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 : 3692814 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 : ['4102347'] with mtr_portfolio_ids : ['28828426'] and first list_photo_ids : [] new path : /proc/3692814/ 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 , WARNING: data may be incomplete, need to offset and complete ! BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 40 ; length of list_pids : 40 ; length of list_args : 40 time to download the photos : 5.764815807342529 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 Mon Nov 24 14:10:33 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 2025-11-24 14:10:37.751058: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-11-24 14:10:39.082513: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2025-11-24 14:10:39.084470: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f6438000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-11-24 14:10:39.084528: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-11-24 14:10:39.088440: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-11-24 14:10:39.440766: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x234205a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-11-24 14:10:39.440825: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-11-24 14:10:39.442307: 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 2025-11-24 14:10:39.444552: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-24 14:10:39.473619: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-24 14:10:39.491164: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-11-24 14:10:39.494915: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-11-24 14:10:39.524844: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-11-24 14:10:39.528836: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-11-24 14:10:39.584888: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-24 14:10:39.586936: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-11-24 14:10:39.587344: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-24 14:10:39.589076: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-11-24 14:10:39.589098: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-11-24 14:10:39.589124: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-11-24 14:10:39.591306: 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. 2025-11-24 14:10:40.067564: 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 2025-11-24 14:10:40.067680: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-24 14:10:40.067697: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-24 14:10:40.067711: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-11-24 14:10:40.067725: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-11-24 14:10:40.067738: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-11-24 14:10:40.067752: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-11-24 14:10:40.067765: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-24 14:10:40.068927: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-11-24 14:10:40.070137: 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 2025-11-24 14:10:40.070165: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-24 14:10:40.070179: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-24 14:10:40.070191: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-11-24 14:10:40.070204: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-11-24 14:10:40.070216: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-11-24 14:10:40.070228: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-11-24 14:10:40.070240: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-24 14:10:40.071407: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-11-24 14:10:40.071441: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-11-24 14:10:40.071449: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-11-24 14:10:40.071456: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-11-24 14:10:40.072657: 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 : [] 2025-11-24 14:10:45.408446: W tensorflow/core/framework/cpu_allocator_impl.cc:81] Allocation of 51380224 exceeds 10% of free system memory. 2025-11-24 14:10:49.912515: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-24 14:10:50.342387: 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 : 40 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 28.00000 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: 1920.00000 nb d'objets trouves : 7 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 25.00000 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: 1920.00000 nb d'objets trouves : 9 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 29.00000 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: 1920.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 32.00000 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: 1920.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 34.00000 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: 1920.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 33.00000 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: 1920.00000 nb d'objets trouves : 8 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 30.00000 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: 1920.00000 nb d'objets trouves : 3 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 37.00000 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: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 26.00000 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: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 31.00000 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: 1920.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 30.00000 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: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 33.00000 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: 1920.00000 nb d'objets trouves : 1 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 36.00000 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: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 31.00000 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: 1920.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 31.00000 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: 1920.