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 : 2237198 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 : ['3903709'] with mtr_portfolio_ids : ['27646360'] and first list_photo_ids : [] new path : /proc/2237198/ 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 , BFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 6 ; length of list_pids : 6 ; length of list_args : 6 time to download the photos : 1.395690679550171 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : 0 number of steps : 10 step1:mask_detect Thu Oct 9 14:40:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10998 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-10-09 14:40:33.001610: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-10-09 14:40:33.026424: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2025-10-09 14:40:33.027878: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f3794000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-10-09 14:40:33.027922: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-10-09 14:40:33.030342: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-10-09 14:40:33.302485: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x26b67e70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-10-09 14:40:33.302540: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-10-09 14:40:33.303568: 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 2025-10-09 14:40:33.304051: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-10-09 14:40:33.307174: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-10-09 14:40:33.310170: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-10-09 14:40:33.310720: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-10-09 14:40:33.314226: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-10-09 14:40:33.315782: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-10-09 14:40:33.320023: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-10-09 14:40:33.321444: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-10-09 14:40:33.321516: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-10-09 14:40:33.322264: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-10-09 14:40:33.322323: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-10-09 14:40:33.322332: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-10-09 14:40:33.323630: 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. 2025-10-09 14:40:33.710358: 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 2025-10-09 14:40:33.710500: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-10-09 14:40:33.710533: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-10-09 14:40:33.710563: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-10-09 14:40:33.710593: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-10-09 14:40:33.710621: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-10-09 14:40:33.710649: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-10-09 14:40:33.710678: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-10-09 14:40:33.712125: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-10-09 14:40:33.713283: 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 2025-10-09 14:40:33.713314: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-10-09 14:40:33.713328: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-10-09 14:40:33.713341: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-10-09 14:40:33.713353: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-10-09 14:40:33.713365: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-10-09 14:40:33.713378: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-10-09 14:40:33.713390: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-10-09 14:40:33.714568: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-10-09 14:40:33.714601: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-10-09 14:40:33.714609: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-10-09 14:40:33.714616: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-10-09 14:40:33.715833: 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 : [] 2025-10-09 14:40:42.132167: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-10-09 14:40:42.335055: 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 : 6 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 : 24 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 : 25 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 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 : 17 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 : 35 Detection mask done ! Trying to reset tf kernel 2237742 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 736 tf kernel not reseted sub process len(results) : 6 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 6 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 : 6025 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl2847 Catched exception ! Connect or reconnect ! thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] time for calcul the mask position with numpy : 0.003451824188232422 nb_pixel_total : 102412 time to create 1 rle with old method : 0.11746644973754883 length of segment : 373 time for calcul the mask position with numpy : 0.00060272216796875 nb_pixel_total : 11879 time to create 1 rle with old method : 0.013607263565063477 length of segment : 134 time for calcul the mask position with numpy : 0.02625751495361328 nb_pixel_total : 1011180 time to create 1 rle with new method : 0.04429340362548828 length of segment : 1538 time for calcul the mask position with numpy : 0.0008616447448730469 nb_pixel_total : 24710 time to create 1 rle with old method : 0.027667522430419922 length of segment : 312 time for calcul the mask position with numpy : 0.002141237258911133 nb_pixel_total : 86684 time to create 1 rle with old method : 0.0974266529083252 length of segment : 469 time for calcul the mask position with numpy : 0.0006690025329589844 nb_pixel_total : 13733 time to create 1 rle with old method : 0.01602935791015625 length of segment : 197 time for calcul the mask position with numpy : 0.0010652542114257812 nb_pixel_total : 37745 time to create 1 rle with old method : 0.04366421699523926 length of segment : 302 time for calcul the mask position with numpy : 0.0008683204650878906 nb_pixel_total : 24666 time to create 1 rle with old method : 0.02861928939819336 length of segment : 231 time for calcul the mask position with numpy : 0.0006163120269775391 nb_pixel_total : 29932 time to create 1 rle with old method : 0.03524637222290039 length of segment : 208 time for calcul the mask position with numpy : 0.0007181167602539062 nb_pixel_total : 22506 time to create 1 rle with old method : 0.026503801345825195 length of segment : 271 time for calcul the mask position with numpy : 0.004953622817993164 nb_pixel_total : 161576 time to create 1 rle with new method : 0.012312889099121094 length of segment : 450 time for calcul the mask position with numpy : 0.0031938552856445312 nb_pixel_total : 64732 time to create 1 rle with old method : 0.0817880630493164 length of segment : 245 time for calcul the mask position with numpy : 0.0012233257293701172 nb_pixel_total : 32247 time to create 1 rle with old method : 0.037119150161743164 length of segment : 218 time for calcul the mask position with numpy : 0.0008559226989746094 nb_pixel_total : 20358 time to create 1 rle with old method : 0.024039030075073242 length of segment : 220 time for calcul the mask position with numpy : 0.0022568702697753906 nb_pixel_total : 65323 time to create 1 rle with old method : 0.07469415664672852 length of segment : 281 time for calcul the mask position with numpy : 0.0006353855133056641 nb_pixel_total : 27626 time to create 1 rle with old method : 0.03216242790222168 length of segment : 212 time for calcul the mask position with numpy : 0.003991365432739258 nb_pixel_total : 110125 time to create 1 rle with old method : 0.1285700798034668 length of segment : 668 time for calcul the mask position with numpy : 0.0018322467803955078 nb_pixel_total : 41127 time to create 1 rle with old method : 0.045828819274902344 length of segment : 424 time for calcul the mask position with numpy : 0.0008077621459960938 nb_pixel_total : 26056 time to create 1 rle with old method : 0.029859066009521484 length of segment : 265 time for calcul the mask position with numpy : 0.00032258033752441406 nb_pixel_total : 7120 