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 : 1649647 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 : ['2557205'] with mtr_portfolio_ids : ['20277523'] and first list_photo_ids : [] new path : /proc/1649647/ Inside batchDatouExec : verbose : 0 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : mask_detect, crop_condition, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final, blur_detection, brightness, velours_tree, send_mail_cod, split_time_score over limit max, limiting to limit_max 40 list_input_json : [] origin We have 1 , BFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 9 ; length of list_pids : 9 ; length of list_args : 9 time to download the photos : 2.3119208812713623 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 Thu Feb 6 01:20:31 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 : 10774 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-02-06 01:20:34.289713: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-06 01:20:34.315301: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-06 01:20:34.316963: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7ff864000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-06 01:20:34.316984: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-06 01:20:34.320974: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-06 01:20:34.550753: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4150e7d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-06 01:20:34.550809: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-06 01:20:34.551651: 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 2025-02-06 01:20:34.552024: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-06 01:20:34.554307: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-06 01:20:34.556711: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-06 01:20:34.557054: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-06 01:20:34.559924: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-06 01:20:34.561186: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-06 01:20:34.567570: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-06 01:20:34.569519: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-06 01:20:34.569689: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-06 01:20:34.570598: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-06 01:20:34.570619: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-06 01:20:34.570629: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-06 01:20:34.572129: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9985 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. 2025-02-06 01:20:34.901353: 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 2025-02-06 01:20:34.901511: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-06 01:20:34.901535: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-06 01:20:34.901553: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-06 01:20:34.901570: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-06 01:20:34.901587: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-06 01:20:34.901604: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-06 01:20:34.901623: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-06 01:20:34.903400: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-06 01:20:34.904855: 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 2025-02-06 01:20:34.904923: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-06 01:20:34.904951: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-06 01:20:34.904972: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-06 01:20:34.904995: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-06 01:20:34.905016: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-06 01:20:34.905038: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-06 01:20:34.905058: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-06 01:20:34.906364: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-06 01:20:34.906416: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-06 01:20:34.906426: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-06 01:20:34.906437: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-06 01:20:34.907922: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9985 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 : [] 2025-02-06 01:20:46.145621: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-06 01:20:46.355074: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-06 01:20:48.148597: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.149239: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.60G (3865470464 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.149853: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.24G (3478923264 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.150513: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.92G (3131030784 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.151118: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.62G (2817927680 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.151743: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.36G (2536134912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.481254: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.481319: W tensorflow/core/common_runtime/bfc_allocator.cc:311] Garbage collection: deallocate free memory regions (i.e., allocations) so that we can re-allocate a larger region to avoid OOM due to memory fragmentation. If you see this message frequently, you are running near the threshold of the available device memory and re-allocation may incur great performance overhead. You may try smaller batch sizes to observe the performance impact. Set TF_ENABLE_GPU_GARBAGE_COLLECTION=false if you'd like to disable this feature. 2025-02-06 01:20:48.535575: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.535663: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.15GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-06 01:20:48.537203: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.537235: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.15GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-06 01:20:48.543938: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.543966: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.08GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-06 01:20:48.544740: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.544760: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.08GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-06 01:20:48.618753: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.618802: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 564.38MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-06 01:20:48.619769: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.619792: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 564.38MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-06 01:20:48.623933: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.623959: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 290.15MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-06 01:20:48.625316: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.625345: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 290.15MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-06 01:20:48.639268: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.639298: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.08GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-06 01:20:48.639890: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.639906: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.08GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-06 01:20:48.644387: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.645016: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.661795: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.662395: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.668344: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.668953: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.773697: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.774326: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.774977: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.775606: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.787402: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 01:20:48.788004: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory local folder : /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23 /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23/mask_model.h5 size_local : 256009536 size in s3 : 256009536 create time local : 2021-08-09 09:43:22 create time in s3 : 2021-08-06 18:54:04 mask_model.h5 already exist and didn't need to update list_images length : 9 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 91 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 94 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 97 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 : 84 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 : 90 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 : 19 Detection mask done ! Trying to reset tf kernel 1650043 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5485 tf kernel not reseted sub process len(results) : 9 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 9 len(list_Values) 0 process is alive process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 10196 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 : 0.09919309616088867 nb_pixel_total : 31999 time to create 1 rle with old method : 0.05218935012817383 length of segment : 211 time for calcul the mask position with numpy : 0.10007500648498535 nb_pixel_total : 39991 time to create 1 rle with old method : 0.04862380027770996 length of segment : 295 time for calcul the mask position with numpy : 0.09089827537536621 nb_pixel_total : 25552 time to create 1 rle with old method : 0.03324747085571289 length of segment : 231 time for calcul the mask position with numpy : 0.07969117164611816 nb_pixel_total : 19039 time to create 1 rle with old method : 0.026890993118286133 length of segment : 202 time for calcul the mask position with numpy : 0.022925376892089844 nb_pixel_total : 9550 time to create 1 rle with old method : 0.017343997955322266 length of segment : 136 time for calcul the mask position with numpy : 0.04153013229370117 nb_pixel_total : 13731 time to create 1 rle with old method : 0.01847243309020996 length of segment : 135 time for calcul the mask position with numpy : 0.001458883285522461 nb_pixel_total : 23912 time to create 1 rle with old method : 0.0274505615234375 length of segment : 187 time for calcul the mask position with numpy : 0.0006973743438720703 nb_pixel_total : 8478 time to create 1 rle with old method : 0.009667634963989258 length of segment : 157 time for calcul the mask position with numpy : 0.04212617874145508 nb_pixel_total : 8806 time to create 1 rle with old method : 0.015088319778442383 length of segment : 130 time for calcul the mask position with numpy : 0.019626617431640625 nb_pixel_total : 6129 time to create 1 rle with old method : 0.012376546859741211 length of segment : 80 time for calcul the mask position with numpy : 0.0005345344543457031 nb_pixel_total : 7813 time to create 1 rle with old method : 0.011266708374023438 length of segment : 85 time for calcul the mask position with numpy : 0.11900091171264648 nb_pixel_total : 60003 time to create 1 rle with old method : 0.06995153427124023 length of segment : 429 time for calcul the mask position with numpy : 0.05727791786193848 nb_pixel_total : 16033 time to create 1 rle with old method : 0.02615952491760254 length of segment : 151 time for calcul the mask position with numpy : 0.09705686569213867 nb_pixel_total : 35794 time to create 1 rle with old method : 0.04184889793395996 length of segment : 267 time for calcul the mask position with numpy : 0.0017254352569580078 nb_pixel_total : 27331 time to create 1 rle with old method : 0.030113935470581055 length of segment : 203 time for calcul the mask position with numpy : 0.02791452407836914 nb_pixel_total : 7762 time to create 1 rle with old method : 0.013383150100708008 length of segment : 119 time for calcul the mask position with numpy : 0.02324199676513672 nb_pixel_total : 306263 time to create 1 rle with new method : 0.022915363311767578 length of segment : 459 time for calcul the mask position with numpy : 0.027647733688354492 nb_pixel_total : 6344 time to create 1 rle with old method : 0.009364843368530273 length of segment : 85 time for calcul the mask position with numpy : 0.05845999717712402 nb_pixel_total : 26255 time to create 1 rle with old method : 0.03209543228149414 length of segment : 231 time for calcul the mask position with numpy : 0.0014865398406982422 nb_pixel_total : 19644 time to create 1 rle with old method : 0.022403717041015625 length of segment : 198 time for calcul the mask position with numpy : 0.0308682918548584 nb_pixel_total : 7734 time to create 1 rle with old method : 0.01110219955444336 length of segment : 159 time for calcul the mask position with numpy : 0.03430438041687012 nb_pixel_total : 12348 time to create 1 rle with old method : 0.017081022262573242 length of segment : 80 time for calcul the mask position with numpy : 0.00017404556274414062 nb_pixel_total : 4800 time to create 1 rle with old method : 0.0058286190032958984 length of segment : 78 time for calcul the mask position with numpy : 0.028113603591918945 nb_pixel_total : 9458 time to create 1 rle with old method : 0.01345682144165039 length of segment : 143 time for calcul the mask position with numpy : 0.07802534103393555 nb_pixel_total : 40841 time to create 1 rle with old method : 0.04806327819824219 length of segment : 380 time for calcul the mask position with numpy : 0.0027618408203125 nb_pixel_total : 36193 time to create 1 rle with old method : 0.04402613639831543 length of segment : 274 time for calcul the mask position with numpy : 0.0012924671173095703 nb_pixel_total : 10454 time to create 1 rle with old method : 0.013061046600341797 length of segment : 159 time for calcul the mask position with numpy : 0.14888715744018555 nb_pixel_total : 44202 time to create 1 rle with old method : 0.05178713798522949 length of segment : 408 time for calcul the mask position with numpy : 0.03788495063781738 nb_pixel_total : 13217 time to create 1 rle with old method : 0.019253969192504883 length of segment : 77 time for calcul the mask position with numpy : 0.0007603168487548828 nb_pixel_total : 7105 time to create 1 rle with old method : 0.008259773254394531 length of segment : 117 time for calcul the mask position with numpy : 0.0009124279022216797 nb_pixel_total : 12334 time to create 1 rle with old method : 0.014592647552490234 length of segment : 146 time for calcul the mask position with numpy : 0.001302957534790039 nb_pixel_total : 16164 time to create 1 rle with old method : 0.01876068115234375 length of segment : 246 time for calcul the mask position with numpy : 0.0007939338684082031 nb_pixel_total : 15563 time to create 1 rle with old method : 0.018500089645385742 length of segment : 143 time for calcul the mask position with numpy : 0.00398564338684082 nb_pixel_total : 20310 time to create 1 rle with old method : 0.024372100830078125 length of segment : 272 time for calcul the mask position with numpy : 0.04822874069213867 nb_pixel_total : 22117 time to create 1 rle with old method : 0.03183484077453613 length of segment : 188 time for calcul the mask position with numpy : 0.0933229923248291 nb_pixel_total : 26367 time to create 1 rle with old method : 0.03146076202392578 length of segment : 190 time for calcul the mask position with numpy : 0.04290485382080078 nb_pixel_total : 34757 time to create 1 rle with old method : 0.04438328742980957 length of segment : 278 time for calcul the mask position with numpy : 0.0003495216369628906 nb_pixel_total : 5411 time to create 1 rle with old method : 0.006268024444580078 length of segment : 97 time for calcul the mask position with numpy : 0.0015976428985595703 nb_pixel_total : 20450 time to create 1 rle with old method : 0.023615360260009766 length of segment : 159 time for calcul the mask position with numpy : 0.0013744831085205078 nb_pixel_total : 15040 time to create 1 rle with old method : 0.019792556762695312 length of segment : 174 time for calcul the mask position with numpy : 0.0007736682891845703 nb_pixel_total : 3500 time to create 1 rle with old method : 0.00549626350402832 length of segment : 82 time for calcul the mask position with numpy : 0.06177473068237305 nb_pixel_total : 10147 time to create 1 rle with old method : 0.014570474624633789 length of segment : 278 time for calcul the mask position with numpy : 0.0003478527069091797 nb_pixel_total : 10533 time to create 1 rle with old method : 0.012207746505737305 length of segment : 127 time for calcul the mask position with numpy : 0.040238142013549805 nb_pixel_total : 17817 time to create 1 rle with old method : 0.02173161506652832 length of segment : 182 time for calcul the mask position with numpy : 0.0028085708618164062 nb_pixel_total : 11010 time to create 1 rle with old method : 0.012308359146118164 length of segment : 100 time for calcul the mask position with numpy : 0.010832786560058594 nb_pixel_total : 12212 time to create 1 rle with old method : 0.018689870834350586 length of segment : 161 time for calcul the mask position with numpy : 0.027324914932250977 nb_pixel_total : 24639 time to create 1 rle with old method : 0.034303903579711914 length of segment : 222 time for calcul the mask position with numpy : 0.00545048713684082 nb_pixel_total : 5454 time to create 1 rle with old method : 0.008303165435791016 length of segment : 101 time for calcul the mask position with numpy : 0.05773425102233887 nb_pixel_total : 37180 time to create 1 rle with old method : 0.05546927452087402 length of segment : 288 time for calcul the mask position with numpy : 0.001107931137084961 nb_pixel_total : 19813 time to create 1 rle with old method : 0.02714395523071289 length of segment : 192 time for calcul the mask position with numpy : 0.03371071815490723 nb_pixel_total : 10863 time to create 1 rle with old method : 0.01715397834777832 length of segment : 123 time for calcul the mask position with numpy : 0.021459579467773438 nb_pixel_total : 16860 time to create 1 rle with old method : 0.02267742156982422 length of segment : 152 time for calcul the mask position with numpy : 0.008647918701171875 nb_pixel_total : 22606 time to create 1 rle with old method : 0.02785778045654297 length of segment : 186 time for calcul the mask position with numpy : 0.01007699966430664 nb_pixel_total : 38220 time to create 1 rle with old method : 0.045805931091308594 length of segment : 222 time for calcul the mask position with numpy : 0.0020470619201660156 nb_pixel_total : 20659 time to create 1 rle with old method : 0.02211308479309082 length of segment : 260 time for calcul the mask position with numpy : 0.000152587890625 nb_pixel_total : 5108 time to create 1 rle with old method : 0.0062961578369140625 length of segment : 103 time for calcul the mask position with numpy : 0.012999773025512695 nb_pixel_total : 4180 time to create 1 rle with old method : 0.008370637893676758 length of segment : 72 time for calcul the mask position with numpy : 0.013455629348754883 nb_pixel_total : 11803 time to create 1 rle with old method : 0.01871180534362793 length of segment : 116 time for calcul the mask position with numpy : 0.0133514404296875 nb_pixel_total : 9707 time to create 1 rle with old method : 0.011383295059204102 length of segment : 128 time for calcul the mask position with numpy : 0.00797128677368164 nb_pixel_total : 3412 time to create 1 rle with old method : 0.004150390625 length of segment : 85 time for calcul the mask position with numpy : 0.0012805461883544922 nb_pixel_total : 15081 time to create 1 rle with old method : 0.01775074005126953 length of segment : 128 time for calcul the mask position with numpy : 0.03937578201293945 nb_pixel_total : 17958 time to create 1 rle with old method : 0.020754337310791016 length of segment : 170 time for calcul the mask position with numpy : 0.08354687690734863 nb_pixel_total : 45615 time to create 1 rle with old method : 0.05035066604614258 length of segment : 288 time for calcul the mask position with numpy : 0.009701251983642578 nb_pixel_total : 6391 time to create 1 rle with old method : 0.008007049560546875 length of segment : 111 time for calcul the mask position with numpy : 0.0007736682891845703 nb_pixel_total : 10615 time to create 1 rle with old method : 0.012825250625610352 length of segment : 128 time for calcul the mask position with numpy : 0.005030393600463867 nb_pixel_total : 5265 time to create 1 rle with old method : 0.006445407867431641 length of segment : 95 time for calcul the mask position with numpy : 0.029246091842651367 nb_pixel_total : 9582 time to create 1 rle with old method : 0.011598825454711914 length of segment : 127 time for calcul the mask position with numpy : 0.022016286849975586 nb_pixel_total : 72928 time to create 1 rle with old method : 0.08456802368164062 length of segment : 357 time for calcul the mask position with numpy : 0.0017669200897216797 nb_pixel_total : 29562 time to create 1 rle with old method : 0.032132863998413086 length of segment : 269 time for calcul the mask position with numpy : 0.015769243240356445 nb_pixel_total : 29902 time to create 1 rle with old method : 0.0406191349029541 length of segment : 228 time for calcul the mask position with numpy : 0.04430031776428223 nb_pixel_total : 39059 time to create 1 rle with old method : 0.04793071746826172 length of segment : 316 time for calcul the mask position with numpy : 0.0010862350463867188 nb_pixel_total : 7179 time to create 1 rle with old method : 0.010409832000732422 length of segment : 130 time for calcul the mask position with numpy : 0.0019330978393554688 nb_pixel_total : 17444 time to create 1 rle with old method : 0.01967620849609375 length of segment : 243 time for calcul the mask position with numpy : 0.0034995079040527344 nb_pixel_total : 25923 time to create 1 rle with old method : 0.03179574012756348 length of segment : 211 time for calcul the mask position with numpy : 0.001413106918334961 nb_pixel_total : 12292 time to create 1 rle with old method : 0.014922142028808594 length of segment : 167 time for calcul the mask position with numpy : 0.0014805793762207031 nb_pixel_total : 10919 time to create 1 rle with old method : 0.01471710205078125 length of segment : 96 time for calcul the mask position with numpy : 0.015105009078979492 nb_pixel_total : 8856 time to create 1 rle with old method : 0.014793157577514648 length of segment : 111 time for calcul the mask position with numpy : 0.0028443336486816406 nb_pixel_total : 26778 time to create 1 rle with old method : 0.03110957145690918 length of segment : 189 time for calcul the mask position with numpy : 0.0157468318939209 nb_pixel_total : 8694 time to create 1 rle with old method : 0.009928226470947266 length of segment : 121 time for calcul the mask position with numpy : 0.03341484069824219 nb_pixel_total : 7820 time to create 1 rle with old method : 0.009101390838623047 length of segment : 101 time for calcul the mask position with numpy : 0.0013957023620605469 nb_pixel_total : 21927 time to create 1 rle with old method : 0.025928497314453125 length of segment : 222 time for calcul the mask position with numpy : 0.02329111099243164 nb_pixel_total : 31941 time to create 1 rle with old method : 0.036303043365478516 length of segment : 396 time for calcul the mask position with numpy : 0.00047135353088378906 nb_pixel_total : 6450 time to create 1 rle with old method : 0.00776362419128418 length of segment : 82 time for calcul the mask position with numpy : 0.02109813690185547 nb_pixel_total : 229212 time to create 1 rle with new method : 0.023193836212158203 length of segment : 286 time for calcul the mask position with numpy : 0.001047372817993164 nb_pixel_total : 23299 time to create 1 rle with old method : 0.03135275840759277 length of segment : 164 time for calcul the mask position with numpy : 0.03894758224487305 nb_pixel_total : 74761 time to create 1 rle with old method : 0.10433197021484375 length of segment : 312 time for calcul the mask position with numpy : 0.00122833251953125 nb_pixel_total : 10930 time to create 1 rle with old method : 0.01620197296142578 length of segment : 149 time for calcul the mask position with numpy : 0.026020288467407227 nb_pixel_total : 27675 time to create 1 rle with old method : 0.035462379455566406 length of segment : 185 time for calcul the mask position with numpy : 0.012426376342773438 nb_pixel_total : 12598 time to create 1 rle with old method : 0.019371986389160156 length of segment : 142 time for calcul the mask position with numpy : 0.025843381881713867 nb_pixel_total : 27342 time to create 1 rle with old method : 0.0313870906829834 length of segment : 490 time for calcul the mask position with numpy : 0.011638402938842773 nb_pixel_total : 20477 time to create 1 rle with old method : 0.05015420913696289 length of segment : 187 time for calcul the mask position with numpy : 0.0038280487060546875 nb_pixel_total : 3084 time to create 1 rle with old method : 0.004350900650024414 length of segment : 170 time for calcul the mask position with numpy : 0.0332188606262207 nb_pixel_total : 18425 time to create 1 rle with old method : 0.026601791381835938 length of segment : 191 time for calcul the mask position with numpy : 0.10932564735412598 nb_pixel_total : 158877 time to create 1 rle with new method : 0.01184988021850586 length of segment : 381 time for calcul the mask position with numpy : 0.002691984176635742 nb_pixel_total : 16912 time to create 1 rle with old method : 0.019928455352783203 length of segment : 185 time for calcul the mask position with numpy : 0.0012769699096679688 nb_pixel_total : 12598 time to create 1 rle with old method : 0.015154838562011719 length of segment : 167 time for calcul the mask position with numpy : 0.0013377666473388672 nb_pixel_total : 5571 time to create 1 rle with old method : 0.0070056915283203125 length of segment : 93 time for calcul the mask position with numpy : 0.002095937728881836 nb_pixel_total : 3895 time to create 1 rle with old method : 0.004923820495605469 length of segment : 101 time for calcul the mask position with numpy : 0.0004723072052001953 nb_pixel_total : 394 time to create 1 rle with old method : 0.001024007797241211 length of segment : 110 time for calcul the mask position with numpy : 0.0005762577056884766 nb_pixel_total : 6187 time to create 1 rle with old method : 0.007653951644897461 length of segment : 97 time for calcul the mask position with numpy : 0.0013778209686279297 nb_pixel_total : 20186 time to create 1 rle with old method : 0.024080753326416016 length of segment : 205 time for calcul the mask position with numpy : 0.01752758026123047 nb_pixel_total : 24220 time to create 1 rle with old method : 0.030181169509887695 length of segment : 188 time for calcul the mask position with numpy : 0.15610694885253906 nb_pixel_total : 127030 time to create 1 rle with old method : 0.14873003959655762 length of segment : 433 time for calcul the mask position with numpy : 0.004463911056518555 nb_pixel_total : 10990 time to create 1 rle with old method : 0.012289762496948242 length of segment : 196 time for calcul the mask position with numpy : 0.020902156829833984 nb_pixel_total : 79591 time to create 1 rle with old method : 0.08976554870605469 length of segment : 891 time for calcul the mask position with numpy : 0.0027632713317871094 nb_pixel_total : 17021 time to create 1 rle with old method : 0.019991397857666016 length of segment : 133 time for calcul the mask position with numpy : 0.0006825923919677734 nb_pixel_total : 4501 time to create 1 rle with old method : 0.005357027053833008 length of segment : 96 time for calcul the mask position with numpy : 0.0010886192321777344 nb_pixel_total : 12633 time to create 1 rle with old method : 0.015730619430541992 length of segment : 122 time for calcul the mask position with numpy : 0.001867055892944336 nb_pixel_total : 17618 time to create 1 rle with old method : 0.019638538360595703 length of segment : 183 time for calcul the mask position with numpy : 0.0007367134094238281 nb_pixel_total : 12236 time to create 1 rle with old method : 0.014412641525268555 length of segment : 119 time for calcul the mask position with numpy : 0.0007569789886474609 nb_pixel_total : 3963 time to create 1 rle with old method : 0.005163908004760742 length of segment : 72 time for calcul the mask position with numpy : 0.0005946159362792969 nb_pixel_total : 3434 time to create 1 rle with old method : 0.004565238952636719 length of segment : 88 time for calcul the mask position with numpy : 0.001556396484375 nb_pixel_total : 5818 time to create 1 rle with old method : 0.007550716400146484 length of segment : 80 time for calcul the mask position with numpy : 0.0004417896270751953 nb_pixel_total : 4395 time to create 1 rle with old method : 0.005801677703857422 length of segment : 94 time for calcul the mask position with numpy : 0.020928144454956055 