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 : 729358 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 : ['2574369'] with mtr_portfolio_ids : ['20425002'] and first list_photo_ids : [] new path : /proc/729358/ 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 , BFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 8 ; length of list_pids : 8 ; length of list_args : 8 time to download the photos : 1.8919298648834229 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 02:20:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10776 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-02-11 02:20:34.591561: 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 02:20:34.599781: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-11 02:20:34.601162: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f59c8000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-11 02:20:34.601217: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-11 02:20:34.603913: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-11 02:20:34.721678: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x371a77c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-11 02:20:34.721739: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-11 02:20:34.723234: 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 02:20:34.723652: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-11 02:20:34.726722: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-11 02:20:34.729860: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-11 02:20:34.730375: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-11 02:20:34.733094: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-11 02:20:34.734163: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-11 02:20:34.738622: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-11 02:20:34.740220: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-11 02:20:34.740305: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-11 02:20:34.741093: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-11 02:20:34.741108: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-11 02:20:34.741117: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-11 02:20:34.742434: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9702 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 02:20:35.007478: 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 02:20:35.007582: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-11 02:20:35.007603: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-11 02:20:35.007621: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-11 02:20:35.007639: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-11 02:20:35.007656: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-11 02:20:35.007673: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-11 02:20:35.007691: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-11 02:20:35.009320: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-11 02:20:35.010803: 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 02:20:35.010847: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-11 02:20:35.010866: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-11 02:20:35.010883: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-11 02:20:35.010935: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-11 02:20:35.010958: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-11 02:20:35.010975: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-11 02:20:35.010992: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-11 02:20:35.012480: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-11 02:20:35.012517: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-11 02:20:35.012526: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-11 02:20:35.012535: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-11 02:20:35.013873: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9702 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 02:20:45.918001: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-11 02:20:46.090164: 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 : 8 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 : 33 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 37 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 Detection mask done ! Trying to reset tf kernel 730065 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 567 tf kernel not reseted sub process len(results) : 8 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 8 len(list_Values) 0 process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5856 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 : 0.014815807342529297 nb_pixel_total : 22202 time to create 1 rle with old method : 0.03026413917541504 length of segment : 265 time for calcul the mask position with numpy : 0.01148080825805664 nb_pixel_total : 41476 time to create 1 rle with old method : 0.047274112701416016 length of segment : 237 time for calcul the mask position with numpy : 0.0008907318115234375 nb_pixel_total : 13136 time to create 1 rle with old method : 0.015409231185913086 length of segment : 142 time for calcul the mask position with numpy : 0.0007762908935546875 nb_pixel_total : 32180 time to create 1 rle with old method : 0.03625822067260742 length of segment : 172 time for calcul the mask position with numpy : 0.007452726364135742 nb_pixel_total : 19210 time to create 1 rle with old method : 0.025041580200195312 length of segment : 176 time for calcul the mask position with numpy : 0.0014765262603759766 nb_pixel_total : 27535 time to create 1 rle with old method : 0.03249239921569824 length of segment : 396 time for calcul the mask position with numpy : 0.020749807357788086 nb_pixel_total : 42512 time to create 1 rle with old method : 0.05102252960205078 length of segment : 514 time for calcul the mask position with numpy : 0.005093812942504883 nb_pixel_total : 14106 time to create 1 rle with old method : 0.018167972564697266 length of segment : 165 time for calcul the mask position with numpy : 0.0019266605377197266 nb_pixel_total : 15960 time to create 1 rle with old method : 0.01944589614868164 length of segment : 145 time for calcul the mask position with numpy : 0.0063555240631103516 nb_pixel_total : 21321 time to create 1 rle with old method : 0.02624201774597168 length of segment : 223 time for calcul the mask position with numpy : 0.026820659637451172 nb_pixel_total : 42775 time to create 1 rle with old method : 0.05152153968811035 length of segment : 353 time for calcul the mask position with numpy : 0.027904987335205078 nb_pixel_total : 66176 time to create 1 rle with old method : 0.0755012035369873 length of segment : 463 time for calcul the mask position with numpy : 0.009597063064575195 nb_pixel_total : 21462 time to create 1 rle with old method : 0.029010295867919922 length of segment : 167 time for calcul the mask position with numpy : 0.006350040435791016 nb_pixel_total : 31406 time to create 1 rle with old method : 0.04278731346130371 length of segment : 182 time for calcul the mask position with numpy : 0.002734661102294922 nb_pixel_total : 17785 time to create 1 rle with old method : 0.020608186721801758 length of segment : 190 time for calcul the mask position with numpy : 0.004563331604003906 nb_pixel_total : 52201 time to create 1 rle with old method : 0.06128048896789551 length of segment : 299 time for calcul the mask position with numpy : 0.016044139862060547 nb_pixel_total : 36923 time to create 1 rle with old method : 0.04437112808227539 length of segment : 262 time for calcul the mask position with numpy : 0.00911855697631836 nb_pixel_total : 17529 time to create 1 rle with old method : 0.022092819213867188 length of segment : 125 time for calcul the mask position with numpy : 0.00043320655822753906 nb_pixel_total : 15257 time to create 1 rle with old method : 0.01785111427307129 length of segment : 158 time for calcul the mask position with numpy : 0.0011181831359863281 nb_pixel_total : 24743 time to create 1 rle with old method : 0.02795267105102539 length of segment : 290 time for calcul the mask position with numpy : 0.0006957054138183594 nb_pixel_total : 18586 time to create 1 rle with old method : 0.021093130111694336 length of segment : 198 time for calcul the mask position with numpy : 0.011216878890991211 nb_pixel_total : 16573 time to create 1 rle with old method : 0.023888826370239258 length of segment : 173 time for calcul the mask position with numpy : 0.00089263916015625 nb_pixel_total : 17617 time to create 1 rle with old method : 0.020229339599609375 length of segment : 235 time for calcul the mask position with numpy : 0.0012819766998291016 nb_pixel_total : 18341 time to create 1 rle with old method : 0.020843505859375 length of segment : 215 time for calcul the mask position with numpy : 0.007653236389160156 nb_pixel_total : 21631 time to create 1 rle with old method : 0.024630308151245117 length of segment : 173 time for calcul the mask position with numpy : 0.019987821578979492 nb_pixel_total : 39931 time to create 1 rle with old method : 0.04294896125793457 length of segment : 289 time for calcul the mask position with numpy : 0.0010116100311279297 nb_pixel_total : 7877 time to create 1 rle with old method : 0.008880138397216797 length of segment : 112 time for calcul the mask position with numpy : 0.00255584716796875 nb_pixel_total : 18191 time to create 1 rle with old method : 0.020014047622680664 length of segment : 175 time for calcul the mask position with numpy : 0.0005297660827636719 nb_pixel_total : 13220 time to create 1 rle with old method : 0.01505589485168457 length of segment : 92 time for calcul the mask position with numpy : 0.0024242401123046875 nb_pixel_total : 22534 time to create 1 rle with old method : 0.02349710464477539 length of segment : 250 time for calcul the mask position with numpy : 0.009953737258911133 nb_pixel_total : 35342 time to create 1 rle with old method : 0.0469517707824707 length of segment : 253 time for calcul the mask position with numpy : 0.0018796920776367188 nb_pixel_total : 9319 time to create 1 rle with old method : 0.010604619979858398 length of segment : 104 time for calcul the mask position with numpy : 0.0037713050842285156 nb_pixel_total : 21634 time to create 1 rle with old method : 0.02635669708251953 length of segment : 227 time for calcul the mask position with numpy : 0.0005381107330322266 nb_pixel_total : 15375 time to create 1 rle with old method : 0.016962051391601562 length of segment : 156 time for calcul the mask position with numpy : 0.00235748291015625 nb_pixel_total : 19260 time to create 1 rle with old method : 0.02164006233215332 length of segment : 160 time for calcul the mask position with numpy : 0.0033893585205078125 nb_pixel_total : 16819 time to create 1 rle with old method : 0.02095770835876465 length of segment : 128 time for calcul the mask position with numpy : 0.004002809524536133 nb_pixel_total : 17167 time to create 1 rle with old method : 0.024743318557739258 length of segment : 152 time for calcul the mask position with numpy : 0.003130674362182617 nb_pixel_total : 23497 time to create 1 rle with old method : 0.026679515838623047 length of segment : 201 time for calcul the mask position with numpy : 0.012536048889160156 nb_pixel_total : 32404 time to create 1 rle with old method : 0.042792558670043945 length of segment : 200 time for calcul the mask position with numpy : 0.0028188228607177734 nb_pixel_total : 13065 time to create 1 rle with old method : 0.014488697052001953 length of segment : 139 time for calcul the mask position with numpy : 0.0032875537872314453 nb_pixel_total : 18383 time to create 1 rle with old method : 0.019925355911254883 length of segment : 137 time for calcul the mask position with numpy : 0.0057086944580078125 nb_pixel_total : 15641 time to create 1 rle with old method : 0.0174868106842041 length of segment : 151 time for calcul the mask position with numpy : 0.002445220947265625 nb_pixel_total : 17429 time to create 1 rle with old method : 0.019787073135375977 length of segment : 209 time for calcul the mask position with numpy : 0.0024635791778564453 nb_pixel_total : 13349 time to create 1 rle with old method : 0.015306949615478516 length of segment : 162 time for calcul the mask position with numpy : 0.004384756088256836 nb_pixel_total : 5301 time to create 1 rle with old method : 0.006227970123291016 length of segment : 115 time for calcul the mask position with numpy : 0.004530191421508789 nb_pixel_total : 14507 time to create 1 rle with old method : 0.018035173416137695 length of segment : 135 time for calcul the mask position with numpy : 0.0002338886260986328 nb_pixel_total : 7671 time to create 1 rle with old method : 0.008554935455322266 length of segment : 114 time for calcul the mask position with numpy : 0.0018153190612792969 nb_pixel_total : 52842 time to create 1 rle with old method : 0.06065058708190918 length of segment : 429 time for calcul the mask position with numpy : 0.0013413429260253906 nb_pixel_total : 25392 time to create 1 rle with old method : 0.032145023345947266 length of segment : 185 time for calcul the mask position with numpy : 0.002473592758178711 nb_pixel_total : 4807 time to create 1 rle with old method : 0.005838871002197266 length of segment : 79 time for calcul the mask position with numpy : 0.0003173351287841797 nb_pixel_total : 6408 time to create 1 rle with old method : 0.0074384212493896484 length of segment : 107 time for calcul the mask position with numpy : 0.017856121063232422 nb_pixel_total : 32212 time to create 1 rle with old method : 0.04082036018371582 length of segment : 245 time for calcul the mask position with numpy : 0.004378795623779297 nb_pixel_total : 22369 time to create 1 rle with old method : 0.028998374938964844 length of segment : 143 time for calcul the mask position with numpy : 0.001725912094116211 nb_pixel_total : 14795 time to create 1 rle with old method : 0.016751527786254883 length of segment : 141 time for calcul the mask position with numpy : 0.021386146545410156 nb_pixel_total : 26395 time to create 1 rle with old method : 0.03311634063720703 length of segment : 294 time for calcul the mask position with numpy : 0.008900165557861328 nb_pixel_total : 21747 time to create 1 rle with old method : 0.02980494499206543 length of segment : 136 time for calcul the mask position with numpy : 0.004026174545288086 nb_pixel_total : 21461 time to create 1 rle with old method : 0.02422785758972168 length of segment : 258 time for calcul the mask position with numpy : 0.0013918876647949219 nb_pixel_total : 14422 time to create 1 rle with old method : 0.01746082305908203 length of segment : 118 time for calcul the mask position with numpy : 0.00408482551574707 nb_pixel_total : 14123 time to create 1 rle with old method : 0.018764495849609375 length of segment : 119 time for calcul the mask position with numpy : 0.0007493495941162109 nb_pixel_total : 14365 time to create 1 rle with old method : 0.016839027404785156 length of segment : 249 time for calcul the mask position with numpy : 0.00021266937255859375 nb_pixel_total : 8038 time to create 1 rle with old method : 0.008880853652954102 length of segment : 105 time for calcul the mask position with numpy : 0.00022363662719726562 nb_pixel_total : 5742 time to create 1 rle with old method : 0.009421348571777344 length of segment : 113 time for calcul the mask position with numpy : 0.0014832019805908203 nb_pixel_total : 13138 time to create 1 rle with old method : 0.015137910842895508 length of segment : 228 time for calcul the mask position with numpy : 0.009188175201416016 nb_pixel_total : 31939 time to create 1 rle with old method : 0.040451765060424805 length of segment : 301 time for calcul the mask position with numpy : 0.021461009979248047 nb_pixel_total : 49209 time to create 1 rle with old method : 0.05798959732055664 length of segment : 375 time for calcul the mask position with numpy : 0.002898693084716797 nb_pixel_total : 9324 time to create 1 rle with old method : 0.010967731475830078 length of segment : 314 time for calcul the mask position with numpy : 0.024964332580566406 nb_pixel_total : 156439 time to create 1 rle with new method : 0.016998767852783203 length of segment : 294 time for calcul the mask position with numpy : 0.0032825469970703125 nb_pixel_total : 23730 time to create 1 rle with old method : 0.025827646255493164 length of segment : 213 time for calcul the mask position with numpy : 0.005623340606689453 nb_pixel_total : 23155 time to create 1 rle with old method : 0.026961565017700195 length of segment : 161 time for calcul the mask position with numpy : 0.0024683475494384766 nb_pixel_total : 26312 time to create 1 rle with old method : 0.02863287925720215 length of segment : 189 time for calcul the mask position with numpy : 0.003632783889770508 nb_pixel_total : 68622 time to create 1 rle with old method : 0.0719153881072998 length of segment : 256 time for calcul the mask position with numpy : 0.00442051887512207 nb_pixel_total : 18207 time to create 1 rle with old method : 0.019285202026367188 length of segment : 145 time for calcul the mask position with numpy : 0.001224517822265625 nb_pixel_total : 4640 time to create 1 rle with old method : 0.005429744720458984 length of segment : 60 time for calcul the mask position with numpy : 0.0011742115020751953 nb_pixel_total : 12224 time to create 1 rle with old method : 0.013278484344482422 length of segment : 127 time for calcul the mask position with numpy : 0.0026242733001708984 nb_pixel_total : 18088 time to create 1 rle with old method : 0.019293546676635742 length of segment : 244 time for calcul the mask position with numpy : 0.0007772445678710938 nb_pixel_total : 13127 time to create 1 rle with old method : 0.0146636962890625 length of segment : 107 time for calcul the mask position with numpy : 0.0004906654357910156 nb_pixel_total : 5725 time to create 1 rle with old method : 0.0064661502838134766 length of segment : 179 time for calcul the mask position with numpy : 0.007500886917114258 nb_pixel_total : 12643 time to create 1 rle with old method : 0.016283273696899414 length of segment : 122 time for calcul the mask position with numpy : 0.0020284652709960938 nb_pixel_total : 33747 time to create 1 rle with old method : 0.04490995407104492 length of segment : 207 time for calcul the mask position with numpy : 0.003643035888671875 nb_pixel_total : 37546 time to create 1 rle with old method : 0.04435133934020996 length of segment : 318 time for calcul the mask position with numpy : 0.002401113510131836 nb_pixel_total : 29678 time to create 1 rle with old method : 0.03287959098815918 length of segment : 205 time for calcul the mask position with numpy : 0.0032727718353271484 nb_pixel_total : 53268 time to create 1 rle with old method : 0.0557096004486084 length of segment : 290 time for calcul the mask position with numpy : 0.0011816024780273438 nb_pixel_total : 16207 time to create 1 rle with old method : 0.016865253448486328 length of segment : 193 time for calcul the mask position with numpy : 0.001219034194946289 nb_pixel_total : 20914 time to create 1 rle with old method : 0.022897005081176758 length of segment : 156 time for calcul the mask position with numpy : 0.0008497238159179688 nb_pixel_total : 11666 time to create 1 rle with old method : 0.013595342636108398 length of segment : 92 time for calcul the mask position with numpy : 0.0027379989624023438 nb_pixel_total : 41948 time to create 1 rle with old method : 0.04591178894042969 length of segment : 254 time for calcul the mask position with numpy : 0.0017268657684326172 nb_pixel_total : 15961 time to create 1 rle with old method : 0.017100095748901367 length of segment : 223 time for calcul the mask position with numpy : 0.0024366378784179688 nb_pixel_total : 38485 time to create 1 rle with old method : 0.04270601272583008 length of segment : 203 time for calcul the mask position with numpy : 0.001817941665649414 nb_pixel_total : 26789 time to create 1 rle with old method : 0.028905391693115234 length of segment : 239 time for calcul the mask position with numpy : 0.0023500919342041016 nb_pixel_total : 34437 time to create 1 rle with old method : 0.03714323043823242 length of segment : 240 time for calcul the mask position with numpy : 0.0034999847412109375 nb_pixel_total : 37604 time to create 1 rle with old method : 0.04400467872619629 length of segment : 413 time for calcul the mask position with numpy : 0.0013685226440429688 nb_pixel_total : 18060 time to create 1 rle with old method : 0.02090740203857422 length of segment : 149 time for calcul the mask position with numpy : 0.0014529228210449219 nb_pixel_total : 17929 time to create 1 rle with old method : 0.020761728286743164 length of segment : 252 time for calcul the mask position with numpy : 0.0008881092071533203 nb_pixel_total : 10205 time to create 1 rle with old method : 0.01098942756652832 length of segment : 206 time for calcul the mask position with numpy : 0.004846811294555664 nb_pixel_total : 53501 time to create 1 rle with old method : 0.06014370918273926 length of segment : 482 time for calcul the mask position with numpy : 0.002335071563720703 nb_pixel_total : 18867 time to create 1 rle with old method : 0.02121901512145996 length of segment : 199 time for calcul the mask position with numpy : 0.0011243820190429688 nb_pixel_total : 17925 time to create 1 rle with old method : 0.019639968872070312 length of segment : 160 time for calcul the mask position with numpy : 0.0013353824615478516 nb_pixel_total : 13859 time to create 1 rle with old method : 0.015403270721435547 length of segment : 173 time for calcul the mask position with numpy : 0.0015170574188232422 nb_pixel_total : 14403 time to create 1 rle with old method : 0.017596721649169922 length of segment : 165 time for calcul the mask position with numpy : 0.0031833648681640625 nb_pixel_total : 36396 time to create 1 rle with old method : 0.04275774955749512 length of segment : 287 time for calcul the mask position with numpy : 0.0017161369323730469 nb_pixel_total : 18330 time to create 1 rle with old method : 0.021632909774780273 length of segment : 217 time for calcul the mask position with numpy : 0.0018787384033203125 nb_pixel_total : 34659 time to create 1 rle with old method : 0.039311885833740234 length of segment : 150 time for calcul the mask position with numpy : 0.0013344287872314453 nb_pixel_total : 18401 time to create 1 rle with old method : 0.02131056785583496 length of segment : 118 time for calcul the mask position with numpy : 0.0012712478637695312 nb_pixel_total : 19812 time to create 1 rle with old method : 0.0226442813873291 length of segment : 145 time for calcul the mask position with numpy : 0.0026721954345703125 nb_pixel_total : 29033 time to create 1 rle with old method : 0.03264784812927246 length of segment : 482 time for calcul the mask position with numpy : 0.001211404800415039 nb_pixel_total : 15218 time to create 1 rle with old method : 0.01776599884033203 length of segment : 196 time for calcul the mask position with numpy : 0.0014791488647460938 nb_pixel_total : 16533 time to create 1 rle with old method : 0.020065784454345703 length of segment : 176 time for calcul the mask position with numpy : 0.0017406940460205078 nb_pixel_total : 19238 time to create 1 rle with old method : 0.022724628448486328 length of segment : 144 time for calcul the mask position with numpy : 0.0003883838653564453 nb_pixel_total : 7812 time to create 1 rle with old method : 0.009429931640625 length of segment : 86 time for calcul the mask position with numpy : 0.0011146068572998047 nb_pixel_total : 14659 time to create 1 rle with old method : 0.01657843589782715 length of segment : 144 time for calcul the mask position with numpy : 0.00036144256591796875 nb_pixel_total : 3744 time to create 1 rle with old method : 0.004323720932006836 length of segment : 74 time for calcul the mask position with numpy : 0.0018031597137451172 nb_pixel_total : 21011 time to create 1 rle with old method : 0.023580074310302734 length of segment : 216 time for calcul the mask position with numpy : 0.0003540515899658203 nb_pixel_total : 3418 time to create 1 rle with old method : 0.003786802291870117 length of segment : 61 time for calcul the mask position with numpy : 0.0003821849822998047 nb_pixel_total : 4701 time to create 1 rle with old method : 0.005240440368652344 length of segment : 66 time for calcul the mask position with numpy : 0.005320072174072266 nb_pixel_total : 82264 time to create 1 rle with old method : 0.09300875663757324 length of segment : 335 time for calcul the mask position with numpy : 0.0057256221771240234 nb_pixel_total : 80081 time to create 1 rle with old method : 0.08698058128356934 length of segment : 266 time for calcul the mask position with numpy : 0.0025866031646728516 nb_pixel_total : 34665 time to create 1 rle with old method : 0.03859353065490723 length of segment : 176 time for calcul the mask position with numpy : 0.0019707679748535156 nb_pixel_total : 17809 time to create 1 rle with old method : 0.019579410552978516 length of segment : 185 time for calcul the mask position with numpy : 0.0010685920715332031 nb_pixel_total : 13045 time to create 1 rle with old method : 0.014359712600708008 length of segment : 156 time for calcul the mask position with numpy : 0.0018606185913085938 nb_pixel_total : 23770 time to create 1 rle with old method : 0.025731801986694336 length of segment : 186 time for calcul the mask position with numpy : 0.0022661685943603516 nb_pixel_total : 43947 time to create 1 rle with old method : 0.04933452606201172 length of segment : 318 time for calcul the mask position with numpy : 0.0024192333221435547 nb_pixel_total : 27808 time to create 1 rle with old method : 0.02889108657836914 length of segment : 276 time for calcul the mask position with numpy : 0.0025565624237060547 nb_pixel_total : 23597 time to create 1 rle with old method : 0.026433944702148438 length of segment : 258 time for calcul the mask position with numpy : 0.0030641555786132812 nb_pixel_total : 52928 time to create 1 rle with old method : 0.056815385818481445 length of segment : 231 time for calcul the mask position with numpy : 0.004766702651977539 nb_pixel_total : 85782 time to create 1 rle with old method : 0.08843183517456055 length of segment : 380 time for calcul the mask position with numpy : 0.0008020401000976562 nb_pixel_total : 9590 time to create 1 rle with old method : 0.010466575622558594 length of segment : 111 time for calcul the mask position with numpy : 0.0021321773529052734 nb_pixel_total : 34164 time to create 1 rle with old method : 0.039319515228271484 length of segment : 236 time for calcul the mask position with numpy : 0.0023725032806396484 nb_pixel_total : 34570 time to create 1 rle with old method : 0.03791046142578125 length of segment : 208 time for calcul the mask position with numpy : 0.0008289813995361328 nb_pixel_total : 12130 time to create 1 rle with old method : 0.01290273666381836 length of segment : 211 time for calcul the mask position with numpy : 0.0007724761962890625 nb_pixel_total : 9370 time to create 1 rle with old method : 0.010371923446655273 length of segment : 109 time for calcul the mask position with numpy : 0.0006313323974609375 nb_pixel_total : 7734 time to create 1 rle with old method : 0.008715152740478516 length of segment : 108 time for calcul the mask position with numpy : 0.00047659873962402344 nb_pixel_total : 13188 time to create 1 rle with old method : 0.014390945434570312 length of segment : 134 time for calcul the mask position with numpy : 0.001165151596069336 nb_pixel_total : 16747 time to create 1 rle with old method : 0.017576217651367188 length of segment : 186 time for calcul the mask position with numpy : 0.00040340423583984375 nb_pixel_total : 3057 time to create 1 rle with old method : 0.02570033073425293 length of segment : 101 time for calcul the mask position with numpy : 0.005915164947509766 nb_pixel_total : 86621 time to create 1 rle with old method : 0.0935356616973877 length of segment : 410 time for calcul the mask position with numpy : 0.0018417835235595703 nb_pixel_total : 19167 time to create 1 rle with old method : 0.022135019302368164 length of segment : 199 time for calcul the mask position with numpy : 0.0014190673828125 nb_pixel_total : 18893 time to create 1 rle with old method : 0.022112369537353516 length of segment : 193 time for calcul the mask position with numpy : 0.0039064884185791016 nb_pixel_total : 49504 time to create 1 rle with old method : 0.05639195442199707 length of segment : 360 time for calcul the mask position with numpy : 0.0007905960083007812 nb_pixel_total : 8210 time to create 1 rle with old method : 0.00955510139465332 length of segment : 110 time for calcul the mask position with numpy : 0.00037360191345214844 nb_pixel_total : 3923 time to create 1 rle with old method : 0.004800319671630859 length of segment : 99 time for calcul the mask position with numpy : 0.0007193088531494141 nb_pixel_total : 7630 time to create 1 rle with old method : 0.008939981460571289 length of segment : 101 time for calcul the mask position with numpy : 0.00022339820861816406 nb_pixel_total : 3741 time to create 1 rle with old method : 0.0046176910400390625 length of segment : 73 time for calcul the mask position with numpy : 0.0010542869567871094 nb_pixel_total : 8430 time to create 1 rle with old method : 0.009980201721191406 length of segment : 126 time for calcul the mask position with numpy : 0.004217386245727539 nb_pixel_total : 33665 time to create 1 rle with old method : 0.03855443000793457 length of segment : 279 time for calcul the mask position with numpy : 0.0019669532775878906 nb_pixel_total : 24733 time to create 1 rle with old method : 0.0289461612701416 length of segment : 137 time for calcul the mask position with numpy : 0.0009992122650146484 nb_pixel_total : 14231 time to create 1 rle with old method : 0.01676034927368164 length of segment : 158 time for calcul the mask position with numpy : 0.002052783966064453 nb_pixel_total : 27044 time to create 1 rle with old method : 0.031093835830688477 length of segment : 200 time for calcul the mask position with numpy : 0.0017330646514892578 nb_pixel_total : 23954 time to create 1 rle with old method : 0.028076648712158203 length of segment : 169 time for calcul the mask position with numpy : 0.0031774044036865234 nb_pixel_total : 39063 time to create 1 rle with old method : 0.043561697006225586 length of segment : 287 time for calcul the mask position with numpy : 0.0017545223236083984 nb_pixel_total : 18569 time to create 1 rle with old method : 0.020761966705322266 length of segment : 232 time for calcul the mask position with numpy : 0.0012745857238769531 nb_pixel_total : 11505 time to create 1 rle with old method : 0.015490055084228516 length of segment : 138 time for calcul the mask position with numpy : 0.003741741180419922 nb_pixel_total : 49575 time to create 1 rle with old method : 0.056792259216308594 length of segment : 400 time for calcul the mask position with numpy : 0.004363536834716797 nb_pixel_total : 71098 time to create 1 rle with old method : 0.07935571670532227 length of segment : 345 time for calcul the mask position with numpy : 0.0019116401672363281 nb_pixel_total : 29615 time to create 1 rle with old method : 0.03457999229431152 length of segment : 197 time for calcul the mask position with numpy : 0.0012362003326416016 nb_pixel_total : 16024 time to create 1 rle with old method : 0.018508195877075195 length of segment : 116 time for calcul the mask position with numpy : 0.0023436546325683594 nb_pixel_total : 27216 time to create 1 rle with old method : 0.030699729919433594 length of segment : 187 time for calcul the mask position with numpy : 0.0020148754119873047 nb_pixel_total : 33237 time to create 1 rle with old method : 0.03876376152038574 length of segment : 204 time for calcul the mask position with numpy : 0.0034394264221191406 nb_pixel_total : 42649 time to create 1 rle with old method : 0.04765772819519043 length of segment : 361 time for calcul the mask position with numpy : 0.0009772777557373047 nb_pixel_total : 11195 time to create 1 rle with old method : 0.013123035430908203 length of segment : 99 time for calcul the mask position with numpy : 0.0008203983306884766 nb_pixel_total : 11894 time to create 1 rle with old method : 0.014079809188842773 length of segment : 95 time for calcul the mask position with numpy : 0.006632328033447266 nb_pixel_total : 99007 time to create 1 rle with old method : 0.10877108573913574 length of segment : 435 time for calcul the mask position with numpy : 0.0015821456909179688 nb_pixel_total : 22076 time to create 1 rle with old method : 0.02481985092163086 length of segment : 209 time for calcul the mask position with numpy : 0.0024864673614501953 nb_pixel_total : 37338 time to create 1 rle with old method : 0.04248404502868652 length of segment : 243 time for calcul the mask position with numpy : 0.0007598400115966797 nb_pixel_total : 15096 time to create 1 rle with old method : 0.017112016677856445 length of segment : 155 time for calcul the mask position with numpy : 0.005203962326049805 nb_pixel_total : 83426 time to create 1 rle with old method : 0.0899193286895752 length of segment : 372 time for calcul the mask position with numpy : 0.000820159912109375 nb_pixel_total : 9554 time to create 1 rle with old method : 0.011437654495239258 length of segment : 141 time for calcul the mask position with numpy : 0.0026209354400634766 nb_pixel_total : 44623 time to create 1 rle with old method : 0.0515894889831543 length of segment : 262 time for calcul the mask position with numpy : 0.0009734630584716797 nb_pixel_total : 17046 time to create 1 rle with old method : 0.01936507225036621 length of segment : 112 time for calcul the mask position with numpy : 0.0007214546203613281 nb_pixel_total : 7585 time to create 1 rle with old method : 0.008846521377563477 length of segment : 129 time for calcul the mask position with numpy : 0.0005774497985839844 nb_pixel_total : 17358 time to create 1 rle with old method : 0.019718647003173828 length of segment : 289 time for calcul the mask position with numpy : 0.0019752979278564453 nb_pixel_total : 37361 time to create 1 rle with old method : 0.042061805725097656 length of segment : 256 time for calcul the mask position with numpy : 0.0008287429809570312 nb_pixel_total : 7620 time to create 1 rle with old method : 0.00922083854675293 length of segment : 120 time for calcul the mask position with numpy : 0.0006892681121826172 nb_pixel_total : 6592 time to create 1 rle with old method : 0.007932662963867188 length of segment : 107 time for calcul the mask position with numpy : 0.0005633831024169922 nb_pixel_total : 7901 time to create 1 rle with old method : 0.009505271911621094 length of segment : 98 time for calcul the mask position with numpy : 0.0012307167053222656 nb_pixel_total : 18917 time to create 1 rle with old method : 0.022220373153686523 length of segment : 162 time for calcul the mask position with numpy : 0.0009455680847167969 nb_pixel_total : 10136 time to create 1 rle with old method : 0.011946916580200195 length of segment : 107 time for calcul the mask position with numpy : 0.002722024917602539 nb_pixel_total : 49813 time to create 1 rle with old method : 0.05298805236816406 length of segment : 477 time for calcul the mask position with numpy : 0.0006496906280517578 nb_pixel_total : 14083 time to create 1 rle with old method : 0.015741348266601562 length of segment : 164 time for calcul the mask