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 : 4094707 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 : ['4179941'] with mtr_portfolio_ids : ['29145272'] and first list_photo_ids : [] new path : /proc/4094707/ 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 , BFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 10 ; length of list_pids : 10 ; length of list_args : 10 time to download the photos : 2.056232452392578 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 Wed Dec 3 14:20:29 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 : 10998 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-12-03 14:20:31.899566: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-12-03 14:20:31.926693: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2025-12-03 14:20:31.928886: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fd278000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-12-03 14:20:31.928947: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-12-03 14:20:31.933082: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-12-03 14:20:32.219083: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x35d44290 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-12-03 14:20:32.219151: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-12-03 14:20:32.220579: 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 2025-12-03 14:20:32.221013: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-12-03 14:20:32.224178: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-12-03 14:20:32.227135: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-12-03 14:20:32.227466: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-12-03 14:20:32.229622: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-12-03 14:20:32.230644: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-12-03 14:20:32.235139: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-12-03 14:20:32.236591: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-12-03 14:20:32.236664: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-12-03 14:20:32.237422: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-12-03 14:20:32.237438: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-12-03 14:20:32.237447: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-12-03 14:20:32.242376: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10193 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. 2025-12-03 14:20:32.585549: 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 2025-12-03 14:20:32.585663: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-12-03 14:20:32.585680: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-12-03 14:20:32.585695: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-12-03 14:20:32.585709: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-12-03 14:20:32.585723: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-12-03 14:20:32.585738: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-12-03 14:20:32.585752: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-12-03 14:20:32.586955: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-12-03 14:20:32.588060: 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 2025-12-03 14:20:32.588087: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-12-03 14:20:32.588101: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-12-03 14:20:32.588115: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-12-03 14:20:32.588128: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-12-03 14:20:32.588141: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-12-03 14:20:32.588154: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-12-03 14:20:32.588167: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-12-03 14:20:32.589342: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-12-03 14:20:32.589376: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-12-03 14:20:32.589384: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-12-03 14:20:32.589391: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-12-03 14:20:32.590652: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10193 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 : [] 2025-12-03 14:20:42.852216: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-12-03 14:20:43.063830: 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 : 10 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 27 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 33 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 27 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 18 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 23 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 15 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 22 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 14 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 18 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 11 Detection mask done ! Trying to reset tf kernel 4095308 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 417 tf kernel not reseted sub process len(results) : 10 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 10 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 : 5706 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'] DEBUG bbox = [486, 1188, 954, 1656] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.004128217697143555 nb_pixel_total : 120242 time to create 1 rle with old method : 0.16511797904968262 length of segment : 448 DEBUG bbox = [12, 66, 588, 756] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0059010982513427734 nb_pixel_total : 249344 time to create 1 rle with new method : 0.015104532241821289 length of segment : 656 DEBUG bbox = [1740, 2244, 1842, 2418] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00026679039001464844 nb_pixel_total : 12189 time to create 1 rle with old method : 0.015002250671386719 length of segment : 88 DEBUG bbox = [1230, 1878, 1602, 2316] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.001739501953125 nb_pixel_total : 82141 time to create 1 rle with old method : 0.0964503288269043 length of segment : 348 DEBUG bbox = [1392, 1734, 1740, 2010] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0009202957153320312 nb_pixel_total : 51622 time to create 1 rle with old method : 0.058217525482177734 length of segment : 549 DEBUG bbox = [594, 2706, 954, 3024] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0010857582092285156 nb_pixel_total : 56240 time to create 1 rle with old method : 0.06307005882263184 length of segment : 323 DEBUG bbox = [222, 2142, 438, 2430] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00083160400390625 nb_pixel_total : 33599 time to create 1 rle with old method : 0.055272579193115234 length of segment : 199 DEBUG bbox = [1830, 1386, 2004, 1560] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0003376007080078125 nb_pixel_total : 17408 time to create 1 rle with old method : 0.019930124282836914 length of segment : 156 DEBUG bbox = [192, 438, 294, 576] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.000202178955078125 nb_pixel_total : 8173 time to create 1 rle with old method : 0.009485721588134766 length of segment : 91 DEBUG bbox = [1470, 1650, 1638, 1920] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00045561790466308594 nb_pixel_total : 28455 time to create 1 rle with old method : 0.031887054443359375 length of segment : 159 DEBUG bbox = [846, 990, 1248, 1206] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0009043216705322266 nb_pixel_total : 42206 time to create 1 rle with old method : 0.046981096267700195 length of segment : 373 DEBUG bbox = [708, 2214, 960, 2418] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0006296634674072266 nb_pixel_total : 34680 time to create 1 rle with old method : 0.039832115173339844 length of segment : 223 DEBUG bbox = [1326, 984, 1488, 1218] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0004932880401611328 nb_pixel_total : 26310 time to create 1 rle with old method : 0.030663728713989258 length of segment : 157 DEBUG bbox = [576, 2076, 936, 2232] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005939006805419922 nb_pixel_total : 20234 time to create 1 rle with old method : 0.02328968048095703 length of segment : 274 DEBUG bbox = [738, 2406, 1002, 2664] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0007202625274658203 nb_pixel_total : 35873 time to create 1 rle with old method : 0.041108131408691406 length of segment : 227 DEBUG bbox = [1734, 480, 1848, 600] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0002181529998779297 nb_pixel_total : 10962 time to create 1 rle with old method : 0.012581110000610352 length of segment : 111 DEBUG bbox = [918, 270, 1362, 798] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.002705097198486328 nb_pixel_total : 145754 time to create 1 rle with old method : 0.16441822052001953 length of segment : 349 DEBUG bbox = [546, 150, 738, 240] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0002608299255371094 nb_pixel_total : 10081 time to create 1 rle with old method : 0.01191401481628418 length of segment : 163 DEBUG bbox = [1704, 2700, 1872, 2898] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00038623809814453125 nb_pixel_total : 18500 time to create 1 rle with old method : 0.021857261657714844 length of segment : 157 DEBUG bbox = [1200, 1848, 1332, 2040] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0003314018249511719 nb_pixel_total : 15757 time to create 1 rle with old method : 0.018754243850708008 length of segment : 108 DEBUG bbox = [1674, 1968, 1830, 2130] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00040841102600097656 nb_pixel_total : 16081 time to create 1 rle with old method : 0.019124269485473633 length of segment : 140 DEBUG bbox = [258, 2046, 546, 2316] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0010769367218017578 nb_pixel_total : 21544 time to create 1 rle with old method : 0.034929752349853516 length of segment : 228 DEBUG bbox = [1290, 1920, 1530, 2298] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.001123666763305664 nb_pixel_total : 50994 time to create 1 rle with old method : 0.09287619590759277 length of segment : 235 DEBUG bbox = [1752, 2370, 1956, 2502] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0004413127899169922 nb_pixel_total : 16918 time to create 1 rle with old method : 0.030514240264892578 length of segment : 170 DEBUG bbox = [1962, 1860, 2142, 1974] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0004265308380126953 nb_pixel_total : 16783 time to create 1 rle with old method : 0.019921302795410156 length of segment : 180 DEBUG bbox = [1158, 2376, 1434, 2694] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.001562356948852539 nb_pixel_total : 30475 time to create 1 rle with old method : 0.049634456634521484 length of segment : 233 DEBUG bbox = [1332, 1602, 1428, 1740] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0003197193145751953 nb_pixel_total : 7308 time to create 1 rle with old method : 0.01192021369934082 length of segment : 79 DEBUG bbox = [1302, 576, 1506, 738] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0007796287536621094 nb_pixel_total : 19626 time to create 1 rle with old method : 0.04096102714538574 length of segment : 245 DEBUG bbox = [1392, 1536, 1812, 1758] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.001154184341430664 nb_pixel_total : 58310 time to create 1 rle with old method : 0.06804347038269043 length of segment : 469 DEBUG bbox = [930, 1992, 1320, 2310] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0015735626220703125 nb_pixel_total : 81111 time to create 1 rle with old method : 0.09421253204345703 length of segment : 370 DEBUG bbox = [1224, 2166, 1824, 2580] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0022830963134765625 nb_pixel_total : 89122 time to create 1 rle with old method : 0.10493731498718262 length of segment : 509 DEBUG bbox = [1608, 894, 1806, 1020] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0004494190216064453 nb_pixel_total : 17626 time to create 1 rle with old method : 0.02511119842529297 length of segment : 170 DEBUG bbox = [1614, 2646, 1812, 2844] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00055694580078125 nb_pixel_total : 23328 time to create 1 rle with old method : 0.02752065658569336 length of segment : 186 DEBUG bbox = [1830, 2034, 1896, 2172] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00020813941955566406 nb_pixel_total : 4356 time to create 1 rle with old method : 0.007764101028442383 length of segment : 56 DEBUG bbox = [114, 1230, 318, 1380] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0007493495941162109 nb_pixel_total : 21885 time to create 1 rle with old method : 0.036066293716430664 length of segment : 201 DEBUG bbox = [984, 3078, 1176, 3258] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0004448890686035156 nb_pixel_total : 25392 time to create 1 rle with old method : 0.02891993522644043 length of segment : 190 DEBUG bbox = [534, 1560, 972, 1866] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0012853145599365234 nb_pixel_total : 78961 time to create 1 rle with old method : 0.08885359764099121 length of segment : 416 DEBUG bbox = [1188, 2340, 1410, 2538] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005578994750976562 nb_pixel_total : 22207 time to create 1 rle with old method : 0.025475263595581055 length of segment : 207 DEBUG bbox = [270, 3090, 492, 3204] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0003445148468017578 nb_pixel_total : 16403 time to create 1 rle with old method : 0.019639968872070312 length of segment : 220 DEBUG bbox = [1290, 1182, 1494, 1374] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.000396728515625 nb_pixel_total : 19229 time to create 1 rle with old method : 0.02171778678894043 length of segment : 201 DEBUG bbox = [300, 2634, 498, 2874] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0009164810180664062 nb_pixel_total : 25052 time to create 1 rle with old method : 0.03192019462585449 length of segment : 190 DEBUG bbox = [1266, 1572, 1560, 1896] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0015611648559570312 nb_pixel_total : 45387 time to create 1 rle with old method : 0.05216193199157715 length of segment : 271 DEBUG bbox = [1914, 2286, 2100, 2424] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0006604194641113281 nb_pixel_total : 16484 time to create 1 rle with old method : 0.018985748291015625 length of segment : 202 DEBUG bbox = [1140, 1776, 1332, 2004] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0006730556488037109 nb_pixel_total : 14036 time to create 1 rle with old method : 0.016664743423461914 length of segment : 164 DEBUG bbox = [942, 2904, 1044, 3000] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00031113624572753906 nb_pixel_total : 6565 time to create 1 rle with old method : 0.007642269134521484 length of segment : 93 DEBUG bbox = [1296, 1902, 1512, 2070] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005736351013183594 nb_pixel_total : 24102 time to create 1 rle with old method : 0.027274370193481445 length of segment : 206 DEBUG bbox = [702, 2940, 1044, 3180] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0013535022735595703 nb_pixel_total : 43695 time to create 1 rle with old method : 0.0485532283782959 length of segment : 345 DEBUG bbox = [1614, 552, 1770, 708] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005509853363037109 nb_pixel_total : 16693 time to create 1 rle with old method : 0.01848292350769043 length of segment : 138 DEBUG bbox = [150, 1266, 408, 1590] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00145721435546875 nb_pixel_total : 44758 time to create 1 rle with old method : 0.05033087730407715 length of segment : 237 DEBUG bbox = [696, 3144, 966, 3324] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.000843048095703125 nb_pixel_total : 24831 time to create 1 rle with old method : 0.027759075164794922 length of segment : 268 DEBUG bbox = [1440, 2226, 1794, 2610] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0017418861389160156 nb_pixel_total : 54124 time to create 1 rle with old method : 0.06488800048828125 length of segment : 387 DEBUG bbox = [444, 2910, 810, 3240] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0015442371368408203 nb_pixel_total : 69881 time to create 1 rle with old method : 0.0770878791809082 length of segment : 348 DEBUG bbox = [138, 462, 420, 792] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0014574527740478516 nb_pixel_total : 59793 time to create 1 rle with old method : 0.06779599189758301 length of segment : 266 DEBUG bbox = [1320, 906, 1434, 1020] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0003635883331298828 nb_pixel_total : 8431 time to create 1 rle with old method : 0.010222434997558594 length of segment : 102 DEBUG bbox = [456, 1350, 618, 1710] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0010068416595458984 nb_pixel_total : 34757 time to create 1 rle with old method : 0.04024958610534668 length of segment : 123 DEBUG bbox = [1044, 1044, 1290, 1266] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.001018524169921875 nb_pixel_total : 23832 time to create 1 rle with old method : 0.027587175369262695 length of segment : 214 DEBUG bbox = [1266, 2106, 1470, 2334] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0008950233459472656 nb_pixel_total : 19993 time to create 1 rle with old method : 0.02527642250061035 length of segment : 144 DEBUG bbox = [678, 2172, 924, 2340] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0009181499481201172 nb_pixel_total : 26028 time to create 1 rle with old method : 0.030523300170898438 length of segment : 244 DEBUG bbox = [48, 2592, 582, 2946] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0032472610473632812 nb_pixel_total : 130175 time to create 1 rle with old method : 0.15619397163391113 length of segment : 511 DEBUG bbox = [1764, 1584, 1950, 1752] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0006890296936035156 nb_pixel_total : 23170 time to create 1 rle with old method : 0.026274919509887695 length of segment : 216 DEBUG bbox = [258, 372, 798, 666] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.002643585205078125 nb_pixel_total : 89179 time to create 1 rle with old method : 0.10191750526428223 length of segment : 509 DEBUG bbox = [1308, 1080, 1524, 1314] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0009799003601074219 nb_pixel_total : 42209 time to create 1 rle with old method : 0.04721546173095703 length of segment : 206 DEBUG bbox = [1026, 1824, 1326, 2052] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0013620853424072266 nb_pixel_total : 37996 time to create 1 rle with old method : 0.043732643127441406 length of segment : 261 DEBUG bbox = [1008, 2646, 1284, 3078] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.002021312713623047 nb_pixel_total : 73917 time to create 1 rle with old method : 0.0842132568359375 length of segment : 275 DEBUG bbox = [1326, 2460, 1620, 2670] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0011343955993652344 nb_pixel_total : 38114 time to create 1 rle with old method : 0.04536795616149902 length of segment : 286 DEBUG bbox = [354, 2532, 576, 2766] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0008292198181152344 nb_pixel_total : 25749 time to create 1 rle with old method : 0.02969074249267578 length of segment : 207 DEBUG bbox = [1074, 1686, 1278, 1806] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0006263256072998047 nb_pixel_total : 15730 time to create 1 rle with old method : 0.026297807693481445 length of segment : 181 DEBUG bbox = [984, 1794, 1176, 1950] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005249977111816406 nb_pixel_total : 14132 time to create 1 rle with old method : 0.026106595993041992 length of segment : 164 DEBUG bbox = [60, 3096, 258, 3288] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0006647109985351562 nb_pixel_total : 26756 time to create 1 rle with old method : 0.0343019962310791 length of segment : 168 DEBUG bbox = [216, 1452, 504, 1842] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0018694400787353516 nb_pixel_total : 90416 time to create 1 rle with old method : 0.10884428024291992 length of segment : 280 DEBUG bbox = [1512, 1008, 1704, 1218] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0006754398345947266 nb_pixel_total : 23707 time to create 1 rle with old method : 0.026759624481201172 length of segment : 167 DEBUG bbox = [270, 1926, 498, 2184] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0008046627044677734 nb_pixel_total : 40300 time to create 1 rle with old method : 0.049687862396240234 length of segment : 216 DEBUG bbox = [1254, 1806, 1428, 2034] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0006377696990966797 nb_pixel_total : 26732 time to create 1 rle with old method : 0.030350923538208008 length of segment : 161 DEBUG bbox = [1356, 1278, 1536, 1500] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0006535053253173828 nb_pixel_total : 24099 time to create 1 rle with old method : 0.027293682098388672 length of segment : 179 DEBUG bbox = [804, 1020, 1038, 1146] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0007011890411376953 nb_pixel_total : 21300 time to create 1 rle with old method : 0.02387857437133789 length of segment : 207 DEBUG bbox = [306, 888, 408, 1068] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00035858154296875 nb_pixel_total : 11021 time to create 1 rle with old method : 0.012875080108642578 length of segment : 91 DEBUG bbox = [24, 2250, 150, 2496] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005438327789306641 nb_pixel_total : 18704 time to create 1 rle with old method : 0.021559953689575195 length of segment : 111 DEBUG bbox = [834, 1374, 1146, 1740] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0015964508056640625 nb_pixel_total : 60610 time to create 1 rle with old method : 0.06856393814086914 length of segment : 353 DEBUG bbox = [1404, 1092, 1668, 1602] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.002075672149658203 nb_pixel_total : 63967 time to create 1 rle with old method : 0.07024431228637695 length of segment : 211 DEBUG bbox = [492, 2688, 792, 2934] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.001383066177368164 nb_pixel_total : 41621 time to create 1 rle with old method : 0.047243595123291016 length of segment : 283 DEBUG bbox = [36, 3000, 264, 3228] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0008549690246582031 nb_pixel_total : 13547 time to create 1 rle with old method : 0.016707420349121094 length of segment : 317 DEBUG bbox = [1626, 1350, 1854, 1578] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0008542537689208984 nb_pixel_total : 33906 time to create 1 rle with old method : 0.0395352840423584 length of segment : 229 DEBUG bbox = [696, 330, 828, 462] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.000514984130859375 nb_pixel_total : 9508 time to create 1 rle with old method : 0.011160135269165039 length of segment : 120 DEBUG bbox = [198, 1110, 414, 1362] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0009839534759521484 nb_pixel_total : 17486 time to create 1 rle with old method : 0.023650646209716797 length of segment : 398 DEBUG bbox = [198, 1584, 384, 1698] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005471706390380859 