nb_pixel_total : 19560 time to create 1 rle with old method : 0.02774214744567871 length of segment : 157 time for calcul the mask position with numpy : 0.004316091537475586 nb_pixel_total : 12252 time to create 1 rle with old method : 0.014031171798706055 length of segment : 228 time for calcul the mask position with numpy : 0.0006318092346191406 nb_pixel_total : 3675 time to create 1 rle with old method : 0.004503965377807617 length of segment : 78 time for calcul the mask position with numpy : 0.00885152816772461 nb_pixel_total : 90220 time to create 1 rle with old method : 0.10085701942443848 length of segment : 434 time for calcul the mask position with numpy : 0.0017786026000976562 nb_pixel_total : 12480 time to create 1 rle with old method : 0.014534235000610352 length of segment : 131 time for calcul the mask position with numpy : 0.016341447830200195 nb_pixel_total : 26573 time to create 1 rle with old method : 0.03429603576660156 length of segment : 212 time for calcul the mask position with numpy : 0.009834051132202148 nb_pixel_total : 10672 time to create 1 rle with old method : 0.015711307525634766 length of segment : 97 time for calcul the mask position with numpy : 0.000576019287109375 nb_pixel_total : 6019 time to create 1 rle with old method : 0.006720304489135742 length of segment : 101 time for calcul the mask position with numpy : 0.006212472915649414 nb_pixel_total : 21223 time to create 1 rle with old method : 0.025483369827270508 length of segment : 512 time for calcul the mask position with numpy : 0.0005216598510742188 nb_pixel_total : 4516 time to create 1 rle with old method : 0.005382537841796875 length of segment : 90 time for calcul the mask position with numpy : 0.002368450164794922 nb_pixel_total : 30347 time to create 1 rle with old method : 0.0357513427734375 length of segment : 299 time for calcul the mask position with numpy : 0.0034530162811279297 nb_pixel_total : 24113 time to create 1 rle with old method : 0.02779221534729004 length of segment : 175 time for calcul the mask position with numpy : 0.0015263557434082031 nb_pixel_total : 10618 time to create 1 rle with old method : 0.012715339660644531 length of segment : 180 time for calcul the mask position with numpy : 0.037683963775634766 nb_pixel_total : 48689 time to create 1 rle with old method : 0.06960725784301758 length of segment : 302 time for calcul the mask position with numpy : 0.001280069351196289 nb_pixel_total : 6466 time to create 1 rle with old method : 0.01401066780090332 length of segment : 83 time for calcul the mask position with numpy : 0.0003597736358642578 nb_pixel_total : 3172 time to create 1 rle with old method : 0.004042387008666992 length of segment : 72 time for calcul the mask position with numpy : 0.001180887222290039 nb_pixel_total : 10185 time to create 1 rle with old method : 0.011941671371459961 length of segment : 153 time for calcul the mask position with numpy : 0.0011258125305175781 nb_pixel_total : 2643 time to create 1 rle with old method : 0.0033330917358398438 length of segment : 100 time for calcul the mask position with numpy : 0.0034704208374023438 nb_pixel_total : 31568 time to create 1 rle with old method : 0.037749290466308594 length of segment : 298 time for calcul the mask position with numpy : 0.018794775009155273 nb_pixel_total : 117841 time to create 1 rle with old method : 0.1345360279083252 length of segment : 545 time for calcul the mask position with numpy : 0.0057451725006103516 nb_pixel_total : 22354 time to create 1 rle with old method : 0.027669191360473633 length of segment : 354 time for calcul the mask position with numpy : 0.0024449825286865234 nb_pixel_total : 13858 time to create 1 rle with old method : 0.015166759490966797 length of segment : 307 time for calcul the mask position with numpy : 0.0002269744873046875 nb_pixel_total : 5486 time to create 1 rle with old method : 0.006360054016113281 length of segment : 87 time for calcul the mask position with numpy : 0.006682872772216797 nb_pixel_total : 23953 time to create 1 rle with old method : 0.025926828384399414 length of segment : 227 time for calcul the mask position with numpy : 0.0020029544830322266 nb_pixel_total : 19640 time to create 1 rle with old method : 0.02250194549560547 length of segment : 176 time for calcul the mask position with numpy : 0.0018813610076904297 nb_pixel_total : 14667 time to create 1 rle with old method : 0.015883684158325195 length of segment : 118 time for calcul the mask position with numpy : 0.0011675357818603516 nb_pixel_total : 8666 time to create 1 rle with old method : 0.009903907775878906 length of segment : 147 time for calcul the mask position with numpy : 0.004844188690185547 nb_pixel_total : 42957 time to create 1 rle with old method : 0.0470120906829834 length of segment : 441 time for calcul the mask position with numpy : 0.0008511543273925781 nb_pixel_total : 8577 time to create 1 rle with old method : 0.010299444198608398 length of segment : 111 time for calcul the mask position with numpy : 0.004140615463256836 nb_pixel_total : 60634 time to create 1 rle with old method : 0.07105779647827148 length of segment : 348 time for calcul the mask position with numpy : 0.0016160011291503906 nb_pixel_total : 11067 time to create 1 rle with old method : 0.01248311996459961 length of segment : 82 time for calcul the mask position with numpy : 0.0006818771362304688 nb_pixel_total : 2666 time to create 1 rle with old method : 0.003551959991455078 length of segment : 148 time for calcul the mask position with numpy : 0.0034580230712890625 nb_pixel_total : 32186 time to create 1 rle with old method : 0.03650641441345215 length of segment : 249 time for calcul the mask position with numpy : 0.002530813217163086 nb_pixel_total : 26652 time to create 1 rle with old method : 0.029721498489379883 length of segment : 235 time for calcul the mask position with numpy : 0.0006301403045654297 nb_pixel_total : 3444 time to create 1 rle with old method : 0.007294416427612305 length of segment : 77 time for calcul the mask position with numpy : 0.0005772113800048828 nb_pixel_total : 16065 time to create 1 rle with old method : 0.018481016159057617 length of segment : 189 time for calcul the mask position with numpy : 0.006585121154785156 nb_pixel_total : 20469 time to create 1 rle with old method : 0.029307842254638672 length of segment : 247 time for calcul the mask position with numpy : 0.0049784183502197266 nb_pixel_total : 32727 time to create 1 rle with old method : 0.04265642166137695 length of segment : 207 time for calcul the mask position with numpy : 0.0037946701049804688 nb_pixel_total : 18928 time to create 1 rle with old method : 0.021676063537597656 length of segment : 238 time for calcul the mask position with numpy : 0.0008854866027832031 nb_pixel_total : 5135 time to create 1 rle with old method : 0.006459712982177734 length of segment : 89 time for calcul the mask position with numpy : 0.0025870800018310547 nb_pixel_total : 28433 time to create 1 rle with old method : 0.035109519958496094 length of segment : 446 time for calcul the mask position with numpy : 0.018434524536132812 nb_pixel_total : 131285 time to create 1 rle with old method : 0.1463024616241455 length of segment : 543 time for calcul the mask position with numpy : 0.022261619567871094 nb_pixel_total : 206069 time to create 1 rle with new method : 0.020071983337402344 length of segment : 484 time for calcul the mask position with numpy : 0.00484776496887207 nb_pixel_total : 19022 time to create 1 rle with old method : 0.023003816604614258 length of segment : 190 time for calcul the mask position with numpy : 0.003387451171875 nb_pixel_total : 13729 time to create 1 rle with old method : 0.019237995147705078 length of segment : 147 time for calcul the mask position with numpy : 0.0010118484497070312 nb_pixel_total : 11023 time to create 1 rle with old method : 0.013149738311767578 length of segment : 95 time for calcul the mask position with numpy : 0.012326240539550781 nb_pixel_total : 14052 time to create 1 rle with old method : 0.01963210105895996 length of segment : 151 time for calcul the mask position with numpy : 0.010165214538574219 nb_pixel_total : 9370 time to create 1 rle with old method : 0.015317440032958984 length of segment : 121 time for calcul the mask position with numpy : 0.12535548210144043 nb_pixel_total : 273194 time to create 1 rle with new method : 0.026057958602905273 length of segment : 629 time for calcul the mask position with numpy : 0.17053961753845215 nb_pixel_total : 37657 time to create 1 rle with old method : 0.0436100959777832 length of segment : 311 time for calcul the mask position with numpy : 0.01224064826965332 nb_pixel_total : 6828 time to create 1 rle with old method : 0.010157346725463867 length of segment : 86 time for calcul the mask position with numpy : 0.13098692893981934 nb_pixel_total : 34591 time to create 1 rle with old method : 0.04183769226074219 length of segment : 251 time for calcul the mask position with numpy : 0.03449201583862305 nb_pixel_total : 7400 time to create 1 rle with old method : 0.013852119445800781 length of segment : 94 time for calcul the mask position with numpy : 0.00676417350769043 nb_pixel_total : 9364 time to create 1 rle with old method : 0.014051198959350586 length of segment : 117 time for calcul the mask position with numpy : 0.5526456832885742 nb_pixel_total : 282825 time to create 1 rle with new method : 0.03783369064331055 length of segment : 433 time for calcul the mask position with numpy : 0.04011034965515137 nb_pixel_total : 197670 time to create 1 rle with new method : 0.014108896255493164 length of segment : 382 time for calcul the mask position with numpy : 0.0010862350463867188 nb_pixel_total : 9557 time to create 1 rle with old method : 0.011515617370605469 length of segment : 76 time for calcul the mask position with numpy : 0.008520126342773438 nb_pixel_total : 8962 time to create 1 rle with old method : 0.01312398910522461 length of segment : 228 time for calcul the mask position with numpy : 0.055086374282836914 nb_pixel_total : 10721 time to create 1 rle with old method : 0.018188953399658203 length of segment : 173 time for calcul the mask position with numpy : 0.06867289543151855 nb_pixel_total : 25414 time to create 1 rle with old method : 0.0331878662109375 length of segment : 280 time for calcul the mask position with numpy : 0.01792287826538086 nb_pixel_total : 11910 time to create 1 rle with old method : 0.018988847732543945 length of segment : 154 time for calcul the mask position with numpy : 0.2718391418457031 nb_pixel_total : 64381 time to create 1 rle with old method : 0.07909655570983887 length of segment : 318 time for calcul the mask position with numpy : 0.05275392532348633 nb_pixel_total : 11005 time to create 1 rle with old method : 0.015082597732543945 length of segment : 118 time for calcul the mask position with numpy : 0.07414698600769043 nb_pixel_total : 9929 time to create 1 rle with old method : 0.01665496826171875 length of segment : 115 time for calcul the mask position with numpy : 0.01663494110107422 nb_pixel_total : 15269 time to create 1 rle with old method : 0.022142410278320312 length of segment : 194 time for calcul the mask position with numpy : 0.1555194854736328 nb_pixel_total : 58515 time to create 1 rle with old method : 0.06994152069091797 length of segment : 356 time for calcul the mask position with numpy : 0.0007543563842773438 nb_pixel_total : 25424 time to create 1 rle with old method : 0.029874801635742188 length of segment : 197 time for calcul the mask position with numpy : 0.04283571243286133 nb_pixel_total : 9246 time to create 1 rle with old method : 0.014819860458374023 length of segment : 129 time for calcul the mask position with numpy : 0.003726482391357422 nb_pixel_total : 3369 time to create 1 rle with old method : 0.004173994064331055 length of segment : 58 time for calcul the mask position with numpy : 0.00627589225769043 nb_pixel_total : 21722 time to create 1 rle with old method : 0.024739503860473633 length of segment : 201 time for calcul the mask position with numpy : 0.0328984260559082 nb_pixel_total : 63578 time to create 1 rle with old method : 0.07088804244995117 length of segment : 356 time for calcul the mask position with numpy : 0.22826099395751953 nb_pixel_total : 102849 time to create 1 rle with old method : 0.12228226661682129 length of segment : 617 time for calcul the mask position with numpy : 0.0034055709838867188 nb_pixel_total : 7630 time to create 1 rle with old method : 0.012581348419189453 length of segment : 116 time for calcul the mask position with numpy : 0.023223876953125 nb_pixel_total : 5729 time to create 1 rle with old method : 0.009799718856811523 length of segment : 89 time for calcul the mask position with numpy : 0.0003478527069091797 nb_pixel_total : 4170 time to create 1 rle with old method : 0.005167245864868164 length of segment : 71 time for calcul the mask position with numpy : 0.011214733123779297 nb_pixel_total : 7814 time to create 1 rle with old method : 0.012139320373535156 length of segment : 117 time for calcul the mask position with numpy : 0.08518242835998535 nb_pixel_total : 34704 time to create 1 rle with old method : 0.04261302947998047 length of segment : 259 time for calcul the mask position with numpy : 0.009377717971801758 nb_pixel_total : 2451 time to create 1 rle with old method : 0.005484580993652344 length of segment : 64 time for calcul the mask position with numpy : 0.008712530136108398 nb_pixel_total : 10989 time to create 1 rle with old method : 0.018258094787597656 length of segment : 164 time for calcul the mask position with numpy : 0.0007970333099365234 nb_pixel_total : 10898 time to create 1 rle with old method : 0.01324772834777832 length of segment : 119 time for calcul the mask position with numpy : 0.008852720260620117 nb_pixel_total : 10312 time to create 1 rle with old method : 0.01587510108947754 length of segment : 111 time for calcul the mask position with numpy : 0.013462305068969727 nb_pixel_total : 45375 time to create 1 rle with old method : 0.05520749092102051 length of segment : 353 time for calcul the mask position with numpy : 0.05316901206970215 nb_pixel_total : 7287 time to create 1 rle with old method : 0.013509035110473633 length of segment : 101 time for calcul the mask position with numpy : 0.0002689361572265625 nb_pixel_total : 3110 time to create 1 rle with old method : 0.0038106441497802734 length of segment : 100 time for calcul the mask position with numpy : 0.08851265907287598 nb_pixel_total : 59236 time to create 1 rle with old method : 0.0695042610168457 length of segment : 351 time for calcul the mask position with numpy : 0.005741596221923828 nb_pixel_total : 6728 time to create 1 rle with old method : 0.009552240371704102 length of segment : 96 time for calcul the mask position with numpy : 0.001055002212524414 nb_pixel_total : 6481 time to create 1 rle with old method : 0.008024454116821289 length of segment : 113 time for calcul the mask position with numpy : 0.009868144989013672 nb_pixel_total : 30601 time to create 1 rle with old method : 0.040238380432128906 length of segment : 331 time for calcul the mask position with numpy : 0.003789663314819336 nb_pixel_total : 6485 time to create 1 rle with old method : 0.009552717208862305 length of segment : 115 time for calcul the mask position with numpy : 0.0010673999786376953 nb_pixel_total : 17767 time to create 1 rle with old method : 0.020312070846557617 length of segment : 97 time for calcul the mask position with numpy : 0.00482487678527832 nb_pixel_total : 17443 time to create 1 rle with old method : 0.021535158157348633 length of segment : 186 time for calcul the mask position with numpy : 0.0016765594482421875 nb_pixel_total : 25891 time to create 1 rle with old method : 0.029459476470947266 length of segment : 204 time for calcul the mask position with numpy : 0.0008873939514160156 nb_pixel_total : 10137 time to create 1 rle with old method : 0.011878490447998047 length of segment : 150 time for calcul the mask position with numpy : 0.0004520416259765625 nb_pixel_total : 6542 time to create 1 rle with old method : 0.007976055145263672 length of segment : 64 time for calcul the mask position with numpy : 0.00510859489440918 nb_pixel_total : 8669 time to create 1 rle with old method : 0.012530088424682617 length of segment : 120 time for calcul the mask position with numpy : 0.013536930084228516 nb_pixel_total : 35828 time to create 1 rle with old method : 0.04402947425842285 length of segment : 195 time for calcul the mask position with numpy : 0.0009546279907226562 nb_pixel_total : 13388 time to create 1 rle with old method : 0.014576911926269531 length of segment : 151 time for calcul the mask position with numpy : 0.0007431507110595703 nb_pixel_total : 9917 time to create 1 rle with old method : 0.010931015014648438 length of segment : 132 time for calcul the mask position with numpy : 0.003650665283203125 nb_pixel_total : 7466 time to create 1 rle with old method : 0.01006770133972168 length of segment : 110 time for calcul the mask position with numpy : 0.000640869140625 nb_pixel_total : 7355 time to create 1 rle with old method : 0.008275270462036133 length of segment : 146 time for calcul the mask position with numpy : 0.004055023193359375 nb_pixel_total : 10216 time to create 1 rle with old method : 0.013265371322631836 length of segment : 111 time for calcul the mask position with numpy : 0.0018401145935058594 nb_pixel_total : 20736 time to create 1 rle with old method : 0.023313283920288086 length of segment : 258 time for calcul the mask position with numpy : 0.005548715591430664 nb_pixel_total : 19311 time to create 1 rle with old method : 0.027806758880615234 length of segment : 189 time for calcul the mask position with numpy : 0.0008273124694824219 nb_pixel_total : 10444 time to create 1 rle with old method : 0.012857913970947266 length of segment : 146 time for calcul the mask position with numpy : 0.007868289947509766 nb_pixel_total : 15464 time to create 1 rle with old method : 0.02251601219177246 length of segment : 185 time for calcul the mask position with numpy : 0.008111953735351562 nb_pixel_total : 27969 time to create 1 rle with old method : 0.03334331512451172 length of segment : 249 time for calcul the mask position with numpy : 0.009352684020996094 nb_pixel_total : 14244 time to create 1 rle with old method : 0.016015052795410156 length of segment : 147 time for calcul the mask position with numpy : 0.009480476379394531 nb_pixel_total : 5948 time to create 1 rle with old method : 0.008572816848754883 length of segment : 76 time for calcul the mask position with numpy : 0.005928754806518555 nb_pixel_total : 13761 time to create 1 rle with old method : 0.0179750919342041 length of segment : 126 time for calcul the mask position with numpy : 0.0013315677642822266 nb_pixel_total : 14768 time to create 1 rle with old method : 0.019704341888427734 length of segment : 310 time for calcul the mask position with numpy : 0.00138092041015625 nb_pixel_total : 13103 time to create 1 rle with old method : 0.015302896499633789 length of segment : 208 time for calcul the mask position with numpy : 0.00021767616271972656 nb_pixel_total : 2808 time to create 1 rle with old method : 0.0035402774810791016 length of segment : 48 time for calcul the mask position with numpy : 0.0014307498931884766 nb_pixel_total : 17113 time to create 1 rle with old method : 0.019820213317871094 length of segment : 153 time for calcul the mask position with numpy : 0.0008816719055175781 nb_pixel_total : 9091 time to create 1 rle with old method : 0.011097431182861328 length of segment : 169 time for calcul the mask position with numpy : 0.0012660026550292969 nb_pixel_total : 10634 time to create 1 rle with old method : 0.01253652572631836 length of segment : 152 time for calcul the mask position with numpy : 0.0014064311981201172 nb_pixel_total : 21490 time to create 1 rle with old method : 0.023947715759277344 length of segment : 152 time for calcul the mask position with numpy : 0.002503633499145508 nb_pixel_total : 30624 time to create 1 rle with old method : 0.03589653968811035 length of segment : 241 time for calcul the mask position with numpy : 0.0041484832763671875 nb_pixel_total : 18515 time to create 1 rle with old method : 0.023274660110473633 length of segment : 132 time for calcul the mask position with numpy : 0.0027806758880615234 nb_pixel_total : 15055 time to create 1 rle with old method : 0.01696944236755371 length of segment : 239 time for calcul the mask position with numpy : 0.00614476203918457 nb_pixel_total : 21179 time to create 1 rle with old method : 0.02544569969177246 length of segment : 188 time for calcul the mask position with numpy : 0.0006477832794189453 nb_pixel_total : 7698 time to create 1 rle with old method : 0.008735418319702148 length of segment : 105 time for calcul the mask position with numpy : 0.0002987384796142578 nb_pixel_total : 3151 time to create 1 rle with old method : 0.0035452842712402344 length of segment : 76 time for calcul the mask position with numpy : 0.00024700164794921875 nb_pixel_total : 3385 time to create 1 rle with old method : 0.003843069076538086 length of segment : 56 time for calcul the mask position with numpy : 0.007357597351074219 nb_pixel_total : 18521 time to create 1 rle with old method : 0.025345325469970703 length of segment : 237 time for calcul the mask position with numpy : 0.012452125549316406 nb_pixel_total : 48405 time to create 1 rle with old method : 0.0562901496887207 length of segment : 261 time for calcul the mask position with numpy : 0.002267599105834961 nb_pixel_total : 37804 time to create 1 rle with old method : 0.04146981239318848 length of segment : 234 time for calcul the mask position with numpy : 0.00038695335388183594 nb_pixel_total : 5798 time to create 1 rle with old method : 0.006463050842285156 length of segment : 97 time for calcul the mask position with numpy : 0.00024366378784179688 nb_pixel_total : 2818 time to create 1 rle with old method : 0.003432035446166992 length of segment : 66 time for calcul the mask position with numpy : 0.006026029586791992 nb_pixel_total : 47074 time to create 1 rle with old method : 0.052263736724853516 length of segment : 217 time for calcul the mask position with numpy : 0.003572702407836914 nb_pixel_total : 8862 time to create 1 rle with old method : 0.012797355651855469 length of segment : 157 time for calcul the mask position with numpy : 0.0022933483123779297 nb_pixel_total : 30279 time to create 1 rle with old method : 0.035036325454711914 length of segment : 272 time for calcul the mask position with numpy : 0.002250194549560547 nb_pixel_total : 5918 time to create 1 rle with old method : 0.007014036178588867 length of segment : 102 time for calcul the mask position with numpy : 0.0005884170532226562 nb_pixel_total : 9980 time to create 1 rle with old method : 0.011595010757446289 length of segment : 149 time for calcul the mask position with numpy : 0.0004239082336425781 nb_pixel_total : 5722 time to create 1 rle with old method : 0.006787538528442383 length of segment : 100 time for calcul the mask position with numpy : 0.00022482872009277344 nb_pixel_total : 6907 time to create 1 rle with old method : 0.007985115051269531 length of segment : 81 time for calcul the mask position with numpy : 0.0008664131164550781 nb_pixel_total : 9158 time to create 1 rle with old method : 0.010897397994995117 length of segment : 133 time for calcul the mask position with numpy : 0.007534503936767578 nb_pixel_total : 46688 time to create 1 rle with old method : 0.05245518684387207 length of segment : 218 time for calcul the mask position with numpy : 0.0019390583038330078 nb_pixel_total : 5007 time to create 1 rle with old method : 0.005943775177001953 length of segment : 82 time for calcul the mask position with numpy : 0.0015208721160888672 nb_pixel_total : 9677 time to create 1 rle with old method : 0.011116266250610352 length of segment : 191 time for calcul the mask position with numpy : 0.008496522903442383 nb_pixel_total : 113539 time to create 1 rle with old method : 0.12637686729431152 length of segment : 456 time for calcul the mask position with numpy : 0.0005013942718505859 nb_pixel_total : 6529 time to create 1 rle with old method : 0.007376909255981445 length of segment : 104 time for calcul the mask position with numpy : 0.003493785858154297 nb_pixel_total : 53829 time to create 1 rle with old method : 0.05727195739746094 length of segment : 357 time for calcul the mask position with numpy : 0.0039348602294921875 nb_pixel_total : 65611 time to create 1 rle with old method : 0.0728294849395752 length of segment : 269 time for calcul the mask position with numpy : 0.009268999099731445 nb_pixel_total : 132175 time to create 1 rle with old method : 0.14415669441223145 length of segment : 452 time for calcul the mask position with numpy : 0.001794576644897461 nb_pixel_total : 31980 time to create 1 rle with old method : 0.03579878807067871 length of segment : 280 time for calcul the mask position with numpy : 0.0004673004150390625 nb_pixel_total : 7310 time to create 1 rle with old method : 0.007954835891723633 length of segment : 151 time for calcul the mask position with numpy : 0.03702235221862793 nb_pixel_total : 581084 time to create 1 rle with new method : 0.04477381706237793 length of segment : 1335 time for calcul the mask position with numpy : 0.0006799697875976562 nb_pixel_total : 8831 time to create 1 rle with old method : 0.010137557983398438 length of segment : 142 time for calcul the mask position with numpy : 0.00026988983154296875 nb_pixel_total : 9376 time to create 1 rle with old method : 0.011408329010009766 length of segment : 118 time for calcul the mask position with numpy : 0.0007841587066650391 nb_pixel_total : 11311 time to create 1 rle with old method : 0.013263225555419922 length of segment : 155 time for calcul the mask position with numpy : 0.001214742660522461 nb_pixel_total : 21785 time to create 1 rle with old method : 0.02734851837158203 length of segment : 177 time for calcul the mask position with numpy : 0.00269317626953125 nb_pixel_total : 49291 time to create 1 rle with old method : 0.05820417404174805 length of segment : 305 time for calcul the mask position with numpy : 0.0005390644073486328 nb_pixel_total : 11378 time to create 1 rle with old method : 0.015058755874633789 length of segment : 138 time for calcul the mask position with numpy : 0.003326416015625 nb_pixel_total : 40137 time to create 1 rle with old method : 0.04745078086853027 length of segment : 284 time for calcul the mask position with numpy : 0.0021343231201171875 nb_pixel_total : 32920 time to create 1 rle with old method : 0.03670358657836914 length of segment : 199 time for calcul the mask position with numpy : 0.003645658493041992 nb_pixel_total : 56176 time to create 1 rle with old method : 0.06190919876098633 length of segment : 366 time for calcul the mask position with numpy : 0.0015802383422851562 nb_pixel_total : 19263 time to create 1 rle with old method : 0.020812273025512695 length of segment : 155 time for calcul the mask position with numpy : 0.002271413803100586 nb_pixel_total : 32803 time to create 1 rle with old method : 0.036170005798339844 length of segment : 228 time for calcul the mask position with numpy : 0.0010471343994140625 nb_pixel_total : 12020 time to create 1 rle with old method : 0.01324009895324707 length of segment : 153 time for calcul the mask position with numpy : 0.001081228256225586 nb_pixel_total : 20735 time to create 1 rle with old method : 0.022753000259399414 length of segment : 207 time for calcul the mask position with numpy : 0.0012128353118896484 nb_pixel_total : 20658 time to create 1 rle with old method : 0.023983240127563477 length of segment : 211 time for calcul the mask position with numpy : 0.00035500526428222656 nb_pixel_total : 12440 time to create 1 rle with old method : 0.014099597930908203 length of segment : 149 time for calcul the mask position with numpy : 0.001459360122680664 nb_pixel_total : 25066 time to create 1 rle with old method : 0.028441667556762695 length of segment : 140 time for calcul the mask position with numpy : 0.002077341079711914 nb_pixel_total : 30290 time to create 1 rle with old method : 0.03464913368225098 length of segment : 171 time for calcul the mask position with numpy : 0.0038001537322998047 nb_pixel_total : 91422 time to create 1 rle with old method : 0.09872317314147949 length of segment : 362 time for calcul the mask position with numpy : 0.002454042434692383 nb_pixel_total : 47913 time to create 1 rle with old method : 0.05231022834777832 length of segment : 334 time for calcul the mask position with numpy : 0.0021359920501708984 nb_pixel_total : 29148 time to create 1 rle with old method : 0.032260894775390625 length of segment : 395 time for calcul the mask position with numpy : 0.0010743141174316406 nb_pixel_total : 11929 time to create 1 rle with old method : 0.013166666030883789 length of segment : 150 time for calcul the mask position with numpy : 0.00016117095947265625 nb_pixel_total : 6803 time to create 1 rle with old method : 0.007767200469970703 length of segment : 