_total : 35872 time to create 1 rle with old method : 0.04058527946472168 length of segment : 295 time for calcul the mask position with numpy : 0.0006642341613769531 nb_pixel_total : 7801 time to create 1 rle with old method : 0.00905609130859375 length of segment : 188 time for calcul the mask position with numpy : 0.0003972053527832031 nb_pixel_total : 6061 time to create 1 rle with old method : 0.007333040237426758 length of segment : 94 time for calcul the mask position with numpy : 0.0008184909820556641 nb_pixel_total : 15914 time to create 1 rle with old method : 0.018045902252197266 length of segment : 152 time for calcul the mask position with numpy : 0.0007278919219970703 nb_pixel_total : 13669 time to create 1 rle with old method : 0.015380859375 length of segment : 172 time for calcul the mask position with numpy : 0.00047516822814941406 nb_pixel_total : 11600 time to create 1 rle with old method : 0.013793230056762695 length of segment : 80 time for calcul the mask position with numpy : 0.0014603137969970703 nb_pixel_total : 23535 time to create 1 rle with old method : 0.028279781341552734 length of segment : 142 time for calcul the mask position with numpy : 0.020461320877075195 nb_pixel_total : 121055 time to create 1 rle with old method : 0.13377690315246582 length of segment : 761 time for calcul the mask position with numpy : 0.0002295970916748047 nb_pixel_total : 3357 time to create 1 rle with old method : 0.004014015197753906 length of segment : 63 time for calcul the mask position with numpy : 0.0009272098541259766 nb_pixel_total : 27581 time to create 1 rle with old method : 0.0309906005859375 length of segment : 170 time for calcul the mask position with numpy : 0.0009303092956542969 nb_pixel_total : 11624 time to create 1 rle with old method : 0.013609170913696289 length of segment : 137 time for calcul the mask position with numpy : 0.0006403923034667969 nb_pixel_total : 10439 time to create 1 rle with old method : 0.012365579605102539 length of segment : 96 time for calcul the mask position with numpy : 0.0025615692138671875 nb_pixel_total : 29339 time to create 1 rle with old method : 0.032700538635253906 length of segment : 187 time for calcul the mask position with numpy : 0.0011739730834960938 nb_pixel_total : 16022 time to create 1 rle with old method : 0.01862931251525879 length of segment : 200 time for calcul the mask position with numpy : 0.007077455520629883 nb_pixel_total : 88531 time to create 1 rle with old method : 0.09830307960510254 length of segment : 540 time for calcul the mask position with numpy : 0.0010287761688232422 nb_pixel_total : 11936 time to create 1 rle with old method : 0.014027595520019531 length of segment : 186 time for calcul the mask position with numpy : 0.0012478828430175781 nb_pixel_total : 14546 time to create 1 rle with old method : 0.01694798469543457 length of segment : 110 time for calcul the mask position with numpy : 0.0016927719116210938 nb_pixel_total : 17788 time to create 1 rle with old method : 0.021251678466796875 length of segment : 279 time for calcul the mask position with numpy : 0.033699750900268555 nb_pixel_total : 502128 time to create 1 rle with new method : 0.03900718688964844 length of segment : 669 time for calcul the mask position with numpy : 0.0006558895111083984 nb_pixel_total : 9317 time to create 1 rle with old method : 0.011019706726074219 length of segment : 130 time for calcul the mask position with numpy : 0.0014357566833496094 nb_pixel_total : 18479 time to create 1 rle with old method : 0.020961284637451172 length of segment : 244 time for calcul the mask position with numpy : 0.002361297607421875 nb_pixel_total : 16755 time to create 1 rle with old method : 0.01978158950805664 length of segment : 184 time for calcul the mask position with numpy : 0.00039386749267578125 nb_pixel_total : 8541 time to create 1 rle with old method : 0.010068655014038086 length of segment : 117 time for calcul the mask position with numpy : 0.0012042522430419922 nb_pixel_total : 6372 time to create 1 rle with old method : 0.007628917694091797 length of segment : 191 time for calcul the mask position with numpy : 0.0009829998016357422 nb_pixel_total : 13106 time to create 1 rle with old method : 0.014764785766601562 length of segment : 240 time for calcul the mask position with numpy : 0.0003261566162109375 nb_pixel_total : 9351 time to create 1 rle with old method : 0.011067628860473633 length of segment : 127 time for calcul the mask position with numpy : 0.0003170967102050781 nb_pixel_total : 3722 time to create 1 rle with old method : 0.004368305206298828 length of segment : 79 time for calcul the mask position with numpy : 0.002415180206298828 nb_pixel_total : 41532 time to create 1 rle with old method : 0.04626727104187012 length of segment : 582 time for calcul the mask position with numpy : 0.00036144256591796875 nb_pixel_total : 6615 time to create 1 rle with old method : 0.00942230224609375 length of segment : 94 time for calcul the mask position with numpy : 0.007842779159545898 nb_pixel_total : 123033 time to create 1 rle with old method : 0.1360785961151123 length of segment : 403 time for calcul the mask position with numpy : 0.0013074874877929688 nb_pixel_total : 16404 time to create 1 rle with old method : 0.019294261932373047 length of segment : 200 time for calcul the mask position with numpy : 0.0015475749969482422 nb_pixel_total : 18808 time to create 1 rle with old method : 0.021824359893798828 length of segment : 136 time for calcul the mask position with numpy : 0.003927707672119141 nb_pixel_total : 47973 time to create 1 rle with old method : 0.053293466567993164 length of segment : 454 time for calcul the mask position with numpy : 0.005131244659423828 nb_pixel_total : 70231 time to create 1 rle with old method : 0.08020687103271484 length of segment : 386 time for calcul the mask position with numpy : 0.0034055709838867188 nb_pixel_total : 38191 time to create 1 rle with old method : 0.04352545738220215 length of segment : 379 time for calcul the mask position with numpy : 0.0017039775848388672 nb_pixel_total : 74747 time to create 1 rle with old method : 0.08258652687072754 length of segment : 413 time for calcul the mask position with numpy : 0.0019659996032714844 nb_pixel_total : 11164 time to create 1 rle with old method : 0.09194445610046387 length of segment : 189 time for calcul the mask position with numpy : 0.0020835399627685547 nb_pixel_total : 15013 time to create 1 rle with old method : 0.028058767318725586 length of segment : 140 time for calcul the mask position with numpy : 0.008180379867553711 nb_pixel_total : 84397 time to create 1 rle with old method : 0.09743666648864746 length of segment : 290 time for calcul the mask position with numpy : 0.0025365352630615234 nb_pixel_total : 32214 time to create 1 rle with old method : 0.04377865791320801 length of segment : 236 time for calcul the mask position with numpy : 0.0009438991546630859 nb_pixel_total : 5084 time to create 1 rle with old method : 0.020693063735961914 length of segment : 115 time for calcul the mask position with numpy : 0.0014655590057373047 nb_pixel_total : 17141 time to create 1 rle with old method : 0.05007338523864746 length of segment : 160 time for calcul the mask position with numpy : 0.0012063980102539062 nb_pixel_total : 12457 time to create 1 rle with old method : 0.014794588088989258 length of segment : 175 time for calcul the mask position with numpy : 0.0005354881286621094 nb_pixel_total : 6907 time to create 1 rle with old method : 0.008388519287109375 length of segment : 133 time for calcul the mask position with numpy : 0.0018393993377685547 nb_pixel_total : 20812 time to create 1 rle with old method : 0.024426937103271484 length of segment : 154 time for calcul the mask position with numpy : 0.001219034194946289 nb_pixel_total : 19496 time to create 1 rle with old method : 0.02225017547607422 length of segment : 200 time for calcul the mask position with numpy : 0.0025413036346435547 nb_pixel_total : 26434 time to create 1 rle with old method : 0.03344464302062988 length of segment : 323 time for calcul the mask position with numpy : 0.0005970001220703125 nb_pixel_total : 5932 time to create 1 rle with old method : 0.010057687759399414 length of segment : 67 time for calcul the mask position with numpy : 0.0003170967102050781 nb_pixel_total : 6271 time to create 1 rle with old method : 0.007305622100830078 length of segment : 81 time for calcul the mask position with numpy : 0.0010724067687988281 nb_pixel_total : 13716 time to create 1 rle with old method : 0.015567302703857422 length of segment : 157 time for calcul the mask position with numpy : 0.0005855560302734375 nb_pixel_total : 7056 time to create 1 rle with old method : 0.008414983749389648 length of segment : 87 time for calcul the mask position with numpy : 0.0010857582092285156 nb_pixel_total : 12245 time to create 1 rle with old method : 0.013886213302612305 length of segment : 148 time for calcul the mask position with numpy : 0.002588987350463867 nb_pixel_total : 43590 time to create 1 rle with old method : 0.04844951629638672 length of segment : 334 time for calcul the mask position with numpy : 0.0005972385406494141 nb_pixel_total : 18320 time to create 1 rle with old method : 0.020799875259399414 length of segment : 168 time for calcul the mask position with numpy : 0.0004277229309082031 nb_pixel_total : 3639 time to create 1 rle with old method : 0.004324436187744141 length of segment : 74 time for calcul the mask position with numpy : 0.00870203971862793 nb_pixel_total : 47763 time to create 1 rle with old method : 0.056554555892944336 length of segment : 408 time for calcul the mask position with numpy : 0.0003044605255126953 nb_pixel_total : 2669 time to create 1 rle with old method : 0.0032808780670166016 length of segment : 55 time for calcul the mask position with numpy : 0.0019648075103759766 nb_pixel_total : 35124 time to create 1 rle with old method : 0.03932762145996094 length of segment : 433 time for calcul the mask position with numpy : 0.0020656585693359375 nb_pixel_total : 24735 time to create 1 rle with old method : 0.030219078063964844 length of segment : 276 time for calcul the mask position with numpy : 0.00079345703125 nb_pixel_total : 6855 time to create 1 rle with old method : 0.008373022079467773 length of segment : 79 time for calcul the mask position with numpy : 0.0005817413330078125 nb_pixel_total : 12938 time to create 1 rle with old method : 0.015243291854858398 length of segment : 258 time for calcul the mask position with numpy : 0.004853248596191406 nb_pixel_total : 51584 time to create 1 rle with old method : 0.0578465461730957 length of segment : 387 time for calcul the mask position with numpy : 0.0012745857238769531 nb_pixel_total : 12339 time to create 1 rle with old method : 0.014757156372070312 length of segment : 154 time for calcul the mask position with numpy : 0.002317190170288086 nb_pixel_total : 45760 time to create 1 rle with old method : 0.05188298225402832 length of segment : 209 time for calcul the mask position with numpy : 0.0005195140838623047 nb_pixel_total : 7288 time to create 1 rle with old method : 0.008699178695678711 length of segment : 133 time for calcul the mask position with numpy : 0.002329587936401367 nb_pixel_total : 45199 time to create 1 rle with old method : 0.05101895332336426 length of segment : 268 time for calcul the mask position with numpy : 0.001018524169921875 nb_pixel_total : 16362 time to create 1 rle with old method : 0.018954753875732422 length of segment : 177 time for calcul the mask position with numpy : 0.0008068084716796875 nb_pixel_total : 10760 time to create 1 rle with old method : 0.01468348503112793 length of segment : 196 time for calcul the mask position with numpy : 0.0004646778106689453 nb_pixel_total : 6796 time to create 1 rle with old method : 0.00804448127746582 length of segment : 93 time for calcul the mask position with numpy : 0.0009372234344482422 nb_pixel_total : 14944 time to create 1 rle with old method : 0.017836570739746094 length of segment : 132 time for calcul the mask position with numpy : 0.0005106925964355469 nb_pixel_total : 8468 time to create 1 rle with old method : 0.010200262069702148 length of segment : 111 time for calcul the mask position with numpy : 0.0032880306243896484 nb_pixel_total : 75889 time to create 1 rle with old method : 0.08505487442016602 length of segment : 274 time for calcul the mask position with numpy : 0.012917757034301758 nb_pixel_total : 297652 time to create 1 rle with new method : 0.017200708389282227 length of segment : 1025 time for calcul the mask position with numpy : 0.014270544052124023 nb_pixel_total : 402171 time to create 1 rle with new method : 0.025760412216186523 length of segment : 720 time for calcul the mask position with numpy : 0.00092315673828125 nb_pixel_total : 17192 time to create 1 rle with old method : 0.019943952560424805 length of segment : 101 time for calcul the mask position with numpy : 0.001007080078125 nb_pixel_total : 17945 time to create 1 rle with old method : 0.02111363410949707 length of segment : 442 time for calcul the mask position with numpy : 0.005921125411987305 nb_pixel_total : 122139 time to create 1 rle with old method : 0.1396503448486328 length of segment : 447 time for calcul the mask position with numpy : 0.0030715465545654297 nb_pixel_total : 59204 time to create 1 rle with old method : 0.06666016578674316 length of segment : 320 time for calcul the mask position with numpy : 0.0004260540008544922 nb_pixel_total : 9339 time to create 1 rle with old method : 0.011026382446289062 length of segment : 100 time for calcul the mask position with numpy : 0.00017690658569335938 nb_pixel_total : 5970 time to create 1 rle with old method : 0.007207393646240234 length of segment : 88 time for calcul the mask position with numpy : 0.004931926727294922 nb_pixel_total : 92250 time to create 1 rle with old method : 0.10083985328674316 length of segment : 452 time for calcul the mask position with numpy : 0.004065513610839844 nb_pixel_total : 93330 time to create 1 rle with old method : 0.10217404365539551 length of segment : 621 time for calcul the mask position with numpy : 0.00077056884765625 nb_pixel_total : 13304 time to create 1 rle with old method : 0.014954328536987305 length of segment : 163 time for calcul the mask position with numpy : 0.0010559558868408203 nb_pixel_total : 24153 time to create 1 rle with old method : 0.027766942977905273 length of segment : 254 time for calcul the mask position with numpy : 0.00026226043701171875 nb_pixel_total : 7213 time to create 1 rle with old method : 0.00850987434387207 length of segment : 100 time for calcul the mask position with numpy : 0.0011374950408935547 nb_pixel_total : 22267 time to create 1 rle with old method : 0.02535843849182129 length of segment : 183 time for calcul the mask position with numpy : 0.0017540454864501953 nb_pixel_total : 56615 time to create 1 rle with old method : 0.06277108192443848 length of segment : 298 time for calcul the mask position with numpy : 0.00131988525390625 nb_pixel_total : 27913 time to create 1 rle with old method : 0.03131222724914551 length of segment : 212 time for calcul the mask position with numpy : 0.0017054080963134766 nb_pixel_total : 45094 time to create 1 rle with old method : 0.05155539512634277 length of segment : 299 time for calcul the mask position with numpy : 0.003040790557861328 nb_pixel_total : 87472 time to create 1 rle with old method : 0.09595727920532227 length of segment : 382 time for calcul the mask position with numpy : 0.001271963119506836 nb_pixel_total : 19268 time to create 1 rle with old method : 0.02190685272216797 length of segment : 193 time for calcul the mask position with numpy : 0.0005710124969482422 nb_pixel_total : 32056 time to create 1 rle with old method : 0.0355837345123291 length of segment : 324 time for calcul the mask position with numpy : 0.0025010108947753906 nb_pixel_total : 60019 time to create 1 rle with old method : 0.06714653968811035 length of segment : 224 time spent for convertir_results : 33.768951416015625 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 509 chid ids of type : 3594 Number RLEs to save : 106042 save missing photos in datou_result : time spend for datou_step_exec : 240.6819064617157 time spend to save output : 13.82183837890625 total time spend for step 1 : 254.50374484062195 step2:crop_condition Tue Feb 11 02:34:44 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 : 9 ! batch 1 Loaded 509 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 ! map_result returned by crop_photo_return_map_crop : length : 419 About to insert : list_path_to_insert length 419 new photo from crops ! About to upload 419 photos upload in portfolio : 3736932 init cache_photo without model_param we have 419 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739237740_744114 we have uploaded 419 photos in the portfolio 3736932 time of upload the photos Elapsed time : 107.13745141029358 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 ! map_result returned by crop_photo_return_map_crop : length : 62 About to insert : list_path_to_insert length 62 new photo from crops ! About to upload 62 photos upload in portfolio : 3736932 init cache_photo without model_param we have 62 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739237863_744114 we have uploaded 62 photos in the portfolio 3736932 time of upload the photos Elapsed time : 18.898241758346558 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 1 About to insert : list_path_to_insert length 1 new photo from crops ! About to upload 1 photos upload in portfolio : 3736932 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739237883_744114 we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.550501823425293 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 ! map_result returned by crop_photo_return_map_crop : length : 14 About to insert : list_path_to_insert length 14 new photo from crops ! About to upload 14 photos upload in portfolio : 3736932 init cache_photo without model_param we have 14 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739237892_744114 we have uploaded 14 photos in the portfolio 3736932 time of upload the photos Elapsed time : 3.371530771255493 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 ! map_result returned by crop_photo_return_map_crop : length : 6 About to insert : list_path_to_insert length 6 new photo from crops ! About to upload 6 photos upload in portfolio : 3736932 init cache_photo without model_param we have 6 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739237899_744114 we have uploaded 6 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.253429412841797 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 1 About to insert : list_path_to_insert length 1 new photo from crops ! About to upload 1 photos upload in portfolio : 3736932 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739237903_744114 we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.0762736797332764 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 ! map_result returned by crop_photo_return_map_crop : length : 6 About to insert : list_path_to_insert length 6 new photo from crops ! About to upload 6 photos upload in portfolio : 3736932 init cache_photo without model_param we have 6 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739237906_744114 we have uploaded 6 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.5422308444976807 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 Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1336630398, 1336630395, 1336630392, 1336630339, 1336630058, 1336630044, 1336629992, 1336629930, 1336629616] Looping around the photos to save general results len do output : 509 /1336666743Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666744Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666745Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666746Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666747Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666748Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666749Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666750Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666751Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666752Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666753Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666754Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666755Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666756Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666757Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666758Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666759Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666760Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666761Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666762Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666763Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666764Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666765Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666766Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666767Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666768Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666769Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666770Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666771Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666772Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666773Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666774Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666775Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666776Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666777Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666778Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666779Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666780Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666781Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666782Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666783Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666784Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666785Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666786Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666787Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666788Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666789Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666790Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666791Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666792Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666793Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666794Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666795Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666796Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666797Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666798Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666799Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666800Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666801Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666802Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666803Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666804Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666805Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666806Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666807Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666808Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666809Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666810Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666811Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666812Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666813Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666814Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666815Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666816Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666817Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666818Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666819Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666820Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666821Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666822Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666823Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666824Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666825Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666826Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666827Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666828Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666829Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666830Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336666831Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630398', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630395', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630392', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630339', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630058', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630044', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336629992', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336629930', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336629616', None, None, None, None, None, '2574366') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1536 time used for this insertion : 0.07636833190917969 save_final save missing photos in datou_result : time spend for datou_step_exec : 222.8887128829956 time spend to save output : 0.10177969932556152 total time spend for step 2 : 222.99049258232117 step3:rle_unique_nms_with_priority Tue Feb 11 02:38:27 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 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 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 509 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 46 nb_hashtags : 5 time to prepare the origin masks : 4.4646148681640625 time for calcul the mask position with numpy : 0.5722646713256836 nb_pixel_total : 4728807 time to create 1 rle with new method : 0.9647386074066162 time for calcul the mask position with numpy : 0.029330015182495117 nb_pixel_total : 13001 time to create 1 rle with old method : 0.018072843551635742 time for calcul the mask position with numpy : 0.02915215492248535 nb_pixel_total : 9601 time to create 1 rle with old method : 0.011131525039672852 time for calcul the mask position with numpy : 0.028102874755859375 nb_pixel_total : 3924 time to create 1 rle with old method : 0.004538536071777344 time for calcul the mask position with numpy : 0.02877640724182129 nb_pixel_total : 18186 time to create 1 rle with old method : 0.020132064819335938 time for calcul the mask position with numpy : 0.028909921646118164 nb_pixel_total : 26101 time to create 1 rle with old method : 0.02866196632385254 time for calcul the mask position with numpy : 0.028888940811157227 nb_pixel_total : 7145 time to create 1 rle with old method : 0.008268356323242188 time for calcul the mask position with numpy : 0.02847743034362793 nb_pixel_total : 15241 time to create 1 rle with old method : 0.016407251358032227 time for calcul the mask position with numpy : 0.028370141983032227 nb_pixel_total : 24688 time to create 1 rle with old method : 0.02740955352783203 time for calcul the mask position with numpy : 0.02796149253845215 nb_pixel_total : 18337 time to create 1 rle with old method : 0.019704818725585938 time for calcul the mask position with numpy : 0.028162240982055664 nb_pixel_total : 7555 time to create 1 rle with old method : 0.00856471061706543 time for calcul the mask position with numpy : 0.027697086334228516 nb_pixel_total : 23058 time to create 1 rle with old method : 0.02475261688232422 time for calcul the mask position with numpy : 0.027601003646850586 nb_pixel_total : 30851 time to create 1 rle with old method : 0.03320431709289551 time for calcul the mask position with numpy : 0.02760028839111328 nb_pixel_total : 6967 time to create 1 rle with old method : 0.011400461196899414 time for calcul the mask position with numpy : 0.032833099365234375 nb_pixel_total : 44461 time to create 1 rle with old method : 0.06027960777282715 time for calcul the mask position with numpy : 0.027756690979003906 nb_pixel_total : 16590 time to create 1 rle with old method : 0.017737150192260742 time for calcul the mask position with numpy : 0.02806258201599121 nb_pixel_total : 38761 time to create 1 rle with old method : 0.042768001556396484 time for calcul the mask position with numpy : 0.028714895248413086 nb_pixel_total : 28368 time to create 1 rle with old method : 0.03155207633972168 time for calcul the mask position with numpy : 0.033168792724609375 nb_pixel_total : 118307 time to create 1 rle with old method : 0.13033008575439453 time for calcul the mask position with numpy : 0.031207799911499023 nb_pixel_total : 22544 time to create 1 rle with old method : 0.025365352630615234 time for calcul the mask position with numpy : 0.030286788940429688 nb_pixel_total : 6746 time to create 1 rle with old method : 0.00799107551574707 time for calcul the mask position with numpy : 0.02997303009033203 nb_pixel_total : 15355 time to create 1 rle with old method : 0.017452239990234375 time for calcul the mask position with numpy : 0.029192686080932617 nb_pixel_total : 21959 time to create 1 rle with old method : 0.02756643295288086 time for calcul the mask position with numpy : 0.02888178825378418 nb_pixel_total : 47228 time to create 1 rle with old method : 0.05179762840270996 time for calcul the mask position with numpy : 0.030168771743774414 nb_pixel_total : 14724 time to create 1 rle with old method : 0.016825437545776367 time for calcul the mask position with numpy : 0.031380414962768555 nb_pixel_total : 21339 time to create 1 rle with old method : 0.023844242095947266 time for calcul the mask position with numpy : 0.028641939163208008 nb_pixel_total : 14464 time to create 1 rle with old method : 0.015952587127685547 time for calcul the mask position with numpy : 0.028696298599243164 nb_pixel_total : 13692 time to create 1 rle with old method : 0.0151824951171875 time for calcul the mask position with numpy : 0.03278064727783203 nb_pixel_total : 56314 time to create 1 rle with old method : 0.07820916175842285 time for calcul the mask position with numpy : 0.03054952621459961 nb_pixel_total : 17703 time to create 1 rle with old method : 0.022378921508789062 time for calcul the mask position with numpy : 0.03468918800354004 nb_pixel_total : 240178 time to create 1 rle with new method : 0.3521747589111328 time for calcul the mask position with numpy : 0.030012845993041992 nb_pixel_total : 133697 time to create 1 rle with old method : 0.1524200439453125 time for calcul the mask position with numpy : 0.030762434005737305 nb_pixel_total : 280364 time to create 1 rle with new method : 0.4872145652770996 time for calcul the mask position with numpy : 0.028242826461791992 nb_pixel_total : 22921 time to create 1 rle with old method : 0.02425527572631836 time for calcul the mask position with numpy : 0.02828240394592285 nb_pixel_total : 51892 time to create 1 rle with old method : 0.058058977127075195 time for calcul the mask position with numpy : 0.028203964233398438 nb_pixel_total : 59830 time to create 1 rle with old method : 0.06543159484863281 time for calcul the mask position with numpy : 0.028741836547851562 nb_pixel_total : 55529 time to create 1 rle with old method : 0.060080528259277344 time for calcul the mask position with numpy : 0.028719663619995117 nb_pixel_total : 3535 time to create 1 rle with old method : 0.004122257232666016 time for calcul the mask position with numpy : 0.028907299041748047 nb_pixel_total : 14162 time to create 1 rle with old method : 0.016211748123168945 time for calcul the mask position with numpy : 0.02913498878479004 nb_pixel_total : 66104 time to create 1 rle with old method : 0.07296085357666016 time for calcul the mask position with numpy : 0.029310226440429688 nb_pixel_total : 181557 time to create 1 rle with new method : 0.5227725505828857 time for calcul the mask position with numpy : 0.029224634170532227 nb_pixel_total : 50629 time to create 1 rle with old method : 0.05558466911315918 time for calcul the mask position with numpy : 0.029529333114624023 nb_pixel_total : 101285 time to create 1 rle with old method : 0.11019086837768555 time for calcul the mask position with numpy : 0.028904438018798828 nb_pixel_total : 34716 time to create 1 rle with old method : 0.03877997398376465 time for calcul the mask position with numpy : 0.029630661010742188 nb_pixel_total : 148539 time to create 1 rle with old method : 0.1608877182006836 time for calcul the mask position with numpy : 0.028748035430908203 nb_pixel_total : 4422 time to create 1 rle with old method : 0.005044221878051758 time for calcul the mask position with numpy : 0.029495954513549805 nb_pixel_total : 168863 time to create 1 rle with new method : 0.5175387859344482 create new chi : 6.52483606338501 time to delete rle : 0.017026424407958984 batch 1 Loaded 93 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 28756 TO DO : save crop sub photo not yet done ! save time : 1.8118150234222412 nb_obj : 45 nb_hashtags : 4 time to prepare the origin masks : 3.718679189682007 time for calcul the mask position with numpy : 0.42907071113586426 nb_pixel_total : 6011177 time to create 1 rle with new method : 0.754967212677002 time for calcul the mask position with numpy : 0.03118276596069336 nb_pixel_total : 12264 time to create 1 rle with old method : 0.014486312866210938 time for calcul the mask position with numpy : 0.029402971267700195 nb_pixel_total : 38409 time to create 1 rle with old method : 0.04197812080383301 time for calcul the mask position with numpy : 0.029241323471069336 nb_pixel_total : 27941 time to create 1 rle with old method : 0.030837297439575195 time for calcul the mask position with numpy : 0.031078100204467773 nb_pixel_total : 9687 time to create 1 rle with old method : 0.011533498764038086 time for calcul the mask position with numpy : 0.029439926147460938 nb_pixel_total : 14152 time to create 1 rle with old method : 0.01569390296936035 time for calcul the mask position with numpy : 0.02893209457397461 nb_pixel_total : 25589 time to create 1 rle with old method : 0.028303146362304688 time for calcul the mask position with numpy : 0.0287477970123291 nb_pixel_total : 12283 time to create 1 rle with old method : 0.014052629470825195 time for calcul the mask position with numpy : 0.02873826026916504 nb_pixel_total : 22889 time to create 1 rle with old method : 0.025170564651489258 time for calcul the mask position with numpy : 0.02888941764831543 nb_pixel_total : 14463 time to create 1 rle with old method : 0.016734600067138672 time for calcul the mask position with numpy : 0.028761625289916992 nb_pixel_total : 23174 time to create 1 rle with old method : 0.02646160125732422 time for calcul the mask position with numpy : 0.028664588928222656 nb_pixel_total : 6303 time to create 1 rle with old method : 0.0073413848876953125 time for calcul the mask position with numpy : 0.028992652893066406 nb_pixel_total : 63959 time to create 1 rle with old method : 0.06980705261230469 time for calcul the mask position with numpy : 0.028679609298706055 nb_pixel_total : 7439 time to create 1 rle with old method : 0.00863337516784668 time for calcul the mask position with numpy : 0.02875208854675293 nb_pixel_total : 23734 time to create 1 rle with old method : 0.027341365814208984 time for calcul the mask position with numpy : 0.02880859375 nb_pixel_total : 28715 time to create 1 rle with old method : 0.03175616264343262 time for calcul the mask position with numpy : 0.02878713607788086 nb_pixel_total : 10045 time to create 1 rle with old method : 0.011587381362915039 time for calcul the mask position with numpy : 0.02878546714782715 nb_pixel_total : 24079 time to create 1 rle with old method : 0.027107715606689453 time for calcul the mask position with numpy : 0.028931140899658203 nb_pixel_total : 16885 time to create 1 rle with old method : 0.01949620246887207 time for calcul the mask position with numpy : 0.028829574584960938 nb_pixel_total : 24712 time to create 1 rle with old method : 0.027085304260253906 time for calcul the mask position with numpy : 0.028970718383789062 nb_pixel_total : 24546 time to create 1 rle with old method : 0.02829766273498535 time for calcul the mask position with numpy : 0.0288236141204834 nb_pixel_total : 53799 time to create 1 rle with old method : 0.057950735092163086 time for calcul the mask position with numpy : 0.02866816520690918 nb_pixel_total : 43521 time to create 1 rle with old method : 0.04762387275695801 time for calcul the mask position with numpy : 0.028847217559814453 nb_pixel_total : 2698 time to create 1 rle with old method : 0.003167867660522461 time for calcul the mask position with numpy : 0.029132604598999023 nb_pixel_total : 10397 time to create 1 rle with old method : 0.01195526123046875 time for calcul the mask position with numpy : 0.029230356216430664 nb_pixel_total : 41995 time to create 1 rle with old method : 0.04627656936645508 time for calcul the mask position with numpy : 0.029320955276489258 nb_pixel_total : 25444 time to create 1 rle with old method : 0.028247833251953125 time for calcul the mask position with numpy : 0.029589176177978516 nb_pixel_total : 17640 time to create 1 rle with old method : 0.020124435424804688 time for calcul the mask position with numpy : 0.029164552688598633 nb_pixel_total : 26370 time to create 1 rle with old method : 0.029873371124267578 time for calcul the mask position with numpy : 0.028999805450439453 nb_pixel_total : 7229 time to create 1 rle with old method : 0.008157730102539062 time for calcul the mask position with numpy : 0.02919602394104004 nb_pixel_total : 21269 time to create 1 rle with old method : 0.02469038963317871 time for calcul the mask position with numpy : 0.029752254486083984 nb_pixel_total : 44075 time to create 1 rle with old method : 0.04828977584838867 time for calcul the mask position with numpy : 0.029067277908325195 nb_pixel_total : 23816 time to create 1 rle with old method : 0.02668905258178711 time for calcul the mask position with numpy : 0.029831886291503906 nb_pixel_total : 13534 time to create 1 rle with old method : 0.015326499938964844 time for calcul the mask position with numpy : 0.031362295150756836 nb_pixel_total : 17493 time to create 1 rle with old method : 0.01968860626220703 time for calcul the mask position with numpy : 0.032357215881347656 nb_pixel_total : 16342 time to create 1 rle with old method : 0.019858837127685547 time for calcul the mask position with numpy : 0.029949665069580078 nb_pixel_total : 33519 time to create 1 rle with old method : 0.03683781623840332 time for calcul the mask position with numpy : 0.02947521209716797 nb_pixel_total : 25938 time to create 1 rle with old method : 0.028609037399291992 time for calcul the mask position with numpy : 0.02902078628540039 nb_pixel_total : 9372 time to create 1 rle with old method : 0.010696172714233398 time for calcul the mask position with numpy : 0.029151439666748047 nb_pixel_total : 60166 time to create 1 rle with old method : 0.06620168685913086 time for calcul the mask position with numpy : 0.029248952865600586 nb_pixel_total : 14792 time to create 1 rle with old method : 0.016790151596069336 time for calcul the mask position with numpy : 0.02912759780883789 nb_pixel_total : 31719 time to create 1 rle with old method : 0.03533315658569336 time for calcul the mask position with numpy : 0.02981734275817871 nb_pixel_total : 3194 time to create 1 rle with old method : 0.0046231746673583984 time for calcul the mask position with numpy : 0.0341334342956543 nb_pixel_total : 11995 time to create 1 rle with old method : 0.019065141677856445 time for calcul the mask position with numpy : 0.03311491012573242 nb_pixel_total : 20371 time to create 1 rle with old method : 0.02280879020690918 time for calcul the mask position with numpy : 0.029520750045776367 nb_pixel_total : 31107 time to create 1 rle with old method : 0.03555750846862793 create new chi : 3.7170047760009766 time to delete rle : 0.004178762435913086 batch 1 Loaded 91 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 18795 TO DO : save crop sub photo not yet done ! save time : 2.1521992683410645 nb_obj : 75 nb_hashtags : 4 time to prepare the origin masks : 4.0458807945251465 time for calcul the mask position with numpy : 0.567572832107544 nb_pixel_total : 5386505 time to create 1 rle with new method : 0.5595319271087646 time for calcul the mask position with numpy : 0.029143095016479492 nb_pixel_total : 59892 time to create 1 rle with old method : 0.0666801929473877 time for calcul the mask position with numpy : 0.02873849868774414 nb_pixel_total : 13487 time to create 1 rle with old method : 0.015317201614379883 time for calcul the mask position with numpy : 0.02864670753479004 nb_pixel_total : 45989 time to create 1 rle with old method : 0.051418304443359375 time for calcul the mask position with numpy : 0.028617143630981445 nb_pixel_total : 7028 time to create 1 rle with old method : 0.007829427719116211 time for calcul the mask position with numpy : 0.028789043426513672 nb_pixel_total : 11556 time to create 1 rle with old method : 0.013337850570678711 time for calcul the mask position with numpy : 0.028788328170776367 nb_pixel_total : 12734 time to create 1 rle with old method : 0.014743328094482422 time for calcul the mask position with numpy : 0.02868199348449707 nb_pixel_total : 23228 time to create 1 rle with old method : 0.026138782501220703 time for calcul the mask position with numpy : 0.028647899627685547 nb_pixel_total : 1061 time to create 1 rle with old method : 0.0014307498931884766 time for calcul the mask position with numpy : 0.028875112533569336 nb_pixel_total : 34637 time to create 1 rle with old method : 0.03878307342529297 time for calcul the mask position with numpy : 0.028740882873535156 nb_pixel_total : 15472 time to create 1 rle with old method : 0.017451047897338867 time for calcul the mask position with numpy : 0.029039859771728516 nb_pixel_total : 58632 time to create 1 rle with old method : 0.06410598754882812 time for calcul the mask position with numpy : 0.028880834579467773 nb_pixel_total : 54168 time to create 1 rle with old method : 0.05949854850769043 time for calcul the mask position with numpy : 0.02882528305053711 nb_pixel_total : 2716 time to create 1 rle with old method : 0.0031664371490478516 time for calcul the mask position with numpy : 0.028775930404663086 nb_pixel_total : 51367 time to create 1 rle with old method : 0.056961774826049805 time for calcul the mask position with numpy : 0.028692007064819336 nb_pixel_total : 5064 time to create 1 rle with old method : 0.005896806716918945 time for calcul the mask position with numpy : 0.028890609741210938 nb_pixel_total : 18249 time to create 1 rle with old method : 0.020188093185424805 time for calcul the mask position with numpy : 0.02878856658935547 nb_pixel_total : 2025 time to create 1 rle with old method : 0.002325296401977539 time for calcul the mask position with numpy : 0.028572797775268555 nb_pixel_total : 2716 time to create 1 rle with old method : 0.003184080123901367 time for calcul the mask position with numpy : 0.028855562210083008 nb_pixel_total : 21533 time to create 1 rle with old method : 0.024197816848754883 time for calcul the mask position with numpy : 0.028789520263671875 nb_pixel_total : 3996 time to create 1 rle with old method : 0.004655361175537109 time for calcul the mask position with numpy : 0.028511524200439453 nb_pixel_total : 945 time to create 1 rle with old method : 0.0012323856353759766 time for calcul the mask position with numpy : 0.028603076934814453 nb_pixel_total : 38493 time to create 1 rle with old method : 0.04220986366271973 time for calcul the mask position with numpy : 0.02878093719482422 nb_pixel_total : 16576 time to create 1 rle with old method : 0.018747329711914062 time for calcul the mask position with numpy : 0.028606891632080078 nb_pixel_total : 20350 time to create 1 rle with old method : 0.023569107055664062 time for calcul the mask position with numpy : 0.0286099910736084 nb_pixel_total : 16944 time to create 1 rle with old method : 0.018941879272460938 time for calcul the mask position with numpy : 0.02865457534790039 nb_pixel_total : 18350 time to create 1 rle with old method : 0.020514249801635742 time for calcul the mask position with numpy : 0.028725147247314453 nb_pixel_total : 16728 time to create 1 rle with old method : 0.01878976821899414 time for calcul the mask position with numpy : 0.02889394760131836 nb_pixel_total : 46766 time to create 1 rle with old method : 0.05128598213195801 time for calcul the mask position with numpy : 0.029062509536743164 nb_pixel_total : 3997 time to create 1 rle with old method : 0.004674673080444336 time for calcul the mask position with numpy : 0.028900861740112305 nb_pixel_total : 20755 time to create 1 rle with old method : 0.02275228500366211 time for calcul the mask position with numpy : 0.028797626495361328 nb_pixel_total : 26908 