time to create 1 rle with old method : 0.008395195007324219 length of segment : 198 time for calcul the mask position with numpy : 0.0008065700531005859 nb_pixel_total : 30830 time to create 1 rle with old method : 0.03533506393432617 length of segment : 298 time for calcul the mask position with numpy : 0.002338409423828125 nb_pixel_total : 114434 time to create 1 rle with old method : 0.13381218910217285 length of segment : 794 time for calcul the mask position with numpy : 0.0012483596801757812 nb_pixel_total : 43434 time to create 1 rle with old method : 0.0519251823425293 length of segment : 389 time for calcul the mask position with numpy : 0.0028235912322998047 nb_pixel_total : 75508 time to create 1 rle with old method : 0.0864558219909668 length of segment : 652 time for calcul the mask position with numpy : 0.00036454200744628906 nb_pixel_total : 14190 time to create 1 rle with old method : 0.016367435455322266 length of segment : 177 time for calcul the mask position with numpy : 0.000579833984375 nb_pixel_total : 32075 time to create 1 rle with old method : 0.036832571029663086 length of segment : 311 time for calcul the mask position with numpy : 0.0009710788726806641 nb_pixel_total : 64570 time to create 1 rle with old method : 0.07382011413574219 length of segment : 272 time for calcul the mask position with numpy : 0.0007925033569335938 nb_pixel_total : 57905 time to create 1 rle with old method : 0.06698203086853027 length of segment : 270 time for calcul the mask position with numpy : 0.00024247169494628906 nb_pixel_total : 10731 time to create 1 rle with old method : 0.012710809707641602 length of segment : 128 time for calcul the mask position with numpy : 0.0004889965057373047 nb_pixel_total : 28828 time to create 1 rle with old method : 0.03375601768493652 length of segment : 212 time for calcul the mask position with numpy : 0.00021886825561523438 nb_pixel_total : 10965 time to create 1 rle with old method : 0.012896537780761719 length of segment : 111 time for calcul the mask position with numpy : 0.0007674694061279297 nb_pixel_total : 54785 time to create 1 rle with old method : 0.0630033016204834 length of segment : 300 time for calcul the mask position with numpy : 0.0013675689697265625 nb_pixel_total : 69245 time to create 1 rle with old method : 0.078582763671875 length of segment : 320 time for calcul the mask position with numpy : 0.0014791488647460938 nb_pixel_total : 70761 time to create 1 rle with old method : 0.08104968070983887 length of segment : 451 time for calcul the mask position with numpy : 0.0005357265472412109 nb_pixel_total : 29045 time to create 1 rle with old method : 0.03325223922729492 length of segment : 241 time for calcul the mask position with numpy : 0.001111745834350586 nb_pixel_total : 37200 time to create 1 rle with old method : 0.04296398162841797 length of segment : 361 time for calcul the mask position with numpy : 0.0012211799621582031 nb_pixel_total : 73599 time to create 1 rle with old method : 0.08377790451049805 length of segment : 466 time for calcul the mask position with numpy : 0.0003020763397216797 nb_pixel_total : 14387 time to create 1 rle with old method : 0.016739845275878906 length of segment : 137 time for calcul the mask position with numpy : 0.002393007278442383 nb_pixel_total : 162667 time to create 1 rle with new method : 0.006497859954833984 length of segment : 531 time for calcul the mask position with numpy : 0.002814769744873047 nb_pixel_total : 102994 time to create 1 rle with old method : 0.11560606956481934 length of segment : 348 time for calcul the mask position with numpy : 0.0026335716247558594 nb_pixel_total : 92693 time to create 1 rle with old method : 0.1067352294921875 length of segment : 293 time for calcul the mask position with numpy : 0.0007653236389160156 nb_pixel_total : 21514 time to create 1 rle with old method : 0.024815797805786133 length of segment : 182 time for calcul the mask position with numpy : 0.0010235309600830078 nb_pixel_total : 29285 time to create 1 rle with old method : 0.03403949737548828 length of segment : 238 time for calcul the mask position with numpy : 0.001155853271484375 nb_pixel_total : 23239 time to create 1 rle with old method : 0.02694106101989746 length of segment : 318 time for calcul the mask position with numpy : 0.0058367252349853516 nb_pixel_total : 190945 time to create 1 rle with new method : 0.01058340072631836 length of segment : 1288 time for calcul the mask position with numpy : 0.0023272037506103516 nb_pixel_total : 68964 time to create 1 rle with old method : 0.07752680778503418 length of segment : 554 time for calcul the mask position with numpy : 0.0009319782257080078 nb_pixel_total : 21618 time to create 1 rle with old method : 0.025421857833862305 length of segment : 251 time for calcul the mask position with numpy : 0.0012171268463134766 nb_pixel_total : 29529 time to create 1 rle with old method : 0.03413820266723633 length of segment : 120 time for calcul the mask position with numpy : 0.0016484260559082031 nb_pixel_total : 36936 time to create 1 rle with old method : 0.043247222900390625 length of segment : 200 time for calcul the mask position with numpy : 0.002306699752807617 nb_pixel_total : 59480 time to create 1 rle with old method : 0.0682065486907959 length of segment : 373 time for calcul the mask position with numpy : 0.0005009174346923828 nb_pixel_total : 18419 time to create 1 rle with old method : 0.021421432495117188 length of segment : 172 time for calcul the mask position with numpy : 0.0005881786346435547 nb_pixel_total : 13661 time to create 1 rle with old method : 0.017458200454711914 length of segment : 134 time for calcul the mask position with numpy : 0.005361795425415039 nb_pixel_total : 68636 time to create 1 rle with old method : 0.09328031539916992 length of segment : 334 time for calcul the mask position with numpy : 0.004089832305908203 nb_pixel_total : 39919 time to create 1 rle with old method : 0.05439639091491699 length of segment : 252 time for calcul the mask position with numpy : 0.0015797615051269531 nb_pixel_total : 42956 time to create 1 rle with old method : 0.04937744140625 length of segment : 197 time for calcul the mask position with numpy : 0.0025610923767089844 nb_pixel_total : 69247 time to create 1 rle with old method : 0.09443449974060059 length of segment : 348 time for calcul the mask position with numpy : 0.0009770393371582031 nb_pixel_total : 23371 time to create 1 rle with old method : 0.02884507179260254 length of segment : 159 time for calcul the mask position with numpy : 0.0010485649108886719 nb_pixel_total : 18721 time to create 1 rle with old method : 0.021990060806274414 length of segment : 144 time spent for convertir_results : 7.58764910697937 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 58 chid ids of type : 3594 Number RLEs to save : 19542 save missing photos in datou_result : time spend for datou_step_exec : 45.737035036087036 time spend to save output : 2.139087200164795 total time spend for step 1 : 47.87612223625183 step2:crop_condition Thu Oct 9 14:41:18 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 : 6 ! batch 1 Loaded 58 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 ! map_result returned by crop_photo_return_map_crop : length : 50 About to insert : list_path_to_insert length 50 new photo from crops ! About to upload 50 photos upload in portfolio : 3736932 init cache_photo without model_param we have 50 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1760013695_2237198 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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679736_0.png', 0, 1449, 1038, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679740_0.png', 0, 213, 251, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679744_0.png', 0, 625, 417, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679739_0.png', 0, 117, 193, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679743_0.png', 0, 243, 185, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679735_0.png', 0, 152, 130, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679738_0.png', 0, 455, 321, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679742_0.png', 0, 217, 208, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679734_0.png', 0, 372, 368, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679741_0.png', 