82 time for calcul the mask position with numpy : 0.0016911029815673828 nb_pixel_total : 21624 time to create 1 rle with old method : 0.024486303329467773 length of segment : 192 time for calcul the mask position with numpy : 0.00028061866760253906 nb_pixel_total : 6899 time to create 1 rle with old method : 0.008725643157958984 length of segment : 138 time for calcul the mask position with numpy : 0.004733562469482422 nb_pixel_total : 80318 time to create 1 rle with old method : 0.08911681175231934 length of segment : 405 time for calcul the mask position with numpy : 0.00348663330078125 nb_pixel_total : 67298 time to create 1 rle with old method : 0.07550477981567383 length of segment : 373 time for calcul the mask position with numpy : 0.006725311279296875 nb_pixel_total : 115522 time to create 1 rle with old method : 0.1452465057373047 length of segment : 356 time for calcul the mask position with numpy : 0.0005059242248535156 nb_pixel_total : 8287 time to create 1 rle with old method : 0.009377717971801758 length of segment : 175 time for calcul the mask position with numpy : 0.0005762577056884766 nb_pixel_total : 8487 time to create 1 rle with old method : 0.009973287582397461 length of segment : 109 time for calcul the mask position with numpy : 0.0005056858062744141 nb_pixel_total : 14061 time to create 1 rle with old method : 0.01644301414489746 length of segment : 147 time for calcul the mask position with numpy : 0.004224061965942383 nb_pixel_total : 87353 time to create 1 rle with old method : 0.09537887573242188 length of segment : 269 time for calcul the mask position with numpy : 0.0015559196472167969 nb_pixel_total : 27469 time to create 1 rle with old method : 0.03129220008850098 length of segment : 192 time for calcul the mask position with numpy : 0.0006973743438720703 nb_pixel_total : 9975 time to create 1 rle with old method : 0.012103557586669922 length of segment : 144 time for calcul the mask position with numpy : 0.0009362697601318359 nb_pixel_total : 11462 time to create 1 rle with old method : 0.013498306274414062 length of segment : 160 time for calcul the mask position with numpy : 0.002568960189819336 nb_pixel_total : 34549 time to create 1 rle with old method : 0.03984427452087402 length of segment : 217 time for calcul the mask position with numpy : 0.0011210441589355469 nb_pixel_total : 15142 time to create 1 rle with old method : 0.018358469009399414 length of segment : 133 time for calcul the mask position with numpy : 0.0009713172912597656 nb_pixel_total : 17120 time to create 1 rle with old method : 0.019820213317871094 length of segment : 106 time for calcul the mask position with numpy : 0.0027942657470703125 nb_pixel_total : 34711 time to create 1 rle with old method : 0.041728973388671875 length of segment : 219 time for calcul the mask position with numpy : 0.002270936965942383 nb_pixel_total : 32323 time to create 1 rle with old method : 0.05046701431274414 length of segment : 92 time for calcul the mask position with numpy : 0.004441499710083008 nb_pixel_total : 59467 time to create 1 rle with old method : 0.06824946403503418 length of segment : 241 time for calcul the mask position with numpy : 0.001604318618774414 nb_pixel_total : 26298 time to create 1 rle with old method : 0.03125929832458496 length of segment : 216 time for calcul the mask position with numpy : 0.0012936592102050781 nb_pixel_total : 12131 time to create 1 rle with old method : 0.014567852020263672 length of segment : 126 time for calcul the mask position with numpy : 0.002320528030395508 nb_pixel_total : 26051 time to create 1 rle with old method : 0.030337810516357422 length of segment : 381 time for calcul the mask position with numpy : 0.0012857913970947266 nb_pixel_total : 19103 time to create 1 rle with old method : 0.023307323455810547 length of segment : 239 time for calcul the mask position with numpy : 0.0058917999267578125 nb_pixel_total : 86726 time to create 1 rle with old method : 0.0981595516204834 length of segment : 346 time for calcul the mask position with numpy : 0.001119852066040039 nb_pixel_total : 15808 time to create 1 rle with old method : 0.018286705017089844 length of segment : 160 time for calcul the mask position with numpy : 0.0013511180877685547 nb_pixel_total : 17758 time to create 1 rle with old method : 0.02111339569091797 length of segment : 174 time for calcul the mask position with numpy : 0.0017313957214355469 nb_pixel_total : 19835 time to create 1 rle with old method : 0.023579835891723633 length of segment : 213 time for calcul the mask position with numpy : 0.0020198822021484375 nb_pixel_total : 25414 time to create 1 rle with old method : 0.029803752899169922 length of segment : 268 time for calcul the mask position with numpy : 0.0011730194091796875 nb_pixel_total : 8808 time to create 1 rle with old method : 0.010820388793945312 length of segment : 123 time for calcul the mask position with numpy : 0.0025441646575927734 nb_pixel_total : 31209 time to create 1 rle with old method : 0.0378422737121582 length of segment : 268 time for calcul the mask position with numpy : 0.0005359649658203125 nb_pixel_total : 10015 time to create 1 rle with old method : 0.01242375373840332 length of segment : 86 time for calcul the mask position with numpy : 0.0023488998413085938 nb_pixel_total : 27078 time to create 1 rle with old method : 0.03185129165649414 length of segment : 294 time for calcul the mask position with numpy : 0.0007495880126953125 nb_pixel_total : 9174 time to create 1 rle with old method : 0.010938167572021484 length of segment : 110 time for calcul the mask position with numpy : 0.00015807151794433594 nb_pixel_total : 3614 time to create 1 rle with old method : 0.0045223236083984375 length of segment : 81 time for calcul the mask position with numpy : 0.0013005733489990234 nb_pixel_total : 14086 time to create 1 rle with old method : 0.016283273696899414 length of segment : 150 time for calcul the mask position with numpy : 0.0010013580322265625 nb_pixel_total : 12600 time to create 1 rle with old method : 0.014560461044311523 length of segment : 141 time for calcul the mask position with numpy : 0.0014531612396240234 nb_pixel_total : 25848 time to create 1 rle with old method : 0.030575275421142578 length of segment : 206 time for calcul the mask position with numpy : 0.0014295578002929688 nb_pixel_total : 11650 time to create 1 rle with old method : 0.01427006721496582 length of segment : 156 time for calcul the mask position with numpy : 0.0015349388122558594 nb_pixel_total : 22999 time to create 1 rle with old method : 0.026689767837524414 length of segment : 207 time for calcul the mask position with numpy : 0.0024476051330566406 nb_pixel_total : 32252 time to create 1 rle with old method : 0.03744339942932129 length of segment : 219 time for calcul the mask position with numpy : 0.0016045570373535156 nb_pixel_total : 25141 time to create 1 rle with old method : 0.029624223709106445 length of segment : 204 time for calcul the mask position with numpy : 0.001966238021850586 nb_pixel_total : 28911 time to create 1 rle with old method : 0.03441333770751953 length of segment : 182 time for calcul the mask position with numpy : 0.0010111331939697266 nb_pixel_total : 16398 time to create 1 rle with old method : 0.01858830451965332 length of segment : 135 time for calcul the mask position with numpy : 0.0034894943237304688 nb_pixel_total : 59977 time to create 1 rle with old method : 0.06640028953552246 length of segment : 244 time for calcul the mask position with numpy : 0.0024154186248779297 nb_pixel_total : 39739 time to create 1 rle with old method : 0.04688739776611328 length of segment : 198 time for calcul the mask position with numpy : 0.0011518001556396484 nb_pixel_total : 15098 time to create 1 rle with old method : 0.017586708068847656 length of segment : 184 time for calcul the mask position with numpy : 0.002615213394165039 nb_pixel_total : 34778 time to create 1 rle with old method : 0.03840827941894531 length of segment : 249 time for calcul the mask position with numpy : 0.0018773078918457031 nb_pixel_total : 31123 time to create 1 rle with old method : 0.03504037857055664 length of segment : 218 time for calcul the mask position with numpy : 0.0012850761413574219 nb_pixel_total : 17125 time to create 1 rle with old method : 0.019939661026000977 length of segment : 154 time for calcul the mask position with numpy : 0.0016150474548339844 nb_pixel_total : 25317 time to create 1 rle with old method : 0.028808116912841797 length of segment : 353 time for calcul the mask position with numpy : 0.0016169548034667969 nb_pixel_total : 17518 time to create 1 rle with old method : 0.01963186264038086 length of segment : 248 time for calcul the mask position with numpy : 0.001772165298461914 nb_pixel_total : 17501 time to create 1 rle with old method : 0.020535945892333984 length of segment : 189 time for calcul the mask position with numpy : 0.002400636672973633 nb_pixel_total : 35631 time to create 1 rle with old method : 0.04171586036682129 length of segment : 235 time for calcul the mask position with numpy : 0.002183198928833008 nb_pixel_total : 19475 time to create 1 rle with old method : 0.022782564163208008 length of segment : 266 time for calcul the mask position with numpy : 0.0031206607818603516 nb_pixel_total : 36118 time to create 1 rle with old method : 0.040999412536621094 length of segment : 406 time for calcul the mask position with numpy : 0.0007727146148681641 nb_pixel_total : 8651 time to create 1 rle with old method : 0.010000228881835938 length of segment : 107 time for calcul the mask position with numpy : 0.0030138492584228516 nb_pixel_total : 36263 time to create 1 rle with old method : 0.04120802879333496 length of segment : 206 time for calcul the mask position with numpy : 0.0008180141448974609 nb_pixel_total : 12810 time to create 1 rle with old method : 0.014934062957763672 length of segment : 109 time for calcul the mask position with numpy : 0.0021600723266601562 nb_pixel_total : 37106 time to create 1 rle with old method : 0.04143691062927246 length of segment : 310 time for calcul the mask position with numpy : 0.0024573802947998047 nb_pixel_total : 23591 time to create 1 rle with old method : 0.02961444854736328 length of segment : 272 time for calcul the mask position with numpy : 0.0009260177612304688 nb_pixel_total : 13082 time to create 1 rle with old method : 0.015162944793701172 length of segment : 114 time for calcul the mask position with numpy : 0.0018854141235351562 nb_pixel_total : 23492 time to create 1 rle with old method : 0.026648759841918945 length of segment : 196 time for calcul the mask position with numpy : 0.0007083415985107422 nb_pixel_total : 9195 time to create 1 rle with old method : 0.010240554809570312 length of segment : 146 time for calcul the mask position with numpy : 0.0018391609191894531 nb_pixel_total : 28453 time to create 1 rle with old method : 0.0320286750793457 length of segment : 192 time for calcul the mask position with numpy : 0.0005135536193847656 nb_pixel_total : 8573 time to create 1 rle with old method : 0.009327888488769531 length of segment : 93 time for calcul the mask position with numpy : 0.0021848678588867188 nb_pixel_total : 44469 time to create 1 rle with old method : 0.054975032806396484 length of segment : 196 time for calcul the mask position with numpy : 0.00104522705078125 nb_pixel_total : 12032 time to create 1 rle with old method : 0.014559507369995117 length of segment : 149 time for calcul the mask position with numpy : 0.0012729167938232422 nb_pixel_total : 15424 time to create 1 rle with old method : 0.018574953079223633 length of segment : 206 time for calcul the mask position with numpy : 0.0016369819641113281 nb_pixel_total : 21996 time to create 1 rle with old method : 0.02684760093688965 length of segment : 194 time for calcul the mask position with numpy : 0.0010955333709716797 nb_pixel_total : 13231 time to create 1 rle with old method : 0.016518115997314453 length of segment : 139 time for calcul the mask position with numpy : 0.0015740394592285156 nb_pixel_total : 17344 time to create 1 rle with old method : 0.022063493728637695 length of segment : 158 time for calcul the mask position with numpy : 0.0033309459686279297 nb_pixel_total : 34057 time to create 1 rle with old method : 0.041352272033691406 length of segment : 251 time for calcul the mask position with numpy : 0.001905679702758789 nb_pixel_total : 19732 time to create 1 rle with old method : 0.02516627311706543 length of segment : 217 time for calcul the mask position with numpy : 0.0011925697326660156 nb_pixel_total : 10365 time to create 1 rle with old method : 0.013057947158813477 length of segment : 217 time for calcul the mask position with numpy : 0.0006606578826904297 nb_pixel_total : 9232 time to create 1 rle with old method : 0.012107133865356445 length of segment : 65 time for calcul the mask position with numpy : 0.006098270416259766 nb_pixel_total : 90782 time to create 1 rle with old method : 0.11344265937805176 length of segment : 271 time for calcul the mask position with numpy : 0.0012853145599365234 nb_pixel_total : 12756 time to create 1 rle with old method : 0.015915632247924805 length of segment : 196 time for calcul the mask position with numpy : 0.0012946128845214844 nb_pixel_total : 15751 time to create 1 rle with old method : 0.0192873477935791 length of segment : 150 time for calcul the mask position with numpy : 0.0033233165740966797 nb_pixel_total : 56124 time to create 1 rle with old method : 0.0668630599975586 length of segment : 330 time for calcul the mask position with numpy : 0.000682830810546875 nb_pixel_total : 10546 time to create 1 rle with old method : 0.013628959655761719 length of segment : 110 time for calcul the mask position with numpy : 0.0007228851318359375 nb_pixel_total : 10567 time to create 1 rle with old method : 0.013214826583862305 length of segment : 138 time for calcul the mask position with numpy : 0.0013098716735839844 nb_pixel_total : 13474 time to create 1 rle with old method : 0.017247676849365234 length of segment : 167 time for calcul the mask position with numpy : 0.002949237823486328 nb_pixel_total : 33321 time to create 1 rle with old method : 0.04050397872924805 length of segment : 185 time for calcul the mask position with numpy : 0.001226663589477539 nb_pixel_total : 12841 time to create 1 rle with old method : 0.016192913055419922 length of segment : 124 time for calcul the mask position with numpy : 0.0010488033294677734 nb_pixel_total : 18075 time to create 1 rle with old method : 0.02283501625061035 length of segment : 180 time for calcul the mask position with numpy : 0.0016083717346191406 nb_pixel_total : 22641 time to create 1 rle with old method : 0.027554035186767578 length of segment : 161 time for calcul the mask position with numpy : 0.001789093017578125 nb_pixel_total : 22246 time to create 1 rle with old method : 0.027379274368286133 length of segment : 167 time for calcul the mask position with numpy : 0.0024542808532714844 nb_pixel_total : 39493 time to create 1 rle with old method : 0.04797053337097168 length of segment : 191 time for calcul the mask position with numpy : 0.0014066696166992188 nb_pixel_total : 14686 time to create 1 rle with old method : 0.018243074417114258 length of segment : 149 time for calcul the mask position with numpy : 0.0009088516235351562 nb_pixel_total : 19320 time to create 1 rle with old method : 0.023684263229370117 length of segment : 238 time for calcul the mask position with numpy : 0.0005166530609130859 nb_pixel_total : 9424 time to create 1 rle with old method : 0.012022733688354492 length of segment : 75 time for calcul the mask position with numpy : 0.0043337345123291016 nb_pixel_total : 33855 time to create 1 rle with old method : 0.04184293746948242 length of segment : 240 time for calcul the mask position with numpy : 0.0005078315734863281 nb_pixel_total : 1999 time to create 1 rle with old method : 0.0028426647186279297 length of segment : 167 time for calcul the mask position with numpy : 0.004145383834838867 nb_pixel_total : 47265 time to create 1 rle with old method : 0.05840921401977539 length of segment : 236 time for calcul the mask position with numpy : 0.0006234645843505859 nb_pixel_total : 10340 time to create 1 rle with old method : 0.012511730194091797 length of segment : 175 time for calcul the mask position with numpy : 0.003416776657104492 nb_pixel_total : 54475 time to create 1 rle with old method : 0.06386971473693848 length of segment : 272 time for calcul the mask position with numpy : 0.0005619525909423828 nb_pixel_total : 9676 time to create 1 rle with old method : 0.011826276779174805 length of segment : 108 time for calcul the mask position with numpy : 0.0009753704071044922 nb_pixel_total : 14193 time to create 1 rle with old method : 0.01652359962463379 length of segment : 187 time for calcul the mask position with numpy : 0.00048732757568359375 nb_pixel_total : 26487 time to create 1 rle with old method : 0.03146815299987793 length of segment : 107 time for calcul the mask position with numpy : 0.0008378028869628906 nb_pixel_total : 16581 time to create 1 rle with old method : 0.01988053321838379 length of segment : 176 time for calcul the mask position with numpy : 0.0010898113250732422 nb_pixel_total : 12482 time to create 1 rle with old method : 0.014284372329711914 length of segment : 221 time for calcul the mask position with numpy : 0.0010900497436523438 nb_pixel_total : 21523 time to create 1 rle with old method : 0.027118206024169922 length of segment : 204 time for calcul the mask position with numpy : 0.00035381317138671875 nb_pixel_total : 8667 time to create 1 rle with old method : 0.011197090148925781 length of segment : 127 time for calcul the mask position with numpy : 0.0017502307891845703 nb_pixel_total : 23913 time to create 1 rle with old method : 0.028848886489868164 length of segment : 164 time for calcul the mask position with numpy : 0.001322031021118164 nb_pixel_total : 20771 time to create 1 rle with old method : 0.026504039764404297 length of segment : 145 time for calcul the mask position with numpy : 0.00015878677368164062 nb_pixel_total : 5126 time to create 1 rle with old method : 0.006078004837036133 length of segment : 63 time for calcul the mask position with numpy : 0.0005934238433837891 nb_pixel_total : 11338 time to create 1 rle with old method : 0.013257741928100586 length of segment : 147 time for calcul the mask position with numpy : 0.0014925003051757812 nb_pixel_total : 34974 time to create 1 rle with old method : 0.04249882698059082 length of segment : 226 time for calcul the mask position with numpy : 0.00037288665771484375 nb_pixel_total : 5160 time to create 1 rle with old method : 0.006336688995361328 length of segment : 93 time for calcul the mask position with numpy : 0.00020384788513183594 nb_pixel_total : 3456 time to create 1 rle with old method : 0.004142045974731445 length of segment : 69 time for calcul the mask position with numpy : 0.0023398399353027344 nb_pixel_total : 37423 time to create 1 rle with old method : 0.043562889099121094 length of segment : 288 time for calcul the mask position with numpy : 0.0038902759552001953 nb_pixel_total : 55495 time to create 1 rle with old method : 0.06279945373535156 length of segment : 441 time for calcul the mask position with numpy : 0.0012388229370117188 nb_pixel_total : 16441 time to create 1 rle with old method : 0.02002739906311035 length of segment : 243 time for calcul the mask position with numpy : 0.0006268024444580078 nb_pixel_total : 8943 time to create 1 rle with old method : 0.010387420654296875 length of segment : 97 time for calcul the mask position with numpy : 0.0008020401000976562 nb_pixel_total : 10997 time to create 1 rle with old method : 0.012299060821533203 length of segment : 132 time for calcul the mask position with numpy : 0.00013375282287597656 nb_pixel_total : 2537 time to create 1 rle with old method : 0.003269195556640625 length of segment : 68 time for calcul the mask position with numpy : 0.0002155303955078125 nb_pixel_total : 4002 time to create 1 rle with old method : 0.004929065704345703 length of segment : 82 time for calcul the mask position with numpy : 0.0015170574188232422 nb_pixel_total : 26144 time to create 1 rle with old method : 0.03009819984436035 length of segment : 234 time for calcul the mask position with numpy : 0.003555774688720703 nb_pixel_total : 56300 time to create 1 rle with old method : 0.06529521942138672 length of segment : 293 time for calcul the mask position with numpy : 0.0017514228820800781 nb_pixel_total : 31249 time to create 1 rle with old method : 0.03522515296936035 length of segment : 208 time for calcul the mask position with numpy : 0.0014188289642333984 nb_pixel_total : 27249 time to create 1 rle with old method : 0.03148818016052246 length of segment : 275 time for calcul the mask position with numpy : 0.0012516975402832031 nb_pixel_total : 27857 time to create 1 rle with old method : 0.03153586387634277 length of segment : 223 time for calcul the mask position with numpy : 0.001834869384765625 nb_pixel_total : 26752 time to create 1 rle with old method : 0.030153512954711914 length of segment : 297 time for calcul the mask position with numpy : 0.003622770309448242 nb_pixel_total : 64887 time to create 1 rle with old method : 0.07375383377075195 length of segment : 238 time for calcul the mask position with numpy : 0.001486063003540039 nb_pixel_total : 53490 time to create 1 rle with old method : 0.06077241897583008 length of segment : 417 time for calcul the mask position with numpy : 0.0005726814270019531 nb_pixel_total : 18159 time to create 1 rle with old method : 0.021521806716918945 length of segment : 122 time for calcul the mask position with numpy : 0.0007722377777099609 nb_pixel_total : 27399 time to create 1 rle with old method : 0.030694007873535156 length of segment : 206 time for calcul the mask position with numpy : 0.00020051002502441406 nb_pixel_total : 4430 time to create 1 rle with old method : 0.00516057014465332 length of segment : 94 time for calcul the mask position with numpy : 0.0007791519165039062 nb_pixel_total : 34416 time to create 1 rle with old method : 0.03787374496459961 length of segment : 263 time for calcul the mask position with numpy : 0.00025391578674316406 nb_pixel_total : 5429 time to create 1 rle with old method : 0.006136655807495117 length of segment : 101 time for calcul the mask position with numpy : 0.00025463104248046875 nb_pixel_total : 5963 time to create 1 rle with old method : 0.006953001022338867 length of segment : 50 time for calcul the mask position with numpy : 0.0002460479736328125 nb_pixel_total : 6309 time to create 1 rle with old method : 0.007206916809082031 length of segment : 101 time for calcul the mask position with numpy : 0.001203775405883789 nb_pixel_total : 31572 time to create 1 rle with old method : 0.03562808036804199 length of segment : 165 time for calcul the mask position with numpy : 0.00032591819763183594 nb_pixel_total : 8980 time to create 1 rle with old method : 0.010315656661987305 length of segment : 95 time for calcul the mask position with numpy : 0.00024509429931640625 nb_pixel_total : 7158 time to create 1 rle with old method : 0.008182525634765625 length of segment : 91 time spent for convertir_results : 29.791430473327637 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 417 chid ids of type : 3594 Number RLEs to save : 82089 save missing photos in datou_result : time spend for datou_step_exec : 171.49125742912292 time spend to save output : 14.980077266693115 total time spend for step 1 : 186.47133469581604 step2:crop_condition Thu Feb 6 01:23:37 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure Loading chi in step crop with photo_hashtag_type : 3594 Loading chi in step crop for list_pids : 9 ! batch 1 Loaded 417 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 ! map_result returned by crop_photo_return_map_crop : length : 336 About to insert : list_path_to_insert length 336 new photo from crops ! About to upload 336 photos upload in portfolio : 3736932 init cache_photo without model_param we have 336 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738801457_1649647 we have uploaded 336 photos in the portfolio 3736932 time of upload the photos Elapsed time : 102.12030053138733 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 ! map_result returned by crop_photo_return_map_crop : length : 42 About to insert : list_path_to_insert length 42 new photo from crops ! About to upload 42 photos upload in portfolio : 3736932 init cache_photo without model_param we have 42 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738801569_1649647 we have uploaded 42 photos in the portfolio 3736932 time of upload the photos Elapsed time : 13.849663496017456 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 7 About to insert : list_path_to_insert length 7 new photo from crops ! About to upload 7 photos upload in portfolio : 3736932 init cache_photo without model_param we have 7 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738801585_1649647 we have uploaded 7 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.892418622970581 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 19 About to insert : list_path_to_insert length 19 new photo from crops ! About to upload 19 photos upload in portfolio : 3736932 init cache_photo without model_param we have 19 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738801597_1649647 we have uploaded 19 photos in the portfolio 3736932 time of upload the photos Elapsed time : 6.581974506378174 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 9 About to insert : list_path_to_insert length 9 new photo from crops ! About to upload 9 photos upload in portfolio : 3736932 init cache_photo without model_param we have 9 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738801606_1649647 we have uploaded 9 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.558239221572876 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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/1738801610_1649647 we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.4610702991485596 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/1738801613_1649647 we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.694162130355835 we have finished the crop for the class : pet_fonce delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1334944838, 1334944834, 1334944830, 1334943171, 1334943139, 1334940964, 1334940961, 1334940958, 1334940953] Looping around the photos to save general results len do output : 417 /1335067778Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067779Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067780Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067781Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067782Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067783Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067784Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067785Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067788Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067791Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067794Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067798Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067801Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067805Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067808Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067811Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067813Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067814Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067815Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067816Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067817Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067818Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067819Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067820Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067821Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067822Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067823Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067824Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067825Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067827Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067828Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067829Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067830Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067836Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067839Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067842Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067844Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067845Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067846Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067847Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067848Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067849Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067853Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067854Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067855Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067856Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067857Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067858Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067859Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067861Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067862Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067863Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067864Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067865Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067866Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067867Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067868Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067869Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067870Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067871Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067872Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067873Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067874Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067877Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067879Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067881Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067883Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067885Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067887Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067889Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067891Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067893Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067895Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067897Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067899Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067901Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067903Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067905Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067908Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067910Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067912Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067914Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067916Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067918Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067920Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067922Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067924Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067927Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067929Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067932Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067934Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067936Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067938Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067940Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067943Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067945Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067949Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067953Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067957Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067958Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067960Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067962Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067964Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067966Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067968Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067970Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067974Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067976Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067978Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067981Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067983Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067985Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067987Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067989Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067991Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067993Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067996Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335067998Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068000Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068003Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068007Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068011Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068013Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068015Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068017Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068019Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068021Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068023Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068025Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068027Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068029Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068031Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068033Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068035Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068037Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068039Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068041Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068043Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068045Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068047Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068049Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068051Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068055Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068058Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068060Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068062Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068064Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068066Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068067Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068069Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068071Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068073Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068075Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068077Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068079Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068081Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068084Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068086Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068089Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068091Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068093Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068095Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068097Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068099Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068101Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068103Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068105Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068107Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068110Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068112Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068114Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068116Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068118Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068120Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068122Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068124Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068126Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068128Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068130Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068132Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068134Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068136Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068138Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068140Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068142Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068144Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068146Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068148Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068150Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068152Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068154Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068156Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068158Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068160Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068162Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068164Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068166Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068167Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068168Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068169Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068170Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068171Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068172Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068173Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068174Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068175Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068176Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068178Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068179Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068180Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068181Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068182Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068183Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068184Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068185Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068186Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068187Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068188Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068189Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068190Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068191Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068192Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068193Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068194Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068195Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068196Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068197Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068198Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068199Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068200Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068201Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068202Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068203Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068204Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068205Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068206Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068207Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068208Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068209Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068210Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068211Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068212Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068213Didn't retrieve data .Didn't retrieve 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/1335068596Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068598Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068600Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068602Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068651Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068652Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068711Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335068713Didn'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, '2557205') ('3318', '20277523', '1334944838', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334944834', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334944830', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334943171', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334943139', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940964', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940961', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940958', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940953', None, None, None, None, None, '2557205') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1260 time used for this insertion : 0.411318302154541 save_final save missing photos in datou_result : time spend for datou_step_exec : 197.1857626438141 time spend to save output : 0.4590795040130615 total time spend for step 2 : 197.64484214782715 step3:rle_unique_nms_with_priority Thu Feb 6 01:26:55 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 417 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 58 nb_hashtags : 3 time to prepare the origin masks : 3.9258415699005127 time for calcul the mask position with numpy : 0.44803309440612793 nb_pixel_total : 5727468 time to create 1 rle with new method : 0.46686410903930664 time for calcul the mask position with numpy : 0.028972387313842773 nb_pixel_total : 25552 time to create 1 rle with old method : 0.029436111450195312 time for calcul the mask position with numpy : 0.0305325984954834 nb_pixel_total : 9550 time to create 1 rle with old method : 0.010772466659545898 time for calcul the mask position with numpy : 0.02900981903076172 nb_pixel_total : 8806 time to create 1 rle with old method : 0.010444879531860352 time for calcul the mask position with numpy : 0.029547691345214844 nb_pixel_total : 40841 time to create 1 rle with old method : 0.04593658447265625 time for calcul the mask position with numpy : 0.029240846633911133 nb_pixel_total : 4180 time to create 1 rle with old method : 0.005105733871459961 time for calcul the mask position with numpy : 0.029913902282714844 nb_pixel_total : 60003 time to create 1 rle with old method : 0.06878829002380371 time for calcul the mask position with numpy : 0.029456377029418945 nb_pixel_total : 7762 time to create 1 rle with old method : 0.008918046951293945 time for calcul the mask position with numpy : 0.02938532829284668 nb_pixel_total : 6129 time to create 1 rle with old method : 0.0075778961181640625 time for calcul the mask position with numpy : 0.030011653900146484 nb_pixel_total : 17817 time to create 1 rle with old method : 0.0234835147857666 time for calcul the mask position with numpy : 0.031743526458740234 nb_pixel_total : 13731 time to create 1 rle with old method : 0.01548004150390625 time for calcul the mask position with numpy : 0.0298006534576416 nb_pixel_total : 16860 time to create 1 rle with old method : 0.01964592933654785 time for calcul the mask position with numpy : 0.03247427940368652 nb_pixel_total : 19039 time to create 1 rle with old method : 0.03148794174194336 time for calcul the mask position with numpy : 0.03618478775024414 nb_pixel_total : 26255 time to create 1 rle with old method : 0.04198765754699707 time for calcul the mask position with numpy : 0.03499245643615723 nb_pixel_total : 13217 time to create 1 rle with old method : 0.02111363410949707 time for calcul the mask position with numpy : 0.029604434967041016 nb_pixel_total : 11803 time to create 1 rle with old method : 0.013974189758300781 time for calcul the mask position with numpy : 0.029117822647094727 nb_pixel_total : 5454 time to create 1 rle with old method : 0.006471157073974609 time for calcul the mask position with numpy : 0.0291593074798584 nb_pixel_total : 31999 time to create 1 rle with old method : 0.036093950271606445 time for calcul the mask position with numpy : 0.029471397399902344 nb_pixel_total : 16033 time to create 1 rle with old method : 0.018381118774414062 time for calcul the mask position with numpy : 0.02933645248413086 nb_pixel_total : 26367 time to create 1 rle with old method : 0.04024481773376465 time for calcul the mask position with numpy : 0.033265113830566406 nb_pixel_total : 35794 time to create 1 rle with old method : 0.04199409484863281 time for calcul the mask position with numpy : 0.02931666374206543 nb_pixel_total : 10863 time to create 1 rle with old method : 0.012330770492553711 time for calcul the mask position with numpy : 0.02926921844482422 nb_pixel_total : 23898 time to create 1 rle with old method : 0.027367830276489258 time for calcul the mask position with numpy : 0.029317378997802734 nb_pixel_total : 12348 time to create 1 rle with old method : 0.013936758041381836 time for calcul the mask position with numpy : 0.029529809951782227 nb_pixel_total : 22117 time to create 1 rle with old method : 0.0256955623626709 time for calcul the mask position with numpy : 0.030459880828857422 nb_pixel_total : 44202 time to create 1 rle with old method : 0.04989123344421387 time for calcul the mask position with numpy : 0.02921605110168457 nb_pixel_total : 9458 time to create 1 rle with old method : 0.011106491088867188 time for calcul the mask position with numpy : 0.029659748077392578 nb_pixel_total : 24639 time to create 1 rle with old method : 0.03352093696594238 time for calcul the mask position with numpy : 0.03306460380554199 nb_pixel_total : 37180 time to create 1 rle with old method : 0.05881619453430176 time for calcul the mask position with numpy : 0.03502917289733887 nb_pixel_total : 6344 time to create 1 rle with old method : 0.007231235504150391 time for calcul the mask position with numpy : 0.029659032821655273 nb_pixel_total : 39991 time to create 1 rle with old method : 0.04541897773742676 time for calcul the mask position with numpy : 0.029103755950927734 nb_pixel_total : 10147 time to create 1 rle with old method : 0.011650800704956055 time for calcul the mask position with numpy : 0.029112815856933594 nb_pixel_total : 7734 time to create 1 rle with old method : 0.008815765380859375 time for calcul the mask position with numpy : 0.02925562858581543 nb_pixel_total : 12212 time to create 1 rle with old method : 0.013901948928833008 time for calcul the mask position with numpy : 0.029523611068725586 nb_pixel_total : 34757 time to create 1 rle with old method : 0.03895378112792969 time for calcul the mask position with numpy : 0.029482603073120117 nb_pixel_total : 22606 time to create 1 rle with old method : 0.025440454483032227 time for calcul the mask position with numpy : 0.029384374618530273 nb_pixel_total : 11010 time to create 1 rle with old method : 0.012668848037719727 time for calcul the mask position with numpy : 0.02941155433654785 nb_pixel_total : 36193 time to create 1 rle with old method : 0.04364418983459473 time for calcul the mask position with numpy : 0.029355764389038086 nb_pixel_total : 15040 time to create 1 rle with old method : 0.017292261123657227 time for calcul the mask position with numpy : 0.02898120880126953 nb_pixel_total : 16164 time to create 1 rle with old method : 0.018769025802612305 time for calcul the mask position with numpy : 0.028980255126953125 nb_pixel_total : 6737 time to create 1 rle with old method : 0.008008241653442383 time for calcul the mask position with numpy : 0.03149724006652832 nb_pixel_total : 15563 time to create 1 rle with old method : 0.017848968505859375 time for calcul the mask position with numpy : 0.029082536697387695 nb_pixel_total : 4786 time to create 1 rle with old method : 0.0057947635650634766 time for calcul the mask position with numpy : 0.02894306182861328 nb_pixel_total : 19644 time to create 1 rle with old method : 0.022207021713256836 time for calcul the mask position with numpy : 0.03325319290161133 nb_pixel_total : 306263 time to create 1 rle with new method : 0.6392486095428467 time for calcul the mask position with numpy : 0.029176950454711914 nb_pixel_total : 27331 time to create 1 rle with old method : 0.040044546127319336 time for calcul the mask position with numpy : 0.03297090530395508 nb_pixel_total : 20659 time to create 1 rle with old method : 0.03390789031982422 time for calcul the mask position with numpy : 0.03342461585998535 nb_pixel_total : 4650 time to create 1 rle with old method : 0.0075531005859375 time for calcul the mask position with numpy : 0.030950307846069336 nb_pixel_total : 19540 time to create 1 rle with old method : 0.02227783203125 time for calcul the mask position with numpy : 0.02935171127319336 nb_pixel_total : 7105 time to create 1 rle with old method : 0.008219480514526367 time for calcul the mask position with numpy : 0.02919316291809082 nb_pixel_total : 1445 time to create 1 rle with old method : 0.0017812252044677734 time for calcul the mask position with numpy : 0.029278039932250977 nb_pixel_total : 8478 time to create 1 rle with old method : 0.009681224822998047 time for calcul the mask position with numpy : 0.02927398681640625 nb_pixel_total : 9269 time to create 1 rle with old method : 0.010593652725219727 time for calcul the mask position with numpy : 0.029234886169433594 nb_pixel_total : 19205 time to create 1 rle with old method : 0.022553443908691406 time for calcul the mask position with numpy : 0.029191970825195312 nb_pixel_total : 10454 time to create 1 rle with old method : 0.012242555618286133 time for calcul the mask position with numpy : 0.029314517974853516 nb_pixel_total : 23912 time to create 1 rle with old method : 0.02758193016052246 time for calcul the mask position with numpy : 0.03086233139038086 nb_pixel_total : 12334 time to create 1 rle with old method : 0.014014482498168945 time for calcul the mask position with numpy : 0.0292510986328125 nb_pixel_total : 7813 time to create 1 rle with old method : 0.00922536849975586 time for calcul the mask position with numpy : 0.029301166534423828 nb_pixel_total : 3489 time to create 1 rle with old method : 0.00422358512878418 create new chi : 4.617602825164795 time to delete rle : 0.01433420181274414 batch 1 Loaded 117 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 22994 TO DO : save crop sub photo not yet done ! save time : 2.570784091949463 nb_obj : 47 nb_hashtags : 6 time to prepare the origin masks : 3.8853421211242676 time for calcul the mask position with numpy : 0.07145047187805176 nb_pixel_total : 5674829 time to create 1 rle with new method : 0.15401935577392578 time for calcul the mask position with numpy : 0.028635263442993164 nb_pixel_total : 29562 time to create 1 rle with old method : 0.03328347206115723 time for calcul the mask position with numpy : 0.028890132904052734 nb_pixel_total : 17444 time to create 1 rle with old method : 0.0200345516204834 time for calcul the mask position with numpy : 0.02893233299255371 nb_pixel_total : 77607 time to create 1 rle with old method : 0.08743548393249512 time for calcul the mask position with numpy : 0.028846263885498047 nb_pixel_total : 21927 time to create 1 rle with old method : 0.025168418884277344 time for calcul the mask position with numpy : 0.02871990203857422 nb_pixel_total : 12586 time to create 1 rle with old method : 0.014757871627807617 time for calcul the mask position with numpy : 0.02870917320251465 nb_pixel_total : 12292 time to create 1 rle with old method : 0.014461517333984375 time for calcul the mask position with numpy : 0.02889418601989746 nb_pixel_total : 23299 time to create 1 rle with old method : 0.029053926467895508 time for calcul the mask position with numpy : 0.032735347747802734 nb_pixel_total : 10930 time to create 1 rle with old method : 0.01768040657043457 time for calcul the mask position with numpy : 0.03182506561279297 nb_pixel_total : 6187 time to create 1 rle with old method : 0.007040739059448242 time for calcul the mask position with numpy : 0.02880573272705078 nb_pixel_total : 25923 time to create 1 rle with old method : 0.02931356430053711 time for calcul the mask position with numpy : 0.029294729232788086 nb_pixel_total : 26778 time to create 1 rle with old method : 0.030121326446533203 time for calcul the mask position with numpy : 0.028910398483276367 nb_pixel_total : 10919 time to create 1 rle with old method : 0.01252293586730957 time for calcul the mask position with numpy : 0.028696537017822266 nb_pixel_total : 16912 time to create 1 rle with old method : 0.019131183624267578 time for calcul the mask position with numpy : 0.029024839401245117 nb_pixel_total : 10615 time to create 1 rle with old method : 0.012232780456542969 time for calcul the mask position with numpy : 0.028791189193725586 nb_pixel_total : 72928 time to create 1 rle with old method : 0.07996296882629395 time for calcul the mask position with numpy : 0.02821826934814453 nb_pixel_total : 5571 time to create 1 rle with old method : 0.006411552429199219 time for calcul the mask position with numpy : 0.028596878051757812 nb_pixel_total : 20477 time to create 1 rle with old method : 0.02308511734008789 time for calcul the mask position with numpy : 0.028540611267089844 nb_pixel_total : 5265 time to create 1 rle with old method : 0.005995750427246094 time for calcul the mask position with numpy : 0.028474807739257812 nb_pixel_total : 29902 time to create 1 rle with old method : 0.03378033638000488 time for calcul the mask position with numpy : 0.028116703033447266 nb_pixel_total : 7179 time to create 1 rle with old method : 0.008283615112304688 time for calcul the mask position with numpy : 0.028550148010253906 nb_pixel_total : 15081 time to create 1 rle with old method : 0.01716017723083496 time for calcul the mask position with numpy : 0.030752182006835938 nb_pixel_total : 74761 time to create 1 rle with old method : 0.08173322677612305 time for calcul the mask position with numpy : 0.028463125228881836 nb_pixel_total : 8694 time to create 1 rle with old method : 0.009892940521240234 time for calcul the mask position with numpy : 0.027832984924316406 nb_pixel_total : 27675 time to create 1 rle with old method : 0.030098438262939453 time for calcul the mask position with numpy : 0.029006004333496094 nb_pixel_total : 154761 time to create 1 rle with new method : 0.1260511875152588 time for calcul the mask position with numpy : 0.027353763580322266 nb_pixel_total : 24220 time to create 1 rle with old method : 0.026276588439941406 time for calcul the mask position with numpy : 0.028194189071655273 nb_pixel_total : 18425 time to create 1 rle with old method : 0.02073216438293457 time for calcul the mask position with numpy : 0.02825140953063965 nb_pixel_total : 3412 time to create 1 rle with old method : 0.004068613052368164 time for calcul the mask position with numpy : 0.028139114379882812 nb_pixel_total : 9707 time to create 1 rle with old method : 0.015434980392456055 time for calcul the mask position with numpy : 0.02822732925415039 nb_pixel_total : 394 time to create 1 rle with old method : 0.0005066394805908203 time for calcul the mask position with numpy : 0.02780771255493164 nb_pixel_total : 45599 time to create 1 rle with old method : 0.05001211166381836 time for calcul the mask position with numpy : 0.02825307846069336 nb_pixel_total : 39059 time to create 1 rle with old method : 0.04356026649475098 time for calcul the mask position with numpy : 0.028356552124023438 nb_pixel_total : 127030 time to create 1 rle with old method : 0.16161704063415527 time for calcul the mask position with numpy : 0.027113914489746094 nb_pixel_total : 7820 time to create 1 rle with old method : 0.009078025817871094 time for calcul the mask position with numpy : 0.0273895263671875 nb_pixel_total : 17958 time to create 1 rle with old method : 0.019069194793701172 time for calcul the mask position with numpy : 0.02664351463317871 nb_pixel_total : 12598 time to create 1 rle with old method : 0.013270378112792969 time for calcul the mask position with numpy : 0.02745509147644043 nb_pixel_total : 9582 time to create 1 rle with old method : 0.010434150695800781 time for calcul the mask position with numpy : 0.027629613876342773 nb_pixel_total : 6322 time to create 1 rle with old method : 0.007053852081298828 time for calcul the mask position with numpy : 0.027850866317749023 nb_pixel_total : 3754 time to create 1 rle with old method : 0.0041408538818359375 time for calcul the mask position with numpy : 0.027869224548339844 nb_pixel_total : 8856 time to create 1 rle with old method : 0.009982585906982422 time for calcul the mask position with numpy : 0.027797460556030273 nb_pixel_total : 26621 time to create 1 rle with old method : 0.029041767120361328 time for calcul the mask position with numpy : 0.028186798095703125 nb_pixel_total : 2505 time to create 1 rle with old method : 0.002924680709838867 time for calcul the mask position with numpy : 0.027623891830444336 nb_pixel_total : 27342 time to create 1 rle with old method : 0.029206037521362305 time for calcul the mask position with numpy : 0.02667713165283203 nb_pixel_total : 3084 time to create 1 rle with old method : 0.003465890884399414 time for calcul the mask position with numpy : 0.0286710262298584 nb_pixel_total : 229212 time to create 1 rle with