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 38.00000 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: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 35.00000 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: 1920.00000 nb d'objets trouves : 3 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 20.00000 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: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 30.00000 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: 1920.00000 nb d'objets trouves : 3 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 33.00000 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: 1920.00000 nb d'objets trouves : 7 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 29.00000 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: 1920.00000 nb d'objets trouves : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 28.00000 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: 1920.00000 nb d'objets trouves : 7 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 33.00000 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: 1920.00000 nb d'objets trouves : 3 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 31.00000 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: 1920.00000 nb d'objets trouves : 9 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 25.00000 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: 1920.00000 nb d'objets trouves : 9 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 27.00000 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: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 36.00000 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: 1920.00000 nb d'objets trouves : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 34.00000 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: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 35.00000 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: 1920.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 35.00000 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: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 24.00000 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: 1920.00000 nb d'objets trouves : 7 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 32.00000 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: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 34.00000 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: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 32.00000 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: 1920.00000 nb d'objets trouves : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 31.00000 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: 1920.00000 nb d'objets trouves : 3 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 37.00000 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: 1920.00000 nb d'objets trouves : 9 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 32.00000 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: 1920.00000 nb d'objets trouves : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 29.00000 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: 1920.00000 nb d'objets trouves : 3 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 34.00000 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: 1920.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 40.00000 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: 1920.00000 nb d'objets trouves : 4 Detection mask done ! Trying to reset tf kernel 3694153 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5706 tf kernel not reseted sub process len(results) : 40 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 40 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 : 10998 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl2847 Catched exception ! Connect or reconnect ! thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] DEBUG bbox = [993, 960, 1071, 1128] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0003154277801513672 nb_pixel_total : 9748 time to create 1 rle with old method : 0.014027833938598633 length of segment : 93 DEBUG bbox = [474, 1512, 1080, 1920] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.002354145050048828 nb_pixel_total : 125727 time to create 1 rle with old method : 0.1310868263244629 length of segment : 551 DEBUG bbox = [315, 1062, 426, 1179] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0001678466796875 nb_pixel_total : 8622 time to create 1 rle with old method : 0.009089946746826172 length of segment : 110 DEBUG bbox = [915, 1230, 999, 1377] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00012040138244628906 nb_pixel_total : 4440 time to create 1 rle with old method : 0.005129337310791016 length of segment : 73 DEBUG bbox = [1038, 321, 1074, 432] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 6.771087646484375e-05 nb_pixel_total : 2535 time to create 1 rle with old method : 0.0029935836791992188 length of segment : 38 DEBUG bbox = [987, 1404, 1071, 1494] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 9.131431579589844e-05 nb_pixel_total : 4131 time to create 1 rle with old method : 0.004687309265136719 length of segment : 80 DEBUG bbox = [0, 0, 1035, 1677] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.04903888702392578 nb_pixel_total : 1542478 time to create 1 rle with new method : 0.2936720848083496 length of segment : 1159 DEBUG bbox = [462, 1068, 585, 1155] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0001475811004638672 nb_pixel_total : 6327 time to create 1 rle with old method : 0.007111549377441406 length of segment : 118 DEBUG bbox = [318, 1086, 420, 1176] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00010395050048828125 nb_pixel_total : 6158 time to create 1 rle with old method : 0.0068759918212890625 length of segment : 95 DEBUG bbox = [495, 1494, 1080, 1905] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.001720428466796875 nb_pixel_total : 118687 time to create 1 rle with old method : 0.13043475151062012 length of segment : 547 DEBUG bbox = [702, 774, 864, 1005] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0005526542663574219 