0, 242, 216, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679737_0.png', 0, 108, 312, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679754_0.png', 0, 158, 293, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679753_0.png', 0, 79, 173, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679755_0.png', 0, 385, 567, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679750_0.png', 0, 269, 659, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679756_0.png', 0, 283, 293, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679747_0.png', 0, 209, 220, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679745_0.png', 0, 372, 218, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679749_0.png', 0, 232, 204, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679758_0.png', 0, 112, 176, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679751_0.png', 0, 262, 424, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679757_0.png', 0, 242, 516, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679748_0.png', 0, 389, 279, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679752_0.png', 0, 157, 229, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414940_7c0131906a124dccef184e49cf7dc01c_rle_crop_3990679759_0.png', 0, 218, 271, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414940_7c0131906a124dccef184e49cf7dc01c_rle_crop_3990679763_0.png', 0, 241, 202, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414940_7c0131906a124dccef184e49cf7dc01c_rle_crop_3990679760_0.png', 0, 327, 290, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414940_7c0131906a124dccef184e49cf7dc01c_rle_crop_3990679762_0.png', 0, 133, 126, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414938_3d41fee7ec732e5cc85516e0baf61e3f_rle_crop_3990679767_0.png', 0, 346, 385, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414938_3d41fee7ec732e5cc85516e0baf61e3f_rle_crop_3990679765_0.png', 0, 237, 293, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414938_3d41fee7ec732e5cc85516e0baf61e3f_rle_crop_3990679770_0.png', 0, 296, 386, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414938_3d41fee7ec732e5cc85516e0baf61e3f_rle_crop_3990679766_0.png', 0, 357, 294, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414938_3d41fee7ec732e5cc85516e0baf61e3f_rle_crop_3990679771_0.png', 0, 185, 136, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414938_3d41fee7ec732e5cc85516e0baf61e3f_rle_crop_3990679769_0.png', 0, 309, 347, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414938_3d41fee7ec732e5cc85516e0baf61e3f_rle_crop_3990679768_0.png', 0, 192, 233, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414930_febc4915cb7407286db037da3c796310_rle_crop_3990679776_0.png', 0, 244, 237, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414930_febc4915cb7407286db037da3c796310_rle_crop_3990679773_0.png', 0, 452, 344, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414930_febc4915cb7407286db037da3c796310_rle_crop_3990679777_0.png', 0, 242, 178, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414930_febc4915cb7407286db037da3c796310_rle_crop_3990679778_0.png', 0, 597, 739, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414930_febc4915cb7407286db037da3c796310_rle_crop_3990679775_0.png', 0, 205, 182, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414930_febc4915cb7407286db037da3c796310_rle_crop_3990679779_0.png', 0, 205, 540, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679785_0.png', 0, 135, 134, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679791_0.png', 0, 239, 133, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679781_0.png', 0, 391, 110, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679787_0.png', 0, 279, 309, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679786_0.png', 0, 333, 330, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679788_0.png', 0, 310, 197, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679790_0.png', 0, 179, 159, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679784_0.png', 0, 138, 172, 0, 1760013709,'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(1760013709), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679782_0.png', 0, 292, 199, 0, 1760013709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 50 photos in the portfolio 3736932 time of upload the photos Elapsed time : 20.265900135040283 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 ! map_result returned by crop_photo_return_map_crop : length : 2 About to insert : list_path_to_insert length 2 new photo from crops ! About to upload 2 photos upload in portfolio : 3736932 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1760013718_2237198 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(1760013718), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679746_0.png', 0, 184, 216, 0, 1760013718,'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(1760013718), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414930_febc4915cb7407286db037da3c796310_rle_crop_3990679780_0.png', 0, 250, 168, 0, 1760013718,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.6089141368865967 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 1 About to insert : list_path_to_insert length 1 new photo from crops ! About to upload 1 photos upload in portfolio : 3736932 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1760013721_2237198 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(1760013721), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414938_3d41fee7ec732e5cc85516e0baf61e3f_rle_crop_3990679764_0.png', 0, 134, 104, 0, 1760013721,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.17171311378479 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/1760013726_2237198 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(1760013727), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414940_7c0131906a124dccef184e49cf7dc01c_rle_crop_3990679761_0.png', 0, 259, 270, 0, 1760013727,'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(1760013727), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414938_3d41fee7ec732e5cc85516e0baf61e3f_rle_crop_3990679772_0.png', 0, 480, 486, 0, 1760013727,'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(1760013727), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414930_febc4915cb7407286db037da3c796310_rle_crop_3990679774_0.png', 0, 583, 271, 0, 1760013727,'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(1760013727), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679783_0.png', 0, 223, 371, 0, 1760013727,'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(1760013727), 0.0, 0.0, 14, '', 0, 0, '1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679789_0.png', 0, 396, 275, 0, 1760013727,'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 : 3.6473684310913086 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 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 Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1388414944, 1388414943, 1388414940, 1388414938, 1388414930, 1388414914] Looping around the photos to save general results len do output : 58 /1388430431Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430432Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430433Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430434Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430435Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430436Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430437Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430438Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430439Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430440Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430441Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430442Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430443Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430444Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430445Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430446Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430447Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430448Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430449Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430450Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430451Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430452Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