time to create 1 rle with old method : 0.029983997344970703 time for calcul the mask position with numpy : 0.028806686401367188 nb_pixel_total : 28615 time to create 1 rle with old method : 0.03183579444885254 time for calcul the mask position with numpy : 0.02908492088317871 nb_pixel_total : 77429 time to create 1 rle with old method : 0.08574652671813965 time for calcul the mask position with numpy : 0.028717041015625 nb_pixel_total : 21698 time to create 1 rle with old method : 0.02409219741821289 time for calcul the mask position with numpy : 0.02861189842224121 nb_pixel_total : 3101 time to create 1 rle with old method : 0.0036237239837646484 time for calcul the mask position with numpy : 0.02863478660583496 nb_pixel_total : 31674 time to create 1 rle with old method : 0.034850120544433594 time for calcul the mask position with numpy : 0.028784990310668945 nb_pixel_total : 42072 time to create 1 rle with old method : 0.04649949073791504 time for calcul the mask position with numpy : 0.02887701988220215 nb_pixel_total : 17762 time to create 1 rle with old method : 0.01976156234741211 time for calcul the mask position with numpy : 0.029380321502685547 nb_pixel_total : 109085 time to create 1 rle with old method : 0.12035036087036133 time for calcul the mask position with numpy : 0.02898693084716797 nb_pixel_total : 63813 time to create 1 rle with old method : 0.07366347312927246 time for calcul the mask position with numpy : 0.028948068618774414 nb_pixel_total : 12387 time to create 1 rle with old method : 0.014171600341796875 time for calcul the mask position with numpy : 0.028767824172973633 nb_pixel_total : 16398 time to create 1 rle with old method : 0.01839590072631836 time for calcul the mask position with numpy : 0.030403614044189453 nb_pixel_total : 17777 time to create 1 rle with old method : 0.01954340934753418 time for calcul the mask position with numpy : 0.02867412567138672 nb_pixel_total : 15264 time to create 1 rle with old method : 0.017816543579101562 time for calcul the mask position with numpy : 0.028859376907348633 nb_pixel_total : 27248 time to create 1 rle with old method : 0.029961347579956055 time for calcul the mask position with numpy : 0.0291287899017334 nb_pixel_total : 62596 time to create 1 rle with old method : 0.06792211532592773 time for calcul the mask position with numpy : 0.0286257266998291 nb_pixel_total : 13413 time to create 1 rle with old method : 0.014954805374145508 time for calcul the mask position with numpy : 0.028684139251708984 nb_pixel_total : 32101 time to create 1 rle with old method : 0.034995079040527344 time for calcul the mask position with numpy : 0.028844356536865234 nb_pixel_total : 10368 time to create 1 rle with old method : 0.011690616607666016 time for calcul the mask position with numpy : 0.028680086135864258 nb_pixel_total : 21334 time to create 1 rle with old method : 0.024091005325317383 time for calcul the mask position with numpy : 0.02875518798828125 nb_pixel_total : 6558 time to create 1 rle with old method : 0.008017301559448242 time for calcul the mask position with numpy : 0.028725385665893555 nb_pixel_total : 6056 time to create 1 rle with old method : 0.0069997310638427734 time for calcul the mask position with numpy : 0.028719186782836914 nb_pixel_total : 19624 time to create 1 rle with old method : 0.022080183029174805 time for calcul the mask position with numpy : 0.028631925582885742 nb_pixel_total : 11299 time to create 1 rle with old method : 0.012487649917602539 time for calcul the mask position with numpy : 0.028560400009155273 nb_pixel_total : 15891 time to create 1 rle with old method : 0.017914533615112305 time for calcul the mask position with numpy : 0.028689146041870117 nb_pixel_total : 13648 time to create 1 rle with old method : 0.015544652938842773 time for calcul the mask position with numpy : 0.029935836791992188 nb_pixel_total : 11337 time to create 1 rle with old method : 0.014440774917602539 time for calcul the mask position with numpy : 0.029182910919189453 nb_pixel_total : 13759 time to create 1 rle with old method : 0.015967369079589844 time for calcul the mask position with numpy : 0.030022144317626953 nb_pixel_total : 45584 time to create 1 rle with old method : 0.05143618583679199 time for calcul the mask position with numpy : 0.02893543243408203 nb_pixel_total : 18612 time to create 1 rle with old method : 0.021625757217407227 time for calcul the mask position with numpy : 0.028861284255981445 nb_pixel_total : 20326 time to create 1 rle with old method : 0.02227163314819336 time for calcul the mask position with numpy : 0.028716325759887695 nb_pixel_total : 4720 time to create 1 rle with old method : 0.005589962005615234 time for calcul the mask position with numpy : 0.028797626495361328 nb_pixel_total : 6015 time to create 1 rle with old method : 0.006978511810302734 time for calcul the mask position with numpy : 0.028842687606811523 nb_pixel_total : 9894 time to create 1 rle with old method : 0.011039495468139648 time for calcul the mask position with numpy : 0.028924226760864258 nb_pixel_total : 22354 time to create 1 rle with old method : 0.025049448013305664 time for calcul the mask position with numpy : 0.03165555000305176 nb_pixel_total : 41914 time to create 1 rle with old method : 0.04632925987243652 time for calcul the mask position with numpy : 0.029130935668945312 nb_pixel_total : 6812 time to create 1 rle with old method : 0.007921218872070312 time for calcul the mask position with numpy : 0.029151439666748047 nb_pixel_total : 10069 time to create 1 rle with old method : 0.011183738708496094 time for calcul the mask position with numpy : 0.0292205810546875 nb_pixel_total : 43145 time to create 1 rle with old method : 0.047675371170043945 time for calcul the mask position with numpy : 0.028969764709472656 nb_pixel_total : 12434 time to create 1 rle with old method : 0.014293909072875977 time for calcul the mask position with numpy : 0.028930187225341797 nb_pixel_total : 11972 time to create 1 rle with old method : 0.013570547103881836 time for calcul the mask position with numpy : 0.02878570556640625 nb_pixel_total : 3102 time to create 1 rle with old method : 0.0036077499389648438 time for calcul the mask position with numpy : 0.028693437576293945 nb_pixel_total : 3862 time to create 1 rle with old method : 0.004522085189819336 time for calcul the mask position with numpy : 0.028785228729248047 nb_pixel_total : 3147 time to create 1 rle with old method : 0.003721475601196289 time for calcul the mask position with numpy : 0.02873539924621582 nb_pixel_total : 5104 time to create 1 rle with old method : 0.005948066711425781 create new chi : 5.190929174423218 time to delete rle : 0.004288196563720703 batch 1 Loaded 151 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 28974 TO DO : save crop sub photo not yet done ! save time : 4.76007080078125 nb_obj : 76 nb_hashtags : 3 time to prepare the origin masks : 4.338984251022339 time for calcul the mask position with numpy : 0.449796199798584 nb_pixel_total : 5020908 time to create 1 rle with new method : 0.6507289409637451 time for calcul the mask position with numpy : 0.033310651779174805 nb_pixel_total : 54421 time to create 1 rle with old method : 0.06497526168823242 time for calcul the mask position with numpy : 0.02870035171508789 nb_pixel_total : 3083 time to create 1 rle with old method : 0.0036416053771972656 time for calcul the mask position with numpy : 0.03000044822692871 nb_pixel_total : 115272 time to create 1 rle with old method : 0.1289658546447754 time for calcul the mask position with numpy : 0.029033899307250977 nb_pixel_total : 9379 time to create 1 rle with old method : 0.010336875915527344 time for calcul the mask position with numpy : 0.02890467643737793 nb_pixel_total : 14724 time to create 1 rle with old method : 0.01671600341796875 time for calcul the mask position with numpy : 0.029739856719970703 nb_pixel_total : 16061 time to create 1 rle with old method : 0.01816391944885254 time for calcul the mask position with numpy : 0.033834218978881836 nb_pixel_total : 26563 time to create 1 rle with old method : 0.04426431655883789 time for calcul the mask position with numpy : 0.030469179153442383 nb_pixel_total : 12283 time to create 1 rle with old method : 0.01392364501953125 time for calcul the mask position with numpy : 0.0286867618560791 nb_pixel_total : 13029 time to create 1 rle with old method : 0.014504432678222656 time for calcul the mask position with numpy : 0.03009510040283203 nb_pixel_total : 14731 time to create 1 rle with old method : 0.016203641891479492 time for calcul the mask position with numpy : 0.028150320053100586 nb_pixel_total : 29338 time to create 1 rle with old method : 0.03171706199645996 time for calcul the mask position with numpy : 0.027701139450073242 nb_pixel_total : 16555 time to create 1 rle with old method : 0.01723647117614746 time for calcul the mask position with numpy : 0.02781391143798828 nb_pixel_total : 58098 time to create 1 rle with old method : 0.06435942649841309 time for calcul the mask position with numpy : 0.028466224670410156 nb_pixel_total : 17593 time to create 1 rle with old method : 0.018720149993896484 time for calcul the mask position with numpy : 0.028247594833374023 nb_pixel_total : 19167 time to create 1 rle with old method : 0.021071434020996094 time for calcul the mask position with numpy : 0.029007673263549805 nb_pixel_total : 4613 time to create 1 rle with old method : 0.005201816558837891 time for calcul the mask position with numpy : 0.029241085052490234 nb_pixel_total : 24775 time to create 1 rle with old method : 0.027608633041381836 time for calcul the mask position with numpy : 0.028573989868164062 nb_pixel_total : 12633 time to create 1 rle with old method : 0.013864755630493164 time for calcul the mask position with numpy : 0.029757022857666016 nb_pixel_total : 12787 time to create 1 rle with old method : 0.014103174209594727 time for calcul the mask position with numpy : 0.028976917266845703 nb_pixel_total : 31198 time to create 1 rle with old method : 0.03363823890686035 time for calcul the mask position with numpy : 0.02878880500793457 nb_pixel_total : 21126 time to create 1 rle with old method : 0.02455925941467285 time for calcul the mask position with numpy : 0.029239654541015625 nb_pixel_total : 23983 time to create 1 rle with old method : 0.026297330856323242 time for calcul the mask position with numpy : 0.02893996238708496 nb_pixel_total : 18733 time to create 1 rle with old method : 0.021165847778320312 time for calcul the mask position with numpy : 0.031097412109375 nb_pixel_total : 374789 time to create 1 rle with new method : 0.7734620571136475 time for calcul the mask position with numpy : 0.027495622634887695 nb_pixel_total : 23351 time to create 1 rle with old method : 0.026054859161376953 time for calcul the mask position with numpy : 0.03010725975036621 nb_pixel_total : 175596 time to create 1 rle with new method : 1.0476431846618652 time for calcul the mask position with numpy : 0.02759265899658203 nb_pixel_total : 14972 time to create 1 rle with old method : 0.017043590545654297 time for calcul the mask position with numpy : 0.027576446533203125 nb_pixel_total : 23779 time to create 1 rle with old method : 0.02656078338623047 time for calcul the mask position with numpy : 0.030313491821289062 nb_pixel_total : 16402 time to create 1 rle with old method : 0.018755435943603516 time for calcul the mask position with numpy : 0.028879404067993164 nb_pixel_total : 24817 time to create 1 rle with old method : 0.027167320251464844 time for calcul the mask position with numpy : 0.027167320251464844 nb_pixel_total : 23489 time to create 1 rle with old method : 0.02489638328552246 time for calcul the mask position with numpy : 0.0274507999420166 nb_pixel_total : 23319 time to create 1 rle with old method : 0.025864839553833008 time for calcul the mask position with numpy : 0.027889013290405273 nb_pixel_total : 16831 time to create 1 rle with old method : 0.018494606018066406 time for calcul the mask position with numpy : 0.02768230438232422 nb_pixel_total : 47632 time to create 1 rle with old method : 0.05134987831115723 time for calcul the mask position with numpy : 0.028231143951416016 nb_pixel_total : 5928 time to create 1 rle with old method : 0.006413698196411133 time for calcul the mask position with numpy : 0.028427600860595703 nb_pixel_total : 18832 time to create 1 rle with old method : 0.02019643783569336 time for calcul the mask position with numpy : 0.029074907302856445 nb_pixel_total : 7570 time to create 1 rle with old method : 0.00851130485534668 time for calcul the mask position with numpy : 0.02909994125366211 nb_pixel_total : 9675 time to create 1 rle with old method : 0.010740280151367188 time for calcul the mask position with numpy : 0.029181241989135742 nb_pixel_total : 39343 time to create 1 rle with old method : 0.04221844673156738 time for calcul the mask position with numpy : 0.028743267059326172 nb_pixel_total : 23172 time to create 1 rle with old method : 0.026066303253173828 time for calcul the mask position with numpy : 0.02900552749633789 nb_pixel_total : 18547 time to create 1 rle with old method : 0.02117133140563965 time for calcul the mask position with numpy : 0.029071569442749023 nb_pixel_total : 13607 time to create 1 rle with old method : 0.015003204345703125 time for calcul the mask position with numpy : 0.028533935546875 nb_pixel_total : 17740 time to create 1 rle with old method : 0.019429445266723633 time for calcul the mask position with numpy : 0.029657840728759766 nb_pixel_total : 88068 time to create 1 rle with old method : 0.09603357315063477 time for calcul the mask position with numpy : 0.02937173843383789 nb_pixel_total : 13615 time to create 1 rle with old method : 0.01569509506225586 time for calcul the mask position with numpy : 0.02912735939025879 nb_pixel_total : 8254 time to create 1 rle with old method : 0.009370803833007812 time for calcul the mask position with numpy : 0.02920055389404297 nb_pixel_total : 9525 time to create 1 rle with old method : 0.010979413986206055 time for calcul the mask position with numpy : 0.029708385467529297 nb_pixel_total : 63880 time to create 1 rle with old method : 0.07042455673217773 time for calcul the mask position with numpy : 0.029534101486206055 nb_pixel_total : 15018 time to create 1 rle with old method : 0.016631364822387695 time for calcul the mask position with numpy : 0.02895331382751465 nb_pixel_total : 18856 time to create 1 rle with old method : 0.02100992202758789 time for calcul the mask position with numpy : 0.02934885025024414 nb_pixel_total : 1083 time to create 1 rle with old method : 0.002324819564819336 time for calcul the mask position with numpy : 0.031083345413208008 nb_pixel_total : 20300 time to create 1 rle with old method : 0.02281928062438965 time for calcul the mask position with numpy : 0.02939319610595703 nb_pixel_total : 20139 time to create 1 rle with old method : 0.032414913177490234 time for calcul the mask position with numpy : 0.03275871276855469 nb_pixel_total : 13748 time to create 1 rle with old method : 0.01784062385559082 time for calcul the mask position with numpy : 0.02867722511291504 nb_pixel_total : 11687 time to create 1 rle with old method : 0.013368368148803711 time for calcul the mask position with numpy : 0.028726577758789062 nb_pixel_total : 2248 time to create 1 rle with old method : 0.0026559829711914062 time for calcul the mask position with numpy : 0.028655529022216797 nb_pixel_total : 16648 time to create 1 rle with old method : 0.01840949058532715 time for calcul the mask position with numpy : 0.028585433959960938 nb_pixel_total : 1317 time to create 1 rle with old method : 0.0015873908996582031 time for calcul the mask position with numpy : 0.028691530227661133 nb_pixel_total : 31665 time to create 1 rle with old method : 0.0350956916809082 time for calcul the mask position with numpy : 0.028606176376342773 nb_pixel_total : 6493 time to create 1 rle with old method : 0.007630825042724609 time for calcul the mask position with numpy : 0.030648231506347656 nb_pixel_total : 7076 time to create 1 rle with old method : 0.011564493179321289 time for calcul the mask position with numpy : 0.03267359733581543 nb_pixel_total : 4418 time to create 1 rle with old method : 0.007130146026611328 time for calcul the mask position with numpy : 0.030422210693359375 nb_pixel_total : 29399 time to create 1 rle with old method : 0.031102895736694336 time for calcul the mask position with numpy : 0.02773737907409668 nb_pixel_total : 11327 time to create 1 rle with old method : 0.01246786117553711 time for calcul the mask position with numpy : 0.027981042861938477 nb_pixel_total : 51502 time to create 1 rle with old method : 0.05497908592224121 time for calcul the mask position with numpy : 0.028710603713989258 nb_pixel_total : 14289 time to create 1 rle with old method : 0.021084308624267578 time for calcul the mask position with numpy : 0.02866649627685547 nb_pixel_total : 13373 time to create 1 rle with old method : 0.014583349227905273 time for calcul the mask position with numpy : 0.028057336807250977 nb_pixel_total : 10702 time to create 1 rle with old method : 0.012536764144897461 time for calcul the mask position with numpy : 0.028367042541503906 nb_pixel_total : 6422 time to create 1 rle with old method : 0.007121562957763672 time for calcul the mask position with numpy : 0.027750015258789062 nb_pixel_total : 4269 time to create 1 rle with old method : 0.00491786003112793 time for calcul the mask position with numpy : 0.028203725814819336 nb_pixel_total : 4304 time to create 1 rle with old method : 0.004937171936035156 time for calcul the mask position with numpy : 0.028575897216796875 nb_pixel_total : 6375 time to create 1 rle with old method : 0.007402181625366211 time for calcul the mask position with numpy : 0.02841472625732422 nb_pixel_total : 8022 time to create 1 rle with old method : 0.009260177612304688 time for calcul the mask position with numpy : 0.0286862850189209 nb_pixel_total : 5680 time to create 1 rle with old method : 0.006448507308959961 time for calcul the mask position with numpy : 0.02803516387939453 nb_pixel_total : 5580 time to create 1 rle with old method : 0.006000518798828125 time for calcul the mask position with numpy : 0.029779911041259766 nb_pixel_total : 14514 time to create 1 rle with old method : 0.018185138702392578 create new chi : 6.901650667190552 time to delete rle : 0.00446009635925293 batch 1 Loaded 153 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 29956 TO DO : save crop sub photo not yet done ! save time : 2.0803115367889404 nb_obj : 62 nb_hashtags : 4 time to prepare the origin masks : 3.8117434978485107 time for calcul the mask position with numpy : 0.543511152267456 nb_pixel_total : 5774257 time to create 1 rle with new method : 0.9813075065612793 time for calcul the mask position with numpy : 0.028975248336791992 nb_pixel_total : 9421 time to create 1 rle with old method : 0.010741233825683594 time for calcul the mask position with numpy : 0.0289304256439209 nb_pixel_total : 20232 time to create 1 rle with old method : 0.023003578186035156 time for calcul the mask position with numpy : 0.028286457061767578 nb_pixel_total : 29048 time to create 1 rle with old method : 0.03231048583984375 time for calcul the mask position with numpy : 0.029570579528808594 nb_pixel_total : 7619 time to create 1 rle with old method : 0.008881330490112305 time for calcul the mask position with numpy : 0.030292272567749023 nb_pixel_total : 2050 time to create 1 rle with old method : 0.002425670623779297 time for calcul the mask position with numpy : 0.0290377140045166 nb_pixel_total : 10607 time to create 1 rle with old method : 0.013117313385009766 time for calcul the mask position with numpy : 0.03307318687438965 nb_pixel_total : 20758 time to create 1 rle with old method : 0.032715559005737305 time for calcul the mask position with numpy : 0.029273033142089844 nb_pixel_total : 12778 time to create 1 rle with old method : 0.014120340347290039 time for calcul the mask position with numpy : 0.02965712547302246 nb_pixel_total : 23652 time to create 1 rle with old method : 0.03356194496154785 time for calcul the mask position with numpy : 0.0307767391204834 nb_pixel_total : 11745 time to create 1 rle with old method : 0.013416528701782227 time for calcul the mask position with numpy : 0.028962135314941406 nb_pixel_total : 13945 time to create 1 rle with old method : 0.015681982040405273 time for calcul the mask position with numpy : 0.030033349990844727 nb_pixel_total : 24707 time to create 1 rle with old method : 0.03988051414489746 time for calcul the mask position with numpy : 0.03289365768432617 nb_pixel_total : 21489 time to create 1 rle with old method : 0.023512601852416992 time for calcul the mask position with numpy : 0.028925657272338867 nb_pixel_total : 21159 time to create 1 rle with old method : 0.023623943328857422 time for calcul the mask position with numpy : 0.02889728546142578 nb_pixel_total : 10659 time to create 1 rle with old method : 0.012070655822753906 time for calcul the mask position with numpy : 0.02901482582092285 nb_pixel_total : 41376 time to create 1 rle with old method : 0.052202701568603516 time for calcul the mask position with numpy : 0.03288412094116211 nb_pixel_total : 30892 time to create 1 rle with old method : 0.0434110164642334 time for calcul the mask position with numpy : 0.028837203979492188 nb_pixel_total : 22062 time to create 1 rle with old method : 0.024646282196044922 time for calcul the mask position with numpy : 0.029178857803344727 nb_pixel_total : 29740 time to create 1 rle with old method : 0.03330564498901367 time for calcul the mask position with numpy : 0.02906203269958496 nb_pixel_total : 34907 time to create 1 rle with old method : 0.04167938232421875 time for calcul the mask position with numpy : 0.029044628143310547 nb_pixel_total : 14848 time to create 1 rle with old method : 0.018421173095703125 time for calcul the mask position with numpy : 0.03287458419799805 nb_pixel_total : 13259 time to create 1 rle with old method : 0.01870584487915039 time for calcul the mask position with numpy : 0.02924656867980957 nb_pixel_total : 35922 time to create 1 rle with old method : 0.03917574882507324 time for calcul the mask position with numpy : 0.03131389617919922 nb_pixel_total : 67988 time to create 1 rle with old method : 0.0764927864074707 time for calcul the mask position with numpy : 0.0289003849029541 nb_pixel_total : 17055 time to create 1 rle with old method : 0.0192413330078125 time for calcul the mask position with numpy : 0.028966188430786133 nb_pixel_total : 64503 time to create 1 rle with old method : 0.07232022285461426 time for calcul the mask position with numpy : 0.02873992919921875 nb_pixel_total : 5808 time to create 1 rle with old method : 0.006753206253051758 time for calcul the mask position with numpy : 0.02876901626586914 nb_pixel_total : 28767 time to create 1 rle with old method : 0.033277273178100586 time for calcul the mask position with numpy : 0.028799057006835938 nb_pixel_total : 10550 time to create 1 rle with old method : 0.012334585189819336 time for calcul the mask position with numpy : 0.028766155242919922 nb_pixel_total : 10612 time to create 1 rle with old method : 0.011905670166015625 time for calcul the mask position with numpy : 0.028782129287719727 nb_pixel_total : 12433 time to create 1 rle with old method : 0.013971805572509766 time for calcul the mask position with numpy : 0.028855323791503906 nb_pixel_total : 16021 time to create 1 rle with old method : 0.01777362823486328 time for calcul the mask position with numpy : 0.029543638229370117 nb_pixel_total : 26075 time to create 1 rle with old method : 0.02947258949279785 time for calcul the mask position with numpy : 0.028772830963134766 nb_pixel_total : 4688 time to create 1 rle with old method : 0.00571441650390625 time for calcul the mask position with numpy : 0.02881145477294922 nb_pixel_total : 12812 time to create 1 rle with old method : 0.014290571212768555 time for calcul the mask position with numpy : 0.02891373634338379 nb_pixel_total : 12193 time to create 1 rle with old method : 0.013566017150878906 time for calcul the mask position with numpy : 0.02899956703186035 nb_pixel_total : 34658 time to create 1 rle with old method : 0.03850054740905762 time for calcul the mask position with numpy : 0.028963565826416016 nb_pixel_total : 23648 time to create 1 rle with old method : 0.02732110023498535 time for calcul the mask position with numpy : 0.02950143814086914 nb_pixel_total : 23761 time to create 1 rle with old method : 0.026576757431030273 time for calcul the mask position with numpy : 0.028876304626464844 nb_pixel_total : 22097 time to create 1 rle with old method : 0.025964736938476562 time for calcul the mask position with numpy : 0.029703140258789062 nb_pixel_total : 4351 time to create 1 rle with old method : 0.004897356033325195 time for calcul the mask position with numpy : 0.030498981475830078 nb_pixel_total : 44108 time to create 1 rle with old method : 0.04880237579345703 time for calcul the mask position with numpy : 0.029386281967163086 nb_pixel_total : 13768 time to create 1 rle with old method : 0.017371416091918945 time for calcul the mask position with numpy : 0.0330357551574707 nb_pixel_total : 6297 time to create 1 rle with old method : 0.007420539855957031 time for calcul the mask position with numpy : 0.029800891876220703 nb_pixel_total : 41793 time to create 1 rle with old method : 0.04587841033935547 time for calcul the mask position with numpy : 0.03125953674316406 nb_pixel_total : 3927 time to create 1 rle with old method : 0.0045719146728515625 time for calcul the mask position with numpy : 0.032549142837524414 nb_pixel_total : 171 time to create 1 rle with old method : 0.0006184577941894531 time for calcul the mask position with numpy : 0.03500771522521973 nb_pixel_total : 19360 time to create 1 rle with old method : 0.02183222770690918 time for calcul the mask position with numpy : 0.0290524959564209 nb_pixel_total : 30243 time to create 1 rle with old method : 0.03671979904174805 time for calcul the mask position with numpy : 0.028827190399169922 nb_pixel_total : 40200 time to create 1 rle with old method : 0.04729962348937988 time for calcul the mask position with numpy : 0.03059840202331543 nb_pixel_total : 5984 time to create 1 rle with old method : 0.007044076919555664 time for calcul the mask position with numpy : 0.028722047805786133 nb_pixel_total : 14872 time to create 1 rle with old method : 0.01690196990966797 time for calcul the mask position with numpy : 0.028662443161010742 nb_pixel_total : 7640 time to create 1 rle with old method : 0.008937358856201172 time for calcul the mask position with numpy : 0.02865290641784668 nb_pixel_total : 5340 time to create 1 rle with old method : 0.006224870681762695 time for calcul the mask position with numpy : 0.02942490577697754 nb_pixel_total : 91685 time to create 1 rle with old method : 0.1033928394317627 time for calcul the mask position with numpy : 0.02880859375 nb_pixel_total : 10644 time to create 1 rle with old method : 0.012026071548461914 time for calcul the mask position with numpy : 0.028734922409057617 nb_pixel_total : 10166 time to create 1 rle with old method : 0.01128840446472168 time for calcul the mask position with numpy : 0.028902292251586914 nb_pixel_total : 29900 time to create 1 rle with old method : 0.0330963134765625 time for calcul the mask position with numpy : 0.02879190444946289 nb_pixel_total : 14850 time to create 1 rle with old method : 0.01631474494934082 time for calcul the mask position with numpy : 0.028742551803588867 nb_pixel_total : 16351 time to create 1 rle with old method : 0.01826953887939453 time for calcul the mask position with numpy : 0.028721094131469727 nb_pixel_total : 5354 time to create 1 rle with old method : 0.00629115104675293 time for calcul the mask position with numpy : 0.02868962287902832 nb_pixel_total : 2435 time to create 1 rle with old method : 0.0028297901153564453 create new chi : 4.892480850219727 time to delete rle : 0.003934621810913086 batch 1 Loaded 125 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 25366 TO DO : save crop sub photo not yet done ! save time : 3.6357216835021973 nb_obj : 63 nb_hashtags : 3 time to prepare the origin masks : 4.341742753982544 time for calcul the mask position with numpy : 0.34137463569641113 nb_pixel_total : 5237287 time to create 1 rle with new method : 0.7383852005004883 time for calcul the mask position with numpy : 0.0309603214263916 nb_pixel_total : 6978 time to create 1 rle with old method : 0.011333942413330078 time for calcul the mask position with numpy : 0.03356504440307617 nb_pixel_total : 33677 time to create 1 rle with old method : 0.04497075080871582 time for calcul the mask position with numpy : 0.029282569885253906 nb_pixel_total : 85508 time to create 1 rle with old method : 0.09287786483764648 time for calcul the mask position with numpy : 0.02901315689086914 nb_pixel_total : 12613 time to create 1 rle with old method : 0.014425039291381836 time for calcul the mask position with numpy : 0.028995752334594727 nb_pixel_total : 30087 time to create 1 rle with old method : 0.03362154960632324 time for calcul the mask position with numpy : 0.029170513153076172 nb_pixel_total : 36562 time to create 1 rle with old method : 0.04171919822692871 time for calcul the mask position with numpy : 0.02928638458251953 nb_pixel_total : 37023 time to create 1 rle with old method : 0.041236162185668945 time for calcul the mask position with numpy : 0.030165672302246094 nb_pixel_total : 10323 time to create 1 rle with old method : 0.014720916748046875 time for calcul the mask position with numpy : 0.029329776763916016 nb_pixel_total : 7853 time to create 1 rle with old method : 0.008900165557861328 time for calcul the mask position with numpy : 0.02911067008972168 nb_pixel_total : 56495 time to create 1 rle with old method : 0.06859421730041504 time for calcul the mask position with numpy : 0.028760671615600586 nb_pixel_total : 19216 time to create 1 rle with old method : 0.02123284339904785 time for calcul the mask position with numpy : 0.029196500778198242 nb_pixel_total : 83180 time to create 1 rle with old method : 0.09560346603393555 time for calcul the mask position with numpy : 0.029398679733276367 nb_pixel_total : 144992 time to create 1 rle with old method : 0.16933751106262207 time for calcul the mask position with numpy : 0.035135507583618164 nb_pixel_total : 103303 time to create 1 rle with old method : 0.11501646041870117 time for calcul the mask position with numpy : 0.03990435600280762 nb_pixel_total : 11806 time to create 1 rle with old method : 0.014447212219238281 time for calcul the mask position with numpy : 0.0343325138092041 nb_pixel_total : 51430 time to create 1 rle with old method : 0.056168317794799805 time for calcul the mask position with numpy : 0.028702974319458008 nb_pixel_total : 5462 time to create 1 rle with old method : 0.006464242935180664 time for calcul the mask position with numpy : 0.028979778289794922 nb_pixel_total : 8620 time to create 1 rle with old method : 0.009881019592285156 time for calcul the mask position with numpy : 0.02893543243408203 nb_pixel_total : 19766 time to create 1 rle with old method : 0.022177457809448242 time for calcul the mask position with numpy : 0.028931617736816406 nb_pixel_total : 13587 time to create 1 rle with old method : 0.015619754791259766 time for calcul the mask position with numpy : 0.02867746353149414 nb_pixel_total : 62189 time to create 1 rle with old method : 0.06843829154968262 time for calcul the mask position with numpy : 0.028769731521606445 nb_pixel_total : 6892 time to create 1 rle with old method : 0.008053779602050781 time for calcul the mask position with numpy : 0.028678178787231445 nb_pixel_total : 21811 time to create 1 rle with old method : 0.024383544921875 time for calcul the mask position with numpy : 0.028723478317260742 nb_pixel_total : 14654 time to create 1 rle with old method : 0.016425371170043945 time for calcul the mask position with numpy : 0.02885293960571289 nb_pixel_total : 29972 time to create 1 rle with old method : 0.033611297607421875 time for calcul the mask position with numpy : 0.02879047393798828 nb_pixel_total : 10510 time to create 1 rle with old method : 0.01177978515625 time for calcul the mask position with numpy : 0.028835535049438477 nb_pixel_total : 20790 time to create 1 rle with old method : 0.024188518524169922 time for calcul the mask position with numpy : 0.028914690017700195 nb_pixel_total : 26819 time to create 1 rle with old method : 0.030011653900146484 time for calcul the mask position with numpy : 0.02894139289855957 nb_pixel_total : 14428 time to create 1 rle with old method : 0.01645374298095703 time for calcul the mask position with numpy : 0.028719663619995117 nb_pixel_total : 10384 time to create 1 rle with old method : 0.011792182922363281 time for calcul the mask position with numpy : 0.02924633026123047 nb_pixel_total : 127167 time to create 1 rle with old method : 0.13891029357910156 time for calcul the mask position with numpy : 0.028882265090942383 nb_pixel_total : 13080 time to create 1 rle with old method : 0.014415264129638672 time for calcul the mask position with numpy : 0.028815507888793945 nb_pixel_total : 9408 time to create 1 rle with old method : 0.010473489761352539 time for calcul the mask position with numpy : 0.02898883819580078 nb_pixel_total : 10645 time to create 1 rle with old method : 0.01233816146850586 time for calcul the mask position with numpy : 0.029124975204467773 nb_pixel_total : 19690 time to create 1 rle with old method : 0.022308826446533203 time for calcul the mask position with numpy : 0.02877950668334961 nb_pixel_total : 38319 time to create 1 rle with old method : 0.0425260066986084 time for calcul the mask position with numpy : 0.0293729305267334 nb_pixel_total : 8823 time to create 1 rle with old method : 0.010440587997436523 time for calcul the mask position with numpy : 0.029160022735595703 nb_pixel_total : 7694 time to create 1 rle with old method : 0.008874177932739258 time for calcul the mask position with numpy : 0.028843164443969727 nb_pixel_total : 8495 time to create 1 rle with old method : 0.009649991989135742 time for calcul the mask position with numpy : 0.02882075309753418 nb_pixel_total : 11930 time to create 1 rle with old method : 0.013324260711669922 time for calcul the mask position with numpy : 0.028783798217773438 nb_pixel_total : 8174 time to create 1 rle with old method : 0.009467124938964844 time for calcul the mask position with numpy : 0.02876877784729004 nb_pixel_total : 8567 time to create 1 rle with old method : 0.009910821914672852 time for calcul the mask position with numpy : 0.02869701385498047 nb_pixel_total : 13465 time to create 1 rle with old method : 0.015480995178222656 time for calcul the mask position with numpy : 0.028897762298583984 nb_pixel_total : 49027 time to create 1 rle with old method : 0.054509639739990234 time for calcul the mask position with numpy : 0.028924226760864258 nb_pixel_total : 8030 time to create 1 rle with old method : 0.009218454360961914 time for calcul the mask position with numpy : 0.02893376350402832 nb_pixel_total : 4744 time to create 1 rle with old method : 0.005338907241821289 time for calcul the mask position with numpy : 0.028934717178344727 nb_pixel_total : 202 time to create 1 rle with old method : 0.00034308433532714844 time for calcul the mask position with numpy : 0.02897357940673828 nb_pixel_total : 7274 time to create 1 rle with old method : 0.008121967315673828 time for calcul the mask position with numpy : 0.0292055606842041 nb_pixel_total : 46739 time to create 1 rle with old method : 0.05199003219604492 time for calcul the mask position with numpy : 0.029352664947509766 nb_pixel_total : 7829 time to create 1 rle with old method : 0.009148836135864258 time for calcul the mask position with numpy : 0.030129194259643555 nb_pixel_total : 57290 time to create 1 rle with old method : 0.06266379356384277 time for calcul the mask position with numpy : 0.02911972999572754 nb_pixel_total : 9181 time to create 1 rle with old method : 0.01063680648803711 time for calcul the mask position with numpy : 0.03205585479736328 nb_pixel_total : 2795 time to create 1 rle with old method : 0.003934383392333984 time for calcul the mask position with numpy : 0.03338336944580078 nb_pixel_total : 14427 time to create 1 rle with old method : 0.019403457641601562 time for calcul the mask position with numpy : 0.029337644577026367 nb_pixel_total : 11849 time to create 1 rle with old method : 0.013483524322509766 time for calcul the mask position with numpy : 0.02933025360107422 nb_pixel_total : 31520 time to create 1 rle with old method : 0.03564262390136719 time for calcul the mask position with numpy : 0.029492616653442383 nb_pixel_total : 29656 time to create 1 rle with old method : 0.033542633056640625 time for calcul the mask position with numpy : 0.029049396514892578 nb_pixel_total : 4593 time to create 1 rle with old method : 0.0053632259368896484 time for calcul the mask position with numpy : 0.029033422470092773 nb_pixel_total : 24455 time to create 1 rle with old method : 0.02705216407775879 time for calcul the mask position with numpy : 0.029590368270874023 nb_pixel_total : 151351 time to create 1 rle with new method : 0.9413206577301025 time for calcul the mask position with numpy : 0.029088973999023438 nb_pixel_total : 46265 time to create 1 rle with old method : 0.05029869079589844 time for calcul the mask position with numpy : 0.02883148193359375 nb_pixel_total : 17113 time to create 1 rle with old method : 0.018915414810180664 time for calcul the mask position with numpy : 0.028746366500854492 nb_pixel_total : 6226 time to create 1 rle with old method : 0.007177114486694336 create new chi : 5.833738327026367 time to delete rle : 0.00407099723815918 batch 1 Loaded 127 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 28058 TO DO : save crop sub photo not yet done ! save time : 2.916435718536377 nb_obj : 62 nb_hashtags : 3 time to prepare the origin masks : 3.8351399898529053 time for calcul the mask position with numpy : 0.07263541221618652 nb_pixel_total : 5844588 time to create 1 rle with new method : 0.5935101509094238 time for calcul the mask position with numpy : 0.029068470001220703 nb_pixel_total : 12573 time to create 1 rle with old method : 0.014226675033569336 time for calcul the mask position with numpy : 0.0293424129486084 nb_pixel_total : 36773 time to create 1 rle with old method : 0.04015660285949707 time for calcul the mask position with numpy : 0.028339385986328125 nb_pixel_total : 7155 time to create 1 rle with old method : 0.008287191390991211 time for calcul the mask position with numpy : 0.028460025787353516 nb_pixel_total : 6650 time to create 1 rle with old method : 0.007699489593505859 time for calcul the mask position with numpy : 0.02816462516784668 nb_pixel_total : 7450 time to create 1 rle with old method : 0.00857996940612793 time for calcul the mask position with numpy : 0.0287933349609375 nb_pixel_total : 11600 time to create 1 rle with old method : 0.012812614440917969 time for calcul the mask position with numpy : 0.028607845306396484 nb_pixel_total : 32077 time to create 1 rle with old method : 0.03531932830810547 time for calcul the mask position with numpy : 0.029351234436035156 nb_pixel_total : 121034 time to create 1 rle with old method : 0.12908673286437988 time for calcul the mask position with numpy : 0.028026819229125977 nb_pixel_total : 10056 time to create 1 rle with old method : 0.011599302291870117 time for calcul the mask position with numpy : 0.02879619598388672 nb_pixel_total : 29339 time to create 1 rle with old method : 0.033219099044799805 time for calcul the mask position with numpy : 0.028879165649414062 nb_pixel_total : 9855 time to create 1 rle with old method : 0.011239767074584961 time for calcul the mask position with numpy : 0.028089284896850586 nb_pixel_total : 21919 time to create 1 rle with old method : 0.024373292922973633 time for calcul the mask position with numpy : 0.028234243392944336 nb_pixel_total : 5543 time to create 1 rle with old method : 0.006256818771362305 time for calcul the mask position with numpy : 0.028384923934936523 nb_pixel_total : 35872 time to create 1 rle with old method : 0.03893923759460449 time for calcul the mask position with numpy : 0.028415441513061523 nb_pixel_total : 9869 time to create 1 rle with old method : 0.010956287384033203 time for calcul the mask position with numpy : 0.02994227409362793 nb_pixel_total : 6776 time to create 1 rle with old method : 0.010976791381835938 time for calcul the mask position with numpy : 0.03272509574890137 nb_pixel_total : 10439 time to create 1 rle with old method : 0.01697087287902832 time for calcul the mask position with numpy : 0.028979778289794922 nb_pixel_total : 9832 time to create 1 rle with old method : 0.011176347732543945 time for calcul the mask position with numpy : 0.028052806854248047 nb_pixel_total : 10917 time to create 1 rle with old method : 0.012391805648803711 time for calcul the mask position with numpy : 0.028143644332885742 nb_pixel_total : 11624 time to create 1 rle with old method : 0.013173818588256836 time for calcul the mask position with numpy : 0.02852773666381836 nb_pixel_total : 23581 time to create 1 rle with old method : 0.026219606399536133 time for calcul the mask position with numpy : 0.02834939956665039 nb_pixel_total : 25353 time to create 1 rle with old method : 0.02731609344482422 time for calcul the mask position with numpy : 0.028092145919799805 nb_pixel_total : 21285 time to create 1 rle with old method : 0.023802518844604492 time for calcul the mask position with numpy : 0.029038667678833008 nb_pixel_total : 15914 time to create 1 rle with old method : 0.018018007278442383 time for calcul the mask position with numpy : 0.029543161392211914 nb_pixel_total : 135901 time to create 1 rle with old method : 0.15063881874084473 time for calcul the mask position with numpy : 0.028196096420288086 nb_pixel_total : 10646 time to create 1 rle with old method : 0.011855840682983398 time for calcul the mask position with numpy : 0.028726816177368164 nb_pixel_total : 26066 time to create 1 rle with old method : 0.02874469757080078 time for calcul the mask position with numpy : 0.02840590476989746 nb_pixel_total : 4916 time to create 1 rle with old method : 0.00581812858581543 time for calcul the mask position with numpy : 0.027899503707885742 nb_pixel_total : 12245 time to create 1 rle with old method : 0.013645648956298828 time for calcul the mask position with numpy : 0.02807903289794922 nb_pixel_total : 10668 time to create 1 rle with old method : 0.011320114135742188 time for calcul the mask position with numpy : 0.027485132217407227 nb_pixel_total : 52396 time to create 1 rle with old method : 0.05458521842956543 time for calcul the mask position with numpy : 0.0283353328704834 nb_pixel_total : 19977 time to create 1 rle with old method : 0.022368669509887695 time for calcul the mask position with numpy : 0.028814077377319336 nb_pixel_total : 17550 time to create 1 rle with old method : 0.02002859115600586 time for calcul the mask position with numpy : 0.02777695655822754 nb_pixel_total : 3357 time to create 1 rle with old method : 0.003875732421875 time for calcul the mask position with numpy : 0.027436256408691406 nb_pixel_total : 7830 time to create 1 rle with old method : 0.009076833724975586 time for calcul the mask position with numpy : 0.028182029724121094 nb_pixel_total : 6061 time to create 1 rle with old method : 0.006795406341552734 time for calcul the mask position with numpy : 0.027451753616333008 nb_pixel_total : 7801 time to create 1 rle with old method : 0.008990049362182617 time for calcul the mask position with numpy : 0.03168082237243652 nb_pixel_total : 20146 time to create 1 rle with old method : 0.023253917694091797 time for calcul the mask position with numpy : 0.028900623321533203 nb_pixel_total : 11832 time to create 1 rle with old method : 0.012706995010375977 time for calcul the mask position with numpy : 0.0302579402923584 nb_pixel_total : 13669 time to create 1 rle with old method : 0.017800331115722656 time for calcul the mask position with numpy : 0.029134273529052734 nb_pixel_total : 13561 time to create 1 rle with old method : 0.014658689498901367 time for calcul the mask position with numpy : 0.028188228607177734 nb_pixel_total : 4233 time to create 1 rle with old method : 0.0047454833984375 time for calcul the mask position with numpy : 0.028433561325073242 nb_pixel_total : 12272 time to create 1 rle with old method : 0.013751983642578125 time for calcul the mask position with numpy : 0.02908015251159668 nb_pixel_total : 975 time to create 1 rle with old method : 0.0013780593872070312 time for calcul the mask position with numpy : 0.02894306182861328 nb_pixel_total : 26808 time to create 1 rle with old method : 0.029196739196777344 time for calcul the mask position with numpy : 0.029309988021850586 nb_pixel_total : 23535 time to create 1 rle with old method : 0.02650308609008789 time for calcul the mask position with numpy : 0.029101848602294922 nb_pixel_total : 9462 time to create 1 rle with old method : 0.010694503784179688 time for calcul the mask