nb_pixel_total : 10310 time to create 1 rle with old method : 0.012137413024902344 length of segment : 174 DEBUG bbox = [1824, 834, 2034, 1020] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0007584095001220703 nb_pixel_total : 28132 time to create 1 rle with old method : 0.03329944610595703 length of segment : 204 DEBUG bbox = [1602, 2106, 1704, 2280] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0003581047058105469 nb_pixel_total : 8919 time to create 1 rle with old method : 0.01064753532409668 length of segment : 84 DEBUG bbox = [6, 702, 90, 804] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00023984909057617188 nb_pixel_total : 6630 time to create 1 rle with old method : 0.008092880249023438 length of segment : 81 DEBUG bbox = [1338, 1728, 1554, 1926] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005784034729003906 nb_pixel_total : 29014 time to create 1 rle with old method : 0.03300046920776367 length of segment : 206 DEBUG bbox = [1230, 702, 1686, 1182] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0026307106018066406 nb_pixel_total : 114920 time to create 1 rle with old method : 0.12892842292785645 length of segment : 420 time spent for convertir_results : 7.28837776184082 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 90 chid ids of type : 3594 Number RLEs to save : 21282 save missing photos in datou_result : time spend for datou_step_exec : 60.99061465263367 time spend to save output : 1.3883166313171387 total time spend for step 1 : 62.378931283950806 step2:crop_condition Wed Dec 3 14:21:31 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 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 : 10 ! batch 1 Loaded 90 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 ! 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/1764768105_4094707 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641233_b47c7c5b4d509caf9ada869f192af1a5_rle_crop_4056922109_0.png', 0, 433, 446, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641233_b47c7c5b4d509caf9ada869f192af1a5_rle_crop_4056922110_0.png', 0, 667, 517, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641233_b47c7c5b4d509caf9ada869f192af1a5_rle_crop_4056922112_0.png', 0, 388, 348, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641233_b47c7c5b4d509caf9ada869f192af1a5_rle_crop_4056922113_0.png', 0, 273, 286, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641233_b47c7c5b4d509caf9ada869f192af1a5_rle_crop_4056922115_0.png', 0, 271, 199, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922116_0.png', 0, 160, 156, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922118_0.png', 0, 228, 157, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922120_0.png', 0, 204, 223, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922121_0.png', 0, 213, 156, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922122_0.png', 0, 134, 303, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922123_0.png', 0, 234, 227, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922126_0.png', 0, 90, 163, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922129_0.png', 0, 160, 131, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922132_0.png', 0, 127, 170, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922133_0.png', 0, 103, 180, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922134_0.png', 0, 299, 233, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922135_0.png', 0, 123, 79, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922136_0.png', 0, 151, 202, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922137_0.png', 0, 212, 415, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922139_0.png', 0, 352, 466, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922140_0.png', 0, 124, 169, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922141_0.png', 0, 190, 178, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922142_0.png', 0, 113, 53, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641206_9605defaf9f6f0b18c417eb1c34a4acc_rle_crop_4056922143_0.png', 0, 150, 201, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641206_9605defaf9f6f0b18c417eb1c34a4acc_rle_crop_4056922146_0.png', 0, 170, 206, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641206_9605defaf9f6f0b18c417eb1c34a4acc_rle_crop_4056922148_0.png', 0, 170, 201, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641205_53649e82ce81c0bfd4470568e925d5c6_rle_crop_4056922150_0.png', 0, 287, 271, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641205_53649e82ce81c0bfd4470568e925d5c6_rle_crop_4056922151_0.png', 0, 135, 184, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641205_53649e82ce81c0bfd4470568e925d5c6_rle_crop_4056922152_0.png', 0, 214, 163, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641205_53649e82ce81c0bfd4470568e925d5c6_rle_crop_4056922154_0.png', 0, 156, 202, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641205_53649e82ce81c0bfd4470568e925d5c6_rle_crop_4056922156_0.png', 0, 154, 130, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641203_e7bb5330f4a6e2e0fd816dbce052e908_rle_crop_4056922159_0.png', 0, 296, 319, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641203_e7bb5330f4a6e2e0fd816dbce052e908_rle_crop_4056922160_0.png', 0, 302, 322, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641203_e7bb5330f4a6e2e0fd816dbce052e908_rle_crop_4056922161_0.png', 0, 325, 263, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922163_0.png', 0, 332, 121, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922165_0.png', 0, 198, 143, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922166_0.png', 0, 162, 244, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922167_0.png', 0, 351, 487, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922168_0.png', 0, 165, 174, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922169_0.png', 0, 284, 509, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922170_0.png', 0, 231, 199, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922171_0.png', 0, 211, 256, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922172_0.png', 0, 412, 275, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922173_0.png', 0, 206, 285, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641185_941b5e06621ade61fb8b9b333731c08f_rle_crop_4056922175_0.png', 0, 96, 181, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641185_941b5e06621ade61fb8b9b333731c08f_rle_crop_4056922178_0.png', 0, 389, 274, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641185_941b5e06621ade61fb8b9b333731c08f_rle_crop_4056922179_0.png', 0, 172, 167, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641185_941b5e06621ade61fb8b9b333731c08f_rle_crop_4056922180_0.png', 0, 253, 215, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641185_941b5e06621ade61fb8b9b333731c08f_rle_crop_4056922181_0.png', 0, 222, 161, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641185_941b5e06621ade61fb8b9b333731c08f_rle_crop_4056922182_0.png', 0, 220, 172, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba_rle_crop_4056922183_0.png', 0, 126, 207, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba_rle_crop_4056922184_0.png', 0, 174, 90, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba_rle_crop_4056922186_0.png', 0, 336, 287, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba_rle_crop_4056922188_0.png', 0, 226, 283, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba_rle_crop_4056922190_0.png', 0, 221, 207, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba_rle_crop_4056922192_0.png', 0, 248, 181, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641180_543010c4eac0fa3949afe3b367642182_rle_crop_4056922193_0.png', 0, 102, 174, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641180_543010c4eac0fa3949afe3b367642182_rle_crop_4056922194_0.png', 0, 184, 202, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641180_543010c4eac0fa3949afe3b367642182_rle_crop_4056922195_0.png', 0, 163, 84, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641180_543010c4eac0fa3949afe3b367642182_rle_crop_4056922196_0.png', 0, 101, 81, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768117), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641180_543010c4eac0fa3949afe3b367642182_rle_crop_4056922198_0.png', 0, 447, 378, 0, 1764768117,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 61 photos in the portfolio 3736932 time of upload the photos Elapsed time : 14.691051006317139 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 ! map_result returned by crop_photo_return_map_crop : length : 16 About to insert : list_path_to_insert length 16 new photo from crops ! About to upload 16 photos upload in portfolio : 3736932 init cache_photo without model_param we have 16 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1764768125_4094707 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768127), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641233_b47c7c5b4d509caf9ada869f192af1a5_rle_crop_4056922114_0.png', 0, 298, 314, 0, 1764768127,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768127), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922117_0.png', 0, 131, 91, 0, 1764768127,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768127), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922119_0.png', 0, 166, 370, 0, 1764768127,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768127), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922128_0.png', 0, 178, 108, 0, 1764768127,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768127), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922130_0.png', 0, 212, 223, 0, 1764768127,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768127), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922131_0.png', 0, 375, 235, 0, 1764768127,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768127), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922138_0.png', 0, 301, 355, 0, 1764768127,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768127), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641206_9605defaf9f6f0b18c417eb1c34a4acc_rle_crop_4056922147_0.png', 0, 103, 218, 0, 1764768127,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768127), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641205_53649e82ce81c0bfd4470568e925d5c6_rle_crop_4056922149_0.png', 0, 231, 170, 0, 1764768127,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768127), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641205_53649e82ce81c0bfd4470568e925d5c6_rle_crop_4056922155_0.png', 0, 213, 337, 0, 1764768127,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768127), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641203_e7bb5330f4a6e2e0fd816dbce052e908_rle_crop_4056922158_0.png', 0, 165, 259, 0, 1764768127,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768127), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922164_0.png', 0, 182, 214, 0, 1764768127,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768127), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641185_941b5e06621ade61fb8b9b333731c08f_rle_crop_4056922174_0.png', 0, 204, 207, 0, 1764768127,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768127), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641185_941b5e06621ade61fb8b9b333731c08f_rle_crop_4056922176_0.png', 0, 144, 164, 0, 1764768127,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768127), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba_rle_crop_4056922185_0.png', 0, 222, 111, 0, 1764768127,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768127), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba_rle_crop_4056922189_0.png', 0, 194, 225, 0, 1764768127,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 16 photos in the portfolio 3736932 time of upload the photos Elapsed time : 4.057839632034302 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 3 new photo from crops ! About to upload 3 photos upload in portfolio : 3736932 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1764768130_4094707 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768130), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641206_9605defaf9f6f0b18c417eb1c34a4acc_rle_crop_4056922144_0.png', 0, 170, 190, 0, 1764768130,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768130), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba_rle_crop_4056922191_0.png', 0, 108, 120, 0, 1764768130,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768130), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641180_543010c4eac0fa3949afe3b367642182_rle_crop_4056922197_0.png', 0, 172, 206, 0, 1764768130,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 3 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.9816129207611084 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 ! 