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 : 1178299 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 : ['2741779'] with mtr_portfolio_ids : ['22248184'] and first list_photo_ids : [] new path : /proc/1178299/ 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 , BFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 12 ; length of list_pids : 12 ; length of list_args : 12 time to download the photos : 2.2602996826171875 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 Fri Apr 11 02:10:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10814 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-11 02:10:32.910267: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-11 02:10:32.935144: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-11 02:10:32.937257: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f402c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-11 02:10:32.937306: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-11 02:10:32.940835: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-11 02:10:33.163380: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x22086b10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-11 02:10:33.163426: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-11 02:10:33.164768: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-11 02:10:33.165137: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-11 02:10:33.168055: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-11 02:10:33.170595: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-11 02:10:33.171080: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-11 02:10:33.174254: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-11 02:10:33.175808: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-11 02:10:33.179846: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-11 02:10:33.181274: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-11 02:10:33.181331: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-11 02:10:33.182071: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-11 02:10:33.182085: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-11 02:10:33.182093: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-11 02:10:33.183420: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-04-11 02:10:33.447527: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-11 02:10:33.447648: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-11 02:10:33.447671: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-11 02:10:33.447691: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-11 02:10:33.447711: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-11 02:10:33.447731: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-11 02:10:33.447750: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-11 02:10:33.447771: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-11 02:10:33.449492: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-11 02:10:33.450839: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-11 02:10:33.450874: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-11 02:10:33.450892: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-11 02:10:33.450924: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-11 02:10:33.450942: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-11 02:10:33.450959: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-11 02:10:33.450976: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-11 02:10:33.450993: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-11 02:10:33.452693: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-11 02:10:33.452733: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-11 02:10:33.452745: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-11 02:10:33.452756: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-11 02:10:33.454513: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl2847 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) {'thcl': {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'], 'list_hashtags_csv': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'svm_hashtag_type_desc': 5275, 'photo_desc_type': 5275, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME learn_RUBBIA_REFUS_AMIENS_23 NUM_CLASSES 9 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : learn_RUBBIA_REFUS_AMIENS_23 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-04-11 02:10:44.698342: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-11 02:10:44.895050: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23 /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23/mask_model.h5 size_local : 256009536 size in s3 : 256009536 create time local : 2021-08-09 09:43:22 create time in s3 : 2021-08-06 18:54:04 mask_model.h5 already exist and didn't need to update list_images length : 12 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 : 50 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 : 50 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 73 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 12 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 10.00000 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 : 23 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 : 21 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 2.00000 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 : 11 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 30 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 35 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 : 25 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 : 95 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 : 89 Detection mask done ! Trying to reset tf kernel 1178821 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5304 tf kernel not reseted sub process len(results) : 12 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 12 len(list_Values) 0 process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 10593 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 : 0.0016734600067138672 nb_pixel_total : 48226 time to create 1 rle with old method : 0.057425737380981445 length of segment : 387 time for calcul the mask position with numpy : 0.0009355545043945312 nb_pixel_total : 44286 time to create 1 rle with old method : 0.049372196197509766 length of segment : 298 time for calcul the mask position with numpy : 0.0002334117889404297 nb_pixel_total : 10501 time to create 1 rle with old method : 0.012351274490356445 length of segment : 122 time for calcul the mask position with numpy : 0.009574174880981445 nb_pixel_total : 467215 time to create 1 rle with new method : 0.03211474418640137 length of segment : 921 time for calcul the mask position with numpy : 0.00031757354736328125 nb_pixel_total : 13108 time to create 1 rle with old method : 0.01522064208984375 length of segment : 135 time for calcul the mask position with numpy : 0.002313852310180664 nb_pixel_total : 124884 time to create 1 rle with old method : 0.14035344123840332 length of segment : 601 time for calcul the mask position with numpy : 0.0011665821075439453 nb_pixel_total : 46352 time to create 1 rle with old method : 0.052558183670043945 length of segment : 288 time for calcul the mask position with numpy : 0.00030922889709472656 nb_pixel_total : 12550 time to create 1 rle with old method : 0.014830827713012695 length of segment : 151 time for calcul the mask position with numpy : 0.0002541542053222656 nb_pixel_total : 11301 time to create 1 rle with old method : 0.013110876083374023 length of segment : 86 time for calcul the mask position with numpy : 0.00014352798461914062 nb_pixel_total : 5548 time to create 1 rle with old method : 0.006491899490356445 length of segment : 77 time for calcul the mask position with numpy : 0.00022172927856445312 nb_pixel_total : 9009 time to create 1 rle with old method : 0.010807275772094727 length of segment : 89 time for calcul the mask position with numpy : 0.0001537799835205078 nb_pixel_total : 5210 time to create 1 rle with old method : 0.0061414241790771484 length of segment : 88 time for calcul the mask position with numpy : 0.0035049915313720703 nb_pixel_total : 9771 time to create 1 rle with old method : 0.011472702026367188 length of segment : 95 time for calcul the mask position with numpy : 0.0004904270172119141 nb_pixel_total : 8047 time to create 1 rle with old method : 0.009238481521606445 length of segment : 128 time for calcul the mask position with numpy : 0.0012912750244140625 nb_pixel_total : 34851 time to create 1 rle with old method : 0.038611412048339844 length of segment : 554 time for calcul the mask position with numpy : 0.0011014938354492188 nb_pixel_total : 18747 time to create 1 rle with old method : 0.02117753028869629 length of segment : 216 time for calcul the mask position with numpy : 0.0008146762847900391 nb_pixel_total : 15533 time to create 1 rle with old method : 0.01845526695251465 length of segment : 141 time for calcul the mask position with numpy : 0.0002384185791015625 nb_pixel_total : 3591 time to create 1 rle with old method : 0.004462003707885742 length of segment : 62 time for calcul the mask position with numpy : 0.0008003711700439453 nb_pixel_total : 12332 time to create 1 rle with old method : 0.014233589172363281 length of segment : 140 time for calcul the mask position with numpy : 0.0005326271057128906 nb_pixel_total : 12246 time to create 1 rle with old method : 0.014564037322998047 length of segment : 97 time for calcul the mask position with numpy : 0.0003643035888671875 nb_pixel_total : 5387 time to create 1 rle with old method : 0.006488800048828125 length of segment : 107 time for calcul the mask position with numpy : 0.008826255798339844 nb_pixel_total : 179012 time to create 1 rle with new method : 0.019997835159301758 length of segment : 896 time for calcul the mask position with numpy : 0.0013632774353027344 nb_pixel_total : 22902 time to create 1 rle with old method : 0.02884531021118164 length of segment : 227 time for calcul the mask position with numpy : 0.0017843246459960938 nb_pixel_total : 36980 time to create 1 rle with old method : 0.0437016487121582 length of segment : 195 time for calcul the mask position with numpy : 0.001104593276977539 nb_pixel_total : 11695 time to create 1 rle with old method : 0.014553546905517578 length of segment : 182 time for calcul the mask position with numpy : 0.0006501674652099609 nb_pixel_total : 12082 time to create 1 rle with old method : 0.013987302780151367 length of segment : 135 time for calcul the mask position with numpy : 0.0037183761596679688 nb_pixel_total : 77321 time to create 1 rle with old method : 0.09163331985473633 length of segment : 614 time for calcul the mask position with numpy : 0.0006999969482421875 nb_pixel_total : 14404 time to create 1 rle with old method : 0.0166473388671875 length of segment : 135 time for calcul the mask position with numpy : 0.006758928298950195 nb_pixel_total : 156812 time to create 1 rle with new method : 0.011748313903808594 length of segment : 471 time for calcul the mask position with numpy : 0.004239797592163086 nb_pixel_total : 73025 time to create 1 rle with old method : 0.08168649673461914 length of segment : 326 time for calcul the mask position with numpy : 0.0012073516845703125 nb_pixel_total : 26467 time to create 1 rle with old method : 0.03021073341369629 length of segment : 285 time for calcul the mask position with numpy : 0.001718759536743164 nb_pixel_total : 41435 time to create 1 rle with old method : 0.047898054122924805 length of segment : 229 time for calcul the mask position with numpy : 0.004815816879272461 nb_pixel_total : 92857 time to create 1 rle with old method : 0.10437369346618652 length of segment : 845 time for calcul the mask position with numpy : 0.0008838176727294922 nb_pixel_total : 16963 time to create 1 rle with old method : 0.01902151107788086 length of segment : 184 time for calcul the mask position with numpy : 0.0004286766052246094 nb_pixel_total : 10276 time to create 1 rle with old method : 0.011724472045898438 length of segment : 133 time for calcul the mask position with numpy : 0.0009601116180419922 nb_pixel_total : 24153 time to create 1 rle with old method : 0.02723097801208496 length of segment : 324 time for calcul the mask position with numpy : 0.0012001991271972656 nb_pixel_total : 26970 time to create 1 rle with old method : 0.031620025634765625 length of segment : 177 time for calcul the mask position with numpy : 0.0013349056243896484 nb_pixel_total : 46391 time to create 1 rle with old method : 0.05311107635498047 length of segment : 298 time for calcul the mask position with numpy : 0.0005404949188232422 nb_pixel_total : 25056 time to create 1 rle with old method : 0.029485702514648438 length of segment : 132 time for calcul the mask position with numpy : 0.0007166862487792969 nb_pixel_total : 8770 time to create 1 rle with old method : 0.014199256896972656 length of segment : 207 time for calcul the mask position with numpy : 0.001567840576171875 nb_pixel_total : 31437 time to create 1 rle with old method : 0.0480656623840332 length of segment : 207 time for calcul the mask position with numpy : 0.0008473396301269531 nb_pixel_total : 18044 time to create 1 rle with old method : 0.02070474624633789 length of segment : 198 time for calcul the mask position with numpy : 0.0008695125579833984 nb_pixel_total : 18088 time to create 1 rle with old method : 0.020360946655273438 length of segment : 165 time for calcul the mask position with numpy : 0.0018954277038574219 nb_pixel_total : 30310 time to create 1 rle with old method : 0.03358316421508789 length of segment : 424 time for calcul the mask position with numpy : 0.0026397705078125 nb_pixel_total : 40688 time to create 1 rle with old method : 0.050676822662353516 length of segment : 666 time for calcul the mask position with numpy : 0.0010950565338134766 nb_pixel_total : 22854 time to create 1 rle with old method : 0.03182077407836914 length of segment : 218 time for calcul the mask position with numpy : 0.0005679130554199219 nb_pixel_total : 7279 time to create 1 rle with old method : 0.011929988861083984 length of segment : 131 time for calcul the mask position with numpy : 0.00023865699768066406 nb_pixel_total : 12974 time to create 1 rle with old method : 0.014780282974243164 length of segment : 136 time for calcul the mask position with numpy : 0.00030732154846191406 nb_pixel_total : 5671 time to create 1 rle with old method : 0.006269931793212891 length of segment : 100 time for calcul the mask position with numpy : 0.0011737346649169922 nb_pixel_total : 26400 time to create 1 rle with old method : 0.02861785888671875 length of segment : 381 time for calcul the mask position with numpy : 0.001959562301635742 nb_pixel_total : 50982 time to create 1 rle with old method : 0.06072282791137695 length of segment : 228 time for calcul the mask position with numpy : 0.0004107952117919922 nb_pixel_total : 8027 time to create 1 rle with old method : 0.00946664810180664 length of segment : 221 time for calcul the mask position with numpy : 0.0007936954498291016 nb_pixel_total : 20093 time to create 1 rle with old method : 0.022456645965576172 length of segment : 163 time for calcul the mask position with numpy : 0.011258840560913086 nb_pixel_total : 203432 time to create 1 rle with new method : 0.019351720809936523 length of segment : 811 time for calcul the mask position with numpy : 0.0016856193542480469 nb_pixel_total : 45618 time to create 1 rle with old method : 0.052366018295288086 length of segment : 482 time for calcul the mask position with numpy : 0.0004303455352783203 nb_pixel_total : 14918 time to create 1 rle with old method : 0.01691722869873047 length of segment : 270 time for calcul the mask position with numpy : 0.0011138916015625 nb_pixel_total : 14486 time to create 1 rle with old method : 0.0162355899810791 length of segment : 151 time for calcul the mask position with numpy : 0.0025026798248291016 nb_pixel_total : 27862 time to create 1 rle with old method : 0.0360865592956543 length of segment : 275 time for calcul the mask position with numpy : 0.003813505172729492 nb_pixel_total : 170090 time to create 1 rle with new method : 0.014262914657592773 length of segment : 524 time for calcul the mask position with numpy : 0.003751516342163086 nb_pixel_total : 75654 time to create 1 rle with old method : 0.08664464950561523 length of segment : 446 time for calcul the mask position with numpy : 0.0004856586456298828 nb_pixel_total : 15181 time to create 1 rle with old method : 0.02234029769897461 length of segment : 124 time for calcul the mask position with numpy : 0.0008666515350341797 nb_pixel_total : 15435 time to create 1 rle with old method : 0.01705002784729004 length of segment : 195 time for calcul the mask position with numpy : 0.009852170944213867 nb_pixel_total : 302862 time to create 1 rle with new method : 0.10221433639526367 length of segment : 1009 time for calcul the mask position with numpy : 0.0009987354278564453 nb_pixel_total : 37330 time to create 1 rle with old method : 0.044061899185180664 length of segment : 236 time for calcul the mask position with numpy : 0.001054525375366211 nb_pixel_total : 26398 time to create 1 rle with old method : 0.031357526779174805 length of segment : 152 time for calcul the mask position with numpy : 0.0006737709045410156 nb_pixel_total : 13905 time to create 1 rle with old method : 0.015938520431518555 length of segment : 201 time for calcul the mask position with numpy : 0.0032236576080322266 nb_pixel_total : 92453 time to create 1 rle with old method : 0.10412764549255371 length of segment : 355 time for calcul the mask position with numpy : 0.006155252456665039 nb_pixel_total : 114964 time to create 1 rle with old method : 0.12832260131835938 length of segment : 360 time for calcul the mask position with numpy : 0.0014500617980957031 nb_pixel_total : 21654 time to create 1 rle with old method : 0.025241851806640625 length of segment : 209 time for calcul the mask position with numpy : 0.010538578033447266 nb_pixel_total : 24315 time to create 1 rle with old method : 0.039476871490478516 length of segment : 271 time for calcul the mask position with numpy : 0.002281665802001953 nb_pixel_total : 23683 time to create 1 rle with old method : 0.026658296585083008 length of segment : 191 time for calcul the mask position with numpy : 0.008872032165527344 nb_pixel_total : 198989 time to create 1 rle with new method : 0.01234579086303711 length of segment : 585 time for calcul the mask position with numpy : 0.0001628398895263672 nb_pixel_total : 5379 time to create 1 rle with old method : 0.006293535232543945 length of segment : 54 time for calcul the mask position with numpy : 0.0005631446838378906 nb_pixel_total : 8756 time to create 1 rle with old method : 0.010108470916748047 length of segment : 109 time for calcul the mask position with numpy : 0.0036818981170654297 nb_pixel_total : 47141 time to create 1 rle with old method : 0.05491161346435547 length of segment : 598 time for calcul the mask position with numpy : 0.0031280517578125 nb_pixel_total : 49061 time to create 1 rle with old method : 0.05388021469116211 length of segment : 399 time for calcul the mask position with numpy : 0.011754512786865234 nb_pixel_total : 214723 time to create 1 rle with new method : 0.017815113067626953 length of segment : 420 time for calcul the mask position with numpy : 0.004710197448730469 nb_pixel_total : 71491 time to create 1 rle with old method : 0.07915115356445312 length of segment : 430 time for calcul the mask position with numpy : 0.0006859302520751953 nb_pixel_total : 12394 time to create 1 rle with old method : 0.013504981994628906 length of segment : 123 time for calcul the mask position with numpy : 0.007790088653564453 nb_pixel_total : 146309 time to create 1 rle with old method : 0.18773603439331055 length of segment : 441 time for calcul the mask position with numpy : 0.0008597373962402344 nb_pixel_total : 12201 time to create 1 rle with old method : 0.013935089111328125 length of segment : 165 time for calcul the mask position with numpy : 0.0007669925689697266 nb_pixel_total : 12343 time to create 1 rle with old method : 0.014729022979736328 length of segment : 272 time for calcul the mask position with numpy : 0.00023674964904785156 nb_pixel_total : 10384 time to create 1 rle with old method : 0.011807918548583984 length of segment : 118 time for calcul the mask position with numpy : 0.010079145431518555 nb_pixel_total : 141899 time to create 1 rle with old method : 0.15936923027038574 length of segment : 422 time for calcul the mask position with numpy : 0.0008981227874755859 nb_pixel_total : 18264 time to create 1 rle with old method : 0.02073049545288086 length of segment : 133 time for calcul the mask position with numpy : 0.0008077621459960938 nb_pixel_total : 20441 time to create 1 rle with old method : 0.023600339889526367 length of segment : 242 time for calcul the mask position with numpy : 0.0010950565338134766 nb_pixel_total : 42736 time to create 1 rle with old method : 0.04665088653564453 length of segment : 265 time for calcul the mask position with numpy : 0.0009701251983642578 nb_pixel_total : 34590 time to create 1 rle with old method : 0.03712058067321777 length of segment : 313 time for calcul the mask position with numpy : 0.0007364749908447266 nb_pixel_total : 22728 time to create 1 rle with old method : 0.03679680824279785 length of segment : 142 time for calcul the mask position with numpy : 0.0005533695220947266 nb_pixel_total : 14494 time to create 1 rle with old method : 0.020725250244140625 length of segment : 168 time for calcul the mask position with numpy : 0.0003788471221923828 nb_pixel_total : 13220 time to create 1 rle with old method : 0.014780521392822266 length of segment : 116 time for calcul the mask position with numpy : 0.000213623046875 nb_pixel_total : 5788 time to create 1 rle with old method : 0.006718158721923828 length of segment : 89 time for calcul the mask position with numpy : 0.002434968948364258 nb_pixel_total : 107388 time to create 1 rle with old method : 0.11372876167297363 length of segment : 396 time for calcul the mask position with numpy : 0.0015277862548828125 nb_pixel_total : 83251 time to create 1 rle with old method : 0.09319639205932617 length of segment : 253 time for calcul the mask position with numpy : 0.0011217594146728516 nb_pixel_total : 29996 time to create 1 rle with old method : 0.03568625450134277 length of segment : 236 time for calcul the mask position with numpy : 0.00231170654296875 nb_pixel_total : 44987 time to create 1 rle with old method : 0.0486445426940918 length of segment : 332 time for calcul the mask position with numpy : 0.0029497146606445312 nb_pixel_total : 108489 time to create 1 rle with old method : 0.11689615249633789 length of segment : 488 time for calcul the mask position with numpy : 0.0017886161804199219 nb_pixel_total : 48209 time to create 1 rle with old method : 0.05473947525024414 length of segment : 372 time for calcul the mask position with numpy : 0.003610372543334961 nb_pixel_total : 153619 time to create 1 rle with new method : 0.005516529083251953 length of segment : 411 time for calcul the mask position with numpy : 0.00133514404296875 nb_pixel_total : 46755 time to create 1 rle with old method : 0.057027339935302734 length of segment : 292 time for calcul the mask position with numpy : 0.0015225410461425781 nb_pixel_total : 70479 time to create 1 rle with old method : 0.07765078544616699 length of segment : 270 time for calcul the mask position with numpy : 0.0003829002380371094 nb_pixel_total : 11908 time to create 1 rle with old method : 0.013967752456665039 length of segment : 120 time for calcul the mask position with numpy : 0.012376785278320312 nb_pixel_total : 476910 time to create 1 rle with new method : 0.03557538986206055 length of segment : 941 time for calcul the mask position with numpy : 0.0006113052368164062 nb_pixel_total : 20172 time to create 1 rle with old method : 0.022887229919433594 length of segment : 151 time for calcul the mask position with numpy : 0.0005614757537841797 nb_pixel_total : 12644 time to create 1 rle with old method : 0.014588356018066406 length of segment : 145 time for calcul the mask position with numpy : 0.011523008346557617 nb_pixel_total : 384224 time to create 1 rle with new method : 0.0283200740814209 length of segment : 827 time for calcul the mask position with numpy : 0.000732421875 nb_pixel_total : 25772 time to create 1 rle with old method : 0.027888059616088867 length of segment : 200 time for calcul the mask position with numpy : 0.008289337158203125 nb_pixel_total : 259870 time to create 1 rle with new method : 0.013965368270874023 length of segment : 843 time for calcul the mask position with numpy : 0.0002472400665283203 nb_pixel_total : 5586 time to create 1 rle with old method : 0.006360769271850586 length of segment : 78 time for calcul the mask position with numpy : 0.0003387928009033203 nb_pixel_total : 17846 time to create 1 rle with old method : 0.02022528648376465 length of segment : 133 time for calcul the mask position with numpy : 0.0010898113250732422 nb_pixel_total : 38738 time to create 1 rle with old method : 0.043164968490600586 length of segment : 295 time for calcul the mask position with numpy : 0.00043773651123046875 nb_pixel_total : 17528 time to create 1 rle with old method : 0.01943802833557129 length of segment : 193 time for calcul the mask position with numpy : 0.00034928321838378906 nb_pixel_total : 11387 time to create 1 rle with old method : 0.013195037841796875 length of segment : 81 time for calcul the mask position with numpy : 0.0013282299041748047 nb_pixel_total : 82170 time to create 1 rle with old method : 0.09135127067565918 length of segment : 304 time for calcul the mask position with numpy : 0.0012753009796142578 nb_pixel_total : 42030 time to create 1 rle with old method : 0.046477556228637695 length of segment : 254 time for calcul the mask position with numpy : 0.00047278404235839844 nb_pixel_total : 14316 time to create 1 rle with old method : 0.016229629516601562 length of segment : 201 time for calcul the mask position with numpy : 0.0005259513854980469 nb_pixel_total : 17561 time to create 1 rle with old method : 0.019173860549926758 length of segment : 157 time for calcul the mask position with numpy : 0.0006568431854248047 nb_pixel_total : 24379 time to create 1 rle with old method : 0.025969743728637695 length of segment : 198 time for calcul the mask position with numpy : 0.0007522106170654297 nb_pixel_total : 20561 time to create 1 rle with old method : 0.023993730545043945 length of segment : 143 time for calcul the mask position with numpy : 0.0052073001861572266 nb_pixel_total : 287623 time to create 1 rle with new method : 0.01200103759765625 length of segment : 684 time for calcul the mask position with numpy : 0.0005917549133300781 nb_pixel_total : 26928 time to create 1 rle with old method : 0.02972102165222168 length of segment : 172 time for calcul the mask position with numpy : 0.0004703998565673828 nb_pixel_total : 14327 time to create 1 rle with old method : 0.016018390655517578 length of segment : 162 time for calcul the mask position with numpy : 0.0034155845642089844 nb_pixel_total : 201067 time to create 1 rle with new method : 0.009981870651245117 length of segment : 526 time for calcul the mask position with numpy : 0.003312826156616211 nb_pixel_total : 133842 time to create 1 rle with old method : 0.14614129066467285 length of segment : 531 time for calcul the mask position with numpy : 0.003373861312866211 nb_pixel_total : 177345 time to create 1 rle with new method : 0.011030197143554688 length of segment : 493 time for calcul the mask position with numpy : 0.0004553794860839844 nb_pixel_total : 13998 time to create 1 rle with old method : 0.015475749969482422 length of segment : 144 time for calcul the mask position with numpy : 0.00438380241394043 nb_pixel_total : 150272 time to create 1 rle with new method : 0.008475065231323242 length of segment : 342 time for calcul the mask position with numpy : 0.013202667236328125 nb_pixel_total : 583402 time to create 1 rle with new method : 0.020837068557739258 length of segment : 1025 time for calcul the mask position with numpy : 0.0023598670959472656 nb_pixel_total : 62072 time to create 1 rle with old method : 0.06732320785522461 length of segment : 564 time for calcul the mask position with numpy : 0.00279998779296875 nb_pixel_total : 106263 time to create 1 rle with old method : 0.11734771728515625 length of segment : 554 time for calcul the mask position with numpy : 0.0078051090240478516 nb_pixel_total : 140427 time to create 1 rle with old method : 0.1505126953125 length of segment : 536 time for calcul the mask position with numpy : 0.0013668537139892578 nb_pixel_total : 61119 time to create 1 rle with old method : 0.08033370971679688 length of segment : 196 time for calcul the mask position with numpy : 0.005513429641723633 nb_pixel_total : 169623 time to create 1 rle with new method : 0.009842872619628906 length of segment : 363 time for calcul the mask position with numpy : 0.0009777545928955078 nb_pixel_total : 32506 time to create 1 rle with old method : 0.03753066062927246 length of segment : 185 time for calcul the mask position with numpy : 0.0004544258117675781 nb_pixel_total : 9311 time to create 1 rle with old method : 0.010650634765625 length of segment : 139 time for calcul the mask position with numpy : 0.0034148693084716797 nb_pixel_total : 131216 time to create 1 rle with old method : 0.14174938201904297 length of segment : 445 time for calcul the mask position with numpy : 0.0044248104095458984 nb_pixel_total : 115897 time to create 1 rle with old method : 0.14212393760681152 length of segment : 350 time for calcul the mask position with numpy : 0.005455732345581055 nb_pixel_total : 234385 time to create 1 rle with new method : 0.009611129760742188 length of segment : 383 time for calcul the mask position with numpy : 0.004633426666259766 nb_pixel_total : 179745 time to create 1 rle with new method : 0.015598773956298828 length of segment : 907 time for calcul the mask position with numpy : 0.004209756851196289 nb_pixel_total : 139288 time to create 1 rle with old method : 0.15407896041870117 length of segment : 646 time for calcul the mask position with numpy : 0.0015168190002441406 nb_pixel_total : 59403 time to create 1 rle with old method : 0.06376838684082031 length of segment : 347 time for calcul the mask position with numpy : 0.0007898807525634766 nb_pixel_total : 19133 time to create 1 rle with old method : 0.021653175354003906 length of segment : 236 time for calcul the mask position with numpy : 0.001272439956665039 nb_pixel_total : 57137 time to create 1 rle with old method : 0.06225109100341797 length of segment : 310 time for calcul the mask position with numpy : 0.0001785755157470703 nb_pixel_total : 3753 time to create 1 rle with old method : 0.004279613494873047 length of segment : 47 time for calcul the mask position with numpy : 0.01547694206237793 nb_pixel_total : 618532 time to create 1 rle with new method : 0.07551813125610352 length of segment : 1926 time for calcul the mask position with numpy : 0.004453182220458984 nb_pixel_total : 147555 time to create 1 rle with old method : 0.1557478904724121 length of segment : 570 time for calcul the mask position with numpy : 0.002033233642578125 nb_pixel_total : 79586 time to create 1 rle with old method : 0.08241868019104004 length of segment : 509 time for calcul the mask position with numpy : 0.004591703414916992 nb_pixel_total : 145222 time to create 1 rle with old method : 0.15337109565734863 length of segment : 487 time for calcul the mask position with numpy : 0.00030612945556640625 nb_pixel_total : 13261 time to create 1 rle with old method : 0.014245986938476562 length of segment : 151 time for calcul the mask position with numpy : 0.010368824005126953 nb_pixel_total : 319335 time to create 1 rle with new method : 0.020646333694458008 length of segment : 1157 time for calcul the mask position with numpy : 0.0002837181091308594 nb_pixel_total : 12408 time to create 1 rle with old method : 0.01354670524597168 length of segment : 149 time for calcul the mask position with numpy : 0.0010781288146972656 nb_pixel_total : 25689 time to create 1 rle with old method : 0.028289318084716797 length of segment : 208 time for calcul the mask position with numpy : 0.008832693099975586 nb_pixel_total : 288510 time to create 1 rle with new method : 0.023205995559692383 length of segment : 1051 time for calcul the mask position with numpy : 0.0029773712158203125 nb_pixel_total : 83355 time to create 1 rle with old method : 0.08938956260681152 length of segment : 605 time for calcul the mask position with numpy : 0.01920008659362793 nb_pixel_total : 401516 time to create 1 rle with new method : 0.08127808570861816 length of segment : 1222 time for calcul the mask position with numpy : 0.0006928443908691406 nb_pixel_total : 15814 time to create 1 rle with old method : 0.01691126823425293 length of segment : 181 time for calcul the mask position with numpy : 0.0018570423126220703 nb_pixel_total : 68410 time to create 1 rle with old method : 0.07149171829223633 length of segment : 323 time for calcul the mask position with numpy : 0.007391691207885742 nb_pixel_total : 264306 time to create 1 rle with new method : 0.010184049606323242 length of segment : 757 time for calcul the mask position with numpy : 0.004789590835571289 nb_pixel_total : 145088 time to create 1 rle with old method : 0.15360283851623535 length of segment : 465 time for calcul the mask position with numpy : 0.00660252571105957 nb_pixel_total : 207061 time to create 1 rle with new method : 0.009373188018798828 length of segment : 596 time for calcul the mask position with numpy : 0.0039424896240234375 nb_pixel_total : 126761 time to create 1 rle with old method : 0.13993287086486816 length of segment : 475 time for calcul the mask position with numpy : 0.005201101303100586 nb_pixel_total : 92530 time to create 1 rle with old method : 0.10018563270568848 length of segment : 628 time for calcul the mask position with numpy : 0.00049591064453125 nb_pixel_total : 10656 time to create 1 rle with old method : 0.012011528015136719 length of segment : 123 time for calcul the mask position with numpy : 0.0002777576446533203 nb_pixel_total : 6104 time to create 1 rle with old method : 0.0069849491119384766 length of segment : 95 time for calcul the mask position with numpy : 0.0031549930572509766 nb_pixel_total : 105186 time to create 1 rle with old method : 0.10816287994384766 length of segment : 616 time for calcul the mask position with numpy : 0.006575345993041992 nb_pixel_total : 164740 time to create 1 rle with new method : 0.02167510986328125 length of segment : 661 time for calcul the mask position with numpy : 0.006063938140869141 nb_pixel_total : 220763 time to create 1 rle with new method : 0.020869731903076172 length of segment : 891 time for calcul the mask position with numpy : 0.0006632804870605469 nb_pixel_total : 35256 time to create 1 rle with old method : 0.04080009460449219 length of segment : 262 time for calcul the mask position with numpy : 0.0009133815765380859 nb_pixel_total : 44427 time to create 1 rle with old method : 0.04884481430053711 length of segment : 452 time for calcul the mask position with numpy : 0.0006291866302490234 nb_pixel_total : 8584 time to create 1 rle with old method : 0.009321928024291992 length of segment : 101 time for calcul the mask position with numpy : 0.0027608871459960938 nb_pixel_total : 39950 time to create 1 rle with old method : 0.04269886016845703 length of segment : 341 time for calcul the mask position with numpy : 0.0031921863555908203 nb_pixel_total : 44688 time to create 1 rle with old method : 0.04829573631286621 length of segment : 252 time for calcul the mask position with numpy : 0.002882242202758789 nb_pixel_total : 39403 time to create 1 rle with old method : 0.04314541816711426 length of segment : 255 time for calcul the mask position with numpy : 0.0019030570983886719 nb_pixel_total : 26009 time to create 1 rle with old method : 0.02749919891357422 length of segment : 340 time for calcul the mask position with numpy : 0.0036954879760742188 nb_pixel_total : 15274 time to create 1 rle with old method : 0.0167081356048584 length of segment : 263 time for calcul the mask position with numpy : 0.0054340362548828125 nb_pixel_total : 89977 time to create 1 rle with old method : 0.09486007690429688 length of segment : 381 time for calcul the mask position with numpy : 0.0015883445739746094 nb_pixel_total : 22694 time to create 1 rle with old method : 0.02413320541381836 length of segment : 235 time for calcul the mask position with numpy : 0.0003991127014160156 nb_pixel_total : 6168 time to create 1 rle with old method : 0.006803750991821289 length of segment : 66 time for calcul the mask position with numpy : 0.001100778579711914 nb_pixel_total : 25705 time to create 1 rle with old method : 0.0279996395111084 length of segment : 183 time for calcul the mask position with numpy : 0.0005958080291748047 nb_pixel_total : 7867 time to create 1 rle with old method : 0.008939743041992188 length of segment : 79 time for calcul the mask position with numpy : 0.0037810802459716797 nb_pixel_total : 60713 time to create 1 rle with old method : 0.06271624565124512 length of segment : 500 time for calcul the mask position with numpy : 0.0005612373352050781 nb_pixel_total : 6711 time to create 1 rle with old method : 0.007395267486572266 length of segment : 95 time for calcul the mask position with numpy : 0.0005028247833251953 nb_pixel_total : 12424 time to create 1 rle with old method : 0.013772726058959961 length of segment : 185 time for calcul the mask position with numpy : 0.0005261898040771484 nb_pixel_total : 9873 time to create 1 rle with old method : 0.010680913925170898 length of segment : 95 time for calcul the mask position with numpy : 0.0018572807312011719 nb_pixel_total : 26752 time to create 1 rle with old method : 0.02768707275390625 length of segment : 209 time for calcul the mask position with numpy : 0.00096893310546875 nb_pixel_total : 7359 time to create 1 rle with old method : 0.008173704147338867 length of segment : 99 time for calcul the mask position with numpy : 0.0012278556823730469 nb_pixel_total : 15571 time to create 1 rle with old method : 0.016936063766479492 length of segment : 168 time for calcul the mask position with numpy : 0.0003178119659423828 nb_pixel_total : 8447 time to create 1 rle with old method : 0.009080648422241211 length of segment : 140 time for calcul the mask position with numpy : 0.003938198089599609 nb_pixel_total : 62953 time to create 1 rle with old method : 0.0657949447631836 length of segment : 423 time for calcul the mask position with numpy : 0.0005309581756591797 nb_pixel_total : 7024 time to create 1 rle with old method : 0.00756382942199707 length of segment : 84 time for calcul the mask position with numpy : 0.0007472038269042969 nb_pixel_total : 12579 time to create 1 rle with old method : 0.013670206069946289 length of segment : 112 time for calcul the mask position with numpy : 0.0022606849670410156 nb_pixel_total : 31449 time to create 1 rle with old method : 0.032738447189331055 length of segment : 282 time for calcul the mask position with numpy : 0.0026307106018066406 nb_pixel_total : 42970 time to create 1 rle with old method : 0.04712986946105957 length of segment : 258 time for calcul the mask position with numpy : 0.0005888938903808594 nb_pixel_total : 7849 time to create 1 rle with old method : 0.008486270904541016 length of segment : 118 time for calcul the mask position with numpy : 0.0007669925689697266 nb_pixel_total : 11022 time to create 1 rle with old method : 0.011982440948486328 length of segment : 146 time for calcul the mask position with numpy : 0.0009856224060058594 nb_pixel_total : 12812 time to create 1 rle with old method : 0.013881206512451172 length of segment : 153 time for calcul the mask position with numpy : 0.0028662681579589844 nb_pixel_total : 38796 time to create 1 rle with old method : 0.04141712188720703 length of segment : 206 time for calcul the mask position with numpy : 0.0006291866302490234 nb_pixel_total : 7129 time to create 1 rle with old method : 0.008762836456298828 length of segment : 123 time for calcul the mask position with numpy : 0.0005042552947998047 nb_pixel_total : 6427 time to create 1 rle with old method : 0.007574796676635742 length of segment : 82 time for calcul the mask position with numpy : 0.002059459686279297 nb_pixel_total : 31232 time to create 1 rle with old method : 0.0351862907409668 length of segment : 269 time for calcul the mask position with numpy : 0.0012211799621582031 nb_pixel_total : 19565 time to create 1 rle with old method : 0.02205038070678711 length of segment : 183 time for calcul the mask position with numpy : 0.00033402442932128906 nb_pixel_total : 2952 time to create 1 rle with old method : 0.0035097599029541016 length of segment : 77 time for calcul the mask position with numpy : 0.0012090206146240234 nb_pixel_total : 15016 time to create 1 rle with old method : 0.01717686653137207 length of segment : 140 time for calcul the mask position with numpy : 0.007585763931274414 nb_pixel_total : 91509 time to create 1 rle with old method : 0.10462212562561035 length of segment : 