nb_pixel_total : 18666 time to create 1 rle with old method : 0.020714759826660156 length of segment : 149 DEBUG bbox = [1038, 234, 1074, 381] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00010776519775390625 nb_pixel_total : 3324 time to create 1 rle with old method : 0.003767728805541992 length of segment : 33 DEBUG bbox = [3, 1290, 123, 1362] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0002243518829345703 nb_pixel_total : 6105 time to create 1 rle with old method : 0.0067901611328125 length of segment : 114 DEBUG bbox = [807, 774, 867, 942] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00017452239990234375 nb_pixel_total : 7326 time to create 1 rle with old method : 0.008346080780029297 length of segment : 72 DEBUG bbox = [0, 0, 1026, 1797] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.027986526489257812 nb_pixel_total : 1455466 time to create 1 rle with new method : 0.10531949996948242 length of segment : 1687 DEBUG bbox = [177, 1089, 411, 1194] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00026679039001464844 nb_pixel_total : 18260 time to create 1 rle with old method : 0.019426345825195312 length of segment : 227 DEBUG bbox = [168, 1242, 330, 1401] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0003752708435058594 nb_pixel_total : 13065 time to create 1 rle with old method : 0.013997077941894531 length of segment : 152 DEBUG bbox = [897, 585, 1071, 837] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0004763603210449219 nb_pixel_total : 8779 time to create 1 rle with old method : 0.009551763534545898 length of segment : 291 DEBUG bbox = [885, 633, 1068, 780] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0004584789276123047 nb_pixel_total : 17559 time to create 1 rle with old method : 0.019115209579467773 length of segment : 217 DEBUG bbox = [144, 1230, 363, 1353] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0004885196685791016 nb_pixel_total : 20452 time to create 1 rle with old method : 0.022154569625854492 length of segment : 220 DEBUG bbox = [807, 1137, 909, 1263] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00023746490478515625 nb_pixel_total : 8802 time to create 1 rle with old method : 0.009870767593383789 length of segment : 105 DEBUG bbox = [516, 1488, 1077, 1899] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.002229928970336914 nb_pixel_total : 123871 time to create 1 rle with old method : 0.13097095489501953 length of segment : 549 DEBUG bbox = [96, 981, 297, 1110] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0004439353942871094 nb_pixel_total : 15591 time to create 1 rle with old method : 0.020532608032226562 length of segment : 186 DEBUG bbox = [72, 1077, 408, 1338] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0012946128845214844 nb_pixel_total : 49697 time to create 1 rle with old method : 0.053125619888305664 length of segment : 357 DEBUG bbox = [534, 1482, 1080, 1890] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0019919872283935547 nb_pixel_total : 87128 time to create 1 rle with old method : 0.09126806259155273 length of segment : 436 DEBUG bbox = [654, 1395, 867, 1590] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0003142356872558594 nb_pixel_total : 14570 time to create 1 rle with old method : 0.015941381454467773 length of segment : 195 DEBUG bbox = [315, 1074, 465, 1191] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0003414154052734375 nb_pixel_total : 8738 time to create 1 rle with old method : 0.009658336639404297 length of segment : 135 DEBUG bbox = [0, 1404, 69, 1512] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00015807151794433594 nb_pixel_total : 4509 time to create 1 rle with old method : 0.005021572113037109 length of segment : 65 DEBUG bbox = [660, 849, 786, 1020] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0003712177276611328 nb_pixel_total : 14062 time to create 1 rle with old method : 0.015504598617553711 length of segment : 113 DEBUG bbox = [3, 1296, 90, 1398] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0002067089080810547 nb_pixel_total : 6021 time to create 1 rle with old method : 0.006808042526245117 length of segment : 82 DEBUG bbox = [309, 1086, 420, 1194] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0002300739288330078 nb_pixel_total : 7229 time to create 1 rle with old method : 0.008072614669799805 length of segment : 102 DEBUG bbox = [879, 12, 1074, 138] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0004532337188720703 nb_pixel_total : 9661 time to create 1 rle with old method : 0.01052403450012207 length of segment : 177 DEBUG bbox = [0, 1560, 60, 1710] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00017023086547851562 nb_pixel_total : 6783 time to create 1 rle with old method : 0.007411479949951172 length of segment : 59 DEBUG bbox = [0, 0, 972, 1734] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.028185606002807617 nb_pixel_total : 1247196 time to create 1 rle with new method : 0.06429004669189453 length of segment : 1438 DEBUG bbox = [3, 1407, 192, 1578] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0005002021789550781 nb_pixel_total : 21817 time to create 1 rle with old method : 0.023427248001098633 length of segment : 179 DEBUG bbox = [603, 969, 687, 1044] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00016546249389648438 nb_pixel_total : 3319 time to create 1 rle with old method : 0.003878355026245117 length of segment : 82 DEBUG bbox = [309, 1089, 438, 1179] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00023293495178222656 nb_pixel_total : 7419 time to create 1 rle with old method : 0.007855653762817383 length of segment : 111 DEBUG bbox = [600, 1221, 741, 1314] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00023674964904785156 nb_pixel_total : 6715 time to create 1 rle with old method : 0.0070667266845703125 length of segment : 115 DEBUG bbox = [828, 1386, 990, 1569] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0004508495330810547 nb_pixel_total : 14767 time to create 1 rle with old method : 0.015904903411865234 length of segment : 150 DEBUG bbox = [315, 1083, 432, 1182] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00021648406982421875 nb_pixel_total : 7420 time to create 1 rle with