430453Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430454Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430455Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430456Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430457Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430458Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430459Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430460Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430461Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430462Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430463Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430464Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430465Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430466Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430467Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430468Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430469Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430470Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430471Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430472Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430473Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430474Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430475Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430476Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430477Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430478Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430479Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430480Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430579Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430580Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430581Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430676Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430677Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430678Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430679Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1388430680Didn'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, '3903709') ('3318', '27646360', '1388414944', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414943', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414940', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414938', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414930', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414914', None, None, None, None, None, '3903709') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 180 time used for this insertion : 0.03921675682067871 save_final save missing photos in datou_result : time spend for datou_step_exec : 50.02039694786072 time spend to save output : 0.04143834114074707 total time spend for step 2 : 50.061835289001465 step3:rle_unique_nms_with_priority Thu Oct 9 14:42:08 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 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 58 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 11 nb_hashtags : 1 time to prepare the origin masks : 0.588716983795166 time for calcul the mask position with numpy : 0.09835934638977051 nb_pixel_total : 6783485 time to create 1 rle with new method : 0.23212146759033203 time for calcul the mask position with numpy : 0.028757333755493164 nb_pixel_total : 161519 time to create 1 rle with new method : 0.16959810256958008 time for calcul the mask position with numpy : 0.025458574295043945 nb_pixel_total : 22506 time to create 1 rle with old method : 0.02545952796936035 time for calcul the mask position with numpy : 0.02523016929626465 nb_pixel_total : 13881 time to create 1 rle with old method : 0.015799522399902344 time for calcul the mask position with numpy : 0.026559114456176758 nb_pixel_total : 24666 time to create 1 rle with old method : 0.02787613868713379 time for calcul the mask position with numpy : 0.025542259216308594 nb_pixel_total : 37745 time to create 1 rle with old method : 0.04255557060241699 time for calcul the mask position with numpy : 0.024512290954589844 nb_pixel_total : 13733 time to create 1 rle with old method : 0.0154876708984375 time for calcul the mask position with numpy : 0.02427983283996582 nb_pixel_total : 86684 time to create 1 rle with old method : 0.09707498550415039 time for calcul the mask position with numpy : 0.023882627487182617 nb_pixel_total : 24710 time to create 1 rle with old method : 0.027852535247802734 time for calcul the mask position with numpy : 0.030857324600219727 nb_pixel_total : 1011180 time to create 1 rle with new method : 0.15491747856140137 time for calcul the mask position with numpy : 0.0235137939453125 nb_pixel_total : 11879 time to create 1 rle with old method : 0.012634515762329102 time for calcul the mask position with numpy : 0.0235748291015625 nb_pixel_total : 102412 time to create 1 rle with old method : 0.1086728572845459 create new chi : 1.3598551750183105 time to delete rle : 0.10233616828918457 batch 1 Loaded 23 chid ids of type : 3594 +++++++++++++++++Number RLEs to save : 10932 TO DO : save crop sub photo not yet done ! save time : 1.214707851409912 nb_obj : 14 nb_hashtags : 2 time to prepare the origin masks : 0.6389102935791016 time for calcul the mask position with numpy : 0.09053587913513184 nb_pixel_total : 7626879 time to create 1 rle with new method : 0.15616178512573242 time for calcul the mask position with numpy : 0.023157119750976562 nb_pixel_total : 14190 time to create 1 rle with old method : 0.014836788177490234 time for calcul the mask position with numpy : 0.022426128387451172 nb_pixel_total : 75508 time to create 1 rle with old method : 0.07841873168945312 time for calcul the mask position with numpy : 0.023315906524658203 nb_pixel_total : 43434 time to create 1 rle with old method : 0.04625749588012695 time for calcul the mask position with numpy : 0.024484634399414062 nb_pixel_total : 114434 time to create 1 rle with old method : 0.11955404281616211 time for calcul the mask position with numpy : 0.022284507751464844 nb_pixel_total : 25241 time to create 1 rle with old method : 0.026082992553710938 time for calcul the mask position with numpy : 0.022322654724121094 nb_pixel_total : 7120 time to create 1 rle with old method : 0.007376194000244141 time for calcul the mask position with numpy : 0.02227497100830078 nb_pixel_total : 26056 time to create 1 rle with old method : 0.02735280990600586 time for calcul the mask position with numpy : 0.023100614547729492 nb_pixel_total : 41127 time to create 1 rle with old method : 0.042360782623291016 time for calcul the mask position with numpy : 0.022622346878051758 nb_pixel_total : 110125 time to create 1 rle with old method : 0.11437630653381348 time for calcul the mask position with numpy : 0.02280139923095703 nb_pixel_total : 27626 time to create 1 rle with old method : 0.029112815856933594 time for calcul the mask position with numpy : 0.02256631851196289 nb_pixel_total : 65323 time to create 1 rle with old method : 0.08087563514709473 time for calcul the mask position with numpy : 0.02565908432006836 nb_pixel_total : 20358 time to create 1 rle with old method : 0.02733325958251953 time for calcul the mask position with numpy : 0.02510833740234375 nb_pixel_total : 32247 time to create 1 rle with old method : 0.03595781326293945 time for calcul the mask position with numpy : 0.02488112449645996 nb_pixel_total : 64732 time to create 1 rle with old method : 0.07236194610595703 create new chi : 1.3125338554382324 time to delete rle : 0.001340627670288086 batch 1 Loaded 29 chid ids of type : 3594 +++++++++++++++++++++++Number RLEs to save : 12119 TO DO : save crop sub photo not yet done ! save time : 1.3259296417236328 nb_obj : 5 nb_hashtags : 2 time to prepare the origin masks : 0.25617074966430664 time for calcul the mask position with numpy : 0.09512567520141602 nb_pixel_total : 8102925 time to create 1 rle with new method : 0.16066813468933105 time for calcul the mask position with numpy : 0.024537086486816406 nb_pixel_total : 28828 time to create 1 rle with old method : 0.03138256072998047 time for calcul the mask position with numpy : 0.024654388427734375 nb_pixel_total : 8097 time to create 1 rle with old method : 0.008619308471679688 time for calcul the mask position with numpy : 0.0248415470123291 nb_pixel_total : 57905 time to create 1 rle with old method : 