556 time for calcul the mask position with numpy : 0.0004382133483886719 nb_pixel_total : 14376 time to create 1 rle with old method : 0.016425371170043945 length of segment : 129 time for calcul the mask position with numpy : 0.0009562969207763672 nb_pixel_total : 9762 time to create 1 rle with old method : 0.010751962661743164 length of segment : 160 time for calcul the mask position with numpy : 0.0065746307373046875 nb_pixel_total : 88240 time to create 1 rle with old method : 0.09521889686584473 length of segment : 341 time for calcul the mask position with numpy : 0.0025985240936279297 nb_pixel_total : 32396 time to create 1 rle with old method : 0.03632545471191406 length of segment : 329 time for calcul the mask position with numpy : 0.0005524158477783203 nb_pixel_total : 6813 time to create 1 rle with old method : 0.00764918327331543 length of segment : 101 time for calcul the mask position with numpy : 0.00021314620971679688 nb_pixel_total : 7749 time to create 1 rle with old method : 0.008310794830322266 length of segment : 120 time for calcul the mask position with numpy : 0.003130674362182617 nb_pixel_total : 42412 time to create 1 rle with old method : 0.044823408126831055 length of segment : 313 time for calcul the mask position with numpy : 0.0014057159423828125 nb_pixel_total : 17519 time to create 1 rle with old method : 0.019151926040649414 length of segment : 205 time for calcul the mask position with numpy : 0.0017805099487304688 nb_pixel_total : 30649 time to create 1 rle with old method : 0.03299212455749512 length of segment : 209 time for calcul the mask position with numpy : 0.0021567344665527344 nb_pixel_total : 27112 time to create 1 rle with old method : 0.029334545135498047 length of segment : 356 time for calcul the mask position with numpy : 0.0014324188232421875 nb_pixel_total : 19808 time to create 1 rle with old method : 0.02173590660095215 length of segment : 172 time for calcul the mask position with numpy : 0.0018756389617919922 nb_pixel_total : 29166 time to create 1 rle with old method : 0.03559398651123047 length of segment : 241 time for calcul the mask position with numpy : 0.0014514923095703125 nb_pixel_total : 17316 time to create 1 rle with old method : 0.02782440185546875 length of segment : 223 time for calcul the mask position with numpy : 0.002899646759033203 nb_pixel_total : 32453 time to create 1 rle with old method : 0.037206172943115234 length of segment : 318 time for calcul the mask position with numpy : 0.0020253658294677734 nb_pixel_total : 25560 time to create 1 rle with old method : 0.029613256454467773 length of segment : 227 time for calcul the mask position with numpy : 0.0046787261962890625 nb_pixel_total : 72633 time to create 1 rle with old method : 0.0778958797454834 length of segment : 504 time for calcul the mask position with numpy : 0.0010182857513427734 nb_pixel_total : 15018 time to create 1 rle with old method : 0.017629623413085938 length of segment : 137 time for calcul the mask position with numpy : 0.0008859634399414062 nb_pixel_total : 18306 time to create 1 rle with old method : 0.02080702781677246 length of segment : 132 time for calcul the mask position with numpy : 0.0008842945098876953 nb_pixel_total : 12109 time to create 1 rle with old method : 0.013890504837036133 length of segment : 164 time for calcul the mask position with numpy : 0.0011150836944580078 nb_pixel_total : 10130 time to create 1 rle with old method : 0.01175236701965332 length of segment : 156 time for calcul the mask position with numpy : 0.00135040283203125 nb_pixel_total : 18442 time to create 1 rle with old method : 0.020721912384033203 length of segment : 149 time for calcul the mask position with numpy : 0.001772165298461914 nb_pixel_total : 33578 time to create 1 rle with old method : 0.03784775733947754 length of segment : 309 time for calcul the mask position with numpy : 0.001882314682006836 nb_pixel_total : 29277 time to create 1 rle with old method : 0.032178640365600586 length of segment : 246 time for calcul the mask position with numpy : 0.0006103515625 nb_pixel_total : 10730 time to create 1 rle with old method : 0.011846065521240234 length of segment : 143 time for calcul the mask position with numpy : 0.0021445751190185547 nb_pixel_total : 31902 time to create 1 rle with old method : 0.03606581687927246 length of segment : 177 time for calcul the mask position with numpy : 0.001331329345703125 nb_pixel_total : 21661 time to create 1 rle with old method : 0.02432084083557129 length of segment : 143 time for calcul the mask position with numpy : 0.0009999275207519531 nb_pixel_total : 18850 time to create 1 rle with old method : 0.021087169647216797 length of segment : 127 time for calcul the mask position with numpy : 0.0023136138916015625 nb_pixel_total : 33805 time to create 1 rle with old method : 0.03835654258728027 length of segment : 238 time for calcul the mask position with numpy : 0.0006999969482421875 nb_pixel_total : 9818 time to create 1 rle with old method : 0.011212825775146484 length of segment : 125 time for calcul the mask position with numpy : 0.00029540061950683594 nb_pixel_total : 4272 time to create 1 rle with old method : 0.004647970199584961 length of segment : 86 time for calcul the mask position with numpy : 0.001184701919555664 nb_pixel_total : 18149 time to create 1 rle with old method : 0.020067453384399414 length of segment : 137 time for calcul the mask position with numpy : 0.0008313655853271484 nb_pixel_total : 14558 time to create 1 rle with old method : 0.0156404972076416 length of segment : 148 time for calcul the mask position with numpy : 0.0006852149963378906 nb_pixel_total : 13064 time to create 1 rle with old method : 0.014393091201782227 length of segment : 134 time for calcul the mask position with numpy : 0.0009245872497558594 nb_pixel_total : 11087 time to create 1 rle with old method : 0.01289057731628418 length of segment : 103 time for calcul the mask position with numpy : 0.0010831356048583984 nb_pixel_total : 18947 time to create 1 rle with old method : 0.020847320556640625 length of segment : 184 time for calcul the mask position with numpy : 0.0015575885772705078 nb_pixel_total : 29396 time to create 1 rle with old method : 0.032190799713134766 length of segment : 255 time for calcul the mask position with numpy : 0.00022935867309570312 nb_pixel_total : 6886 time to create 1 rle with old method : 0.00775909423828125 length of segment : 105 time for calcul the mask position with numpy : 0.004376888275146484 nb_pixel_total : 46190 time to create 1 rle with old method : 0.04995107650756836 length of segment : 275 time for calcul the mask position with numpy : 0.0006780624389648438 nb_pixel_total : 16117 time to create 1 rle with old method : 0.017426729202270508 length of segment : 198 time for calcul the mask position with numpy : 0.0010652542114257812 nb_pixel_total : 13740 time to create 1 rle with old method : 0.015059947967529297 length of segment : 169 time for calcul the mask position with numpy : 0.0028023719787597656 nb_pixel_total : 45459 time to create 1 rle with old method : 0.04849863052368164 length of segment : 251 time for calcul the mask position with numpy : 0.002336740493774414 nb_pixel_total : 37035 time to create 1 rle with old method : 0.03908848762512207 length of segment : 459 time for calcul the mask position with numpy : 0.0018377304077148438 nb_pixel_total : 19408 time to create 1 rle with old method : 0.020504474639892578 length of segment : 289 time for calcul the mask position with numpy : 0.0007603168487548828 nb_pixel_total : 13081 time to create 1 rle with old method : 0.014560461044311523 length of segment : 209 time for calcul the mask position with numpy : 0.0016982555389404297 nb_pixel_total : 29452 time to create 1 rle with old method : 0.03186750411987305 length of segment : 222 time for calcul the mask position with numpy : 0.0013892650604248047 nb_pixel_total : 17084 time to create 1 rle with old method : 0.018103837966918945 length of segment : 141 time for calcul the mask position with numpy : 0.0025501251220703125 nb_pixel_total : 37750 time to create 1 rle with old method : 0.04035210609436035 length of segment : 445 time for calcul the mask position with numpy : 0.0030128955841064453 nb_pixel_total : 43862 time to create 1 rle with old method : 0.045590877532958984 length of segment : 335 time for calcul the mask position with numpy : 0.00037932395935058594 nb_pixel_total : 11471 time to create 1 rle with old method : 0.012125253677368164 length of segment : 119 time for calcul the mask position with numpy : 0.0017292499542236328 nb_pixel_total : 29334 time to create 1 rle with old method : 0.03132343292236328 length of segment : 247 time for calcul the mask position with numpy : 0.004555225372314453 nb_pixel_total : 66467 time to create 1 rle with old method : 0.06969928741455078 length of segment : 402 time for calcul the mask position with numpy : 0.0009379386901855469 nb_pixel_total : 16889 time to create 1 rle with old method : 0.018484830856323242 length of segment : 147 time for calcul the mask position with numpy : 0.00021338462829589844 nb_pixel_total : 4082 time to create 1 rle with old method : 0.004464626312255859 length of segment : 101 time for calcul the mask position with numpy : 0.0013599395751953125 nb_pixel_total : 19003 time to create 1 rle with old method : 0.02226114273071289 length of segment : 184 time for calcul the mask position with numpy : 0.001369476318359375 nb_pixel_total : 27343 time to create 1 rle with old method : 0.02875232696533203 length of segment : 197 time for calcul the mask position with numpy : 0.0007748603820800781 nb_pixel_total : 13542 time to create 1 rle with old method : 0.014464855194091797 length of segment : 123 time for calcul the mask position with numpy : 0.0007495880126953125 nb_pixel_total : 16936 time to create 1 rle with old method : 0.01833033561706543 length of segment : 121 time for calcul the mask position with numpy : 0.0032711029052734375 nb_pixel_total : 58338 time to create 1 rle with old method : 0.06243610382080078 length of segment : 220 time for calcul the mask position with numpy : 0.003199338912963867 nb_pixel_total : 66750 time to create 1 rle with old method : 0.07012653350830078 length of segment : 304 time for calcul the mask position with numpy : 0.0013608932495117188 nb_pixel_total : 25238 time to create 1 rle with old method : 0.02726125717163086 length of segment : 136 time for calcul the mask position with numpy : 0.0011496543884277344 nb_pixel_total : 12002 time to create 1 rle with old method : 0.01322484016418457 length of segment : 190 time for calcul the mask position with numpy : 0.0007224082946777344 nb_pixel_total : 11947 time to create 1 rle with old method : 0.012841224670410156 length of segment : 193 time for calcul the mask position with numpy : 0.0011379718780517578 nb_pixel_total : 19827 time to create 1 rle with old method : 0.021068572998046875 length of segment : 210 time for calcul the mask position with numpy : 0.0009908676147460938 nb_pixel_total : 14120 time to create 1 rle with old method : 0.01523900032043457 length of segment : 120 time for calcul the mask position with numpy : 0.0019850730895996094 nb_pixel_total : 28513 time to create 1 rle with old method : 0.030445337295532227 length of segment : 214 time for calcul the mask position with numpy : 0.0017964839935302734 nb_pixel_total : 22752 time to create 1 rle with old method : 0.024096250534057617 length of segment : 152 time for calcul the mask position with numpy : 0.0017385482788085938 nb_pixel_total : 22774 time to create 1 rle with old method : 0.024358272552490234 length of segment : 208 time for calcul the mask position with numpy : 0.0014462471008300781 nb_pixel_total : 26437 time to create 1 rle with old method : 0.027835607528686523 length of segment : 171 time for calcul the mask position with numpy : 0.0012063980102539062 nb_pixel_total : 16730 time to create 1 rle with old method : 0.01796889305114746 length of segment : 194 time for calcul the mask position with numpy : 0.0033888816833496094 nb_pixel_total : 25748 time to create 1 rle with old method : 0.027559518814086914 length of segment : 407 time for calcul the mask position with numpy : 0.0006837844848632812 nb_pixel_total : 18037 time to create 1 rle with old method : 0.019694805145263672 length of segment : 188 time spent for convertir_results : 22.483461141586304 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 275 chid ids of type : 3594 Number RLEs to save : 81129 save missing photos in datou_result : time spend for datou_step_exec : 117.0720579624176 time spend to save output : 92.07550549507141 total time spend for step 1 : 209.147563457489 step2:crop_condition Fri Apr 11 02:13:59 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 ! 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 : 12 ! batch 1 Loaded 275 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 ! map_result returned by crop_photo_return_map_crop : length : 190 About to insert : list_path_to_insert length 190 new photo from crops ! About to upload 190 photos upload in portfolio : 3736932 init cache_photo without model_param we have 190 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744330489_1178299 we have uploaded 190 photos in the portfolio 3736932 time of upload the photos Elapsed time : 83.21185064315796 we have finished the crop for the class : papier begin to crop the class : carton param for this class : {'min_score': 0.7} filtre for class : carton hashtag_id of this class : 492774966 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 61 About to insert : list_path_to_insert length 61 new photo from crops ! About to upload 61 photos upload in portfolio : 3736932 init cache_photo without model_param we have 61 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744330590_1178299 we have uploaded 61 photos in the portfolio 3736932 time of upload the photos Elapsed time : 16.589463233947754 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 1 About to insert : list_path_to_insert length 1 new photo from crops ! About to upload 1 photos upload in portfolio : 3736932 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744330609_1178299 we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.649284839630127 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 ! map_result returned by crop_photo_return_map_crop : length : 17 About to insert : list_path_to_insert length 17 new photo from crops ! About to upload 17 photos upload in portfolio : 3736932 init cache_photo without model_param we have 17 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744330619_1178299 we have uploaded 17 photos in the portfolio 3736932 time of upload the photos Elapsed time : 4.353280544281006 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 5 About to insert : list_path_to_insert length 5 new photo from crops ! About to upload 5 photos upload in portfolio : 3736932 init cache_photo without model_param we have 5 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744330627_1178299 we have uploaded 5 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.681267261505127 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 1 About to insert : list_path_to_insert length 1 new photo from crops ! About to upload 1 photos upload in portfolio : 3736932 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744330631_1178299 we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.856330394744873 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 delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1351370182, 1351370131, 1351370124, 1351370121, 1351196530, 1351196272, 1351196185, 1351195670, 1351195664, 1351195594, 1351195504, 1351195429] Looping around the photos to save general results len do output : 275 /1351407645Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407647Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407648Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407649Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407651Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407652Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407653Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407655Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407656Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407657Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407658Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407659Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407660Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407661Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407662Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407663Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407664Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407665Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407666Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407667Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407669Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407670Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407671Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407672Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407673Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407674Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407675Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407676Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407677Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407678Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407679Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407680Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407681Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407682Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407683Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407684Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407685Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407686Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407687Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407688Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407689Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407690Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407691Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407692Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407693Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407694Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407695Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407696Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407697Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407698Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407699Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407700Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407701Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407702Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407704Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407705Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407706Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407707Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407708Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407709Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407711Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407712Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407713Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407714Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407715Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407717Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407718Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407719Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407720Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407721Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407722Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407723Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407724Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407725Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407726Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407727Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407728Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407729Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407730Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407731Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407732Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407733Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407734Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407735Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407736Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351407737Didn't 