new method : 0.1277637481689453 time for calcul the mask position with numpy : 0.0273592472076416 nb_pixel_total : 20186 time to create 1 rle with old method : 0.02200174331665039 time for calcul the mask position with numpy : 0.027052640914916992 nb_pixel_total : 6450 time to create 1 rle with old method : 0.007214784622192383 create new chi : 2.9969589710235596 time to delete rle : 0.002920389175415039 batch 1 Loaded 95 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 21000 TO DO : save crop sub photo not yet done ! save time : 1.6265842914581299 nb_obj : 50 nb_hashtags : 5 time to prepare the origin masks : 3.3519153594970703 time for calcul the mask position with numpy : 0.068328857421875 nb_pixel_total : 6065869 time to create 1 rle with new method : 0.14217400550842285 time for calcul the mask position with numpy : 0.026804685592651367 nb_pixel_total : 23953 time to create 1 rle with old method : 0.025957584381103516 time for calcul the mask position with numpy : 0.02704787254333496 nb_pixel_total : 48689 time to create 1 rle with old method : 0.05339956283569336 time for calcul the mask position with numpy : 0.026955366134643555 nb_pixel_total : 19560 time to create 1 rle with old method : 0.021399974822998047 time for calcul the mask position with numpy : 0.0273587703704834 nb_pixel_total : 20469 time to create 1 rle with old method : 0.02230095863342285 time for calcul the mask position with numpy : 0.027330636978149414 nb_pixel_total : 26573 time to create 1 rle with old method : 0.02800607681274414 time for calcul the mask position with numpy : 0.026480436325073242 nb_pixel_total : 10672 time to create 1 rle with old method : 0.011608123779296875 time for calcul the mask position with numpy : 0.027083158493041992 nb_pixel_total : 13858 time to create 1 rle with old method : 0.015166997909545898 time for calcul the mask position with numpy : 0.026869773864746094 nb_pixel_total : 90220 time to create 1 rle with old method : 0.09567070007324219 time for calcul the mask position with numpy : 0.02651381492614746 nb_pixel_total : 5135 time to create 1 rle with old method : 0.005700349807739258 time for calcul the mask position with numpy : 0.026670217514038086 nb_pixel_total : 11067 time to create 1 rle with old method : 0.01204061508178711 time for calcul the mask position with numpy : 0.02625584602355957 nb_pixel_total : 12252 time to create 1 rle with old method : 0.013320446014404297 time for calcul the mask position with numpy : 0.02672410011291504 nb_pixel_total : 2643 time to create 1 rle with old method : 0.0029227733612060547 time for calcul the mask position with numpy : 0.02649855613708496 nb_pixel_total : 12633 time to create 1 rle with old method : 0.013945341110229492 time for calcul the mask position with numpy : 0.028188228607177734 nb_pixel_total : 60630 time to create 1 rle with old method : 0.06323051452636719 time for calcul the mask position with numpy : 0.02647566795349121 nb_pixel_total : 19640 time to create 1 rle with old method : 0.020425796508789062 time for calcul the mask position with numpy : 0.026998281478881836 nb_pixel_total : 22354 time to create 1 rle with old method : 0.024404287338256836 time for calcul the mask position with numpy : 0.026911020278930664 nb_pixel_total : 5818 time to create 1 rle with old method : 0.006263017654418945 time for calcul the mask position with numpy : 0.027136564254760742 nb_pixel_total : 16065 time to create 1 rle with old method : 0.017427921295166016 time for calcul the mask position with numpy : 0.02696967124938965 nb_pixel_total : 8666 time to create 1 rle with old method : 0.009561777114868164 time for calcul the mask position with numpy : 0.026909351348876953 nb_pixel_total : 12480 time to create 1 rle with old method : 0.013769865036010742 time for calcul the mask position with numpy : 0.026234149932861328 nb_pixel_total : 42957 time to create 1 rle with old method : 0.04497075080871582 time for calcul the mask position with numpy : 0.026566505432128906 nb_pixel_total : 18928 time to create 1 rle with old method : 0.0202634334564209 time for calcul the mask position with numpy : 0.027480125427246094 nb_pixel_total : 117841 time to create 1 rle with old method : 0.12688493728637695 time for calcul the mask position with numpy : 0.02735137939453125 nb_pixel_total : 24113 time to create 1 rle with old method : 0.02600550651550293 time for calcul the mask position with numpy : 0.026922225952148438 nb_pixel_total : 4395 time to create 1 rle with old method : 0.004858970642089844 time for calcul the mask position with numpy : 0.026363372802734375 nb_pixel_total : 3675 time to create 1 rle with old method : 0.004090070724487305 time for calcul the mask position with numpy : 0.02677440643310547 nb_pixel_total : 3142 time to create 1 rle with old method : 0.0033338069915771484 time for calcul the mask position with numpy : 0.026560306549072266 nb_pixel_total : 31568 time to create 1 rle with old method : 0.03442263603210449 time for calcul the mask position with numpy : 0.026817798614501953 nb_pixel_total : 3434 time to create 1 rle with old method : 0.0036253929138183594 time for calcul the mask position with numpy : 0.026559114456176758 nb_pixel_total : 26652 time to create 1 rle with old method : 0.028099536895751953 time for calcul the mask position with numpy : 0.02633213996887207 nb_pixel_total : 4501 time to create 1 rle with old method : 0.004898548126220703 time for calcul the mask position with numpy : 0.026488542556762695 nb_pixel_total : 4473 time to create 1 rle with old method : 0.0048253536224365234 time for calcul the mask position with numpy : 0.02733302116394043 nb_pixel_total : 30347 time to create 1 rle with old method : 0.03334164619445801 time for calcul the mask position with numpy : 0.027635574340820312 nb_pixel_total : 10185 time to create 1 rle with old method : 0.011586189270019531 time for calcul the mask position with numpy : 0.027812719345092773 nb_pixel_total : 4516 time to create 1 rle with old method : 0.0050160884857177734 time for calcul the mask position with numpy : 0.027637004852294922 nb_pixel_total : 20838 time to create 1 rle with old method : 0.023685693740844727 time for calcul the mask position with numpy : 0.02809762954711914 nb_pixel_total : 21223 time to create 1 rle with old method : 0.023777246475219727 time for calcul the mask position with numpy : 0.0283966064453125 nb_pixel_total : 2666 time to create 1 rle with old method : 0.0031855106353759766 time for calcul the mask position with numpy : 0.0289914608001709 nb_pixel_total : 8577 time to create 1 rle with old method : 0.00972437858581543 time for calcul the mask position with numpy : 0.029120683670043945 nb_pixel_total : 10618 time to create 1 rle with old method : 0.012350320816040039 time for calcul the mask position with numpy : 0.028517484664916992 nb_pixel_total : 6019 time to create 1 rle with old method : 0.0069828033447265625 time for calcul the mask position with numpy : 0.027842283248901367 nb_pixel_total : 14667 time to create 1 rle with old method : 0.0164639949798584 time for calcul the mask position with numpy : 0.02771615982055664 nb_pixel_total : 12236 time to create 1 rle with old method : 0.01389312744140625 time for calcul the mask position with numpy : 0.027914047241210938 nb_pixel_total : 32727 time to create 1 rle with old method : 0.03656888008117676 time for calcul the mask position with numpy : 0.028074264526367188 nb_pixel_total : 3963 time to create 1 rle with old method : 0.0044972896575927734 time for calcul the mask position with numpy : 0.02778792381286621 nb_pixel_total : 17618 time to create 1 rle with old method : 0.019271373748779297 time for calcul the mask position with numpy : 0.02729010581970215 nb_pixel_total : 32186 time to create 1 rle with old method : 0.034601688385009766 time for calcul the mask position with numpy : 0.02794957160949707 nb_pixel_total : 6466 time to create 1 rle with old method : 0.0076940059661865234 time for calcul the mask position with numpy : 0.02756953239440918 nb_pixel_total : 3442 time to create 1 rle with old method : 0.003736734390258789 time for calcul the mask position with numpy : 0.026735305786132812 nb_pixel_total : 17021 time to create 1 rle with old method : 0.018724679946899414 create new chi : 2.655705451965332 time to delete rle : 0.0027742385864257812 batch 1 Loaded 101 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 21425 TO DO : save crop sub photo not yet done ! save time : 8.040777683258057 nb_obj : 46 nb_hashtags : 4 time to prepare the origin masks : 4.1909120082855225 time for calcul the mask position with numpy : 0.07010531425476074 nb_pixel_total : 5133348 time to create 1 rle with new method : 0.15148258209228516 time for calcul the mask position with numpy : 0.028922080993652344 nb_pixel_total : 8962 time to create 1 rle with old method : 0.01002049446105957 time for calcul the mask position with numpy : 0.030470848083496094 nb_pixel_total : 197670 time to create 1 rle with new method : 0.13024425506591797 time for calcul the mask position with numpy : 0.028962373733520508 nb_pixel_total : 11023 time to create 1 rle with old method : 0.012641668319702148 time for calcul the mask position with numpy : 0.02952408790588379 nb_pixel_total : 131285 time to create 1 rle with old method : 0.14805197715759277 time for calcul the mask position with numpy : 0.029031038284301758 nb_pixel_total : 10898 time to create 1 rle with old method : 0.01241302490234375 time for calcul the mask position with numpy : 0.02915024757385254 nb_pixel_total : 25424 time to create 1 rle with old method : 0.028458356857299805 time for calcul the mask position with numpy : 0.028992891311645508 nb_pixel_total : 63578 time to create 1 rle with old method : 0.07227659225463867 time for calcul the mask position with numpy : 0.029711484909057617 nb_pixel_total : 198344 time to create 1 rle with new method : 0.12622547149658203 time for calcul the mask position with numpy : 0.029125690460205078 nb_pixel_total : 9364 time to create 1 rle with old method : 0.010768890380859375 time for calcul the mask position with numpy : 0.02928948402404785 nb_pixel_total : 45246 time to create 1 rle with old method : 0.05068707466125488 time for calcul the mask position with numpy : 0.029349803924560547 nb_pixel_total : 21722 time to create 1 rle with old method : 0.024600744247436523 time for calcul the mask position with numpy : 0.029201984405517578 nb_pixel_total : 7630 time to create 1 rle with old method : 0.008673429489135742 time for calcul the mask position with numpy : 0.03035449981689453 nb_pixel_total : 273194 time to create 1 rle with new method : 0.12743496894836426 time for calcul the mask position with numpy : 0.029119253158569336 nb_pixel_total : 19022 time to create 1 rle with old method : 0.021549224853515625 time for calcul the mask position with numpy : 0.028987646102905273 nb_pixel_total : 13729 time to create 1 rle with old method : 0.016212940216064453 time for calcul the mask position with numpy : 0.029194355010986328 nb_pixel_total : 10989 time to create 1 rle with old method : 0.01272726058959961 time for calcul the mask position with numpy : 0.029052019119262695 nb_pixel_total : 58515 time to create 1 rle with old method : 0.06621503829956055 time for calcul the mask position with numpy : 0.02968573570251465 nb_pixel_total : 64381 time to create 1 rle with old method : 0.11410069465637207 time for calcul the mask position with numpy : 0.0372617244720459 nb_pixel_total : 10721 time to create 1 rle with old method : 0.01946711540222168 time for calcul the mask position with numpy : 0.032036542892456055 nb_pixel_total : 34704 time to create 1 rle with old method : 0.038790225982666016 time for calcul the mask position with numpy : 0.029764890670776367 nb_pixel_total : 102849 time to create 1 rle with old method : 0.11493372917175293 time for calcul the mask position with numpy : 0.029297828674316406 nb_pixel_total : 5729 time to create 1 rle with old method : 0.006619691848754883 time for calcul the mask position with numpy : 0.02886962890625 nb_pixel_total : 9929 time to create 1 rle with old method : 0.011181831359863281 time for calcul the mask position with numpy : 0.02901601791381836 nb_pixel_total : 25414 time to create 1 rle with old method : 0.028596878051757812 time for calcul the mask position with numpy : 0.02900218963623047 nb_pixel_total : 10312 time to create 1 rle with old method : 0.012180566787719727 time for calcul the mask position with numpy : 0.029180526733398438 nb_pixel_total : 34591 time to create 1 rle with old method : 0.038913726806640625 time for calcul the mask position with numpy : 0.028888225555419922 nb_pixel_total : 6728 time to create 1 rle with old method : 0.00802159309387207 time for calcul the mask position with numpy : 0.02884984016418457 nb_pixel_total : 6481 time to create 1 rle with old method : 0.007756710052490234 time for calcul the mask position with numpy : 0.028923749923706055 nb_pixel_total : 7814 time to create 1 rle with old method : 0.009000301361083984 time for calcul the mask position with numpy : 0.028938770294189453 nb_pixel_total : 9370 time to create 1 rle with old method : 0.01112055778503418 time for calcul the mask position with numpy : 0.027896881103515625 nb_pixel_total : 11005 time to create 1 rle with old method : 0.01243448257446289 time for calcul the mask position with numpy : 0.02790665626525879 nb_pixel_total : 15269 time to create 1 rle with old method : 0.017120838165283203 time for calcul the mask position with numpy : 0.02857661247253418 nb_pixel_total : 9246 time to create 1 rle with old method : 0.01051473617553711 time for calcul the mask position with numpy : 0.028061628341674805 nb_pixel_total : 3110 time to create 1 rle with old method : 0.003667116165161133 time for calcul the mask position with numpy : 0.028018951416015625 nb_pixel_total : 11910 time to create 1 rle with old method : 0.013033628463745117 time for calcul the mask position with numpy : 0.027536392211914062 nb_pixel_total : 37657 time to create 1 rle with old method : 0.04059600830078125 time for calcul the mask position with numpy : 0.029514312744140625 nb_pixel_total : 282825 time to create 1 rle with new method : 0.14516949653625488 time for calcul the mask position with numpy : 0.028138160705566406 nb_pixel_total : 59236 time to create 1 rle with old method : 0.06435322761535645 time for calcul the mask position with numpy : 0.0278623104095459 nb_pixel_total : 72 time to create 1 rle with old method : 0.00014066696166992188 time for calcul the mask position with numpy : 0.027556657791137695 nb_pixel_total : 7400 time to create 1 rle with old method : 0.008409261703491211 time for calcul the mask position with numpy : 0.02789783477783203 nb_pixel_total : 7287 time to create 1 rle with old method : 0.008238792419433594 time for calcul the mask position with numpy : 0.02849864959716797 nb_pixel_total : 14052 time to create 1 rle with old method : 0.015638351440429688 time for calcul the mask position with numpy : 0.032740116119384766 nb_pixel_total : 2451 time to create 1 rle with old method : 0.002955913543701172 time for calcul the mask position with numpy : 0.029098987579345703 nb_pixel_total : 3369 time to create 1 rle with old method : 0.004002094268798828 time for calcul the mask position with numpy : 0.02875542640686035 nb_pixel_total : 6828 time to create 1 rle with old method : 0.008152961730957031 time for calcul the mask position with numpy : 0.02892136573791504 nb_pixel_total : 9557 time to create 1 rle with old method : 0.011061429977416992 create new chi : 3.302716016769409 time to delete rle : 0.0034008026123046875 batch 1 Loaded 93 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 21639 TO DO : save crop sub photo not yet done ! save time : 1.581855297088623 nb_obj : 52 nb_hashtags : 2 time to prepare the origin masks : 3.4799821376800537 time for calcul the mask position with numpy : 0.07267212867736816 nb_pixel_total : 6233358 time to create 1 rle with new method : 0.14343976974487305 time for calcul the mask position with numpy : 0.02839827537536621 nb_pixel_total : 3151 time to create 1 rle with old method : 0.003655672073364258 time for calcul the mask position with numpy : 0.0283205509185791 nb_pixel_total : 2818 time to create 1 rle with old method : 0.003445863723754883 time for calcul the mask position with numpy : 0.02796173095703125 nb_pixel_total : 6611 time to create 1 rle with old method : 0.007574558258056641 time for calcul the mask position with numpy : 0.028072118759155273 nb_pixel_total : 7698 time to create 1 rle with old method : 0.008599042892456055 time for calcul the mask position with numpy : 0.028104305267333984 nb_pixel_total : 9917 time to create 1 rle with old method : 0.011441946029663086 time for calcul the mask position with numpy : 0.027962446212768555 nb_pixel_total : 3385 time to create 1 rle with old method : 0.003998279571533203 time for calcul the mask position with numpy : 0.026896953582763672 nb_pixel_total : 6485 time to create 1 rle with old method : 0.007017374038696289 time for calcul the mask position with numpy : 0.027589797973632812 nb_pixel_total : 21179 time to create 1 rle with old method : 0.023395061492919922 time for calcul the mask position with numpy : 0.026815176010131836 nb_pixel_total : 5722 time to create 1 rle with old method : 0.006429910659790039 time for calcul the mask position with numpy : 0.026849746704101562 nb_pixel_total : 8862 time to create 1 rle with old method : 0.00965428352355957 time for calcul the mask position with numpy : 0.0271759033203125 nb_pixel_total : 47074 time to create 1 rle with old method : 0.05061960220336914 time for calcul the mask position with numpy : 0.028119802474975586 nb_pixel_total : 27969 time to create 1 rle with old method : 0.031909942626953125 time for calcul the mask position with numpy : 0.0289154052734375 nb_pixel_total : 30601 time to create 1 rle with old method : 0.03434014320373535 time for calcul the mask position with numpy : 0.02915668487548828 nb_pixel_total : 13388 time to create 1 rle with old method : 0.015079975128173828 time for calcul the mask position with numpy : 0.028137683868408203 nb_pixel_total : 9158 time to create 1 rle with old method : 0.010250329971313477 time for calcul the mask position with numpy : 0.027669906616210938 nb_pixel_total : 46688 time to create 1 rle with old method : 0.05207490921020508 time for calcul the mask position with numpy : 0.028789520263671875 nb_pixel_total : 14244 time to create 1 rle with old method : 0.01600813865661621 time for calcul the mask position with numpy : 0.02909231185913086 nb_pixel_total : 5007 time to create 1 rle with old method : 0.005900144577026367 time for calcul the mask position with numpy : 0.028912067413330078 nb_pixel_total : 2502 time to create 1 rle with old method : 0.003011465072631836 time for calcul the mask position with numpy : 0.02861928939819336 nb_pixel_total : 19311 time to create 1 rle with old method : 0.022049665451049805 time for calcul the mask position with numpy : 0.02904963493347168 nb_pixel_total : 5948 time to create 1 rle with old method : 0.006979227066040039 time for calcul the mask position with numpy : 0.028987646102905273 nb_pixel_total : 35828 time to create 1 rle with old method : 0.03981161117553711 time for calcul the mask position with numpy : 0.028855562210083008 nb_pixel_total : 18515 time to create 1 rle with old method : 0.02095174789428711 time for calcul the mask position with numpy : 0.0290524959564209 nb_pixel_total : 10634 time to create 1 rle with old method : 0.012251853942871094 time for calcul the mask position with numpy : 0.028872966766357422 nb_pixel_total : 18521 time to create 1 rle with old method : 0.021351337432861328 time for calcul the mask position with numpy : 0.028828859329223633 nb_pixel_total : 5918 time to create 1 rle with old method : 0.006716489791870117 time for calcul the mask position with numpy : 0.028951644897460938 nb_pixel_total : 13761 time to create 1 rle with old method : 0.015871763229370117 time for calcul the mask position with numpy : 0.029061317443847656 nb_pixel_total : 17443 time to create 1 rle with old method : 0.019643306732177734 time for calcul the mask position with numpy : 0.0290830135345459 nb_pixel_total : 48405 time to create 1 rle with old method : 0.054319143295288086 time for calcul the mask position with numpy : 0.029154300689697266 nb_pixel_total : 8669 time to create 1 rle with old method : 0.00998544692993164 time for calcul the mask position with numpy : 0.02898550033569336 nb_pixel_total : 9091 time to create 1 rle with old method : 0.013455390930175781 time for calcul the mask position with numpy : 0.0332334041595459 nb_pixel_total : 15055 time to create 1 rle with old method : 0.024051904678344727 time for calcul the mask position with numpy : 0.03232979774475098 nb_pixel_total : 10444 time to create 1 rle with old method : 0.012027978897094727 time for calcul the mask position with numpy : 0.029018402099609375 nb_pixel_total : 15464 time to create 1 rle with old method : 0.017338037490844727 time for calcul the mask position with numpy : 0.02906060218811035 nb_pixel_total : 10216 time to create 1 rle with old method : 0.012028932571411133 time for calcul the mask position with numpy : 0.029016971588134766 nb_pixel_total : 17113 time to create 1 rle with old method : 0.01962137222290039 time for calcul the mask position with numpy : 0.028502225875854492 nb_pixel_total : 13103 time to create 1 rle with old method : 0.015075922012329102 time for calcul the mask position with numpy : 0.02868199348449707 nb_pixel_total : 25891 time to create 1 rle with old method : 0.029007911682128906 time for calcul the mask position with numpy : 0.0283658504486084 nb_pixel_total : 20736 time to create 1 rle with old method : 0.02341175079345703 time for calcul the mask position with numpy : 0.028645038604736328 nb_pixel_total : 9677 time to create 1 rle with old method : 0.011342763900756836 time for calcul the mask position with numpy : 0.02926945686340332 nb_pixel_total : 7355 time to create 1 rle with old method : 0.008384466171264648 time for calcul the mask position with numpy : 0.028462886810302734 nb_pixel_total : 7466 time to create 1 rle with old method : 0.008781909942626953 time for calcul the mask position with numpy : 0.0291445255279541 nb_pixel_total : 37804 time to create 1 rle with old method : 0.04244589805603027 time for calcul the mask position with numpy : 0.02878284454345703 nb_pixel_total : 30624 time to create 1 rle with old method : 0.03483772277832031 time for calcul the mask position with numpy : 0.028817415237426758 nb_pixel_total : 30279 time to create 1 rle with old method : 0.03600740432739258 time for calcul the mask position with numpy : 0.028679847717285156 nb_pixel_total : 21490 time to create 1 rle with old method : 0.024669647216796875 time for calcul the mask position with numpy : 0.028973817825317383 nb_pixel_total : 14185 time to create 1 rle with old method : 0.016168832778930664 time for calcul the mask position with numpy : 0.028335094451904297 nb_pixel_total : 10137 time to create 1 rle with old method : 0.011605262756347656 time for calcul the mask position with numpy : 0.02846693992614746 nb_pixel_total : 17767 time to create 1 rle with old method : 0.0204012393951416 time for calcul the mask position with numpy : 0.02836894989013672 nb_pixel_total : 5798 time to create 1 rle with old method : 0.0068471431732177734 time for calcul the mask position with numpy : 0.028591156005859375 nb_pixel_total : 5233 time to create 1 rle with old method : 0.006043910980224609 time for calcul the mask position with numpy : 0.028766155242919922 nb_pixel_total : 6542 time to create 1 rle with old method : 0.007775068283081055 create new chi : 2.6615562438964844 time to delete rle : 0.0024101734161376953 batch 1 Loaded 105 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 18534 TO DO : save crop sub photo not yet done ! save time : 1.6878998279571533 nb_obj : 41 nb_hashtags : 5 time to prepare the origin masks : 4.2677202224731445 time for calcul the mask position with numpy : 0.0704646110534668 nb_pixel_total : 5037380 time to create 1 rle with new method : 0.15343475341796875 time for calcul the mask position with numpy : 0.02922797203063965 nb_pixel_total : 6529 time to create 1 rle with old method : 0.007578611373901367 time for calcul the mask position with numpy : 0.03468203544616699 nb_pixel_total : 581084 time to create 1 rle with new method : 0.12862229347229004 time for calcul the mask position with numpy : 0.029651165008544922 nb_pixel_total : 80318 time to create 1 rle with old method : 0.09017419815063477 time for calcul the mask position with numpy : 0.02904987335205078 nb_pixel_total : 25066 time to create 1 rle with old method : 0.028388261795043945 time for calcul the mask position with numpy : 0.028695344924926758 nb_pixel_total : 53829 time to create 1 rle with old method : 0.05999875068664551 time for calcul the mask position with numpy : 0.028009653091430664 nb_pixel_total : 11311 time to create 1 rle with old method : 0.012788057327270508 time for calcul the mask position with numpy : 0.028049945831298828 nb_pixel_total : 9975 time to create 1 rle with old method : 0.011106729507446289 time for calcul the mask position with numpy : 0.028481483459472656 nb_pixel_total : 115522 time to create 1 rle with old method : 0.14974164962768555 time for calcul the mask position with numpy : 0.030256271362304688 nb_pixel_total : 56176 time to create 1 rle with old method : 0.061452388763427734 time for calcul the mask position with numpy : 0.027007579803466797 nb_pixel_total : 27469 time to create 1 rle with old method : 0.02973175048828125 time for calcul the mask position with numpy : 0.028279781341552734 nb_pixel_total : 7310 time to create 1 rle with old method : 0.008439302444458008 time for calcul the mask position with numpy : 0.02800917625427246 nb_pixel_total : 19263 time to create 1 rle with old method : 0.02237248420715332 time for calcul the mask position with numpy : 0.028462648391723633 nb_pixel_total : 31980 time to create 1 rle with old method : 0.03619742393493652 time for calcul the mask position with numpy : 0.029007911682128906 nb_pixel_total : 8487 time to create 1 rle with old method : 0.009486198425292969 time for calcul the mask position with numpy : 0.028826475143432617 nb_pixel_total : 11929 time to create 1 rle with old method : 0.013685464859008789 time for calcul the mask position with numpy : 0.02938222885131836 nb_pixel_total : 32920 time to create 1 rle with old method : 0.037291765213012695 time for calcul the mask position with numpy : 0.029223918914794922 nb_pixel_total : 8287 time to create 1 rle with old method : 0.009944915771484375 time for calcul the mask position with numpy : 0.03037858009338379 nb_pixel_total : 20735 time to create 1 rle with old method : 0.023778200149536133 time for calcul the mask position with numpy : 0.02925848960876465 nb_pixel_total : 21785 time to create 1 rle with old method : 0.024720430374145508 time for calcul the mask position with numpy : 0.029580354690551758 nb_pixel_total : 40137 time to create 1 rle with old method : 0.04532575607299805 time for calcul the mask position with numpy : 0.029858827590942383 nb_pixel_total : 90090 time to create 1 rle with old method : 0.10108017921447754 time for calcul the mask position with numpy : 0.029166460037231445 nb_pixel_total : 12020 time to create 1 rle with old method : 0.01416015625 time for calcul the mask position with numpy : 0.02969837188720703 nb_pixel_total : 67298 time to create 1 rle with old method : 0.07568955421447754 time for calcul the mask position with numpy : 0.0294647216796875 nb_pixel_total : 113539 time to create 1 rle with old method : 0.12632536888122559 time for calcul the mask position with numpy : 0.02907109260559082 nb_pixel_total : 30290 time to create 1 rle with old method : 0.033976078033447266 time for calcul the mask position with numpy : 0.029541492462158203 nb_pixel_total : 65611 time to create 1 rle with old method : 0.07681608200073242 time for calcul the mask position with numpy : 0.029181480407714844 nb_pixel_total : 29148 time to create 1 rle with old method : 0.033184051513671875 time for calcul the mask position with numpy : 0.02882838249206543 nb_pixel_total : 20658 time to create 1 rle with old method : 0.024102210998535156 time for calcul the mask position with numpy : 0.029179096221923828 nb_pixel_total : 47913 time to create 1 rle with old method : 0.054128170013427734 time for calcul the mask position with numpy : 0.02963995933532715 nb_pixel_total : 8831 time to create 1 rle with old method : 0.010049819946289062 time for calcul the mask position with numpy : 0.02879810333251953 nb_pixel_total : 11378 time to create 1 rle with old method : 0.013416767120361328 time for calcul the mask position with numpy : 0.029072999954223633 nb_pixel_total : 579 time to create 1 rle with old method : 0.0007698535919189453 time for calcul the mask position with numpy : 0.02912449836730957 nb_pixel_total : 32803 time to create 1 rle with old method : 0.03721737861633301 time for calcul the mask position with numpy : 0.0289461612701416 nb_pixel_total : 6899 time to create 