0.06335759162902832 time for calcul the mask position with numpy : 0.02379322052001953 nb_pixel_total : 64570 time to create 1 rle with old method : 0.06887960433959961 time for calcul the mask position with numpy : 0.02512955665588379 nb_pixel_total : 32075 time to create 1 rle with old method : 0.03437614440917969 create new chi : 0.602487325668335 time to delete rle : 0.0006759166717529297 batch 1 Loaded 11 chid ids of type : 3594 +++++++Number RLEs to save : 4488 TO DO : save crop sub photo not yet done ! save time : 0.5901718139648438 nb_obj : 9 nb_hashtags : 3 time to prepare the origin masks : 0.38439345359802246 time for calcul the mask position with numpy : 0.08453059196472168 nb_pixel_total : 7773576 time to create 1 rle with new method : 0.1502244472503662 time for calcul the mask position with numpy : 0.024351835250854492 nb_pixel_total : 160837 time to create 1 rle with new method : 0.14886879920959473 time for calcul the mask position with numpy : 0.024014949798583984 nb_pixel_total : 14387 time to create 1 rle with old method : 0.016010522842407227 time for calcul the mask position with numpy : 0.024080991744995117 nb_pixel_total : 73599 time to create 1 rle with old method : 0.07936453819274902 time for calcul the mask position with numpy : 0.02407360076904297 nb_pixel_total : 37200 time to create 1 rle with old method : 0.040714263916015625 time for calcul the mask position with numpy : 0.024540424346923828 nb_pixel_total : 29045 time to create 1 rle with old method : 0.031363487243652344 time for calcul the mask position with numpy : 0.024043560028076172 nb_pixel_total : 70761 time to create 1 rle with old method : 0.07659649848937988 time for calcul the mask position with numpy : 0.02596902847290039 nb_pixel_total : 69245 time to create 1 rle with old method : 0.07431697845458984 time for calcul the mask position with numpy : 0.023631811141967773 nb_pixel_total : 54785 time to create 1 rle with old method : 0.059270381927490234 time for calcul the mask position with numpy : 0.022982120513916016 nb_pixel_total : 10965 time to create 1 rle with old method : 0.012183427810668945 create new chi : 1.0196566581726074 time to delete rle : 0.0009961128234863281 batch 1 Loaded 19 chid ids of type : 3594 +++++++++++Number RLEs to save : 7887 TO DO : save crop sub photo not yet done ! save time : 0.9496932029724121 nb_obj : 8 nb_hashtags : 3 time to prepare the origin masks : 0.3792898654937744 time for calcul the mask position with numpy : 0.08893251419067383 nb_pixel_total : 7743149 time to create 1 rle with new method : 0.16101622581481934 time for calcul the mask position with numpy : 0.026859283447265625 nb_pixel_total : 21618 time to create 1 rle with old method : 0.023957490921020508 time for calcul the mask position with numpy : 0.024024248123168945 nb_pixel_total : 68964 time to create 1 rle with old method : 0.07346296310424805 time for calcul the mask position with numpy : 0.023743152618408203 nb_pixel_total : 190944 time to create 1 rle with new method : 0.14460229873657227 time for calcul the mask position with numpy : 0.02329254150390625 nb_pixel_total : 23239 time to create 1 rle with old method : 0.025079965591430664 time for calcul the mask position with numpy : 0.022733449935913086 nb_pixel_total : 29285 time to create 1 rle with old method : 0.030955791473388672 time for calcul the mask position with numpy : 0.022697925567626953 nb_pixel_total : 21514 time to create 1 rle with old method : 0.02259993553161621 time for calcul the mask position with numpy : 0.02311396598815918 nb_pixel_total : 92693 time to create 1 rle with old method : 0.09893584251403809 time for calcul the mask position with numpy : 0.02405381202697754 nb_pixel_total : 102994 time to create 1 rle with old method : 0.1084597110748291 create new chi : 0.9971108436584473 time to delete rle : 0.0010530948638916016 batch 1 Loaded 17 chid ids of type : 3594 +++++++++++++++Number RLEs to save : 9098 TO DO : save crop sub photo not yet done ! save time : 1.0716753005981445 nb_obj : 11 nb_hashtags : 2 time to prepare the origin masks : 0.5146551132202148 time for calcul the mask position with numpy : 0.089447021484375 nb_pixel_total : 7873564 time to create 1 rle with new method : 0.1450028419494629 time for calcul the mask position with numpy : 0.022809505462646484 nb_pixel_total : 18721 time to create 1 rle with old method : 0.019896268844604492 time for calcul the mask position with numpy : 0.02272939682006836 nb_pixel_total : 23371 time to create 1 rle with old method : 0.02486252784729004 time for calcul the mask position with numpy : 0.022869586944580078 nb_pixel_total : 69247 time to create 1 rle with old method : 0.07245969772338867 time for calcul the mask position with numpy : 0.02459549903869629 nb_pixel_total : 42956 time to create 1 rle with old method : 0.045063018798828125 time for calcul the mask position with numpy : 0.022934913635253906 nb_pixel_total : 39919 time to create 1 rle with old method : 0.041977643966674805 time for calcul the mask position with numpy : 0.022647857666015625 nb_pixel_total : 68636 time to create 1 rle with old method : 0.07248115539550781 time for calcul the mask position with numpy : 0.022804737091064453 nb_pixel_total : 13661 time to create 1 rle with old method : 0.014354467391967773 time for calcul the mask position with numpy : 0.022403717041015625 nb_pixel_total : 18380 time to create 1 rle with old method : 0.019449234008789062 time for calcul the mask position with numpy : 0.022778987884521484 nb_pixel_total : 59480 time to create 1 rle with old method : 0.060573577880859375 time for calcul the mask position with numpy : 0.02293705940246582 nb_pixel_total : 36936 time to create 1 rle with old method : 0.039237022399902344 time for calcul the mask position with numpy : 0.023688316345214844 nb_pixel_total : 29529 time to create 1 rle with old method : 0.030869722366333008 create new chi : 0.9429037570953369 time to delete rle : 0.0009553432464599609 batch 1 Loaded 23 chid ids of type : 3594 ++++++++++++Number RLEs to save : 7002 TO DO : save crop sub photo not yet done ! save time : 0.8703675270080566 map_output_result : {1388414944: (0.0, 'Should be the crop_list due to order', 0), 1388414943: (0.0, 'Should be the crop_list due to order', 0), 1388414940: (0.0, 'Should be the crop_list due to order', 0), 1388414938: (0.0, 'Should be the crop_list due to order', 0), 1388414930: (0.0, 'Should be the crop_list due to order', 0), 1388414914: (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 [1388414944, 1388414943, 1388414940, 1388414938, 1388414930, 1388414914] Looping around the photos to save general results len do output : 6 /1388414944.Didn't retrieve data . /1388414943.Didn't retrieve data . /1388414940.Didn't retrieve data . /1388414938.Didn't retrieve data . /1388414930.Didn't retrieve data . /1388414914.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, '3903709') ('3318', '27646360', '1388414944', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414943', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414940', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414938', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414930', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414914', None, None, None, None, None, '3903709') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 18 time used for this insertion : 0.03590583801269531 save_final save missing photos in datou_result : time spend for datou_step_exec : 15.93283200263977 time spend to save output : 0.0362401008605957 total time spend for step 3 : 15.969072103500366 step4:ventilate_hashtags_in_portfolio Thu Oct 9 14:42:24 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 27646360 get user id for portfolio 27646360 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`=27646360 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('environnement','pet_clair','mal_croppe','papier','pet_fonce','background','metal','flou','autre','carton','pehd')) AND mptpi.`min_score`=0.5 To do To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=27646360 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('environnement','pet_clair','mal_croppe','papier','pet_fonce','background','metal','flou','autre','carton','pehd')) AND mptpi.