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output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351370182', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351370131', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351370124', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351370121', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351196530', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351196272', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351196185', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195670', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195664', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195594', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195504', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195429', None, None, None, None, None, '2741779') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 837 time used for this insertion : 0.04472923278808594 save_final save missing photos in datou_result : time spend for datou_step_exec : 192.5435938835144 time spend to save output : 0.05180215835571289 total time spend for step 2 : 192.59539604187012 step3:rle_unique_nms_with_priority Fri Apr 11 02:17:12 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 275 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 22 nb_hashtags : 3 time to prepare the origin masks : 7.227193832397461 time for calcul the mask position with numpy : 1.1840898990631104 nb_pixel_total : 5952658 time to create 1 rle with new method : 0.9033758640289307 time for calcul the mask position with numpy : 0.023110628128051758 nb_pixel_total : 179012 time to create 1 rle with new method : 0.48857879638671875 time for calcul the mask position with numpy : 0.0215303897857666 nb_pixel_total : 5387 time to create 1 rle with old method : 0.006240367889404297 time for calcul the mask position with numpy : 0.021616697311401367 nb_pixel_total : 12246 time to create 1 rle with old method : 0.01355123519897461 time for calcul the mask position with numpy : 0.02184772491455078 nb_pixel_total : 12332 time to create 1 rle with old method : 0.013679027557373047 time for calcul the mask position with numpy : 0.02212977409362793 nb_pixel_total : 3591 time to create 1 rle with old method : 0.0041332244873046875 time for calcul the mask position with numpy : 0.021686792373657227 nb_pixel_total : 15533 time to create 1 rle with old method : 0.01767420768737793 time for calcul the mask position with numpy : 0.021377086639404297 nb_pixel_total : 18747 time to create 1 rle with old method : 0.020650625228881836 time for calcul the mask position with numpy : 0.021823644638061523 nb_pixel_total : 34851 time to create 1 rle with old method : 0.039449453353881836 time for calcul the mask position with numpy : 0.0214080810546875 nb_pixel_total : 8047 time to create 1 rle with old method : 0.009217262268066406 time for calcul the mask position with numpy : 0.022613525390625 nb_pixel_total : 9771 time to create 1 rle with old method : 0.011298418045043945 time for calcul the mask position with numpy : 0.021641969680786133 nb_pixel_total : 5210 time to create 1 rle with old method : 0.0058171749114990234 time for calcul the mask position with numpy : 0.020934581756591797 nb_pixel_total : 9009 time to create 1 rle with old method : 0.01052403450012207 time for calcul the mask position with numpy : 0.02211594581604004 nb_pixel_total : 5548 time to create 1 rle with old method : 0.0062389373779296875 time for calcul the mask position with numpy : 0.021386384963989258 nb_pixel_total : 11301 time to create 1 rle with old method : 0.012342453002929688 time for calcul the mask position with numpy : 0.021451473236083984 nb_pixel_total : 12550 time to create 1 rle with old method : 0.015543699264526367 time for calcul the mask position with numpy : 0.024817943572998047 nb_pixel_total : 46227 time to create 1 rle with old method : 0.07063484191894531 time for calcul the mask position with numpy : 0.022938251495361328 nb_pixel_total : 124884 time to create 1 rle with old method : 0.13608312606811523 time for calcul the mask position with numpy : 0.021346092224121094 nb_pixel_total : 13108 time to create 1 rle with old method : 0.014441728591918945 time for calcul the mask position with numpy : 0.025298595428466797 nb_pixel_total : 467215 time to create 1 rle with new method : 0.4735579490661621 time for calcul the mask position with numpy : 0.02912116050720215 nb_pixel_total : 10501 time to create 1 rle with old method : 0.011264562606811523 time for calcul the mask position with numpy : 0.03247189521789551 nb_pixel_total : 44286 time to create 1 rle with old method : 0.04790806770324707 time for calcul the mask position with numpy : 0.033878326416015625 nb_pixel_total : 48226 time to create 1 rle with old method : 0.054077863693237305 create new chi : 4.165210485458374 time to delete rle : 0.022986650466918945 batch 1 Loaded 45 chid ids of type : 3594 +++++++++++++++++++++++++++Number RLEs to save : 13488 TO DO : save crop sub photo not yet done ! save time : 18.146272659301758 nb_obj : 31 nb_hashtags : 3 time to prepare the origin masks : 3.723754644393921 time for calcul the mask position with numpy : 0.6714165210723877 nb_pixel_total : 6052397 time to create 1 rle with new method : 0.5887746810913086 time for calcul the mask position with numpy : 0.02782583236694336 nb_pixel_total : 12082 time to create 1 rle with old method : 0.013313531875610352 time for calcul the mask position with numpy : 0.02811717987060547 nb_pixel_total : 14404 time to create 1 rle with old method : 0.01592087745666504 time for calcul the mask position with numpy : 0.028888702392578125 nb_pixel_total : 45278 time to create 1 rle with old method : 0.04952359199523926 time for calcul the mask position with numpy : 0.028888940811157227 nb_pixel_total : 156812 time to create 1 rle with new method : 0.39960145950317383 time for calcul the mask position with numpy : 0.03342723846435547 nb_pixel_total : 41435 time to create 1 rle with old method : 0.05808424949645996 time for calcul the mask position with numpy : 0.03011488914489746 nb_pixel_total : 22902 time to create 1 rle with old method : 0.026834964752197266 time for calcul the mask position with numpy : 0.029056072235107422 nb_pixel_total : 24153 time to create 1 rle with old method : 0.027778148651123047 time for calcul the mask position with numpy : 0.02837371826171875 nb_pixel_total : 36980 time to create 1 rle with old method : 0.04003286361694336 time for calcul the mask position with numpy : 0.02781820297241211 nb_pixel_total : 26970 time to create 1 rle with old method : 0.02917003631591797 time for calcul the mask position with numpy : 0.028863191604614258 nb_pixel_total : 11695 time to create 1 rle with old method : 0.013087272644042969 time for calcul the mask position with numpy : 0.02866363525390625 nb_pixel_total : 26467 time to create 1 rle with old method : 0.029405593872070312 time for calcul the mask position with numpy : 0.02851247787475586 nb_pixel_total : 18044 time to create 1 rle with old method : 0.020238637924194336 time for calcul the mask position with numpy : 0.029240846633911133 nb_pixel_total : 30310 time to create 1 rle with old method : 0.032720327377319336 time for calcul the mask position with numpy : 0.028338193893432617 nb_pixel_total : 8770 time to create 1 rle with old method : 0.009636402130126953 time for calcul the mask position with numpy : 0.02794671058654785 nb_pixel_total : 20093 time to create 1 rle with old method : 0.022398948669433594 time for calcul the mask position with numpy : 0.029488801956176758 nb_pixel_total : 16963 time to create 1 rle with old method : 0.019268035888671875 time for calcul the mask position with numpy : 0.032479047775268555 nb_pixel_total : 18088 time to create 1 rle with old method : 0.0292971134185791 time for calcul the mask position with numpy : 0.032370567321777344 nb_pixel_total : 92857 time to create 1 rle with old method : 0.09979963302612305 time for calcul the mask position with numpy : 0.02781510353088379 nb_pixel_total : 26167 time to create 1 rle with old method : 0.027920007705688477 time for calcul the mask position with numpy : 0.02808070182800293 nb_pixel_total : 22854 time to create 1 rle with old method : 0.024828672409057617 time for calcul the mask position with numpy : 0.027711868286132812 nb_pixel_total : 10276 time to create 1 rle with old method : 0.011114358901977539 time for calcul the mask position with numpy : 0.027727127075195312 nb_pixel_total : 13875 time to create 1 rle with old method : 0.01535654067993164 time for calcul the mask position with numpy : 0.028610944747924805 nb_pixel_total : 73025 time to create 1 rle with old method : 0.08039450645446777 time for calcul the mask position with numpy : 0.028254032135009766 nb_pixel_total : 7279 time to create 1 rle with old method : 0.008004426956176758 time for calcul the mask position with numpy : 0.027373313903808594 nb_pixel_total : 5671 time to create 1 rle with old method : 0.006158351898193359 time for calcul the mask position with numpy : 0.02868342399597168 nb_pixel_total : 9151 time to create 1 rle with old method : 0.010297775268554688 time for calcul the mask position with numpy : 0.028179407119750977 nb_pixel_total : 40688 time to create 1 rle with old method : 0.046897172927856445 time for calcul the mask position with numpy : 0.028661727905273438 nb_pixel_total : 77321 time to create 1 rle with old method : 0.08621978759765625 time for calcul the mask position with numpy : 0.02738356590270996 nb_pixel_total : 4814 time to create 1 rle with old method : 0.005584001541137695 time for calcul the mask position with numpy : 0.026891231536865234 nb_pixel_total : 50982 time to create 1 rle with old method : 0.05529284477233887 time for calcul the mask position with numpy : 0.02843022346496582 nb_pixel_total : 31437 time to create 1 rle with old method : 0.03559255599975586 create new chi : 3.5642879009246826 time to delete rle : 0.0024802684783935547 batch 1 Loaded 63 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 18476 TO DO : save crop sub photo not yet done ! save time : 26.87400984764099 nb_obj : 32 nb_hashtags : 4 time to prepare the origin masks : 4.120767831802368 time for calcul the mask position with numpy : 0.42990970611572266 nb_pixel_total : 4902129 time to create 1 rle with new method : 0.7501609325408936 time for calcul the mask position with numpy : 0.03078293800354004 nb_pixel_total : 302862 time to create 1 rle with new method : 0.49305033683776855 time for calcul the mask position with numpy : 0.0276336669921875 nb_pixel_total : 14918 time to create 1 rle with old method : 0.01700568199157715 time for calcul the mask position with numpy : 0.02980494499206543 nb_pixel_total : 49061 time to create 1 rle with old method : 0.05504608154296875 time for calcul the mask position with numpy : 0.030449628829956055 nb_pixel_total : 170090 time to create 1 rle with new method : 0.7954738140106201 time for calcul the mask position with numpy : 0.02788853645324707 nb_pixel_total : 37330 time to create 1 rle with old method : 0.03846240043640137 time for calcul the mask position with numpy : 0.02796173095703125 nb_pixel_total : 141899 time to create 1 rle with old method : 0.17815899848937988 time for calcul the mask position with numpy : 0.0279083251953125 nb_pixel_total : 12394 time to create 1 rle with old method : 0.013483285903930664 time for calcul the mask position with numpy : 0.028896331787109375 nb_pixel_total : 114964 time to create 1 rle with old method : 0.1227121353149414 time for calcul the mask position with numpy : 0.028262853622436523 nb_pixel_total : 12201 time to create 1 rle with old method : 0.013062238693237305 time for calcul the mask position with numpy : 0.027873516082763672 nb_pixel_total : 71491 time to create 1 rle with old method : 0.07525014877319336 time for calcul the mask position with numpy : 0.0293276309967041 nb_pixel_total : 198989 time to create 1 rle with new method : 0.4989278316497803 time for calcul the mask position with numpy : 0.03281402587890625 nb_pixel_total : 8756 time to create 1 rle with old method : 0.014171361923217773 time for calcul the mask position with numpy : 0.03227663040161133 nb_pixel_total : 146184 time to create 1 rle with old method : 0.15340447425842285 time for calcul the mask position with numpy : 0.02823925018310547 nb_pixel_total : 15435 time to create 1 rle with old method : 0.016429424285888672 time for calcul the mask position with numpy : 0.027883291244506836 nb_pixel_total : 203432 time to create 1 rle with new method : 0.6046695709228516 time for calcul the mask position with numpy : 0.029160022735595703 nb_pixel_total : 19290 time to create 1 rle with old method : 0.020640134811401367 time for calcul the mask position with numpy : 0.026806116104125977 nb_pixel_total : 13905 time to create 1 rle with old method : 0.014422178268432617 time for calcul the mask position with numpy : 0.027151107788085938 nb_pixel_total : 75654 time to create 1 rle with old method : 0.07963681221008301 time for calcul the mask position with numpy : 0.026148080825805664 nb_pixel_total : 14486 time to create 1 rle with old method : 0.014647483825683594 time for calcul the mask position with numpy : 0.027346372604370117 nb_pixel_total : 214723 time to create 1 rle with new method : 0.5165562629699707 time for calcul the mask position with numpy : 0.04769492149353027 nb_pixel_total : 20461 time to create 1 rle with old method : 0.03931236267089844 time for calcul the mask position with numpy : 0.03751111030578613 nb_pixel_total : 45618 time to create 1 rle with old method : 0.08442497253417969 time for calcul the mask position with numpy : 0.031052589416503906 nb_pixel_total : 26200 time to create 1 rle with old method : 0.029841184616088867 time for calcul the mask position with numpy : 0.030008554458618164 nb_pixel_total : 92453 time to create 1 rle with old method : 0.10935020446777344 time for calcul the mask position with numpy : 0.029252052307128906 nb_pixel_total : 23683 time to create 1 rle with old method : 0.026396989822387695 time for calcul the mask position with numpy : 0.029928207397460938 nb_pixel_total : 24315 time to create 1 rle with old method : 0.02714061737060547 time for calcul the mask position with numpy : 0.030976533889770508 nb_pixel_total : 17153 time to create 1 rle with old method : 0.019143342971801758 time for calcul the mask position with numpy : 0.02925562858581543 nb_pixel_total : 7752 time to create 1 rle with old method : 0.008768081665039062 time for calcul the mask position with numpy : 0.029381990432739258 nb_pixel_total : 26398 time to create 1 rle with old method : 0.02958846092224121 time for calcul the mask position with numpy : 0.029317140579223633 nb_pixel_total : 5454 time to create 1 rle with old method : 0.006297588348388672 time for calcul the mask position with numpy : 0.02939772605895996 nb_pixel_total : 15181 time to create 1 rle with old method : 0.016693830490112305 time for calcul the mask position with numpy : 0.02728414535522461 nb_pixel_total : 5379 time to create 1 rle with old method : 0.005791187286376953 create new chi : 6.431606769561768 time to delete rle : 0.003315448760986328 batch 1 Loaded 65 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 21963 TO DO : save crop sub photo not yet done ! save time : 37.327720642089844 nb_obj : 9 nb_hashtags : 3 time to prepare the origin masks : 3.491166591644287 time for calcul the mask position with numpy : 1.4385735988616943 nb_pixel_total : 6707248 time to create 1 rle with new method : 1.108708143234253 time for calcul the mask position with numpy : 0.03782916069030762 nb_pixel_total : 83251 time to create 1 rle with old method : 0.0971670150756836 time for calcul the mask position with numpy : 0.036580801010131836 nb_pixel_total : 107388 time to create 1 rle with old method : 0.11823725700378418 time for calcul the mask position with numpy : 0.034139394760131836 nb_pixel_total : 5788 time to create 1 rle with old method : 0.006943464279174805 time for calcul the mask position with numpy : 0.03506064414978027 nb_pixel_total : 11576 time to create 1 rle with old method : 0.012856721878051758 time for calcul the mask position with numpy : 0.028798341751098633 nb_pixel_total : 14494 time to create 1 rle with old method : 0.016976594924926758 time for calcul the mask position with numpy : 0.027750015258789062 nb_pixel_total : 22728 time to create 1 rle with old method : 0.0257260799407959 time for calcul the mask position with numpy : 0.023401260375976562 nb_pixel_total : 34590 time to create 1 rle with old method : 0.04128217697143555 time for calcul the mask position with numpy : 0.02268075942993164 nb_pixel_total : 42736 time to create 1 rle with old method : 0.04710817337036133 time for calcul the mask position with numpy : 0.022840499877929688 nb_pixel_total : 20441 time to create 1 rle with old method : 0.024112701416015625 create new chi : 3.2407987117767334 time to delete rle : 0.000995635986328125 batch 1 Loaded 19 chid ids of type : 3594 ++++++++++Number RLEs to save : 6042 TO DO : save crop sub photo not yet done ! save time : 4.493907928466797 nb_obj : 10 nb_hashtags : 3 time to prepare the origin masks : 5.445894002914429 time for calcul the mask position with numpy : 0.6181769371032715 nb_pixel_total : 6038716 time to create 1 rle with new method : 0.8322031497955322 time for calcul the mask position with numpy : 0.03480815887451172 nb_pixel_total : 20172 time to create 1 rle with old method : 0.022881746292114258 time for calcul the mask position with numpy : 0.04079031944274902 nb_pixel_total : 476910 time to create 1 rle with new method : 0.4654207229614258 time for calcul the mask position with numpy : 0.033536434173583984 nb_pixel_total : 11908 time to create 1 rle with old method : 0.013174057006835938 time for calcul the mask position with numpy : 0.03502202033996582 nb_pixel_total : 70479 time to create 1 rle with old method : 0.07754659652709961 time for calcul the mask position with numpy : 0.03530097007751465 nb_pixel_total : 46755 time to create 1 rle with old method : 0.051403045654296875 time for calcul the mask position with numpy : 0.04029536247253418 nb_pixel_total : 153619 time to create 1 rle with new method : 0.7119519710540771 time for calcul the mask position with numpy : 0.033239126205444336 nb_pixel_total : 48209 time to create 1 rle with old method : 0.06052684783935547 time for calcul the mask position with numpy : 0.039313316345214844 nb_pixel_total : 108489 time to create 1 rle with old method : 0.1197364330291748 time for calcul the mask position with numpy : 0.03354501724243164 nb_pixel_total : 44987 time to create 1 rle with old method : 0.04935765266418457 time for calcul the mask position with numpy : 0.03440117835998535 nb_pixel_total : 29996 time to create 1 rle with old method : 0.0335693359375 create new chi : 3.500903606414795 time to delete rle : 0.0023941993713378906 batch 1 Loaded 21 chid ids of type : 3594 ++++++++++++++Number RLEs to save : 9385 TO DO : save crop sub photo not yet done ! save time : 14.06261157989502 nb_obj : 13 nb_hashtags : 4 time to prepare the origin masks : 9.393814086914062 time for calcul the mask position with numpy : 0.5543241500854492 nb_pixel_total : 6127777 time to create 1 rle with new method : 1.4172463417053223 time for calcul the mask position with numpy : 0.034989356994628906 nb_pixel_total : 17561 time to create 1 rle with old method : 0.02344799041748047 time for calcul the mask position with numpy : 0.03487896919250488 nb_pixel_total : 14316 time to create 1 rle with old method : 0.017354488372802734 time for calcul the mask position with numpy : 0.03448939323425293 nb_pixel_total : 42030 time to create 1 rle with old method : 0.047414541244506836 time for calcul the mask position with numpy : 0.03473496437072754 nb_pixel_total : 82170 time to create 1 rle with old method : 0.09113264083862305 time for calcul the mask position with numpy : 0.03443789482116699 nb_pixel_total : 11387 time to create 1 rle with old method : 0.012732982635498047 time for calcul the mask position with numpy : 0.034546852111816406 nb_pixel_total : 17528 time to create 1 rle with old method : 0.01952528953552246 time for calcul the mask position with numpy : 0.03566288948059082 nb_pixel_total : 38738 time to create 1 rle with old method : 0.062124013900756836 time for calcul the mask position with numpy : 0.040895938873291016 nb_pixel_total : 17846 time to create 1 rle with old method : 0.019194841384887695 time for calcul the mask position with numpy : 0.03435015678405762 nb_pixel_total : 5586 time to create 1 rle with old method : 0.0062673091888427734 time for calcul the mask position with numpy : 0.03694796562194824 nb_pixel_total : 259870 time to create 1 rle with new method : 0.4474153518676758 time for calcul the mask position with numpy : 0.03686237335205078 nb_pixel_total : 25772 time to create 1 rle with old method : 0.02971792221069336 time for calcul the mask position with numpy : 0.03951740264892578 nb_pixel_total : 377015 time to create 1 rle with new method : 0.48779940605163574 time for calcul the mask position with numpy : 0.021245479583740234 nb_pixel_total : 12644 time to create 1 rle with old method : 0.014035940170288086 create new chi : 3.7930405139923096 time to delete rle : 0.0022912025451660156 batch 1 Loaded 27 chid ids of type : 3594 ++++++++++++++++++Number RLEs to save : 9449 TO DO : save crop sub photo not yet done ! save time : 18.398131132125854 nb_obj : 9 nb_hashtags : 2 time to prepare the origin masks : 5.235576629638672 time for calcul the mask position with numpy : 0.8626582622528076 nb_pixel_total : 6150170 time to create 1 rle with new method : 0.7116940021514893 time for calcul the mask position with numpy : 0.033513545989990234 nb_pixel_total : 13998 time to create 1 rle with old method : 0.015323877334594727 time for calcul the mask position with numpy : 0.022520065307617188 nb_pixel_total : 177345 time to create 1 rle with new method : 0.966942548751831 time for calcul the mask position with numpy : 0.03426933288574219 nb_pixel_total : 133842 time to create 1 rle with old method : 0.1468524932861328 time for calcul the mask position with numpy : 0.03433990478515625 nb_pixel_total : 201067 time to create 1 rle with new method : 0.7134740352630615 time for calcul the mask position with numpy : 0.03398394584655762 nb_pixel_total : 14327 time to create 1 rle with old method : 0.01618814468383789 time for calcul the mask position with numpy : 0.023197412490844727 nb_pixel_total : 26928 time to create 1 rle with old method : 0.0300600528717041 time for calcul the mask position with numpy : 0.025789499282836914 nb_pixel_total : 287623 time to create 1 rle with new method : 0.873802661895752 time for calcul the mask position with numpy : 0.021088361740112305 nb_pixel_total : 20561 time to create 1 rle with old method : 0.022815465927124023 time for calcul the mask position with numpy : 0.023204565048217773 nb_pixel_total : 24379 time to create 1 rle with old method : 0.027144432067871094 create new chi : 4.748772859573364 time to delete rle : 0.0013594627380371094 batch 1 Loaded 19 chid ids of type : 3594 +++++++++++Number RLEs to save : 8266 TO DO : save crop sub photo not yet done ! save time : 7.7317118644714355 nb_obj : 19 nb_hashtags : 3 time to prepare the origin masks : 7.105355739593506 time for calcul the mask position with numpy : 0.45006799697875977 nb_pixel_total : 4277488 time to create 1 rle with new method : 0.5465576648712158 time for calcul the mask position with numpy : 0.04427027702331543 nb_pixel_total : 618532 time to create 1 rle with new method : 0.8603975772857666 time for calcul the mask position with numpy : 0.0363008975982666 nb_pixel_total : 3753 time to create 1 rle with old method : 0.004211902618408203 time for calcul the mask position with numpy : 0.0384373664855957 nb_pixel_total : 57137 time to create 1 rle with old method : 0.06504034996032715 time for calcul the mask position with numpy : 0.041251420974731445 nb_pixel_total : 19133 time to create 1 rle with old method : 0.02270340919494629 time for calcul the mask position with numpy : 0.03613543510437012 nb_pixel_total : 59403 time to create 1 rle with old method : 0.0682215690612793 time for calcul the mask position with numpy : 0.04024648666381836 nb_pixel_total : 139288 time to create 1 rle with old method : 0.15513086318969727 time for calcul the mask position with numpy : 0.03947591781616211 nb_pixel_total : 116860 time to create 1 rle with old method : 0.137070894241333 time for calcul the mask position with numpy : 0.04128623008728027 nb_pixel_total : 234385 time to create 1 rle with new method : 0.9051098823547363 time for calcul the mask position with numpy : 0.03190946578979492 nb_pixel_total : 115897 time to create 1 rle with old method : 0.13178372383117676 time for calcul the mask position with numpy : 0.02375316619873047 nb_pixel_total : 131216 time to create 1 rle with old method : 0.16113066673278809 time for calcul the mask position with numpy : 0.025563478469848633 nb_pixel_total : 9311 time to create 1 rle with old method : 0.010768651962280273 time for calcul the mask position with numpy : 0.02496790885925293 nb_pixel_total : 32506 time to create 1 rle with old method : 0.03628826141357422 time for calcul the mask position with numpy : 0.0260312557220459 nb_pixel_total : 169623 time to create 1 rle with new method : 1.195927619934082 time for calcul the mask position with numpy : 0.03539228439331055 nb_pixel_total : 61119 time to create 1 rle with old method : 0.0686190128326416 time for calcul the mask position with numpy : 0.03945589065551758 nb_pixel_total : 140427 time to create 1 rle with old method : 0.15622162818908691 time for calcul the mask position with numpy : 0.036093711853027344 nb_pixel_total : 68416 time to create 1 rle with old method : 0.07657885551452637 time for calcul the mask position with numpy : 0.036232709884643555 nb_pixel_total : 62072 time to create 1 rle with old method : 0.07011222839355469 time for calcul the mask position with numpy : 0.03968024253845215 nb_pixel_total : 583402 time to create 1 rle with new method : 1.023669958114624 time for calcul the mask position with numpy : 0.03777360916137695 nb_pixel_total : 150272 time to create 1 rle with new method : 1.0526952743530273 create new chi : 8.02746868133545 time to delete rle : 0.003535747528076172 batch 1 Loaded 39 chid ids of type : 3594 ++++++++++++++++++++++++++++Number RLEs to save : 20362 TO DO : save crop sub photo not yet done ! save time : 29.44171905517578 nb_obj : 14 nb_hashtags : 4 time to prepare the origin masks : 4.976690292358398 time for calcul the mask position with numpy : 0.31035733222961426 nb_pixel_total : 5051956 time to create 1 rle with new method : 0.7645905017852783 time for calcul the mask position with numpy : 0.03087472915649414 nb_pixel_total : 145088 time to create 1 rle with old method : 0.1656966209411621 time for calcul the mask position with numpy : 0.03000950813293457 nb_pixel_total : 264306 time to create 1 rle with new method : 0.6137328147888184 time for calcul the mask position with numpy : 0.027175188064575195 nb_pixel_total : 68410 time to create 1 rle with old method : 0.07670354843139648 time for calcul the mask position with numpy : 0.029323101043701172 nb_pixel_total : 15814 time to create 1 rle with old method : 0.017935514450073242 time for calcul the mask position with numpy : 0.03361392021179199 nb_pixel_total : 401516 time to create 1 rle with new method : 0.9172577857971191 time for calcul the mask position with numpy : 0.04664945602416992 nb_pixel_total : 83355 time to create 1 rle with old method : 0.09611010551452637 time for calcul the mask position with numpy : 0.0383298397064209 nb_pixel_total : 288510 time to create 1 rle with new method : 1.0368616580963135 time for calcul the mask position with numpy : 0.04981541633605957 nb_pixel_total : 25689 time to create 1 rle with old method : 0.03231358528137207 time for calcul the mask position with numpy : 0.049503326416015625 nb_pixel_total : 12367 time to create 1 rle with old method : 0.014236927032470703 time for calcul the mask position with numpy : 0.048267364501953125 nb_pixel_total : 318828 time to create 1 rle with new method : 0.9490129947662354 time for calcul the mask position with numpy : 0.04039502143859863 nb_pixel_total : 2882 time to create 1 rle with old method : 0.0034410953521728516 time for calcul the mask position with numpy : 0.043476104736328125 nb_pixel_total : 145222 time to create 1 rle with old method : 0.16226744651794434 time for calcul the mask position with numpy : 0.03636598587036133 nb_pixel_total : 78742 time to create 1 rle with old method : 0.08594393730163574 time for calcul the mask position with numpy : 0.0411226749420166 nb_pixel_total : 147555 time to create 1 rle with old method : 0.18988776206970215 create new chi : 6.1625378131866455 time to delete rle : 0.008421897888183594 batch 1 Loaded 29 chid ids of type : 3594 +++++++++++++++++++++++++Number RLEs to save : 17589 TO DO : save crop sub photo not yet done ! save time : 12.906147003173828 nb_obj : 10 nb_hashtags : 2 time to prepare the origin masks : 5.501007318496704 time for calcul the mask position with numpy : 1.322838544845581 nb_pixel_total : 6094856 time to create 1 rle with new method : 1.263758659362793 time for calcul the mask position with numpy : 0.04527592658996582 nb_pixel_total : 44427 time to create 1 rle with old method : 0.052704811096191406 time for calcul the mask position with numpy : 0.028914451599121094 nb_pixel_total : 6263 time to create 1 rle with old method : 0.008568048477172852 time for calcul the mask position with numpy : 0.0314936637878418 nb_pixel_total : 194275 time to create 1 rle with new method : 1.1097044944763184 time for calcul the mask position with numpy : 0.04465889930725098 nb_pixel_total : 162121 time to create 1 rle with new method : 0.44859790802001953 time for calcul the mask position with numpy : 0.04323554039001465 nb_pixel_total : 105186 time to create 1 rle with old method : 0.11605954170227051 time for calcul the mask position with numpy : 0.044211387634277344 nb_pixel_total : 6104 time to create 1 rle with old method : 0.010057687759399414 time for calcul the mask position with numpy : 0.047846317291259766 nb_pixel_total : 10656 time to create 1 rle with old method : 0.011860132217407227 time for calcul the mask position with numpy : 0.04411935806274414 nb_pixel_total : 92530 time to create 1 rle with old method : 0.10334229469299316 time for calcul the mask position with numpy : 0.04536628723144531 nb_pixel_total : 126761 time to create 1 rle with old method : 0.15334343910217285 time for calcul the mask position with numpy : 0.045409440994262695 nb_pixel_total : 207061 time to create 1 rle with new method : 0.6448321342468262 create new chi : 5.862595558166504 time to delete rle : 0.003992795944213867 batch 1 Loaded 21 chid ids of type : 3594 ++++++++++++++++Number RLEs to save : 10937 TO DO : save crop sub photo not yet done ! save time : 13.996116161346436 nb_obj : 47 nb_hashtags : 3 time to prepare the origin masks : 4.171472787857056 time for calcul the mask position with numpy : 0.7430362701416016 nb_pixel_total : 5860312 time to create 1 rle with new method : 0.617957592010498 time for calcul the mask position with numpy : 0.02910161018371582 nb_pixel_total : 26752 time to create 1 rle with old method : 0.03202319145202637 time for calcul the mask position with numpy : 0.029664039611816406 nb_pixel_total : 8438 time to create 1 rle with old method : 0.009646415710449219 time for calcul the mask position with numpy : 0.03178739547729492 nb_pixel_total : 30649 time to create 1 rle with old method : 0.038002967834472656 time for calcul the mask position with numpy : 0.029903411865234375 nb_pixel_total : 7359 time to create 1 rle with old method : 0.008591651916503906 time for calcul the mask position with numpy : 0.02914571762084961 nb_pixel_total : 26009 time to create 1 rle with old method : 0.02995133399963379 time for calcul the mask position with numpy : 0.03279376029968262 nb_pixel_total : 6427 time to create 1 rle with old method : 0.007488727569580078 time for calcul the mask position with numpy : 0.029536724090576172 nb_pixel_total : 19808 time to create 1 rle with old method : 0.02214980125427246 time for calcul the mask position with numpy : 0.02921891212463379 nb_pixel_total : 15571 time to create 1 rle with old method : 0.01723504066467285 time for calcul the mask position with numpy : 0.028653860092163086 nb_pixel_total : 15274 time to create 1 rle with old method : 0.017055988311767578 time for calcul the mask position with numpy : 0.02916264533996582 nb_pixel_total : 2952 time to create 1 rle with old method : 0.0032968521118164062 time for calcul the mask position with numpy : 0.028762340545654297 nb_pixel_total : 15016 time to create 1 rle with old method : 0.017148494720458984 time for calcul the mask position with numpy : 0.028934001922607422 nb_pixel_total : 32396 time to create 1 rle with old method : 0.03634285926818848 time for calcul the mask position with numpy : 0.029811620712280273 nb_pixel_total : 11022 time to create 1 rle with old method : 0.012136220932006836 time for calcul the mask position with numpy : 0.030391693115234375 nb_pixel_total : 88240 time to create 1 rle with old method : 0.09745097160339355 time for calcul the mask position with numpy : 0.029085397720336914 nb_pixel_total : 31232 time to create 1 rle with old method : 0.03457951545715332 time for calcul the mask position with numpy : 0.029043197631835938 nb_pixel_total : 8584 time to create 1 rle with old method : 0.009731292724609375 time for calcul the mask position with numpy : 0.0292510986328125 nb_pixel_total : 62953 time to create 1 rle with old method : 0.07713699340820312 time for calcul the mask position with numpy : 0.02960991859436035 nb_pixel_total : 89977 time to create 1 rle with old method : 0.1002347469329834 time for calcul the mask position with numpy : 0.0290677547454834 nb_pixel_total : 31449 time to create 1 rle with old method : 0.03443145751953125 time for calcul the mask position with numpy : 0.02888941764831543 nb_pixel_total : 42970 time to create 1 rle with old method : 0.04752612113952637 time for calcul the mask position with numpy : 0.028565406799316406 nb_pixel_total : 19565 time to create 1 rle with old method : 0.02154254913330078 time for calcul the mask position with numpy : 0.028363466262817383 nb_pixel_total : 22515 time to create 1 rle with old method : 0.025053977966308594 time for calcul the mask position with numpy : 0.028447389602661133 nb_pixel_total : 17519 time to create 1 rle with old method : 0.019492149353027344 time for calcul the mask position with numpy : 0.028310060501098633 nb_pixel_total : 12413 time to create 1 rle with old method : 0.013753175735473633 time for calcul the mask position with numpy : 0.028735876083374023 nb_pixel_total : 29166 time to create 1 rle with old method : 0.0321347713470459 time for calcul the mask position with numpy : 0.02858281135559082 nb_pixel_total : 3190 time to create 1 rle with old method : 0.00351715087890625 time for calcul the mask position with numpy : 0.02884197235107422 nb_pixel_total : 60713 time to create 1 rle with old method : 0.06743669509887695 time for calcul the mask position with numpy : 0.02888321876525879 nb_pixel_total : 25705 time to create 1 rle with old method : 0.028327465057373047 time for calcul the mask position with numpy : 0.028612136840820312 nb_pixel_total : 91509 time to create 1 rle with old method : 0.09874320030212402 time for calcul the mask position with numpy : 0.0289614200592041 nb_pixel_total : 9873 time to create 1 rle with old method : 0.010764598846435547 time for calcul the mask position with numpy : 0.02852654457092285 nb_pixel_total : 7849 time to create 1 rle with old method : 0.008421897888183594 time for calcul the mask position with numpy : 0.027903079986572266 nb_pixel_total : 39950 time to create 1 rle with old method : 0.04322624206542969 time for calcul the mask position with numpy : 0.028339862823486328 nb_pixel_total : 7867 time to create 1 rle with old method : 0.008577823638916016 time for calcul the mask position with numpy : 0.028004884719848633 nb_pixel_total : 6813 time to create 1 rle with old method : 0.007234096527099609 time for calcul the mask position with numpy : 0.028482437133789062 nb_pixel_total : 12812 time to create 1 rle with old method : 0.013599634170532227 time for calcul the mask position with numpy : 0.027385711669921875 nb_pixel_total : 42412 time to create 1 rle with old method : 0.0457916259765625 time for calcul the mask position with numpy : 0.02808403968811035 nb_pixel_total : 27112 time to create 1 rle with old method : 0.02942061424255371 time for calcul the mask position with numpy : 0.02759861946105957 nb_pixel_total : 44688 time to create 1 rle with old method : 0.04813742637634277 time for calcul the mask position with numpy : 0.027863025665283203 nb_pixel_total : 7129 time to create 1 rle with old method : 0.007740497589111328 time for calcul the mask position with numpy : 0.02750396728515625 nb_pixel_total : 6711 time to create 1 rle with old method : 0.007132768630981445 time for calcul the mask position with numpy : 0.02783966064453125 nb_pixel_total : 39403 time to create 1 rle with old method : 0.04222464561462402 time for calcul the mask position with numpy : 0.02905869483947754 nb_pixel_total : 6168 time to create 1 rle with old method : 0.007039546966552734 time for calcul the mask position with numpy : 0.02910637855529785 nb_pixel_total : 9762 time to create 1 rle with old method : 0.01105046272277832 time for calcul the mask position with numpy : 0.028618812561035156 nb_pixel_total : 7024 time to create 1 rle with old method : 0.00789499282836914 time for calcul the mask position with numpy : 0.02849864959716797 nb_pixel_total : 38796 time to create 1 rle with old method : 0.04296541213989258 time for calcul the mask position with numpy : 0.02939605712890625 nb_pixel_total : 7607 time to create 1 rle with old method : 0.008856773376464844 time for calcul the mask position with numpy : 0.029326438903808594 nb_pixel_total : 12579 time to create 1 rle with old method : 0.014447689056396484 create new chi : 4.083652019500732 time to delete rle : 0.00418400764465332 batch 1 Loaded 95 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 21575 TO DO : save crop sub photo not yet done ! save time : 27.89957094192505 nb_obj : 59 nb_hashtags : 4 time to prepare the origin masks : 4.1738502979278564 time for calcul the mask position with numpy : 0.7899067401885986 nb_pixel_total : 5645333 time to create 1 rle with new method : 1.2569031715393066 time for calcul the mask position with numpy : 0.032089948654174805 nb_pixel_total : 18306 time to create 1 rle with old method : 0.021712064743041992 time for calcul the mask position with numpy : 0.03235316276550293 nb_pixel_total : 9818 time to create 1 rle with old method : 0.012196063995361328 time for calcul the mask position with numpy : 0.030263662338256836 nb_pixel_total : 8145 time to create 1 rle with old method : 0.011651039123535156 time for calcul the mask position with numpy : 0.03142356872558594 nb_pixel_total : 22752 time to create 1 rle with old method : 0.030129432678222656 time for calcul the mask position with numpy : 0.03176403045654297 nb_pixel_total : 19827 time to create 1 rle with old method : 0.023833513259887695 time for calcul the mask position with numpy : 0.02952122688293457 nb_pixel_total : 11087 time to create 1 rle with old method : 0.013916730880737305 time for calcul the mask position with numpy : 0.030467987060546875 nb_pixel_total : 18850 time to create 1 rle with old method : 0.02184891700744629 time for calcul the mask position with numpy : 0.029599428176879883 nb_pixel_total : 10730 time to create 1 rle with old method : 0.012360334396362305 time for calcul the mask position with numpy : 0.03057098388671875 nb_pixel_total : 19003 time to create 1 rle with old method : 0.02186417579650879 time for calcul the mask position with numpy : 0.030404090881347656 nb_pixel_total : 13740 time to create 1 rle with old method : 0.016752243041992188 time for calcul the mask position with numpy : 0.03018355369567871 nb_pixel_total : 4082 time to create 1 rle with old method : 0.004945516586303711 time for calcul the mask position with numpy : 0.0314173698425293 nb_pixel_total : 37750 time to create 1 rle with old method : 0.04477834701538086 time for calcul the mask position with numpy : 0.029064178466796875 nb_pixel_total : 18442 time to create 1 rle with old method : 0.020736217498779297 time for calcul the mask position with numpy : 0.02959728240966797 nb_pixel_total : 18149 time to create 1 rle with old method : 0.020890474319458008 time for calcul the mask position with numpy : 0.030150651931762695 nb_pixel_total : 33578 time to create 1 rle with old method : 0.05459713935852051 time for calcul the mask position with numpy : 0.03263235092163086 nb_pixel_total : 10130 time to create 1 rle with old method : 0.016610145568847656 time for calcul the mask position with numpy : 0.03339242935180664 nb_pixel_total : 12109 time to create 1 rle with old method : 0.020995616912841797 time