position with numpy : 0.0016057491302490234 nb_pixel_total : 15220 time to create 1 rle with old method : 0.017705678939819336 length of segment : 180 time for calcul the mask position with numpy : 0.0008294582366943359 nb_pixel_total : 9989 time to create 1 rle with old method : 0.011040925979614258 length of segment : 148 time for calcul the mask position with numpy : 0.00023365020751953125 nb_pixel_total : 4756 time to create 1 rle with old method : 0.005359172821044922 length of segment : 73 time for calcul the mask position with numpy : 0.0013976097106933594 nb_pixel_total : 11978 time to create 1 rle with old method : 0.012759923934936523 length of segment : 147 time for calcul the mask position with numpy : 0.001203298568725586 nb_pixel_total : 18697 time to create 1 rle with old method : 0.020391464233398438 length of segment : 164 time for calcul the mask position with numpy : 0.00048232078552246094 nb_pixel_total : 8316 time to create 1 rle with old method : 0.009006261825561523 length of segment : 154 time for calcul the mask position with numpy : 0.0011088848114013672 nb_pixel_total : 18294 time to create 1 rle with old method : 0.01931285858154297 length of segment : 142 time for calcul the mask position with numpy : 0.0011568069458007812 nb_pixel_total : 18743 time to create 1 rle with old method : 0.01912236213684082 length of segment : 172 time for calcul the mask position with numpy : 0.0010085105895996094 nb_pixel_total : 28559 time to create 1 rle with old method : 0.030312061309814453 length of segment : 243 time for calcul the mask position with numpy : 0.0012483596801757812 nb_pixel_total : 23627 time to create 1 rle with old method : 0.025154829025268555 length of segment : 203 time for calcul the mask position with numpy : 0.0027856826782226562 nb_pixel_total : 57810 time to create 1 rle with old method : 0.061203718185424805 length of segment : 341 time for calcul the mask position with numpy : 0.0011153221130371094 nb_pixel_total : 14445 time to create 1 rle with old method : 0.015145301818847656 length of segment : 220 time for calcul the mask position with numpy : 0.0005414485931396484 nb_pixel_total : 7684 time to create 1 rle with old method : 0.008571624755859375 length of segment : 141 time for calcul the mask position with numpy : 0.001565694808959961 nb_pixel_total : 20994 time to create 1 rle with old method : 0.02271294593811035 length of segment : 174 time for calcul the mask position with numpy : 0.0015206336975097656 nb_pixel_total : 18991 time to create 1 rle with old method : 0.020402908325195312 length of segment : 185 time for calcul the mask position with numpy : 0.0014925003051757812 nb_pixel_total : 14950 time to create 1 rle with old method : 0.016858339309692383 length of segment : 168 time for calcul the mask position with numpy : 0.0010051727294921875 nb_pixel_total : 19776 time to create 1 rle with old method : 0.022301673889160156 length of segment : 211 time for calcul the mask position with numpy : 0.0015265941619873047 nb_pixel_total : 28436 time to create 1 rle with old method : 0.03161120414733887 length of segment : 186 time for calcul the mask position with numpy : 0.0005011558532714844 nb_pixel_total : 9536 time to create 1 rle with old method : 0.010716438293457031 length of segment : 151 time for calcul the mask position with numpy : 0.0013158321380615234 nb_pixel_total : 19107 time to create 1 rle with old method : 0.02089977264404297 length of segment : 249 time for calcul the mask position with numpy : 0.0010371208190917969 nb_pixel_total : 17328 time to create 1 rle with old method : 0.01781463623046875 length of segment : 245 time for calcul the mask position with numpy : 0.0004074573516845703 nb_pixel_total : 8016 time to create 1 rle with old method : 0.008996963500976562 length of segment : 118 time for calcul the mask position with numpy : 0.0009140968322753906 nb_pixel_total : 12690 time to create 1 rle with old method : 0.01496124267578125 length of segment : 123 time for calcul the mask position with numpy : 0.0006825923919677734 nb_pixel_total : 10739 time to create 1 rle with old method : 0.01253652572631836 length of segment : 204 time for calcul the mask position with numpy : 0.003147125244140625 nb_pixel_total : 46532 time to create 1 rle with old method : 0.05105757713317871 length of segment : 250 time for calcul the mask position with numpy : 0.0010979175567626953 nb_pixel_total : 25009 time to create 1 rle with old method : 0.028397321701049805 length of segment : 172 time for calcul the mask position with numpy : 0.0011532306671142578 nb_pixel_total : 12981 time to create 1 rle with old method : 0.014957666397094727 length of segment : 182 time for calcul the mask position with numpy : 0.0012416839599609375 nb_pixel_total : 10291 time to create 1 rle with old method : 0.012048482894897461 length of segment : 182 time for calcul the mask position with numpy : 0.0008552074432373047 nb_pixel_total : 15421 time to create 1 rle with old method : 0.017502546310424805 length of segment : 157 time for calcul the mask position with numpy : 0.003762960433959961 nb_pixel_total : 52131 time to create 1 rle with old method : 0.059422969818115234 length of segment : 249 time for calcul the mask position with numpy : 0.0032782554626464844 nb_pixel_total : 49489 time to create 1 rle with old method : 0.056766510009765625 length of segment : 231 time for calcul the mask position with numpy : 0.0017132759094238281 nb_pixel_total : 22945 time to create 1 rle with old method : 0.02644634246826172 length of segment : 197 time for calcul the mask position with numpy : 0.0015854835510253906 nb_pixel_total : 25912 time to create 1 rle with old method : 0.0299985408782959 length of segment : 229 time for calcul the mask position with numpy : 0.000667572021484375 nb_pixel_total : 18604 time to create 1 rle with old method : 0.021142959594726562 length of segment : 249 time for calcul the mask position with numpy : 0.004325389862060547 nb_pixel_total : 50805 time to create 1 rle with old method : 0.05746936798095703 length of segment : 326 time for calcul the mask position with numpy : 0.0006954669952392578 nb_pixel_total : 10899 time to create 1 rle with old method : 0.01262664794921875 length of segment : 80 time for calcul the mask position with numpy : 0.0008449554443359375 nb_pixel_total : 10541 time to create 1 rle with old method : 0.011957168579101562 length of segment : 211 time for calcul the mask position with numpy : 0.00047278404235839844 nb_pixel_total : 10193 time to create 1 rle with old method : 0.011910200119018555 length of segment : 224 time for calcul the mask position with numpy : 0.0018541812896728516 nb_pixel_total : 18853 time to create 1 rle with old method : 0.022057533264160156 length of segment : 312 time for calcul the mask position with numpy : 0.002643585205078125 nb_pixel_total : 47110 time to create 1 rle with old method : 0.055077552795410156 length of segment : 257 time for calcul the mask position with numpy : 0.0011143684387207031 nb_pixel_total : 11195 time to create 1 rle with old method : 0.015564203262329102 length of segment : 102 time for calcul the mask position with numpy : 0.0005726814270019531 nb_pixel_total : 7375 time to create 1 rle with old method : 0.00969552993774414 length of segment : 102 time for calcul the mask position with numpy : 0.0014755725860595703 nb_pixel_total : 16665 time to create 1 rle with old method : 0.021563053131103516 length of segment : 153 time for calcul the mask position with numpy : 0.001039266586303711 nb_pixel_total : 13463 time to create 1 rle with old method : 0.017766952514648438 length of segment : 122 time for calcul the mask position with numpy : 0.003534555435180664 nb_pixel_total : 37005 time to create 1 rle with old method : 0.04715251922607422 length of segment : 312 time for calcul the mask position with numpy : 0.0008740425109863281 nb_pixel_total : 11720 time to create 1 rle with old method : 0.015276193618774414 length of segment : 142 time for calcul the mask position with numpy : 0.003042459487915039 nb_pixel_total : 43786 time to create 1 rle with old method : 0.05464625358581543 length of segment : 230 time for calcul the mask position with numpy : 0.00048732757568359375 nb_pixel_total : 5969 time to create 1 rle with old method : 0.008185863494873047 length of segment : 75 time for calcul the mask position with numpy : 0.006972074508666992 nb_pixel_total : 108029 time to create 1 rle with old method : 0.13010144233703613 length of segment : 552 time for calcul the mask position with numpy : 0.013329029083251953 nb_pixel_total : 175141 time to create 1 rle with new method : 0.02140021324157715 length of segment : 652 time for calcul the mask position with numpy : 0.0005867481231689453 nb_pixel_total : 6303 time to create 1 rle with old method : 0.007714509963989258 length of segment : 93 time for calcul the mask position with numpy : 0.0017409324645996094 nb_pixel_total : 28141 time to create 1 rle with old method : 0.03375673294067383 length of segment : 209 time for calcul the mask position with numpy : 0.0052683353424072266 nb_pixel_total : 141594 time to create 1 rle with old method : 0.17538070678710938 length of segment : 404 time for calcul the mask position with numpy : 0.00041604042053222656 nb_pixel_total : 5505 time to create 1 rle with old method : 0.00630640983581543 length of segment : 134 time for calcul the mask position with numpy : 0.0018029212951660156 nb_pixel_total : 22204 time to create 1 rle with old method : 0.025667905807495117 length of segment : 177 time for calcul the mask position with numpy : 0.0013840198516845703 nb_pixel_total : 11163 time to create 1 rle with old method : 0.012816905975341797 length of segment : 135 time for calcul the mask position with numpy : 0.003744363784790039 nb_pixel_total : 62993 time to create 1 rle with old method : 0.07111454010009766 length of segment : 285 time for calcul the mask position with numpy : 0.0064737796783447266 nb_pixel_total : 76960 time to create 1 rle with old method : 0.09224843978881836 length of segment : 301 time for calcul the mask position with numpy : 0.0018587112426757812 nb_pixel_total : 30437 time to create 1 rle with old method : 0.035837411880493164 length of segment : 213 time for calcul the mask position with numpy : 0.0011873245239257812 nb_pixel_total : 22147 time to create 1 rle with old method : 0.025397062301635742 length of segment : 169 time for calcul the mask position with numpy : 0.001104116439819336 nb_pixel_total : 8056 time to create 1 rle with old method : 0.012054920196533203 length of segment : 250 time for calcul the mask position with numpy : 0.0009000301361083984 nb_pixel_total : 12717 time to create 1 rle with old method : 0.015761852264404297 length of segment : 207 time for calcul the mask position with numpy : 0.013913393020629883 nb_pixel_total : 289041 time to create 1 rle with new method : 0.024059295654296875 length of segment : 390 time for calcul the mask position with numpy : 0.0012350082397460938 nb_pixel_total : 19259 time to create 1 rle with old method : 0.024720430374145508 length of segment : 165 time for calcul the mask position with numpy : 0.002271413803100586 nb_pixel_total : 37299 time to create 1 rle with old method : 0.04175734519958496 length of segment : 158 time for calcul the mask position with numpy : 0.004373073577880859 nb_pixel_total : 78857 time to create 1 rle with old method : 0.0879368782043457 length of segment : 374 time for calcul the mask position with numpy : 0.0005481243133544922 nb_pixel_total : 7480 time to create 1 rle with old method : 0.0088043212890625 length of segment : 98 time for calcul the mask position with numpy : 0.0006918907165527344 nb_pixel_total : 11649 time to create 1 rle with old method : 0.013709783554077148 length of segment : 144 time for calcul the mask position with numpy : 0.0028960704803466797 nb_pixel_total : 40139 time to create 1 rle with old method : 0.04643750190734863 length of segment : 229 time for calcul the mask position with numpy : 0.0005359649658203125 nb_pixel_total : 7824 time to create 1 rle with old method : 0.00925588607788086 length of segment : 105 time for calcul the mask position with numpy : 0.0010998249053955078 nb_pixel_total : 15664 time to create 1 rle with old method : 0.018038272857666016 length of segment : 176 time for calcul the mask position with numpy : 0.004496335983276367 nb_pixel_total : 62064 time to create 1 rle with old method : 0.0690460205078125 length of segment : 306 time for calcul the mask position with numpy : 0.0013992786407470703 nb_pixel_total : 20827 time to create 1 rle with old method : 0.02384161949157715 length of segment : 189 time for calcul the mask position with numpy : 0.006164073944091797 nb_pixel_total : 72278 time to create 1 rle with old method : 0.09251523017883301 length of segment : 584 time for calcul the mask position with numpy : 0.001308441162109375 nb_pixel_total : 13993 time to create 1 rle with old method : 0.021425485610961914 length of segment : 177 time for calcul the mask position with numpy : 0.0007712841033935547 nb_pixel_total : 23060 time to create 1 rle with old method : 0.038457393646240234 length of segment : 199 time for calcul the mask position with numpy : 0.0003235340118408203 nb_pixel_total : 2754 time to create 1 rle with old method : 0.0034263134002685547 length of segment : 48 time for calcul the mask position with numpy : 0.0010819435119628906 nb_pixel_total : 12997 time to create 1 rle with old method : 0.01566624641418457 length of segment : 139 time for calcul the mask position with numpy : 0.0026159286499023438 nb_pixel_total : 122925 time to create 1 rle with old method : 0.13294744491577148 length of segment : 402 time for calcul the mask position with numpy : 0.0010175704956054688 nb_pixel_total : 12302 time to create 1 rle with old method : 0.014005422592163086 length of segment : 128 time for calcul the mask position with numpy : 0.0011749267578125 nb_pixel_total : 55450 time to create 1 rle with old method : 0.06020545959472656 length of segment : 257 time for calcul the mask position with numpy : 0.0016231536865234375 nb_pixel_total : 36363 time to create 1 rle with old method : 0.0399775505065918 length of segment : 201 time for calcul the mask position with numpy : 0.002622842788696289 nb_pixel_total : 42148 time to create 1 rle with old method : 0.04461812973022461 length of segment : 453 time for calcul the mask position with numpy : 0.0006618499755859375 nb_pixel_total : 10751 time to create 1 rle with old method : 0.012210369110107422 length of segment : 85 time for calcul the mask position with numpy : 0.0012602806091308594 nb_pixel_total : 12287 time to create 1 rle with old method : 0.013389825820922852 length of segment : 167 time for calcul the mask position with numpy : 0.001220703125 nb_pixel_total : 15581 time to create 1 rle with old method : 0.01767420768737793 length of segment : 184 time for calcul the mask position with numpy : 0.007082223892211914 nb_pixel_total : 62863 time to create 1 rle with old method : 0.07928466796875 length of segment : 342 time for calcul the mask position with numpy : 0.0003237724304199219 nb_pixel_total : 2572 time to create 1 rle with old method : 0.003101348876953125 length of segment : 51 time for calcul the mask position with numpy : 0.0018358230590820312 nb_pixel_total : 20186 time to create 1 rle with old method : 0.022919416427612305 length of segment : 216 time for calcul the mask position with numpy : 0.0013136863708496094 nb_pixel_total : 16071 time to create 1 rle with old method : 0.018075227737426758 length of segment : 218 time for calcul the mask position with numpy : 0.0011303424835205078 nb_pixel_total : 19726 time to create 1 rle with old method : 0.022459745407104492 length of segment : 103 time for calcul the mask position with numpy : 0.0014252662658691406 nb_pixel_total : 18695 time to create 1 rle with old method : 0.020955801010131836 length of segment : 269 time for calcul the mask position with numpy : 0.001996278762817383 nb_pixel_total : 29891 time to create 1 rle with old method : 0.03400087356567383 length of segment : 201 time for calcul the mask position with numpy : 0.0009608268737792969 nb_pixel_total : 7415 time to create 1 rle with old method : 0.008686065673828125 length of segment : 250 time for calcul the mask position with numpy : 0.0009131431579589844 nb_pixel_total : 11457 time to create 1 rle with old method : 0.013475418090820312 length of segment : 185 time for calcul the mask position with numpy : 0.0009622573852539062 nb_pixel_total : 12867 time to create 1 rle with old method : 0.016716957092285156 length of segment : 158 time for calcul the mask position with numpy : 0.0014231204986572266 nb_pixel_total : 17218 time to create 1 rle with old method : 0.019995927810668945 length of segment : 155 time for calcul the mask position with numpy : 0.0016069412231445312 nb_pixel_total : 19257 time to create 1 rle with old method : 0.022731304168701172 length of segment : 277 time for calcul the mask position with numpy : 0.0009810924530029297 nb_pixel_total : 16946 time to create 1 rle with old method : 0.019274234771728516 length of segment : 154 time for calcul the mask position with numpy : 0.002218484878540039 nb_pixel_total : 37039 time to create 1 rle with old method : 0.041840314865112305 length of segment : 238 time for calcul the mask position with numpy : 0.0016641616821289062 nb_pixel_total : 21044 time to create 1 rle with old method : 0.024213790893554688 length of segment : 208 time for calcul the mask position with numpy : 0.000537872314453125 nb_pixel_total : 13622 time to create 1 rle with old method : 0.015792369842529297 length of segment : 141 time for calcul the mask position with numpy : 0.0037412643432617188 nb_pixel_total : 126087 time to create 1 rle with old method : 0.1410808563232422 length of segment : 502 time for calcul the mask position with numpy : 0.0006361007690429688 nb_pixel_total : 19661 time to create 1 rle with old method : 0.02248406410217285 length of segment : 127 time for calcul the mask position with numpy : 0.003571748733520508 nb_pixel_total : 91998 time to create 1 rle with old method : 0.10045957565307617 length of segment : 418 time for calcul the mask position with numpy : 0.0004088878631591797 nb_pixel_total : 18882 time to create 1 rle with old method : 0.021094083786010742 length of segment : 217 time for calcul the mask position with numpy : 0.0009012222290039062 nb_pixel_total : 19822 time to create 1 rle with old method : 0.022371292114257812 length of segment : 324 time for calcul the mask position with numpy : 0.002595186233520508 nb_pixel_total : 43722 time to create 1 rle with old method : 0.049302101135253906 length of segment : 619 time for calcul the mask position with numpy : 0.003603696823120117 nb_pixel_total : 103086 time to create 1 rle with old method : 0.11325550079345703 length of segment : 512 time for calcul the mask position with numpy : 0.0008640289306640625 nb_pixel_total : 19615 time to create 1 rle with old method : 0.022261619567871094 length of segment : 244 time for calcul the mask position with numpy : 0.0033559799194335938 nb_pixel_total : 98701 time to create 1 rle with old method : 0.11049365997314453 length of segment : 532 time for calcul the mask position with numpy : 0.0004913806915283203 nb_pixel_total : 10769 time to create 1 rle with old method : 0.012282609939575195 length of segment : 115 time for calcul the mask position with numpy : 0.0009894371032714844 nb_pixel_total : 23985 time to create 1 rle with old method : 0.027721881866455078 length of segment : 309 time for calcul the mask position with numpy : 0.0002474784851074219 nb_pixel_total : 9819 time to create 1 rle with old method : 0.011707544326782227 length of segment : 142 time for calcul the mask position with numpy : 0.0008521080017089844 nb_pixel_total : 31445 time to create 1 rle with old method : 0.035967111587524414 length of segment : 287 time for calcul the mask position with numpy : 0.0007746219635009766 nb_pixel_total : 30818 time to create 1 rle with old method : 0.03459954261779785 length of segment : 249 time for calcul the mask position with numpy : 0.0002944469451904297 nb_pixel_total : 10879 time to create 1 rle with old method : 0.012479066848754883 length of segment : 147 time for calcul the mask position with numpy : 0.0013377666473388672 nb_pixel_total : 17940 time to create 1 rle with old method : 0.020385265350341797 length of segment : 221 time for calcul the mask position with numpy : 0.020594120025634766 nb_pixel_total : 297980 time to create 1 rle with new method : 0.022258281707763672 length of segment : 452 time for calcul the mask position with numpy : 0.0007340908050537109 nb_pixel_total : 11518 time to create 1 rle with old method : 0.013134002685546875 length of segment : 128 time for calcul the mask position with numpy : 0.0032067298889160156 nb_pixel_total : 39042 time to create 1 rle with old method : 0.04485726356506348 length of segment : 251 time for calcul the mask position with numpy : 0.0003440380096435547 nb_pixel_total : 3757 time to create 1 rle with old method : 0.004616975784301758 length of segment : 67 time for calcul the mask position with numpy : 0.002714872360229492 nb_pixel_total : 41195 time to create 1 rle with old method : 0.04670834541320801 length of segment : 258 time for calcul the mask position with numpy : 0.0014820098876953125 nb_pixel_total : 16862 time to create 1 rle with old method : 0.018722057342529297 length of segment : 237 time for calcul the mask position with numpy : 0.0013453960418701172 nb_pixel_total : 21971 time to create 1 rle with old method : 0.02419734001159668 length of segment : 171 time for calcul the mask position with numpy : 0.0012295246124267578 nb_pixel_total : 18093 time to create 1 rle with old method : 0.019782066345214844 length of segment : 155 time for calcul the mask position with numpy : 0.004872322082519531 nb_pixel_total : 72997 time to create 1 rle with old method : 0.0792698860168457 length of segment : 308 time for calcul the mask position with numpy : 0.0005395412445068359 nb_pixel_total : 7659 time to create 1 rle with old method : 0.008292198181152344 length of segment : 119 time for calcul the mask position with numpy : 0.00048041343688964844 nb_pixel_total : 5870 time to create 1 rle with old method : 0.006597757339477539 length of segment : 90 time for calcul the mask position with numpy : 0.0006034374237060547 nb_pixel_total : 18287 time to create 1 rle with old method : 0.020278215408325195 length of segment : 310 time for calcul the mask position with numpy : 0.0007832050323486328 nb_pixel_total : 8793 time to create 1 rle with old method : 0.009950876235961914 length of segment : 164 time for calcul the mask position with numpy : 0.001192331314086914 nb_pixel_total : 14200 time to create 1 rle with old method : 0.015159130096435547 length of segment : 144 time for calcul the mask position with numpy : 0.0005712509155273438 nb_pixel_total : 7314 time to create 1 rle with old method : 0.00817108154296875 length of segment : 110 time for calcul the mask position with numpy : 0.0033965110778808594 nb_pixel_total : 44869 time to create 1 rle with old method : 0.04877424240112305 length of segment : 345 time for calcul the mask position with numpy : 0.00455474853515625 nb_pixel_total : 59833 time to create 1 rle with old method : 0.061861276626586914 length of segment : 348 time for calcul the mask position with numpy : 0.021668672561645508 nb_pixel_total : 357254 time to create 1 rle with new method : 0.030188560485839844 length of segment : 822 time for calcul the mask position with numpy : 0.001947641372680664 nb_pixel_total : 18804 time to create 1 rle with old method : 0.021508216857910156 length of segment : 320 time for calcul the mask position with numpy : 0.0017652511596679688 nb_pixel_total : 25208 time to create 1 rle with old method : 0.028708219528198242 length of segment : 202 time for calcul the mask position with numpy : 0.0008900165557861328 nb_pixel_total : 10552 time to create 1 rle with old method : 0.012333393096923828 length of segment : 111 time for calcul the mask position with numpy : 0.0023267269134521484 nb_pixel_total : 30005 time to create 1 rle with old method : 0.03389549255371094 length of segment : 296 time for calcul the mask position with numpy : 0.0002951622009277344 nb_pixel_total : 7448 time to create 1 rle with old method : 0.008676767349243164 length of segment : 126 time for calcul the mask position with numpy : 0.0012383460998535156 nb_pixel_total : 17249 time to create 1 rle with old method : 0.01987934112548828 length of segment : 177 time for calcul the mask position with numpy : 0.003865480422973633 nb_pixel_total : 41806 time to create 1 rle with old method : 0.048117876052856445 length of segment : 343 time for calcul the mask position with numpy : 0.0018007755279541016 nb_pixel_total : 34119 time to create 1 rle with old method : 0.03688859939575195 length of segment : 325 time for calcul the mask position with numpy : 0.0003108978271484375 nb_pixel_total : 2987 time to create 1 rle with old method : 0.0035216808319091797 length of segment : 92 time for calcul the mask position with numpy : 0.0021924972534179688 nb_pixel_total : 28394 time to create 1 rle with old method : 0.03491973876953125 length of segment : 298 time for calcul the mask position with numpy : 0.0008261203765869141 nb_pixel_total : 12663 time to create 1 rle with old method : 0.014379024505615234 length of segment : 156 time for calcul the mask position with numpy : 0.0004487037658691406 nb_pixel_total : 6257 time to create 1 rle with old method : 0.007130861282348633 length of segment : 142 time for calcul the mask position with numpy : 0.0008420944213867188 nb_pixel_total : 12391 time to create 1 rle with old method : 0.013609647750854492 length of segment : 148 time for calcul the mask position with numpy : 0.002089262008666992 nb_pixel_total : 37733 time to create 1 rle with old method : 0.04293942451477051 length of segment : 266 time for calcul the mask position with numpy : 0.0348362922668457 nb_pixel_total : 667747 time to create 1 rle with new method : 0.055970191955566406 length of segment : 1134 time for calcul the mask position with numpy : 0.0025658607482910156 nb_pixel_total : 41669 time to create 1 rle with old method : 0.051686763763427734 length of segment : 290 time for calcul the mask position with numpy : 0.001314401626586914 nb_pixel_total : 53627 time to create 1 rle with old method : 0.05858349800109863 length of segment : 303 time for calcul the mask position with numpy : 0.0004036426544189453 nb_pixel_total : 7477 time to create 1 rle with old method : 0.008404016494750977 length of segment : 118 time for calcul the mask position with numpy : 0.0011801719665527344 nb_pixel_total : 12561 time to create 1 rle with old method : 0.0141143798828125 length of segment : 181 time for calcul the mask position with numpy : 0.0030641555786132812 nb_pixel_total : 42475 time to create 1 rle with old method : 0.04563164710998535 length of segment : 284 time for calcul the mask position with numpy : 0.005421876907348633 nb_pixel_total : 117264 time to create 1 rle with old method : 0.12627363204956055 length of segment : 357 time for calcul the mask position with numpy : 0.00017762184143066406 nb_pixel_total : 2635 time to create 1 rle with old method : 0.003238677978515625 length of segment : 100 time for calcul the mask position with numpy : 0.0003867149353027344 nb_pixel_total : 6216 time to create 1 rle with old method : 0.007430076599121094 length of segment : 120 time for calcul the mask position with numpy : 0.000621795654296875 nb_pixel_total : 8123 time to create 1 rle with old method : 0.009350776672363281 length of segment : 138 time for calcul the mask position with numpy : 0.003543376922607422 nb_pixel_total : 43576 time to create 1 rle with old method : 0.04901480674743652 length of segment : 406 time for calcul the mask position with numpy : 0.0008184909820556641 nb_pixel_total : 41554 time to create 1 rle with old method : 0.04761624336242676 length of segment : 174 time for calcul the mask position with numpy : 0.001543283462524414 nb_pixel_total : 21530 time to create 1 rle with old method : 0.02422618865966797 length of segment : 215 time for calcul the mask position with numpy : 0.0006663799285888672 nb_pixel_total : 28696 time to create 1 rle with old method : 0.03345823287963867 length of segment : 241 time for calcul the mask position with numpy : 0.0009455680847167969 nb_pixel_total : 9353 time to create 1 rle with old method : 0.010807991027832031 length of segment : 120 time for calcul the mask position with numpy : 0.003193378448486328 nb_pixel_total : 41287 time to create 1 rle with old method : 0.04936933517456055 length of segment : 329 time for calcul the mask position with numpy : 0.00040841102600097656 nb_pixel_total : 3186 time to create 1 rle with old method : 0.004091978073120117 length of segment : 89 time for calcul the mask position with numpy : 0.0016264915466308594 nb_pixel_total : 38396 time to create 1 rle with old method : 0.04263782501220703 length of segment : 461 time for calcul the mask position with numpy : 0.0008130073547363281 nb_pixel_total : 41134 time to create 1 rle with old method : 0.046918392181396484 length of segment : 175 time for calcul the mask position with numpy : 9.1552734375e-05 nb_pixel_total : 1645 time to create 1 rle with old method : 0.0020744800567626953 length of segment : 49 time for calcul the mask position with numpy : 0.0023343563079833984 nb_pixel_total : 41245 time to create 1 rle with old method : 0.046375274658203125 length of segment : 189 time for calcul the mask position with numpy : 0.001505136489868164 nb_pixel_total : 20136 time to create 1 rle with old method : 0.021991729736328125 length of segment : 265 time for calcul the mask position with numpy : 0.00018835067749023438 nb_pixel_total : 2394 time to create 1 rle with old method : 0.0029556751251220703 length of segment : 96 time for calcul the mask position with numpy : 0.00036597251892089844 nb_pixel_total : 1574 time to create 1 rle with old method : 0.001764059066772461 length of segment : 155 time for calcul the mask position with numpy : 0.0014615058898925781 nb_pixel_total : 13616 time to create 1 rle with old method : 0.014963388442993164 length of segment : 155 time for calcul the mask position with numpy : 0.0006759166717529297 nb_pixel_total : 9919 time to create 1 rle with old method : 0.01125478744506836 length of segment : 136 time for calcul the mask position with numpy : 0.00751185417175293 nb_pixel_total : 172384 time to create 1 rle with new method : 0.009116172790527344 length of segment : 540 time for calcul the mask position with numpy : 0.0014498233795166016 nb_pixel_total : 29259 time to create 1 rle with old method : 0.03264427185058594 length of segment : 227 time for calcul the mask position with numpy : 0.00027680397033691406 nb_pixel_total : 12766 time to create 1 rle with old method : 0.014776945114135742 length of segment : 162 time for calcul the mask position with numpy : 0.0004169940948486328 nb_pixel_total : 9825 time to create 1 rle with old method : 0.011404275894165039 length of segment : 149 time for calcul the mask position with numpy : 0.009257316589355469 nb_pixel_total : 390163 time to create 1 rle with new method : 0.018741846084594727 length of segment : 947 time for calcul the mask position with numpy : 0.001110076904296875 nb_pixel_total : 39638 time to create 1 rle with old method : 0.04399871826171875 length of segment : 313 time for calcul the mask position with numpy : 0.002259492874145508 nb_pixel_total : 68847 time to create 1 rle with old method : 0.07611894607543945 length of segment : 344 time for calcul the mask position with numpy : 0.0006384849548339844 nb_pixel_total : 22349 time to create 1 rle with old method : 0.025249242782592773 length of segment : 185 time for calcul the mask position with numpy : 0.005410194396972656 nb_pixel_total : 200473 time to create 1 rle with new method : 0.011066675186157227 length of segment : 602 time for calcul the mask position with numpy : 0.00041866302490234375 nb_pixel_total : 11017 time to create 1 rle with old method : 0.01260519027709961 length of segment : 149 time for calcul the mask position with numpy : 0.0005400180816650391 nb_pixel_total : 14457 time to create 1 rle with old method : 0.016574859619140625 length of segment : 152 time for calcul the mask position with numpy : 0.0007345676422119141 nb_pixel_total : 23217 time to create 1 rle with old method : 0.024865150451660156 length of segment : 233 time for calcul the mask position with numpy : 0.009592533111572266 nb_pixel_total : 457311 time to create 1 rle with new method : 0.02496957778930664 length of segment : 737 time for calcul the mask position with numpy : 0.0028319358825683594 nb_pixel_total : 105817 time to create 1 rle with old method : 0.11349153518676758 length of segment : 586 time for calcul the mask position with numpy : 0.004171848297119141 nb_pixel_total : 207303 time to create 1 rle with new method : 0.012631416320800781 length of segment : 617 time for calcul the mask position with numpy : 0.0008878707885742188 nb_pixel_total : 32577 time to create 1 rle with old method : 0.05177116394042969 length of segment : 221 time for calcul the mask position with numpy : 0.00037217140197753906 nb_pixel_total : 14889 time to create 1 rle with old method : 0.017446041107177734 length of segment : 130 time for calcul the mask position with numpy : 0.003337860107421875 nb_pixel_total : 130846 time to create 1 rle with old method : 0.15073633193969727 length of segment : 550 time for calcul the mask position with numpy : 0.0012450218200683594 nb_pixel_total : 28806 time to create 1 rle with old method : 0.0321810245513916 length of segment : 261 time for calcul the mask position with numpy : 0.0008566379547119141 nb_pixel_total : 20088 time to create 1 rle with old method : 0.022769689559936523 length of segment : 164 time for calcul the mask position with numpy : 0.0006163120269775391 nb_pixel_total : 19717 time to create 1 rle with old method : 0.02202010154724121 length of segment : 191 time for calcul the mask position with numpy : 0.0009946823120117188 nb_pixel_total : 21812 time to create 1 rle with old method : 0.024557828903198242 length of segment : 197 time for calcul the mask position with numpy : 0.003914594650268555 nb_pixel_total : 53625 time to create 1 rle with old method : 0.0596158504486084 length of segment : 242 time for calcul the mask position with numpy : 0.0010657310485839844 nb_pixel_total : 15844 time to create 1 rle with old method : 0.018056392669677734 length of segment : 122 time for calcul the mask position with numpy : 0.002435445785522461 nb_pixel_total : 33449 time to create 1 rle with old method : 0.037386178970336914 length of segment : 197 time for calcul the mask position with numpy : 0.006053924560546875 nb_pixel_total : 102223 time to create 1 rle with old method : 0.11267256736755371 length of segment : 489 time for calcul the mask position with numpy : 0.0042688846588134766 nb_pixel_total : 55976 time to create 1 rle with old method : 0.0626528263092041 length of segment : 449 time for calcul the mask position with numpy : 0.003507375717163086 nb_pixel_total : 48436 time to create 1 rle with old method : 0.05347871780395508 length of segment : 348 time for calcul the mask position with numpy : 0.001611948013305664 nb_pixel_total : 21136 time to create 1 rle with old method : 0.023680925369262695 length of segment : 288 time for calcul the mask position with numpy : 0.001554250717163086 nb_pixel_total : 20415 time to create 1 rle with old method : 0.02352452278137207 length of segment : 185 time for calcul the mask position with numpy : 0.0012116432189941406 nb_pixel_total : 14431 time to create 1 rle with old method : 0.015835285186767578 length of segment : 125 time for calcul the mask position with numpy : 0.0008840560913085938 nb_pixel_total : 11950 time to create 1 rle with old method : 0.012962102890014648 length of segment : 187 time for calcul the mask position with numpy : 0.0013275146484375 nb_pixel_total : 14724 time to create 1 rle with old method : 0.016427993774414062 length of segment : 170 time for calcul the mask position with numpy : 0.0017044544219970703 nb_pixel_total : 25250 time to create 1 rle with old method : 0.028193235397338867 length of segment : 132 time for calcul the mask position with numpy : 0.0003402233123779297 nb_pixel_total : 3373 time to create 1 rle with old method : 0.0038537979125976562 length of segment : 78 time for calcul the mask position with numpy : 0.0011096000671386719 nb_pixel_total : 15114 time to create 1 rle with old method : 0.01710796356201172 length of segment : 135 time for calcul the mask position with numpy : 0.0013954639434814453 nb_pixel_total : 24292 time to create 1 rle with old method : 0.025928258895874023 length of segment : 200 time for calcul the mask position with numpy : 0.002801179885864258 nb_pixel_total : 30581 time to create 1 rle with old method : 0.03342866897583008 length of segment : 354 time for calcul the mask position with numpy : 0.002843141555786133 nb_pixel_total : 39062 time to create 1 rle with old method : 0.03997683525085449 length of segment : 299 time for calcul the mask position with numpy : 0.0006582736968994141 nb_pixel_total : 11189 time to create 1 rle with old method : 0.01198434829711914 length of segment : 351 time for calcul the mask position with numpy : 0.000713348388671875 nb_pixel_total : 10330 time to create 1 rle with old method : 0.010819673538208008 length of segment : 86 time for calcul the mask position with numpy : 0.0011925697326660156 nb_pixel_total : 14527 time to create 1 rle with old method : 0.016131877899169922 length of segment : 110 time for calcul the mask position with numpy : 0.0009741783142089844 nb_pixel_total : 11187 time to create 1 rle with old method : 0.012403249740600586 length of segment : 250 time for calcul the mask position with numpy : 0.0016248226165771484 nb_pixel_total : 25589 time to create 1 rle with old method : 0.02801656723022461 length of segment : 271 time for calcul the mask position with numpy : 0.003739595413208008 nb_pixel_total : 44380 time to create 1 rle with old method : 0.047705650329589844 length of segment : 452 time for calcul the mask position with numpy : 0.001842498779296875 nb_pixel_total : 20466 time to create 1 rle with old method : 0.022107839584350586 length of segment : 348 time for calcul the mask position with numpy : 0.0012879371643066406 nb_pixel_total : 14504 time to create 1 rle with old method : 0.01667308807373047 length of segment : 177 time for calcul the mask position with numpy : 0.002574920654296875 nb_pixel_total : 39578 time to create 1 rle with old method : 0.04562258720397949 length of segment : 254 time for calcul the mask position with numpy : 0.0006320476531982422 nb_pixel_total : 10447 time to create 1 rle with old method : 0.011727571487426758 length of segment : 135 time for calcul the mask position with numpy : 0.0010712146759033203 nb_pixel_total : 13005 time to create 1 rle with old method : 0.015403032302856445 length of segment : 174 time for calcul the mask position with numpy : 0.0025277137756347656 nb_pixel_total : 31585 time to create 1 rle with old method : 0.03560686111450195 length of segment : 243 time for calcul the mask position with numpy : 0.0005512237548828125 nb_pixel_total : 11591 time to create 1 rle with old method : 0.013428211212158203 length of segment : 110 time for calcul the mask position with numpy : 0.009614229202270508 nb_pixel_total : 154014 time to create 1 rle with new method : 0.011809587478637695 length of segment : 506 time for calcul the mask position with numpy : 0.002295970916748047 nb_pixel_total : 33979 time to create 1 rle with old method : 0.0348818302154541 length of segment : 206 time for calcul the mask position with numpy : 0.001056671142578125 nb_pixel_total : 14526 time to create 1 rle with old method : 0.015580177307128906 length of segment : 146 time for calcul the mask position with numpy : 0.0012006759643554688 nb_pixel_total : 29989 time to create 1 rle with old method : 0.03274822235107422 length of segment : 174 time for calcul the mask position with numpy : 0.0005965232849121094 nb_pixel_total : 8243 time to create 1 rle with old method : 0.008933782577514648 length of segment : 84 time for calcul the mask position with numpy : 0.002185344696044922 nb_pixel_total : 27484 time to create 1 rle with old method : 0.029128551483154297 length of segment : 216 time for calcul the mask position with numpy : 0.0008459091186523438 nb_pixel_total : 15963 time to create 1 rle with old method : 0.016541719436645508 length of segment : 216 time for calcul the mask position with numpy : 0.001634359359741211 nb_pixel_total : 18439 time to create 1 rle with old method : 0.01945662498474121 length of segment : 308 time for calcul the mask position with numpy : 0.002309560775756836 nb_pixel_total : 27946 time to create 1 rle with old method : 0.030134916305541992 length of segment : 184 time for calcul the mask position with numpy : 0.0036478042602539062 nb_pixel_total : 30348 time to create 1 rle with old method : 0.03424382209777832 length of segment : 581 time for calcul the mask position with numpy : 0.0007436275482177734 nb_pixel_total : 8033 time to create 1 rle with old method : 0.00930333137512207 length of segment : 120 time for calcul the mask position with numpy : 0.002532482147216797 nb_pixel_total : 20128 time to create 1 rle with old method : 0.023078441619873047 length of segment : 325 time for calcul the mask position with numpy : 0.00022673606872558594 nb_pixel_total : 6977 time to create 1 rle with old method : 0.008271455764770508 length of segment : 116 time for calcul the mask position with numpy : 0.0009119510650634766 nb_pixel_total : 14103 time to create 1 rle with old method : 0.016221284866333008 length of segment : 217 time for calcul the mask position with numpy : 0.0019571781158447266 nb_pixel_total : 29106 time to create 1 rle with old method : 0.03288626670837402 length of segment : 388 time for calcul the mask position with numpy : 0.000518798828125 nb_pixel_total : 5936 time to create 1 rle with old method : 0.007183074951171875 length of segment : 89 time for calcul the mask position with numpy : 0.002155780792236328 nb_pixel_total : 39539 time to create 1 rle with old method : 0.04527401924133301 length of segment : 403 time for calcul the mask position with numpy : 0.0023233890533447266 nb_pixel_total : 34537 time to create 1 rle with old method : 0.03935551643371582 length of segment : 312 time for calcul the mask position with numpy : 0.00980830192565918 nb_pixel_total : 127381 time to create 1 rle with old method : 0.13814187049865723 length of segment : 707 time for calcul the mask position with numpy : 0.0005214214324951172 nb_pixel_total : 8268 time to create 1 rle with old method : 0.009520530700683594 length of segment : 95 time for calcul the mask position with numpy : 0.0008940696716308594 nb_pixel_total : 17018 time to create 1 rle with old method : 0.018656253814697266 length of segment : 108 time for calcul the mask position with numpy : 0.0008747577667236328 nb_pixel_total : 21868 time to create 1 rle with old method : 0.023761272430419922 length of segment : 196 time for calcul the mask position with numpy : 0.0019674301147460938 nb_pixel_total : 22677 time to create 1 rle with old method : 0.02508401870727539 length of segment : 215 time for calcul the mask position with numpy : 0.0018162727355957031 nb_pixel_total : 34661 time to create 1 rle with old method : 0.036928415298461914 length of segment : 231 time for calcul the mask position with numpy : 0.0006246566772460938 nb_pixel_total : 12373 time to create 1 rle with old method : 0.01411747932434082 length of segment : 175 time for calcul the mask position with numpy : 0.0005714893341064453 nb_pixel_total : 11514 time to create 1 rle with old method : 0.012609720230102539 length of segment : 142 time for calcul the mask position with numpy : 0.0013909339904785156 nb_pixel_total : 19827 time to create 1 rle with old method : 0.02172064781188965 length of segment : 207 time for calcul the mask position with numpy : 0.0006268024444580078 nb_pixel_total : 16611 time to create 1 rle with old method : 0.01958751678466797 length of segment : 178 time for calcul the mask position with numpy : 0.0004279613494873047 nb_pixel_total : 10438 time to create 1 rle with old method : 0.013804197311401367 length of segment : 195 time for calcul the mask position with numpy : 0.0033702850341796875 nb_pixel_total : 25494 time to create 1 rle with old method : 0.04324960708618164 length of segment : 437 time for calcul the mask position with numpy : 0.00027060508728027344 nb_pixel_total : 6261 time to create 1 rle with old method : 0.009288311004638672 length of segment : 86 time for calcul the mask position with numpy : 0.00238800048828125 nb_pixel_total : 30877 time to create 1 rle with old method : 0.043175458908081055 length of segment : 193 time for calcul the mask position with numpy : 0.009563446044921875 nb_pixel_total : 159988 time to create 1 rle with new method : 0.010278701782226562 length of segment : 414 time for calcul the mask position with numpy : 0.003770112991333008 nb_pixel_total : 45452 time to create 1 rle with old method : 0.050478219985961914 length of segment : 251 time for calcul the mask position with numpy : 0.0018470287322998047 nb_pixel_total : 26409 time to create 1 rle with old method : 0.0305788516998291 length of segment : 226 time for calcul the mask position with numpy : 0.006871461868286133 nb_pixel_total : 60786 time to create 1 rle with old method : 0.06748414039611816 length of segment : 363 time for calcul the mask position with numpy : 0.0026645660400390625 nb_pixel_total : 44153 time to create 1 rle with old method : 0.04943394660949707 length of segment : 221 time for calcul the mask position with numpy : 0.003034353256225586 nb_pixel_total : 50875 time to create 1 rle with old method : 0.05759167671203613 length of segment : 240 time for calcul the mask position with numpy : 0.0006513595581054688 nb_pixel_total : 13342 time to create 1 rle with old method : 0.023146867752075195 length of segment : 188 time for calcul the mask position with numpy : 0.0009469985961914062 nb_pixel_total : 23719 time to create 1 rle with old method : 0.026854276657104492 length of segment : 217 time for calcul the mask position with numpy : 0.0002875328063964844 nb_pixel_total : 4845 time to create 1 rle with old method : 0.006166219711303711 length of segment : 61 time for calcul the mask position with numpy : 0.0006456375122070312 nb_pixel_total : 11172 time to create 1 rle with old method : 0.013421773910522461 length of segment : 120 time for calcul the mask position with numpy : 0.0026819705963134766 nb_pixel_total : 41024 time to create 1 rle with old method : 0.048874616622924805 length of segment : 305 time for calcul the mask position with numpy : 0.004214286804199219 nb_pixel_total : 62339 time to create 1 rle with old method : 0.06944990158081055 length of segment : 326 time for calcul the mask position with numpy : 0.0010900497436523438 nb_pixel_total : 6655 time to create 1 rle with old method : 0.00819087028503418 length of segment : 164 time for calcul the mask position with numpy : 0.0005691051483154297 nb_pixel_total : 6906 time to create 1 rle with old method : 0.008666753768920898 length of segment : 116 time for calcul the mask position with numpy : 0.0003440380096435547 nb_pixel_total : 8710 time to create 1 rle with old method : 0.010863780975341797 length of segment : 128 time for calcul the mask position with numpy : 0.0003058910369873047 nb_pixel_total : 5145 time to create 1 rle with old method : 0.006089210510253906 length of segment : 55 time for calcul the mask position with numpy : 0.0013849735260009766 nb_pixel_total : 18938 time to create 1 rle with old method : 0.021597623825073242 length of segment : 236 time spent for convertir_results : 29.09219002723694 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 455 chid ids of type : 3594 Number RLEs to save : 102404 save missing photos in datou_result : time spend for datou_step_exec : 166.2006323337555 time spend to save output : 14.657142162322998 total time spend for step 1 : 180.8577744960785 step2:crop_condition Tue Feb 11 02:23:32 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 : 8 ! batch 1 Loaded 455 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 ! map_result returned by crop_photo_return_map_crop : length : 378 About to insert : list_path_to_insert length 378 new photo from crops ! About to upload 378 photos upload in portfolio : 3736932 init cache_photo without model_param we have 378 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739237068_729358 we have uploaded 378 photos in the portfolio 3736932 time of upload the photos Elapsed time : 91.64352583885193 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 ! map_result returned by crop_photo_return_map_crop : length : 56 About to insert : list_path_to_insert length 56 new photo from crops ! About to upload 56 photos upload in portfolio : 3736932 init cache_photo without model_param we have 56 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739237175_729358 we have uploaded 56 photos in the portfolio 3736932 time of upload the photos Elapsed time : 15.624692916870117 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/1739237192_729358 we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.9864487648010254 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 ! map_result returned by crop_photo_return_map_crop : length : 12 About to insert : list_path_to_insert length 12 new photo from crops ! About to upload 12 photos upload in portfolio : 3736932 init cache_photo without model_param we have 12 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739237200_729358 we have uploaded 12 photos in the portfolio 3736932 time of upload the photos Elapsed time : 3.469468832015991 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 ! map_result returned by crop_photo_return_map_crop : length : 5 About to insert : list_path_to_insert length 5 new photo from crops ! About to upload 5 photos upload in portfolio : 3736932 init cache_photo without model_param we have 5 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739237206_729358 we have uploaded 5 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.4854562282562256 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/1739237209_729358 we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.5739331245422363 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 ! map_result returned by crop_photo_return_map_crop : length : 2 About to insert : list_path_to_insert length 2 new photo from crops ! About to upload 2 photos upload in portfolio : 3736932 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739237212_729358 we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.7922742366790771 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 Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1336619587, 1336619584, 1336619541, 1336619539, 1336537676, 1336537660, 1336537656, 1336537599] Looping around the photos to save general results len do output : 455 /1336664603Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664604Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664605Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664606Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664607Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664608Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664609Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664610Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664611Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664612Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664613Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664614Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664615Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664616Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664617Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664618Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664619Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664620Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664621Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664622Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664623Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664625Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664626Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664627Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664628Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664629Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664630Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664631Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664632Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664633Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664634Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664636Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664637Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664638Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664639Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664640Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664641Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664642Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664643Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664644Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664645Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664646Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664647Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664648Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664649Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664650Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664651Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664652Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664653Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664654Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664655Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664656Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664657Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664658Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664659Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664660Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664661Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664662Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664663Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664664Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664665Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664666Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664667Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664668Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664669Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664670Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664671Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664672Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664673Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664674Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664675Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664676Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664677Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664678Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664679Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664680Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664681Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664683Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664684Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664685Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664686Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664687Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664689Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664690Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664691Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664692Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664693Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664694Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664695Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664696Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664697Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664698Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664699Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664700Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664701Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664702Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664703Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664704Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664705Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664706Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664707Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664708Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664709Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664710Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664711Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664712Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664713Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664714Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664715Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664716Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664717Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664718Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664719Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664721Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664724Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664726Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664727Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664728Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664729Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664730Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664731Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664732Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664733Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664734Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664735Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664736Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664737Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664738Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664739Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664740Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664741Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664742Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664743Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664744Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664745Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664746Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664747Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664748Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664750Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664751Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664752Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664753Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664754Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664755Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664756Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664757Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664758Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664759Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664760Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664761Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664762Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664763Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664764Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664766Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664767Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664769Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664770Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664771Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664772Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664773Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664774Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664775Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664776Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664777Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664778Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664779Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664780Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664781Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664782Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664783Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664784Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664785Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664786Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664787Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664788Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664789Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664790Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664791Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664792Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664793Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664794Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664795Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664796Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664797Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664798Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664799Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664800Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664801Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664802Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664803Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664804Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664805Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664806Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664807Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664808Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664809Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664810Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664811Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664812Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664813Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664814Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664815Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664816Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664817Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664818Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664819Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664820Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664821Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664823Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664824Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664825Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664826Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664827Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664828Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664829Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664830Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664831Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664832Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664833Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664834Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664835Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664836Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664837Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664838Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664839Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664840Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664841Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664842Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664843Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664844Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664845Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664847Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664848Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664849Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664850Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664851Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664852Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664853Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664854Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664855Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664856Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664857Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664858Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664859Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664860Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664862Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664865Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664868Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664872Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664873Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664874Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664875Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664876Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664877Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664878Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664879Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664880Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664881Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664882Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664883Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664884Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664885Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664886Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664887Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664888Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664889Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664890Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664891Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664892Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664893Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664894Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664895Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664896Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664897Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664898Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664899Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664900Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664901Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664903Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664904Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664905Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664906Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664907Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664908Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664909Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664910Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664911Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664912Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664913Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664914Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664915Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664916Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664917Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664918Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664919Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664920Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664921Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664922Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664923Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664924Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664925Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664926Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664927Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664928Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664929Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664930Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664931Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664932Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664933Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664934Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664935Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664936Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664937Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664938Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664939Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664940Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664941Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664942Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664943Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664944Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664945Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664946Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664947Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664948Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664949Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664950Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664951Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664952Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664953Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664954Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664955Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664956Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664957Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664958Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664959Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664961Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664962Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664963Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664964Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664965Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664966Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664967Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664968Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664969Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664970Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664971Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664972Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664973Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664974Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664975Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664976Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664977Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664978Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664980Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664981Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664982Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664983Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664984Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664985Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664986Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664987Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664988Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664989Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664990Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664991Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664992Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664993Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664994Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664995Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664996Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664997Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664998Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336664999Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665000Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665001Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665002Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665003Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665004Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665024Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665025Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665026Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665027Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665028Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665029Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665030Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665031Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665032Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665033Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665034Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665035Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665036Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665037Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665039Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665040Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665041Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665042Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665043Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665044Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665045Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665046Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665047Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665048Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665049Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665050Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665051Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665052Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665053Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665054Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665055Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665056Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665057Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665058Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665059Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665060Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665061Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665062Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665063Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665064Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665065Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665066Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665067Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665068Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665069Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665070Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665071Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665072Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665073Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665074Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665075Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665076Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665077Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665078Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665079Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665080Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665083Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665098Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665099Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665100Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665101Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665102Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665103Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665104Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665105Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665106Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665107Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665108Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665109Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665112Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665113Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665114Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665115Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665116Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665117Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665120Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336665121Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336619587', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336619584', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336619541', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336619539', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537676', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537660', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537656', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537599', None, None, None, None, None, '2574369') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1373 time used for this insertion : 0.1272437572479248 save_final save missing photos in datou_result : time spend for datou_step_exec : 200.01357555389404 time spend to save output : 0.13714861869812012 total time spend for step 2 : 200.15072417259216 step3:rle_unique_nms_with_priority Tue Feb 11 02:26:52 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 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 455 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 78 nb_hashtags : 4 time to prepare the origin masks : 4.1464598178863525 time for calcul the mask position with numpy : 0.2305912971496582 nb_pixel_total : 5276496 time to create 1 rle with new method : 0.546764612197876 time for calcul the mask position with numpy : 0.02868795394897461 nb_pixel_total : 17167 time to create 1 rle with old method : 0.01898479461669922 time for calcul the mask position with numpy : 0.028927326202392578 nb_pixel_total : 39931 time to create 1 rle with old method : 0.04468655586242676 time for calcul the mask position with numpy : 0.030260801315307617 nb_pixel_total : 26395 time to create 1 rle with old method : 0.03151512145996094 time for calcul the mask position with numpy : 0.03187966346740723 nb_pixel_total : 21747 time to create 1 rle with old method : 0.024018049240112305 time for calcul the mask position with numpy : 0.028493642807006836 nb_pixel_total : 18383 time to create 1 rle with old method : 0.019653797149658203 time for calcul the mask position with numpy : 0.03033280372619629 nb_pixel_total : 22369 time to create 1 rle with old method : 0.02632451057434082 time for calcul the mask position with numpy : 0.03032517433166504 nb_pixel_total : 9825 time to create 1 rle with old method : 0.011436700820922852 time for calcul the mask position with numpy : 0.027951955795288086 nb_pixel_total : 3852 time to create 1 rle with old method : 0.004537105560302734 time for calcul the mask position with numpy : 0.028547048568725586 nb_pixel_total : 16819 time to create 1 rle with old method : 0.01839923858642578 time for calcul the mask position with numpy : 0.026905298233032227 nb_pixel_total : 7926 time to create 1 rle with old method : 0.008594751358032227 time for calcul the mask position with numpy : 0.02752232551574707 nb_pixel_total : 23730 time to create 1 rle with old method : 0.025463342666625977 time for calcul the mask position with numpy : 0.02827596664428711 nb_pixel_total : 31939 time to create 1 rle with old method : 0.035340309143066406 time for calcul the mask position with numpy : 0.02776956558227539 nb_pixel_total : 21461 time to create 1 rle with old method : 0.022755861282348633 time for calcul the mask position with numpy : 0.028007984161376953 nb_pixel_total : 17214 time to create 1 rle with old method : 0.01809215545654297 time for calcul the mask position with numpy : 0.028234481811523438 nb_pixel_total : 187 time to create 1 rle with old method : 0.0003871917724609375 time for calcul the mask position with numpy : 0.029361724853515625 nb_pixel_total : 21634 time to create 1 rle with old method : 0.023560285568237305 time for calcul the mask position with numpy : 0.029177427291870117 nb_pixel_total : 41476 time to create 1 rle with old method : 0.045946359634399414 time for calcul the mask position with numpy : 0.028879404067993164 nb_pixel_total : 13349 time to create 1 rle with old method : 0.015814542770385742 time for calcul the mask position with numpy : 0.02877664566040039 nb_pixel_total : 17785 time to create 1 rle with old method : 0.01976799964904785 time for calcul the mask position with numpy : 0.02884531021118164 nb_pixel_total : 15960 time to create 1 rle with old method : 0.01792287826538086 time for calcul the mask position with numpy : 0.028331518173217773 nb_pixel_total : 19260 time to create 1 rle with old method : 0.021981477737426758 time for calcul the mask position with numpy : 0.027723312377929688 nb_pixel_total : 18207 time to create 1 rle with old method : 0.020227432250976562 time for calcul the mask position with numpy : 0.02786421775817871 nb_pixel_total : 13065 time to create 1 rle with old method : 0.015047788619995117 time for calcul the mask position with numpy : 0.028285980224609375 nb_pixel_total : 9319 time to create 1 rle with old method : 0.010549545288085938 time for calcul the mask position with numpy : 0.028372526168823242 nb_pixel_total : 15860 time to create 1 rle with old method : 0.018329858779907227 time for calcul the mask position with numpy : 0.028374195098876953 nb_pixel_total : 14106 time to create 1 rle with old method : 0.01531982421875 time for calcul the mask position with numpy : 0.028030872344970703 nb_pixel_total : 21631 time to create 1 rle with old method : 0.023061513900756836 time for calcul the mask position with numpy : 0.028529644012451172 nb_pixel_total : 17529 time to create 1 rle with old method : 0.019538164138793945 time for calcul the mask position with numpy : 0.02958846092224121 nb_pixel_total : 14507 time to create 1 rle with old method : 0.015678882598876953 time for calcul the mask position with numpy : 0.02776813507080078 nb_pixel_total : 23497 time to create 1 rle with old method : 0.024777889251708984 time for calcul the mask position with numpy : 0.02787613868713379 nb_pixel_total : 31406 time to create 1 rle with old method : 0.0328831672668457 time for calcul the mask position with numpy : 0.030834436416625977 nb_pixel_total : 5056 time to create 1 rle with old method : 0.005973339080810547 time for calcul the mask position with numpy : 0.028231143951416016 nb_pixel_total : 9324 time to create 1 rle with old method : 0.010357141494750977 time for calcul the mask position with numpy : 0.027933597564697266 nb_pixel_total : 5742 time to create 1 rle with old method : 0.006082057952880859 time for calcul the mask position with numpy : 0.028632640838623047 nb_pixel_total : 18191 time to create 1 rle with old method : 0.019399642944335938 time for calcul the mask position with numpy : 0.02822089195251465 nb_pixel_total : 24694 time to create 1 rle with old method : 0.027804851531982422 time for calcul the mask position with numpy : 0.029026269912719727 nb_pixel_total : 16187 time to create 1 rle with old method : 0.017450571060180664 time for calcul the mask position with numpy : 0.02815532684326172 nb_pixel_total : 22202 time to create 1 rle with old method : 0.023975372314453125 time for calcul the mask position with numpy : 0.029175281524658203 nb_pixel_total : 14123 time to create 1 rle with old method : 0.016042470932006836 time for calcul the mask position with numpy : 0.03055429458618164 nb_pixel_total : 21321 time to create 1 rle with old method : 0.024657487869262695 time for calcul the mask position with numpy : 0.029438257217407227 nb_pixel_total : 14795 time to create 1 rle with old method : 0.022922039031982422 time for calcul the mask position with numpy : 0.031305551528930664 nb_pixel_total : 42775 time to create 1 rle with old method : 0.04789996147155762 time for calcul the mask position with numpy : 0.03550839424133301 nb_pixel_total : 923 time to create 1 rle with old method : 0.0011661052703857422 time for calcul the mask position with numpy : 0.0306093692779541 nb_pixel_total : 19210 time to create 1 rle with old method : 0.023355960845947266 time for calcul the mask position with numpy : 0.03392529487609863 nb_pixel_total : 23155 time to create 1 rle with old method : 0.026827096939086914 time for calcul the mask position with numpy : 0.02948594093322754 nb_pixel_total : 35342 time to create 1 rle with old method : 0.03767824172973633 time for calcul the mask position with numpy : 0.02770233154296875 nb_pixel_total : 36923 time to create 1 rle with old method : 0.03892087936401367 time for calcul the mask position with numpy : 0.02747321128845215 nb_pixel_total : 21462 time to create 1 rle with old method : 0.022639036178588867 time for calcul the mask position with numpy : 0.0277402400970459 nb_pixel_total : 12643 time to create 1 rle with old method : 0.015324592590332031 time for calcul the mask position with numpy : 0.028469085693359375 nb_pixel_total : 5301 time to create 1 rle with old method : 0.0058596134185791016 time for calcul the mask position with numpy : 0.0285031795501709 nb_pixel_total : 47519 time to create 1 rle with old method : 0.05294060707092285 time for calcul the mask position with numpy : 0.0280911922454834 nb_pixel_total : 32404 time to create 1 rle with old method : 0.03544926643371582 time for calcul the mask position with numpy : 0.029060840606689453 nb_pixel_total : 42512 time to create 1 rle with old method : 0.04571127891540527 time for calcul the mask position with numpy : 0.029087305068969727 nb_pixel_total : 66176 time to create 1 rle with old method : 0.07381057739257812 time for calcul the mask position with numpy : 0.030220746994018555 nb_pixel_total : 32212 time to create 1 rle with old method : 0.03628683090209961 time for calcul the mask position with numpy : 0.030237913131713867 nb_pixel_total : 52201 time to create 1 rle with old method : 0.06035208702087402 time for calcul the mask position with numpy : 0.029550552368164062 nb_pixel_total : 4807 time to create 1 rle with old method : 0.0056035518646240234 time for calcul the mask position with numpy : 0.029384851455688477 nb_pixel_total : 22534 time to create 1 rle with old method : 0.025003910064697266 time for calcul the mask position with numpy : 0.02869892120361328 nb_pixel_total : 25392 time to create 1 rle with old method : 0.02696990966796875 time for calcul the mask position with numpy : 0.029828786849975586 nb_pixel_total : 1262 time to create 1 rle with old method : 0.0015461444854736328 time for calcul the mask position with numpy : 0.02781200408935547 nb_pixel_total : 15641 time to create 1 rle with old method : 0.016866207122802734 time for calcul the mask position with numpy : 0.02852606773376465 nb_pixel_total : 14365 time to create 1 rle with old method : 0.01564788818359375 time for calcul the mask position with numpy : 0.02804422378540039 nb_pixel_total : 68622 time to create 1 rle with old method : 0.07244110107421875 time for calcul the mask position with numpy : 0.027537822723388672 nb_pixel_total : 17617 time to create 1 rle with old method : 0.01904463768005371 time for calcul the mask position with numpy : 0.026669025421142578 nb_pixel_total : 7671 time to create 1 rle with old method : 0.008304834365844727 time for calcul the mask position with numpy : 0.027184009552001953 nb_pixel_total : 52842 time to create 1 rle with old method : 0.05582451820373535 time for calcul the mask position with numpy : 0.027809858322143555 nb_pixel_total : 32180 time to create 1 rle with old method : 0.033792972564697266 time for calcul the mask position with numpy : 0.027359962463378906 nb_pixel_total : 12224 time to create 1 rle with old method : 0.013253927230834961 time for calcul the mask position with numpy : 0.02756190299987793 nb_pixel_total : 15257 time to create 1 rle with old method : 0.01716303825378418 time for calcul the mask position with numpy : 0.02802252769470215 nb_pixel_total : 27535 time to create 1 rle with old method : 0.028995752334594727 time for calcul the mask position with numpy : 0.028879404067993164 nb_pixel_total : 156439 time to create 1 rle with new method : 0.3680732250213623 time for calcul the mask position with numpy : 0.029244422912597656 nb_pixel_total : 14989 time to create 1 rle with old method : 0.017392396926879883 time for calcul the mask position with numpy : 0.028784751892089844 nb_pixel_total : 18136 time to create 1 rle with old method : 0.019396066665649414 time for calcul the mask position with numpy : 0.027832508087158203 nb_pixel_total : 13136 time to create 1 rle with old method : 0.014081478118896484 time for calcul the mask position with numpy : 0.02771472930908203 nb_pixel_total : 17429 time to create 1 rle with old method : 0.018347978591918945 time for calcul the mask position with numpy : 0.026744365692138672 nb_pixel_total : 7877 time to create 1 rle with old method : 0.008688688278198242 time for calcul the mask position with numpy : 0.026918649673461914 nb_pixel_total : 13220 time to create 1 rle with old method : 0.014091730117797852 time for calcul the mask position with numpy : 0.026755571365356445 nb_pixel_total : 4640 time to create 1 rle with old method : 0.0050373077392578125 create new chi : 5.237654447555542 time to delete rle : 0.014485359191894531 batch 1 Loaded 157 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 31980 TO DO : save crop sub photo not yet done ! save time : 3.9101572036743164 nb_obj : 67 nb_hashtags : 4 time to prepare the origin masks : 4.147514343261719 time for calcul the mask position with numpy : 0.43791794776916504 nb_pixel_total : 5365957 time to create 1 rle with new method : 0.4908897876739502 time for calcul the mask position with numpy : 0.029423236846923828 nb_pixel_total : 20914 time to create 1 rle with old method : 0.024844646453857422 time for calcul the mask position with numpy : 0.02931356430053711 nb_pixel_total : 3744 time to create 1 rle with old method : 0.004374265670776367 time for calcul the mask position with numpy : 0.02914118766784668 nb_pixel_total : 10205 time to create 1 rle with old method : 0.01178598403930664 time for calcul the mask position with numpy : 0.029711008071899414 nb_pixel_total : 18867 time to create 1 rle with old method : 0.021011829376220703 time for calcul the mask position with numpy : 0.029545068740844727 nb_pixel_total : 82264 time to create 1 rle with old method : 0.09255051612854004 time for calcul the mask position with numpy : 0.028000831604003906 nb_pixel_total : 34164 time to create 1 rle with old method : 0.0371851921081543 time for calcul the mask position with numpy : 0.03028559684753418 nb_pixel_total : 34659 time to create 1 rle with old method : 0.04073596000671387 time for calcul the mask position with numpy : 0.028490543365478516 nb_pixel_total : 4701 time to create 1 rle with old method : 0.005251407623291016 time for calcul the mask position with numpy : 0.027263641357421875 nb_pixel_total : 15218 time to create 1 rle with old method : 0.016081571578979492 time for calcul the mask position with numpy : 0.02779388427734375 nb_pixel_total : 29033 time to create 1 rle with old method : 0.03246355056762695 time for calcul the mask position with numpy : 0.028026103973388672 nb_pixel_total : 9590 time to create 1 rle with old method : 0.010879278182983398 time for calcul the mask position with numpy : 0.029666900634765625 nb_pixel_total : 37604 time to create 1 rle with old method : 0.04147672653198242 time for calcul the mask position with numpy : 0.029019832611083984 nb_pixel_total : 36396 time to create 1 rle with old method : 0.04012441635131836 time for calcul the mask position with numpy : 0.029093027114868164 nb_pixel_total : 36135 time to create 1 rle with old method : 0.039757490158081055 time for calcul the mask position with numpy : 0.029158830642700195 nb_pixel_total : 8210 time to create 1 rle with old method : 0.00938272476196289 time for calcul the mask position with numpy : 0.029196977615356445 nb_pixel_total : 16747 time to create 1 rle with old method : 0.018479585647583008 time for calcul the mask position with numpy : 0.028348445892333984 nb_pixel_total : 17925 time to create 1 rle with old method : 0.01990485191345215 time for calcul the mask position with numpy : 0.029135704040527344 nb_pixel_total : 12130 time to create 1 rle with old method : 0.013650655746459961 time for calcul the mask position with numpy : 0.029304981231689453 nb_pixel_total : 14403 time to create 1 rle with old method : 0.01598334312438965 time for calcul the mask position with numpy : 0.029462814331054688 nb_pixel_total : 37546 time to create 1 rle with old method : 0.04150032997131348 time for calcul the mask position with numpy : 0.028545141220092773 nb_pixel_total : 49504 time to create 1 rle with old method : 0.05926632881164551 time for calcul the mask position with numpy : 0.02848052978515625 nb_pixel_total : 38485 time to create 1 rle with old method : 0.04169106483459473 time for calcul the mask position with numpy : 0.028425216674804688 nb_pixel_total : 32752 time to create 1 rle with old method : 0.036160945892333984 time for calcul the mask position with numpy : 0.029227495193481445 nb_pixel_total : 23597 time to create 1 rle with old method : 0.026775360107421875 time for calcul the mask position with numpy : 0.02935171127319336 nb_pixel_total : 41948 time to create 1 rle with old method : 0.04662823677062988 time for calcul the mask position with numpy : 0.02886056900024414 nb_pixel_total : 24733 time to create 1 rle with old method : 0.026696205139160156 time for calcul the mask position with numpy : 0.027212858200073242 nb_pixel_total : 13188 time to create 1 rle with old method : 0.014047384262084961 time for calcul the mask position with numpy : 0.027437210083007812 nb_pixel_total : 29678 time to create 1 rle with old method : 0.032697200775146484 time for calcul the mask position with numpy : 0.02791881561279297 nb_pixel_total : 27808 time to create 1 rle with old method : 0.032062530517578125 time for calcul the mask position with numpy : 0.028095245361328125 nb_pixel_total : 53268 time to create 1 rle with old method : 0.05746340751647949 time for calcul the mask position with numpy : 0.028487205505371094 nb_pixel_total : 26789 time to create 1 rle with old method : 0.029692888259887695 time for calcul the mask position with numpy : 0.02996683120727539 nb_pixel_total : 34665 time to create 1 rle with old method : 0.05700397491455078 time for calcul the mask position with numpy : 0.030559062957763672 nb_pixel_total : 53501 time to create 1 rle with old method : 0.06466150283813477 time for calcul the mask position with numpy : 0.02843165397644043 nb_pixel_total : 7812 time to create 1 rle with old method : 0.01035165786743164 time for calcul the mask position with numpy : 0.029042959213256836 nb_pixel_total : 15961 time to create 1 rle with old method : 0.017492055892944336 time for calcul the mask position with numpy : 0.028554677963256836 nb_pixel_total : 14659 time to create 1 rle with old method : 0.016495466232299805 time for calcul the mask position with numpy : 0.028897762298583984 nb_pixel_total : 16533 time to create 1 rle with old method : 0.018346548080444336 time for calcul the mask position with numpy : 0.02944803237915039 nb_pixel_total : 33747 time to create 1 rle with old method : 0.03686952590942383 time for calcul the mask position with numpy : 0.02895355224609375 nb_pixel_total : 17929 time to create 1 rle with old method : 0.019631147384643555 time for calcul the mask position with numpy : 0.029278993606567383 nb_pixel_total : 73408 time to create 1 rle with old method : 0.0783994197845459 time for calcul the mask position with numpy : 0.02834916114807129 nb_pixel_total : 34570 time to create 1 rle with old method : 0.03708791732788086 time for calcul the mask position with numpy : 0.028713703155517578 nb_pixel_total : 13859 time to create 1 rle with old method : 0.015234947204589844 time for calcul the mask position with numpy : 0.02826833724975586 nb_pixel_total : 19812 time to create 1 rle with old method : 0.022443771362304688 time for calcul the mask position with numpy : 0.028305768966674805 nb_pixel_total : 3057 time to create 1 rle with old method : 0.003309488296508789 time for calcul the mask position with numpy : 0.0281374454498291 nb_pixel_total : 13045 time to create 1 rle with old method : 0.014169692993164062 time for calcul the mask position with numpy : 0.02915048599243164 nb_pixel_total : 14231 time to create 1 rle with old method : 0.023194551467895508 time for calcul the mask position with numpy : 0.033757925033569336 nb_pixel_total : 73398 time to create 1 rle with old method : 0.08535146713256836 time for calcul the mask position with numpy : 0.029039621353149414 nb_pixel_total : 17809 time to create 1 rle with old method : 0.019853830337524414 time for calcul the mask position with numpy : 0.02870774269104004 nb_pixel_total : 86621 time to create 1 rle with old method : 0.09162092208862305 time for calcul the mask position with numpy : 0.02887892723083496 nb_pixel_total : 52928 time to create 1 rle with old method : 0.058469295501708984 time for calcul the mask position with numpy : 0.029636859893798828 nb_pixel_total : 34437 time to create 1 rle with old method : 0.03886747360229492 time for calcul the mask position with numpy : 0.0296628475189209 nb_pixel_total : 11666 time to create 1 rle with old method : 0.012897491455078125 time for calcul the mask position with numpy : 0.029115915298461914 nb_pixel_total : 3923 time to create 1 rle with old method : 0.0043582916259765625 time for calcul the mask position with numpy : 0.029346704483032227 nb_pixel_total : 18330 time to create 1 rle with old method : 0.020731687545776367 time for calcul the mask position with numpy : 0.02995443344116211 nb_pixel_total : 3741 time to create 1 rle with old method : 0.004405498504638672 time for calcul the mask position with numpy : 0.029786348342895508 nb_pixel_total : 19238 time to create 1 rle with old method : 0.022232770919799805 time for calcul the mask position with numpy : 0.029277563095092773 nb_pixel_total : 19167 time to create 1 rle with old method : 0.02161097526550293 time for calcul the mask position with numpy : 0.02901458740234375 nb_pixel_total : 7630 time to create 1 rle with old method : 0.008885860443115234 time for calcul the mask position with numpy : 0.02937793731689453 nb_pixel_total : 21011 time to create 1 rle with old method : 0.023494720458984375 time for calcul the mask position with numpy : 0.029534101486206055 nb_pixel_total : 23770 time to create 1 rle with old method : 0.027279376983642578 time for calcul the mask position with numpy : 0.02974414825439453 nb_pixel_total : 18060 time to create 1 rle with old method : 0.020395278930664062 time for calcul the mask position with numpy : 0.029542922973632812 nb_pixel_total : 7734 time to create 1 rle with old method : 0.009027957916259766 time for calcul the mask position with numpy : 0.029233932495117188 nb_pixel_total : 16207 time to create 1 rle with old method : 0.018370389938354492 time for calcul the mask position with numpy : 0.02958369255065918 nb_pixel_total : 3418 time to create 1 rle with old method : 0.004134178161621094 time for calcul the mask position with numpy : 0.029639482498168945 nb_pixel_total : 18401 time to create 1 rle with old method : 0.02065873146057129 time for calcul the mask position with numpy : 0.02991318702697754 nb_pixel_total : 8430 time to create 1 rle with old method : 0.00992727279663086 time for calcul the mask position with numpy : 0.029643535614013672 nb_pixel_total : 9370 time to create 1 rle with old method : 0.011231184005737305 create new chi : 4.823566198348999 time to delete rle : 0.006381034851074219 batch 1 Loaded 136 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 28826 TO DO : save crop sub photo not yet done ! save time : 2.962944746017456 nb_obj : 76 nb_hashtags : 4 time to prepare the origin masks : 4.195579767227173 time for calcul the mask position with numpy : 0.2274789810180664 nb_pixel_total : 5264734 time to create 1 rle with new method : 0.3135244846343994 time for calcul the mask position with numpy : 0.03194427490234375 nb_pixel_total : 10899 time to create 1 rle with old method : 0.015474557876586914 time for calcul the mask position with numpy : 0.03295540809631348 nb_pixel_total : 6592 time to create 1 rle with old method : 0.011200904846191406 time for calcul the mask position with numpy : 0.032373666763305664 nb_pixel_total : 10136 time to create 1 rle with old method : 0.012298583984375 time for calcul the mask position with numpy : 0.029500722885131836 nb_pixel_total : 7901 time to create 1 rle with old method : 0.009192943572998047 time for calcul the mask position with numpy : 0.029361486434936523 nb_pixel_total : 20994 time to create 1 rle with old method : 0.023129701614379883 time for calcul the mask position with numpy : 0.029100656509399414 nb_pixel_total : 11978 time to create 1 rle with old method : 0.01397252082824707 time for calcul the mask position with numpy : 0.029330968856811523 nb_pixel_total : 17046 time to create 1 rle with old method : 0.019907474517822266 time for calcul the mask position with numpy : 0.029311180114746094 nb_pixel_total : 50805 time to create 1 rle with old method : 0.05881190299987793 time for calcul the mask position with numpy : 0.02899909019470215 nb_pixel_total : 10541 time to create 1 rle with old method : 0.012114763259887695 time for calcul the mask position with numpy : 0.030446767807006836 nb_pixel_total : 216 time to create 1 rle with old method : 0.0004258155822753906 time for calcul the mask position with numpy : 0.03163027763366699 nb_pixel_total : 46532 time to create 1 rle with old method : 0.060652732849121094 time for calcul the mask position with numpy : 0.03212904930114746 nb_pixel_total : 11195 time to create 1 rle with old method : 0.014951229095458984 time for calcul the mask position with numpy : 0.032297372817993164 nb_pixel_total : 50336 time to create 1 rle with old method : 0.06602001190185547 time for calcul the mask position with numpy : 0.03264188766479492 nb_pixel_total : 19479 time to create 1 rle with old method : 0.02554035186767578 time for calcul the mask position with numpy : 0.03258180618286133 nb_pixel_total : 14950 time to create 1 rle with old method : 0.018854379653930664 time for calcul the mask position with numpy : 0.03223133087158203 nb_pixel_total : 14445 time to create 1 rle with old method : 0.016367673873901367 time for calcul the mask position with numpy : 0.02936100959777832 nb_pixel_total : 12690 time to create 1 rle with old method : 0.014547586441040039 time for calcul the mask position with numpy : 0.029091596603393555 nb_pixel_total : 27216 time to create 1 rle with old method : 0.030423402786254883 time for calcul the mask position with numpy : 0.02966022491455078 nb_pixel_total : 17328 time to create 1 rle with old method : 0.02858591079711914 time for calcul the mask position with numpy : 0.03339719772338867 nb_pixel_total : 15220 time to create 1 rle with old method : 0.022730112075805664 time for calcul the mask position with numpy : 0.0599367618560791 nb_pixel_total : 18991 time to create 1 rle with old method : 0.03563499450683594 time for calcul the mask position with numpy : 0.04276561737060547 nb_pixel_total : 22076 time to create 1 rle with old method : 0.025137901306152344 time for calcul the mask position with numpy : 0.03258466720581055 nb_pixel_total : 7620 time to create 1 rle with old method : 0.010208845138549805 time for calcul the mask position with numpy : 0.03387093544006348 nb_pixel_total : 18836 time to create 1 rle with old method : 0.021046876907348633 time for calcul the mask position with numpy : 0.02951192855834961 nb_pixel_total : 12981 time to create 1 rle with old method : 0.014872312545776367 time for calcul the mask position with numpy : 0.029598712921142578 nb_pixel_total : 24992 time to create 1 rle with old method : 0.028606414794921875 time for calcul the mask position with numpy : 0.03409838676452637 nb_pixel_total : 49489 time to create 1 rle with old method : 0.07881855964660645 time for calcul the mask position with numpy : 0.041243791580200195 nb_pixel_total : 15733 time to create 1 rle with old method : 0.020154953002929688 time for calcul the mask position with numpy : 0.036237239837646484 nb_pixel_total : 7585 time to create 1 rle with old method : 0.011133670806884766 time for calcul the mask position with numpy : 0.0431210994720459 nb_pixel_total : 39063 time to create 1 rle with old method : 0.05049777030944824 time for calcul the mask position with numpy : 0.03516745567321777 nb_pixel_total : 83426 time to create 1 rle with old method : 0.09448623657226562 time for calcul the mask position with numpy : 0.0341494083404541 nb_pixel_total : 99007 time to create 1 rle with old method : 0.14179325103759766 