`min_score`=0.5 To do Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=27646360 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('environnement','pet_clair','mal_croppe','papier','pet_fonce','background','metal','flou','autre','carton','pehd')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://marlene.fotonower.com/velours/27646628,27646629,27646630,27646631,27646632,27646633,27646634,27646635,27646636,27646637,27646638?tags=environnement,pet_clair,mal_croppe,papier,pet_fonce,background,metal,flou,autre,carton,pehd Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1388414944, 1388414943, 1388414940, 1388414938, 1388414930, 1388414914] Looping around the photos to save general results len do output : 1 /27646360. 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, '3903709') ('3318', '27646360', '1388414944', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414943', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414940', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414938', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414930', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414914', None, None, None, None, None, '3903709') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 7 time used for this insertion : 0.03773212432861328 save_final save missing photos in datou_result : time spend for datou_step_exec : 5.702716588973999 time spend to save output : 0.038025856018066406 total time spend for step 4 : 5.740742444992065 step5:final Thu Oct 9 14:42:29 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 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 : {1388414944: ('0.07761907632458846',), 1388414943: ('0.07761907632458846',), 1388414940: ('0.07761907632458846',), 1388414938: ('0.07761907632458846',), 1388414930: ('0.07761907632458846',), 1388414914: ('0.07761907632458846',)} new output for save of step final : {1388414944: ('0.07761907632458846',), 1388414943: ('0.07761907632458846',), 1388414940: ('0.07761907632458846',), 1388414938: ('0.07761907632458846',), 1388414930: ('0.07761907632458846',), 1388414914: ('0.07761907632458846',)} [1388414944, 1388414943, 1388414940, 1388414938, 1388414930, 1388414914] Looping around the photos to save general results len do output : 6 /1388414944.Didn't retrieve data . /1388414943.Didn't retrieve data . /1388414940.Didn't retrieve data . /1388414938.Didn't retrieve data . /1388414930.Didn't retrieve data . /1388414914.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, '3903709') ('3318', '27646360', '1388414944', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414943', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414940', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414938', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414930', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414914', None, None, None, None, None, '3903709') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 18 time used for this insertion : 0.03641653060913086 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.37253522872924805 time spend to save output : 0.03678560256958008 total time spend for step 5 : 0.4093208312988281 step6:blur_detection Thu Oct 9 14:42:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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/1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175.jpg resize: (2160, 3840) 1388414944 -7.024897380013097 treat image : temp/1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b.jpg resize: (2160, 3840) 1388414943 -6.973365085163435 treat image : temp/1760013629_2237198_1388414940_7c0131906a124dccef184e49cf7dc01c.jpg resize: (2160, 3840) 1388414940 -7.075646733631397 treat image : temp/1760013629_2237198_1388414938_3d41fee7ec732e5cc85516e0baf61e3f.jpg resize: (2160, 3840) 1388414938 -6.974452279635691 treat image : temp/1760013629_2237198_1388414930_febc4915cb7407286db037da3c796310.jpg resize: (2160, 3840) 1388414930 -6.894389630426321 treat image : temp/1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd.jpg resize: (2160, 3840) 1388414914 -7.052051258899446 treat image : temp/1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679736_0.png resize: (1038, 1449) 1388430431 -4.593315485396261 treat image : temp/1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679740_0.png resize: (251, 213) 1388430432 -3.9088915605519805 treat image : temp/1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679744_0.png resize: (417, 625) 1388430433 -4.935073069710411 treat image : temp/1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679739_0.png resize: (193, 117) 1388430434 -3.367421492973702 treat image : temp/1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679743_0.png resize: (185, 243) 1388430435 -4.028812972267082 treat image : temp/1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679735_0.png resize: (130, 152) 1388430436 -2.4751688462496135 treat image : temp/1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679738_0.png resize: (321, 455) 1388430437 -3.0780764168573898 treat image : temp/1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679742_0.png resize: (208, 217) 1388430438 -2.525665449757292 treat image : temp/1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679734_0.png resize: (368, 372) 1388430439 -3.1751126420574662 treat image : temp/1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679741_0.png resize: (216, 242) 1388430440 -3.0478128567934877 treat image : temp/1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679737_0.png resize: (312, 108) 1388430441 -3.5761237905960925 treat image : temp/1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679754_0.png resize: (293, 158) 1388430442 -3.3548030178177553 treat image : temp/1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679753_0.png resize: (173, 79) 1388430443 -4.00094337394543 treat image : temp/1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679755_0.png resize: (567, 385) 1388430444 -4.603300010427222 treat image : temp/1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679750_0.png resize: (659, 269) 1388430445 -1.7300639477182937 treat image : temp/1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679756_0.png resize: (293, 283) 1388430446 -4.375539956531822 treat image : temp/1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679747_0.png resize: (220, 209) 1388430447 -1.364243922371396 treat image : temp/1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679745_0.png resize: (218, 372) 1388430448 -2.499482763453015 treat image : temp/1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679749_0.png resize: (204, 232) 1388430449 -4.18226496507643 treat image : temp/1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679758_0.png resize: (176, 112) 1388430450 -0.3308953899127546 treat image : temp/1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679751_0.png resize: (424, 262) 1388430451 -2.1894088149992728 treat image : temp/1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679757_0.png resize: (516, 242) 1388430452 -4.666867334952832 treat image : temp/1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679748_0.png resize: (279, 389) 1388430453 -4.520509220008199 treat image : temp/1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679752_0.png resize: (229, 157) 1388430454 -4.49976653306851 treat image : temp/1760013629_2237198_1388414940_7c0131906a124dccef184e49cf7dc01c_rle_crop_3990679759_0.png resize: (271, 218) 1388430455 -1.9171623944106144 treat image : temp/1760013629_2237198_1388414940_7c0131906a124dccef184e49cf7dc01c_rle_crop_3990679763_0.png resize: (202, 241) 1388430456 -4.045990892026383 treat image : temp/1760013629_2237198_1388414940_7c0131906a124dccef184e49cf7dc01c_rle_crop_3990679760_0.png resize: (290, 327) 1388430457 -4.939386863823382 treat image : temp/1760013629_2237198_1388414940_7c0131906a124dccef184e49cf7dc01c_rle_crop_3990679762_0.png resize: (126, 133) 1388430458 -2.9933995896323147 treat image : temp/1760013629_2237198_1388414938_3d41fee7ec732e5cc85516e0baf61e3f_rle_crop_3990679767_0.png resize: (385, 346) 1388430459 -4.20875439977397 treat image : temp/1760013629_2237198_1388414938_3d41fee7ec732e5cc85516e0baf61e3f_rle_crop_3990679765_0.png resize: (293, 237) 1388430460 -4.435858759960815 treat image : temp/1760013629_2237198_1388414938_3d41fee7ec732e5cc85516e0baf61e3f_rle_crop_3990679770_0.png resize: (386, 296) 1388430461 -4.539064738787709 treat image : temp/1760013629_2237198_1388414938_3d41fee7ec732e5cc85516e0baf61e3f_rle_crop_3990679766_0.png