position with numpy : 0.02967238426208496 nb_pixel_total : 23397 time to create 1 rle with old method : 0.02730274200439453 time for calcul the mask position with numpy : 0.02907872200012207 nb_pixel_total : 37411 time to create 1 rle with old method : 0.04150867462158203 time for calcul the mask position with numpy : 0.029099464416503906 nb_pixel_total : 4774 time to create 1 rle with old method : 0.0053615570068359375 time for calcul the mask position with numpy : 0.028777122497558594 nb_pixel_total : 13228 time to create 1 rle with old method : 0.014742136001586914 time for calcul the mask position with numpy : 0.02849292755126953 nb_pixel_total : 30300 time to create 1 rle with old method : 0.033151865005493164 time for calcul the mask position with numpy : 0.02782893180847168 nb_pixel_total : 20977 time to create 1 rle with old method : 0.023031234741210938 time for calcul the mask position with numpy : 0.027849674224853516 nb_pixel_total : 12513 time to create 1 rle with old method : 0.013810157775878906 time for calcul the mask position with numpy : 0.02825188636779785 nb_pixel_total : 17511 time to create 1 rle with old method : 0.01932048797607422 time for calcul the mask position with numpy : 0.027712345123291016 nb_pixel_total : 10434 time to create 1 rle with old method : 0.011893272399902344 time for calcul the mask position with numpy : 0.028139352798461914 nb_pixel_total : 34858 time to create 1 rle with old method : 0.03873944282531738 time for calcul the mask position with numpy : 0.028923511505126953 nb_pixel_total : 5257 time to create 1 rle with old method : 0.006177425384521484 time for calcul the mask position with numpy : 0.02900218963623047 nb_pixel_total : 26273 time to create 1 rle with old method : 0.030129194259643555 time for calcul the mask position with numpy : 0.02894306182861328 nb_pixel_total : 11566 time to create 1 rle with old method : 0.013352632522583008 time for calcul the mask position with numpy : 0.0284121036529541 nb_pixel_total : 4194 time to create 1 rle with old method : 0.0047702789306640625 time for calcul the mask position with numpy : 0.02816009521484375 nb_pixel_total : 7566 time to create 1 rle with old method : 0.008825063705444336 create new chi : 3.820878028869629 time to delete rle : 0.0034689903259277344 batch 1 Loaded 125 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 23638 TO DO : save crop sub photo not yet done ! save time : 3.6562752723693848 nb_obj : 49 nb_hashtags : 5 time to prepare the origin masks : 4.283282518386841 time for calcul the mask position with numpy : 0.43246889114379883 nb_pixel_total : 5361948 time to create 1 rle with new method : 0.45919227600097656 time for calcul the mask position with numpy : 0.03041529655456543 nb_pixel_total : 32214 time to create 1 rle with old method : 0.036895036697387695 time for calcul the mask position with numpy : 0.028801679611206055 nb_pixel_total : 6475 time to create 1 rle with old method : 0.007035970687866211 time for calcul the mask position with numpy : 0.028913259506225586 nb_pixel_total : 38191 time to create 1 rle with old method : 0.04147768020629883 time for calcul the mask position with numpy : 0.030879735946655273 nb_pixel_total : 16404 time to create 1 rle with old method : 0.017777204513549805 time for calcul the mask position with numpy : 0.028390169143676758 nb_pixel_total : 3639 time to create 1 rle with old method : 0.00429844856262207 time for calcul the mask position with numpy : 0.028389453887939453 nb_pixel_total : 5932 time to create 1 rle with old method : 0.006640434265136719 time for calcul the mask position with numpy : 0.028354644775390625 nb_pixel_total : 18808 time to create 1 rle with old method : 0.020490169525146484 time for calcul the mask position with numpy : 0.027777671813964844 nb_pixel_total : 12457 time to create 1 rle with old method : 0.01404881477355957 time for calcul the mask position with numpy : 0.028022289276123047 nb_pixel_total : 6855 time to create 1 rle with old method : 0.007630109786987305 time for calcul the mask position with numpy : 0.0283355712890625 nb_pixel_total : 2669 time to create 1 rle with old method : 0.0031557083129882812 time for calcul the mask position with numpy : 0.02846217155456543 nb_pixel_total : 24498 time to create 1 rle with old method : 0.027897119522094727 time for calcul the mask position with numpy : 0.028141021728515625 nb_pixel_total : 43590 time to create 1 rle with old method : 0.04780435562133789 time for calcul the mask position with numpy : 0.02875065803527832 nb_pixel_total : 6271 time to create 1 rle with old method : 0.0073757171630859375 time for calcul the mask position with numpy : 0.028654813766479492 nb_pixel_total : 13716 time to create 1 rle with old method : 0.015551090240478516 time for calcul the mask position with numpy : 0.02887248992919922 nb_pixel_total : 46482 time to create 1 rle with old method : 0.051915645599365234 time for calcul the mask position with numpy : 0.028765439987182617 nb_pixel_total : 5084 time to create 1 rle with old method : 0.005917787551879883 time for calcul the mask position with numpy : 0.028711557388305664 nb_pixel_total : 9317 time to create 1 rle with old method : 0.010610103607177734 time for calcul the mask position with numpy : 0.02888178825378418 nb_pixel_total : 16755 time to create 1 rle with old method : 0.018973588943481445 time for calcul the mask position with numpy : 0.02879500389099121 nb_pixel_total : 15013 time to create 1 rle with old method : 0.016915082931518555 time for calcul the mask position with numpy : 0.02906012535095215 nb_pixel_total : 84397 time to create 1 rle with old method : 0.09269881248474121 time for calcul the mask position with numpy : 0.029090166091918945 nb_pixel_total : 51584 time to create 1 rle with old method : 0.05704379081726074 time for calcul the mask position with numpy : 0.02877521514892578 nb_pixel_total : 7056 time to create 1 rle with old method : 0.00788736343383789 time for calcul the mask position with numpy : 0.03022027015686035 nb_pixel_total : 47973 time to create 1 rle with old method : 0.05271005630493164 time for calcul the mask position with numpy : 0.02871251106262207 nb_pixel_total : 12245 time to create 1 rle with old method : 0.014038324356079102 time for calcul the mask position with numpy : 0.028730154037475586 nb_pixel_total : 20812 time to create 1 rle with old method : 0.023278474807739258 time for calcul the mask position with numpy : 0.02921319007873535 nb_pixel_total : 88531 time to create 1 rle with old method : 0.09713077545166016 time for calcul the mask position with numpy : 0.02887749671936035 nb_pixel_total : 18320 time to create 1 rle with old method : 0.020562410354614258 time for calcul the mask position with numpy : 0.028794050216674805 nb_pixel_total : 18479 time to create 1 rle with old method : 0.020404338836669922 time for calcul the mask position with numpy : 0.02872157096862793 nb_pixel_total : 5467 time to create 1 rle with old method : 0.00680851936340332 time for calcul the mask position with numpy : 0.028745174407958984 nb_pixel_total : 41532 time to create 1 rle with old method : 0.04610705375671387 time for calcul the mask position with numpy : 0.029024362564086914 nb_pixel_total : 74747 time to create 1 rle with old method : 0.0825047492980957 time for calcul the mask position with numpy : 0.0289456844329834 nb_pixel_total : 31622 time to create 1 rle with old method : 0.03666210174560547 time for calcul the mask position with numpy : 0.03747296333312988 nb_pixel_total : 502128 time to create 1 rle with new method : 0.45021986961364746 time for calcul the mask position with numpy : 0.036069631576538086 nb_pixel_total : 6372 time to create 1 rle with old method : 0.0076596736907958984 time for calcul the mask position with numpy : 0.03124213218688965 nb_pixel_total : 3443 time to create 1 rle with old method : 0.00408625602722168 time for calcul the mask position with numpy : 0.02870965003967285 nb_pixel_total : 13106 time to create 1 rle with old method : 0.014788389205932617 time for calcul the mask position with numpy : 0.028875350952148438 nb_pixel_total : 26434 time to create 1 rle with old method : 0.029589176177978516 time for calcul the mask position with numpy : 0.029132604598999023 nb_pixel_total : 70231 time to create 1 rle with old method : 0.07773065567016602 time for calcul the mask position with numpy : 0.029294967651367188 nb_pixel_total : 16968 time to create 1 rle with old method : 0.027508974075317383 time for calcul the mask position with numpy : 0.032671451568603516 nb_pixel_total : 11164 time to create 1 rle with old method : 0.01812887191772461 time for calcul the mask position with numpy : 0.029522180557250977 nb_pixel_total : 14546 time to create 1 rle with old method : 0.01635909080505371 time for calcul the mask position with numpy : 0.02908492088317871 nb_pixel_total : 16022 time to create 1 rle with old method : 0.01830148696899414 time for calcul the mask position with numpy : 0.02894139289855957 nb_pixel_total : 6615 time to create 1 rle with old method : 0.007727384567260742 time for calcul the mask position with numpy : 0.029451608657836914 nb_pixel_total : 123033 time to create 1 rle with old method : 0.13402867317199707 time for calcul the mask position with numpy : 0.02949213981628418 nb_pixel_total : 11936 time to create 1 rle with old method : 0.01432180404663086 time for calcul the mask position with numpy : 0.028894901275634766 nb_pixel_total : 8512 time to create 1 rle with old method : 0.010033607482910156 time for calcul the mask position with numpy : 0.028862953186035156 nb_pixel_total : 17788 time to create 1 rle with old method : 0.020055055618286133 time for calcul the mask position with numpy : 0.028908729553222656 nb_pixel_total : 9137 time to create 1 rle with old method : 0.01064920425415039 time for calcul the mask position with numpy : 0.028759002685546875 nb_pixel_total : 3722 time to create 1 rle with old method : 0.004327058792114258 create new chi : 4.159956455230713 time to delete rle : 0.0036134719848632812 batch 1 Loaded 99 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 24222 TO DO : save crop sub photo not yet done ! save time : 2.890299081802368 nb_obj : 31 nb_hashtags : 6 time to prepare the origin masks : 4.141608715057373 time for calcul the mask position with numpy : 0.4817512035369873 nb_pixel_total : 5326818 time to create 1 rle with new method : 0.6191420555114746 time for calcul the mask position with numpy : 0.029039382934570312 nb_pixel_total : 16362 time to create 1 rle with old method : 0.01813817024230957 time for calcul the mask position with numpy : 0.028777360916137695 nb_pixel_total : 27913 time to create 1 rle with old method : 0.03131747245788574 time for calcul the mask position with numpy : 0.03360891342163086 nb_pixel_total : 45199 time to create 1 rle with old method : 0.04933452606201172 time for calcul the mask position with numpy : 0.02886033058166504 nb_pixel_total : 19268 time to create 1 rle with old method : 0.022907257080078125 time for calcul the mask position with numpy : 0.030785322189331055 nb_pixel_total : 59204 time to create 1 rle with old method : 0.06748676300048828 time for calcul the mask position with numpy : 0.02875661849975586 nb_pixel_total : 22267 time to create 1 rle with old method : 0.02485823631286621 time for calcul the mask position with numpy : 0.02942061424255371 nb_pixel_total : 75889 time to create 1 rle with old method : 0.0832977294921875 time for calcul the mask position with numpy : 0.029134035110473633 nb_pixel_total : 14944 time to create 1 rle with old method : 0.016926050186157227 time for calcul the mask position with numpy : 0.02883124351501465 nb_pixel_total : 9339 time to create 1 rle with old method : 0.010623693466186523 time for calcul the mask position with numpy : 0.029011249542236328 nb_pixel_total : 92250 time to create 1 rle with old method : 0.09897422790527344 time for calcul the mask position with numpy : 0.029100656509399414 nb_pixel_total : 56615 time to create 1 rle with old method : 0.06275534629821777 time for calcul the mask position with numpy : 0.02915477752685547 nb_pixel_total : 93330 time to create 1 rle with old method : 0.10190868377685547 time for calcul the mask position with numpy : 0.030643463134765625 nb_pixel_total : 402171 time to create 1 rle with new method : 0.3744983673095703 time for calcul the mask position with numpy : 0.029059886932373047 nb_pixel_total : 87472 time to create 1 rle with old method : 0.09691691398620605 time for calcul the mask position with numpy : 0.029056787490844727 nb_pixel_total : 45760 time to create 1 rle with old method : 0.05019521713256836 time for calcul the mask position with numpy : 0.029074907302856445 nb_pixel_total : 12566 time to create 1 rle with old method : 0.014525890350341797 time for calcul the mask position with numpy : 0.030482769012451172 nb_pixel_total : 297652 time to create 1 rle with new method : 0.4320371150970459 time for calcul the mask position with numpy : 0.029223918914794922 nb_pixel_total : 122139 time to create 1 rle with old method : 0.1318221092224121 time for calcul the mask position with numpy : 0.028792381286621094 nb_pixel_total : 13304 time to create 1 rle with old method : 0.015421628952026367 time for calcul the mask position with numpy : 0.029157161712646484 nb_pixel_total : 45094 time to create 1 rle with old method : 0.049278974533081055 time for calcul the mask position with numpy : 0.02878713607788086 nb_pixel_total : 10760 time to create 1 rle with old method : 0.012488365173339844 time for calcul the mask position with numpy : 0.028771162033081055 nb_pixel_total : 17192 time to create 1 rle with old method : 0.019441843032836914 time for calcul the mask position with numpy : 0.028754711151123047 nb_pixel_total : 11793 time to create 1 rle with old method : 0.013373136520385742 time for calcul the mask position with numpy : 0.029215097427368164 nb_pixel_total : 7288 time to create 1 rle with old method : 0.008079767227172852 time for calcul the mask position with numpy : 0.02885270118713379 nb_pixel_total : 5070 time to create 1 rle with old method : 0.005910634994506836 time for calcul the mask position with numpy : 0.028782129287719727 nb_pixel_total : 12339 time to create 1 rle with old method : 0.013878822326660156 time for calcul the mask position with numpy : 0.029068946838378906 nb_pixel_total : 60019 time to create 1 rle with old method : 0.06615138053894043 time for calcul the mask position with numpy : 0.02887272834777832 nb_pixel_total : 24153 time to create 1 rle with old method : 0.027388572692871094 time for calcul the mask position with numpy : 0.028785228729248047 nb_pixel_total : 8468 time to create 1 rle with old method : 0.009728193283081055 time for calcul the mask position with numpy : 0.028980255126953125 nb_pixel_total : 806 time to create 1 rle with old method : 0.0010714530944824219 time for calcul the mask position with numpy : 0.0288240909576416 nb_pixel_total : 6796 time to create 1 rle with old method : 0.007880449295043945 create new chi : 4.019641399383545 time to delete rle : 0.0025031566619873047 batch 1 Loaded 63 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 18805 TO DO : save crop sub photo not yet done ! save time : 2.9631385803222656 map_output_result : {1336630398: (0.0, 'Should be the crop_list due to order', 0.0), 1336630395: (0.0, 'Should be the crop_list due to order', 0.0), 1336630392: (0.0, 'Should be the crop_list due to order', 0.0), 1336630339: (0.0, 'Should be the crop_list due to order', 0.0), 1336630058: (0.0, 'Should be the crop_list due to order', 0.0), 1336630044: (0.0, 'Should be the crop_list due to order', 0.0), 1336629992: (0.0, 'Should be the crop_list due to order', 0.0), 1336629930: (0.0, 'Should be the crop_list due to order', 0.0), 1336629616: (0.0, 'Should be the crop_list due to order', 0.0)} End step rle-unique-nms Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : rle_unique_nms_with_priority we use saveGeneral [1336630398, 1336630395, 1336630392, 1336630339, 1336630058, 1336630044, 1336629992, 1336629930, 1336629616] Looping around the photos to save general results len do output : 9 /1336630398.Didn't retrieve data . /1336630395.Didn't retrieve data . /1336630392.Didn't retrieve data . /1336630339.Didn't retrieve data . /1336630058.Didn't retrieve data . /1336630044.Didn't retrieve data . /1336629992.Didn't retrieve data . /1336629930.Didn't retrieve data . /1336629616.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, '2574366') ('3318', '20424999', '1336630398', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630395', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630392', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630339', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630058', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630044', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336629992', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336629930', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336629616', None, None, None, None, None, '2574366') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 27 time used for this insertion : 0.21080684661865234 save_final save missing photos in datou_result : time spend for datou_step_exec : 111.90443897247314 time spend to save output : 0.2112894058227539 total time spend for step 3 : 112.1157283782959 step4:ventilate_hashtags_in_portfolio Tue Feb 11 02:40:19 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 : 20424999 get user id for portfolio 20424999 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`=20424999 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('autre','pehd','background','pet_fonce','environnement','pet_clair','metal','flou','mal_croppe','papier','carton')) AND mptpi.`min_score`=0.5 To do To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20424999 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('autre','pehd','background','pet_fonce','environnement','pet_clair','metal','flou','mal_croppe','papier','carton')) AND mptpi.`min_score`=0.5 To do Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20424999 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('autre','pehd','background','pet_fonce','environnement','pet_clair','metal','flou','mal_croppe','papier','carton')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/20425275,20425276,20425277,20425278,20425279,20425280,20425281,20425282,20425283,20425284,20425285?tags=autre,pehd,background,pet_fonce,environnement,pet_clair,metal,flou,mal_croppe,papier,carton Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1336630398, 1336630395, 1336630392, 1336630339, 1336630058, 1336630044, 1336629992, 1336629930, 1336629616] Looping around the photos to save general results len do output : 1 /20424999. 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, '2574366') ('3318', '20424999', '1336630398', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630395', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630392', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630339', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630058', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630044', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336629992', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336629930', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336629616', None, None, None, None, None, '2574366') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 10 time used for this insertion : 0.28467369079589844 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.802790641784668 time spend to save output : 0.28510069847106934 total time spend for step 4 : 2.0878913402557373 step5:final Tue Feb 11 02:40:21 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : {1336630398: ('0.23261406703885257',), 1336630395: ('0.23261406703885257',), 1336630392: ('0.23261406703885257',), 1336630339: ('0.23261406703885257',), 1336630058: ('0.23261406703885257',), 1336630044: ('0.23261406703885257',), 1336629992: ('0.23261406703885257',), 1336629930: ('0.23261406703885257',), 1336629616: ('0.23261406703885257',)} new output for save of step final : {1336630398: ('0.23261406703885257',), 1336630395: ('0.23261406703885257',), 1336630392: ('0.23261406703885257',), 1336630339: ('0.23261406703885257',), 1336630058: ('0.23261406703885257',), 1336630044: ('0.23261406703885257',), 1336629992: ('0.23261406703885257',), 1336629930: ('0.23261406703885257',), 1336629616: ('0.23261406703885257',)} [1336630398, 1336630395, 1336630392, 1336630339, 1336630058, 1336630044, 1336629992, 1336629930, 1336629616] Looping around the photos to save general results len do output : 9 /1336630398.Didn't retrieve data . /1336630395.Didn't retrieve data . /1336630392.Didn't retrieve data . /1336630339.Didn't retrieve data . /1336630058.Didn't retrieve data . /1336630044.Didn't retrieve data . /1336629992.Didn't retrieve data . /1336629930.Didn't retrieve data . /1336629616.