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/1764768134_4094707 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768135), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922125_0.png', 0, 488, 336, 0, 1764768135,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768135), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641206_9605defaf9f6f0b18c417eb1c34a4acc_rle_crop_4056922145_0.png', 0, 281, 407, 0, 1764768135,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768135), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641205_53649e82ce81c0bfd4470568e925d5c6_rle_crop_4056922157_0.png', 0, 292, 237, 0, 1764768135,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768135), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641185_941b5e06621ade61fb8b9b333731c08f_rle_crop_4056922177_0.png', 0, 181, 164, 0, 1764768135,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768135), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba_rle_crop_4056922187_0.png', 0, 454, 201, 0, 1764768135,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 5 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.4675266742706299 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/1764768138_4094707 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768139), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641233_b47c7c5b4d509caf9ada869f192af1a5_rle_crop_4056922111_0.png', 0, 167, 86, 0, 1764768139,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768139), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922124_0.png', 0, 118, 111, 0, 1764768139,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768139), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922127_0.png', 0, 175, 157, 0, 1764768139,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768139), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641205_53649e82ce81c0bfd4470568e925d5c6_rle_crop_4056922153_0.png', 0, 83, 93, 0, 1764768139,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1764768139), 0.0, 0.0, 14, '', 0, 0, '1764768027_4094707_1397641203_e7bb5330f4a6e2e0fd816dbce052e908_rle_crop_4056922162_0.png', 0, 110, 97, 0, 1764768139,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 5 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.4149339199066162 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 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 Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1397641233, 1397641232, 1397641231, 1397641206, 1397641205, 1397641203, 1397641186, 1397641185, 1397641184, 1397641180] Looping around the photos to save general results len do output : 90 /1397651020Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651021Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651022Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651023Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651024Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651025Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651026Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651027Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651028Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651029Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651030Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651031Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651032Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651033Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651034Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651035Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651036Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651037Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651038Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651039Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651040Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651041Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651042Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651043Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651044Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651045Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651046Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651047Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651048Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651049Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651050Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651051Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651052Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651053Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651054Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651055Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651056Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651057Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651058Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651059Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651060Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651061Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651062Didn't retrieve data .Didn't retrieve data .Didn't 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retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651107Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651108Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651109Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651111Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651112Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651113Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651114Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1397651115Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641233', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641232', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641231', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641206', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641205', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641203', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641186', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641185', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641184', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641180', None, None, None, None, None, '4179941') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 280 time used for this insertion : 0.04425811767578125 save_final save missing photos in datou_result : time spend for datou_step_exec : 47.89984703063965 time spend to save output : 0.04667305946350098 total time spend for step 2 : 47.94652009010315 step3:rle_unique_nms_with_priority Wed Dec 3 14:22:19 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 90 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 7 nb_hashtags : 3 time to prepare the origin masks : 2.8373725414276123 time for calcul the mask position with numpy : 0.3891932964324951 nb_pixel_total : 7689023 time to create 1 rle with new method : 1.0586400032043457 time for calcul the mask position with numpy : 0.024930477142333984 nb_pixel_total : 33599 time to create 1 rle with old method : 0.04394793510437012 time for calcul the mask position with numpy : 0.026383638381958008 nb_pixel_total : 56240 time to create 1 rle with old method : 0.07529211044311523 time for calcul the mask position with numpy : 0.0249173641204834 nb_pixel_total : 51622 time to create 1 rle with old method : 0.05686211585998535 time for calcul the mask position with numpy : 0.02501654624938965 nb_pixel_total : 82141 time to create 1 rle with old method : 0.08981204032897949 time for calcul the mask position with numpy : 0.025364398956298828 nb_pixel_total : 12189 time to create 1 rle with old method : 0.013636589050292969 time for calcul the mask position with numpy : 0.02668619155883789 nb_pixel_total : 249344 time to create 1 rle with new method : 1.257199764251709 time for calcul the mask position with numpy : 0.03482389450073242 nb_pixel_total : 120242 time to create 1 rle with old method : 0.1350536346435547 create new chi : 3.3773610591888428 time to delete rle : 0.026549339294433594 batch 1 Loaded 15 chid ids of type : 3594 +++++++++Number RLEs to save : 7382 TO DO : save crop sub photo not yet done ! save time : 0.5344386100769043 nb_obj : 16 nb_hashtags : 4 time to prepare the origin masks : 5.565772533416748 time for calcul the mask position with numpy : 0.6313354969024658 nb_pixel_total : 7791388 time to create 1 rle with new method : 0.9101426601409912 time for calcul the mask position with numpy : 0.026190519332885742 nb_pixel_total : 50994 time to create 1 rle with old method : 0.05878472328186035 time for calcul the mask position with numpy : 0.030380725860595703 nb_pixel_total : 21544 time to create 1 rle with old method : 0.029611587524414062 time for calcul the mask position with numpy : 0.03171586990356445 nb_pixel_total : 16081 time to create 1 rle with old method : 0.021759033203125 time for calcul the mask position with numpy : 0.026540040969848633 nb_pixel_total : 15757 time to create 1 rle with old method : 0.017058610916137695 time for calcul the mask position with numpy : 0.025260448455810547 nb_pixel_total : 18500 time to create 1 rle with old method : 0.019941091537475586 time for calcul the mask position with numpy : 0.02764415740966797 nb_pixel_total : 10081 time to create 1 rle with old method : 0.011462926864624023 time for calcul the mask position with numpy : 0.02578282356262207 nb_pixel_total : 145754 time to create 1 rle with old method : 0.17984676361083984 time for calcul the mask position with numpy : 0.025119304656982422 nb_pixel_total : 10962 time to create 1 rle with old method : 0.011886358261108398 time for calcul the mask position with numpy : 0.026484966278076172 nb_pixel_total : 35873 time to create 1 rle with old method : 0.03920793533325195 time for calcul the mask position with numpy : 0.025700092315673828 nb_pixel_total : 20234 time to create 1 rle with old method : 0.022075653076171875 time for calcul the mask position with numpy : 0.025471210479736328 nb_pixel_total : 26310 time to create 1 rle with old method : 0.02895331382751465 time for calcul the mask position with numpy : 0.024985551834106445 nb_pixel_total : 34680 time to create 1 rle with old method : 0.03885841369628906 time for calcul the mask position with numpy : 0.026972293853759766 nb_pixel_total : 42206 time to create 1 rle with old method : 0.04933524131774902 time for calcul