resize: (294, 357) 1388430462 -4.961204581159469 treat image : temp/1760013629_2237198_1388414938_3d41fee7ec732e5cc85516e0baf61e3f_rle_crop_3990679771_0.png resize: (136, 185) 1388430463 -2.005336787662352 treat image : temp/1760013629_2237198_1388414938_3d41fee7ec732e5cc85516e0baf61e3f_rle_crop_3990679769_0.png resize: (347, 309) 1388430464 -3.825230132831944 treat image : temp/1760013629_2237198_1388414938_3d41fee7ec732e5cc85516e0baf61e3f_rle_crop_3990679768_0.png resize: (233, 192) 1388430465 -3.419410457116106 treat image : temp/1760013629_2237198_1388414930_febc4915cb7407286db037da3c796310_rle_crop_3990679776_0.png resize: (237, 244) 1388430466 -2.3475598153783257 treat image : 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temp/1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679791_0.png resize: (133, 239) 1388430473 -3.6454413699491153 treat image : temp/1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679781_0.png resize: (110, 391) 1388430474 -2.843672206221968 treat image : temp/1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679787_0.png resize: (309, 279) 1388430475 -3.5070914460940013 treat image : temp/1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679786_0.png resize: (330, 333) 1388430476 -3.3789526703783426 treat image : temp/1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679788_0.png resize: (197, 310) 1388430477 -3.871942043866307 treat image : temp/1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679790_0.png resize: (159, 179) 1388430478 -2.734220124412981 treat image : temp/1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679784_0.png resize: (172, 138) 1388430479 0.6691078751541857 treat image : temp/1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679782_0.png resize: (199, 292) 1388430480 -0.5295209931979055 treat image : temp/1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b_rle_crop_3990679746_0.png resize: (216, 184) 1388430579 -3.478248804318337 treat image : temp/1760013629_2237198_1388414930_febc4915cb7407286db037da3c796310_rle_crop_3990679780_0.png resize: (168, 250) 1388430580 -3.7802005196267037 treat image : temp/1760013629_2237198_1388414938_3d41fee7ec732e5cc85516e0baf61e3f_rle_crop_3990679764_0.png resize: (104, 134) 1388430581 -3.5460513516135093 treat image : temp/1760013629_2237198_1388414940_7c0131906a124dccef184e49cf7dc01c_rle_crop_3990679761_0.png resize: (270, 259) 1388430676 -6.256053695966837 treat image : temp/1760013629_2237198_1388414938_3d41fee7ec732e5cc85516e0baf61e3f_rle_crop_3990679772_0.png resize: (486, 480) 1388430677 -4.921752890159219 treat image : temp/1760013629_2237198_1388414930_febc4915cb7407286db037da3c796310_rle_crop_3990679774_0.png resize: (271, 583) 1388430678 -3.9741510952904573 treat image : temp/1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679783_0.png resize: (371, 223) 1388430679 -4.432343206875827 treat image : temp/1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679789_0.png resize: (275, 396) 1388430680 -4.518472171226213 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 : 64 time used for this insertion : 0.037230730056762695 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 64 time used for this insertion : 0.036884307861328125 save missing photos in datou_result : time spend for datou_step_exec : 20.793656826019287 time spend to save output : 0.09274482727050781 total time spend for step 6 : 20.886401653289795 step7:brightness Thu Oct 9 14:42:51 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/1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175.jpg treat image : temp/1760013629_2237198_1388414943_eefbe3444679b4199f6d0a34aadd509b.jpg treat image : temp/1760013629_2237198_1388414940_7c0131906a124dccef184e49cf7dc01c.jpg treat image : temp/1760013629_2237198_1388414938_3d41fee7ec732e5cc85516e0baf61e3f.jpg treat image : temp/1760013629_2237198_1388414930_febc4915cb7407286db037da3c796310.jpg treat image : temp/1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd.jpg treat image : temp/1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679736_0.png treat image : temp/1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679740_0.png treat image : temp/1760013629_2237198_1388414944_2b9d86b1460145fbda0775f948070175_rle_crop_3990679744_0.png treat image : 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temp/1760013629_2237198_1388414914_d4f220d62fcb6dac7977670c51dfd9fd_rle_crop_3990679789_0.png Inside saveOutput : final : False verbose : 0 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 64 time used for this insertion : 0.036974191665649414 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 64 time used for this insertion : 0.03650212287902832 save missing photos in datou_result : time spend for datou_step_exec : 5.99250054359436 time spend to save output : 0.09006619453430176 total time spend for step 7 : 6.082566738128662 step8:velours_tree Thu Oct 9 14:42:57 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 : 0.10003662109375 time spend to save output : 4.267692565917969e-05 total time spend for step 8 : 0.10007929801940918 step9:send_mail_cod Thu Oct 9 14:42:57 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_P27646360_09-10-2025_14_42_57.pdf 27646629 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette276466291760013777 27646630 imagette276466301760013779 27646631 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 .imagette276466311760013779 27646632 imagette276466321760013781 27646633 imagette276466331760013781 27646634 change filename to text .imagette276466341760013781 27646635 imagette276466351760013781 27646636 imagette276466361760013781 27646637 change filename to text .change filename to text .imagette276466371760013781 27646638 imagette276466381760013781 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=27646360 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://marlene.fotonower.com/velours/27646628,27646629,27646630,27646631,27646632,27646633,27646634,27646635,27646636,27646637,27646638?tags=environnement,pet_clair,mal_croppe,papier,pet_fonce,background,metal,flou,autre,carton,pehd args[1388414944] : ((1388414944, -7.024897380013097, 492609224), (1388414944, 1.5804228544113215, 2107752395), '0.07761907632458846') We are sending mail with results at report@fotonower.com args[1388414943] : ((1388414943, -6.973365085163435, 492609224), (1388414943, 0.9942519746087801, 2107752395), '0.07761907632458846') We are sending mail with results at report@fotonower.com args[1388414940] : ((1388414940, -7.075646733631397, 492609224), (1388414940, 1.176895660819398, 2107752395), '0.07761907632458846') We are sending mail with results at report@fotonower.com args[1388414938] : ((1388414938, -6.974452279635691, 492609224), (1388414938, 0.8050743479302318, 2107752395), '0.07761907632458846') We are sending mail with results at report@fotonower.com args[1388414930] : ((1388414930, -6.894389630426321, 492609224), (1388414930, 1.5968493119476777, 2107752395), '0.07761907632458846') We are sending mail with results at report@fotonower.com args[1388414914] : ((1388414914, -7.052051258899446, 492609224), (1388414914, 0.9066974400900619, 2107752395), '0.07761907632458846') We are sending mail with results at report@fotonower.com refus_total : 0.07761907632458846 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=27646360 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_P27646360_09-10-2025_14_42_57.pdf results_Auto_P27646360_09-10-2025_14_42_57.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27646360_09-10-2025_14_42_57.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','27646360','results_Auto_P27646360_09-10-2025_14_42_57.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27646360_09-10-2025_14_42_57.pdf','pdf','','0.41','0.07761907632458846') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/27646360

https://www.fotonower.com/image?json=false&list_photos_id=1388414944
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1388414943
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1388414940
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1388414938
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1388414930
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1388414914
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 7.76%
Veuillez trouver les photos des contaminants.