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, '2574366') ('3318', '20424999', '1336630398', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630395', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630392', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630339', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630058', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630044', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336629992', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336629930', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336629616', None, None, None, None, None, '2574366') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 27 time used for this insertion : 0.01533365249633789 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.13580870628356934 time spend to save output : 0.015760183334350586 total time spend for step 5 : 0.15156888961791992 step6:blur_detection Tue Feb 11 02:40:22 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection methode: ratio et variance treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5.jpg resize: (2160, 3264) 1336630398 -5.427840207989237 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a.jpg resize: (2160, 3264) 1336630395 -6.259900673143691 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7.jpg resize: (2160, 3264) 1336630392 -7.015344358835143 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023.jpg resize: (2160, 3264) 1336630339 -6.656494736424185 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef.jpg resize: (2160, 3264) 1336630058 -7.262126727809535 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26.jpg resize: (2160, 3264) 1336630044 -6.360437831775804 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f.jpg resize: (2160, 3264) 1336629992 -8.165957469892088 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423.jpg resize: (2160, 3264) 1336629930 -6.340671507157082 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad.jpg resize: (2160, 3264) 1336629616 -6.00885583711349 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995635_0.png resize: (426, 168) 1336666743 -4.4466345240827465 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995626_0.png resize: (236, 397) 1336666744 -3.6040172325921533 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995606_0.png resize: (213, 95) 1336666745 -3.558682677584289 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995608_0.png resize: (495, 453) 1336666746 -4.4842739760773185 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995607_0.png resize: (196, 324) 1336666747 -3.2814892322919866 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995617_0.png resize: (181, 98) 1336666748 -2.492283093166334 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995615_0.png resize: (134, 168) 1336666749 -4.488225712905898 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995623_0.png resize: (204, 107) 1336666750 -2.681027171955053 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995618_0.png resize: (143, 177) 1336666751 -4.102937067849504 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995634_0.png resize: (172, 173) 1336666752 -4.234698766088618 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995646_0.png resize: (311, 273) 1336666753 -3.216271853053623 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995601_0.png resize: (386, 562) 1336666754 -3.776655631081398 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995603_0.png resize: (172, 143) 1336666755 -4.10579901469258 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995639_0.png resize: (255, 431) 1336666756 -4.084437370478079 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995632_0.png resize: (177, 166) 1336666757 -2.278225285901281 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995645_0.png resize: (216, 170) 1336666758 -3.861429722214378 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995624_0.png resize: (183, 184) 1336666759 -2.7494936008708937 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995644_0.png resize: (77, 83) 1336666760 -2.5359004941005767 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995611_0.png resize: (175, 204) 1336666761 -2.6338172670730176 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995620_0.png resize: (122, 186) 1336666762 -3.39533516279573 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995630_0.png resize: (235, 109) 1336666763 -4.617449929564819 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995627_0.png resize: (144, 110) 1336666764 -4.02825464303099 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995602_0.png resize: (207, 100) 1336666765 -4.45875631823021 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995625_0.png resize: (129, 147) 1336666766 -3.1693997885210523 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995610_0.png resize: (117, 88) 1336666767 -2.671758577651999 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995604_0.png resize: (165, 111) 1336666768 -1.5737280442245443 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995622_0.png resize: (253, 238) 1336666769 -3.9709598926352316 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995616_0.png resize: (124, 96) 1336666770 -2.3797920369183374 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995631_0.png resize: (335, 581) 1336666771 -1.7758575122048248 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995640_0.png resize: (533, 555) 1336666772 -2.037268595147679 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995638_0.png resize: (240, 151) 1336666773 -4.259802574829276 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995609_0.png resize: (397, 174) 1336666774 -4.129162758831937 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995614_0.png resize: (157, 165) 1336666775 -2.1686346895747763 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995642_0.png resize: (246, 394) 1336666776 -2.312606422720336 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995613_0.png resize: (268, 153) 1336666777 -3.649394950483577 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995637_0.png resize: (133, 98) 1336666778 -3.9874277704246106 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995656_0.png resize: (151, 248) 1336666779 -3.105328715832364 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995659_0.png resize: (194, 190) 1336666780 -2.9345018939060967 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995664_0.png resize: (161, 262) 1336666781 -3.210748372214081 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995682_0.png resize: (153, 175) 1336666782 -3.026202708893345 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995651_0.png resize: (197, 136) 1336666783 -2.691034197023808 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995672_0.png resize: (156, 141) 1336666784 -2.8663298193597924 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995662_0.png resize: (231, 161) 1336666785 -3.9098315809660886 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995668_0.png resize: (144, 336) 1336666786 -2.389427335648168 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995667_0.png resize: (219, 148) 1336666787 -3.6022455035422243 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995691_0.png resize: (330, 357) 1336666788 -4.127182778373643 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995653_0.png resize: (261, 228) 1336666789 -2.3521642532071017 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995663_0.png resize: (200, 188) 1336666790 -3.666549627471243 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995674_0.png resize: (127, 246) 1336666791 -3.854359506123748 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995688_0.png resize: (137, 152) 1336666792 -4.161196193969399 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995649_0.png resize: (217, 207) 1336666793 -3.472374326447644 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995679_0.png resize: (341, 643) 1336666794 -3.259952294850002 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995648_0.png resize: (180, 179) 1336666795 -3.676670802982608 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995654_0.png resize: (129, 139) 1336666796 -4.283415524468029 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995650_0.png resize: (174, 86) 1336666797 -1.6233348352618007 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995658_0.png resize: (265, 172) 1336666798 -4.4439717626366075 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995680_0.png resize: (116, 200) 1336666799 -4.205547168590564 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995690_0.png resize: (165, 161) 1336666800 -4.320050262537483 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995669_0.png resize: (164, 211) 1336666801 -3.9665848812662077 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995684_0.png resize: (140, 77) 1336666802 -3.698664329696149 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995685_0.png resize: (220, 314) 1336666803 -4.0528913518776255 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995652_0.png resize: (289, 269) 1336666804 -2.195020756143029 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995671_0.png resize: (125, 196) 1336666805 -2.344869002094528 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995661_0.png resize: (205, 357) 1336666806 -3.531206320148992 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995678_0.png resize: (108, 92) 1336666807 -4.3950922697277655 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995655_0.png resize: (131, 157) 1336666808 -3.808690112851797 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995677_0.png resize: (222, 210) 1336666809 -3.34983557768061 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995676_0.png resize: (340, 293) 1336666810 -2.846242195427769 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995673_0.png resize: (102, 129) 1336666811 -4.727420546174966 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995665_0.png resize: (113, 121) 1336666812 -1.9830626280422556 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995666_0.png resize: (91, 136) 1336666813 -1.522897617526987 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995657_0.png resize: (135, 214) 1336666814 -1.938423653910424 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995683_0.png resize: (177, 286) 1336666815 -4.366782518271944 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995647_0.png resize: (228, 177) 1336666816 -2.411117090102605 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995709_0.png resize: (137, 141) 1336666817 -3.6628097127049823 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995750_0.png resize: (146, 195) 1336666818 -2.0984868770476086 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995761_0.png resize: (226, 379) 1336666819 -3.975141599891173 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995757_0.png resize: (136, 143) 1336666820 -3.1802908745415186 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995760_0.png resize: (228, 362) 1336666821 -4.392844932646693 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995723_0.png resize: (182, 171) 1336666822 -4.001704111018911 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995759_0.png resize: (146, 180) 1336666823 -4.315325067446705 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995701_0.png resize: (130, 229) 1336666824 -4.429008056167605 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995700_0.png resize: (103, 89) 1336666825 -3.3317789069557904 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995751_0.png resize: (135, 190) 1336666826 -5.053746262483647 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995711_0.png resize: (171, 133) 1336666827 -0.7395450518438146 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995716_0.png resize: (195, 185) 1336666828 -3.9420932128136292 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995737_0.png resize: (119, 116) 1336666829 -1.9225726270820147 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995714_0.png resize: (280, 190) 1336666830 -3.5775355225784757 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995743_0.png resize: (81, 50) 1336666831 -3.4662286977711014 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995733_0.png resize: (147, 97) 1336666832 -2.5545174729352036 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995756_0.png resize: (157, 160) 1336666833 -3.381059318397126 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995696_0.png resize: (202, 215) 1336666834 -4.190661676264901 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995705_0.png resize: (240, 382) 1336666835 -4.941326101101248 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995713_0.png resize: (126, 196) 1336666836 -3.899250612861276 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995692_0.png resize: (186, 237) 1336666837 -3.6061506400378525 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995739_0.png resize: (198, 218) 1336666838 -4.627816030819463 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995707_0.png resize: (233, 133) 1336666839 -4.126301717393022 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995730_0.png resize: (118, 204) 1336666840 -4.649598215616853 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995718_0.png resize: (231, 247) 1336666841 -4.212317039000755 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995766_0.png resize: (123, 148) 1336666842 -3.866246862770912 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995734_0.png resize: (156, 140) 1336666843 -5.0184700527735435 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995719_0.png resize: (370, 244) 1336666844 -3.739994090145357 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995694_0.png resize: (60, 95) 1336666845 -0.6062570659558021 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995736_0.png resize: (162, 138) 1336666846 -2.8558543958203284 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995703_0.png resize: (56, 92) 1336666847 -1.264749505926645 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995749_0.png resize: (203, 219) 1336666848 -3.9890166751125853 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995738_0.png resize: (270, 278) 1336666849 -4.566022611693491 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995731_0.png resize: (152, 188) 1336666850 -4.571953571775873 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995765_0.png resize: (395, 280) 1336666851 -4.7857205222461925 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995706_0.png resize: (140, 128) 1336666852 -5.244904704513929 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995715_0.png resize: (168, 140) 1336666853 -4.2223836478572006 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995695_0.png resize: (259, 328) 1336666855 -3.1811279349879276 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995698_0.png resize: (247, 237) 1336666856 -5.000682838389714 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995717_0.png resize: (129, 184) 1336666857 -4.707810203659206 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995758_0.png resize: (212, 132) 1336666858 -4.874962162210011 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995764_0.png resize: (50, 85) 1336666859 -0.9513374750131863 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995699_0.png resize: (310, 308) 1336666860 -3.14374291799071 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995745_0.png resize: (74, 63) 1336666861 -5.041885133321373 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995735_0.png resize: (69, 66) 1336666862 1.427806420546423 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995752_0.png resize: (150, 201) 1336666863 -2.9311507930388037 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995724_0.png resize: (132, 105) 1336666864 -3.6390694762908438 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995741_0.png resize: (163, 247) 1336666865 -4.76908488044471 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995729_0.png resize: (106, 79) 1336666866 -3.8979136673258568 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995725_0.png resize: (286, 203) 1336666867 -4.946289796422298 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995708_0.png resize: (104, 85) 1336666868 -2.6734636898250597 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995702_0.png resize: (66, 105) 1336666869 -3.853676579103098 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995732_0.png resize: (160, 227) 1336666870 -4.324492030368773 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995748_0.png resize: (134, 70) 1336666871 -4.137573184181554 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995697_0.png resize: (113, 136) 1336666872 -2.832321229467794 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995722_0.png resize: (75, 90) 1336666873 -2.610442882988572 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995728_0.png resize: (64, 45) 1336666874 -2.05776234262999 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995742_0.png resize: (39, 84) 1336666875 -0.03047674512847255 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995755_0.png resize: (148, 205) 1336666876 -4.433145382842001 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995704_0.png resize: (126, 78) 1336666877 -2.303577202330356 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995747_0.png resize: (74, 53) 1336666878 -3.035590726420334 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995727_0.png resize: (112, 150) 1336666879 -3.1904120749748612 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995753_0.png resize: (127, 147) 1336666880 -3.795561727635013 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995746_0.png resize: (224, 184) 1336666881 -4.467729157449463 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995762_0.png resize: (70, 82) 1336666882 -0.9807264612781609 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995720_0.png resize: (114, 163) 1336666883 -3.310843725314596 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995819_0.png resize: (544, 578) 1336666884 -2.722320389814284 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995785_0.png resize: (193, 361) 1336666885 -3.5499916314897897 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995794_0.png resize: (295, 428) 1336666886 -3.463047224542679 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995834_0.png resize: (340, 281) 1336666887 -4.326178533210259 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995814_0.png resize: (93, 140) 1336666888 -2.7131082946627942 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995820_0.png resize: (278, 672) 1336666889 -2.338262961469999 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995837_0.png resize: (131, 234) 1336666890 -2.727027719811516 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995791_0.png resize: (72, 176) 1336666891 -3.309225991641371 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995828_0.png resize: (114, 202) 1336666892 -4.816688973660704 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995830_0.png resize: (120, 49) 1336666893 -1.3513405331136306 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995842_0.png resize: (201, 192) 1336666894 -3.825719503055408 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995810_0.png resize: (72, 104) 1336666895 -2.9046501686245474 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995784_0.png resize: (210, 171) 1336666896 -2.912057470019952 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995841_0.png resize: (155, 135) 1336666897 -4.084691613508844 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995838_0.png resize: (170, 105) 1336666898 -3.024140832287662 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995825_0.png resize: (217, 422) 1336666899 -4.67105946877701 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995823_0.png resize: (190, 418) 1336666900 -2.4439934118436875 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995806_0.png resize: (141, 129) 1336666901 -2.7711861515359697 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995818_0.png resize: (90, 87) 1336666902 -3.1428935814980914 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995831_0.png resize: (29, 101) 1336666903 -2.5452762079352267 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995811_0.png resize: (46, 94) 1336666904 -3.977577204076421 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995796_0.png resize: (100, 146) 1336666905 -4.715360230973845 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995801_0.png resize: (69, 111) 1336666906 -3.306390066585961 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995817_0.png resize: (74, 251) 1336666907 -5.386057831905507 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995783_0.png resize: (173, 157) 1336666908 -3.4117901146232157 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995826_0.png resize: (250, 167) 1336666909 -4.427882408069805 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995803_0.png resize: (278, 104) 1336666910 -4.560101318924078 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995770_0.png resize: (201, 258) 1336666911 -3.9038281388004594 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995777_0.png resize: (120, 144) 1336666912 -4.309706978799682 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995782_0.png resize: (169, 162) 1336666913 -4.51266488582099 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995779_0.png resize: (171, 123) 1336666914 -4.4435171617522276 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995808_0.png resize: (85, 110) 1336666915 -3.2703840997055793 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995797_0.png resize: (130, 245) 1336666916 -4.877167892815484 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995829_0.png resize: (161, 197) 1336666917 -3.6015334530819403 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995768_0.png resize: (104, 104) 1336666918 -3.0426429874143106 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995816_0.png resize: (220, 188) 1336666919 -4.6093706551573295 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995773_0.png resize: (82, 75) 1336666920 -2.814801130318617 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995827_0.png resize: (121, 181) 1336666921 -4.73330080599151 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995815_0.png resize: (148, 176) 1336666922 -4.911901044913762 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995790_0.png resize: (123, 163) 1336666923 -3.6210905696082754 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995771_0.png resize: (206, 231) 1336666924 -3.972127589684302 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995786_0.png resize: (96, 116) 1336666925 -4.512670383045877 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995795_0.png resize: (77, 97) 1336666926 -4.1903070770275725 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995807_0.png resize: (224, 160) 1336666928 -4.68101986894238 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995793_0.png resize: (141, 197) 1336666929 -4.721220869110269 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995804_0.png resize: (158, 191) 1336666930 -4.354949323406047 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995833_0.png resize: (116, 199) 1336666932 -4.617840767908774 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995824_0.png resize: (177, 378) 1336666933 -3.594472453886173 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995772_0.png resize: (135, 142) 1336666934 -3.1948045886906864 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995775_0.png resize: (138, 172) 1336666935 -3.892701186846293 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995835_0.png resize: (182, 154) 1336666936 -4.049745834435687 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995832_0.png resize: (93, 62) 1336666937 -4.339064091768213 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995802_0.png resize: (144, 123) 1336666938 -4.751132051414387 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995809_0.png resize: (64, 103) 1336666939 -3.6003543746968227 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995781_0.png resize: (165, 139) 1336666940 -3.8558394611326423 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995787_0.png resize: (267, 335) 1336666941 -4.284657606008471 