time for calcul the mask position with numpy : 0.03018784523010254 nb_pixel_total : 42649 time to create 1 rle with old method : 0.047774314880371094 time for calcul the mask position with numpy : 0.03494715690612793 nb_pixel_total : 29615 time to create 1 rle with old method : 0.03411674499511719 time for calcul the mask position with numpy : 0.03640341758728027 nb_pixel_total : 18697 time to create 1 rle with old method : 0.021558523178100586 time for calcul the mask position with numpy : 0.040537118911743164 nb_pixel_total : 7684 time to create 1 rle with old method : 0.00875234603881836 time for calcul the mask position with numpy : 0.04198145866394043 nb_pixel_total : 23954 time to create 1 rle with old method : 0.0265657901763916 time for calcul the mask position with numpy : 0.033690690994262695 nb_pixel_total : 9536 time to create 1 rle with old method : 0.011082172393798828 time for calcul the mask position with numpy : 0.029187917709350586 nb_pixel_total : 10291 time to create 1 rle with old method : 0.011813163757324219 time for calcul the mask position with numpy : 0.03358769416809082 nb_pixel_total : 15421 time to create 1 rle with old method : 0.018709182739257812 time for calcul the mask position with numpy : 0.045410871505737305 nb_pixel_total : 9554 time to create 1 rle with old method : 0.026577472686767578 time for calcul the mask position with numpy : 0.04800748825073242 nb_pixel_total : 25912 time to create 1 rle with old method : 0.028736591339111328 time for calcul the mask position with numpy : 0.029382944107055664 nb_pixel_total : 47110 time to create 1 rle with old method : 0.05466341972351074 time for calcul the mask position with numpy : 0.03134655952453613 nb_pixel_total : 27044 time to create 1 rle with old method : 0.029925823211669922 time for calcul the mask position with numpy : 0.029727935791015625 nb_pixel_total : 18569 time to create 1 rle with old method : 0.021050453186035156 time for calcul the mask position with numpy : 0.029384613037109375 nb_pixel_total : 11505 time to create 1 rle with old method : 0.014645099639892578 time for calcul the mask position with numpy : 0.030313491821289062 nb_pixel_total : 22945 time to create 1 rle with old method : 0.02683234214782715 time for calcul the mask position with numpy : 0.030292987823486328 nb_pixel_total : 71098 time to create 1 rle with old method : 0.08090329170227051 time for calcul the mask position with numpy : 0.031184911727905273 nb_pixel_total : 18294 time to create 1 rle with old method : 0.022490739822387695 time for calcul the mask position with numpy : 0.03076958656311035 nb_pixel_total : 9989 time to create 1 rle with old method : 0.012515544891357422 time for calcul the mask position with numpy : 0.03096771240234375 nb_pixel_total : 37361 time to create 1 rle with old method : 0.04285931587219238 time for calcul the mask position with numpy : 0.02935051918029785 nb_pixel_total : 16665 time to create 1 rle with old method : 0.018726825714111328 time for calcul the mask position with numpy : 0.02937483787536621 nb_pixel_total : 19107 time to create 1 rle with old method : 0.022040605545043945 time for calcul the mask position with numpy : 0.02999567985534668 nb_pixel_total : 16024 time to create 1 rle with old method : 0.018719911575317383 time for calcul the mask position with numpy : 0.030438899993896484 nb_pixel_total : 18743 time to create 1 rle with old method : 0.030913114547729492 time for calcul the mask position with numpy : 0.03274869918823242 nb_pixel_total : 8316 time to create 1 rle with old method : 0.009374856948852539 time for calcul the mask position with numpy : 0.029255151748657227 nb_pixel_total : 37338 time to create 1 rle with old method : 0.041243791580200195 time for calcul the mask position with numpy : 0.0290677547454834 nb_pixel_total : 18917 time to create 1 rle with old method : 0.021037578582763672 time for calcul the mask position with numpy : 0.02906632423400879 nb_pixel_total : 7375 time to create 1 rle with old method : 0.008619070053100586 time for calcul the mask position with numpy : 0.029210567474365234 nb_pixel_total : 11195 time to create 1 rle with old method : 0.012865304946899414 time for calcul the mask position with numpy : 0.032491207122802734 nb_pixel_total : 14083 time to create 1 rle with old method : 0.01601576805114746 time for calcul the mask position with numpy : 0.0315699577331543 nb_pixel_total : 49813 time to create 1 rle with old method : 0.05532979965209961 time for calcul the mask position with numpy : 0.030315637588500977 nb_pixel_total : 49575 time to create 1 rle with old method : 0.056314945220947266 time for calcul the mask position with numpy : 0.030646085739135742 nb_pixel_total : 28559 time to create 1 rle with old method : 0.03235197067260742 time for calcul the mask position with numpy : 0.029799699783325195 nb_pixel_total : 15096 time to create 1 rle with old method : 0.01881885528564453 time for calcul the mask position with numpy : 0.028828144073486328 nb_pixel_total : 23627 time to create 1 rle with old method : 0.027277708053588867 time for calcul the mask position with numpy : 0.028969287872314453 nb_pixel_total : 44623 time to create 1 rle with old method : 0.049630165100097656 time for calcul the mask position with numpy : 0.029085397720336914 nb_pixel_total : 10381 time to create 1 rle with old method : 0.011695384979248047 time for calcul the mask position with numpy : 0.028988361358642578 nb_pixel_total : 4756 time to create 1 rle with old method : 0.00552678108215332 time for calcul the mask position with numpy : 0.028892040252685547 nb_pixel_total : 28436 time to create 1 rle with old method : 0.03216862678527832 time for calcul the mask position with numpy : 0.03559374809265137 nb_pixel_total : 13463 time to create 1 rle with old method : 0.01505732536315918 time for calcul the mask position with numpy : 0.03960371017456055 nb_pixel_total : 52002 time to create 1 rle with old method : 0.05817532539367676 time for calcul the mask position with numpy : 0.03885030746459961 nb_pixel_total : 10739 time to create 1 rle with old method : 0.012218952178955078 time for calcul the mask position with numpy : 0.0371401309967041 nb_pixel_total : 33237 time to create 1 rle with old method : 0.0365147590637207 time for calcul the mask position with numpy : 0.029055356979370117 nb_pixel_total : 8016 time to create 1 rle with old method : 0.010399103164672852 time for calcul the mask position with numpy : 0.030255794525146484 nb_pixel_total : 11894 time to create 1 rle with old method : 0.013389825820922852 create new chi : 5.259198188781738 time to delete rle : 0.0070421695709228516 batch 1 Loaded 153 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 31576 TO DO : save crop sub photo not yet done ! save time : 4.731853246688843 nb_obj : 57 nb_hashtags : 3 time to prepare the origin masks : 5.639987945556641 time for calcul the mask position with numpy : 0.6483583450317383 nb_pixel_total : 5132437 time to create 1 rle with new method : 1.0593340396881104 time for calcul the mask position with numpy : 0.028315305709838867 nb_pixel_total : 29891 time to create 1 rle with old method : 0.032858848571777344 time for calcul the mask position with numpy : 0.02791595458984375 nb_pixel_total : 76960 time to create 1 rle with old method : 0.08302140235900879 time for calcul the mask position with numpy : 0.02788233757019043 nb_pixel_total : 13993 time to create 1 rle with old method : 0.014997005462646484 time for calcul the mask position with numpy : 0.02732253074645996 nb_pixel_total : 6303 time to create 1 rle with old method : 0.007379293441772461 time for calcul the mask position with numpy : 0.028300046920776367 nb_pixel_total : 40139 time to create 1 rle with old method : 0.04311990737915039 time for calcul the mask position with numpy : 0.02843165397644043 nb_pixel_total : 72124 time to create 1 rle with old method : 0.0788576602935791 time for calcul the mask position with numpy : 0.029085874557495117 nb_pixel_total : 19105 time to create 1 rle with old method : 0.021326303482055664 time for calcul the mask position with numpy : 0.029963254928588867 nb_pixel_total : 15581 time to create 1 rle with old method : 0.018179655075073242 time for calcul the mask position with numpy : 0.029507875442504883 nb_pixel_total : 12287 time to create 1 rle with old method : 0.014184236526489258 time for calcul the mask position with numpy : 0.028909921646118164 nb_pixel_total : 11720 time to create 1 rle with old method : 0.013127565383911133 time for calcul the mask position with numpy : 0.029237031936645508 nb_pixel_total : 62993 time to create 1 rle with old method : 0.07051229476928711 time for calcul the mask position with numpy : 0.030002832412719727 nb_pixel_total : 78857 time to create 1 rle with old method : 0.0877375602722168 time for calcul the mask position with numpy : 0.029384136199951172 nb_pixel_total : 20827 time to create 1 rle with old method : 0.02333664894104004 time for calcul the mask position with numpy : 0.029386281967163086 nb_pixel_total : 20186 time to create 1 rle with old method : 0.023646831512451172 time for calcul the mask position with numpy : 0.02939152717590332 nb_pixel_total : 28141 time to create 1 rle with old method : 0.03181195259094238 time for calcul the mask position with numpy : 0.02996659278869629 nb_pixel_total : 18695 time to create 1 rle with old method : 0.02198004722595215 time for calcul the mask position with numpy : 0.02991318702697754 nb_pixel_total : 19726 time to create 1 rle with old method : 0.027870655059814453 time for calcul the mask position with numpy : 0.04234647750854492 nb_pixel_total : 37039 time to create 1 rle with old method : 0.042310237884521484 time for calcul the mask position with numpy : 0.03001713752746582 nb_pixel_total : 62064 time to create 1 rle with old method : 0.0706949234008789 time for calcul the mask position with numpy : 0.03227877616882324 nb_pixel_total : 21044 time to create 1 rle with old method : 0.0249936580657959 time for calcul the mask position with numpy : 0.030504703521728516 nb_pixel_total : 22147 time to create 1 rle with old method : 0.025505542755126953 time for calcul the mask position with numpy : 0.029740095138549805 nb_pixel_total : 11457 time to create 1 rle with old method : 0.047231197357177734 time for calcul the mask position with numpy : 0.0312039852142334 nb_pixel_total : 62863 time to create 1 rle with old method : 0.07161784172058105 time for calcul the mask position with numpy : 0.030054569244384766 nb_pixel_total : 30437 time to create 1 rle with old method : 0.03450512886047363 time for calcul the mask position with numpy : 0.03120708465576172 nb_pixel_total : 2754 time to create 1 rle with old method : 0.0032126903533935547 time for calcul the mask position with numpy : 0.029740571975708008 nb_pixel_total : 17218 time to create 1 rle with old method : 0.020684480667114258 time for calcul the mask position with numpy : 0.029833555221557617 nb_pixel_total : 12997 time to create 1 rle with old method : 0.015566587448120117 time for calcul the mask position with numpy : 0.03077554702758789 nb_pixel_total : 175141 time to create 1 rle with new method : 0.14562153816223145 time for calcul the mask position with numpy : 0.03125715255737305 nb_pixel_total : 16946 time to create 1 rle with old method : 0.019384384155273438 time for calcul the mask position with numpy : 0.029401063919067383 nb_pixel_total : 7824 time to create 1 rle with old method : 0.008857011795043945 time for calcul the mask position with numpy : 0.02953958511352539 nb_pixel_total : 22204 time to create 1 rle with old method : 0.02498483657836914 time for calcul the mask position with numpy : 0.0296022891998291 nb_pixel_total : 42148 time to create 1 rle with old method : 0.04745316505432129 time for calcul the mask position with numpy : 0.030000686645507812 nb_pixel_total : 108029 time to create 1 rle with old method : 0.12083125114440918 time for calcul the mask position with numpy : 0.02905726432800293 nb_pixel_total : 15664 time to create 1 rle with old method : 0.018204689025878906 time for calcul the mask position with numpy : 0.029032468795776367 nb_pixel_total : 11649 time to create 1 rle with old method : 0.012972116470336914 time for calcul the mask position with numpy : 0.029510498046875 nb_pixel_total : 104822 time to create 1 rle with old method : 0.11578178405761719 time for calcul the mask position with numpy : 0.028849363327026367 nb_pixel_total : 1107 time to create 1 rle with old method : 0.0017657279968261719 time for calcul the mask position with numpy : 0.030294418334960938 nb_pixel_total : 37005 time to create 1 rle with old method : 0.04219484329223633 time for calcul the mask position with numpy : 0.03001570701599121 nb_pixel_total : 23060 time to create 1 rle with old method : 0.028143882751464844 time for calcul the mask position with numpy : 0.030634641647338867 nb_pixel_total : 4802 time to create 1 rle with old method : 0.005804777145385742 time for calcul the mask position with numpy : 0.031029939651489258 nb_pixel_total : 12717 time to create 1 rle with old method : 0.014725446701049805 time for calcul the mask position with numpy : 0.028635263442993164 nb_pixel_total : 5505 time to create 1 rle with old method : 0.006400585174560547 time for calcul the mask position with numpy : 0.03130650520324707 nb_pixel_total : 289041 time to create 1 rle with new method : 0.1499326229095459 time for calcul the mask position with numpy : 0.029078245162963867 nb_pixel_total : 11163 time to create 1 rle with old method : 0.012772321701049805 time for calcul the mask position with numpy : 0.029016733169555664 nb_pixel_total : 2900 time to create 1 rle with old method : 0.004170894622802734 time for calcul the mask position with numpy : 0.03387308120727539 nb_pixel_total : 19079 time to create 1 rle with old method : 0.024929523468017578 time for calcul the mask position with numpy : 0.029561519622802734 nb_pixel_total : 2572 time to create 1 rle with old method : 0.003028392791748047 time for calcul the mask position with numpy : 0.030774593353271484 nb_pixel_total : 43786 time to create 1 rle with old method : 0.07478809356689453 time for calcul the mask position with numpy : 0.03163719177246094 nb_pixel_total : 5969 time to create 1 rle with old method : 0.007020473480224609 time for calcul the mask position with numpy : 0.029091596603393555 nb_pixel_total : 8056 time to create 1 rle with old method : 0.009107351303100586 time for calcul the mask position with numpy : 0.02913069725036621 nb_pixel_total : 31284 time to create 1 rle with old method : 0.03811240196228027 time for calcul the mask position with numpy : 0.03277015686035156 nb_pixel_total : 7480 time to create 1 rle with old method : 0.008940935134887695 time for calcul the mask position with numpy : 0.03056502342224121 nb_pixel_total : 12867 time to create 1 rle with old method : 0.021761417388916016 time for calcul the mask position with numpy : 0.03321337699890137 nb_pixel_total : 37299 time to create 1 rle with old method : 0.049569129943847656 time for calcul the mask position with numpy : 0.031723976135253906 nb_pixel_total : 7083 time to create 1 rle with old method : 0.008287906646728516 time for calcul the mask position with numpy : 0.029501676559448242 nb_pixel_total : 12302 time to create 1 rle with old method : 0.013752460479736328 time for calcul the mask position with numpy : 0.0290677547454834 nb_pixel_total : 10751 time to create 1 rle with old method : 0.01366734504699707 create new chi : 5.502331018447876 time to delete rle : 0.005274295806884766 batch 1 Loaded 115 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 26406 TO DO : save crop sub photo not yet done ! save time : 1.5490612983703613 nb_obj : 16 nb_hashtags : 3 time to prepare the origin masks : 4.180757522583008 time for calcul the mask position with numpy : 0.38596558570861816 nb_pixel_total : 6395307 time to create 1 rle with new method : 0.5512816905975342 time for calcul the mask position with numpy : 0.023164749145507812 nb_pixel_total : 10879 time to create 1 rle with old method : 0.012595891952514648 time for calcul the mask position with numpy : 0.021532773971557617 nb_pixel_total : 30818 time to create 1 rle with old method : 0.03404831886291504 time for calcul the mask position with numpy : 0.022559165954589844 nb_pixel_total : 26737 time to create 1 rle with old method : 0.031256675720214844 time for calcul the mask position with numpy : 0.022792577743530273 nb_pixel_total : 9507 time to create 1 rle with old method : 0.01088404655456543 time for calcul the mask position with numpy : 0.02274298667907715 nb_pixel_total : 23985 time to create 1 rle with old method : 0.027851581573486328 time for calcul the mask position with numpy : 0.021835803985595703 nb_pixel_total : 10769 time to create 1 rle with old method : 0.011945724487304688 time for calcul the mask position with numpy : 0.02597641944885254 nb_pixel_total : 98701 time to create 1 rle with old method : 0.1113893985748291 time for calcul the mask position with numpy : 0.02526545524597168 nb_pixel_total : 19615 time to create 1 rle with old method : 0.032242536544799805 time for calcul the mask position with numpy : 0.023288488388061523 nb_pixel_total : 103086 time to create 1 rle with old method : 0.12280797958374023 time for calcul the mask position with numpy : 0.0246732234954834 nb_pixel_total : 43722 time to create 1 rle with old method : 0.058046579360961914 time for calcul the mask position with numpy : 0.023258686065673828 nb_pixel_total : 19822 time to create 1 rle with old method : 0.022289276123046875 time for calcul the mask position with numpy : 0.02669239044189453 nb_pixel_total : 5924 time to create 1 rle with old method : 0.006983041763305664 time for calcul the mask position with numpy : 0.02309131622314453 nb_pixel_total : 91998 time to create 1 rle with old method : 0.10205769538879395 time for calcul the mask position with numpy : 0.026726484298706055 nb_pixel_total : 19661 time to create 1 rle with old method : 0.021995067596435547 time for calcul the mask position with numpy : 0.023526668548583984 nb_pixel_total : 126087 time to create 1 rle with old method : 0.14578485488891602 time for calcul the mask position with numpy : 0.02165699005126953 nb_pixel_total : 13622 time to create 1 rle with old method : 0.015043497085571289 create new chi : 2.1179003715515137 time to delete rle : 0.0015511512756347656 batch 1 Loaded 33 chid ids of type : 3594 +++++++++++++++++++++++Number RLEs to save : 11416 TO DO : save crop sub photo not yet done ! save time : 0.7309830188751221 nb_obj : 60 nb_hashtags : 3 time to prepare the origin masks : 4.897664785385132 time for calcul the mask position with numpy : 0.05887746810913086 nb_pixel_total : 4385261 time to create 1 rle with new method : 0.1437385082244873 time for calcul the mask position with numpy : 0.028805971145629883 nb_pixel_total : 10552 time to create 1 rle with old method : 0.01181483268737793 time for calcul the mask position with numpy : 0.02885270118713379 nb_pixel_total : 18287 time to create 1 rle with old method : 0.020256757736206055 time for calcul the mask position with numpy : 0.029193639755249023 nb_pixel_total : 72997 time to create 1 rle with old method : 0.08026123046875 time for calcul the mask position with numpy : 0.029108762741088867 nb_pixel_total : 41669 time to create 1 rle with old method : 0.04694795608520508 time for calcul the mask position with numpy : 0.029236316680908203 nb_pixel_total : 17940 time to create 1 rle with old method : 0.0207827091217041 time for calcul the mask position with numpy : 0.02943253517150879 nb_pixel_total : 43576 time to create 1 rle with old method : 0.049523353576660156 time for calcul the mask position with numpy : 0.030875444412231445 nb_pixel_total : 41043 time to create 1 rle with old method : 0.045458078384399414 time for calcul the mask position with numpy : 0.02885293960571289 nb_pixel_total : 9353 time to create 1 rle with old method : 0.010935306549072266 time for calcul the mask position with numpy : 0.029227256774902344 nb_pixel_total : 38396 time to create 1 rle with old method : 0.04480457305908203 time for calcul the mask position with numpy : 0.02918720245361328 nb_pixel_total : 37733 time to create 1 rle with old method : 0.0422673225402832 time for calcul the mask position with numpy : 0.029701709747314453 nb_pixel_total : 5870 time to create 1 rle with old method : 0.008351325988769531 time for calcul the mask position with numpy : 0.02942514419555664 nb_pixel_total : 53627 time to create 1 rle with old method : 0.05932450294494629 time for calcul the mask position with numpy : 0.03279566764831543 nb_pixel_total : 565079 time to create 1 rle with new method : 0.12711834907531738 time for calcul the mask position with numpy : 0.029004335403442383 nb_pixel_total : 20136 time to create 1 rle with old method : 0.022706985473632812 time for calcul the mask position with numpy : 0.02892899513244629 nb_pixel_total : 41195 time to create 1 rle with old method : 0.04535555839538574 time for calcul the mask position with numpy : 0.028743743896484375 nb_pixel_total : 30005 time to create 1 rle with old method : 0.0333256721496582 time for calcul the mask position with numpy : 0.028821706771850586 nb_pixel_total : 39042 time to create 1 rle with old method : 0.0435025691986084 time for calcul the mask position with numpy : 0.028913021087646484 nb_pixel_total : 41287 time to create 1 rle with old method : 0.04523468017578125 time for calcul the mask position with numpy : 0.029375314712524414 nb_pixel_total : 13616 time to create 1 rle with old method : 0.015110969543457031 time for calcul the mask position with numpy : 0.02889537811279297 nb_pixel_total : 1574 time to create 1 rle with old method : 0.00189971923828125 time for calcul the mask position with numpy : 0.028830528259277344 nb_pixel_total : 44869 time to create 1 rle with old method : 0.04914379119873047 time for calcul the mask position with numpy : 0.030194997787475586 nb_pixel_total : 357254 time to create 1 rle with new method : 0.1346912384033203 time for calcul the mask position with numpy : 0.02866196632385254 nb_pixel_total : 28394 time to create 1 rle with old method : 0.03134036064147949 time for calcul the mask position with numpy : 0.028767108917236328 nb_pixel_total : 25208 time to create 1 rle with old method : 0.028918981552124023 time for calcul the mask position with numpy : 0.028654813766479492 nb_pixel_total : 41806 time to create 1 rle with old method : 0.04597902297973633 time for calcul the mask position with numpy : 0.028864622116088867 nb_pixel_total : 17249 time to create 1 rle with old method : 0.01959705352783203 time for calcul the mask position with numpy : 0.028903961181640625 nb_pixel_total : 14200 time to create 1 rle with old method : 0.016369104385375977 time for calcul the mask position with numpy : 0.028683185577392578 nb_pixel_total : 1005 time to create 1 rle with old method : 0.001192331314086914 time for calcul the mask position with numpy : 0.028980493545532227 nb_pixel_total : 9919 time to create 1 rle with old method : 0.011336803436279297 time for calcul the mask position with numpy : 0.02879190444946289 nb_pixel_total : 12663 time to create 1 rle with old method : 0.014654397964477539 time for calcul the mask position with numpy : 0.02912116050720215 nb_pixel_total : 117264 time to create 1 rle with old method : 0.1297163963317871 time for calcul the mask position with numpy : 0.028802156448364258 nb_pixel_total : 21530 time to create 1 rle with old method : 0.02430248260498047 time for calcul the mask position with numpy : 0.028809785842895508 nb_pixel_total : 29259 time to create 1 rle with old method : 0.03267097473144531 time for calcul the mask position with numpy : 0.028864383697509766 nb_pixel_total : 21971 time to create 1 rle with old method : 0.02447962760925293 time for calcul the mask position with numpy : 0.02896571159362793 nb_pixel_total : 7659 time to create 1 rle with old method : 0.009174108505249023 time for calcul the mask position with numpy : 0.029097318649291992 nb_pixel_total : 11518 time to create 1 rle with old method : 0.013026237487792969 time for calcul the mask position with numpy : 0.03063058853149414 nb_pixel_total : 6257 time to create 1 rle with old method : 0.006969451904296875 time for calcul the mask position with numpy : 0.029258251190185547 nb_pixel_total : 18804 time to create 1 rle with old method : 0.0221250057220459 time for calcul the mask position with numpy : 0.02903127670288086 nb_pixel_total : 8793 time to create 1 rle with old method : 0.009787797927856445 time for calcul the mask position with numpy : 0.02943277359008789 nb_pixel_total : 12391 time to create 1 rle with old method : 0.014198064804077148 time for calcul the mask position with numpy : 0.029292821884155273 nb_pixel_total : 42475 time to create 1 rle with old method : 0.04743242263793945 time for calcul the mask position with numpy : 0.029095888137817383 nb_pixel_total : 2552 time to create 1 rle with old method : 0.0030078887939453125 time for calcul the mask position with numpy : 0.029703617095947266 nb_pixel_total : 172384 time to create 1 rle with new method : 0.12574172019958496 time for calcul the mask position with numpy : 0.032334089279174805 nb_pixel_total : 6197 time to create 1 rle with old method : 0.007158756256103516 time for calcul the mask position with numpy : 0.028841495513916016 nb_pixel_total : 7477 time to create 1 rle with old method : 0.008471488952636719 time for calcul the mask position with numpy : 0.028971433639526367 nb_pixel_total : 18093 time to create 1 rle with old method : 0.02319812774658203 time for calcul the mask position with numpy : 0.03580212593078613 nb_pixel_total : 12561 time to create 1 rle with old method : 0.01429605484008789 time for calcul the mask position with numpy : 0.029018402099609375 nb_pixel_total : 8123 time to create 1 rle with old method : 0.009280681610107422 time for calcul the mask position with numpy : 0.02889084815979004 nb_pixel_total : 1645 time to create 1 rle with old method : 0.0019474029541015625 time for calcul the mask position with numpy : 0.028767108917236328 nb_pixel_total : 3186 time to create 1 rle with old method : 0.003815889358520508 time for calcul the mask position with numpy : 0.031014442443847656 nb_pixel_total : 297980 time to create 1 rle with new method : 0.12173080444335938 time for calcul the mask position with numpy : 0.0327301025390625 nb_pixel_total : 33535 time to create 1 rle with old method : 0.05532693862915039 time for calcul the mask position with numpy : 0.031206607818603516 nb_pixel_total : 7314 time to create 1 rle with old method : 0.008493900299072266 time for calcul the mask position with numpy : 0.029105186462402344 nb_pixel_total : 16862 time to create 1 rle with old method : 0.01865363121032715 time for calcul the mask position with numpy : 0.028983592987060547 nb_pixel_total : 2394 time to create 1 rle with old method : 0.003594636917114258 time for calcul the mask position with numpy : 0.03182578086853027 nb_pixel_total : 3757 time to create 1 rle with old method : 0.00670933723449707 time for calcul the mask position with numpy : 0.03856205940246582 nb_pixel_total : 2987 time to create 1 rle with old method : 0.0055124759674072266 time for calcul the mask position with numpy : 0.03777337074279785 nb_pixel_total : 2032 time to create 1 rle with old method : 0.004159450531005859 time for calcul the mask position with numpy : 0.03601503372192383 nb_pixel_total : 59833 time to create 1 rle with old method : 0.09062027931213379 time for calcul the mask position with numpy : 0.029955387115478516 nb_pixel_total : 12602 time to create 1 rle with old method : 0.014482498168945312 create new chi : 4.0500359535217285 time to delete rle : 0.0048940181732177734 batch 1 Loaded 123 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 30098 TO DO : save crop sub photo not yet done ! save time : 6.128360748291016 nb_obj : 19 nb_hashtags : 5 time to prepare the origin masks : 9.555925369262695 time for calcul the mask position with numpy : 0.6316788196563721 nb_pixel_total : 5431759 time to create 1 rle with new method : 0.951453447341919 time for calcul the mask position with numpy : 0.023040771484375 nb_pixel_total : 21812 time to create 1 rle with old method : 0.02533888816833496 time for calcul the mask position with numpy : 0.027410507202148438 nb_pixel_total : 19639 time to create 1 rle with old method : 0.021982192993164062 time for calcul the mask position with numpy : 0.03578805923461914 nb_pixel_total : 20088 time to create 1 rle with old method : 0.022371530532836914 time for calcul the mask position with numpy : 0.03920912742614746 nb_pixel_total : 28806 time to create 1 rle with old method : 0.03415417671203613 time for calcul the mask position with numpy : 0.035753726959228516 nb_pixel_total : 130846 time to create 1 rle with old method : 0.14603066444396973 time for calcul the mask position with numpy : 0.035181522369384766 nb_pixel_total : 14889 time to create 1 rle with old method : 0.01680731773376465 time for calcul the mask position with numpy : 0.026540517807006836 nb_pixel_total : 32577 time to create 1 rle with old method : 0.036926984786987305 time for calcul the mask position with numpy : 0.023903369903564453 nb_pixel_total : 8721 time to create 1 rle with old method : 0.012234687805175781 time for calcul the mask position with numpy : 0.022989511489868164 nb_pixel_total : 105817 time to create 1 rle with old method : 0.1372687816619873 time for calcul the mask position with numpy : 0.030440092086791992 nb_pixel_total : 456465 time to create 1 rle with new method : 0.7934770584106445 time for calcul the mask position with numpy : 0.021947145462036133 nb_pixel_total : 22052 time to create 1 rle with old method : 0.024767398834228516 time for calcul the mask position with numpy : 0.02743983268737793 nb_pixel_total : 14457 time to create 1 rle with old method : 0.015916824340820312 time for calcul the mask position with numpy : 0.022899866104125977 nb_pixel_total : 11017 time to create 1 rle with old method : 0.012403011322021484 time for calcul the mask position with numpy : 0.025104999542236328 nb_pixel_total : 200473 time to create 1 rle with new method : 0.8939192295074463 time for calcul the mask position with numpy : 0.03706812858581543 nb_pixel_total : 22349 time to create 1 rle with old method : 0.025995731353759766 time for calcul the mask position with numpy : 0.03904461860656738 nb_pixel_total : 68847 time to create 1 rle with old method : 0.0811457633972168 time for calcul the mask position with numpy : 0.03724837303161621 nb_pixel_total : 39638 time to create 1 rle with old method : 0.044257402420043945 time for calcul the mask position with numpy : 0.03909420967102051 nb_pixel_total : 390163 time to create 1 rle with new method : 0.6351301670074463 time for calcul the mask position with numpy : 0.0352940559387207 nb_pixel_total : 9825 time to create 1 rle with old method : 0.011367321014404297 create new chi : 5.274098634719849 time to delete rle : 0.0022356510162353516 batch 1 Loaded 39 chid ids of type : 3594 +++++++++++++++++++++++Number RLEs to save : 14852 TO DO : save crop sub photo not yet done ! save time : 1.3038742542266846 nb_obj : 79 nb_hashtags : 4 time to prepare the origin masks : 4.89025354385376 time for calcul the mask position with numpy : 0.08183050155639648 nb_pixel_total : 4849444 time to create 1 rle with new method : 0.5570013523101807 time for calcul the mask position with numpy : 0.02947235107421875 nb_pixel_total : 17018 time to create 1 rle with old method : 0.019088029861450195 time for calcul the mask position with numpy : 0.029725313186645508 nb_pixel_total : 8243 time to create 1 rle with old method : 0.009639978408813477 time for calcul the mask position with numpy : 0.029956340789794922 nb_pixel_total : 55976 time to create 1 rle with old method : 0.062334537506103516 time for calcul the mask position with numpy : 0.029982328414916992 nb_pixel_total : 50875 time to create 1 rle with old method : 0.056168556213378906 time for calcul the mask position with numpy : 0.031219959259033203 nb_pixel_total : 26409 time to create 1 rle with old method : 0.03009486198425293 time for calcul the mask position with numpy : 0.0293271541595459 nb_pixel_total : 14526 time to create 1 rle with old method : 0.016436100006103516 time for calcul the mask position with numpy : 0.029781579971313477 nb_pixel_total : 22677 time to create 1 rle with old method : 0.02583146095275879 time for calcul the mask position with numpy : 0.030120372772216797 nb_pixel_total : 6655 time to create 1 rle with old method : 0.013047456741333008 time for calcul the mask position with numpy : 0.03302812576293945 nb_pixel_total : 11187 time to create 1 rle with old method : 0.012884140014648438 time for calcul the mask position with numpy : 0.029412508010864258 nb_pixel_total : 14527 time to create 1 rle with old method : 0.016582727432250977 time for calcul the mask position with numpy : 0.029126644134521484 nb_pixel_total : 18938 time to create 1 rle with old method : 0.0245974063873291 time for calcul the mask position with numpy : 0.03034806251525879 nb_pixel_total : 159988 time to create 1 rle with new method : 0.13859963417053223 time for calcul the mask position with numpy : 0.029674768447875977 nb_pixel_total : 62339 time to create 1 rle with old method : 0.06798958778381348 time for calcul the mask position with numpy : 0.02908015251159668 nb_pixel_total : 41024 time to create 1 rle with old method : 0.04655122756958008 time for calcul the mask position with numpy : 0.0292356014251709 nb_pixel_total : 60786 time to create 1 rle with old method : 0.0781395435333252 time for calcul the mask position with numpy : 0.033223867416381836 nb_pixel_total : 113834 time to create 1 rle with old method : 0.12812256813049316 time for calcul the mask position with numpy : 0.04320859909057617 nb_pixel_total : 27946 time to create 1 rle with old method : 0.030563831329345703 time for calcul the mask position with numpy : 0.02916407585144043 nb_pixel_total : 14431 time to create 1 rle with old method : 0.0160980224609375 time for calcul the mask position with numpy : 0.029211759567260742 nb_pixel_total : 19827 time to create 1 rle with old method : 0.022365331649780273 time for calcul the mask position with numpy : 0.02935338020324707 nb_pixel_total : 21136 time to create 1 rle with old method : 0.0243074893951416 time for calcul the mask position with numpy : 0.0289461612701416 nb_pixel_total : 8033 time to create 1 rle with old method : 0.009265422821044922 time for calcul the mask position with numpy : 0.028886079788208008 nb_pixel_total : 30877 time to create 1 rle with old method : 0.036173343658447266 time for calcul the mask position with numpy : 0.029461145401000977 nb_pixel_total : 12373 time to create 1 rle with old method : 0.01396632194519043 time for calcul the mask position with numpy : 0.02882838249206543 nb_pixel_total : 11172 time to create 1 rle with old method : 0.013108253479003906 time for calcul the mask position with numpy : 0.032991647720336914 nb_pixel_total : 39062 time to create 1 rle with old method : 0.05788683891296387 time for calcul the mask position with numpy : 0.02878117561340332 nb_pixel_total : 45452 time to create 1 rle with old method : 0.0501556396484375 time for calcul the mask position with numpy : 0.0290372371673584 nb_pixel_total : 15114 time to create 1 rle with old method : 0.017070293426513672 time for calcul the mask