for calcul the mask position with numpy : 0.03813576698303223 nb_pixel_total : 29334 time to create 1 rle with old method : 0.049629926681518555 time for calcul the mask position with numpy : 0.029503822326660156 nb_pixel_total : 26437 time to create 1 rle with old method : 0.029671907424926758 time for calcul the mask position with numpy : 0.02965521812438965 nb_pixel_total : 15018 time to create 1 rle with old method : 0.017068862915039062 time for calcul the mask position with numpy : 0.029744386672973633 nb_pixel_total : 17316 time to create 1 rle with old method : 0.019414186477661133 time for calcul the mask position with numpy : 0.029246091842651367 nb_pixel_total : 25748 time to create 1 rle with old method : 0.029075145721435547 time for calcul the mask position with numpy : 0.030287504196166992 nb_pixel_total : 16889 time to create 1 rle with old method : 0.01887369155883789 time for calcul the mask position with numpy : 0.029387712478637695 nb_pixel_total : 33805 time to create 1 rle with old method : 0.037961483001708984 time for calcul the mask position with numpy : 0.02944803237915039 nb_pixel_total : 29452 time to create 1 rle with old method : 0.032860755920410156 time for calcul the mask position with numpy : 0.02965688705444336 nb_pixel_total : 27343 time to create 1 rle with old method : 0.03061056137084961 time for calcul the mask position with numpy : 0.03172636032104492 nb_pixel_total : 16936 time to create 1 rle with old method : 0.019021272659301758 time for calcul the mask position with numpy : 0.02939891815185547 nb_pixel_total : 13542 time to create 1 rle with old method : 0.015198945999145508 time for calcul the mask position with numpy : 0.030353784561157227 nb_pixel_total : 31902 time to create 1 rle with old method : 0.035744667053222656 time for calcul the mask position with numpy : 0.0295257568359375 nb_pixel_total : 37035 time to create 1 rle with old method : 0.041390419006347656 time for calcul the mask position with numpy : 0.029892444610595703 nb_pixel_total : 46190 time to create 1 rle with old method : 0.07437849044799805 time for calcul the mask position with numpy : 0.02937459945678711 nb_pixel_total : 19408 time to create 1 rle with old method : 0.021754741668701172 time for calcul the mask position with numpy : 0.029248714447021484 nb_pixel_total : 17084 time to create 1 rle with old method : 0.022811174392700195 time for calcul the mask position with numpy : 0.030691862106323242 nb_pixel_total : 11471 time to create 1 rle with old method : 0.013447046279907227 time for calcul the mask position with numpy : 0.031145095825195312 nb_pixel_total : 18037 time to create 1 rle with old method : 0.02190995216369629 time for calcul the mask position with numpy : 0.029804468154907227 nb_pixel_total : 66467 time to create 1 rle with old method : 0.07445144653320312 time for calcul the mask position with numpy : 0.029615402221679688 nb_pixel_total : 18947 time to create 1 rle with old method : 0.021754980087280273 time for calcul the mask position with numpy : 0.029689550399780273 nb_pixel_total : 45459 time to create 1 rle with old method : 0.050960540771484375 time for calcul the mask position with numpy : 0.03143620491027832 nb_pixel_total : 72633 time to create 1 rle with old method : 0.10077929496765137 time for calcul the mask position with numpy : 0.032645225524902344 nb_pixel_total : 32453 time to create 1 rle with old method : 0.043755292892456055 time for calcul the mask position with numpy : 0.029569387435913086 nb_pixel_total : 29277 time to create 1 rle with old method : 0.03257179260253906 time for calcul the mask position with numpy : 0.02895379066467285 nb_pixel_total : 11302 time to create 1 rle with old method : 0.012650012969970703 time for calcul the mask position with numpy : 0.02885746955871582 nb_pixel_total : 25238 time to create 1 rle with old method : 0.0316007137298584 time for calcul the mask position with numpy : 0.02995467185974121 nb_pixel_total : 29396 time to create 1 rle with old method : 0.032984018325805664 time for calcul the mask position with numpy : 0.02947711944580078 nb_pixel_total : 4272 time to create 1 rle with old method : 0.00484156608581543 time for calcul the mask position with numpy : 0.03305506706237793 nb_pixel_total : 43791 time to create 1 rle with old method : 0.05306673049926758 time for calcul the mask position with numpy : 0.0321650505065918 nb_pixel_total : 6886 time to create 1 rle with old method : 0.011597633361816406 time for calcul the mask position with numpy : 0.0332636833190918 nb_pixel_total : 16117 time to create 1 rle with old method : 0.018164396286010742 time for calcul the mask position with numpy : 0.029930830001831055 nb_pixel_total : 11928 time to create 1 rle with old method : 0.013700246810913086 time for calcul the mask position with numpy : 0.03061389923095703 nb_pixel_total : 25560 time to create 1 rle with old method : 0.028706073760986328 time for calcul the mask position with numpy : 0.029674291610717773 nb_pixel_total : 14558 time to create 1 rle with old method : 0.01673603057861328 time for calcul the mask position with numpy : 0.029556751251220703 nb_pixel_total : 16730 time to create 1 rle with old method : 0.018686771392822266 time for calcul the mask position with numpy : 0.029668092727661133 nb_pixel_total : 66750 time to create 1 rle with old method : 0.07486939430236816 time for calcul the mask position with numpy : 0.030257701873779297 nb_pixel_total : 28513 time to create 1 rle with old method : 0.03194451332092285 time for calcul the mask position with numpy : 0.03065013885498047 nb_pixel_total : 21661 time to create 1 rle with old method : 0.02799677848815918 time for calcul the mask position with numpy : 0.029996871948242188 nb_pixel_total : 12002 time to create 1 rle with old method : 0.01432657241821289 time for calcul the mask position with numpy : 0.029773712158203125 nb_pixel_total : 13064 time to create 1 rle with old method : 0.014981985092163086 time for calcul the mask position with numpy : 0.034778594970703125 nb_pixel_total : 58338 time to create 1 rle with old method : 0.06943893432617188 time for calcul the mask position with numpy : 0.029953479766845703 nb_pixel_total : 14120 time to create 1 rle with old method : 0.016663789749145508 create new chi : 5.6080474853515625 time to delete rle : 0.009509086608886719 batch 1 Loaded 119 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 26343 TO DO : save crop sub photo not yet done ! save time : 30.95333695411682 map_output_result : {1351370182: (0.0, 'Should be the crop_list due to order', 0), 1351370131: (0.0, 'Should be the crop_list due to order', 0), 1351370124: (0.0, 'Should be the crop_list due to order', 0), 1351370121: (0.0, 'Should be the crop_list due to order', 0), 1351196530: (0.0, 'Should be the crop_list due to order', 0), 1351196272: (0.0, 'Should be the crop_list due to order', 0), 1351196185: (0.0, 'Should be the crop_list due to order', 0), 1351195670: (0.0, 'Should be the crop_list due to order', 0), 1351195664: (0.0, 'Should be the crop_list due to order', 0), 1351195594: (0.0, 'Should be the crop_list due to order', 0), 1351195504: (0.0, 'Should be the crop_list due to order', 0), 1351195429: (0.0, 'Should be the crop_list due to order', 0)} End step rle-unique-nms Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : rle_unique_nms_with_priority we use saveGeneral [1351370182, 1351370131, 1351370124, 1351370121, 1351196530, 1351196272, 1351196185, 1351195670, 1351195664, 1351195594, 1351195504, 1351195429] Looping around the photos to save general results len do output : 12 /1351370182.Didn't retrieve data . /1351370131.Didn't retrieve data . /1351370124.Didn't retrieve data . /1351370121.Didn't retrieve data . /1351196530.Didn't retrieve data . /1351196272.Didn't retrieve data . /1351196185.Didn't retrieve data . /1351195670.Didn't retrieve data . /1351195664.Didn't retrieve data . /1351195594.Didn't retrieve data . /1351195504.Didn't retrieve data . /1351195429.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, '2741779') ('3318', '22248184', '1351370182', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351370131', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351370124', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351370121', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351196530', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351196272', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351196185', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195670', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195664', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195594', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195504', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195429', None, None, None, None, None, '2741779') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 36 time used for this insertion : 0.5919342041015625 save_final save missing photos in datou_result : time spend for datou_step_exec : 369.6478772163391 time spend to save output : 0.5937395095825195 total time spend for step 3 : 370.24161672592163 step4:ventilate_hashtags_in_portfolio Fri Apr 11 02:23:22 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 22248184 get user id for portfolio 22248184 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`=22248184 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('background','metal','pet_clair','carton','autre','environnement','mal_croppe','flou','papier','pehd','pet_fonce')) 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`=22248184 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('background','metal','pet_clair','carton','autre','environnement','mal_croppe','flou','papier','pehd','pet_fonce')) 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`=22248184 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('background','metal','pet_clair','carton','autre','environnement','mal_croppe','flou','papier','pehd','pet_fonce')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/22248668,22248669,22248670,22248671,22248672,22248673,22248674,22248675,22248676,22248677,22248678?tags=background,metal,pet_clair,carton,autre,environnement,mal_croppe,flou,papier,pehd,pet_fonce Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1351370182, 1351370131, 1351370124, 1351370121, 1351196530, 1351196272, 1351196185, 1351195670, 1351195664, 1351195594, 1351195504, 1351195429] Looping around the photos to save general results len do output : 1 /22248184. 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, '2741779') ('3318', '22248184', '1351370182', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351370131', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351370124', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351370121', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351196530', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351196272', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351196185', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195670', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195664', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195594', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195504', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195429', None, None, None, None, None, '2741779') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 13 time used for this insertion : 0.017345190048217773 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.132152795791626 time spend to save output : 0.017805099487304688 total time spend for step 4 : 2.1499578952789307 step5:final Fri Apr 11 02:23:24 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : {1351370182: ('0.1860674246550472',), 1351370131: ('0.1860674246550472',), 1351370124: ('0.1860674246550472',), 1351370121: ('0.1860674246550472',), 1351196530: ('0.1860674246550472',), 1351196272: ('0.1860674246550472',), 1351196185: ('0.1860674246550472',), 1351195670: ('0.1860674246550472',), 1351195664: ('0.1860674246550472',), 1351195594: ('0.1860674246550472',), 1351195504: ('0.1860674246550472',), 1351195429: ('0.1860674246550472',)} new output for save of step final : {1351370182: ('0.1860674246550472',), 1351370131: ('0.1860674246550472',), 1351370124: ('0.1860674246550472',), 1351370121: ('0.1860674246550472',), 1351196530: ('0.1860674246550472',), 1351196272: ('0.1860674246550472',), 1351196185: ('0.1860674246550472',), 1351195670: ('0.1860674246550472',), 1351195664: ('0.1860674246550472',), 1351195594: ('0.1860674246550472',), 1351195504: ('0.1860674246550472',), 1351195429: ('0.1860674246550472',)} [1351370182, 1351370131, 1351370124, 1351370121, 1351196530, 1351196272, 1351196185, 1351195670, 1351195664, 1351195594, 1351195504, 1351195429] Looping around the photos to save general results len do output : 12 /1351370182.Didn't retrieve data . /1351370131.Didn't retrieve data . /1351370124.Didn't retrieve data . /1351370121.Didn't retrieve data . /1351196530.Didn't retrieve data . /1351196272.Didn't retrieve data . /1351196185.Didn't retrieve data . /1351195670.Didn't retrieve data . /1351195664.Didn't retrieve data . /1351195594.Didn't retrieve data . /1351195504.Didn't retrieve data . /1351195429.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, '2741779') ('3318', '22248184', '1351370182', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351370131', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351370124', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351370121', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351196530', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351196272', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351196185', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195670', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195664', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195594', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195504', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195429', None, None, None, None, None, '2741779') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 36 time used for this insertion : 0.013685941696166992 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.14252305030822754 time spend to save output : 0.014333963394165039 total time spend for step 5 : 0.15685701370239258 step6:blur_detection Fri Apr 11 02:23:24 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc.jpg resize: (2160, 3264) 1351370182 -2.540675911047674 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2.jpg resize: (2160, 3264) 1351370131 -3.9189381303688533 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a.jpg resize: (2160, 3264) 1351370124 -3.3630812415575297 treat image : temp/1744330228_1178299_1351370121_5e3ac64af827cd2595d939aa318981ac.jpg resize: (2160, 3264) 1351370121 -1.043999122453061 treat image : temp/1744330228_1178299_1351196530_170639c249362f7596ecfe1384661e3e.jpg resize: (2160, 3264) 1351196530 3.7109137684798 treat image : temp/1744330228_1178299_1351196272_2c668f566e2d6f861befb96cd4c18de0.jpg resize: (2160, 3264) 1351196272 -0.1822105619538249 treat image : temp/1744330228_1178299_1351196185_4807275af30ae878c17b01613421739c.jpg resize: (2160, 3264) 1351196185 -2.2568925245038436 treat image : temp/1744330228_1178299_1351195670_fe3f6c8af926e65cf79de3e2811e70a7.jpg resize: (2160, 3264) 1351195670 -1.6469224572852488 treat image : temp/1744330228_1178299_1351195664_6fb49d18dd6b7df304615d14d54ff6c9.jpg resize: (2160, 3264) 1351195664 -2.670134375653883 treat image : temp/1744330228_1178299_1351195594_cf51d31723c4ea5908487b254d93e056.jpg resize: (2160, 3264) 1351195594 -2.9816738080003486 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d.jpg resize: (2160, 3264) 1351195504 -5.687550568855551 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53.jpg resize: (2160, 3264) 1351195429 -4.366737580338417 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665152_0.png resize: (298, 240) 1351407645 -1.7651965346934606 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665154_0.png resize: (921, 745) 1351407647 -0.4972959040734872 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665158_0.png resize: (149, 136) 1351407648 -1.3820717993613079 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665161_0.png resize: (88, 154) 1351407649 -2.1228660318179116 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665159_0.png resize: (86, 179) 1351407651 -1.1030947040068153 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665165_0.png resize: (386, 171) 1351407652 -1.9787783354652306 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665170_0.png resize: (94, 180) 1351407653 -1.581898732877607 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665169_0.png resize: (140, 127) 1351407655 -2.2286719902131304 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665160_0.png resize: (77, 88) 1351407656 1.4025633612853639 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665151_0.png resize: (295, 281) 1351407657 -1.2994165904069441 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665155_0.png resize: (135, 146) 1351407658 -2.342239968020779 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665172_0.png resize: (594, 585) 1351407659 -1.8889225356359929 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665153_0.png resize: (122, 125) 1351407660 -1.8608306377363677 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665167_0.png resize: (126, 184) 1351407661 -1.4487529714881133 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665156_0.png resize: (601, 325) 1351407662 -2.399722617130517 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665163_0.png resize: (95, 155) 1351407663 -1.6787224693785037 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665166_0.png resize: (216, 158) 1351407664 -0.9930472901232754 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665162_0.png resize: (88, 76) 1351407665 -1.1592872932644962 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665174_0.png resize: (194, 289) 1351407666 -2.125571067101883 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665185_0.png resize: (132, 98) 1351407667 -1.0139868193226722 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665183_0.png resize: (549, 321) 1351407669 -2.8569420128963 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665198_0.png resize: (134, 141) 1351407670 -2.1729782515910943 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665192_0.png resize: (186, 155) 1351407671 -2.8466566155428366 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665191_0.png resize: (207, 214) 1351407672 -1.7769962394818406 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665176_0.png resize: (135, 126) 1351407673 -1.9354913018903703 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665188_0.png resize: (298, 257) 1351407674 -3.5217429820774786 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665179_0.png resize: (440, 488) 1351407675 -2.243302840210023 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665195_0.png resize: (381, 352) 1351407676 -2.5619268843450627 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665178_0.png resize: (135, 151) 1351407677 -1.672150875062831 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665202_0.png resize: (212, 159) 1351407678 -2.160448533029736 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665184_0.png resize: (184, 154) 1351407679 -3.0858283263693562 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665177_0.png resize: (392, 318) 1351407680 -2.4963984179713474 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665200_0.png resize: (343, 116) 1351407681 -2.5554458164561056 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665182_0.png resize: (224, 269) 1351407682 -4.198479877627974 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665187_0.png resize: (158, 310) 1351407683 -0.7131707845263033 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665181_0.png resize: (218, 188) 1351407684 -1.9176110948085245 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665175_0.png resize: (182, 119) 1351407685 -2.1303153797783416 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665193_0.png resize: (161, 173) 1351407686 -2.176833742028973 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665186_0.png resize: (262, 111) 1351407687 -3.0506796803354197 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665190_0.png resize: (207, 60) 1351407688 -3.070662128124568 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665173_0.png resize: (220, 166) 1351407689 -0.21952840457495246 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665194_0.png resize: (302, 178) 1351407690 -3.5048594577590255 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665197_0.png