old method : 0.00802922248840332 length of segment : 111 DEBUG bbox = [921, 1104, 1023, 1290] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00022101402282714844 nb_pixel_total : 11134 time to create 1 rle with old method : 0.011913537979125977 length of segment : 81 DEBUG bbox = [804, 1098, 885, 1284] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0002052783966064453 nb_pixel_total : 10920 time to create 1 rle with old method : 0.01172184944152832 length of segment : 75 DEBUG bbox = [462, 1488, 1080, 1887] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.002515077590942383 nb_pixel_total : 118240 time to create 1 rle with old method : 0.12316036224365234 length of segment : 566 DEBUG bbox = [306, 1086, 435, 1185] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0002396106719970703 nb_pixel_total : 7677 time to create 1 rle with old method : 0.008933782577514648 length of segment : 116 DEBUG bbox = [0, 0, 1080, 1287] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.012001752853393555 nb_pixel_total : 735731 time to create 1 rle with new method : 0.05891227722167969 length of segment : 1312 DEBUG bbox = [321, 1080, 423, 1173] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00023674964904785156 nb_pixel_total : 6541 time to create 1 rle with old method : 0.0074160099029541016 length of segment : 96 DEBUG bbox = [3, 1089, 81, 1158] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00017905235290527344 nb_pixel_total : 4498 time to create 1 rle with old method : 0.006338834762573242 length of segment : 75 DEBUG bbox = [315, 1089, 429, 1185] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00024628639221191406 nb_pixel_total : 7142 time to create 1 rle with old method : 0.009764671325683594 length of segment : 106 DEBUG bbox = [450, 1569, 588, 1680] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0003552436828613281 nb_pixel_total : 10801 time to create 1 rle with old method : 0.01300048828125 length of segment : 131 DEBUG bbox = [1005, 270, 1080, 345] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 9.1552734375e-05 nb_pixel_total : 4094 time to create 1 rle with old method : 0.0048792362213134766 length of segment : 70 DEBUG bbox = [324, 891, 765, 1488] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0024003982543945312 nb_pixel_total : 112676 time to create 1 rle with old method : 0.12269306182861328 length of segment : 502 DEBUG bbox = [510, 1497, 1080, 1893] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0016903877258300781 nb_pixel_total : 107108 time to create 1 rle with old method : 0.11857891082763672 length of segment : 517 DEBUG bbox = [312, 1089, 411, 1185] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0002048015594482422 nb_pixel_total : 7180 time to create 1 rle with old method : 0.008063793182373047 length of segment : 95 DEBUG bbox = [315, 1083, 414, 1185] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00017070770263671875 nb_pixel_total : 6560 time to create 1 rle with old method : 0.007521867752075195 length of segment : 94 DEBUG bbox = [312, 1083, 411, 1185] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0002105236053466797 nb_pixel_total : 6973 time to create 1 rle with old method : 0.007737636566162109 length of segment : 93 DEBUG bbox = [6, 1512, 90, 1599] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00019311904907226562 nb_pixel_total : 3916 time to create 1 rle with old method : 0.004395961761474609 length of segment : 82 DEBUG bbox = [300, 930, 468, 1110] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0004184246063232422 nb_pixel_total : 17765 time to create 1 rle with old method : 0.01901841163635254 length of segment : 164 DEBUG bbox = [1032, 231, 1074, 330] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00010848045349121094 nb_pixel_total : 2869 time to create 1 rle with old method : 0.003406524658203125 length of segment : 41 DEBUG bbox = [945, 777, 1068, 843] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.000217437744140625 nb_pixel_total : 3949 time to create 1 rle with old method : 0.004273176193237305 length of segment : 107 DEBUG bbox = [3, 774, 1071, 1878] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.017942428588867188 nb_pixel_total : 974146 time to create 1 rle with new method : 0.07063436508178711 length of segment : 1522 time spent for convertir_results : 6.041033983230591 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 60 chid ids of type : 3594 Number RLEs to save : 16618 save missing photos in datou_result : time spend for datou_step_exec : 49.87859749794006 time spend to save output : 1.1312179565429688 total time spend for step 1 : 51.00981545448303 step2:crop_condition Mon Nov 24 14:11:24 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : 40 ! batch 1 Loaded 60 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 ! map_result returned by crop_photo_return_map_crop : length : 14 About to insert : list_path_to_insert length 14 new photo from crops ! About to upload 14 photos upload in portfolio : 3736932 init cache_photo without model_param we have 14 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1763989889_3692814 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(1763989891), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872579_3979aa494baf1b8d98d7ff95564ba374_rle_crop_4043702973_0.png', 0, 103, 106, 0, 1763989891,'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(1763989891), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872579_3979aa494baf1b8d98d7ff95564ba374_rle_crop_4043702974_0.png', 0, 144, 73, 0, 1763989891,'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(1763989891), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872570_b5afc6b02cc1a0ae7962af9168cd2b84_rle_crop_4043702984_0.png', 0, 168, 58, 0, 1763989891,'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(1763989891), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872568_dfbbbd723dfcb3e84c0d8bb3b88cadb6_rle_crop_4043702987_0.png', 0, 153, 149, 0, 1763989891,'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(1763989891), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872485_16f3defcbbf58ce749793790755c60ed_rle_crop_4043702991_0.png', 0, 116, 102, 0, 