the mask position with numpy : 0.02530193328857422 nb_pixel_total : 28455 time to create 1 rle with old method : 0.03122425079345703 time for calcul the mask position with numpy : 0.025214672088623047 nb_pixel_total : 8173 time to create 1 rle with old method : 0.00911855697631836 time for calcul the mask position with numpy : 0.026187419891357422 nb_pixel_total : 17408 time to create 1 rle with old method : 0.01914072036743164 create new chi : 2.6032772064208984 time to delete rle : 0.0010693073272705078 batch 1 Loaded 33 chid ids of type : 3594 +++++++++++++++++Number RLEs to save : 8462 TO DO : save crop sub photo not yet done ! save time : 0.5791378021240234 nb_obj : 11 nb_hashtags : 2 time to prepare the origin masks : 4.351325511932373 time for calcul the mask position with numpy : 0.6014144420623779 nb_pixel_total : 7931730 time to create 1 rle with new method : 0.8522889614105225 time for calcul the mask position with numpy : 0.03997182846069336 nb_pixel_total : 4356 time to create 1 rle with old method : 0.004700899124145508 time for calcul the mask position with numpy : 0.04086804389953613 nb_pixel_total : 23328 time to create 1 rle with old method : 0.024860620498657227 time for calcul the mask position with numpy : 0.040245771408081055 nb_pixel_total : 17626 time to create 1 rle with old method : 0.018786907196044922 time for calcul the mask position with numpy : 0.040476083755493164 nb_pixel_total : 89122 time to create 1 rle with old method : 0.09687352180480957 time for calcul the mask position with numpy : 0.04208731651306152 nb_pixel_total : 81111 time to create 1 rle with old method : 0.08982110023498535 time for calcul the mask position with numpy : 0.026019811630249023 nb_pixel_total : 56017 time to create 1 rle with old method : 0.0614013671875 time for calcul the mask position with numpy : 0.02563953399658203 nb_pixel_total : 19626 time to create 1 rle with old method : 0.021122217178344727 time for calcul the mask position with numpy : 0.025455951690673828 nb_pixel_total : 7308 time to create 1 rle with old method : 0.007733345031738281 time for calcul the mask position with numpy : 0.030462026596069336 nb_pixel_total : 30475 time to create 1 rle with old method : 0.03234410285949707 time for calcul the mask position with numpy : 0.0401761531829834 nb_pixel_total : 16783 time to create 1 rle with old method : 0.017754077911376953 time for calcul the mask position with numpy : 0.03864908218383789 nb_pixel_total : 16918 time to create 1 rle with old method : 0.018726348876953125 create new chi : 2.2827048301696777 time to delete rle : 0.0016069412231445312 batch 1 Loaded 23 chid ids of type : 3594 +++++++++++Number RLEs to save : 7483 TO DO : save crop sub photo not yet done ! save time : 0.5283534526824951 nb_obj : 6 nb_hashtags : 4 time to prepare the origin masks : 2.9145493507385254 time for calcul the mask position with numpy : 0.6402018070220947 nb_pixel_total : 8110323 time to create 1 rle with new method : 0.8085875511169434 time for calcul the mask position with numpy : 0.03571820259094238 nb_pixel_total : 19229 time to create 1 rle with old method : 0.023701190948486328 time for calcul the mask position with numpy : 0.02618694305419922 nb_pixel_total : 16403 time to create 1 rle with old method : 0.018363475799560547 time for calcul the mask position with numpy : 0.025809288024902344 nb_pixel_total : 22207 time to create 1 rle with old method : 0.025530099868774414 time for calcul the mask position with numpy : 0.025537729263305664 nb_pixel_total : 78961 time to create 1 rle with old method : 0.08886528015136719 time for calcul the mask position with numpy : 0.02582097053527832 nb_pixel_total : 25392 time to create 1 rle with old method : 0.02892279624938965 time for calcul the mask position with numpy : 0.027902841567993164 nb_pixel_total : 21885 time to create 1 rle with old method : 0.024901390075683594 create new chi : 1.87078857421875 time to delete rle : 0.0006864070892333984 batch 1 Loaded 13 chid ids of type : 3594 ++++++++Number RLEs to save : 5030 TO DO : save crop sub photo not yet done ! save time : 0.3609273433685303 nb_obj : 9 nb_hashtags : 4 time to prepare the origin masks : 4.148651123046875 time for calcul the mask position with numpy : 0.7429251670837402 nb_pixel_total : 8057628 time to create 1 rle with new method : 0.7928798198699951 time for calcul the mask position with numpy : 0.023698091506958008 nb_pixel_total : 44758 time to create 1 rle with old method : 0.04886960983276367 time for calcul the mask position with numpy : 0.03851604461669922 nb_pixel_total : 16693 time to create 1 rle with old method : 0.018163442611694336 time for calcul the mask position with numpy : 0.04010939598083496 nb_pixel_total : 43695 time to create 1 rle with old method : 0.04717135429382324 time for calcul the mask position with numpy : 0.03644561767578125 nb_pixel_total : 24102 time to create 1 rle with old method : 0.025421857833862305 time for calcul the mask position with numpy : 0.036309003829956055 nb_pixel_total : 6565 time to create 1 rle with old method : 0.00708317756652832 time for calcul the mask position with numpy : 0.0242159366607666 nb_pixel_total : 14036 time to create 1 rle with old method : 0.015972614288330078 time for calcul the mask position with numpy : 0.0254669189453125 nb_pixel_total : 16484 time to create 1 rle with old method : 0.018225431442260742 time for calcul the mask position with numpy : 0.02514791488647461 nb_pixel_total : 45387 time to create 1 rle with old method : 0.04868030548095703 time for calcul the mask position with numpy : 0.024614810943603516 nb_pixel_total : 25052 time to create 1 rle with old method : 0.027703046798706055 create new chi : 2.1092000007629395 time to delete rle : 0.0009243488311767578 batch 1 Loaded 19 chid ids of type : 3594 +++++++++++++Number RLEs to save : 5852 TO DO : save crop sub photo not yet done ! save time : 0.44025731086730957 nb_obj : 5 nb_hashtags : 3 time to prepare the origin masks : 2.045304298400879 time for calcul the mask position with numpy : 0.49567365646362305 nb_pixel_total : 8077340 time to create 1 rle with new method : 0.6599948406219482 time for calcul the mask position with numpy : 0.02612161636352539 nb_pixel_total : 8431 time to create 1 rle with old method : 0.009626150131225586 time for calcul the mask position with numpy : 0.0258181095123291 nb_pixel_total : 59793 time to create 1 rle with old method : 0.06700825691223145 time for calcul the mask position with numpy : 0.02592301368713379 nb_pixel_total : 69881 time to create 1 rle with old method : 0.07734441757202148 time for calcul the mask position with numpy : 0.02444314956665039 nb_pixel_total : 54124 time to create 1 rle with old method : 0.061374664306640625 time for calcul the mask position with numpy : 0.02446913719177246 nb_pixel_total : 24831 time to create 1 rle with old method : 0.027606725692749023 create new chi : 1.5735867023468018 time to delete rle : 0.0007221698760986328 batch 1 Loaded 11 chid ids of type : 3594 +++++Number RLEs to save : 4902 TO DO : save crop sub photo not yet done ! save time : 0.35167407989501953 nb_obj : 11 nb_hashtags : 2 time to prepare the origin masks : 4.22076940536499 time for calcul the mask position with numpy : 0.4622220993041992 nb_pixel_total : 7755030 time to create 1 rle with new method : 0.8307645320892334 time for calcul the mask position with numpy : 0.024120569229125977 nb_pixel_total : 38114 time to create 1 rle with old method : 0.040814876556396484 time for calcul the mask position with numpy : 0.02405381202697754 nb_pixel_total : 73917 time to create 1 rle with old method : 0.07804298400878906 time for calcul the mask position with numpy : 0.02387094497680664 nb_pixel_total : 37996 time to create 1 rle with old method : 0.040584564208984375 time for calcul the mask position with numpy : 0.024436473846435547 nb_pixel_total : 42209 time to create 1 rle with old method : 0.04542350769042969 time for calcul the mask position with numpy : 0.025664806365966797 nb_pixel_total : 89179 time to create 1 rle with old method : 0.12157821655273438 time for calcul the mask position with numpy : 0.026009559631347656 nb_pixel_total : 23170 time to create 1 rle with old method : 0.025209903717041016 time for calcul the mask position with numpy : 0.025736570358276367 nb_pixel_total : 130175 time to create 1 rle with old method : 0.14240741729736328 time for calcul the mask position with numpy : 0.02729201316833496 nb_pixel_total : 26028 time to create 1 rle with old method : 0.028692007064819336 time for calcul the mask position with numpy : 0.02647852897644043 nb_pixel_total : 19993 time to create 1 rle with old method : 0.021969318389892578 time for calcul the mask position with numpy : 0.025251150131225586 nb_pixel_total : 23832 time to create 1 rle with old method : 0.026406288146972656 time for calcul the mask position with numpy : 0.039359092712402344 nb_pixel_total : 34757 time to create 1 rle with old method : 0.03845071792602539 create new chi : 2.2374684810638428 time to delete rle : 0.0013761520385742188 batch 1 Loaded 23 chid ids of type : 3594 +++++++++++++Number RLEs to save : 8138 TO DO : save crop sub photo not yet done ! save time : 0.5716285705566406 nb_obj : 9 nb_hashtags : 3 time to prepare the origin masks : 4.000112533569336 time for calcul the mask position with numpy : 0.5498409271240234 nb_pixel_total : 8006779 time to create 1 rle with new method : 0.8090548515319824 time for calcul the mask position with numpy : 0.025638580322265625 nb_pixel_total : 24099 time to create 1 rle with old method : 0.028529882431030273 time for calcul the mask position with numpy : 0.028790712356567383 nb_pixel_total : 26732 time to create 1 rle with old method : 0.035265445709228516 time for calcul the mask position with numpy : 0.030680179595947266 nb_pixel_total : 40300 time to create 1 rle with old method : 0.05694866180419922 time for calcul the mask position with numpy : 0.03213024139404297 nb_pixel_total : 23707 time to create 1 rle with old method : 0.03336501121520996 time for calcul the mask position with numpy : 0.04783201217651367 nb_pixel_total : 90416 time to create 1 rle with old method : 0.10724806785583496 time for calcul the mask position with numpy : 0.03821754455566406 nb_pixel_total : 26756 time to create 1 rle with old method : 0.02917766571044922 time for calcul the mask position with numpy : 0.02453756332397461 nb_pixel_total : 14132 time to create 1 rle with old method : 0.015095949172973633 time for calcul the mask position with numpy : 0.025281906127929688 nb_pixel_total : 15730 time to create 1 rle with old method : 0.016986370086669922 time for calcul the mask position with numpy : 0.025563716888427734 nb_pixel_total : 25749 time to create 1 rle with old method : 0.03169608116149902 create new chi : 2.036531925201416 time to delete rle : 0.0009109973907470703 batch 1 Loaded 19 chid ids of type : 3594 +++++++++Number RLEs to save : 5606 TO DO : save crop sub photo not yet done ! save time : 0.4033524990081787 nb_obj : 10 nb_hashtags : 4 time to prepare the origin masks : 4.058392524719238 time for calcul the mask position with numpy : 0.5731086730957031 nb_pixel_total : 8002730 time to create 1 rle with new method : 0.8911805152893066 time for calcul the mask position with numpy : 0.034336090087890625 nb_pixel_total : 17486 time to create 1 rle with old method : 0.027295827865600586 time for calcul the mask position with numpy : 0.04096484184265137 nb_pixel_total : 9508 time to create 1 rle with old method : 0.01070261001586914 time for calcul the mask position with numpy : 0.04186439514160156 nb_pixel_total : 