resize: (130, 86) 1351407691 -0.7285684606941712 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665199_0.png resize: (100, 71) 1351407692 -1.124826234210098 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665213_0.png resize: (1008, 511) 1351407693 -0.5322087063501592 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665217_0.png resize: (334, 408) 1351407694 -1.1147814172200927 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665218_0.png resize: (360, 422) 1351407695 -2.070064046175566 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665222_0.png resize: (585, 486) 1351407696 -0.7797905948588341 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665226_0.png resize: (399, 214) 1351407697 -3.070532446910136 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665212_0.png resize: (195, 103) 1351407698 -3.7944364611603403 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665223_0.png resize: (51, 119) 1351407699 -0.7686421091088971 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665233_0.png resize: (110, 131) 1351407700 -2.3703571329457995 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665229_0.png resize: (123, 130) 1351407701 -1.3789192782202921 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665234_0.png resize: (340, 710) 1351407702 -2.3138645625092686 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665206_0.png resize: (222, 112) 1351407704 -1.5044133440163892 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665214_0.png resize: (236, 197) 1351407705 -0.29307955041382266 treat image : temp/1744330228_1178299_1351370121_5e3ac64af827cd2595d939aa318981ac_rle_crop_3755665236_0.png resize: (242, 191) 1351407706 -2.031082637320819 treat image : temp/1744330228_1178299_1351370121_5e3ac64af827cd2595d939aa318981ac_rle_crop_3755665242_0.png resize: (89, 81) 1351407707 -1.237744691853155 treat image : temp/1744330228_1178299_1351370121_5e3ac64af827cd2595d939aa318981ac_rle_crop_3755665237_0.png resize: (264, 231) 1351407708 0.9312333585433177 treat image : temp/1744330228_1178299_1351370121_5e3ac64af827cd2595d939aa318981ac_rle_crop_3755665238_0.png resize: (303, 185) 1351407709 -0.2169833720127512 treat image : temp/1744330228_1178299_1351370121_5e3ac64af827cd2595d939aa318981ac_rle_crop_3755665241_0.png resize: (116, 143) 1351407711 0.6900486853853199 treat image : temp/1744330228_1178299_1351370121_5e3ac64af827cd2595d939aa318981ac_rle_crop_3755665243_0.png resize: (390, 475) 1351407712 -0.41305776017929413 treat image : temp/1744330228_1178299_1351196530_170639c249362f7596ecfe1384661e3e_rle_crop_3755665248_0.png resize: (272, 403) 1351407713 -1.1153808421006608 treat image : temp/1744330228_1178299_1351196530_170639c249362f7596ecfe1384661e3e_rle_crop_3755665246_0.png resize: (331, 369) 1351407714 -1.6684269971331238 treat image : temp/1744330228_1178299_1351196530_170639c249362f7596ecfe1384661e3e_rle_crop_3755665251_0.png resize: (265, 279) 1351407715 7.072867765301763 treat image : temp/1744330228_1178299_1351196530_170639c249362f7596ecfe1384661e3e_rle_crop_3755665254_0.png resize: (148, 156) 1351407717 1.9963790260181522 treat image : temp/1744330228_1178299_1351196530_170639c249362f7596ecfe1384661e3e_rle_crop_3755665245_0.png resize: (236, 184) 1351407718 0.5449856258551327 treat image : temp/1744330228_1178299_1351196530_170639c249362f7596ecfe1384661e3e_rle_crop_3755665250_0.png resize: (273, 237) 1351407719 0.1414317165639878 treat image : temp/1744330228_1178299_1351196530_170639c249362f7596ecfe1384661e3e_rle_crop_3755665252_0.png resize: (120, 148) 1351407720 -1.1458997922643002 treat image : temp/1744330228_1178299_1351196272_2c668f566e2d6f861befb96cd4c18de0_rle_crop_3755665262_0.png resize: (191, 116) 1351407721 -0.8043828897779568 treat image : temp/1744330228_1178299_1351196272_2c668f566e2d6f861befb96cd4c18de0_rle_crop_3755665267_0.png resize: (157, 147) 1351407722 -1.0836208172379282 treat image : temp/1744330228_1178299_1351196272_2c668f566e2d6f861befb96cd4c18de0_rle_crop_3755665259_0.png resize: (78, 95) 1351407723 -0.9042733995557919 treat image : temp/1744330228_1178299_1351196272_2c668f566e2d6f861befb96cd4c18de0_rle_crop_3755665263_0.png resize: (81, 178) 1351407724 -1.2924257042874003 treat image : temp/1744330228_1178299_1351196272_2c668f566e2d6f861befb96cd4c18de0_rle_crop_3755665256_0.png resize: (692, 839) 1351407725 -1.1604437463779504 treat image : temp/1744330228_1178299_1351196272_2c668f566e2d6f861befb96cd4c18de0_rle_crop_3755665261_0.png resize: (295, 212) 1351407726 -1.0039185520095322 treat image : temp/1744330228_1178299_1351196185_4807275af30ae878c17b01613421739c_rle_crop_3755665269_0.png resize: (143, 484) 1351407727 -2.0916312665115897 treat image : temp/1744330228_1178299_1351196185_4807275af30ae878c17b01613421739c_rle_crop_3755665268_0.png resize: (198, 182) 1351407728 -1.0328865945155938 treat image : temp/1744330228_1178299_1351196185_4807275af30ae878c17b01613421739c_rle_crop_3755665272_0.png resize: (162, 126) 1351407729 -1.4721715882056643 treat image : temp/1744330228_1178299_1351196185_4807275af30ae878c17b01613421739c_rle_crop_3755665274_0.png resize: (531, 426) 1351407730 -2.1611150700258053 treat image : temp/1744330228_1178299_1351196185_4807275af30ae878c17b01613421739c_rle_crop_3755665276_0.png resize: (142, 134) 1351407731 -1.109346883062603 treat image : temp/1744330228_1178299_1351195670_fe3f6c8af926e65cf79de3e2811e70a7_rle_crop_3755665278_0.png resize: (867, 854) 1351407732 -0.9716646973261115 treat image : temp/1744330228_1178299_1351195670_fe3f6c8af926e65cf79de3e2811e70a7_rle_crop_3755665280_0.png resize: (550, 308) 1351407733 -0.7812414226060111 treat image : temp/1744330228_1178299_1351195670_fe3f6c8af926e65cf79de3e2811e70a7_rle_crop_3755665288_0.png resize: (338, 866) 1351407734 -1.1239839117391655 treat image : temp/1744330228_1178299_1351195670_fe3f6c8af926e65cf79de3e2811e70a7_rle_crop_3755665284_0.png resize: (183, 244) 1351407735 -0.7355900493803478 treat image : temp/1744330228_1178299_1351195670_fe3f6c8af926e65cf79de3e2811e70a7_rle_crop_3755665286_0.png resize: (339, 667) 1351407736 -1.983541851705565 treat image : temp/1744330228_1178299_1351195670_fe3f6c8af926e65cf79de3e2811e70a7_rle_crop_3755665282_0.png resize: (196, 373) 1351407737 0.39640542864236883 treat image : temp/1744330228_1178299_1351195670_fe3f6c8af926e65cf79de3e2811e70a7_rle_crop_3755665283_0.png resize: (350, 799) 1351407739 -0.9488402017996029 treat image : temp/1744330228_1178299_1351195670_fe3f6c8af926e65cf79de3e2811e70a7_rle_crop_3755665292_0.png resize: (223, 139) 1351407740 -0.9631154168717622 treat image : temp/1744330228_1178299_1351195670_fe3f6c8af926e65cf79de3e2811e70a7_rle_crop_3755665291_0.png resize: (278, 303) 1351407741 2.1226670280608837 treat image : temp/1744330228_1178299_1351195670_fe3f6c8af926e65cf79de3e2811e70a7_rle_crop_3755665285_0.png resize: (137, 80) 1351407742 0.785221756118655 treat image : temp/1744330228_1178299_1351195670_fe3f6c8af926e65cf79de3e2811e70a7_rle_crop_3755665289_0.png resize: (600, 558) 1351407744 -1.6068348355014248 treat image : temp/1744330228_1178299_1351195670_fe3f6c8af926e65cf79de3e2811e70a7_rle_crop_3755665294_0.png resize: (46, 98) 1351407745 0.8615871603169397 treat image : temp/1744330228_1178299_1351195664_6fb49d18dd6b7df304615d14d54ff6c9_rle_crop_3755665303_0.png resize: (775, 709) 1351407746 -0.8220686927377459 treat image : temp/1744330228_1178299_1351195664_6fb49d18dd6b7df304615d14d54ff6c9_rle_crop_3755665300_0.png resize: (1145, 465) 1351407748 -1.5851694193727162 treat image : temp/1744330228_1178299_1351195664_6fb49d18dd6b7df304615d14d54ff6c9_rle_crop_3755665305_0.png resize: (1099, 952) 1351407749 -1.8631452851458157 treat image : temp/1744330228_1178299_1351195664_6fb49d18dd6b7df304615d14d54ff6c9_rle_crop_3755665304_0.png resize: (428, 309) 1351407750 -2.652667760746323 treat image : temp/1744330228_1178299_1351195664_6fb49d18dd6b7df304615d14d54ff6c9_rle_crop_3755665296_0.png resize: (475, 512) 1351407752 -3.533898259193115 treat image : temp/1744330228_1178299_1351195664_6fb49d18dd6b7df304615d14d54ff6c9_rle_crop_3755665299_0.png resize: (150, 130) 1351407753 -2.995742560597367 treat image : temp/1744330228_1178299_1351195664_6fb49d18dd6b7df304615d14d54ff6c9_rle_crop_3755665297_0.png resize: (508, 239) 1351407754 -1.203630930798131 treat image : temp/1744330228_1178299_1351195594_cf51d31723c4ea5908487b254d93e056_rle_crop_3755665315_0.png resize: (601, 213) 1351407756 -1.3922011233590965 treat image : temp/1744330228_1178299_1351195594_cf51d31723c4ea5908487b254d93e056_rle_crop_3755665313_0.png resize: (123, 124) 1351407757 -1.7238831909496646 treat image : temp/1744330228_1178299_1351195594_cf51d31723c4ea5908487b254d93e056_rle_crop_3755665314_0.png resize: (95, 93) 1351407759 0.019727444122348815 treat image : temp/1744330228_1178299_1351195594_cf51d31723c4ea5908487b254d93e056_rle_crop_3755665317_0.png resize: (765, 660) 1351407760 -0.1514143095814704 treat image : temp/1744330228_1178299_1351195594_cf51d31723c4ea5908487b254d93e056_rle_crop_3755665312_0.png resize: (540, 630) 1351407761 -0.6132247991654697 treat image : temp/1744330228_1178299_1351195594_cf51d31723c4ea5908487b254d93e056_rle_crop_3755665310_0.png resize: (511, 678) 1351407763 -1.0669390284711573 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665359_0.png resize: (101, 98) 1351407765 -1.805067555694347 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665324_0.png resize: (229, 153) 1351407766 -2.599652313713491 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665326_0.png resize: (338, 356) 1351407768 -2.331680794123417 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665347_0.png resize: (123, 479) 1351407769 -2.2748268738873034 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665333_0.png resize: (151, 171) 1351407770 -2.9249944163247035 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665346_0.png resize: (135, 168) 1351407772 -2.684166446068818 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665328_0.png resize: (65, 115) 1351407773 -0.4019017885088613 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665334_0.png resize: (95, 135) 1351407774 -3.793566641948612 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665332_0.png resize: (92, 117) 1351407776 -2.5180159584739523 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665330_0.png resize: (76, 153) 1351407777 -2.503745525145844 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665364_0.png resize: (344, 139) 1351407778 -2.6859462499213484 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665355_0.png resize: (118, 196) 1351407780 -1.758084361762432 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665327_0.png resize: (155, 241) 1351407782 -2.8821632837896596 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665344_0.png resize: (115, 108) 1351407783 -1.9747575545498297 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665361_0.png resize: (282, 217) 1351407784 -2.882695759512396 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665325_0.png resize: (263, 209) 1351407787 -2.357466229617273 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665362_0.png resize: (192, 173) 1351407788 -4.014524018426601 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665323_0.png resize: (252, 232) 1351407789 -2.7574449529904634 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665335_0.png resize: (200, 194) 1351407792 -2.1930780670661063 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665349_0.png resize: (75, 106) 1351407793 0.30576285832770805 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665338_0.png resize: (140, 78) 1351407794 -1.9420784003303388 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665339_0.png resize: (319, 291) 1351407797 -4.04800742354649 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665337_0.png resize: (167, 136) 1351407798 -3.0795709543013214 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665354_0.png resize: (545, 338) 1351407800 -3.570880835835292 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665341_0.png resize: (111, 166) 1351407803 -2.167974495596833 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665356_0.png resize: (160, 108) 1351407804 -2.3087791489858493 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665357_0.png resize: (341, 344) 1351407805 -4.640892058070927 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665363_0.png resize: (194, 219) 1351407808 -4.261991633477403 treat image : 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temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665382_0.png resize: (234, 251) 1351407839 -2.1537054343548423 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665370_0.png resize: (326, 367) 1351407840 -3.113735423957232 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665391_0.png resize: (100, 99) 1351407842 -1.8886535575928205 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665425_0.png resize: (188, 155) 1351407844 -1.9871937234595556 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665423_0.png resize: (192, 164) 1351407845 -2.8136441521686093 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665418_0.png resize: (120, 173) 1351407848 -1.6495671280905466 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665389_0.png resize: (184, 201) 1351407850 -1.8895164160488056 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665398_0.png resize: (208, 123) 1351407851 -2.9822070390061914 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665411_0.png resize: (116, 171) 1351407853 -1.0665046208873605 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665395_0.png resize: (241, 274) 1351407855 -2.3406260612291754 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665405_0.png resize: (257, 499) 1351407856 -2.534562732593401 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665386_0.png resize: (124, 179) 1351407858 -2.197204862942309 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665387_0.png resize: (131, 145) 1351407860 -1.4731644706729898 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665409_0.png resize: (197, 171) 1351407861 -2.8946281327950105 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665380_0.png resize: (143, 225) 1351407863 -1.7414395471838713 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665388_0.png resize: (103, 171) 1351407865 -2.246239604201018 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665399_0.png resize: (219, 219) 1351407867 -2.9850525976594913 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665420_0.png resize: (119, 296) 1351407869 -2.1920060031854787 treat image : 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temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665381_0.png resize: (118, 194) 1351407881 -0.2943163447932538 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665375_0.png resize: (146, 214) 1351407882 -0.7558349537439004 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665419_0.png resize: (214, 231) 1351407885 -3.3829209525561468 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665378_0.png resize: (142, 107) 1351407886 -1.313932695034427 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665401_0.png resize: (300, 223) 1351407887 -2.2322589955346146 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665404_0.png resize: (231, 199) 1351407890 -3.0611252921214236 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665413_0.png resize: (261, 341) 1351407891 -2.6802534547766 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665400_0.png resize: (131, 221) 1351407893 -3.102308351118404 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665424_0.png resize: (280, 267) 1351407895 -3.814852499213614 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665369_0.png resize: (227, 218) 1351407896 -2.102272419099534 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665384_0.png resize: (86, 62) 1351407898 -1.449740638421609 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665393_0.png resize: (188, 129) 1351407900 -3.728632033815548 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665402_0.png resize: (283, 217) 1351407901 -4.008894691975904 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665414_0.png resize: (119, 288) 1351407903 -3.1656079123416814 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665408_0.png resize: (184, 157) 1351407905 -1.78682546663664 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665171_0.png resize: (106, 69) 1351408019 -1.0758600558093228 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665168_0.png resize: (58, 82) 1351408020 -0.5547249852285986 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665157_0.png resize: (277, 270) 1351408021 -1.278021474251012 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665180_0.png resize: (288, 460) 1351408022 -2.3809676611050303 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665189_0.png resize: (129, 243) 1351408023 -2.2313521712017073 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665201_0.png resize: (228, 280) 1351408024 -1.8010366303480476 treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2_rle_crop_3755665196_0.png resize: (147, 254) 1351408026 -2.773490116006241 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665204_0.png resize: (625, 710) 1351408027 -2.359905513004037 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665225_0.png resize: (260, 339) 1351408028 -3.143038402119117 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665231_0.png resize: (165, 131) 1351408029 -1.352358612875206 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665221_0.png resize: (186, 173) 1351408030 -2.3050827807250185 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665216_0.png resize: (201, 130) 1351408031 -2.151933182161642 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665209_0.png resize: (470, 615) 1351408032 -0.9228702778434424 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665205_0.png resize: (289, 298) 1351408034 -3.5603972679368714 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665228_0.png resize: (411, 264) 1351408035 -1.5012415252102973 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665211_0.png resize: (119, 180) 1351408036 -1.9429036543881728 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665220_0.png resize: (272, 202) 1351408037 -2.3619488154390984 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665232_0.png resize: (173, 235) 1351408038 -3.093489127076939 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665235_0.png resize: (133, 230) 1351408039 -2.1948408594162747 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665207_0.png resize: (150, 163) 1351408040 -2.328303130780316 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665208_0.png resize: (165, 460) 1351408042 -2.9998585419491124 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665227_0.png resize: (420, 885) 1351408043 -0.7805796717144124 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665219_0.png resize: (154, 224) 1351408044 -3.0169226532130473 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665215_0.png resize: (142, 311) 1351408045 -2.536369806307326 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665210_0.png resize: (331, 351) 1351408046 -2.1126265348805755 treat image : temp/1744330228_1178299_1351370121_5e3ac64af827cd2595d939aa318981ac_rle_crop_3755665239_0.png resize: (136, 215) 1351408047 -1.8421349784913852 treat image : temp/1744330228_1178299_1351370121_5e3ac64af827cd2595d939aa318981ac_rle_crop_3755665240_0.png resize: (168, 103) 1351408048 0.663391861477504 treat image : temp/1744330228_1178299_1351196272_2c668f566e2d6f861befb96cd4c18de0_rle_crop_3755665258_0.png resize: (843, 422) 1351408049 -1.078184006899387 treat image : temp/1744330228_1178299_1351196272_2c668f566e2d6f861befb96cd4c18de0_rle_crop_3755665265_0.png resize: (246, 320) 1351408050 -1.5209987006709016 treat image : temp/1744330228_1178299_1351196272_2c668f566e2d6f861befb96cd4c18de0_rle_crop_3755665255_0.png resize: (145, 140) 1351408051 -0.5930847047492311 treat image : temp/1744330228_1178299_1351196272_2c668f566e2d6f861befb96cd4c18de0_rle_crop_3755665257_0.png resize: (199, 180) 1351408052 -2.0075950499074224 treat image : temp/1744330228_1178299_1351195670_fe3f6c8af926e65cf79de3e2811e70a7_rle_crop_3755665295_0.png resize: (1058, 1059) 1351408053 -2.1662908018586022 treat image : temp/1744330228_1178299_1351195670_fe3f6c8af926e65cf79de3e2811e70a7_rle_crop_3755665287_0.png resize: (349, 498) 1351408054 -1.1834232491151082 treat image : temp/1744330228_1178299_1351195670_fe3f6c8af926e65cf79de3e2811e70a7_rle_crop_3755665281_0.png resize: (536, 734) 1351408055 -1.0470428960548968 treat image : temp/1744330228_1178299_1351195670_fe3f6c8af926e65cf79de3e2811e70a7_rle_crop_3755665293_0.png resize: (310, 297) 1351408056 -1.2082726227206724 treat image : temp/1744330228_1178299_1351195670_fe3f6c8af926e65cf79de3e2811e70a7_rle_crop_3755665290_0.png resize: (553, 412) 1351408057 -2.6266095583254954 treat image : temp/1744330228_1178299_1351195664_6fb49d18dd6b7df304615d14d54ff6c9_rle_crop_3755665306_0.png resize: (149, 168) 1351408058 -2.6480902473319636 treat image : temp/1744330228_1178299_1351195664_6fb49d18dd6b7df304615d14d54ff6c9_rle_crop_3755665301_0.png resize: (121, 234) 1351408059 -2.0653720890589065 treat image : temp/1744330228_1178299_1351195664_6fb49d18dd6b7df304615d14d54ff6c9_rle_crop_3755665298_0.png resize: (401, 569) 1351408060 -0.7485437983986135 treat image : temp/1744330228_1178299_1351195664_6fb49d18dd6b7df304615d14d54ff6c9_rle_crop_3755665302_0.png resize: (182, 304) 1351408061 -2.6616258711798584 treat image : temp/1744330228_1178299_1351195594_cf51d31723c4ea5908487b254d93e056_rle_crop_3755665319_0.png resize: (294, 239) 1351408062 -2.5561288447272785 treat image : temp/1744330228_1178299_1351195594_cf51d31723c4ea5908487b254d93e056_rle_crop_3755665316_0.png resize: (661, 863) 1351408063 -3.4490200681006273 treat image : temp/1744330228_1178299_1351195594_cf51d31723c4ea5908487b254d93e056_rle_crop_3755665318_0.png resize: (256, 216) 1351408064 -1.24566697847408 treat image : temp/1744330228_1178299_1351195594_cf51d31723c4ea5908487b254d93e056_rle_crop_3755665311_0.png resize: (388, 659) 1351408065 -0.7121186432759254 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665322_0.png resize: (241, 283) 1351408066 -2.2355451740749794 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665366_0.png resize: (226, 264) 1351408067 -1.7940347345702434 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665336_0.png resize: (98, 122) 1351408068 -1.9603872088762762 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665345_0.png resize: (132, 121) 1351408069 -3.4035245560660248 treat image : temp/1744330228_1178299_1351195504_9905ce3a81ac88779efe16f580ea731d_rle_crop_3755665351_0.png resize: (183, 134) 1351408070 -1.027053360504681 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665421_0.png resize: (175, 256) 1351408071 -2.231905152019788 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665417_0.png resize: (195, 150) 1351408072 -1.5316320480609447 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665372_0.png resize: (123, 172) 1351408073 -0.742486207042469 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665410_0.png resize: (123, 168) 1351408074 -1.52917696062611 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665385_0.png resize: (136, 215) 1351408075 -1.9891852363144156 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665383_0.png resize: (100, 184) 1351408076 -2.080173918125306 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665406_0.png resize: (147, 156) 1351408077 -0.9487912791302164 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665394_0.png resize: (164, 146) 1351408078 -1.601462539314632 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665377_0.png resize: (213, 192) 1351408079 -1.5864342531588376 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665374_0.png resize: (141, 148) 1351408080 -2.290055411448294 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665412_0.png resize: (172, 531) 1351408081 -3.0195359125114094 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665422_0.png resize: (171, 195) 1351408082 -3.0130247978074656 treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a_rle_crop_3755665224_0.png resize: (108, 99) 1351408093 -3.22687041967725 treat image : temp/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc_rle_crop_3755665164_0.png resize: (128, 83) 1351408107 -1.479548902175015 treat image : 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temp/1744330228_1178299_1351195664_6fb49d18dd6b7df304615d14d54ff6c9_rle_crop_3755665308_0.png resize: (634, 587) 1351408126 -3.194846671336264 treat image : temp/1744330228_1178299_1351195664_6fb49d18dd6b7df304615d14d54ff6c9_rle_crop_3755665309_0.png resize: (427, 659) 1351408127 -2.405629201206587 treat image : temp/1744330228_1178299_1351195429_75c98636b93d69abb4064565bafb2b53_rle_crop_3755665407_0.png resize: (101, 45) 1351408128 20.0 treat image : temp/1744330228_1178299_1351196530_170639c249362f7596ecfe1384661e3e_rle_crop_3755665247_0.png resize: (488, 324) 1351408131 0.12527636704619796 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 : 287 time used for this insertion : 0.026830196380615234 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 287 time used for this insertion : 0.0479426383972168 save missing photos in datou_result : time spend for datou_step_exec : 50.92642068862915 time spend to save output : 0.08263754844665527 total time spend for step 6 : 51.009058237075806 step7:brightness Fri Apr 11 02:24:15 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/1744330228_1178299_1351370182_b1ea9d92a427f33c0db8789eff3c0bcc.jpg treat image : temp/1744330228_1178299_1351370131_5c4d5594240b39ebc98cc7401a03a9e2.jpg treat image : temp/1744330228_1178299_1351370124_616835c3d087aa24afebd7cbcb476e4a.jpg treat image : temp/1744330228_1178299_1351370121_5e3ac64af827cd2595d939aa318981ac.jpg treat image : temp/1744330228_1178299_1351196530_170639c249362f7596ecfe1384661e3e.jpg treat image : temp/1744330228_1178299_1351196272_2c668f566e2d6f861befb96cd4c18de0.jpg treat image : temp/1744330228_1178299_1351196185_4807275af30ae878c17b01613421739c.jpg treat image : temp/1744330228_1178299_1351195670_fe3f6c8af926e65cf79de3e2811e70a7.jpg treat image : temp/1744330228_1178299_1351195664_6fb49d18dd6b7df304615d14d54ff6c9.jpg treat image : temp/1744330228_1178299_1351195594_cf51d31723c4ea5908487b254d93e056.jpg treat image : 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photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 287 time used for this insertion : 0.04965376853942871 save missing photos in datou_result : time spend for datou_step_exec : 12.587618112564087 time spend to save output : 0.08572268486022949 total time spend for step 7 : 12.673340797424316 step8:velours_tree Fri Apr 11 02:24:28 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure can't find the photo_desc_type Inside saveOutput : final : False verbose : 0 ouput is None No outpout to save, returning out of save general time spend for datou_step_exec : 2.7645444869995117 time spend to save output : 4.673004150390625e-05 total time spend for step 8 : 2.7645912170410156 step9:send_mail_cod Fri Apr 11 02:24:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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_P22248184_11-04-2025_02_24_31.pdf 22248668 imagette222486681744331071 22248669 change filename to text .imagette222486691744331071 22248670 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 .imagette222486701744331071 22248671 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 .imagette222486711744331072 22248672 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette222486721744331074 22248674 imagette222486741744331074 22248675 imagette222486751744331074 22248676 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 .imagette222486761744331074 22248677 change filename to text .imagette222486771744331075 22248678 imagette222486781744331076 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=22248184 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/22248668,22248669,22248670,22248671,22248672,22248673,22248674,22248675,22248676,22248677,22248678?tags=background,metal,pet_clair,carton,autre,environnement,mal_croppe,flou,papier,pehd,pet_fonce args[1351370182] : ((1351370182, -2.540675911047674, 492609224), (1351370182, 0.06705921995937447, 2107752395), '0.1860674246550472') We are sending mail with results at report@fotonower.com args[1351370131] : ((1351370131, -3.9189381303688533, 492609224), (1351370131, -0.28509014203522537, 496442774), '0.1860674246550472') We are sending mail with results at report@fotonower.com args[1351370124] : ((1351370124, -3.3630812415575297, 492609224), (1351370124, -0.07827603193542118, 496442774), '0.1860674246550472') We are sending mail with results at report@fotonower.com args[1351370121] : ((1351370121, -1.043999122453061, 492688767), (1351370121, -0.12183573934386678, 496442774), '0.1860674246550472') We are sending mail with results at report@fotonower.com args[1351196530] : ((1351196530, 3.7109137684798, 492688767), (1351196530, -0.08964057681011614, 496442774), '0.1860674246550472') We are sending mail with results at report@fotonower.com args[1351196272] : ((1351196272, -0.1822105619538249, 492688767), (1351196272, -0.22076800310205513, 496442774), '0.1860674246550472') We are sending mail with results at report@fotonower.com args[1351196185] : ((1351196185, -2.2568925245038436, 492609224), (1351196185, -0.2353305542133998, 496442774), '0.1860674246550472') We are sending mail with results at report@fotonower.com args[1351195670] : ((1351195670, -1.6469224572852488, 492688767), (1351195670, 0.08926526273647868, 2107752395), '0.1860674246550472') We are sending mail with results at report@fotonower.com args[1351195664] : ((1351195664, -2.670134375653883, 492609224), (1351195664, -0.39228174233636987, 496442774), '0.1860674246550472') We are sending mail with results at report@fotonower.com args[1351195594] : ((1351195594, -2.9816738080003486, 492609224), (1351195594, -0.20501098865817236, 496442774), '0.1860674246550472') We are sending mail with results at report@fotonower.com args[1351195504] : ((1351195504, -5.687550568855551, 492609224), (1351195504, -0.007975179178710566, 2107752395), '0.1860674246550472') We are sending mail with results at report@fotonower.com args[1351195429] : ((1351195429, -4.366737580338417, 492609224), (1351195429, -0.14851173811433324, 496442774), '0.1860674246550472') We are sending mail with results at report@fotonower.com refus_total : 0.1860674246550472 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=22248184 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1351195664,1351195670,1351195429,1351195504,1351195594,1351196185,1351196272,1351196530,1351370121,1351370124,1351370131,1351370182) Found this number of photos: 12 begin to download photo : 1351195664 begin to download photo : 1351195504 begin to download photo : 1351196272 begin to download photo : 1351370124 download finish for photo 1351196272 begin to download photo : 1351196530 download finish for photo 1351370124 begin to download photo : 1351370131 download finish for photo 1351195504 begin to download photo : 1351195594 download finish for photo 1351195664 begin to download photo : 1351195670 download finish for photo 1351196530 begin to download photo : 1351370121 download finish for photo 1351370131 begin to download photo : 1351370182 download finish for photo 1351195670 begin to download photo : 1351195429 download finish for photo 1351370121 download finish for photo 1351195594 begin to download photo : 1351196185 download finish for photo 1351195429 download finish for photo 1351196185 download finish for photo 1351370182 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22248184_11-04-2025_02_24_31.pdf results_Auto_P22248184_11-04-2025_02_24_31.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22248184_11-04-2025_02_24_31.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','22248184','results_Auto_P22248184_11-04-2025_02_24_31.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22248184_11-04-2025_02_24_31.pdf','pdf','','0.96','0.1860674246550472') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/22248184

https://www.fotonower.com/image?json=false&list_photos_id=1351370182
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1351370131
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1351370124
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1351370121
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1351196530
La photo est trop floue, merci de reprendre une photo.(avec le score = 3.7109137684798)
https://www.fotonower.com/image?json=false&list_photos_id=1351196272
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1351196185
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1351195670
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1351195664
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1351195594
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1351195504
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1351195429
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 18.61%
Veuillez trouver les photos des contaminants.

exemples de contaminants: metal: https://www.fotonower.com/view/22248669?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/22248670?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/22248671?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/22248672?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/22248676?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/22248677?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22248184_11-04-2025_02_24_31.pdf.

Lien vers velours :https://www.fotonower.com/velours/22248668,22248669,22248670,22248671,22248672,22248673,22248674,22248675,22248676,22248677,22248678?tags=background,metal,pet_clair,carton,autre,environnement,mal_croppe,flou,papier,pehd,pet_fonce.


L'équipe Fotonower 202 b'' Server: nginx Date: Fri, 11 Apr 2025 00:24:40 GMT Content-Length: 0 Connection: close X-Message-Id: yqI8aGZUTta_cJuobwX00w 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 [1351370182, 1351370131, 1351370124, 1351370121, 1351196530, 1351196272, 1351196185, 1351195670, 1351195664, 1351195594, 1351195504, 1351195429] 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, '2741779') ('3318', '22248184', '1351370182', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351370131', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351370124', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351370121', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351196530', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351196272', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351196185', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195670', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195664', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195594', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195504', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195429', None, None, None, None, None, '2741779') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 12 time used for this insertion : 0.1808319091796875 save_final save missing photos in datou_result : time spend for datou_step_exec : 9.102825164794922 time spend to save output : 0.18118858337402344 total time spend for step 9 : 9.284013748168945 step10:split_time_score Fri Apr 11 02:24:40 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure begin split time score Catched exception ! Connect or reconnect ! TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('16', 12),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 10042025 22248184 Nombre de photos uploadées : 12 / 23040 (0%) 10042025 22248184 Nombre de photos taguées (types de déchets): 0 / 12 (0%) 10042025 22248184 Nombre de photos taguées (volume) : 0 / 12 (0%) elapsed_time : load_data_split_time_score 3.337860107421875e-06 elapsed_time : order_list_meta_photo_and_scores 9.5367431640625e-06 ???????????? elapsed_time : fill_and_build_computed_from_old_data 0.0006744861602783203 elapsed_time : insert_dashboard_record_day_entry 0.028411388397216797 We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps Qualite : 0.08146285908317973 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22246522_11-04-2025_00_21_32.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22246522 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`=22246522 AND mptpi.`type`=3726 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22248171 order by id desc limit 1 Qualite : 0.30404372758941545 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22222738_10-04-2025_13_49_21.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22222738 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`=22222738 AND mptpi.`type`=3594 To do Qualite : 0.13080439814814812 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22222739_10-04-2025_12_31_33.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22222739 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`=22222739 AND mptpi.`type`=3594 To do Qualite : 0.08491796816652654 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22222743_10-04-2025_12_25_42.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22222743 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`=22222743 AND mptpi.`type`=3726 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22248172 order by id desc limit 1 Qualite : 0.06472482777268794 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22234331_10-04-2025_17_50_57.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22234331 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`=22234331 AND mptpi.`type`=3726 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22248175 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22248176 order by id desc limit 1 Qualite : 0.13695543617250847 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22237479_10-04-2025_19_32_24.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22237479 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`=22237479 AND mptpi.`type`=3726 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22248183 order by id desc limit 1 Qualite : 0.1860674246550472 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22248184_11-04-2025_02_24_31.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22248184 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`=22248184 AND mptpi.`type`=3594 To do Qualite : 0.06080137778204751 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22246525_11-04-2025_00_35_35.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22246525 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`=22246525 AND mptpi.`type`=3726 To do Qualite : 0.18220796130952377 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22246530_11-04-2025_01_04_15.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22246530 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`=22246530 AND mptpi.`type`=3594 To do Qualite : 0.2303195134000895 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22243654_10-04-2025_22_48_38.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22243654 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`=22243654 AND mptpi.`type`=3594 To do Qualite : 0.17880971427923026 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22243656_10-04-2025_22_30_13.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22243656 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`=22243656 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'10042025': {'nb_upload': 12, '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 [1351370182, 1351370131, 1351370124, 1351370121, 1351196530, 1351196272, 1351196185, 1351195670, 1351195664, 1351195594, 1351195504, 1351195429] Looping around the photos to save general results len do output : 1 /22248184Didn'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, '2741779') ('3318', '22248184', '1351370182', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351370131', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351370124', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351370121', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351196530', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351196272', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351196185', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195670', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195664', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195594', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195504', None, None, None, None, None, '2741779') ('3318', None, None, None, None, None, None, None, '2741779') ('3318', '22248184', '1351195429', None, None, None, None, None, '2741779') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 13 time used for this insertion : 0.029380321502685547 save_final save missing photos in datou_result : time spend for datou_step_exec : 5.057990074157715 time spend to save output : 0.02960491180419922 total time spend for step 10 : 5.087594985961914 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 12 set_done_treatment 241.79user 166.04system 14:19.53elapsed 47%CPU (0avgtext+0avgdata 7467300maxresident)k 1189096inputs+177528outputs (20148major+22928332minor)pagefaults 0swaps