1763989891,'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(1763989891), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872457_afd1ace169b3c6fd5c2db37bc0375596_rle_crop_4043703002_0.png', 0, 106, 177, 0, 1763989891,'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(1763989891), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872385_36aff5158e6066a17050b839c05ec728_rle_crop_4043703008_0.png', 0, 90, 110, 0, 1763989891,'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(1763989891), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872383_345b49333ee83a9ef167100b6a5a33bc_rle_crop_4043703010_0.png', 0, 88, 111, 0, 1763989891,'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(1763989891), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872380_851ad8630341ca23ff1980b323891d8a_rle_crop_4043703014_0.png', 0, 90, 116, 0, 1763989891,'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(1763989891), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872380_851ad8630341ca23ff1980b323891d8a_rle_crop_4043703015_0.png', 0, 1241, 1014, 0, 1763989891,'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(1763989891), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872296_ad2911233becf91ea6fad2f58d0f8c12_rle_crop_4043703019_0.png', 0, 106, 130, 0, 1763989891,'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(1763989891), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872072_89c82f9dd784691fe0ba37eccbb79676_rle_crop_4043703023_0.png', 0, 89, 95, 0, 1763989891,'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(1763989891), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872068_0675e7c7a1abfb4e696aab122424356f_rle_crop_4043703025_0.png', 0, 92, 93, 0, 1763989891,'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(1763989891), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872035_b790a9166126391af82e3ca94b6fde3a_rle_crop_4043703029_0.png', 0, 57, 107, 0, 1763989891,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 14 photos in the portfolio 3736932 time of upload the photos Elapsed time : 3.3448832035064697 we have finished the crop for the class : papier begin to crop the class : carton param for this class : {'min_score': 0.7} filtre for class : carton hashtag_id of this class : 492774966 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 9 About to insert : list_path_to_insert length 9 new photo from crops ! About to upload 9 photos upload in portfolio : 3736932 init cache_photo without model_param we have 9 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1763989894_3692814 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(1763989896), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872574_8e1c7ced7093a59e325368bd88eb6eea_rle_crop_4043702978_0.png', 0, 77, 118, 0, 1763989896,'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(1763989896), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872574_8e1c7ced7093a59e325368bd88eb6eea_rle_crop_4043702979_0.png', 0, 82, 95, 0, 1763989896,'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(1763989896), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872459_ee29e632dec6210b25fae73d2121c706_rle_crop_4043702997_0.png', 0, 91, 135, 0, 1763989896,'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(1763989896), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872457_afd1ace169b3c6fd5c2db37bc0375596_rle_crop_4043703001_0.png', 0, 87, 102, 0, 1763989896,'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(1763989896), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872385_36aff5158e6066a17050b839c05ec728_rle_crop_4043703007_0.png', 0, 83, 111, 0, 1763989896,'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(1763989896), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872298_644addd48cdbd0d3470b7831d29dd666_rle_crop_4043703016_0.png', 0, 87, 96, 0, 1763989896,'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(1763989896), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872296_ad2911233becf91ea6fad2f58d0f8c12_rle_crop_4043703018_0.png', 0, 89, 106, 0, 1763989896,'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(1763989896), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872213_418119d17f09b71fd77a0dfc274e863a_rle_crop_4043703021_0.png', 0, 576, 393, 0, 1763989896,'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(1763989896), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872070_723c25bda3efb2fd3f11cc4cdd55eb6f_rle_crop_4043703024_0.png', 0, 87, 94, 0, 1763989896,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 9 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.5524935722351074 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 4 About to insert : list_path_to_insert length 4 new photo from crops ! About to upload 4 photos upload in portfolio : 3736932 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1763989897_3692814 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(1763989898), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872579_3979aa494baf1b8d98d7ff95564ba374_rle_crop_4043702975_0.png', 0, 104, 34, 0, 1763989898,'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(1763989898), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872573_7ed280bdd0e60327716c8321af406bea_rle_crop_4043702982_0.png', 0, 131, 32, 0, 1763989898,'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(1763989898), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872248_7eacf15e24c40dd2dde9a6d7cba90ebf_rle_crop_4043703020_0.png', 0, 70, 70, 0, 1763989898,'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(1763989898), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872065_35452833fd59bd99313b7d388a5685a5_rle_crop_4043703028_0.png', 0, 93, 40, 0, 1763989898,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 4 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.0514354705810547 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 32 About to insert : list_path_to_insert length 32 new photo from crops ! About to upload 32 photos upload in portfolio : 3736932 init cache_photo without model_param we have 32 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1763989923_3692814 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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872581_2f19095de53eb044e4eb0526ae3879e4_rle_crop_4043702971_0.png', 