position with numpy : 0.0290830135345459 nb_pixel_total : 23990 time to create 1 rle with old method : 0.028229236602783203 time for calcul the mask position with numpy : 0.029062986373901367 nb_pixel_total : 24458 time to create 1 rle with old method : 0.026735782623291016 time for calcul the mask position with numpy : 0.028780460357666016 nb_pixel_total : 11950 time to create 1 rle with old method : 0.013843536376953125 time for calcul the mask position with numpy : 0.028958559036254883 nb_pixel_total : 5936 time to create 1 rle with old method : 0.006970643997192383 time for calcul the mask position with numpy : 0.02893972396850586 nb_pixel_total : 8268 time to create 1 rle with old method : 0.00941610336303711 time for calcul the mask position with numpy : 0.02912449836730957 nb_pixel_total : 28644 time to create 1 rle with old method : 0.0319826602935791 time for calcul the mask position with numpy : 0.028738737106323242 nb_pixel_total : 20466 time to create 1 rle with old method : 0.02268362045288086 time for calcul the mask position with numpy : 0.02880382537841797 nb_pixel_total : 15844 time to create 1 rle with old method : 0.01838207244873047 time for calcul the mask position with numpy : 0.02874612808227539 nb_pixel_total : 48436 time to create 1 rle with old method : 0.05427670478820801 time for calcul the mask position with numpy : 0.02893376350402832 nb_pixel_total : 27484 time to create 1 rle with old method : 0.0307464599609375 time for calcul the mask position with numpy : 0.028693199157714844 nb_pixel_total : 13758 time to create 1 rle with old method : 0.015794038772583008 time for calcul the mask position with numpy : 0.028832435607910156 nb_pixel_total : 34531 time to create 1 rle with old method : 0.03814816474914551 time for calcul the mask position with numpy : 0.03060603141784668 nb_pixel_total : 25494 time to create 1 rle with old method : 0.028748273849487305 time for calcul the mask position with numpy : 0.02890181541442871 nb_pixel_total : 14504 time to create 1 rle with old method : 0.017407655715942383 time for calcul the mask position with numpy : 0.030222177505493164 nb_pixel_total : 102223 time to create 1 rle with old method : 0.14149689674377441 time for calcul the mask position with numpy : 0.02905726432800293 nb_pixel_total : 12770 time to create 1 rle with old method : 0.014720439910888672 time for calcul the mask position with numpy : 0.028773784637451172 nb_pixel_total : 29272 time to create 1 rle with old method : 0.03584146499633789 time for calcul the mask position with numpy : 0.0286865234375 nb_pixel_total : 9161 time to create 1 rle with old method : 0.010601520538330078 time for calcul the mask position with numpy : 0.02874898910522461 nb_pixel_total : 44153 time to create 1 rle with old method : 0.0508570671081543 time for calcul the mask position with numpy : 0.032498836517333984 nb_pixel_total : 13005 time to create 1 rle with old method : 0.016311168670654297 time for calcul the mask position with numpy : 0.028882503509521484 nb_pixel_total : 33449 time to create 1 rle with old method : 0.03702592849731445 time for calcul the mask position with numpy : 0.0290529727935791 nb_pixel_total : 33979 time to create 1 rle with old method : 0.037363529205322266 time for calcul the mask position with numpy : 0.0284576416015625 nb_pixel_total : 6816 time to create 1 rle with old method : 0.00801539421081543 time for calcul the mask position with numpy : 0.027838706970214844 nb_pixel_total : 10330 time to create 1 rle with old method : 0.010857105255126953 time for calcul the mask position with numpy : 0.027878761291503906 nb_pixel_total : 11514 time to create 1 rle with old method : 0.01327967643737793 time for calcul the mask position with numpy : 0.028881549835205078 nb_pixel_total : 12625 time to create 1 rle with old method : 0.014657020568847656 time for calcul the mask position with numpy : 0.028733491897583008 nb_pixel_total : 6906 time to create 1 rle with old method : 0.007946968078613281 time for calcul the mask position with numpy : 0.028537988662719727 nb_pixel_total : 18439 time to create 1 rle with old method : 0.019853830337524414 time for calcul the mask position with numpy : 0.028822898864746094 nb_pixel_total : 21868 time to create 1 rle with old method : 0.0239717960357666 time for calcul the mask position with numpy : 0.02849411964416504 nb_pixel_total : 4737 time to create 1 rle with old method : 0.0053789615631103516 time for calcul the mask position with numpy : 0.02824878692626953 nb_pixel_total : 25250 time to create 1 rle with old method : 0.02808547019958496 time for calcul the mask position with numpy : 0.028873443603515625 nb_pixel_total : 29989 time to create 1 rle with old method : 0.03306245803833008 time for calcul the mask position with numpy : 0.03185081481933594 nb_pixel_total : 53625 time to create 1 rle with old method : 0.05708456039428711 time for calcul the mask position with numpy : 0.026918649673461914 nb_pixel_total : 20415 time to create 1 rle with old method : 0.021526098251342773 time for calcul the mask position with numpy : 0.027603626251220703 nb_pixel_total : 30581 time to create 1 rle with old method : 0.03275775909423828 time for calcul the mask position with numpy : 0.028232574462890625 nb_pixel_total : 3373 time to create 1 rle with old method : 0.0038313865661621094 time for calcul the mask position with numpy : 0.02728748321533203 nb_pixel_total : 11446 time to create 1 rle with old method : 0.012268304824829102 time for calcul the mask position with numpy : 0.029174089431762695 nb_pixel_total : 153846 time to create 1 rle with new method : 0.1356344223022461 time for calcul the mask position with numpy : 0.028428316116333008 nb_pixel_total : 10205 time to create 1 rle with old method : 0.011440515518188477 time for calcul the mask position with numpy : 0.028281688690185547 nb_pixel_total : 8710 time to create 1 rle with old method : 0.00997471809387207 time for calcul the mask position with numpy : 0.02881312370300293 nb_pixel_total : 39578 time to create 1 rle with old method : 0.043137311935424805 time for calcul the mask position with numpy : 0.029201269149780273 nb_pixel_total : 44380 time to create 1 rle with old method : 0.04826688766479492 time for calcul the mask position with numpy : 0.02927875518798828 nb_pixel_total : 34661 time to create 1 rle with old method : 0.038318634033203125 time for calcul the mask position with numpy : 0.029035329818725586 nb_pixel_total : 14074 time to create 1 rle with old method : 0.016018390655517578 time for calcul the mask position with numpy : 0.03148937225341797 nb_pixel_total : 5145 time to create 1 rle with old method : 0.0059430599212646484 time for calcul the mask position with numpy : 0.02814769744873047 nb_pixel_total : 1930 time to create 1 rle with old method : 0.002306222915649414 time for calcul the mask position with numpy : 0.0283203125 nb_pixel_total : 14724 time to create 1 rle with old method : 0.01601576805114746 time for calcul the mask position with numpy : 0.028258323669433594 nb_pixel_total : 10447 time to create 1 rle with old method : 0.011763572692871094 time for calcul the mask position with numpy : 0.02869129180908203 nb_pixel_total : 31585 time to create 1 rle with old method : 0.03390383720397949 time for calcul the mask position with numpy : 0.028474092483520508 nb_pixel_total : 20128 time to create 1 rle with old method : 0.021724462509155273 time for calcul the mask position with numpy : 0.028139352798461914 nb_pixel_total : 6977 time to create 1 rle with old method : 0.007814407348632812 time for calcul the mask position with numpy : 0.028060436248779297 nb_pixel_total : 24292 time to create 1 rle with old method : 0.027617692947387695 create new chi : 5.461178541183472 time to delete rle : 0.0057675838470458984 batch 1 Loaded 159 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 37201 TO DO : save crop sub photo not yet done ! save time : 3.836191177368164 map_output_result : {1336619587: (0.0, 'Should be the crop_list due to order', 0.0), 1336619584: (0.0, 'Should be the crop_list due to order', 0.0), 1336619541: (0.0, 'Should be the crop_list due to order', 0.0), 1336619539: (0.0, 'Should be the crop_list due to order', 0.0), 1336537676: (0.0, 'Should be the crop_list due to order', 0.0), 1336537660: (0.0, 'Should be the crop_list due to order', 0.0), 1336537656: (0.0, 'Should be the crop_list due to order', 0.0), 1336537599: (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 [1336619587, 1336619584, 1336619541, 1336619539, 1336537676, 1336537660, 1336537656, 1336537599] Looping around the photos to save general results len do output : 8 /1336619587.Didn't retrieve data . /1336619584.Didn't retrieve data . /1336619541.Didn't retrieve data . /1336619539.Didn't retrieve data . /1336537676.Didn't retrieve data . /1336537660.Didn't retrieve data . /1336537656.Didn't retrieve data . /1336537599.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, '2574369') ('3318', '20425002', '1336619587', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336619584', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336619541', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336619539', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537676', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537660', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537656', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537599', None, None, None, None, None, '2574369') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 24 time used for this insertion : 0.015747785568237305 save_final save missing photos in datou_result : time spend for datou_step_exec : 105.82452464103699 time spend to save output : 0.01629185676574707 total time spend for step 3 : 105.84081649780273 step4:ventilate_hashtags_in_portfolio Tue Feb 11 02:28:38 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 : 20425002 get user id for portfolio 20425002 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 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('flou','autre','environnement','pet_fonce','mal_croppe','pehd','metal','carton','pet_clair','papier','background')) AND mptpi.`min_score`=0.5 To do To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20425002 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('flou','autre','environnement','pet_fonce','mal_croppe','pehd','metal','carton','pet_clair','papier','background')) 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`=20425002 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('flou','autre','environnement','pet_fonce','mal_croppe','pehd','metal','carton','pet_clair','papier','background')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/20425149,20425150,20425151,20425152,20425153,20425154,20425155,20425156,20425157,20425158,20425159?tags=flou,autre,environnement,pet_fonce,mal_croppe,pehd,metal,carton,pet_clair,papier,background Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1336619587, 1336619584, 1336619541, 1336619539, 1336537676, 1336537660, 1336537656, 1336537599] Looping around the photos to save general results len do output : 1 /20425002. 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, '2574369') ('3318', '20425002', '1336619587', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336619584', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336619541', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336619539', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537676', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537660', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537656', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537599', None, None, None, None, None, '2574369') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 9 time used for this insertion : 0.016246557235717773 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.0467123985290527 time spend to save output : 0.016663312911987305 total time spend for step 4 : 2.06337571144104 step5:final Tue Feb 11 02:28:40 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 : {1336619587: ('0.2535467764217956',), 1336619584: ('0.2535467764217956',), 1336619541: ('0.2535467764217956',), 1336619539: ('0.2535467764217956',), 1336537676: ('0.2535467764217956',), 1336537660: ('0.2535467764217956',), 1336537656: ('0.2535467764217956',), 1336537599: ('0.2535467764217956',)} new output for save of step final : {1336619587: ('0.2535467764217956',), 1336619584: ('0.2535467764217956',), 1336619541: ('0.2535467764217956',), 1336619539: ('0.2535467764217956',), 1336537676: ('0.2535467764217956',), 1336537660: ('0.2535467764217956',), 1336537656: ('0.2535467764217956',), 1336537599: ('0.2535467764217956',)} [1336619587, 1336619584, 1336619541, 1336619539, 1336537676, 1336537660, 1336537656, 1336537599] Looping around the photos to save general results len do output : 8 /1336619587.Didn't retrieve data . /1336619584.Didn't retrieve data . /1336619541.Didn't retrieve data . /1336619539.Didn't retrieve data . /1336537676.Didn't retrieve data . /1336537660.Didn't retrieve data . /1336537656.Didn't retrieve data . /1336537599.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, '2574369') ('3318', '20425002', '1336619587', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336619584', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336619541', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336619539', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537676', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537660', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537656', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537599', None, None, None, None, None, '2574369') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 24 time used for this insertion : 0.015547037124633789 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.10989928245544434 time spend to save output : 0.016016483306884766 total time spend for step 5 : 0.1259157657623291 step6:blur_detection Tue Feb 11 02:28:40 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/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60.jpg resize: (2160, 3264) 1336619587 -6.515442956549997 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c.jpg resize: (2160, 3264) 1336619584 -6.7477139240461534 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da.jpg resize: (2160, 3264) 1336619541 -6.415736676068564 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a.jpg resize: (2160, 3264) 1336619539 -6.588129656097259 treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31.jpg resize: (2160, 3264) 1336537676 -1.302297726903151 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62.jpg resize: (2160, 3264) 1336537660 -5.6044903508365405 treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e.jpg resize: (2160, 3264) 1336537656 -2.6768334386734938 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423.jpg resize: (2160, 3264) 1336537599 -6.0447965436167514 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979423_0.png resize: (155, 158) 1336664603 -3.945761823214492 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979407_0.png resize: (131, 121) 1336664604 -2.137889929311802 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979430_0.png resize: (270, 257) 1336664605 -2.777577448096842 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979473_0.png resize: (161, 206) 1336664606 -3.9773010031669624 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979463_0.png resize: (116, 160) 1336664607 -1.806291435559101 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979446_0.png resize: (151, 168) 1336664608 -1.878202207951421 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979455_0.png resize: (106, 124) 1336664609 -3.936901624962186 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979410_0.png resize: (199, 241) 1336664610 -4.538214836403525 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979477_0.png resize: (57, 108) 1336664611 -4.022196911627471 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979482_0.png resize: (102, 184) 1336664612 -4.317163296177494 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979481_0.png resize: (95, 105) 1336664613 -3.9627829457053734 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979437_0.png resize: (197, 159) 1336664614 -3.871447557514661 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979466_0.png resize: (111, 77) 1336664615 -4.268112623534042 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979425_0.png resize: (193, 160) 1336664616 -3.597688705911805 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979476_0.png resize: (144, 185) 1336664617 -2.592608276768485 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979411_0.png resize: (400, 188) 1336664618 -3.8673920355026588 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979417_0.png resize: (167, 208) 1336664619 -3.4730686464056344 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979431_0.png resize: (110, 148) 1336664620 -2.8041422951643207 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979452_0.png resize: (322, 362) 1336664621 -3.928263598842826 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979461_0.png resize: (255, 127) 1336664622 -4.837753032912037 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979438_0.png resize: (156, 187) 1336664623 -3.4840820443778107 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979427_0.png resize: (213, 213) 1336664625 -3.3964258428980356 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979451_0.png resize: (109, 121) 1336664626 -3.8981052900006388 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979432_0.png resize: (175, 166) 1336664627 -4.749412579207988 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979447_0.png resize: (156, 192) 1336664628 -4.035569377589191 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979458_0.png resize: (141, 173) 1336664629 -3.602739961335198 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979472_0.png resize: (178, 198) 1336664630 -4.704781751962397 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979415_0.png resize: (275, 334) 1336664631 -4.54434074553343 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979436_0.png resize: (103, 130) 1336664632 -3.8978145715007475 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979450_0.png resize: (116, 197) 1336664633 -4.248196328344856 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979406_0.png resize: (236, 233) 1336664634 -4.211271982665821 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979456_0.png resize: (234, 284) 1336664636 -4.386434771337222 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979422_0.png resize: (118, 221) 1336664637 -3.820729017088038 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979435_0.png resize: (253, 196) 1336664638 -2.352835125669886 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979468_0.png resize: (301, 167) 1336664639 -4.7408364639925615 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979433_0.png resize: (87, 197) 1336664640 -2.530545988693618 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979419_0.png resize: (190, 136) 1336664641 -1.3655339842352505 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979464_0.png resize: (242, 153) 1336664642 -4.0891443250801265 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979443_0.png resize: (167, 315) 1336664643 -4.4362531106397896 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979457_0.png resize: (125, 317) 1336664644 -4.006609902596425 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979420_0.png resize: (287, 293) 1336664645 -4.357463045260607 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979467_0.png resize: (133, 182) 1336664646 -4.1886575176480525 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979421_0.png resize: (262, 193) 1336664647 -4.516468774642668 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979460_0.png resize: (130, 232) 1336664648 -3.395147908079648 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979416_0.png resize: (463, 337) 1336664649 -4.078971335218114 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979440_0.png resize: (128, 230) 1336664650 -3.122029898101562 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979418_0.png resize: (180, 206) 1336664651 -4.289029657046801 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979459_0.png resize: (202, 266) 1336664652 -4.6885848838788595 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979439_0.png resize: (160, 163) 1336664653 -3.8437140449717258 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979426_0.png resize: (155, 193) 1336664654 -4.4231799248423656 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979462_0.png resize: (116, 214) 1336664655 -5.1148265846887915 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979405_0.png resize: (251, 182) 1336664656 -3.6560971171079166 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979413_0.png resize: (145, 136) 1336664657 -3.681454257589997 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979424_0.png resize: (170, 200) 1336664658 -5.17750011284189 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979444_0.png resize: (136, 134) 1336664659 -4.029650513467676 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979479_0.png resize: (229, 140) 1336664660 -4.55323552496149 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979428_0.png resize: (185, 195) 1336664661 -4.220262415448151 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979409_0.png resize: (175, 165) 1336664662 -4.812897113500562 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979429_0.png resize: (173, 182) 1336664663 -4.471688806462514 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979465_0.png resize: (104, 127) 1336664664 -2.8524185958777712 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979445_0.png resize: (137, 178) 1336664665 -3.5093649972051284 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979414_0.png resize: (206, 184) 1336664666 -3.925804324789711 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979469_0.png resize: (298, 438) 1336664667 -3.7631567151658287 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979442_0.png resize: (198, 161) 1336664668 -4.300656863129113 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979454_0.png resize: (79, 83) 1336664669 -0.33542408400077656 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979448_0.png resize: (160, 113) 1336664670 -1.6297877037156254 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979453_0.png resize: (181, 242) 1336664671 -3.1306297970428565 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979449_0.png resize: (112, 72) 1336664672 -4.146916065043163 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979412_0.png resize: (165, 120) 1336664673 -3.35940378559886 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979434_0.png resize: (245, 146) 1336664674 -3.3608830937725283 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979470_0.png resize: (216, 101) 1336664675 -3.5229175892898197 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979488_0.png resize: (151, 186) 1336664676 -2.8759159789287683 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979497_0.png resize: (252, 115) 1336664677 -3.220496798284021 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979528_0.png resize: (221, 294) 1336664678 -3.1493928390059347 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979536_0.png resize: (132, 130) 1336664679 -3.0719690405354076 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979486_0.png resize: (271, 261) 1336664680 -4.139717549425272 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979527_0.png resize: (258, 180) 1336664681 -4.271490576721109 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979508_0.png resize: (132, 186) 1336664683 -3.9004725307689547 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979517_0.png resize: (55, 77) 1336664684 -1.6178566816561417 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979533_0.png resize: (170, 122) 1336664685 -4.164880346336368 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979521_0.png resize: (166, 315) 1336664686 -4.689757198318325 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979492_0.png resize: (190, 306) 1336664687 -2.9884533648647045 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979487_0.png resize: (191, 105) 1336664689 -2.1518525753022337 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979520_0.png resize: (237, 500) 1336664690 -4.359984294063473 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979501_0.png resize: (160, 137) 1336664691 -1.8999587911562215 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979495_0.png resize: (311, 246) 1336664692 -4.622062343019906 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979548_0.png resize: (172, 331) 1336664693 -4.489530855959433 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979499_0.png resize: (362, 291) 1336664694 -4.591836003071007 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979507_0.png resize: (118, 226) 1336664695 -3.287429938590233 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979490_0.png resize: (229, 289) 1336664696 -4.635463146098278 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979531_0.png resize: (232, 225) 1336664697 -3.6072348743559477 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979496_0.png resize: (147, 186) 1336664698 -3.3130489247966026 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979537_0.png resize: (177, 146) 1336664699 -4.062927154817506 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979547_0.png resize: (126, 156) 1336664700 -3.348199863926515 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979503_0.png resize: (165, 155) 1336664701 -2.7794218039615823 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979540_0.png resize: (145, 203) 1336664702 -3.6673388585452686 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979522_0.png resize: (179, 196) 1336664703 -3.503459123429105 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979534_0.png resize: (109, 130) 1336664704 -4.517383415796472 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979526_0.png resize: (275, 173) 1336664705 -3.2067832650162384 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979546_0.png resize: (71, 78) 1336664706 -3.6359651046796126 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979544_0.png resize: (88, 69) 1336664707 -3.7719767881006447 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979529_0.png resize: (354, 378) 1336664708 -4.3418102572634565 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979515_0.png resize: (73, 64) 1336664709 0.59860395341081 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979541_0.png resize: (187, 134) 1336664710 -3.3143140089246748 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979532_0.png resize: (208, 274) 1336664711 -4.347901973081956 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979511_0.png resize: (173, 160) 1336664712 -4.304284968484992 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979494_0.png resize: (231, 225) 1336664713 -4.438560785287659 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979549_0.png resize: (129, 284) 1336664714 -4.3225462388154625 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979542_0.png resize: (215, 413) 1336664715 -4.9495806354518095 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979523_0.png resize: (154, 123) 1336664716 -3.58806868173048 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979483_0.png resize: (196, 226) 1336664717 -4.7265226919143455 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979535_0.png resize: (108, 104) 1336664718 -3.0488148660074375 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979498_0.png resize: (167, 100) 1336664719 -4.526160291699566 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979509_0.png resize: (290, 217) 1336664721 -5.247681235255928 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979500_0.png resize: (199, 182) 1336664724 -4.087935663493783 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979485_0.png resize: (199, 251) 1336664726 -2.826520791278201 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979512_0.png resize: (135, 230) 1336664727 -5.141286061736745 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979525_0.png resize: (314, 219) 1336664728 -5.163451008332327 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979504_0.png resize: (242, 272) 1336664729 -4.854315137438757 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979545_0.png resize: (101, 103) 1336664730 -2.802032540369411 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979491_0.png resize: (223, 129) 1336664731 -3.9208703412596395 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979516_0.png resize: (210, 213) 1336664732 -3.926174320367608 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979489_0.png resize: (86, 187) 1336664733 -4.24550118866467 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979513_0.png resize: (85, 129) 1336664734 -3.2870547502213734 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979550_0.png resize: (158, 153) 1336664735 -4.1900963779136 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979505_0.png resize: (213, 164) 1336664736 -3.923445477928909 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979539_0.png resize: (405, 409) 1336664737 -3.683258994402193 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979543_0.png resize: (110, 105) 1336664738 -4.009884964219589 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979510_0.png resize: (184, 118) 1336664739 -3.8644426303054265 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979484_0.png resize: (318, 201) 1336664740 -4.225393555049154 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979538_0.png resize: (101, 44) 1336664741 -1.4356199301640653 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979514_0.png resize: (134, 174) 1336664742 -3.6435948324820306 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979524_0.png resize: (186, 233) 1336664743 -4.030957869546083 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979493_0.png resize: (218, 147) 1336664744 -3.6892131454305273 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979575_0.png resize: (253, 186) 1336664745 -3.714792733523791 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979552_0.png resize: (164, 193) 1336664746 -3.7262271572049523 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979621_0.png resize: (221, 219) 1336664747 -4.080330737214357 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979551_0.png resize: (196, 195) 1336664748 -2.1193146043330127 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979581_0.png resize: (348, 213) 1336664750 -4.192658800667686 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979557_0.png resize: (281, 385) 1336664751 -2.196240515061549 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979569_0.png resize: (368, 313) 1336664752 -3.585191242292628 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979586_0.png resize: (143, 182) 1336664753 -4.274765193552789 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979597_0.png resize: (144, 214) 1336664754 -4.137888488775368 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979612_0.png resize: (243, 455) 1336664755 -2.826345454720075 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979564_0.png resize: (82, 222) 1336664756 -2.6431601611294617 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979593_0.png resize: (286, 281) 1336664757 -4.297646422725369 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979577_0.png resize: (102, 108) 1336664758 -2.5112639365307627 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979563_0.png resize: (93, 158) 1336664759 -2.5546414991890543 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979613_0.png resize: (188, 380) 1336664760 -4.083811211398355 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979561_0.png resize: (201, 224) 1336664761 -1.5559186108515484 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979584_0.png resize: (137, 163) 1336664762 -2.39832260161372 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979623_0.png resize: (102, 190) 1336664763 -4.090481556993707 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979606_0.png resize: (119, 169) 1336664764 -4.726289250539832 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979604_0.png resize: (118, 88) 1336664766 -4.453901539982092 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979592_0.png resize: (202, 168) 1336664767 -3.925102937411462 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979594_0.png resize: (201, 105) 1336664769 -3.65262193995208 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979599_0.png resize: (211, 145) 1336664770 -3.806422229171788 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979579_0.png resize: (151, 200) 1336664771 -4.569414957433531 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979587_0.png resize: (164, 167) 1336664772 -3.0619516389084733 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979559_0.png resize: (115, 196) 1336664773 -3.8471632527667845 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979610_0.png resize: (181, 83) 1336664774 -4.17352511890477 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979590_0.png resize: (172, 139) 1336664775 -4.326266750757165 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979617_0.png resize: (326, 324) 1336664776 -2.6664127157268354 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979553_0.png resize: (278, 204) 1336664777 -2.776268186236816 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979568_0.png resize: (146, 138) 1336664778 -1.9547131622155727 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979602_0.png resize: (245, 133) 1336664779 -4.177624229308362 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979558_0.png resize: (169, 276) 1336664780 -3.344946657140326 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979596_0.png resize: (174, 226) 1336664781 -2.744274044071117 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979614_0.png resize: (170, 215) 1336664782 -3.7930994858198654 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979595_0.png resize: (141, 79) 1336664783 -2.8924929001403363 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979582_0.png resize: (162, 152) 1336664784 -2.2127039334011984 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979611_0.png resize: (157, 127) 1336664785 -3.966561307724621 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979556_0.png resize: (323, 282) 1336664786 -4.255061456038086 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979573_0.png resize: (129, 78) 1336664787 -2.469388187920285 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979616_0.png resize: (224, 124) 1336664788 -3.7722324492190813 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979583_0.png resize: (170, 155) 1336664789 -2.9436582289543285 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979562_0.png resize: (272, 264) 1336664790 -4.471754813110908 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979603_0.png resize: (241, 120) 1336664791 -4.599624372585923 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979555_0.png resize: (118, 142) 1336664792 -3.8597159872665596 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979567_0.png resize: (241, 213) 1336664793 -4.147249739607493 