33906 time to create 1 rle with old method : 0.03876137733459473 time for calcul the mask position with numpy : 0.04486703872680664 nb_pixel_total : 13547 time to create 1 rle with old method : 0.01853346824645996 time for calcul the mask position with numpy : 0.041289329528808594 nb_pixel_total : 41621 time to create 1 rle with old method : 0.046566009521484375 time for calcul the mask position with numpy : 0.042642831802368164 nb_pixel_total : 63967 time to create 1 rle with old method : 0.07519173622131348 time for calcul the mask position with numpy : 0.04074382781982422 nb_pixel_total : 60610 time to create 1 rle with old method : 0.06788158416748047 time for calcul the mask position with numpy : 0.03866839408874512 nb_pixel_total : 18704 time to create 1 rle with old method : 0.023865222930908203 time for calcul the mask position with numpy : 0.04102802276611328 nb_pixel_total : 11021 time to create 1 rle with old method : 0.013790369033813477 time for calcul the mask position with numpy : 0.04043126106262207 nb_pixel_total : 21300 time to create 1 rle with old method : 0.023617267608642578 create new chi : 2.2613611221313477 time to delete rle : 0.0012192726135253906 batch 1 Loaded 21 chid ids of type : 3594 ++++++++++++++Number RLEs to save : 6800 TO DO : save crop sub photo not yet done ! save time : 0.4947226047515869 nb_obj : 6 nb_hashtags : 2 time to prepare the origin masks : 3.1323304176330566 time for calcul the mask position with numpy : 0.6734387874603271 nb_pixel_total : 8096475 time to create 1 rle with new method : 0.9104769229888916 time for calcul the mask position with numpy : 0.024697303771972656 nb_pixel_total : 114920 time to create 1 rle with old method : 0.1561903953552246 time for calcul the mask position with numpy : 0.04151773452758789 nb_pixel_total : 29014 time to create 1 rle with old method : 0.033091068267822266 time for calcul the mask position with numpy : 0.044086456298828125 nb_pixel_total : 6630 time to create 1 rle with old method : 0.008011341094970703 time for calcul the mask position with numpy : 0.04147148132324219 nb_pixel_total : 8919 time to create 1 rle with old method : 0.010252714157104492 time for calcul the mask position with numpy : 0.034868478775024414 nb_pixel_total : 28132 time to create 1 rle with old method : 0.03675723075866699 time for calcul the mask position with numpy : 0.03850817680358887 nb_pixel_total : 10310 time to create 1 rle with old method : 0.017931699752807617 create new chi : 2.1135878562927246 time to delete rle : 0.00165557861328125 batch 1 Loaded 13 chid ids of type : 3594 ++++++Number RLEs to save : 4498 TO DO : save crop sub photo not yet done ! save time : 0.33510804176330566 map_output_result : {1397641233: (0.0, 'Should be the crop_list due to order', 0), 1397641232: (0.0, 'Should be the crop_list due to order', 0), 1397641231: (0.0, 'Should be the crop_list due to order', 0), 1397641206: (0.0, 'Should be the crop_list due to order', 0), 1397641205: (0.0, 'Should be the crop_list due to order', 0), 1397641203: (0.0, 'Should be the crop_list due to order', 0), 1397641186: (0.0, 'Should be the crop_list due to order', 0), 1397641185: (0.0, 'Should be the crop_list due to order', 0), 1397641184: (0.0, 'Should be the crop_list due to order', 0), 1397641180: (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 [1397641233, 1397641232, 1397641231, 1397641206, 1397641205, 1397641203, 1397641186, 1397641185, 1397641184, 1397641180] Looping around the photos to save general results len do output : 10 /1397641233.Didn't retrieve data . /1397641232.Didn't retrieve data . /1397641231.Didn't retrieve data . /1397641206.Didn't retrieve data . /1397641205.Didn't retrieve data . /1397641203.Didn't retrieve data . /1397641186.Didn't retrieve data . /1397641185.Didn't retrieve data . /1397641184.Didn't retrieve data . /1397641180.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, '4179941') ('3318', '29145272', '1397641233', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641232', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641231', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641206', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641205', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641203', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641186', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641185', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641184', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641180', None, None, None, None, None, '4179941') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 30 time used for this insertion : 0.01681351661682129 save_final save missing photos in datou_result : time spend for datou_step_exec : 65.52283835411072 time spend to save output : 0.017236948013305664 total time spend for step 3 : 65.54007530212402 step4:ventilate_hashtags_in_portfolio Wed Dec 3 14:23:25 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 29145272 get user id for portfolio 29145272 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`=29145272 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('background','carton','flou','metal','pet_fonce','mal_croppe','pehd','environnement','pet_clair','papier','autre')) 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`=29145272 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('background','carton','flou','metal','pet_fonce','mal_croppe','pehd','environnement','pet_clair','papier','autre')) 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`=29145272 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('background','carton','flou','metal','pet_fonce','mal_croppe','pehd','environnement','pet_clair','papier','autre')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://marlene.fotonower.com/velours/29146835,29146836,29146837,29146838,29146839,29146840,29146841,29146842,29146843,29146844,29146845?tags=background,carton,flou,metal,pet_fonce,mal_croppe,pehd,environnement,pet_clair,papier,autre Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1397641233, 1397641232, 1397641231, 1397641206, 1397641205, 1397641203, 1397641186, 1397641185, 1397641184, 1397641180] Looping around the photos to save general results len do output : 1 /29145272. 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, '4179941') ('3318', '29145272', '1397641233', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641232', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641231', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641206', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641205', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641203', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641186', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641185', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641184', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641180', None, None, None, None, None, '4179941') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 11 time used for this insertion : 0.02041149139404297 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.095923900604248 time spend to save output : 0.0207521915435791 total time spend for step 4 : 2.116676092147827 step5:final Wed Dec 3 14:23:27 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : {1397641233: ('0.04129959972993828',), 1397641232: ('0.04129959972993828',), 1397641231: ('0.04129959972993828',), 1397641206: ('0.04129959972993828',), 1397641205: ('0.04129959972993828',), 1397641203: ('0.04129959972993828',), 1397641186: ('0.04129959972993828',), 1397641185: ('0.04129959972993828',), 1397641184: ('0.04129959972993828',), 1397641180: ('0.04129959972993828',)} new output for save of step final : {1397641233: ('0.04129959972993828',), 1397641232: ('0.04129959972993828',), 1397641231: ('0.04129959972993828',), 1397641206: ('0.04129959972993828',), 1397641205: ('0.04129959972993828',), 1397641203: ('0.04129959972993828',), 1397641186: ('0.04129959972993828',), 1397641185: ('0.04129959972993828',), 1397641184: ('0.04129959972993828',), 1397641180: ('0.04129959972993828',)} [1397641233, 1397641232, 1397641231, 1397641206, 1397641205, 1397641203, 1397641186, 1397641185, 1397641184, 1397641180] Looping around the photos to save general results len do output : 10 /1397641233.Didn't retrieve data . /1397641232.Didn't retrieve data . /1397641231.Didn't retrieve data . /1397641206.Didn't retrieve data . /1397641205.Didn't retrieve data . /1397641203.Didn't retrieve data . /1397641186.Didn't retrieve data . /1397641185.Didn't retrieve data . /1397641184.Didn't retrieve data . /1397641180.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, '4179941') ('3318', '29145272', '1397641233', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641232', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641231', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641206', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641205', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641203', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641186', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641185', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641184', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641180', None, None, None, None, None, '4179941') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 30 time used for this insertion : 0.016702651977539062 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.17291259765625 time spend to save output : 0.017206907272338867 total time spend for step 5 : 0.19011950492858887 step6:blur_detection Wed Dec 3 14:23:27 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection methode: ratio et variance treat image : temp/1764768027_4094707_1397641233_b47c7c5b4d509caf9ada869f192af1a5.jpg resize: (2160, 3840) 1397641233 -6.996578496402057 treat image : temp/1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8.jpg resize: (2160, 3840) 1397641232 -6.978961017022594 treat image : temp/1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e.jpg resize: (2160, 3840) 1397641231 -7.1277741831115105 treat image : temp/1764768027_4094707_1397641206_9605defaf9f6f0b18c417eb1c34a4acc.jpg resize: (2160, 3840) 1397641206 -7.348368487149834 treat image : temp/1764768027_4094707_1397641205_53649e82ce81c0bfd4470568e925d5c6.jpg resize: (2160, 3840) 1397641205 -7.1880248943075795 treat image : temp/1764768027_4094707_1397641203_e7bb5330f4a6e2e0fd816dbce052e908.jpg resize: (2160, 3840) 1397641203 -7.177491902016461 treat image : temp/1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409.jpg resize: (2160, 3840) 1397641186 -7.040054115557637 treat image : temp/1764768027_4094707_1397641185_941b5e06621ade61fb8b9b333731c08f.jpg resize: (2160, 3840) 1397641185 -7.202899765488644 treat image : temp/1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba.jpg resize: (2160, 3840) 1397641184 -7.180094794334863 treat image : temp/1764768027_4094707_1397641180_543010c4eac0fa3949afe3b367642182.jpg resize: (2160, 3840) 1397641180 -4.7361886816765155 treat image : temp/1764768027_4094707_1397641233_b47c7c5b4d509caf9ada869f192af1a5_rle_crop_4056922109_0.png resize: (446, 433) 1397651020 -4.495879667009088 treat image : temp/1764768027_4094707_1397641233_b47c7c5b4d509caf9ada869f192af1a5_rle_crop_4056922110_0.png resize: (517, 667) 1397651021 -5.235110528578843 treat image : temp/1764768027_4094707_1397641233_b47c7c5b4d509caf9ada869f192af1a5_rle_crop_4056922112_0.png resize: (348, 388) 1397651022 -4.594859771846534 treat image : temp/1764768027_4094707_1397641233_b47c7c5b4d509caf9ada869f192af1a5_rle_crop_4056922113_0.png resize: (286, 273) 1397651023 -5.373463322605934 treat image : temp/1764768027_4094707_1397641233_b47c7c5b4d509caf9ada869f192af1a5_rle_crop_4056922115_0.png resize: (199, 271) 1397651024 -2.9096360761944147 treat image : temp/1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922116_0.png resize: (156, 160) 1397651025 -3.3167596739545697 treat image : temp/1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922118_0.png resize: (157, 228) 1397651026 -5.041666149244102 treat image : temp/1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922120_0.png resize: (223, 204) 1397651027 -3.801271822628186 treat image : temp/1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922121_0.png resize: (156, 213) 1397651028 -4.566327516227372 treat image : temp/1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922122_0.png resize: (303, 134) 1397651029 -3.895625722369484 treat image : temp/1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922123_0.png resize: (227, 234) 1397651030 -4.252089845539934 treat image : temp/1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922126_0.png resize: (163, 90) 1397651031 -1.6767856552583975 treat image : temp/1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922129_0.png resize: (131, 160) 1397651032 -1.6817466198379931 treat image : temp/1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922132_0.png resize: (170, 127) 1397651033 -4.991499763950521 treat image : temp/1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922133_0.png resize: (180, 103) 1397651034 -4.072391869646509 treat image : temp/1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922134_0.png resize: (233, 299) 1397651035 -3.470570466689257 treat image : temp/1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922135_0.png resize: (79, 123) 1397651036 -3.6953981580490347 treat image : temp/1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922136_0.png resize: (202, 151) 1397651037 -3.9858418689413835 treat image : temp/1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922137_0.png resize: (415, 212) 1397651038 -5.314016484897677 treat image : temp/1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922139_0.png resize: (466, 352) 1397651039 -4.906212229176056 treat image : temp/1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922140_0.png resize: (169, 124) 1397651040 -2.771738292005135 treat image : temp/1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922141_0.png resize: (178, 190) 1397651041 -4.2326920968155575 treat image : temp/1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922142_0.png resize: (53, 113) 1397651042 -3.7347816231713242 treat image : temp/1764768027_4094707_1397641206_9605defaf9f6f0b18c417eb1c34a4acc_rle_crop_4056922143_0.png resize: (201, 150) 1397651043 1.5055925926594684 treat image : temp/1764768027_4094707_1397641206_9605defaf9f6f0b18c417eb1c34a4acc_rle_crop_4056922146_0.png resize: (206, 170) 1397651044 -4.0950591500509494 treat image : temp/1764768027_4094707_1397641206_9605defaf9f6f0b18c417eb1c34a4acc_rle_crop_4056922148_0.png resize: (201, 170) 1397651045 -3.2840725252368483 treat image : temp/1764768027_4094707_1397641205_53649e82ce81c0bfd4470568e925d5c6_rle_crop_4056922150_0.png resize: (271, 287) 1397651046 -3.7830779866479274 treat image : temp/1764768027_4094707_1397641205_53649e82ce81c0bfd4470568e925d5c6_rle_crop_4056922151_0.png resize: (184, 135) 1397651047 -4.515815929408968 treat image : temp/1764768027_4094707_1397641205_53649e82ce81c0bfd4470568e925d5c6_rle_crop_4056922152_0.png resize: (163, 214) 1397651048 -3.5859675242131335 treat image : temp/1764768027_4094707_1397641205_53649e82ce81c0bfd4470568e925d5c6_rle_crop_4056922154_0.png resize: (202, 156) 1397651049 -4.575322560846387 treat image : temp/1764768027_4094707_1397641205_53649e82ce81c0bfd4470568e925d5c6_rle_crop_4056922156_0.png resize: (130, 154) 1397651050 -4.142969336893498 treat image : temp/1764768027_4094707_1397641203_e7bb5330f4a6e2e0fd816dbce052e908_rle_crop_4056922159_0.png resize: (319, 296) 1397651051 -5.124027364085385 treat image : temp/1764768027_4094707_1397641203_e7bb5330f4a6e2e0fd816dbce052e908_rle_crop_4056922160_0.png resize: (322, 302) 1397651052 -3.5086854976745023 treat image : temp/1764768027_4094707_1397641203_e7bb5330f4a6e2e0fd816dbce052e908_rle_crop_4056922161_0.png resize: (263, 325) 1397651053 -3.2805695264864823 treat image : temp/1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922163_0.png resize: (121, 332) 1397651054 -2.353372487271928 treat image : temp/1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922165_0.png resize: (143, 198) 1397651055 -3.6245356254601857 treat image : temp/1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922166_0.png resize: (244, 162) 1397651056 -3.9146387608546034 treat image : temp/1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922167_0.png resize: (487, 351) 1397651057 -3.7660258511663236 treat image : temp/1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922168_0.png resize: (174, 165) 1397651058 -4.185519458687155 treat image : temp/1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922169_0.png resize: (509, 284) 1397651059 -3.902977535468883 treat image : temp/1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922170_0.png resize: (199, 231) 1397651060 -3.900708893512898 treat image : temp/1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922171_0.png resize: (256, 211) 1397651061 -4.67701336635109 treat image : temp/1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922172_0.png resize: (275, 412) 1397651062 -4.417853624937736 treat image : temp/1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922173_0.png resize: (285, 206) 1397651063 -3.9456907866721984 treat image : temp/1764768027_4094707_1397641185_941b5e06621ade61fb8b9b333731c08f_rle_crop_4056922175_0.png resize: (181, 96) 1397651064 -3.2099379409651063 treat image : temp/1764768027_4094707_1397641185_941b5e06621ade61fb8b9b333731c08f_rle_crop_4056922178_0.png resize: (274, 389) 1397651065 -4.449578819407593 treat image : temp/1764768027_4094707_1397641185_941b5e06621ade61fb8b9b333731c08f_rle_crop_4056922179_0.png resize: (167, 172) 1397651066 -5.018378083124226 treat image : temp/1764768027_4094707_1397641185_941b5e06621ade61fb8b9b333731c08f_rle_crop_4056922180_0.png resize: (215, 253) 1397651067 -4.406980603089213 treat image : temp/1764768027_4094707_1397641185_941b5e06621ade61fb8b9b333731c08f_rle_crop_4056922181_0.png resize: (161, 222) 1397651068 -5.3758063553565005 treat image : temp/1764768027_4094707_1397641185_941b5e06621ade61fb8b9b333731c08f_rle_crop_4056922182_0.png resize: (172, 220) 1397651069 -4.373620147620658 treat image : temp/1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba_rle_crop_4056922183_0.png resize: (207, 126) 1397651070 -2.7845789892545847 treat image : temp/1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba_rle_crop_4056922184_0.png resize: (90, 174) 1397651071 -3.1935232279929933 treat image : temp/1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba_rle_crop_4056922186_0.png resize: (287, 336) 1397651072 -4.028794143466124 treat image : temp/1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba_rle_crop_4056922188_0.png resize: (283, 226) 1397651074 -4.270800508222561 treat image : temp/1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba_rle_crop_4056922190_0.png resize: (207, 221) 1397651075 -4.719877453918418 treat image : temp/1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba_rle_crop_4056922192_0.png resize: (181, 248) 1397651076 -2.0207233814290584 treat image : temp/1764768027_4094707_1397641180_543010c4eac0fa3949afe3b367642182_rle_crop_4056922193_0.png resize: (174, 102) 1397651077 -3.697605339846437 treat image : temp/1764768027_4094707_1397641180_543010c4eac0fa3949afe3b367642182_rle_crop_4056922194_0.png resize: (202, 184) 1397651078 -4.746570228754132 treat image : temp/1764768027_4094707_1397641180_543010c4eac0fa3949afe3b367642182_rle_crop_4056922195_0.png resize: (84, 163) 1397651079 -4.952696272677014 treat image : temp/1764768027_4094707_1397641180_543010c4eac0fa3949afe3b367642182_rle_crop_4056922196_0.png resize: (81, 101) 1397651080 -4.006325832072216 treat image : temp/1764768027_4094707_1397641180_543010c4eac0fa3949afe3b367642182_rle_crop_4056922198_0.png resize: (378, 447) 1397651081 -5.058024578237792 treat image : temp/1764768027_4094707_1397641233_b47c7c5b4d509caf9ada869f192af1a5_rle_crop_4056922114_0.png resize: (314, 298) 1397651084 -4.231251500915121 treat image : temp/1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922117_0.png resize: (91, 131) 1397651085 -3.0284393745843365 treat image : temp/1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922119_0.png resize: (370, 166) 1397651086 -3.9973846698663418 treat image : temp/1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922128_0.png resize: (108, 178) 1397651087 -3.1763297375789126 treat image : temp/1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922130_0.png resize: (223, 212) 1397651088 -2.1913750906898626 treat image : temp/1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922131_0.png resize: (235, 375) 1397651089 -4.28960839711081 treat image : temp/1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e_rle_crop_4056922138_0.png resize: (355, 301) 1397651090 -3.9131915897375182 treat image : temp/1764768027_4094707_1397641206_9605defaf9f6f0b18c417eb1c34a4acc_rle_crop_4056922147_0.png resize: (218, 103) 1397651091 -2.8217359899897123 treat image : temp/1764768027_4094707_1397641205_53649e82ce81c0bfd4470568e925d5c6_rle_crop_4056922149_0.png resize: (170, 231) 1397651092 -3.2339322705415374 treat image : temp/1764768027_4094707_1397641205_53649e82ce81c0bfd4470568e925d5c6_rle_crop_4056922155_0.png resize: (337, 213) 1397651093 -4.215455015556522 treat image : temp/1764768027_4094707_1397641203_e7bb5330f4a6e2e0fd816dbce052e908_rle_crop_4056922158_0.png resize: (259, 165) 1397651094 -4.17764801875662 treat image : temp/1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409_rle_crop_4056922164_0.png resize: (214, 182) 1397651095 -3.668537811287494 treat image : temp/1764768027_4094707_1397641185_941b5e06621ade61fb8b9b333731c08f_rle_crop_4056922174_0.png resize: (207, 204) 1397651096 -3.163643362522084 treat image : temp/1764768027_4094707_1397641185_941b5e06621ade61fb8b9b333731c08f_rle_crop_4056922176_0.png resize: (164, 144) 1397651097 -3.725728744579093 treat image : temp/1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba_rle_crop_4056922185_0.png resize: (111, 222) 1397651098 -2.1582600632418854 treat image : temp/1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba_rle_crop_4056922189_0.png resize: (225, 194) 1397651099 -3.067589679430922 treat image : temp/1764768027_4094707_1397641206_9605defaf9f6f0b18c417eb1c34a4acc_rle_crop_4056922144_0.png resize: (190, 170) 1397651101 -5.193755865412657 treat image : temp/1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba_rle_crop_4056922191_0.png resize: (120, 108) 1397651102 -4.555754631429962 treat image : temp/1764768027_4094707_1397641180_543010c4eac0fa3949afe3b367642182_rle_crop_4056922197_0.png resize: (206, 172) 1397651103 -4.534748136772048 treat image : temp/1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922125_0.png resize: (336, 488) 1397651105 -4.488557460133211 treat image : temp/1764768027_4094707_1397641206_9605defaf9f6f0b18c417eb1c34a4acc_rle_crop_4056922145_0.png resize: (407, 281) 1397651106 -5.121513001562954 treat image : temp/1764768027_4094707_1397641205_53649e82ce81c0bfd4470568e925d5c6_rle_crop_4056922157_0.png resize: (237, 292) 1397651107 -4.092514436787184 treat image : temp/1764768027_4094707_1397641185_941b5e06621ade61fb8b9b333731c08f_rle_crop_4056922177_0.png