1 rle with old method : 0.008222579956054688 time for calcul the mask position with numpy : 0.028882503509521484 nb_pixel_total : 7289 time to create 1 rle with old method : 0.008301019668579102 time for calcul the mask position with numpy : 0.028822660446166992 nb_pixel_total : 49291 time to create 1 rle with old method : 0.05562567710876465 time for calcul the mask position with numpy : 0.029384136199951172 nb_pixel_total : 132175 time to create 1 rle with old method : 0.14716100692749023 time for calcul the mask position with numpy : 0.029497861862182617 nb_pixel_total : 87353 time to create 1 rle with old method : 0.12211132049560547 time for calcul the mask position with numpy : 0.02877497673034668 nb_pixel_total : 21624 time to create 1 rle with old method : 0.024509191513061523 time for calcul the mask position with numpy : 0.02914881706237793 nb_pixel_total : 3703 time to create 1 rle with old method : 0.004479646682739258 time for calcul the mask position with numpy : 0.029005050659179688 nb_pixel_total : 4256 time to create 1 rle with old method : 0.005154132843017578 create new chi : 3.23779559135437 time to delete rle : 0.0033235549926757812 batch 1 Loaded 83 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 22540 TO DO : save crop sub photo not yet done ! save time : 1.6775853633880615 nb_obj : 54 nb_hashtags : 6 time to prepare the origin masks : 3.7604105472564697 time for calcul the mask position with numpy : 0.07095885276794434 nb_pixel_total : 5721806 time to create 1 rle with new method : 0.15829062461853027 time for calcul the mask position with numpy : 0.029015064239501953 nb_pixel_total : 17120 time to create 1 rle with old method : 0.019208908081054688 time for calcul the mask position with numpy : 0.029357194900512695 nb_pixel_total : 25317 time to create 1 rle with old method : 0.028555870056152344 time for calcul the mask position with numpy : 0.02886176109313965 nb_pixel_total : 36118 time to create 1 rle with old method : 0.040583133697509766 time for calcul the mask position with numpy : 0.029315710067749023 nb_pixel_total : 86726 time to create 1 rle with old method : 0.0968027114868164 time for calcul the mask position with numpy : 0.02875041961669922 nb_pixel_total : 12810 time to create 1 rle with old method : 0.015098094940185547 time for calcul the mask position with numpy : 0.028926372528076172 nb_pixel_total : 9174 time to create 1 rle with old method : 0.010871410369873047 time for calcul the mask position with numpy : 0.029152631759643555 nb_pixel_total : 28911 time to create 1 rle with old method : 0.033925771713256836 time for calcul the mask position with numpy : 0.029141664505004883 nb_pixel_total : 34711 time to create 1 rle with old method : 0.03900456428527832 time for calcul the mask position with numpy : 0.02892279624938965 nb_pixel_total : 59467 time to create 1 rle with old method : 0.06656122207641602 time for calcul the mask position with numpy : 0.03265261650085449 nb_pixel_total : 34778 time to create 1 rle with old method : 0.04147052764892578 time for calcul the mask position with numpy : 0.029836416244506836 nb_pixel_total : 44469 time to create 1 rle with old method : 0.0498507022857666 time for calcul the mask position with numpy : 0.029448986053466797 nb_pixel_total : 25414 time to create 1 rle with old method : 0.032395362854003906 time for calcul the mask position with numpy : 0.03089451789855957 nb_pixel_total : 14086 time to create 1 rle with old method : 0.018786191940307617 time for calcul the mask position with numpy : 0.029078245162963867 nb_pixel_total : 19835 time to create 1 rle with old method : 0.022446155548095703 time for calcul the mask position with numpy : 0.028404712677001953 nb_pixel_total : 8808 time to create 1 rle with old method : 0.009910345077514648 time for calcul the mask position with numpy : 0.028712749481201172 nb_pixel_total : 11650 time to create 1 rle with old method : 0.013905525207519531 time for calcul the mask position with numpy : 0.028292417526245117 nb_pixel_total : 16398 time to create 1 rle with old method : 0.01848316192626953 time for calcul the mask position with numpy : 0.02870488166809082 nb_pixel_total : 28453 time to create 1 rle with old method : 0.031375885009765625 time for calcul the mask position with numpy : 0.02839374542236328 nb_pixel_total : 37106 time to create 1 rle with old method : 0.04149317741394043 time for calcul the mask position with numpy : 0.02856922149658203 nb_pixel_total : 22999 time to create 1 rle with old method : 0.0259096622467041 time for calcul the mask position with numpy : 0.0279691219329834 nb_pixel_total : 12600 time to create 1 rle with old method : 0.013653755187988281 time for calcul the mask position with numpy : 0.027167320251464844 nb_pixel_total : 17501 time to create 1 rle with old method : 0.01966381072998047 time for calcul the mask position with numpy : 0.026843786239624023 nb_pixel_total : 31123 time to create 1 rle with old method : 0.035573720932006836 time for calcul the mask position with numpy : 0.0288236141204834 nb_pixel_total : 17758 time to create 1 rle with old method : 0.01990675926208496 time for calcul the mask position with numpy : 0.02901744842529297 nb_pixel_total : 23492 time to create 1 rle with old method : 0.026487112045288086 time for calcul the mask position with numpy : 0.02888202667236328 nb_pixel_total : 8573 time to create 1 rle with old method : 0.010187387466430664 time for calcul the mask position with numpy : 0.027855634689331055 nb_pixel_total : 26298 time to create 1 rle with old method : 0.028724193572998047 time for calcul the mask position with numpy : 0.027546167373657227 nb_pixel_total : 25141 time to create 1 rle with old method : 0.0279843807220459 time for calcul the mask position with numpy : 0.028827428817749023 nb_pixel_total : 39739 time to create 1 rle with old method : 0.04442453384399414 time for calcul the mask position with numpy : 0.02811598777770996 nb_pixel_total : 17125 time to create 1 rle with old method : 0.018799781799316406 time for calcul the mask position with numpy : 0.028792381286621094 nb_pixel_total : 17518 time to create 1 rle with old method : 0.019500017166137695 time for calcul the mask position with numpy : 0.028083086013793945 nb_pixel_total : 26051 time to create 1 rle with old method : 0.02919316291809082 time for calcul the mask position with numpy : 0.028386831283569336 nb_pixel_total : 19475 time to create 1 rle with old method : 0.023151159286499023 time for calcul the mask position with numpy : 0.03016066551208496 nb_pixel_total : 36263 time to create 1 rle with old method : 0.040358781814575195 time for calcul the mask position with numpy : 0.028688430786132812 nb_pixel_total : 8651 time to create 1 rle with old method : 0.009855031967163086 time for calcul the mask position with numpy : 0.029024839401245117 nb_pixel_total : 23394 time to create 1 rle with old method : 0.027170896530151367 time for calcul the mask position with numpy : 0.02963089942932129 nb_pixel_total : 59977 time to create 1 rle with old method : 0.06745076179504395 time for calcul the mask position with numpy : 0.028475522994995117 nb_pixel_total : 35631 time to create 1 rle with old method : 0.03938794136047363 time for calcul the mask position with numpy : 0.02812933921813965 nb_pixel_total : 34549 time to create 1 rle with old method : 0.03769707679748535 time for calcul the mask position with numpy : 0.028309106826782227 nb_pixel_total : 27078 time to create 1 rle with old method : 0.030410051345825195 time for calcul the mask position with numpy : 0.028776168823242188 nb_pixel_total : 9195 time to create 1 rle with old method : 0.010637283325195312 time for calcul the mask position with numpy : 0.029048442840576172 nb_pixel_total : 10015 time to create 1 rle with old method : 0.011576414108276367 time for calcul the mask position with numpy : 0.029354095458984375 nb_pixel_total : 15098 time to create 1 rle with old method : 0.020786523818969727 time for calcul the mask position with numpy : 0.02921772003173828 nb_pixel_total : 13082 time to create 1 rle with old method : 0.014675140380859375 time for calcul the mask position with numpy : 0.02885580062866211 nb_pixel_total : 32252 time to create 1 rle with old method : 0.03617548942565918 time for calcul the mask position with numpy : 0.02940058708190918 nb_pixel_total : 3614 time to create 1 rle with old method : 0.004461765289306641 time for calcul the mask position with numpy : 0.029069185256958008 nb_pixel_total : 25848 time to create 1 rle with old method : 0.029337406158447266 time for calcul the mask position with numpy : 0.028675317764282227 nb_pixel_total : 12131 time to create 1 rle with old method : 0.014299869537353516 time for calcul the mask position with numpy : 0.028080224990844727 nb_pixel_total : 15808 time to create 1 rle with old method : 0.017364025115966797 time for calcul the mask position with numpy : 0.02837514877319336 nb_pixel_total : 31074 time to create 1 rle with old method : 0.03484535217285156 time for calcul the mask position with numpy : 0.028606653213500977 nb_pixel_total : 15142 time to create 1 rle with old method : 0.017637968063354492 time for calcul the mask position with numpy : 0.0284271240234375 nb_pixel_total : 19103 time to create 1 rle with old method : 0.02147984504699707 time for calcul the mask position with numpy : 0.028220176696777344 nb_pixel_total : 11462 time to create 1 rle with old method : 0.015944719314575195 time for calcul the mask position with numpy : 0.03130936622619629 nb_pixel_total : 32323 time to create 1 rle with old method : 0.05170035362243652 create new chi : 3.331503391265869 time to delete rle : 0.004540681838989258 batch 1 Loaded 109 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 23568 TO DO : save crop sub photo not yet done ! save time : 3.437525510787964 nb_obj : 59 nb_hashtags : 4 time to prepare the origin masks : 3.805166006088257 time for calcul the mask position with numpy : 0.08477020263671875 nb_pixel_total : 5721903 time to create 1 rle with new method : 0.7738606929779053 time for calcul the mask position with numpy : 0.03141617774963379 nb_pixel_total : 90782 time to create 1 rle with old method : 0.1027834415435791 time for calcul the mask position with numpy : 0.028964757919311523 nb_pixel_total : 8943 time to create 1 rle with old method : 0.01043558120727539 time for calcul the mask position with numpy : 0.028862476348876953 nb_pixel_total : 5160 time to create 1 rle with old method : 0.006031513214111328 time for calcul the mask position with numpy : 0.02825307846069336 nb_pixel_total : 23913 time to create 1 rle with old method : 0.02770256996154785 time for calcul the mask position with numpy : 0.028627634048461914 nb_pixel_total : 54475 time to create 1 rle with old method : 0.06068682670593262 time for calcul the mask position with numpy : 0.028964519500732422 nb_pixel_total : 39493 time to create 1 rle with old method : 0.04437375068664551 time for calcul the mask position with numpy : 0.029259443283081055 nb_pixel_total : 31249 time to create 1 rle with old method : 0.03491663932800293 time for calcul the mask position with numpy : 0.03269195556640625 nb_pixel_total : 12841 time to create 1 rle with old method : 0.021953821182250977 time for calcul the mask position with numpy : 0.02901315689086914 nb_pixel_total : 15751 time to create 1 rle with old method : 0.017668485641479492 time for calcul the mask position with numpy : 0.02895951271057129 nb_pixel_total : 47265 time to create 1 rle with old method : 0.05563044548034668 time for calcul the mask position with numpy : 0.029190778732299805 nb_pixel_total : 56300 time to create 1 rle with old method : 0.07115817070007324 time for calcul the mask position with numpy : 0.030617713928222656 nb_pixel_total : 34057 time to create 1 rle with old method : 0.038724422454833984 time for calcul the mask position with numpy : 0.028961658477783203 nb_pixel_total : 16441 time to create 1 rle with old method : 0.018559694290161133 time for calcul the mask position with numpy : 0.02928948402404785 nb_pixel_total : 27857 time to create 1 rle with old method : 0.04428601264953613 time for calcul the mask position with numpy : 0.033270835876464844 nb_pixel_total : 37423 time to create 1 rle with old method : 0.042041778564453125 time for calcul the mask position with numpy : 0.0276336669921875 nb_pixel_total : 64887 time to create 1 rle with old method : 0.0704038143157959 time for calcul the mask position with numpy : 0.02852487564086914 nb_pixel_total : 55495 time to create 1 rle with old method : 0.06642365455627441 time for calcul the mask position with numpy : 0.028756141662597656 nb_pixel_total : 33321 time to create 1 rle with old method : 0.03786897659301758 time for calcul the mask position with numpy : 0.02875208854675293 nb_pixel_total : 22641 time to create 1 rle with old method : 0.026168346405029297 time for calcul the mask position with numpy : 0.028420448303222656 nb_pixel_total : 13474 time to create 1 rle with old method : 0.01503753662109375 time for calcul the mask position with numpy : 0.02722454071044922 nb_pixel_total : 20771 time to create 1 rle with old method : 0.02178812026977539 time for calcul the mask position with numpy : 0.026995420455932617 nb_pixel_total : 17344 time to create 1 rle with old method : 0.019203901290893555 time for calcul the mask position with numpy : 0.02768111228942871 nb_pixel_total : 21523 time to create 1 rle with old method : 0.022992610931396484 time for calcul the mask position with numpy : 0.028752565383911133 nb_pixel_total : 14686 time to create 1 rle with old method : 0.01690506935119629 time for calcul the mask position with numpy : 0.028160810470581055 nb_pixel_total : 26144 time to create 1 rle with old method : 0.027784109115600586 time for calcul the mask position with numpy : 0.027445554733276367 nb_pixel_total : 12482 time to create 1 rle with old method : 0.01368403434753418 time for calcul the mask position with numpy : 0.027205944061279297 nb_pixel_total : 3554 time to create 1 rle with old method : 0.0040454864501953125 time for calcul the mask position with numpy : 0.027112245559692383 nb_pixel_total : 13231 time to create 1 rle with old method : 0.014148473739624023 time for calcul the mask position with numpy : 0.02703547477722168 nb_pixel_total : 21996 time to create 1 rle with old method : 0.025244712829589844 time for calcul the mask position with numpy : 0.027126550674438477 nb_pixel_total : 10567 time to create 1 rle with old method : 0.011194705963134766 time for calcul the mask position with numpy : 0.027117013931274414 nb_pixel_total : 22246 time to create 1 rle with old method : 0.023488283157348633 time for calcul the mask position with numpy : 0.02707839012145996 nb_pixel_total : 9232 time to create 1 rle with old method : 0.010271549224853516 time for calcul the mask position with numpy : 0.03886270523071289 nb_pixel_total : 9424 time to create 1 rle with old method : 0.010556697845458984 time for calcul the mask position with numpy : 0.02722454071044922 nb_pixel_total : 26312 time to create 1 rle with old method : 0.027790546417236328 time for calcul the mask position with numpy : 0.026918411254882812 nb_pixel_total : 14193 time to create 1 rle with old method : 0.015446186065673828 time for calcul the mask position with numpy : 0.02813410758972168 nb_pixel_total : 34974 time to create 1 rle with old method : 0.039288997650146484 time for calcul the mask position with numpy : 0.03331303596496582 nb_pixel_total : 16581 time to create 1 rle with old method : 0.02898406982421875 time for calcul the mask position with numpy : 0.029534578323364258 nb_pixel_total : 9676 time to create 1 rle with old method : 0.011482477188110352 time for calcul the mask position with numpy : 0.03802990913391113 nb_pixel_total : 11338 time to create 1 rle with old method : 0.016863107681274414 time for calcul the mask position with numpy : 0.030028581619262695 nb_pixel_total : 27249 time to create 1 rle with old method : 0.031623125076293945 time for calcul the mask position with numpy : 0.029040813446044922 nb_pixel_total : 10997 time to create 1 rle with old method : 0.012512683868408203 time for calcul the mask position with numpy : 0.03033900260925293 nb_pixel_total : 10546 time to create 1 rle with old method : 0.012195110321044922 time for calcul the mask position with numpy : 0.029207944869995117 nb_pixel_total : 12032 time to create 1 rle with old method : 0.015297651290893555 time for calcul the mask position with numpy : 0.02932000160217285 nb_pixel_total : 56124 time to create 1 rle with old method : 0.07235407829284668 time for calcul the mask position with numpy : 0.029478073120117188 nb_pixel_total : 1999 time to create 1 rle with old method : 0.0024709701538085938 time for calcul the mask position with numpy : 0.029633045196533203 nb_pixel_total : 33057 time to create 1 rle with old method : 0.03881192207336426 time for calcul the mask position with numpy : 0.02789473533630371 nb_pixel_total : 18075 time to create 1 rle with old method : 0.019736766815185547 time for calcul the mask position with numpy : 0.029099702835083008 nb_pixel_total : 16947 time to create 1 rle with old method : 0.0191347599029541 time for calcul the mask position with numpy : 0.041269779205322266 nb_pixel_total : 19320 time to create 1 rle with old method : 0.024698495864868164 time for calcul the mask position with numpy : 0.029056549072265625 nb_pixel_total : 10365 time to create 1 rle with old method : 0.012040853500366211 time for calcul the mask position with numpy : 0.029126405715942383 nb_pixel_total : 15424 time to create 1 rle with old method : 0.017815351486206055 time for calcul the mask position with numpy : 0.02902531623840332 nb_pixel_total : 19732 time to create 1 rle with old method : 0.022641897201538086 time for calcul the mask position with numpy : 0.02879166603088379 nb_pixel_total : 8667 time to create 1 rle with old method : 0.01032567024230957 time for calcul the mask position with numpy : 0.028693675994873047 nb_pixel_total : 12756 time to create 1 rle with old method : 0.015321493148803711 time for calcul the mask position with numpy : 0.028711557388305664 nb_pixel_total : 9649 time to create 1 rle with old method : 0.010907411575317383 time for calcul the mask position with numpy : 0.028627395629882812 nb_pixel_total : 2537 time to create 1 rle with old method : 0.003017425537109375 time for calcul the mask position with numpy : 0.02881789207458496 nb_pixel_total : 26237 time to create 1 rle with old method : 0.02929377555847168 time for calcul the mask position with numpy : 0.028722524642944336 nb_pixel_total : 3456 time to create 1 rle with old method : 0.004067659378051758 time for calcul the mask position with numpy : 0.030732393264770508 nb_pixel_total : 5126 time to create 1 rle with old method : 0.005956172943115234 create new chi : 4.171780824661255 time to delete rle : 0.006657123565673828 batch 1 Loaded 119 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 23596 TO DO : save crop sub photo not yet done ! save time : 2.890502452850342 nb_obj : 10 nb_hashtags : 3 time to prepare the origin masks : 3.7113122940063477 time for calcul the mask position with numpy : 0.3051004409790039 nb_pixel_total : 6900425 time to create 1 rle with new method : 0.6791577339172363 time for calcul the mask position with numpy : 0.022177457809448242 nb_pixel_total : 7158 time to create 1 rle with old method : 0.008538007736206055 time for calcul the mask position with numpy : 0.021787643432617188 nb_pixel_total : 8980 time to create 1 rle with old method : 0.010335683822631836 time for calcul the mask position with numpy : 0.02175140380859375 nb_pixel_total : 31572 time to create 1 rle with old method : 0.03598809242248535 time for calcul the mask position with numpy : 0.02117776870727539 nb_pixel_total : 6309 time to create 1 rle with old method : 0.00717616081237793 time for calcul the mask position with numpy : 0.0220797061920166 nb_pixel_total : 5963 time to create 1 rle with old method : 0.007196903228759766 time for calcul the mask position with numpy : 0.021596431732177734 nb_pixel_total : 5429 time to create 1 rle with old method : 0.006274223327636719 time for calcul the mask position with numpy : 0.023096561431884766 nb_pixel_total : 34416 time to create 1 rle with old method : 0.038956642150878906 time for calcul the mask position with numpy : 0.024695158004760742 nb_pixel_total : 4430 time to create 1 rle with old method : 0.005134105682373047 time for calcul the mask position with numpy : 0.02278876304626465 nb_pixel_total : 27399 time to create 1 rle with old method : 0.03060460090637207 time for calcul the mask position with numpy : 0.025945425033569336 nb_pixel_total : 18159 time to create 1 rle with old method : 0.02208113670349121 create new chi : 1.4134995937347412 time to delete rle : 0.0007565021514892578 batch 1 Loaded 21 chid ids of type : 3594 ++++++++++++Number RLEs to save : 4736 TO DO : save crop sub photo not yet done ! save time : 0.4277799129486084 map_output_result : {1334944838: (0.0, 'Should be the crop_list due to order', 0.0), 1334944834: (0.0, 'Should be the crop_list due to order', 0.0), 1334944830: (0.0, 'Should be the crop_list due to order', 0.0), 1334943171: (0.0, 'Should be the crop_list due to order', 0.0), 1334943139: (0.0, 'Should be the crop_list due to order', 0.0), 1334940964: (0.0, 'Should be the crop_list due to order', 0.0), 1334940961: (0.0, 'Should be the crop_list due to order', 0.0), 1334940958: (0.0, 'Should be the crop_list due to order', 0.0), 1334940953: (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 [1334944838, 1334944834, 1334944830, 1334943171, 1334943139, 1334940964, 1334940961, 1334940958, 1334940953] Looping around the photos to save general results len do output : 9 /1334944838.Didn't retrieve data . /1334944834.Didn't retrieve data . /1334944830.Didn't retrieve data . /1334943171.Didn't retrieve data . /1334943139.Didn't retrieve data . /1334940964.Didn't retrieve data . /1334940961.Didn't retrieve data . /1334940958.Didn't retrieve data . /1334940953.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, '2557205') ('3318', '20277523', '1334944838', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334944834', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334944830', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334943171', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334943139', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940964', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940961', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940958', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940953', None, None, None, None, None, '2557205') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 27 time used for this insertion : 0.017255067825317383 save_final save missing photos in datou_result : time spend for datou_step_exec : 88.1089563369751 time spend to save output : 0.01938605308532715 total time spend for step 3 : 88.12834239006042 step4:ventilate_hashtags_in_portfolio Thu Feb 6 01:28:23 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : 20277523 get user id for portfolio 20277523 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`=20277523 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('metal','autre','flou','background','papier','environnement','pet_clair','carton','mal_croppe','pet_fonce','pehd')) 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`=20277523 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('metal','autre','flou','background','papier','environnement','pet_clair','carton','mal_croppe','pet_fonce','pehd')) 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`=20277523 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('metal','autre','flou','background','papier','environnement','pet_clair','carton','mal_croppe','pet_fonce','pehd')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/20278038,20278039,20278040,20278041,20278042,20278043,20278044,20278045,20278046,20278047,20278048?tags=metal,autre,flou,background,papier,environnement,pet_clair,carton,mal_croppe,pet_fonce,pehd Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1334944838, 1334944834, 1334944830, 1334943171, 1334943139, 1334940964, 1334940961, 1334940958, 1334940953] Looping around the photos to save general results len do output : 1 /20277523. 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, '2557205') ('3318', '20277523', '1334944838', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334944834', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334944830', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334943171', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334943139', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940964', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940961', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940958', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940953', None, None, None, None, None, '2557205') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 10 time used for this insertion : 0.015564680099487305 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.6058290004730225 time spend to save output : 0.015802621841430664 total time spend for step 4 : 1.6216316223144531 step5:final Thu Feb 6 01:28:25 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 : {1334944838: ('0.1770747284253208',), 1334944834: ('0.1770747284253208',), 1334944830: ('0.1770747284253208',), 1334943171: ('0.1770747284253208',), 1334943139: ('0.1770747284253208',), 1334940964: ('0.1770747284253208',), 1334940961: ('0.1770747284253208',), 1334940958: ('0.1770747284253208',), 1334940953: ('0.1770747284253208',)} new output for save of step final : {1334944838: ('0.1770747284253208',), 1334944834: ('0.1770747284253208',), 1334944830: ('0.1770747284253208',), 1334943171: ('0.1770747284253208',), 1334943139: ('0.1770747284253208',), 1334940964: ('0.1770747284253208',), 1334940961: ('0.1770747284253208',), 1334940958: ('0.1770747284253208',), 1334940953: ('0.1770747284253208',)} [1334944838, 1334944834, 1334944830, 1334943171, 1334943139, 1334940964, 1334940961, 1334940958, 1334940953] Looping around the photos to save general results len do output : 9 /1334944838.Didn't retrieve data . /1334944834.Didn't retrieve data . /1334944830.Didn't retrieve data . /1334943171.Didn't retrieve data . /1334943139.Didn't retrieve data . /1334940964.Didn't retrieve data . /1334940961.Didn't retrieve data . /1334940958.Didn't retrieve data . /1334940953.