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995836_0.png resize: (176, 202) 1336666942 -5.861049888728069 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995769_0.png resize: (138, 134) 1336666943 -4.315206044698567 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995805_0.png resize: (130, 200) 1336666944 -4.199894572382491 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995776_0.png resize: (175, 208) 1336666945 -4.177966032981825 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995812_0.png resize: (90, 104) 1336666946 -1.9879484996251533 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995774_0.png resize: (157, 132) 1336666947 -1.6443140730813792 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995780_0.png resize: (281, 135) 1336666948 -4.5932655135117475 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995889_0.png resize: (448, 194) 1336666949 -4.311613583880979 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995903_0.png resize: (352, 302) 1336666950 -5.424330153010718 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995867_0.png resize: (240, 114) 1336666951 -4.254985250803793 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995873_0.png resize: (148, 116) 1336666952 -4.62063963882681 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995854_0.png resize: (130, 142) 1336666953 -2.8565876040241007 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995893_0.png resize: (61, 137) 1336666954 -0.5683891168180405 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995900_0.png resize: (147, 113) 1336666955 -5.214930226378428 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995848_0.png resize: (299, 223) 1336666956 -3.8302719071599385 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995891_0.png resize: (145, 135) 1336666957 -3.290485968557503 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995876_0.png resize: (230, 379) 1336666958 -2.1528612567460232 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995851_0.png resize: (106, 97) 1336666959 -5.042806885084515 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995844_0.png resize: (120, 129) 1336666960 -2.7732904759575185 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995852_0.png resize: (142, 190) 1336666961 -3.698802687645383 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995864_0.png resize: (267, 295) 1336666962 -3.984952387199105 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995853_0.png resize: (142, 107) 1336666963 -3.654991947087275 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995847_0.png resize: (157, 223) 1336666964 -3.9900641023069565 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995879_0.png resize: (63, 51) 1336666965 0.8950754865014646 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995883_0.png resize: (297, 150) 1336666966 -4.591708119042314 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995872_0.png resize: (112, 141) 1336666967 -3.112222717801167 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995902_0.png resize: (187, 168) 1336666968 -3.947408554465362 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995845_0.png resize: (171, 136) 1336666969 -3.720010265604769 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995865_0.png resize: (186, 275) 1336666970 -3.9151646295699707 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995858_0.png resize: (195, 188) 1336666971 -4.580674590607555 treat image : 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temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995878_0.png resize: (240, 382) 1336666978 -5.5536461329695666 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995860_0.png resize: (59, 51) 1336666979 0.11889511140255973 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995866_0.png resize: (192, 260) 1336666980 -4.465293862777993 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995882_0.png resize: (177, 167) 1336666981 -4.741448344921256 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995874_0.png resize: (191, 163) 1336666982 -4.237188103769262 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995870_0.png resize: (233, 129) 1336666983 -3.4293373711891326 treat image : 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temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995862_0.png resize: (195, 190) 1336666990 -5.1055455630482 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995901_0.png resize: (139, 141) 1336666991 -3.5109262298974175 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995899_0.png resize: (81, 62) 1336666992 -3.1450048145758944 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995895_0.png resize: (287, 181) 1336666993 -4.787161530685458 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995869_0.png resize: (174, 237) 1336666994 -3.8508233500218276 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995875_0.png resize: (264, 202) 1336666995 -4.627071563804198 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995849_0.png resize: (161, 169) 1336666996 -4.4245415433183055 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995855_0.png resize: (311, 179) 1336666997 -4.496676820850941 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995877_0.png resize: (95, 85) 1336666998 -4.264381041011186 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995890_0.png resize: (134, 156) 1336666999 -1.936756890189528 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995898_0.png resize: (122, 49) 1336667000 -2.5092344767473973 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995915_0.png resize: (422, 379) 1336667001 -4.333730325810871 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995906_0.png resize: (173, 201) 1336667002 -2.8618583621110547 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995960_0.png resize: (249, 188) 1336667003 -3.0202562642324957 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995912_0.png resize: (132, 105) 1336667004 -3.3980027519696008 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995909_0.png resize: (75, 151) 1336667005 -4.0283973099929105 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995965_0.png resize: (253, 323) 1336667006 -2.913474255641893 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995931_0.png resize: (130, 118) 1336667007 -4.5109973690774146 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995940_0.png resize: (212, 236) 1336667008 -3.7612259046988243 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995927_0.png resize: (298, 316) 1336667010 -4.998475223954417 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995961_0.png resize: (344, 282) 1336667011 -4.844566850414779 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995908_0.png resize: (116, 169) 1336667012 -2.177889375037936 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995911_0.png resize: (127, 84) 1336667013 -3.912979811335099 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995948_0.png resize: (65, 131) 1336667014 -3.6404816956061636 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995939_0.png resize: (121, 145) 1336667015 -3.469790593528085 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995907_0.png resize: (363, 210) 1336667016 -2.839872479783794 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995951_0.png resize: (184, 243) 1336667018 -3.96242419124009 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995947_0.png resize: (840, 788) 1336667019 -1.437920857716573 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995920_0.png resize: (84, 152) 1336667020 -1.930424368892556 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995913_0.png resize: (212, 171) 1336667021 -3.8214618213203675 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995926_0.png resize: (451, 300) 1336667022 -4.517702314707988 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995917_0.png resize: (150, 98) 1336667023 -4.50676899959174 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995922_0.png resize: (122, 131) 1336667024 -3.9025289175290356 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995916_0.png resize: (266, 306) 1336667025 -3.972997856154918 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995936_0.png resize: (83, 71) 1336667026 -3.546054182132766 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995944_0.png resize: (158, 143) 1336667027 -2.320950909597859 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995958_0.png resize: (116, 132) 1336667028 -5.008745333787205 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995959_0.png resize: (282, 249) 1336667029 -3.377707693523024 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995945_0.png resize: (90, 126) 1336667030 -3.6606702021689617 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995967_0.png resize: (107, 109) 1336667031 -1.8476168046555408 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995935_0.png resize: (205, 270) 1336667032 -1.674729347975293 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995921_0.png resize: (103, 132) 1336667033 -4.776308147311738 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995942_0.png resize: (84, 92) 1336667034 -3.6282423513442494 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995950_0.png resize: (61, 59) 1336667035 -1.791638215962502 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995919_0.png resize: (115, 146) 1336667036 -4.152428560831513 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995963_0.png resize: (143, 124) 1336667037 -3.3111600674529766 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995923_0.png resize: (151, 76) 1336667038 -3.288827074285304 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995955_0.png resize: (175, 208) 1336667039 -4.292541359142367 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995929_0.png resize: (93, 143) 1336667040 -3.329520521001882 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995910_0.png resize: (96, 184) 1336667041 -4.22322637877398 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995956_0.png resize: (91, 207) 1336667042 -4.88678904900664 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995925_0.png resize: (231, 283) 1336667043 -3.773011679779446 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995966_0.png resize: (212, 118) 1336667044 -4.348708009552519 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995949_0.png resize: (144, 101) 1336667045 -2.635926260538007 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995933_0.png resize: (111, 98) 1336667046 -3.8550255566582896 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995962_0.png resize: (165, 200) 1336667047 -4.022610449907495 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995937_0.png resize: (315, 196) 1336667048 -3.245663879338269 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995905_0.png resize: (284, 110) 1336667049 -4.429547281309295 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995941_0.png resize: (98, 93) 1336667050 -2.2305219160989362 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995924_0.png resize: (169, 83) 1336667051 -3.605587199409189 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996008_0.png resize: (158, 91) 1336667052 -3.1521038428267953 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996021_0.png resize: (172, 108) 1336667053 -4.254647229630463 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996010_0.png resize: (133, 243) 1336667054 -4.5708088018421416 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996001_0.png resize: (112, 125) 1336667055 -5.084823000287346 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995971_0.png resize: (156, 263) 1336667056 -3.7215409502790293 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996013_0.png resize: (101, 64) 1336667057 -2.6750271179223546 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995993_0.png resize: (155, 183) 1336667058 -4.830664197677054 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996012_0.png resize: (123, 150) 1336667059 -4.344328838237023 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996020_0.png resize: (148, 139) 1336667060 -4.295538534843954 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995992_0.png resize: (65, 106) 1336667061 -4.353583381092367 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996028_0.png resize: (93, 149) 1336667062 -3.6399417398174956 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996027_0.png resize: (129, 112) 1336667063 -3.4947673893176403 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996004_0.png resize: (149, 123) 1336667064 -4.8144730098927475 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995987_0.png resize: (157, 346) 1336667065 -4.975663470402717 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995974_0.png resize: (152, 130) 1336667066 -2.930689633484438 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995978_0.png resize: (213, 158) 1336667067 -5.695473726504212 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996023_0.png resize: (124, 255) 1336667068 -4.722630555307313 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995988_0.png resize: (128, 225) 1336667069 -4.626087629007059 treat image : 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temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996006_0.png resize: (105, 125) 1336667077 -5.298583421698081 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996025_0.png resize: (63, 70) 1336667078 -4.070910672347153 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995973_0.png resize: (121, 80) 1336667079 -1.804057933245273 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996019_0.png resize: (94, 81) 1336667081 -5.0934560458006874 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996005_0.png resize: (385, 514) 1336667082 -6.6109872332717465 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995969_0.png resize: (257, 225) 1336667083 -4.012349426162724 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996017_0.png resize: (245, 214) 1336667084 -5.550820131496615 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995972_0.png resize: (102, 135) 1336667085 -2.780536804904317 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995986_0.png resize: (193, 126) 1336667086 -4.90748900868872 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996015_0.png resize: (112, 112) 1336667087 -6.5094716296788455 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995983_0.png resize: (77, 98) 1336667088 -2.9228949825319934 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995975_0.png resize: (136, 151) 1336667089 -4.795388607685668 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996026_0.png resize: (169, 279) 1336667090 -3.0603181706807967 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995996_0.png resize: (201, 103) 1336667091 -4.724994699834092 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995980_0.png resize: (218, 132) 1336667092 -5.832787114527164 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995970_0.png resize: (88, 88) 1336667093 -4.453967789071253 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995985_0.png resize: (82, 89) 1336667094 -3.7430124103400986 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995968_0.png resize: (191, 233) 1336667095 -5.146541960691953 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996016_0.png resize: (136, 152) 1336667096 -4.840001853485549 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995994_0.png resize: (161, 70) 1336667097 -2.6985212306797988 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995995_0.png resize: (311, 193) 1336667098 -4.234553276298915 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995989_0.png resize: (256, 193) 1336667099 -5.48426548136437 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995976_0.png resize: (242, 102) 1336667100 -4.901652671391839 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995990_0.png resize: (114, 148) 1336667101 -4.505115051842237 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995977_0.png resize: (196, 234) 1336667102 -2.369425862586168 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996009_0.png resize: (112, 110) 1336667103 -5.229880049423488 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996000_0.png resize: (103, 108) 1336667104 -3.607517912581422 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996018_0.png resize: (166, 83) 1336667105 -4.643384727573993 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996055_0.png resize: (279, 373) 1336667106 -1.260304222025644 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996051_0.png resize: (282, 201) 1336667107 -2.9778518841777832 treat image : 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temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996043_0.png resize: (79, 68) 1336667114 -3.8225755169045996 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996038_0.png resize: (184, 234) 1336667115 -2.316520583369757 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996075_0.png resize: (275, 184) 1336667116 -3.508897337667416 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996049_0.png resize: (378, 191) 1336667117 -4.7363485274764185 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996062_0.png resize: (200, 143) 1336667118 -3.1809006499509755 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996072_0.png resize: (294, 522) 1336667119 -4.013059037262951 treat image : 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temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996030_0.png resize: (198, 104) 1336667126 -4.436619220938292 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996059_0.png resize: (175, 86) 1336667127 -4.485673105632323 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996065_0.png resize: (79, 99) 1336667128 -3.4744292529631866 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996048_0.png resize: (123, 196) 1336667129 -4.443070799361054 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996045_0.png resize: (93, 86) 1336667130 -2.322855145933124 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996050_0.png resize: (280, 464) 1336667131 -4.59759153689492 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996032_0.png resize: (160, 100) 1336667132 -3.7517719691330162 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996064_0.png resize: (63, 111) 1336667133 -1.85046042089889 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996039_0.png resize: (117, 88) 1336667134 -3.3134010827770237 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996066_0.png resize: (143, 132) 1336667135 -4.492857395484555 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996074_0.png resize: (260, 245) 1336667136 -4.783338113903751 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996033_0.png resize: (109, 229) 1336667137 -4.245041225235636 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996040_0.png resize: (134, 115) 1336667138 -3.586225797867527 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996071_0.png resize: (72, 64) 1336667139 -3.8141688143009294 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996044_0.png resize: (388, 183) 1336667140 -4.244281614952737 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996077_0.png resize: (236, 81) 1336667141 -4.428556822473043 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996041_0.png resize: (191, 112) 1336667142 -3.6757861790625808 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996037_0.png resize: (244, 107) 1336667143 -4.004809514077282 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996060_0.png resize: (113, 102) 1336667145 -3.601819051320568 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996073_0.png resize: (55, 60) 1336667146 1.7395169109002495 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996036_0.png resize: (130, 89) 1336667147 -4.765892308311386 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996104_0.png resize: (198, 173) 1336667148 -2.906320254078344 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996102_0.png resize: (168, 199) 1336667149 -4.678343241117462 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996098_0.png resize: (379, 375) 1336667150 -4.754907009666297 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996083_0.png resize: (177, 135) 1336667151 -2.241432034911101 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996088_0.png resize: (264, 400) 1336667152 -2.630108460578727 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996080_0.png resize: (205, 315) 1336667153 -4.079517797308171 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996082_0.png resize: (266, 235) 1336667154 -4.32520777477634 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996097_0.png resize: (409, 336) 1336667155 -4.569219491422783 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996085_0.png resize: (93, 110) 1336667156 -4.459494170497655 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996092_0.png resize: (270, 178) 1336667158 -3.959263824541564 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996087_0.png resize: (108, 100) 1336667159 -3.460808591228154 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996100_0.png resize: (149, 410) 1336667160 -3.1280083471306543 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996099_0.png resize: (162, 133) 1336667161 -4.289330571816616 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996101_0.png resize: (97, 164) 1336667162 -3.230120565190397 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996081_0.png resize: (133, 89) 1336667163 -3.7400581644146325 treat image : 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temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995712_0.png resize: (245, 693) 1336667200 -5.855567259099013 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995740_0.png resize: (191, 166) 1336667201 -2.7242632736471917 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995710_0.png resize: (148, 88) 1336667202 -2.466864877950179 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995726_0.png resize: (352, 342) 1336667203 -3.603706939973808 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995693_0.png resize: (237, 357) 1336667204 -5.392617620591436 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995744_0.png resize: (408, 249) 1336667205 -4.925001664366845 treat image : 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temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995800_0.png resize: (211, 185) 1336667212 -4.786853588700233 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995789_0.png resize: (185, 171) 1336667213 -2.130319269061811 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995798_0.png