exemples de contaminants: pet_clair: https://www.fotonower.com/view/27646629?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/27646631?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/27646634?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/27646637?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27646360_09-10-2025_14_42_57.pdf.

Lien vers velours :https://marlene.fotonower.com/velours/27646628,27646629,27646630,27646631,27646632,27646633,27646634,27646635,27646636,27646637,27646638?tags=environnement,pet_clair,mal_croppe,papier,pet_fonce,background,metal,flou,autre,carton,pehd.


L'équipe Fotonower 202 b'' Server: nginx Date: Thu, 09 Oct 2025 12:43:03 GMT Content-Length: 0 Connection: close X-Message-Id: Xetf7U7CSpOVZbREcVK9fw Access-Control-Allow-Origin: https://sendgrid.api-docs.io Access-Control-Allow-Methods: POST Access-Control-Allow-Headers: Authorization, Content-Type, On-behalf-of, x-sg-elas-acl Access-Control-Max-Age: 600 X-No-CORS-Reason: https://sendgrid.com/docs/Classroom/Basics/API/cors.html Strict-Transport-Security: max-age=31536000; includeSubDomains Content-Security-Policy: frame-ancestors 'none' Cache-Control: no-cache X-Content-Type-Options: no-sniff Referrer-Policy: strict-origin-when-cross-origin Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : send_mail_cod we use saveGeneral [1388414944, 1388414943, 1388414940, 1388414938, 1388414930, 1388414914] 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, '3903709') ('3318', '27646360', '1388414944', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414943', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414940', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414938', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414930', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414914', None, None, None, None, None, '3903709') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.03565478324890137 save_final save missing photos in datou_result : time spend for datou_step_exec : 6.344643831253052 time spend to save output : 0.03589320182800293 total time spend for step 9 : 6.380537033081055 step10:split_time_score Thu Oct 9 14:43:03 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'}] (('13', 6),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 09102025 27646360 Nombre de photos uploadées : 6 / 23040 (0%) 09102025 27646360 Nombre de photos taguées (types de déchets): 0 / 6 (0%) 09102025 27646360 Nombre de photos taguées (volume) : 0 / 6 (0%) elapsed_time : load_data_split_time_score 1.430511474609375e-06 elapsed_time : order_list_meta_photo_and_scores 5.245208740234375e-06 ?????? elapsed_time : fill_and_build_computed_from_old_data 0.0003619194030761719 Catched exception ! Connect or reconnect ! Catched exception ! Connect or reconnect ! elapsed_time : insert_dashboard_record_day_entry 0.6793889999389648 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 : 0.006979999940995782 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27627388_09-10-2025_07_31_50.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27627388 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`=27627388 AND mptpi.`type`=3726 To do Qualite : 0.032817041216563786 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27627402_09-10-2025_07_23_03.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27627402 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`=27627402 AND mptpi.`type`=3594 To do Qualite : 0.03697135180695989 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27627420_09-10-2025_07_16_46.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27627420 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`=27627420 AND mptpi.`type`=3726 To do Qualite : 0.029398137638777324 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27634485_09-10-2025_09_47_25.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27634485 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`=27634485 AND mptpi.`type`=3726 To do Qualite : 0.03744780642448261 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27642712_09-10-2025_12_47_12.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27642712 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`=27642712 AND mptpi.`type`=3726 To do Qualite : 0.06490990306712963 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27643653_09-10-2025_14_24_04.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27643653 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`=27643653 AND mptpi.`type`=3594 To do Qualite : 0.07761907632458846 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27646360_09-10-2025_14_42_57.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27646360 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`=27646360 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27646365 order by id desc limit 1 NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'09102025': {'nb_upload': 6, '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 [1388414944, 1388414943, 1388414940, 1388414938, 1388414930, 1388414914] Looping around the photos to save general results len do output : 1 /27646360Didn'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, '3903709') ('3318', '27646360', '1388414944', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414943', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414940', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414938', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414930', None, None, None, None, None, '3903709') ('3318', None, None, None, None, None, None, None, '3903709') ('3318', '27646360', '1388414914', None, None, None, None, None, '3903709') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 7 time used for this insertion : 0.04299211502075195 save_final save missing photos in datou_result : time spend for datou_step_exec : 3.4036643505096436 time spend to save output : 0.04318428039550781 total time spend for step 10 : 3.4468486309051514 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 6 set_done_treatment 81.59user 29.55system 2:42.74elapsed 68%CPU (0avgtext+0avgdata 4104844maxresident)k 23680inputs+73224outputs (608major+5140849minor)pagefaults 0swaps