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, '2557205') ('3318', '20277523', '1334944838', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334944834', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334944830', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334943171', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334943139', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940964', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940961', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940958', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940953', None, None, None, None, None, '2557205') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 27 time used for this insertion : 0.013691186904907227 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.19548726081848145 time spend to save output : 0.014218568801879883 total time spend for step 5 : 0.20970582962036133 step6:blur_detection Thu Feb 6 01:28:25 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/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e.jpg resize: (2160, 3264) 1334944838 -6.3554754316203725 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf.jpg resize: (2160, 3264) 1334944834 -7.576396063129005 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056.jpg resize: (2160, 3264) 1334944830 -6.849263437473965 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770.jpg resize: (2160, 3264) 1334943171 -6.629196626552226 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc.jpg resize: (2160, 3264) 1334943139 -6.635106431196048 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb.jpg resize: (2160, 3264) 1334940964 -6.043742423063105 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d.jpg resize: (2160, 3264) 1334940961 -6.474051258854378 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6.jpg resize: (2160, 3264) 1334940958 -6.494277652677899 treat image : temp/1738801229_1649647_1334940953_1e7a44457954b54c9e9cb93325a0a32e.jpg resize: (2160, 3264) 1334940953 -2.9276254900647114 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828516_0.png resize: (178, 156) 1335067778 -4.203318353952872 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828500_0.png resize: (275, 472) 1335067779 -4.167960693110401 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828526_0.png resize: (187, 290) 1335067780 -4.511591180628667 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828485_0.png resize: (149, 133) 1335067781 -3.3142757238570475 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828506_0.png resize: (214, 523) 1335067782 -2.935160725006178 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828515_0.png resize: (127, 141) 1335067783 -4.481731023656804 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828499_0.png resize: (92, 225) 1335067784 -4.26378955753514 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828511_0.png resize: (157, 228) 1335067785 -4.531087648504471 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828498_0.png resize: (272, 177) 1335067788 -4.027151626632222 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828521_0.png resize: (288, 193) 1335067791 -1.5595781874118637 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828523_0.png resize: (123, 116) 1335067794 -2.93154208111668 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828502_0.png resize: (112, 112) 1335067798 -3.9109154907633843 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828527_0.png resize: (240, 233) 1335067801 -4.046473008496728 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828503_0.png resize: (146, 110) 1335067805 -1.423149355975814 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828490_0.png resize: (85, 90) 1335067808 -2.227619395262676 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828496_0.png resize: (141, 89) 1335067811 -4.282430807102539 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828525_0.png resize: (183, 174) 1335067813 -4.847508419845906 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828528_0.png resize: (92, 89) 1335067814 -4.61336582725843 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828497_0.png resize: (304, 230) 1335067815 -2.790698361497537 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828512_0.png resize: (200, 144) 1335067816 -4.586329906497219 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828513_0.png resize: (74, 93) 1335067817 -4.230972195732725 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828507_0.png resize: (186, 167) 1335067818 -2.731926252967841 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828508_0.png resize: (177, 231) 1335067819 -2.9998108194078457 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828483_0.png resize: (85, 107) 1335067820 -1.494753142742328 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828509_0.png resize: (278, 189) 1335067821 -3.270567438330985 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828484_0.png resize: (276, 384) 1335067822 -4.137211154667094 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828494_0.png resize: (78, 189) 1335067823 -1.6477728167847325 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828504_0.png resize: (158, 180) 1335067824 -4.376564168454767 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828487_0.png resize: (171, 222) 1335067825 -4.745093390772985 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828479_0.png resize: (182, 205) 1335067827 -3.2926945428752235 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828514_0.png resize: (135, 164) 1335067828 -3.810486847143331 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828477_0.png resize: (136, 102) 1335067829 -3.6336577846189395 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828518_0.png resize: (161, 112) 1335067830 -3.533098041168833 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828480_0.png resize: (157, 74) 1335067836 -2.9350891656236575 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828492_0.png resize: (177, 191) 1335067839 -5.311493254196141 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828475_0.png resize: (213, 205) 1335067842 -3.473193627264499 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828510_0.png resize: (96, 79) 1335067844 -3.958179243177265 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828495_0.png resize: (74, 81) 1335067845 -2.94925916351238 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828505_0.png resize: (142, 128) 1335067846 -4.334915805579179 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828478_0.png resize: (123, 155) 1335067847 -4.032969747656272 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828488_0.png resize: (117, 94) 1335067848 -5.2065590805549755 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828524_0.png resize: (152, 151) 1335067849 -4.963382578657457 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828517_0.png resize: (99, 134) 1335067853 -4.419850578608168 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828529_0.png resize: (72, 76) 1335067854 -3.1648923433510165 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828530_0.png resize: (116, 140) 1335067855 -4.177960798533043 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828486_0.png resize: (266, 179) 1335067856 -4.695682587843228 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828493_0.png resize: (157, 64) 1335067857 -4.527787786689064 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828476_0.png resize: (202, 148) 1335067858 -4.572773480369472 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828481_0.png resize: (130, 101) 1335067859 -4.6745369061586395 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828482_0.png resize: (80, 106) 1335067861 -4.971464936861163 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828520_0.png resize: (99, 91) 1335067862 -3.4585711130170806 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828562_0.png resize: (316, 182) 1335067863 -4.818083014482635 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828533_0.png resize: (128, 159) 1335067864 -4.735900565526864 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828551_0.png resize: (121, 125) 1335067865 -3.258146765209992 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828573_0.png resize: (197, 148) 1335067866 -5.299050994716505 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828540_0.png resize: (337, 292) 1335067867 -4.87928863203388 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828557_0.png resize: (159, 209) 1335067868 -5.0215729823567985 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828576_0.png resize: (177, 112) 1335067869 -4.803010402702516 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828554_0.png resize: (274, 227) 1335067870 -4.543198008114057 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828539_0.png resize: (127, 116) 1335067871 -4.90984405295632 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828542_0.png resize: (172, 288) 1335067872 -4.227379190923291 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828537_0.png resize: (112, 132) 1335067873 -3.273300303893932 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828561_0.png resize: (137, 139) 1335067874 -5.218619563253675 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828564_0.png resize: (127, 69) 1335067877 -4.638581641687107 treat image : 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temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828538_0.png resize: (95, 72) 1335067891 -4.196406638231574 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828559_0.png resize: (149, 103) 1335067893 -3.800791857449583 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828568_0.png resize: (144, 159) 1335067895 -4.669830954934127 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828553_0.png resize: (222, 186) 1335067897 -4.326526800642174 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828543_0.png resize: (251, 228) 1335067899 -5.141953487859655 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828534_0.png resize: (159, 170) 1335067901 -4.236801689094176 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828572_0.png resize: (97, 86) 1335067903 -5.40637529725305 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828570_0.png resize: (91, 62) 1335067905 -3.8144880779897834 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828541_0.png resize: (269, 182) 1335067908 -4.656016016482892 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828535_0.png resize: (284, 222) 1335067910 -5.7864860150146304 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828536_0.png resize: (111, 96) 1335067912 -3.0495885991698968 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828532_0.png resize: (85, 49) 1335067914 -4.153561239377259 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828563_0.png resize: (164, 193) 1335067916 -5.401410259211133 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828550_0.png resize: (174, 227) 1335067918 -5.218167153558932 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828571_0.png resize: (70, 25) 1335067920 20.0 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828544_0.png resize: (129, 79) 1335067922 -4.54380721349545 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828531_0.png resize: (128, 85) 1335067924 -4.357859610062387 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828547_0.png resize: (141, 133) 1335067927 -2.8301861315848824 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828607_0.png resize: (295, 117) 1335067929 -4.114843072669145 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828604_0.png resize: (99, 56) 1335067932 -4.798531243709085 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828614_0.png resize: (430, 202) 1335067934 -3.353211529211903 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828624_0.png resize: (204, 278) 1335067936 -3.4602129240404036 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828578_0.png resize: (130, 256) 1335067938 -3.8216929222713256 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828580_0.png resize: (122, 140) 1335067940 -3.143194177968569 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828595_0.png resize: (345, 167) 1335067943 -4.01466742945271 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828591_0.png resize: (131, 164) 1335067945 -2.6765388654189475 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828619_0.png resize: (222, 255) 1335067949 -4.180048626821286 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828581_0.png resize: (183, 151) 1335067953 -4.849419152619936 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828613_0.png resize: (146, 100) 1335067957 -3.089997510112268 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828590_0.png resize: (383, 353) 1335067958 -4.500874702173376 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828606_0.png resize: (406, 519) 1335067960 -5.247271211613583 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828587_0.png resize: (154, 204) 1335067962 -4.276412414587638 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828582_0.png resize: (108, 141) 1335067964 -4.552596348048256 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828622_0.png resize: (189, 118) 1335067966 -2.528110005445942 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828599_0.png resize: (179, 110) 1335067968 -3.85655457541415 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828627_0.png resize: (293, 206) 1335067970 -3.7046668949994395 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828588_0.png resize: (208, 180) 1335067974 -3.4362036104935885 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828623_0.png resize: (230, 142) 1335067976 -4.7666664169573965 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828609_0.png resize: (86, 91) 1335067978 -3.311695533501732 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828597_0.png resize: (250, 211) 1335067981 -4.54470303930954 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828610_0.png resize: (227, 161) 1335067983 -3.279334924122976 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828618_0.png resize: (134, 72) 1335067985 -4.013924253100528 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828583_0.png resize: (70, 79) 1335067987 -3.4567905854256726 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828625_0.png resize: (207, 148) 1335067989 -4.8438126582744 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828600_0.png resize: (277, 243) 1335067991 -4.893160918465694 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828579_0.png resize: (96, 55) 1335067993 -1.6494312403663127 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828602_0.png resize: (72, 67) 1335067996 -4.271137390587624 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828617_0.png resize: (80, 167) 1335067998 -3.1656465478166944 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828584_0.png resize: (88, 52) 1335068000 -3.2347410475587455 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828589_0.png resize: (78, 63) 1335068003 -2.367090846436867 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828620_0.png resize: (213, 165) 1335068007 -3.4461393477103353 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828603_0.png resize: (130, 111) 1335068011 -3.136036268229995 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828585_0.png resize: (79, 131) 1335068013 -4.362683816137005 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828586_0.png resize: (94, 58) 1335068015 -3.730398194027555 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828626_0.png resize: (89, 84) 1335068017 -4.566763389217392 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828608_0.png resize: (254, 97) 1335068019 -3.8609889813370533 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828596_0.png resize: (87, 67) 1335068021 -1.9435990902998208 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828671_0.png resize: (349, 237) 1335068023 -3.9952896915364517 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828658_0.png resize: (428, 414) 1335068025 -3.8816141947924847 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828640_0.png resize: (102, 144) 1335068027 -3.787220479832865 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828652_0.png resize: (355, 278) 1335068029 -4.460837334060837 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828646_0.png resize: (277, 156) 1335068031 -3.1067363088678586 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828659_0.png resize: (115, 80) 1335068033 -3.7247303195402304 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828669_0.png resize: (101, 93) 1335068035 -2.1494417623706235 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828655_0.png resize: (54, 89) 1335068037 -3.1020102230050224 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828634_0.png resize: (121, 96) 1335068039 -2.0425878845698744 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828637_0.png resize: (83, 118) 1335068041 -3.7674767907673643 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828662_0.png resize: (111, 94) 1335068043 -4.161587733187148 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828664_0.png resize: (61, 56) 1335068045 -1.6581043933958215 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828670_0.png resize: (64, 77) 1335068047 -4.022568496657732 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828647_0.png resize: (154, 116) 1335068049 -4.267838002918245 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828661_0.png resize: (71, 78) 1335068051 -3.430572246319814 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828673_0.png resize: (102, 85) 1335068055 -4.267001497664474 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828639_0.png resize: (67, 166) 1335068058 -4.455556318255619 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828638_0.png resize: (242, 217) 1335068060 -4.161306780296983 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828636_0.png resize: (281, 235) 1335068062 -4.449854800795288 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828651_0.png resize: (193, 124) 1335068064 -4.572758193713068 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828643_0.png resize: (73, 176) 1335068066 -4.338382939852181 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828633_0.png resize: (149, 166) 1335068067 -3.3303852581968933 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828656_0.png resize: (181, 194) 1335068069 -4.49561655919634 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828672_0.png resize: (96, 97) 1335068071 -4.04140523377084 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828650_0.png resize: (113, 117) 1335068073 -5.628736026794106 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828667_0.png resize: (108, 138) 1335068075 -5.360305351021443 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828663_0.png resize: (248, 206) 1335068077 -4.030382063697428 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828648_0.png resize: (314, 289) 1335068079 -4.608533576493314 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828630_0.png resize: (190, 170) 1335068081 -2.031257980802856 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828649_0.png resize: (113, 122) 1335068084 -4.169285582217801 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828631_0.png resize: (123, 153) 1335068086 -4.081290180317087 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828644_0.png resize: (218, 113) 1335068089 -3.4285879845581535 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828665_0.png resize: (116, 176) 1335068091 -3.7649212495559383 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828645_0.png resize: (109, 171) 1335068093 -4.731328491420472 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828632_0.png resize: (95, 176) 1335068095 -3.32864584757106 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828688_0.png resize: (233, 168) 1335068097 -3.7989193881109613 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828702_0.png resize: (151, 190) 1335068099 -2.3824573196142715 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828690_0.png resize: (146, 108) 1335068101 -1.7810427060543537 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828713_0.png resize: (95, 74) 1335068103 -2.2073473385739315 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828699_0.png resize: (136, 186) 1335068105 -2.5409489061188237 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828677_0.png resize: (181, 165) 1335068107 -4.531068967568792 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828696_0.png resize: (220, 128) 1335068110 -3.9784450005005447 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828693_0.png resize: (147, 142) 1335068112 -2.2684821003578928 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828686_0.png resize: (144, 79) 1335068114 -2.9634574594310465 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828682_0.png resize: (190, 295) 1335068116 -4.752961013244429 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828679_0.png resize: (150, 101) 1335068118 -2.885703417238952 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828680_0.png resize: (64, 134) 1335068120 -0.9272368846702891 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828725_0.png resize: (162, 120) 1335068122 -3.7156036783001167 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828703_0.png resize: (236, 235) 1335068124 -3.4612108696343578 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828721_0.png resize: (80, 114) 1335068126 -1.3750836423865727 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828674_0.png resize: (309, 214) 1335068128 -3.2328970736143665 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828678_0.png resize: (177, 209) 1335068130 -4.75208442976186 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828723_0.png resize: (204, 345) 1335068132 -4.869128852891925 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828675_0.png resize: (113, 80) 1335068134 -2.189199183236094 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828685_0.png resize: (101, 126) 1335068136 -4.427579831202972 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828709_0.png resize: (55, 70) 1335068138 -0.06107334239404469 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828687_0.png resize: (111, 129) 1335068140 -3.4753363284210756 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828720_0.png resize: (100, 73) 1335068142 -3.134566745302572 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828697_0.png resize: (168, 134) 1335068144 -4.147073814334432 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828704_0.png resize: (124, 204) 1335068146 -4.452409270838266 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828716_0.png resize: (152, 71) 1335068148 -2.5385636838555783 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828676_0.png resize: (87, 228) 1335068150 -3.8036244066984097 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828706_0.png resize: (185, 168) 1335068152 -3.270645931967353 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828701_0.png resize: (144, 114) 1335068154 -4.509865046705093 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828712_0.png resize: (211, 226) 1335068156 -4.7755400633152245 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828722_0.png resize: (149, 106) 1335068158 -4.972857702562398 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828705_0.png resize: (200, 131) 1335068160 -4.17570942324075 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828684_0.png resize: (132, 113) 1335068162 -3.353860258986701 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828694_0.png resize: (74, 90) 1335068164 -2.677579354001223 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828698_0.png resize: (48, 69) 1335068166 -3.8967646403918335 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828714_0.png resize: (65, 58) 1335068167 -1.426810656935527 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828710_0.png resize: (234, 96) 1335068168 -4.960858081621011 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828689_0.png resize: (173, 157) 1335068169 -5.2453928065637125 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828681_0.png resize: (114, 99) 1335068170 -3.590336930561791 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828683_0.png resize: (138, 131) 1335068171 -4.20249355685283 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828691_0.png resize: (175, 106) 1335068172 -3.2157550882997636 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828692_0.png resize: (240, 169) 1335068173 -4.870782089793051 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828719_0.png resize: (121, 122) 1335068174 -4.052776951827535 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828707_0.png resize: (102, 122) 1335068175 -4.117565543744417 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828718_0.png resize: (95, 98) 1335068176 -3.3483152581712026 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828724_0.png resize: (82, 86) 1335068178 -3.2032541220484942 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828726_0.png resize: (453, 393) 1335068179 -3.8965963364106577 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828733_0.png resize: (1138, 850) 1335068180 -3.729728113103245 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828746_0.png resize: (207, 175) 1335068181 -0.6314445779832359 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828744_0.png resize: (228, 236) 1335068182 -1.714173155338704 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828756_0.png resize: (184, 202) 1335068183 -4.006370609686467 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828766_0.png resize: (144, 97) 1335068184 -1.3992578987688824 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828728_0.png resize: (357, 254) 1335068185 -0.8146013922897252 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828755_0.png resize: (82, 109) 1335068186 -3.510273407745913 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828747_0.png resize: (191, 155) 1335068187 -4.275795546770951 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828757_0.png resize: (101, 129) 1335068188 -4.78872007902133 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828730_0.png resize: (272, 709) 1335068189 -4.961490925923348 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828734_0.png resize: (142, 109) 1335068190 -3.118546395147202 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828743_0.png resize: (147, 210) 1335068191 -2.923255752934019 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828735_0.png resize: (118, 115) 1335068192 -3.014991346342146 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828741_0.png resize: (174, 273) 1335068193 -4.563075516546439 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828750_0.png resize: (133, 310) 1335068194 -2.5657338998124777 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828748_0.png resize: (126, 153) 1335068195 -3.6692498194790595 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828739_0.png resize: (136, 118) 1335068196 -4.212328250075532 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828745_0.png resize: (151, 141) 1335068197 -3.4861689748564038 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828761_0.png resize: (136, 98) 1335068198 -3.828016520677316 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828763_0.png resize: (130, 168) 1335068199 -3.5210322578495656 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828737_0.png resize: (170, 166) 1335068200 -3.8058063927924515 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828754_0.png resize: (149, 152) 1335068201 -3.510211120351758 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828762_0.png resize: (109, 104) 1335068202 -4.044939788624219 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828753_0.png resize: (393, 156) 1335068203 -2.7699152331205705 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828752_0.png resize: (246, 227) 1335068204 -4.173703858767639 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828731_0.png resize: (278, 156) 1335068205 -2.9266901371107292 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828736_0.png resize: (151, 100) 1335068206 -3.1108711095377615 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828727_0.png resize: (102, 78) 1335068207 -3.290786701332702 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828732_0.png resize: (149, 58) 1335068208 -3.8357781269668565 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828804_0.png resize: (318, 115) 1335068209 -3.098598979569876 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828777_0.png resize: (161, 181) 1335068210 -3.1269590478181413 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828802_0.png resize: (218, 206) 1335068211 -3.205083478755393 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828794_0.png resize: (214, 210) 1335068212 -3.1861222169319894 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828780_0.png resize: (174, 164) 1335068213 -3.546926929579027 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828772_0.png resize: (91, 451) 1335068214 -2.8388730371340345 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828801_0.png resize: (220, 278) 1335068215 -4.46838225731112 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828803_0.png resize: (154, 176) 1335068216 -3.2468648522427666 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828817_0.png resize: (146, 98) 1335068217 -1.592890642058237 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828778_0.png resize: (328, 419) 1335068218 -4.212516619561964 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828814_0.png resize: (172, 275) 1335068219 -4.3583101594891245 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828783_0.png resize: (123, 158) 1335068220 -2.892108451845839 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828790_0.png resize: (141, 117) 1335068221 -2.7395764498841353 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828771_0.png resize: (179, 268) 1335068222 -4.7433576462426865 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828791_0.png resize: (200, 202) 1335068223 -3.7884135817018603 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828797_0.png resize: (110, 221) 1335068224 -4.1701854518381785 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828813_0.png resize: (302, 199) 1335068225 -4.456490917052269 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828789_0.png resize: (146, 155) 1335068226 -4.224751039710326 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828810_0.png resize: (107, 128) 1335068227 -3.321962634466015 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828774_0.png resize: (213, 143) 1335068228 -4.673696070766395 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828784_0.png resize: (238, 243) 1335068229 -4.290688744262304 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828782_0.png resize: (261, 149) 1335068230 -5.17989397567773 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828806_0.png resize: (179, 149) 1335068231 -3.168263291551853 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828767_0.png resize: (160, 99) 1335068232 -3.367530931458684 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828776_0.png resize: (290, 155) 1335068233 -4.253014161271009 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828785_0.png resize: (71, 187) 1335068235 -4.560398434168204 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828818_0.png resize: (192, 230) 1335068237 -3.753865889560204 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828808_0.png resize: (192, 186) 1335068239 -4.242301396579779 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828786_0.png resize: (292, 183) 1335068242 -4.611473964890508 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828788_0.png