resize: (130, 270) 1336667214 -4.711439474377174 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995822_0.png resize: (163, 205) 1336667215 -3.505138123653067 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995839_0.png resize: (154, 159) 1336667216 -5.1425387300744205 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995813_0.png resize: (176, 173) 1336667217 -4.145398846384278 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995788_0.png resize: (121, 173) 1336667218 -2.8006059166743755 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995843_0.png resize: (113, 293) 1336667219 -2.9331989436335526 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995892_0.png resize: (58, 155) 1336667220 -4.456419379858487 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995904_0.png resize: (188, 169) 1336667221 -3.9306059781840124 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995861_0.png resize: (187, 162) 1336667222 -1.7538340250514894 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995887_0.png resize: (183, 223) 1336667223 -4.470987846123617 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995894_0.png resize: (305, 408) 1336667224 -4.047314318083522 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995896_0.png resize: (204, 166) 1336667225 -4.3404083643614975 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995859_0.png resize: (131, 325) 1336667226 -2.9988206587754473 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995918_0.png resize: (200, 269) 1336667227 -2.7057268889565935 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995938_0.png resize: (282, 262) 1336667228 -1.8170361971507114 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995934_0.png resize: (239, 328) 1336667229 -4.728592912922811 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995928_0.png resize: (88, 128) 1336667231 -2.464487046170872 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995930_0.png resize: (103, 104) 1336667232 -1.0910386703979797 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995914_0.png resize: (157, 118) 1336667233 -3.1103766101780588 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995946_0.png resize: (85, 84) 1336667234 -3.9131708069469275 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995953_0.png resize: (188, 97) 1336667235 -1.1754665304905503 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995979_0.png resize: (153, 210) 1336667236 -3.388839310380896 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996007_0.png resize: (49, 206) 1336667237 -2.3823222198970884 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995981_0.png resize: (130, 153) 1336667238 -3.810256767378486 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995991_0.png resize: (165, 272) 1336667239 -3.032769594524365 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995997_0.png resize: (111, 102) 1336667240 -4.449313921249018 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995982_0.png resize: (143, 220) 1336667241 -4.854446070430943 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996024_0.png resize: (761, 650) 1336667242 -2.529145461822814 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996011_0.png resize: (184, 238) 1336667243 -5.165563267785256 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668995999_0.png resize: (140, 233) 1336667244 -4.42262100329384 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996035_0.png resize: (661, 1239) 1336667245 -1.3937181765293527 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996068_0.png resize: (143, 139) 1336667246 -4.7915470308125 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996061_0.png resize: (153, 221) 1336667247 -2.8408703991872857 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996089_0.png resize: (618, 719) 1336667248 -4.728854228835098 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996108_0.png resize: (324, 121) 1336667249 -3.898416227540183 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996093_0.png resize: (413, 409) 1336667250 -4.036781775539822 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996086_0.png resize: (132, 152) 1336667251 -4.319165516200572 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996095_0.png resize: (100, 116) 1336667252 -5.178493102636672 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995633_0.png resize: (463, 478) 1336667256 -2.364743867254158 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995754_0.png resize: (244, 281) 1336667257 -3.3762274279173963 treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023_rle_crop_3668995840_0.png resize: (757, 642) 1336667258 -3.367981298460565 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995884_0.png resize: (231, 196) 1336667259 -3.5560706594677667 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995957_0.png resize: (250, 142) 1336667260 -3.4643761433628724 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995932_0.png resize: (216, 390) 1336667261 -3.3443627959379163 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995964_0.png resize: (263, 155) 1336667262 -2.3540632549368845 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995943_0.png resize: (223, 506) 1336667263 -4.962644161655615 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995954_0.png resize: (386, 351) 1336667264 -3.122748389569682 treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26_rle_crop_3668995952_0.png resize: (272, 951) 1336667265 -2.0701167780062044 treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f_rle_crop_3668996029_0.png resize: (187, 281) 1336667266 -4.563681660168197 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996069_0.png resize: (334, 160) 1336667267 -4.926433040983637 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996046_0.png resize: (275, 709) 1336667268 -3.190072956112815 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996105_0.png resize: (299, 199) 1336667269 -3.7009308555839855 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995629_0.png resize: (153, 267) 1336667270 -1.1129640107436864 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995670_0.png resize: (140, 160) 1336667271 -1.7680679223263818 treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7_rle_crop_3668995721_0.png resize: (161, 159) 1336667272 -4.978103652674883 treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef_rle_crop_3668995897_0.png resize: (150, 112) 1336667273 -2.402060521103849 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996106_0.png resize: (364, 345) 1336667274 -6.125222549965246 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996090_0.png resize: (710, 727) 1336667275 -3.3480061487052817 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996047_0.png resize: (171, 136) 1336667276 -3.8060000725920835 treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995619_0.png resize: (89, 99) 1336667277 -3.480627703093596 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995689_0.png resize: (160, 86) 1336667278 -1.8872223538987745 treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995675_0.png resize: (63, 53) 1336667279 0.6804614698199168 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996063_0.png resize: (275, 184) 1336667280 -4.029753394042423 treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996058_0.png resize: (159, 134) 1336667281 -4.646539173568893 treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996109_0.png resize: (216, 444) 1336667282 -2.9910808376275084 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 : 518 time used for this insertion : 0.04518008232116699 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 518 time used for this insertion : 0.13118839263916016 save missing photos in datou_result : time spend for datou_step_exec : 39.381818532943726 time spend to save output : 0.1836564540863037 total time spend for step 6 : 39.56547498703003 step7:brightness Tue Feb 11 02:41:01 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 ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5.jpg treat image : temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a.jpg treat image : temp/1739237428_744114_1336630392_c9512604de89e346fe025b25ab83f7a7.jpg treat image : temp/1739237428_744114_1336630339_30e1f49c4fc5aa6186e68a93eb562023.jpg treat image : temp/1739237428_744114_1336630058_ba9d8291b4dd1c4b255db746233f6bef.jpg treat image : temp/1739237428_744114_1336630044_9fd90748f71e47b9456a6f7085477d26.jpg treat image : temp/1739237428_744114_1336629992_f3502000cb996073388e68125551048f.jpg treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423.jpg treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad.jpg treat image : temp/1739237428_744114_1336630398_67a51a2b0d34d832652a8c986d9083b5_rle_crop_3668995635_0.png treat image : 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temp/1739237428_744114_1336630395_acc1b62e51e2874d0dafcc47f762092a_rle_crop_3668995675_0.png treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996063_0.png treat image : temp/1739237428_744114_1336629930_93ff8d3b897b657d3ea8f38a3de3b423_rle_crop_3668996058_0.png treat image : temp/1739237428_744114_1336629616_17999e98a50c0c1c0eaca0b361f2acad_rle_crop_3668996109_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 : 518 time used for this insertion : 0.05558943748474121 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 518 time used for this insertion : 0.16018319129943848 save missing photos in datou_result : time spend for datou_step_exec : 10.28220534324646 time spend to save output : 0.2220442295074463 total time spend for step 7 : 10.504249572753906 step8:velours_tree Tue Feb 11 02:41:12 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure can't find the photo_desc_type Inside saveOutput : final : False verbose : 0 ouput is None No outpout to save, returning out of save general time spend for datou_step_exec : 0.17740535736083984 time spend to save output : 4.601478576660156e-05 total time spend for step 8 : 0.17745137214660645 step9:send_mail_cod Tue Feb 11 02:41:12 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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_P20424999_11-02-2025_02_41_12.pdf 20425275 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette204252751739238072 20425276 change filename to text .imagette204252761739238072 20425277 imagette204252771739238072 20425278 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette204252781739238072 20425280 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 .imagette204252801739238073 20425281 change filename to text .imagette204252811739238074 20425282 imagette204252821739238074 20425283 imagette204252831739238074 20425284 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 .imagette204252841739238074 20425285 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 .imagette204252851739238075 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=20424999 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/20425275,20425276,20425277,20425278,20425279,20425280,20425281,20425282,20425283,20425284,20425285?tags=autre,pehd,background,pet_fonce,environnement,pet_clair,metal,flou,mal_croppe,papier,carton args[1336630398] : ((1336630398, -5.427840207989237, 492609224), (1336630398, -0.07164048821803431, 496442774), '0.23261406703885257') apple ((1336630398, -5.427840207989237, 492609224), (1336630398, -0.07164048821803431, 496442774), '0.23261406703885257') We are sending mail with results at report@fotonower.com args[1336630395] : ((1336630395, -6.259900673143691, 492609224), (1336630395, -0.1043244517151294, 496442774), '0.23261406703885257') apple ((1336630395, -6.259900673143691, 492609224), (1336630395, -0.1043244517151294, 496442774), '0.23261406703885257') We are sending mail with results at report@fotonower.com args[1336630392] : ((1336630392, -7.015344358835143, 492609224), (1336630392, -0.05580241077004776, 2107752395), '0.23261406703885257') apple ((1336630392, -7.015344358835143, 492609224), (1336630392, -0.05580241077004776, 2107752395), '0.23261406703885257') We are sending mail with results at report@fotonower.com args[1336630339] : ((1336630339, -6.656494736424185, 492609224), (1336630339, -0.163220625197218, 496442774), '0.23261406703885257') apple ((1336630339, -6.656494736424185, 492609224), (1336630339, -0.163220625197218, 496442774), '0.23261406703885257') We are sending mail with results at report@fotonower.com args[1336630058] : ((1336630058, -7.262126727809535, 492609224), (1336630058, -0.13891441124039056, 496442774), '0.23261406703885257') apple ((1336630058, -7.262126727809535, 492609224), (1336630058, -0.13891441124039056, 496442774), '0.23261406703885257') We are sending mail with results at report@fotonower.com args[1336630044] : ((1336630044, -6.360437831775804, 492609224), (1336630044, -0.24266647501082467, 496442774), '0.23261406703885257') apple ((1336630044, -6.360437831775804, 492609224), (1336630044, -0.24266647501082467, 496442774), '0.23261406703885257') We are sending mail with results at report@fotonower.com args[1336629992] : ((1336629992, -8.165957469892088, 492609224), (1336629992, -0.1400206494463254, 496442774), '0.23261406703885257') apple ((1336629992, -8.165957469892088, 492609224), (1336629992, -0.1400206494463254, 496442774), '0.23261406703885257') We are sending mail with results at report@fotonower.com args[1336629930] : ((1336629930, -6.340671507157082, 492609224), (1336629930, -0.28704896239223127, 496442774), '0.23261406703885257') apple ((1336629930, -6.340671507157082, 492609224), (1336629930, -0.28704896239223127, 496442774), '0.23261406703885257') We are sending mail with results at report@fotonower.com args[1336629616] : ((1336629616, -6.00885583711349, 492609224), (1336629616, -0.2849690210917985, 496442774), '0.23261406703885257') apple ((1336629616, -6.00885583711349, 492609224), (1336629616, -0.2849690210917985, 496442774), '0.23261406703885257') We are sending mail with results at report@fotonower.com refus_total : 0.23261406703885257 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=20424999 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1336630398,1336630058,1336630395,1336629616,1336629930,1336629992,1336630044,1336630339,1336630392) Found this number of photos: 9 begin to download photo : 1336630398 begin to download photo : 1336630395 begin to download photo : 1336629930 begin to download photo : 1336630044 begin to download photo : 1336630392 download finish for photo 1336630392 download finish for photo 1336629930 begin to download photo : 1336629992 download finish for photo 1336630044 begin to download photo : 1336630339 download finish for photo 1336630398 begin to download photo : 1336630058 download finish for photo 1336630395 begin to download photo : 1336629616 download finish for photo 1336629992 download finish for photo 1336630339 download finish for photo 1336630058 download finish for photo 1336629616 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20424999_11-02-2025_02_41_12.pdf results_Auto_P20424999_11-02-2025_02_41_12.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20424999_11-02-2025_02_41_12.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','20424999','results_Auto_P20424999_11-02-2025_02_41_12.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20424999_11-02-2025_02_41_12.pdf','pdf','','0.81','0.23261406703885257') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/20424999

https://www.fotonower.com/image?json=false&list_photos_id=1336630398
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1336630395
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1336630392
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1336630339
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1336630058
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1336630044
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1336629992
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1336629930
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1336629616
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 23.26%
Veuillez trouver les photos des contaminants.

exemples de contaminants: autre: https://www.fotonower.com/view/20425275?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/20425276?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/20425278?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/20425280?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/20425281?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/20425284?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/20425285?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20424999_11-02-2025_02_41_12.pdf.

Lien vers velours :https://www.fotonower.com/velours/20425275,20425276,20425277,20425278,20425279,20425280,20425281,20425282,20425283,20425284,20425285?tags=autre,pehd,background,pet_fonce,environnement,pet_clair,metal,flou,mal_croppe,papier,carton.


L'équipe Fotonower 202 b'' Server: nginx Date: Tue, 11 Feb 2025 01:41:19 GMT Content-Length: 0 Connection: close X-Message-Id: c7CyH6nHStK_8fzwnY1aJw 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 [1336630398, 1336630395, 1336630392, 1336630339, 1336630058, 1336630044, 1336629992, 1336629930, 1336629616] 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, '2574366') ('3318', '20424999', '1336630398', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630395', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630392', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630339', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630058', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630044', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336629992', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336629930', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336629616', None, None, None, None, None, '2574366') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 9 time used for this insertion : 0.11227917671203613 save_final save missing photos in datou_result : time spend for datou_step_exec : 7.544375419616699 time spend to save output : 0.11256742477416992 total time spend for step 9 : 7.656942844390869 step10:split_time_score Tue Feb 11 02:41:20 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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'}] (('15', 9),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 10022025 20424999 Nombre de photos uploadées : 9 / 23040 (0%) 10022025 20424999 Nombre de photos taguées (types de déchets): 0 / 9 (0%) 10022025 20424999 Nombre de photos taguées (volume) : 0 / 9 (0%) elapsed_time : load_data_split_time_score 2.384185791015625e-06 elapsed_time : order_list_meta_photo_and_scores 6.4373016357421875e-06 ????????? elapsed_time : fill_and_build_computed_from_old_data 0.0005433559417724609 elapsed_time : insert_dashboard_record_day_entry 0.023622751235961914 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 : 0.20770710642474585 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20423267 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`=20423267 AND mptpi.`type`=3594 To do Qualite : 0.08218852237472649 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20406980_10-02-2025_15_39_20.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20406980 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`=20406980 AND mptpi.`type`=3726 To do Qualite : 0.22224855111315364 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20406982_10-02-2025_16_37_17.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20406982 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`=20406982 AND mptpi.`type`=3594 To do Qualite : 0.23261406703885257 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20424999_11-02-2025_02_41_12.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20424999 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`=20424999 AND mptpi.`type`=3594 To do Qualite : 0.19914179581765348 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20424312_11-02-2025_01_42_58.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20424312 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`=20424312 AND mptpi.`type`=3594 To do Qualite : 0.2535467764217956 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20425002_11-02-2025_02_29_22.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20425002 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`=20425002 AND mptpi.`type`=3594 To do Qualite : 0.08109472723835193 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20423281_11-02-2025_00_24_28.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20423281 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`=20423281 AND mptpi.`type`=3726 To do Qualite : 0.18296439199232933 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20423284_11-02-2025_00_27_40.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20423284 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`=20423284 AND mptpi.`type`=3594 To do Qualite : 0.19405417404230213 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20423288_11-02-2025_00_19_07.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20423288 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`=20423288 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'10022025': {'nb_upload': 9, '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 [1336630398, 1336630395, 1336630392, 1336630339, 1336630058, 1336630044, 1336629992, 1336629930, 1336629616] Looping around the photos to save general results len do output : 1 /20424999Didn'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, '2574366') ('3318', '20424999', '1336630398', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630395', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630392', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630339', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630058', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336630044', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336629992', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336629930', None, None, None, None, None, '2574366') ('3318', None, None, None, None, None, None, None, '2574366') ('3318', '20424999', '1336629616', None, None, None, None, None, '2574366') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 10 time used for this insertion : 0.01647329330444336 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.5442185401916504 time spend to save output : 0.016703367233276367 total time spend for step 10 : 1.5609219074249268 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 9 set_done_treatment 327.27user 131.07system 10:56.68elapsed 69%CPU (0avgtext+0avgdata 7625192maxresident)k 7994840inputs+241200outputs (203827major+31664647minor)pagefaults 0swaps