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979585_0.png resize: (72, 85) 1336664794 -2.9999463273811684 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979560_0.png resize: (184, 209) 1336664795 -3.908747207990723 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979608_0.png resize: (172, 230) 1336664796 -4.419120958801491 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979601_0.png resize: (146, 101) 1336664797 -4.085454621607963 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979589_0.png resize: (142, 186) 1336664798 -4.479244898354098 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979607_0.png resize: (233, 287) 1336664799 -3.9247434087005653 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979565_0.png resize: (414, 376) 1336664800 -5.086826221235348 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979574_0.png resize: (198, 153) 1336664801 -4.600554092278226 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979554_0.png resize: (232, 104) 1336664802 -2.700463434010131 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979588_0.png resize: (133, 125) 1336664803 -4.843312354602727 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979571_0.png resize: (244, 323) 1336664804 -3.9378943259336903 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979605_0.png resize: (123, 151) 1336664805 -2.444721521906962 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979620_0.png resize: (142, 116) 1336664806 -2.929963925911101 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979609_0.png resize: (177, 128) 1336664807 -3.09522672534474 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979615_0.png resize: (229, 151) 1336664808 -4.052331770936199 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979598_0.png resize: (168, 182) 1336664809 -2.732004811629404 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979576_0.png resize: (120, 101) 1336664810 -3.396789314637701 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979570_0.png resize: (131, 106) 1336664811 -4.212550659000347 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979624_0.png resize: (102, 93) 1336664812 -2.4949097916496186 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979628_0.png resize: (141, 99) 1336664813 -2.5656804520662773 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979647_0.png resize: (155, 335) 1336664814 -3.1935238465550833 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979631_0.png resize: (390, 430) 1336664815 -4.078339710411479 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979646_0.png resize: (126, 247) 1336664816 -3.5882252350944874 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979642_0.png resize: (169, 174) 1336664817 -3.6015376009933613 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979633_0.png resize: (93, 107) 1336664818 -3.155561011686678 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979672_0.png resize: (180, 141) 1336664819 -2.944984824727231 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979630_0.png resize: (75, 105) 1336664820 -2.7557388424345794 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979636_0.png resize: (129, 67) 1336664821 -2.3209109284490506 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979676_0.png resize: (136, 125) 1336664823 -4.165612947709628 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979650_0.png resize: (140, 116) 1336664824 -3.0043841850884365 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979682_0.png resize: (237, 232) 1336664825 -5.118920376346451 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979662_0.png resize: (127, 155) 1336664826 -3.667433794278044 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979670_0.png resize: (50, 63) 1336664827 7.370045312869422 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979637_0.png resize: (175, 204) 1336664828 -2.5536992957797846 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979651_0.png resize: (223, 253) 1336664829 -4.771951260191426 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979627_0.png resize: (312, 225) 1336664830 -3.252025409932425 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979673_0.png resize: (103, 249) 1336664831 -2.9726585605427003 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979643_0.png resize: (188, 119) 1336664832 -3.704404068949881 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979652_0.png resize: (105, 112) 1336664833 -3.7331406182710225 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979653_0.png resize: (145, 184) 1336664834 -5.120278431770884 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979660_0.png resize: (138, 143) 1336664835 -4.574283149725788 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979654_0.png resize: (246, 420) 1336664836 -4.815467199784455 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979644_0.png resize: (157, 103) 1336664837 -4.2059690039483515 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979681_0.png resize: (154, 142) 1336664838 -3.7542828528693426 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979679_0.png resize: (152, 150) 1336664839 -2.6741473509151814 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979638_0.png resize: (124, 116) 1336664840 -3.3245607975746654 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979671_0.png resize: (180, 191) 1336664841 -4.536860876775745 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979675_0.png resize: (198, 229) 1336664842 -4.84932464545932 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979649_0.png resize: (98, 102) 1336664843 -3.901290506481767 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979640_0.png resize: (300, 459) 1336664844 -4.68068633889228 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979632_0.png resize: (570, 655) 1336664845 -3.7441583633535416 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979639_0.png resize: (277, 321) 1336664847 -4.225609480091382 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979661_0.png resize: (361, 509) 1336664848 -4.109636926232826 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979677_0.png resize: (185, 85) 1336664849 -3.2765086311971507 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979634_0.png resize: (193, 206) 1336664850 -3.881718733839587 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979663_0.png resize: (253, 302) 1336664851 -3.8748204115380425 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979657_0.png resize: (177, 110) 1336664852 -5.03448914626087 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979648_0.png resize: (348, 316) 1336664853 -5.1802388540010345 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979668_0.png resize: (140, 197) 1336664854 -4.996433203266372 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979659_0.png resize: (49, 76) 1336664855 -2.8013828075312293 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979667_0.png resize: (167, 147) 1336664856 -4.124256443774573 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979674_0.png resize: (264, 101) 1336664857 -1.5719453176714715 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979680_0.png resize: (277, 114) 1336664858 -2.7568761410045703 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979656_0.png resize: (478, 232) 1336664859 -3.5681170852924122 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979655_0.png resize: (187, 156) 1336664860 -3.182354425536776 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979641_0.png resize: (210, 199) 1336664862 -3.2989940550552483 treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979691_0.png resize: (491, 378) 1336664865 -1.659363645180691 treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979698_0.png resize: (245, 225) 1336664868 -2.37460619699927 treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979687_0.png resize: (401, 408) 1336664872 -1.0262174747350177 treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979686_0.png resize: (127, 227) 1336664873 -1.012926151011902 treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979688_0.png resize: (217, 131) 1336664874 -1.7248970065938476 treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979684_0.png resize: (141, 136) 1336664875 0.2423504454692441 treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979695_0.png resize: (284, 209) 1336664876 -2.706505833634561 treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979696_0.png resize: (141, 114) 1336664877 -2.0174197714566717 treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979692_0.png resize: (244, 146) 1336664878 -2.3650700836267027 treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979685_0.png resize: (487, 438) 1336664879 0.16948311215617964 treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979689_0.png resize: (324, 89) 1336664880 -2.766866103496384 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979756_0.png resize: (157, 29) 1336664881 -2.865904448880871 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979716_0.png resize: (332, 232) 1336664882 -2.5987997356366064 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979712_0.png resize: (309, 81) 1336664883 -1.5204795194570135 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979717_0.png resize: (258, 413) 1336664884 -3.747409782033186 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979761_0.png resize: (119, 132) 1336664885 -3.5047702237891016 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979733_0.png resize: (902, 1139) 1336664886 -4.184002325759538 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979735_0.png resize: (303, 242) 1336664887 -2.0934300530427086 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979723_0.png resize: (125, 103) 1336664888 -2.4394768949051264 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979750_0.png resize: (310, 223) 1336664889 -3.482252838182569 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979746_0.png resize: (241, 205) 1336664890 -2.8475861568912557 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979707_0.png resize: (124, 231) 1336664891 -3.3603180699253636 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979725_0.png resize: (274, 305) 1336664892 -3.6500221930638936 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979706_0.png resize: (219, 133) 1336664893 -4.532075205762644 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979737_0.png resize: (176, 145) 1336664894 -4.191687821343469 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979727_0.png resize: (91, 47) 1336664895 -3.7903933969549177 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979751_0.png resize: (164, 315) 1336664896 -2.3259440940553224 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979738_0.png resize: (282, 232) 1336664897 -3.66065822173909 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979732_0.png resize: (261, 223) 1336664898 -3.8726467947447762 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979704_0.png resize: (59, 84) 1336664899 -5.691021645281818 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979703_0.png resize: (249, 227) 1336664900 -3.7169018430486265 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979720_0.png resize: (202, 170) 1336664901 -3.583388906004476 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979755_0.png resize: (69, 48) 1336664903 -4.660618115948218 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979749_0.png resize: (124, 104) 1336664904 -1.8443129304112262 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979752_0.png resize: (49, 43) 1336664905 -1.423034980972614 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979713_0.png resize: (164, 74) 1336664906 -3.2282381540007856 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979719_0.png resize: (206, 174) 1336664907 -4.470215031249028 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979726_0.png resize: (283, 199) 1336664908 -4.5379898929567535 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979708_0.png resize: (153, 159) 1336664909 -4.766891654595724 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979715_0.png resize: (81, 121) 1336664910 -3.196165576045574 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979736_0.png resize: (107, 91) 1336664911 -4.325673346151363 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979748_0.png resize: (302, 201) 1336664912 -3.8912974511309306 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979700_0.png resize: (175, 167) 1336664913 -3.877768781219971 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979741_0.png resize: (119, 73) 1336664914 -3.445951930907976 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979747_0.png resize: (117, 149) 1336664915 -3.026305433459224 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979714_0.png resize: (144, 152) 1336664916 -4.25711409282548 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979754_0.png resize: (199, 179) 1336664917 -3.2144194623870077 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979730_0.png resize: (138, 51) 1336664918 -0.9573040699440282 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979740_0.png resize: (75, 54) 1336664919 -3.9898243009645786 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979724_0.png resize: (176, 133) 1336664920 -3.2360678798050766 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979702_0.png resize: (128, 108) 1336664921 -3.410860001858484 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979731_0.png resize: (138, 136) 1336664922 -4.16389143625233 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979729_0.png resize: (155, 115) 1336664923 -3.982802675227668 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979757_0.png resize: (155, 168) 1336664924 -3.475916866372811 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979710_0.png resize: (116, 85) 1336664925 -3.0363432577143974 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979758_0.png resize: (131, 140) 1336664926 -3.486534571629295 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979728_0.png resize: (214, 212) 1336664927 -4.773898201433598 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979722_0.png resize: (246, 166) 1336664928 -4.359582058534947 treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979774_0.png resize: (221, 179) 1336664929 -1.6937732787248685 treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979765_0.png resize: (344, 353) 1336664930 -4.272008421562232 treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979767_0.png resize: (576, 589) 1336664931 -2.32190144043902 treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979779_0.png resize: (185, 157) 1336664932 -1.1789258191575058 treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979762_0.png resize: (149, 108) 1336664933 -2.401903480738317 treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979771_0.png resize: (737, 1011) 1336664934 -0.7009266431975951 treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979764_0.png resize: (304, 286) 1336664935 -2.5548691876795995 treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979770_0.png resize: (169, 295) 1336664936 -3.957121763730932 treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979780_0.png resize: (188, 161) 1336664937 -3.880713824122097 treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979769_0.png resize: (152, 150) 1336664938 -3.056425005465492 treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979772_0.png resize: (506, 431) 1336664939 -1.6844178215290075 treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979777_0.png resize: (261, 182) 1336664940 -2.812442412876689 treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979768_0.png resize: (149, 91) 1336664941 -2.2789186948829134 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979854_0.png resize: (322, 240) 1336664942 -2.713640943802256 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979785_0.png resize: (359, 280) 1336664943 -3.233877415116163 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979803_0.png resize: (437, 195) 1336664944 -3.8084552218053744 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979808_0.png resize: (172, 116) 1336664945 -3.7295699717681616 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979825_0.png resize: (287, 142) 1336664946 -3.6074016941535176 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979836_0.png resize: (141, 116) 1336664947 -3.267537733849547 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979810_0.png resize: (106, 177) 1336664948 -4.332291230157562 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979824_0.png resize: (212, 147) 1336664949 -2.928921263493187 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979812_0.png resize: (203, 217) 1336664950 -3.6825216909610425 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979844_0.png resize: (242, 264) 1336664951 -4.005542183495344 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979805_0.png resize: (173, 122) 1336664952 -1.6999071977802362 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979781_0.png resize: (242, 335) 1336664953 -1.5345113779122694 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979787_0.png resize: (288, 121) 1336664954 -3.1634319430103903 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979789_0.png resize: (125, 187) 1336664955 -4.0821174777200895 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979832_0.png resize: (172, 147) 1336664956 -2.584875839461531 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979800_0.png resize: (110, 216) 1336664957 -3.8584525942714825 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979816_0.png resize: (215, 191) 1336664958 -4.0973504996282895 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979788_0.png resize: (131, 239) 1336664959 -3.6897049853406636 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979806_0.png resize: (234, 334) 1336664961 -4.798791635778472 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979829_0.png resize: (536, 438) 1336664962 -3.904781544966909 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979804_0.png resize: (340, 107) 1336664963 -2.2703287741559213 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979792_0.png resize: (121, 264) 1336664964 -4.060316578745478 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979828_0.png resize: (298, 202) 1336664965 -3.7032213498086652 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979791_0.png resize: (170, 171) 1336664966 -3.5727031868714034 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979858_0.png resize: (51, 118) 1336664967 -2.1636566385876304 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979798_0.png resize: (205, 106) 1336664968 -3.7091613159897423 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979823_0.png resize: (116, 87) 1336664969 -1.4430140974659988 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979782_0.png resize: (118, 171) 1336664970 -3.484289640282832 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979786_0.png resize: (343, 289) 1336664971 -3.246431869409299 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979822_0.png resize: (239, 234) 1336664972 -3.086662969825879 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979796_0.png resize: (196, 299) 1336664973 -4.834614253125399 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979856_0.png resize: (116, 80) 1336664974 -2.1298827372487694 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979820_0.png resize: (289, 248) 1336664975 -4.786962007304288 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979807_0.png resize: (131, 111) 1336664976 -2.35498851332381 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979851_0.png resize: (55, 149) 1336664977 -3.5536267506507757 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979799_0.png resize: (83, 160) 1336664978 -2.0521019774397486 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979857_0.png resize: (119, 142) 1336664980 -4.4967336534122 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979838_0.png resize: (172, 144) 1336664981 -3.6680337600293105 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979783_0.png resize: (194, 251) 1336664982 -4.134816903449052 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979797_0.png resize: (283, 193) 1336664983 -4.205121602776481 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979845_0.png resize: (221, 180) 1336664984 -3.281615008389074 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979814_0.png resize: (174, 256) 1336664985 -4.458503520351257 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979852_0.png resize: (120, 123) 1336664986 -4.358461736717885 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979830_0.png resize: (95, 112) 1336664987 -3.4223400266244766 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979839_0.png resize: (144, 122) 1336664988 -4.30106599898607 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979840_0.png resize: (219, 296) 1336664989 -3.970422761265449 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979790_0.png resize: (162, 105) 1336664990 -2.490376899249234 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979859_0.png resize: (236, 154) 1336664991 -3.722642407693161 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979846_0.png resize: (363, 286) 1336664992 -4.196519868934618 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979826_0.png resize: (89, 109) 1336664993 -3.554668058656094 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979849_0.png resize: (188, 134) 1336664994 -3.9000639644798802 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979818_0.png resize: (226, 155) 1336664995 -4.062169033796519 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979855_0.png resize: (113, 118) 1336664996 -3.8117700382686577 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979801_0.png resize: (151, 130) 1336664997 -4.490602691660343 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979813_0.png resize: (146, 147) 1336664998 -2.6640495016456045 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979833_0.png resize: (199, 207) 1336664999 -3.223976010874565 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979853_0.png resize: (207, 295) 1336665000 -4.051799168509245 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979817_0.png resize: (216, 117) 1336665001 -4.22527063768542 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979793_0.png resize: (76, 67) 1336665002 -4.040006662984578 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979821_0.png resize: (120, 88) 1336665003 -3.055084598572442 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979850_0.png resize: (198, 164) 1336665004 -3.336522316201634 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979480_0.png resize: (107, 192) 1336665024 -5.125588849950979 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979475_0.png resize: (250, 391) 1336665025 -4.163952576499958 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979474_0.png resize: (180, 270) 1336665026 -3.209587751632541 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979408_0.png resize: (165, 288) 1336665027 -2.8023792652329513 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979506_0.png resize: (147, 285) 1336665028 -2.307358359634959 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979502_0.png resize: (173, 155) 1336665029 -2.830847395288915 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979518_0.png resize: (63, 102) 1336665030 -3.032977596080507 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979618_0.png resize: (72, 191) 1336665031 -0.9725223688445888 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979600_0.png resize: (166, 282) 1336665032 -4.6653778384887215 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979566_0.png resize: (209, 149) 1336665033 -1.847874472475288 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979578_0.png resize: (98, 109) 1336665034 -3.451241899594389 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979622_0.png resize: (257, 292) 1336665035 -1.9538315094182632 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979619_0.png resize: (141, 120) 1336665036 -2.776197947401151 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979580_0.png resize: (106, 196) 1336665037 -4.11290954109405 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979625_0.png resize: (151, 177) 1336665039 -3.356263442351972 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979664_0.png resize: (185, 314) 1336665040 -3.8449814733438012 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979678_0.png resize: (158, 104) 1336665041 -3.2633998382223925 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979658_0.png resize: (194, 217) 1336665042 -3.8471656732097252 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979635_0.png resize: (402, 513) 1336665043 -4.040594480858258 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979629_0.png resize: (168, 379) 1336665044 -3.898863320626708 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979666_0.png resize: (80, 227) 1336665045 -3.744008203051182 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979683_0.png resize: (168, 192) 1336665046 -4.061256377439454 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979665_0.png resize: (278, 280) 1336665047 -4.089086934414434 treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979697_0.png resize: (247, 325) 1336665048 -1.1010280944289996 treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979694_0.png resize: (115, 144) 1336665049 -3.036588230730723 treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979699_0.png resize: (143, 113) 1336665050 -1.5267256062959889 treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979690_0.png resize: (396, 363) 1336665051 -2.4956014662235755 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979718_0.png resize: (822, 647) 1336665052 -2.533658104023233 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979709_0.png resize: (294, 334) 1336665053 -1.2429243565142234 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979759_0.png resize: (537, 393) 1336665054 -3.1385867451312435 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979744_0.png resize: (168, 315) 1336665055 -2.8195364895193435 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979760_0.png resize: (216, 214) 1336665056 -3.509364380072591 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979711_0.png resize: (90, 94) 1336665057 -4.7115467125422565 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979745_0.png resize: (209, 144) 1336665058 -4.554652077374079 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979742_0.png resize: (138, 78) 1336665059 -3.382713411563956 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979743_0.png resize: (364, 194) 1336665060 -2.9736625106984933 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979721_0.png resize: (111, 150) 1336665061 -3.7450835244418026 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979705_0.png resize: (254, 240) 1336665062 -5.141788833782008 treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979773_0.png resize: (617, 592) 1336665063 -2.386135379117019 treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979766_0.png resize: (150, 216) 1336665064 -2.0221869641955634 treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979763_0.png resize: (893, 652) 1336665065 -2.1986310693229614 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979831_0.png resize: (106, 178) 1336665066 -2.4702609158444773 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979835_0.png resize: (165, 124) 1336665067 -2.8149684647078597 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979819_0.png resize: (180, 247) 1336665068 -2.8856013052725733 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979848_0.png resize: (204, 345) 1336665069 -4.792155641546994 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979843_0.png resize: (411, 670) 1336665070 -2.4214752067057623 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979815_0.png resize: (82, 115) 1336665071 -3.208836485196165 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979794_0.png resize: (130, 168) 1336665072 -2.7865890576735826 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979802_0.png resize: (166, 308) 1336665073 -3.9154925934250246 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979827_0.png resize: (256, 332) 1336665074 -4.035705279781289 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979811_0.png resize: (487, 573) 1336665075 -3.150391552396509 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979841_0.png resize: (80, 147) 1336665076 -4.143695258843192 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979809_0.png resize: (243, 230) 1336665077 -1.6838550811629054 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979837_0.png resize: (206, 135) 1336665078 -1.3204617880860896 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979834_0.png resize: (215, 228) 1336665079 -4.281595806072158 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979784_0.png resize: (402, 315) 1336665080 -3.7467392658379812 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979842_0.png resize: (193, 252) 1336665083 -4.532830279532456 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979471_0.png resize: (293, 901) 1336665098 -1.9506344159134223 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979519_0.png resize: (296, 361) 1336665099 -3.3730151056544746 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979645_0.png resize: (360, 1019) 1336665100 -2.987150843745611 treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979669_0.png resize: (305, 318) 1336665101 -3.2975557359462573 treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979693_0.png resize: (513, 273) 1336665102 -1.083320057214401 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979734_0.png resize: (197, 274) 1336665103 -2.133727527836908 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979753_0.png resize: (161, 369) 1336665104 -3.2753307973993837 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979701_0.png resize: (404, 1230) 1336665105 -1.9023989670850907 treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979739_0.png resize: (355, 425) 1336665106 -4.106333367535379 treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979776_0.png resize: (503, 363) 1336665107 -4.359523004689209 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979847_0.png resize: (204, 316) 1336665108 -4.311531270690434 treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979795_0.png resize: (199, 143) 1336665109 -4.10257736505211 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979478_0.png resize: (127, 124) 1336665112 -2.6171552902802984 treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979441_0.png resize: (144, 156) 1336665113 -4.572411747267834 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979572_0.png resize: (108, 176) 1336665114 2.0734251914809776 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979626_0.png resize: (122, 174) 1336665115 -4.393906993075934 treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979775_0.png resize: (130, 146) 1336665116 -0.2683348358441866 treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979530_0.png resize: (110, 122) 1336665117 -2.210364596250116 treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979591_0.png resize: (243, 145) 1336665120 -4.669141536609412 treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979778_0.png resize: (164, 158) 1336665121 -3.149424881884989 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 : 463 time used for this insertion : 0.03798961639404297 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 463 time used for this insertion : 0.11379170417785645 save missing photos in datou_result : time spend for datou_step_exec : 31.148980379104614 time spend to save output : 0.15929079055786133 total time spend for step 6 : 31.308271169662476 step7:brightness Tue Feb 11 02:29:12 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 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/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60.jpg treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c.jpg treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da.jpg treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a.jpg treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31.jpg treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62.jpg treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e.jpg treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423.jpg treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979423_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979407_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979430_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979473_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979463_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979446_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979455_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979410_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979477_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979482_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979481_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979437_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979466_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979425_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979476_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979411_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979417_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979431_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979452_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979461_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979438_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979427_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979451_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979432_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979447_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979458_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979472_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979415_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979436_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979450_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979406_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979456_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979422_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979435_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979468_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979433_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979419_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979464_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979443_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979457_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979420_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979467_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979421_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979460_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979416_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979440_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979418_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979459_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979439_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979426_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979462_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979405_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979413_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979424_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979444_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979479_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979428_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979409_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979429_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979465_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979445_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979414_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979469_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979442_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979454_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979448_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979453_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979449_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979412_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979434_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979470_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979488_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979497_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979528_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979536_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979486_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979527_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979508_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979517_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979533_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979521_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979492_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979487_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979520_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979501_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979495_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979548_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979499_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979507_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979490_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979531_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979496_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979537_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979547_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979503_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979540_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979522_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979534_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979526_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979546_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979544_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979529_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979515_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979541_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979532_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979511_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979494_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979549_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979542_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979523_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979483_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979535_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979498_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979509_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979500_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979485_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979512_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979525_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979504_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979545_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979491_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979516_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979489_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979513_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979550_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979505_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979539_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979543_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979510_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979484_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979538_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979514_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979524_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979493_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979575_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979552_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979621_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979551_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979581_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979557_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979569_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979586_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979597_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979612_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979564_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979593_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979577_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979563_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979613_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979561_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979584_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979623_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979606_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979604_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979592_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979594_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979599_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979579_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979587_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979559_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979610_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979590_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979617_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979553_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979568_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979602_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979558_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979596_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979614_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979595_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979582_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979611_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979556_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979573_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979616_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979583_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979562_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979603_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979555_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979567_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979585_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979560_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979608_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979601_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979589_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979607_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979565_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979574_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979554_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979588_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979571_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979605_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979620_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979609_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979615_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979598_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979576_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979570_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979624_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979628_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979647_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979631_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979646_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979642_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979633_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979672_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979630_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979636_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979676_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979650_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979682_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979662_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979670_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979637_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979651_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979627_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979673_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979643_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979652_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979653_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979660_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979654_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979644_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979681_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979679_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979638_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979671_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979675_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979649_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979640_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979632_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979639_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979661_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979677_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979634_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979663_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979657_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979648_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979668_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979659_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979667_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979674_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979680_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979656_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979655_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979641_0.png treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979691_0.png treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979698_0.png treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979687_0.png treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979686_0.png treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979688_0.png treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979684_0.png treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979695_0.png treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979696_0.png treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979692_0.png treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979685_0.png treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979689_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979756_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979716_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979712_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979717_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979761_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979733_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979735_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979723_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979750_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979746_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979707_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979725_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979706_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979737_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979727_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979751_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979738_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979732_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979704_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979703_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979720_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979755_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979749_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979752_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979713_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979719_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979726_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979708_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979715_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979736_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979748_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979700_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979741_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979747_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979714_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979754_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979730_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979740_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979724_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979702_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979731_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979729_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979757_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979710_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979758_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979728_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979722_0.png treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979774_0.png treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979765_0.png treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979767_0.png treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979779_0.png treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979762_0.png treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979771_0.png treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979764_0.png treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979770_0.png treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979780_0.png treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979769_0.png treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979772_0.png treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979777_0.png treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979768_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979854_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979785_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979803_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979808_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979825_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979836_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979810_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979824_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979812_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979844_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979805_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979781_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979787_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979789_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979832_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979800_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979816_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979788_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979806_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979829_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979804_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979792_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979828_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979791_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979858_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979798_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979823_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979782_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979786_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979822_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979796_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979856_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979820_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979807_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979851_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979799_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979857_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979838_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979783_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979797_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979845_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979814_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979852_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979830_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979839_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979840_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979790_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979859_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979846_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979826_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979849_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979818_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979855_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979801_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979813_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979833_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979853_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979817_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979793_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979821_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979850_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979480_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979475_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979474_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979408_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979506_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979502_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979518_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979618_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979600_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979566_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979578_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979622_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979619_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979580_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979625_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979664_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979678_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979658_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979635_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979629_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979666_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979683_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979665_0.png treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979697_0.png treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979694_0.png treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979699_0.png treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979690_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979718_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979709_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979759_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979744_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979760_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979711_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979745_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979742_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979743_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979721_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979705_0.png treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979773_0.png treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979766_0.png treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979763_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979831_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979835_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979819_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979848_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979843_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979815_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979794_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979802_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979827_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979811_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979841_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979809_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979837_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979834_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979784_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979842_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979471_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979519_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979645_0.png treat image : temp/1739236829_729358_1336619539_846f5ebfc7845e8b60d2f5f5e3c2d69a_rle_crop_3668979669_0.png treat image : temp/1739236829_729358_1336537676_44c5e259ccb779c2e54d425b9013fd31_rle_crop_3668979693_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979734_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979753_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979701_0.png treat image : temp/1739236829_729358_1336537660_bf001890a4501a1ec58af224005c8b62_rle_crop_3668979739_0.png treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979776_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979847_0.png treat image : temp/1739236829_729358_1336537599_40c1c4f96e232f9c0d3b771075b3d423_rle_crop_3668979795_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979478_0.png treat image : temp/1739236829_729358_1336619587_82df78a48cc0ea935d59bc3ee4072a60_rle_crop_3668979441_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979572_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979626_0.png treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979775_0.png treat image : temp/1739236829_729358_1336619584_7c61b4aedf0af902e1fa2694f658dd3c_rle_crop_3668979530_0.png treat image : temp/1739236829_729358_1336619541_967256f28be7855791716203ecf502da_rle_crop_3668979591_0.png treat image : temp/1739236829_729358_1336537656_60419b5b89f9d8478aef30a18e270c0e_rle_crop_3668979778_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 : 463 time used for this insertion : 0.04065442085266113 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 463 time used for this insertion : 0.32955169677734375 save missing photos in datou_result : time spend for datou_step_exec : 9.414159297943115 time spend to save output : 0.3774433135986328 total time spend for step 7 : 9.791602611541748 step8:velours_tree Tue Feb 11 02:29:21 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 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 : 0.18621015548706055 time spend to save output : 4.1961669921875e-05 total time spend for step 8 : 0.18625211715698242 step9:send_mail_cod Tue Feb 11 02:29:22 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 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_P20425002_11-02-2025_02_29_22.pdf 20425149 imagette204251491739237362 20425150 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette204251501739237362 20425152 change filename to text .change filename to text .imagette204251521739237362 20425153 imagette204251531739237362 20425154 change filename to text .imagette204251541739237362 20425155 change filename to text .imagette204251551739237362 20425156 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 .imagette204251561739237362 20425157 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 .imagette204251571739237364 20425158 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 .imagette204251581739237364 20425159 imagette204251591739237366 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=20425002 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/20425149,20425150,20425151,20425152,20425153,20425154,20425155,20425156,20425157,20425158,20425159?tags=flou,autre,environnement,pet_fonce,mal_croppe,pehd,metal,carton,pet_clair,papier,background args[1336619587] : ((1336619587, -6.515442956549997, 492609224), (1336619587, -0.08323118457913709, 496442774), '0.2535467764217956') apple ((1336619587, -6.515442956549997, 492609224), (1336619587, -0.08323118457913709, 496442774), '0.2535467764217956') We are sending mail with results at report@fotonower.com args[1336619584] : ((1336619584, -6.7477139240461534, 492609224), (1336619584, -0.11880799200492184, 496442774), '0.2535467764217956') apple ((1336619584, -6.7477139240461534, 492609224), (1336619584, -0.11880799200492184, 496442774), '0.2535467764217956') We are sending mail with results at report@fotonower.com args[1336619541] : ((1336619541, -6.415736676068564, 492609224), (1336619541, -0.07045552212304548, 496442774), '0.2535467764217956') apple ((1336619541, -6.415736676068564, 492609224), (1336619541, -0.07045552212304548, 496442774), '0.2535467764217956') We are sending mail with results at report@fotonower.com args[1336619539] : ((1336619539, -6.588129656097259, 492609224), (1336619539, -0.21413388779610124, 496442774), '0.2535467764217956') apple ((1336619539, -6.588129656097259, 492609224), (1336619539, -0.21413388779610124, 496442774), '0.2535467764217956') We are sending mail with results at report@fotonower.com args[1336537676] : ((1336537676, -1.302297726903151, 492688767), (1336537676, -0.0935303350877977, 496442774), '0.2535467764217956') apple ((1336537676, -1.302297726903151, 492688767), (1336537676, -0.0935303350877977, 496442774), '0.2535467764217956') We are sending mail with results at report@fotonower.com args[1336537660] : ((1336537660, -5.6044903508365405, 492609224), (1336537660, -0.23137987395968426, 496442774), '0.2535467764217956') apple ((1336537660, -5.6044903508365405, 492609224), (1336537660, -0.23137987395968426, 496442774), '0.2535467764217956') We are sending mail with results at report@fotonower.com args[1336537656] : ((1336537656, -2.6768334386734938, 492609224), (1336537656, -0.30338011220354205, 496442774), '0.2535467764217956') apple ((1336537656, -2.6768334386734938, 492609224), (1336537656, -0.30338011220354205, 496442774), '0.2535467764217956') We are sending mail with results at report@fotonower.com args[1336537599] : ((1336537599, -6.0447965436167514, 492609224), (1336537599, -0.04592278120260091, 2107752395), '0.2535467764217956') apple ((1336537599, -6.0447965436167514, 492609224), (1336537599, -0.04592278120260091, 2107752395), '0.2535467764217956') We are sending mail with results at report@fotonower.com refus_total : 0.2535467764217956 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=20425002 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1336619587,1336537599,1336537656,1336537660,1336537676,1336619539,1336619541,1336619584) Found this number of photos: 8 begin to download photo : 1336619587 begin to download photo : 1336537656 begin to download photo : 1336537676 begin to download photo : 1336619541 download finish for photo 1336537676 begin to download photo : 1336619539 download finish for photo 1336537656 begin to download photo : 1336537660 download finish for photo 1336619587 begin to download photo : 1336537599 download finish for photo 1336619541 begin to download photo : 1336619584 download finish for photo 1336537660 download finish for photo 1336619539 download finish for photo 1336537599 download finish for photo 1336619584 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20425002_11-02-2025_02_29_22.pdf results_Auto_P20425002_11-02-2025_02_29_22.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20425002_11-02-2025_02_29_22.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','20425002','results_Auto_P20425002_11-02-2025_02_29_22.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20425002_11-02-2025_02_29_22.pdf','pdf','','0.72','0.2535467764217956') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/20425002

https://www.fotonower.com/image?json=false&list_photos_id=1336619587
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1336619584
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1336619541
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1336619539
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1336537676
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1336537660
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1336537656
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1336537599
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 25.35%
Veuillez trouver les photos des contaminants.

exemples de contaminants: autre: https://www.fotonower.com/view/20425150?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/20425152?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/20425154?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/20425155?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/20425156?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/20425157?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/20425158?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20425002_11-02-2025_02_29_22.pdf.

Lien vers velours :https://www.fotonower.com/velours/20425149,20425150,20425151,20425152,20425153,20425154,20425155,20425156,20425157,20425158,20425159?tags=flou,autre,environnement,pet_fonce,mal_croppe,pehd,metal,carton,pet_clair,papier,background.


L'équipe Fotonower 202 b'' Server: nginx Date: Tue, 11 Feb 2025 01:29:30 GMT Content-Length: 0 Connection: close X-Message-Id: f7pV_9lgRMGNY2LeE_jtYA 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 [1336619587, 1336619584, 1336619541, 1336619539, 1336537676, 1336537660, 1336537656, 1336537599] 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, '2574369') ('3318', '20425002', '1336619587', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336619584', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336619541', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336619539', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537676', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537660', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537656', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537599', None, None, None, None, None, '2574369') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 8 time used for this insertion : 0.018661022186279297 save_final save missing photos in datou_result : time spend for datou_step_exec : 8.162693977355957 time spend to save output : 0.01885676383972168 total time spend for step 9 : 8.181550741195679 step10:split_time_score Tue Feb 11 02:29:30 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 Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure begin split time score Catched exception ! Connect or reconnect ! TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('16', 8),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 10022025 20425002 Nombre de photos uploadées : 8 / 23040 (0%) 10022025 20425002 Nombre de photos taguées (types de déchets): 0 / 8 (0%) 10022025 20425002 Nombre de photos taguées (volume) : 0 / 8 (0%) elapsed_time : load_data_split_time_score 1.6689300537109375e-06 elapsed_time : order_list_meta_photo_and_scores 4.76837158203125e-06 ???????? elapsed_time : fill_and_build_computed_from_old_data 0.0004146099090576172 elapsed_time : insert_dashboard_record_day_entry 0.02358388900756836 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 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20424999 order by id desc limit 1 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': 8, '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 [1336619587, 1336619584, 1336619541, 1336619539, 1336537676, 1336537660, 1336537656, 1336537599] Looping around the photos to save general results len do output : 1 /20425002Didn'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, '2574369') ('3318', '20425002', '1336619587', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336619584', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336619541', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336619539', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537676', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537660', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537656', None, None, None, None, None, '2574369') ('3318', None, None, None, None, None, None, None, '2574369') ('3318', '20425002', '1336537599', None, None, None, None, None, '2574369') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 9 time used for this insertion : 0.017199993133544922 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.248903512954712 time spend to save output : 0.01740407943725586 total time spend for step 10 : 2.2663075923919678 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 8 set_done_treatment 271.39user 115.55system 9:07.28elapsed 70%CPU (0avgtext+0avgdata 7276304maxresident)k 1980568inputs+214912outputs (73958major+25583037minor)pagefaults 0swaps