resize: (80, 63) 1335068244 -4.84681413222177 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828773_0.png resize: (240, 354) 1335068246 -4.855631756048562 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828792_0.png resize: (156, 164) 1335068248 -4.02104521185159 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828807_0.png resize: (235, 217) 1335068250 -5.039060758603141 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828811_0.png resize: (178, 322) 1335068252 -4.717693626054028 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828781_0.png resize: (213, 169) 1335068254 -3.8083681079253178 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828787_0.png resize: (110, 111) 1335068256 -4.170967064814908 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828793_0.png resize: (206, 128) 1335068258 -4.93917774280234 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828815_0.png resize: (105, 173) 1335068260 -4.9233775820069114 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828805_0.png resize: (199, 165) 1335068262 -4.153369617813306 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828779_0.png resize: (155, 123) 1335068264 -2.95685105327561 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828800_0.png resize: (179, 136) 1335068266 -4.86987412109224 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828769_0.png resize: (108, 186) 1335068268 -4.120356445208674 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828830_0.png resize: (264, 493) 1335068270 -0.9937795842449314 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828856_0.png resize: (185, 135) 1335068272 -3.3661281408990407 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828841_0.png resize: (165, 230) 1335068274 -5.424584083940714 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828862_0.png resize: (179, 256) 1335068276 -4.2232206430085055 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828835_0.png resize: (135, 111) 1335068278 -3.407762224968844 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828829_0.png resize: (64, 192) 1335068279 -2.665717596138762 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828845_0.png resize: (72, 180) 1335068280 -3.6907800238110156 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828865_0.png resize: (220, 243) 1335068281 -4.471550500244724 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828846_0.png resize: (253, 428) 1335068282 -2.3826429672200127 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828825_0.png resize: (158, 170) 1335068283 -3.3793965675840862 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828854_0.png resize: (138, 187) 1335068284 -4.17418832112558 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828823_0.png resize: (174, 186) 1335068285 -4.740792637589256 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828834_0.png resize: (109, 116) 1335068286 -1.5913330302403457 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828867_0.png resize: (190, 133) 1335068287 -1.7640688246704193 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828859_0.png resize: (138, 259) 1335068288 -3.6284609285634826 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828861_0.png resize: (147, 107) 1335068289 -3.211186336575925 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828831_0.png resize: (195, 130) 1335068290 -4.030040229102333 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828836_0.png resize: (165, 153) 1335068291 -2.5260401692203986 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828821_0.png resize: (131, 132) 1335068293 -4.596206870023928 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828870_0.png resize: (61, 56) 1335068294 -1.2384704193895204 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828849_0.png resize: (169, 103) 1335068295 -5.4729161170996345 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828851_0.png resize: (106, 132) 1335068296 -3.987010231410973 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828828_0.png resize: (205, 69) 1335068297 -4.023636685440778 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828847_0.png resize: (89, 95) 1335068298 -4.715341171866055 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828848_0.png resize: (223, 319) 1335068299 -3.771622457328585 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828833_0.png resize: (273, 268) 1335068300 -4.040988002325718 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828872_0.png resize: (233, 190) 1335068301 -3.82779327015079 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828877_0.png resize: (223, 218) 1335068302 -4.109499661661753 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828838_0.png resize: (115, 157) 1335068303 -2.3939638347701413 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828843_0.png resize: (134, 191) 1335068304 -4.496489061184245 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828844_0.png resize: (230, 109) 1335068305 -4.159838496517487 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828874_0.png resize: (199, 206) 1335068306 -3.9970037671356855 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828855_0.png resize: (220, 88) 1335068307 -3.139633801169032 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828863_0.png resize: (77, 106) 1335068308 -4.031867516679584 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828822_0.png resize: (181, 117) 1335068309 -5.402209554119556 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828826_0.png resize: (251, 214) 1335068310 -4.18280301440772 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828876_0.png resize: (200, 206) 1335068311 -4.680192976511023 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828864_0.png resize: (65, 80) 1335068312 -3.3054147449687448 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828850_0.png resize: (255, 322) 1335068313 -4.743596311951871 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828852_0.png resize: (169, 145) 1335068314 -3.290720701026071 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828827_0.png resize: (201, 153) 1335068315 -3.4152937662399454 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828860_0.png resize: (63, 100) 1335068316 -3.2436309896764977 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828832_0.png resize: (150, 123) 1335068318 -4.041733779994787 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828837_0.png resize: (183, 276) 1335068319 -3.2635532506980085 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828875_0.png resize: (215, 208) 1335068320 -3.9701734638054362 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828824_0.png resize: (139, 135) 1335068321 -3.542230708769933 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828839_0.png resize: (169, 168) 1335068322 -4.030553169259169 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828871_0.png resize: (76, 83) 1335068323 -4.68125477840568 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828869_0.png resize: (129, 106) 1335068324 -3.7238863628842114 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828857_0.png resize: (126, 86) 1335068325 -4.666558783824169 treat image : temp/1738801229_1649647_1334940953_1e7a44457954b54c9e9cb93325a0a32e_rle_crop_3656828885_0.png resize: (47, 202) 1335068326 -4.358753378860887 treat image : temp/1738801229_1649647_1334940953_1e7a44457954b54c9e9cb93325a0a32e_rle_crop_3656828889_0.png resize: (91, 117) 1335068327 -1.4438200935513132 treat image : temp/1738801229_1649647_1334940953_1e7a44457954b54c9e9cb93325a0a32e_rle_crop_3656828887_0.png resize: (141, 462) 1335068328 -2.5918784393178558 treat image : temp/1738801229_1649647_1334940953_1e7a44457954b54c9e9cb93325a0a32e_rle_crop_3656828888_0.png resize: (95, 120) 1335068329 -5.318648787127736 treat image : temp/1738801229_1649647_1334940953_1e7a44457954b54c9e9cb93325a0a32e_rle_crop_3656828886_0.png resize: (101, 84) 1335068330 -1.3100331868612987 treat image : temp/1738801229_1649647_1334940953_1e7a44457954b54c9e9cb93325a0a32e_rle_crop_3656828884_0.png resize: (101, 72) 1335068331 -2.5873013552976865 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828491_0.png resize: (231, 152) 1335068446 -2.092913338104995 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828522_0.png resize: (162, 186) 1335068447 -4.835041001019069 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828474_0.png resize: (295, 251) 1335068448 -3.316556154979919 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828501_0.png resize: (76, 251) 1335068449 -3.6359041619553842 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828473_0.png resize: (209, 226) 1335068450 -5.769100305743015 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828519_0.png resize: (209, 193) 1335068451 -4.607216394012961 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828548_0.png resize: (93, 172) 1335068453 -3.7507655518418757 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828566_0.png resize: (369, 499) 1335068454 -7.165746679184079 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828546_0.png resize: (211, 185) 1335068455 -2.86055532789145 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828574_0.png resize: (188, 191) 1335068456 -5.543437974413005 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828593_0.png resize: (95, 175) 1335068457 -4.102854024804518 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828612_0.png resize: (116, 181) 1335068458 -4.709996094148779 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828592_0.png resize: (212, 201) 1335068459 -3.066419514667719 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828605_0.png resize: (236, 246) 1335068460 -4.884379694306441 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828668_0.png resize: (353, 266) 1335068461 -3.7405653442719307 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828635_0.png resize: (621, 568) 1335068462 -5.138437495539575 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828628_0.png resize: (409, 631) 1335068463 -3.142416080713109 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828629_0.png resize: (443, 721) 1335068464 -3.8167764812140113 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828654_0.png resize: (129, 127) 1335068465 -4.113378000325989 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828657_0.png resize: (297, 326) 1335068466 -4.934541889497032 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828666_0.png resize: (117, 133) 1335068467 -4.564083534123383 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828715_0.png resize: (211, 336) 1335068468 -4.310193298623524 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828695_0.png resize: (119, 166) 1335068469 -2.693389562450808 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828700_0.png resize: (169, 93) 1335068470 -3.4877395652586136 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828717_0.png resize: (223, 266) 1335068471 -4.15706574968218 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828708_0.png resize: (76, 56) 1335068472 -0.5999308596232235 treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc_rle_crop_3656828711_0.png resize: (244, 283) 1335068473 -4.281081205444399 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828742_0.png resize: (277, 352) 1335068474 -3.9610529007657806 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828796_0.png resize: (177, 238) 1335068475 -3.1141829714868807 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828770_0.png resize: (104, 197) 1335068476 -2.962934332695852 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828768_0.png resize: (214, 252) 1335068477 -3.3942929733780547 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828809_0.png resize: (322, 203) 1335068478 -4.567934789175227 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828775_0.png resize: (124, 143) 1335068479 -3.8720522799840094 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828816_0.png resize: (158, 240) 1335068480 -4.5007192633712005 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828795_0.png resize: (204, 156) 1335068481 -3.469961663654099 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828840_0.png resize: (157, 194) 1335068482 -2.8601677722284644 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828866_0.png resize: (332, 286) 1335068483 -4.37013649525011 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828878_0.png resize: (229, 446) 1335068484 -3.311581570738339 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828868_0.png resize: (97, 135) 1335068485 -2.804660131485989 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828858_0.png resize: (158, 235) 1335068486 -3.3870263727836036 treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828842_0.png resize: (152, 349) 1335068487 -4.358636345348453 treat image : temp/1738801229_1649647_1334940953_1e7a44457954b54c9e9cb93325a0a32e_rle_crop_3656828883_0.png resize: (262, 159) 1335068488 -4.486162613307377 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828555_0.png resize: (82, 118) 1335068504 -5.811017398665819 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828598_0.png resize: (175, 183) 1335068505 -4.0766479815132355 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828621_0.png resize: (77, 58) 1335068506 -3.0562365368599544 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828601_0.png resize: (83, 113) 1335068507 -4.965932993580344 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828611_0.png resize: (175, 159) 1335068508 -5.534060323354804 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828765_0.png resize: (192, 193) 1335068509 -2.475041612362237 treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d_rle_crop_3656828820_0.png resize: (189, 279) 1335068510 -4.932964473811414 treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828489_0.png resize: (459, 1150) 1335068555 -1.9778867875685977 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828558_0.png resize: (274, 387) 1335068556 -3.7059137173994348 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828575_0.png resize: (433, 411) 1335068557 -5.468678708845995 treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf_rle_crop_3656828556_0.png resize: (273, 1343) 1335068558 -2.36965481278235 treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056_rle_crop_3656828616_0.png resize: (313, 252) 1335068559 -3.551523177560086 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828642_0.png resize: (379, 688) 1335068561 -3.5834796577241685 treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770_rle_crop_3656828641_0.png resize: (407, 1367) 1335068562 -2.0808538689983465 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828738_0.png resize: (243, 301) 1335068563 -3.6141005282318286 treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828764_0.png resize: (268, 406) 1335068564 -2.8451261366657743 treat image : 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insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 426 time used for this insertion : 0.21535491943359375 save missing photos in datou_result : time spend for datou_step_exec : 33.672935009002686 time spend to save output : 0.26276564598083496 total time spend for step 6 : 33.93570065498352 step7:brightness Thu Feb 6 01:28:59 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e.jpg treat image : temp/1738801229_1649647_1334944834_149fb6493a5740fa345858a7eb6051cf.jpg treat image : temp/1738801229_1649647_1334944830_7dd29ef30cd946781627ba8d57336056.jpg treat image : temp/1738801229_1649647_1334943171_d085a9ec658171d130b73fd79d2b6770.jpg treat image : temp/1738801229_1649647_1334943139_f74f3a992d338bab8a7a09442ae95dcc.jpg treat image : temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb.jpg treat image : temp/1738801229_1649647_1334940961_8ce264edfd96e65039360592f1b3483d.jpg treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6.jpg treat image : temp/1738801229_1649647_1334940953_1e7a44457954b54c9e9cb93325a0a32e.jpg treat image : temp/1738801229_1649647_1334944838_5dba5781f1e1ff872027a285deb74a8e_rle_crop_3656828516_0.png treat image : 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temp/1738801229_1649647_1334940964_e6d4a64397e2cd7a0bcd46cf21b382eb_rle_crop_3656828749_0.png treat image : temp/1738801229_1649647_1334940958_3bb98ac4fe1c03a3a4ecf38f7a6eedd6_rle_crop_3656828853_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 : 426 time used for this insertion : 0.03057551383972168 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 426 time used for this insertion : 0.12763643264770508 save missing photos in datou_result : time spend for datou_step_exec : 9.577129125595093 time spend to save output : 0.16466021537780762 total time spend for step 7 : 9.7417893409729 step8:velours_tree Thu Feb 6 01:29:09 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 0.8864648342132568 time spend to save output : 4.8160552978515625e-05 total time spend for step 8 : 0.8865129947662354 step9:send_mail_cod Thu Feb 6 01:29:09 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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_P20277523_06-02-2025_01_29_09.pdf 20278038 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 .imagette202780381738801749 20278039 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 .imagette202780391738801750 20278040 imagette202780401738801750 20278041 imagette202780411738801750 20278042 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 .imagette202780421738801750 20278044 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 .imagette202780441738801752 20278045 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 .imagette202780451738801753 20278046 imagette202780461738801754 20278047 change filename to text .change filename to text .imagette202780471738801754 20278048 change filename to text .change filename to text .imagette202780481738801754 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=20277523 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/20278038,20278039,20278040,20278041,20278042,20278043,20278044,20278045,20278046,20278047,20278048?tags=metal,autre,flou,background,papier,environnement,pet_clair,carton,mal_croppe,pet_fonce,pehd args[1334944838] : ((1334944838, -6.3554754316203725, 492609224), (1334944838, -0.32223241573255895, 496442774), '0.1770747284253208') apple ((1334944838, -6.3554754316203725, 492609224), (1334944838, -0.32223241573255895, 496442774), '0.1770747284253208') We are sending mail with results at report@fotonower.com args[1334944834] : ((1334944834, -7.576396063129005, 492609224), (1334944834, -0.3045136424666567, 496442774), '0.1770747284253208') apple ((1334944834, -7.576396063129005, 492609224), (1334944834, -0.3045136424666567, 496442774), '0.1770747284253208') We are sending mail with results at report@fotonower.com args[1334944830] : ((1334944830, -6.849263437473965, 492609224), (1334944830, -0.3462665745431176, 496442774), '0.1770747284253208') apple ((1334944830, -6.849263437473965, 492609224), (1334944830, -0.3462665745431176, 496442774), '0.1770747284253208') We are sending mail with results at report@fotonower.com args[1334943171] : ((1334943171, -6.629196626552226, 492609224), (1334943171, -0.32232100162469657, 496442774), '0.1770747284253208') apple ((1334943171, -6.629196626552226, 492609224), (1334943171, -0.32232100162469657, 496442774), '0.1770747284253208') We are sending mail with results at report@fotonower.com args[1334943139] : ((1334943139, -6.635106431196048, 492609224), (1334943139, -0.4097484308475365, 496442774), '0.1770747284253208') apple ((1334943139, -6.635106431196048, 492609224), (1334943139, -0.4097484308475365, 496442774), '0.1770747284253208') We are sending mail with results at report@fotonower.com args[1334940964] : ((1334940964, -6.043742423063105, 492609224), (1334940964, 0.06312186016733609, 2107752395), '0.1770747284253208') apple ((1334940964, -6.043742423063105, 492609224), (1334940964, 0.06312186016733609, 2107752395), '0.1770747284253208') We are sending mail with results at report@fotonower.com args[1334940961] : ((1334940961, -6.474051258854378, 492609224), (1334940961, -0.14540619227494006, 496442774), '0.1770747284253208') apple ((1334940961, -6.474051258854378, 492609224), (1334940961, -0.14540619227494006, 496442774), '0.1770747284253208') We are sending mail with results at report@fotonower.com args[1334940958] : ((1334940958, -6.494277652677899, 492609224), (1334940958, -0.1056531975912329, 496442774), '0.1770747284253208') apple ((1334940958, -6.494277652677899, 492609224), (1334940958, -0.1056531975912329, 496442774), '0.1770747284253208') We are sending mail with results at report@fotonower.com args[1334940953] : ((1334940953, -2.9276254900647114, 492609224), (1334940953, 0.19314589330693543, 2107752395), '0.1770747284253208') apple ((1334940953, -2.9276254900647114, 492609224), (1334940953, 0.19314589330693543, 2107752395), '0.1770747284253208') We are sending mail with results at report@fotonower.com refus_total : 0.1770747284253208 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=20277523 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1334940958,1334940961,1334940964,1334940953,1334943139,1334943171,1334944830,1334944834,1334944838) Found this number of photos: 9 begin to download photo : 1334940958 begin to download photo : 1334940964 begin to download photo : 1334943139 begin to download photo : 1334944830 begin to download photo : 1334944838 download finish for photo 1334944838 download finish for photo 1334940964 begin to download photo : 1334940953 download finish for photo 1334943139 begin to download photo : 1334943171 download finish for photo 1334940958 begin to download photo : 1334940961 download finish for photo 1334944830 begin to download photo : 1334944834 download finish for photo 1334940953 download finish for photo 1334943171 download finish for photo 1334940961 download finish for photo 1334944834 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20277523_06-02-2025_01_29_09.pdf results_Auto_P20277523_06-02-2025_01_29_09.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20277523_06-02-2025_01_29_09.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','20277523','results_Auto_P20277523_06-02-2025_01_29_09.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20277523_06-02-2025_01_29_09.pdf','pdf','','0.8','0.1770747284253208') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/20277523

https://www.fotonower.com/image?json=false&list_photos_id=1334944838
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1334944834
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1334944830
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1334943171
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1334943139
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1334940964
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1334940961
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1334940958
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1334940953
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 17.71%
Veuillez trouver les photos des contaminants.

exemples de contaminants: metal: https://www.fotonower.com/view/20278038?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/20278039?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/20278042?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/20278044?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/20278045?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/20278047?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/20278048?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20277523_06-02-2025_01_29_09.pdf.

Lien vers velours :https://www.fotonower.com/velours/20278038,20278039,20278040,20278041,20278042,20278043,20278044,20278045,20278046,20278047,20278048?tags=metal,autre,flou,background,papier,environnement,pet_clair,carton,mal_croppe,pet_fonce,pehd.


L'équipe Fotonower 202 b'' Server: nginx Date: Thu, 06 Feb 2025 00:29:17 GMT Content-Length: 0 Connection: close X-Message-Id: HGVAN9sATtyetdlRPy3YQw 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 [1334944838, 1334944834, 1334944830, 1334943171, 1334943139, 1334940964, 1334940961, 1334940958, 1334940953] 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, '2557205') ('3318', '20277523', '1334944838', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334944834', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334944830', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334943171', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334943139', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940964', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940961', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940958', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940953', None, None, None, None, None, '2557205') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 9 time used for this insertion : 0.012389898300170898 save_final save missing photos in datou_result : time spend for datou_step_exec : 7.725209951400757 time spend to save output : 0.012635231018066406 total time spend for step 9 : 7.737845182418823 step10:split_time_score Thu Feb 6 01:29:17 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure begin split time score Catched exception ! Connect or reconnect ! TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('14', 9),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 05022025 20277523 Nombre de photos uploadées : 9 / 23040 (0%) 05022025 20277523 Nombre de photos taguées (types de déchets): 0 / 9 (0%) 05022025 20277523 Nombre de photos taguées (volume) : 0 / 9 (0%) elapsed_time : load_data_split_time_score 1.1920928955078125e-06 elapsed_time : order_list_meta_photo_and_scores 4.0531158447265625e-06 ????????? elapsed_time : fill_and_build_computed_from_old_data 0.00034928321838378906 elapsed_time : insert_dashboard_record_day_entry 0.02367568016052246 We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20277502 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20277503 order by id desc limit 1 Qualite : 0.06442282072148928 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20277508_06-02-2025_00_55_54.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20277508 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`=20277508 AND mptpi.`type`=3726 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20277509 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20277511 order by id desc limit 1 Qualite : 0.1770747284253208 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20277523_06-02-2025_01_29_09.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20277523 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`=20277523 AND mptpi.`type`=3594 To do Qualite : 0.06769655641860853 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20277527_06-02-2025_00_59_31.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20277527 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`=20277527 AND mptpi.`type`=3726 To do Qualite : 0.21480488134682896 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20277530_06-02-2025_01_27_03.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20277530 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`=20277530 AND mptpi.`type`=3594 To do Qualite : 0.180145604972313 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20277549_06-02-2025_00_58_40.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20277549 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`=20277549 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20277551 order by id desc limit 1 NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'05022025': {'nb_upload': 9, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} Inside saveOutput : final : True verbose : 0 saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1334944838, 1334944834, 1334944830, 1334943171, 1334943139, 1334940964, 1334940961, 1334940958, 1334940953] Looping around the photos to save general results len do output : 1 /20277523Didn'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, '2557205') ('3318', '20277523', '1334944838', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334944834', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334944830', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334943171', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334943139', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940964', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940961', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940958', None, None, None, None, None, '2557205') ('3318', None, None, None, None, None, None, None, '2557205') ('3318', '20277523', '1334940953', None, None, None, None, None, '2557205') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 10 time used for this insertion : 0.016681671142578125 save_final save missing photos in datou_result : time spend for datou_step_exec : 4.34697151184082 time spend to save output : 0.016884326934814453 total time spend for step 10 : 4.363855838775635 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 9 set_done_treatment 256.13user 100.00system 8:55.92elapsed 66%CPU (0avgtext+0avgdata 8749308maxresident)k 5184768inputs+201848outputs (172729major+25229854minor)pagefaults 0swaps