resize: (164, 181) 1397651108 -2.4774497560292272 treat image : temp/1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba_rle_crop_4056922187_0.png resize: (201, 454) 1397651109 -5.575025096006091 treat image : temp/1764768027_4094707_1397641233_b47c7c5b4d509caf9ada869f192af1a5_rle_crop_4056922111_0.png resize: (86, 167) 1397651111 -4.125862794346987 treat image : temp/1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922124_0.png resize: (111, 118) 1397651112 -3.9022656904604633 treat image : temp/1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8_rle_crop_4056922127_0.png resize: (157, 175) 1397651113 -2.07150733673393 treat image : temp/1764768027_4094707_1397641205_53649e82ce81c0bfd4470568e925d5c6_rle_crop_4056922153_0.png resize: (93, 83) 1397651114 -4.313642767087118 treat image : temp/1764768027_4094707_1397641203_e7bb5330f4a6e2e0fd816dbce052e908_rle_crop_4056922162_0.png resize: (97, 110) 1397651115 -3.102585286290836 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 : 100 time used for this insertion : 0.019021272659301758 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 100 time used for this insertion : 0.020357370376586914 save missing photos in datou_result : time spend for datou_step_exec : 38.844908237457275 time spend to save output : 0.04558992385864258 total time spend for step 6 : 38.89049816131592 step7:brightness Wed Dec 3 14:24:06 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/1764768027_4094707_1397641233_b47c7c5b4d509caf9ada869f192af1a5.jpg treat image : temp/1764768027_4094707_1397641232_4d747f9dc864849686c0366646c7b8d8.jpg treat image : temp/1764768027_4094707_1397641231_99d7907b56043b9ff444a793cd3a8b7e.jpg treat image : temp/1764768027_4094707_1397641206_9605defaf9f6f0b18c417eb1c34a4acc.jpg treat image : temp/1764768027_4094707_1397641205_53649e82ce81c0bfd4470568e925d5c6.jpg treat image : temp/1764768027_4094707_1397641203_e7bb5330f4a6e2e0fd816dbce052e908.jpg treat image : temp/1764768027_4094707_1397641186_0b132b2b1114549918a0ccb6df9bf409.jpg treat image : temp/1764768027_4094707_1397641185_941b5e06621ade61fb8b9b333731c08f.jpg treat image : temp/1764768027_4094707_1397641184_eac5f78976fa336f2226089bcd8b68ba.jpg treat image : temp/1764768027_4094707_1397641180_543010c4eac0fa3949afe3b367642182.jpg treat image : 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: 0 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 100 time used for this insertion : 0.018703460693359375 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 100 time used for this insertion : 0.02101302146911621 save missing photos in datou_result : time spend for datou_step_exec : 9.514374732971191 time spend to save output : 0.045346975326538086 total time spend for step 7 : 9.55972170829773 step8:velours_tree Wed Dec 3 14:24:15 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 complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure can't find the photo_desc_type Inside saveOutput : final : False verbose : 0 ouput is None No outpout to save, returning out of save general time spend for datou_step_exec : 0.14551877975463867 time spend to save output : 4.696846008300781e-05 total time spend for step 8 : 0.14556574821472168 step9:send_mail_cod Wed Dec 3 14:24:15 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 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_P29145272_03-12-2025_14_24_15.pdf 29146835 imagette291468351764768255 29146836 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 .imagette291468361764768255 29146837 imagette291468371764768257 29146838 change filename to text .change filename to text .change filename to text .imagette291468381764768257 29146839 imagette291468391764768257 29146840 imagette291468401764768257 29146841 imagette291468411764768257 29146843 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette291468431764768257 29146844 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 .imagette291468441764768258 29146845 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette291468451764768260 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=29145272 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://marlene.fotonower.com/velours/29146835,29146836,29146837,29146838,29146839,29146840,29146841,29146842,29146843,29146844,29146845?tags=background,carton,flou,metal,pet_fonce,mal_croppe,pehd,environnement,pet_clair,papier,autre your option no_mail is active, we will not send the real mail to your client args[1397641233] : ((1397641233, -6.996578496402057, 492609224), (1397641233, 0.2683470126727146, 2107752395), '0.04129959972993828') We are sending mail with results at report@fotonower.com args[1397641232] : ((1397641232, -6.978961017022594, 492609224), (1397641232, 0.26205035557708745, 2107752395), '0.04129959972993828') We are sending mail with results at report@fotonower.com args[1397641231] : ((1397641231, -7.1277741831115105, 492609224), (1397641231, 0.3323347607035417, 2107752395), '0.04129959972993828') We are sending mail with results at report@fotonower.com args[1397641206] : ((1397641206, -7.348368487149834, 492609224), (1397641206, 0.30759375844376996, 2107752395), '0.04129959972993828') We are sending mail with results at report@fotonower.com args[1397641205] : ((1397641205, -7.1880248943075795, 492609224), (1397641205, 0.28219144762561454, 2107752395), '0.04129959972993828') We are sending mail with results at report@fotonower.com args[1397641203] : ((1397641203, -7.177491902016461, 492609224), (1397641203, 0.305675374686939, 2107752395), '0.04129959972993828') We are sending mail with results at report@fotonower.com args[1397641186] : ((1397641186, -7.040054115557637, 492609224), (1397641186, 0.27826262705092897, 2107752395), '0.04129959972993828') We are sending mail with results at report@fotonower.com args[1397641185] : ((1397641185, -7.202899765488644, 492609224), (1397641185, 0.20640782157340457, 2107752395), '0.04129959972993828') We are sending mail with results at report@fotonower.com args[1397641184] : ((1397641184, -7.180094794334863, 492609224), (1397641184, 0.25554023441642326, 2107752395), '0.04129959972993828') We are sending mail with results at report@fotonower.com args[1397641180] : ((1397641180, -4.7361886816765155, 492609224), (1397641180, -1.3882069913182427, 501862349), '0.04129959972993828') We are sending mail with results at report@fotonower.com refus_total : 0.04129959972993828 2022-04-13 10:29:59 0 SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=29145272 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P29145272_03-12-2025_14_24_15.pdf results_Auto_P29145272_03-12-2025_14_24_15.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P29145272_03-12-2025_14_24_15.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','29145272','results_Auto_P29145272_03-12-2025_14_24_15.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P29145272_03-12-2025_14_24_15.pdf','pdf','','0.58','0.04129959972993828') Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : send_mail_cod we use saveGeneral [1397641233, 1397641232, 1397641231, 1397641206, 1397641205, 1397641203, 1397641186, 1397641185, 1397641184, 1397641180] 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, '4179941') ('3318', '29145272', '1397641233', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641232', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641231', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641206', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641205', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641203', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641186', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641185', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641184', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641180', None, None, None, None, None, '4179941') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 10 time used for this insertion : 0.01801896095275879 save_final save missing photos in datou_result : time spend for datou_step_exec : 7.184643507003784 time spend to save output : 0.018158674240112305 total time spend for step 9 : 7.2028021812438965 step10:split_time_score Wed Dec 3 14:24:23 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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'}] (('12', 10),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 03122025 29145272 Nombre de photos uploadées : 10 / 23040 (0%) 03122025 29145272 Nombre de photos taguées (types de déchets): 0 / 10 (0%) 03122025 29145272 Nombre de photos taguées (volume) : 0 / 10 (0%) elapsed_time : load_data_split_time_score 3.0994415283203125e-06 elapsed_time : order_list_meta_photo_and_scores 1.0967254638671875e-05 ?????????? elapsed_time : fill_and_build_computed_from_old_data 0.0004677772521972656 Catched exception ! Connect or reconnect ! Catched exception ! Connect or reconnect ! elapsed_time : insert_dashboard_record_day_entry 0.26587486267089844 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 : 0.007722137017795443 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P29133113_03-12-2025_07_08_20.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 29133113 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`=29133113 AND mptpi.`type`=3726 To do Qualite : 0.020481419839787338 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P29138301_03-12-2025_09_36_08.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 29138301 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`=29138301 AND mptpi.`type`=3726 To do Qualite : 0.04129959972993828 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P29145272_03-12-2025_14_24_15.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 29145272 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`=29145272 AND mptpi.`type`=3594 To do Qualite : 0.017082562409380303 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P29146298_03-12-2025_14_08_41.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 29146298 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`=29146298 AND mptpi.`type`=3726 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'03122025': {'nb_upload': 10, '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 [1397641233, 1397641232, 1397641231, 1397641206, 1397641205, 1397641203, 1397641186, 1397641185, 1397641184, 1397641180] Looping around the photos to save general results len do output : 1 /29145272Didn'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, '4179941') ('3318', '29145272', '1397641233', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641232', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641231', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641206', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641205', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641203', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641186', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641185', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641184', None, None, None, None, None, '4179941') ('3318', None, None, None, None, None, None, None, '4179941') ('3318', '29145272', '1397641180', None, None, None, None, None, '4179941') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 11 time used for this insertion : 0.018118858337402344 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.8155372142791748 time spend to save output : 0.018340349197387695 total time spend for step 10 : 0.8338775634765625 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 10 set_done_treatment 108.00user 97.78system 3:59.14elapsed 86%CPU (0avgtext+0avgdata 4820712maxresident)k 641976inputs+87128outputs (5149major+10647024minor)pagefaults 0swaps