0, 152, 76, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872581_2f19095de53eb044e4eb0526ae3879e4_rle_crop_4043702972_0.png', 0, 377, 546, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872579_3979aa494baf1b8d98d7ff95564ba374_rle_crop_4043702976_0.png', 0, 78, 80, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872576_a4f9d62300e8f0e83406d230e7279fac_rle_crop_4043702977_0.png', 0, 1642, 999, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872574_8e1c7ced7093a59e325368bd88eb6eea_rle_crop_4043702980_0.png', 0, 385, 541, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872574_8e1c7ced7093a59e325368bd88eb6eea_rle_crop_4043702981_0.png', 0, 208, 142, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872572_9ee42c5520f538cb0f9761665b267b2e_rle_crop_4043702983_0.png', 0, 72, 114, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872570_b5afc6b02cc1a0ae7962af9168cd2b84_rle_crop_4043702985_0.png', 0, 1762, 999, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872570_b5afc6b02cc1a0ae7962af9168cd2b84_rle_crop_4043702986_0.png', 0, 104, 226, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872568_dfbbbd723dfcb3e84c0d8bb3b88cadb6_rle_crop_4043702988_0.png', 0, 158, 142, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872565_543a2c2022d4688ef97d64d9d34b7e81_rle_crop_4043702989_0.png', 0, 141, 181, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872487_fd17c6be5782fec25fe42247e8070d45_rle_crop_4043702990_0.png', 0, 123, 202, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872485_16f3defcbbf58ce749793790755c60ed_rle_crop_4043702992_0.png', 0, 381, 542, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872485_16f3defcbbf58ce749793790755c60ed_rle_crop_4043702993_0.png', 0, 112, 186, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872463_a041d104d258cf1753a4f0fad9bbee6e_rle_crop_4043702994_0.png', 0, 228, 318, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872463_a041d104d258cf1753a4f0fad9bbee6e_rle_crop_4043702995_0.png', 0, 373, 431, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872463_a041d104d258cf1753a4f0fad9bbee6e_rle_crop_4043702996_0.png', 0, 165, 195, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872459_ee29e632dec6210b25fae73d2121c706_rle_crop_4043702998_0.png', 0, 98, 64, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872459_ee29e632dec6210b25fae73d2121c706_rle_crop_4043702999_0.png', 0, 167, 109, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872457_afd1ace169b3c6fd5c2db37bc0375596_rle_crop_4043703000_0.png', 0, 98, 81, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872435_7b9892492896ad4a272ddcf9008cbf76_rle_crop_4043703003_0.png', 0, 150, 58, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872435_7b9892492896ad4a272ddcf9008cbf76_rle_crop_4043703004_0.png', 0, 1709, 953, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872405_e0772a99928e5b17d7bfce0a03dc4054_rle_crop_4043703005_0.png', 0, 169, 179, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872383_345b49333ee83a9ef167100b6a5a33bc_rle_crop_4043703009_0.png', 0, 160, 150, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872383_345b49333ee83a9ef167100b6a5a33bc_rle_crop_4043703011_0.png', 0, 181, 81, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872383_345b49333ee83a9ef167100b6a5a33bc_rle_crop_4043703012_0.png', 0, 177, 75, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872383_345b49333ee83a9ef167100b6a5a33bc_rle_crop_4043703013_0.png', 0, 370, 556, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872298_644addd48cdbd0d3470b7831d29dd666_rle_crop_4043703017_0.png', 0, 69, 73, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872185_1f57192815d5efa759e21c5ed39c4bf6_rle_crop_4043703022_0.png', 0, 354, 517, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872066_873f649ad41fd420d511839f9101c67f_rle_crop_4043703026_0.png', 0, 77, 82, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872066_873f649ad41fd420d511839f9101c67f_rle_crop_4043703027_0.png', 0, 150, 164, 0, 1763989930,'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(1763989930), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872035_b790a9166126391af82e3ca94b6fde3a_rle_crop_4043703030_0.png', 0, 1097, 1011, 0, 1763989930,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 32 photos in the portfolio 3736932 time of upload the photos Elapsed time : 9.60201621055603 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! 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/1763989934_3692814 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(1763989934), 0.0, 0.0, 14, '', 0, 0, '1763989828_3692814_1395872385_36aff5158e6066a17050b839c05ec728_rle_crop_4043703006_0.png', 0, 70, 72, 0, 1763989934,'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 : 0.6643939018249512 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1395872581, 1395872579, 1395872577, 1395872576, 1395872575, 1395872574, 1395872573, 1395872572, 1395872570, 1395872568, 1395872565, 1395872512, 1395872487, 1395872485, 1395872484, 1395872476, 1395872469, 1395872463, 1395872461, 1395872459, 1395872457, 1395872435, 1395872405, 1395872385, 1395872383, 1395872380, 1395872298, 1395872296, 1395872281, 1395872248, 1395872213, 1395872185, 1395872072, 1395872070, 1395872068, 1395872066, 1395872065, 1395872064, 1395872037, 1395872035] Looping around the photos to save general results len do output : 60 /1395902737Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395902738Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395902741Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395902742Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395902744Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395902745Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395902746Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395902748Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395902749Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395902750Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395902752Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395902753Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395902754Didn't retrieve data .Didn't retrieve data .Didn't 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data .Didn't retrieve data . /1395902779Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903062Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903063Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903064Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903065Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903066Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903067Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903068Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903069Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903070Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903071Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903072Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903073Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903074Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903075Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903076Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903077Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903078Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903079Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903080Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903081Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903082Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903083Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903084Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903085Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903086Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903087Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903088Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903089Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903090Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903091Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903092Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903093Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395903095Didn'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, '4102347') ('3318', None, '1395872581', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872579', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872577', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872576', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872575', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872574', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872573', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872572', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872570', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872568', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872565', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872512', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872487', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872485', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872484', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872476', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872469', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872463', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872461', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872459', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872457', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872435', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872405', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872385', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872383', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872380', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872298', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872296', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872281', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872248', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872213', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872185', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872072', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872070', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872068', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872066', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872065', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872064', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872037', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872035', None, None, None, None, None, '4102347') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 220 time used for this insertion : 0.030352354049682617 save_final save missing photos in datou_result : time spend for datou_step_exec : 49.76174974441528 time spend to save output : 0.03300213813781738 total time spend for step 2 : 49.7947518825531 step3:rle_unique_nms_with_priority Mon Nov 24 14:12:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array 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 60 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.1389906406402588 time for calcul the mask position with numpy : 0.035691022872924805 nb_pixel_total : 1938125 time to create 1 rle with new method : 0.13748693466186523 time for calcul the mask position with numpy : 0.007581472396850586 nb_pixel_total : 125727 time to create 1 rle with old method : 0.13960766792297363 time for calcul the mask position with numpy : 0.006676435470581055 nb_pixel_total : 9748 time to create 1 rle with old method : 0.010705232620239258 create new chi : 0.34772539138793945 time to delete rle : 0.026479721069335938 batch 1 Loaded 5 chid ids of type : 3594 +++++Number RLEs to save : 2368 TO DO : save crop sub photo not yet done ! save time : 0.2071065902709961 nb_obj : 4 nb_hashtags : 3 time to prepare the origin masks : 0.3365182876586914 time for calcul the mask position with numpy : 0.12083983421325684 nb_pixel_total : 2053872 time to create 1 rle with new method : 0.08406662940979004 time for calcul the mask position with numpy : 0.006304264068603516 nb_pixel_total : 4131 time to create 1 rle with old method : 0.004636526107788086 time for calcul the mask position with numpy : 0.00615239143371582 nb_pixel_total : 2535 time to create 1 rle with old method : 0.0028603076934814453 time for calcul the mask position with numpy : 0.006001949310302734 nb_pixel_total : 4440 time to create 1 rle with old method : 0.005011796951293945 time for calcul the mask position with numpy : 0.0063855648040771484 nb_pixel_total : 8622 time to create 1 rle with old method : 0.009463787078857422 create new chi : 0.2619056701660156 time to delete rle : 0.0003380775451660156 batch 1 Loaded 9 chid ids of type : 3594 +++++Number RLEs to save : 1682 TO DO : save crop sub photo not yet done ! save time : 0.15388226509094238 No data in photo_id : 1395872577 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.04207730293273926 time for calcul the mask position with numpy : 0.010339498519897461 nb_pixel_total : 531122 time to create 1 rle with new method : 0.030334949493408203 time for calcul the mask position with numpy : 0.018628358840942383 nb_pixel_total : 1542478 time to create 1 rle with new method : 0.03232312202453613 create new chi : 0.09206414222717285 time to delete rle : 0.0005221366882324219 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 3398 TO DO : save crop sub photo not yet done ! save time : 0.25936222076416016 No data in photo_id : 1395872575 nb_obj : 4 nb_hashtags : 2 time to prepare the origin masks : 0.15639352798461914 time for calcul the mask position with numpy : 0.14557456970214844 nb_pixel_total : 1923762 time to create 1 rle with new method : 0.12761211395263672 time for calcul the mask position with numpy : 0.006100177764892578 nb_pixel_total : 18666 time to create 1 rle with old method : 0.019959688186645508 time for calcul the mask position with numpy : 0.008069038391113281 nb_pixel_total : 118687 time to create 1 rle with old method : 0.127485990524292 time for calcul the mask position with numpy : 0.010544538497924805 nb_pixel_total : 6158 time to create 1 rle with old method : 0.007176876068115234 time for calcul the mask position with numpy : 0.011055946350097656 nb_pixel_total : 6327 time to create 1 rle with old method : 0.007025480270385742 create new chi : 0.48120856285095215 time to delete rle : 0.0007081031799316406 batch 1 Loaded 9 chid ids of type : 3594 ++++Number RLEs to save : 2898 TO DO : save crop sub photo not yet done ! save time : 0.2471308708190918 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.05021214485168457 time for calcul the mask position with numpy : 0.019625186920166016 nb_pixel_total : 2070276 time to create 1 rle with new method : 0.14281415939331055 time for calcul the mask position with numpy : 0.006223201751708984 nb_pixel_total : 3324 time to create 1 rle with old method : 0.003552675247192383 create new chi : 0.1724395751953125 time to delete rle : 0.0002338886260986328 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1146 TO DO : save crop sub photo not yet done ! save time : 0.12117552757263184 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.040656089782714844 time for calcul the mask position with numpy : 0.06678891181945801 nb_pixel_total : 2067495 time to create 1 rle with new method : 0.10859489440917969 time for calcul the mask position with numpy : 0.006289005279541016 nb_pixel_total : 6105 time to create 1 rle with old method : 0.00662994384765625 create new chi : 0.1922752857208252 time to delete rle : 0.0003337860107421875 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1308 TO DO : save crop sub photo not yet done ! save time : 0.12716221809387207 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.052176475524902344 time for calcul the mask position with numpy : 0.012737035751342773 nb_pixel_total : 610808 time to create 1 rle with new method : 0.09423089027404785 time for calcul the mask position with numpy : 0.05073046684265137 nb_pixel_total : 1455466 time to create 1 rle with new method : 0.09190082550048828 time for calcul the mask position with numpy : 0.00625920295715332 nb_pixel_total : 7326 time to create 1 rle with old method : 0.007952451705932617 create new chi : 0.27838659286499023 time to delete rle : 0.0009467601776123047 batch 1 Loaded 6 chid ids of type : 3594 +++Number RLEs to save : 4598 TO DO : save crop sub photo not yet done ! save time : 0.34463047981262207 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.039626359939575195 time for calcul the mask position with numpy : 0.07023262977600098 nb_pixel_total : 2051756 time to create 1 rle with new method : 0.07981729507446289 time for calcul the mask position with numpy : 0.006124973297119141 nb_pixel_total : 8779 time to create 1 rle with old method : 0.009862899780273438 time for calcul the mask position with numpy : 0.0063323974609375 nb_pixel_total : 13065 time to create 1 rle with old method : 0.014716148376464844 create new chi : 0.19658827781677246 time to delete rle : 0.00034546852111816406 batch 1 Loaded 5 chid ids of type : 3594 ++++Number RLEs to save : 1966 TO DO : save crop sub photo not yet done ! save time : 0.16756057739257812 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.0330810546875 time for calcul the mask position with numpy : 0.04343819618225098 nb_pixel_total : 2056041 time to create 1 rle with new method : 0.13020086288452148 time for calcul the mask position with numpy : 0.0067844390869140625 nb_pixel_total : 17559 time to create 1 rle with old method : 0.04641866683959961 create new chi : 0.23503375053405762 time to delete rle : 0.0005328655242919922 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1514 TO DO : save crop sub photo not yet done ! save time : 0.15611720085144043 No data in photo_id : 1395872512 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.04028463363647461 time for calcul the mask position with numpy : 0.08394956588745117 nb_pixel_total : 2053148 time to create 1 rle with new method : 0.13099074363708496 time for calcul the mask position with numpy : 0.0064678192138671875 nb_pixel_total : 20452 time to create 1 rle with old method : 0.022671222686767578 create new chi : 0.2530081272125244 time to delete rle : 0.0003337860107421875 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1520 TO DO : save crop sub photo not yet done ! save time : 0.15505647659301758 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.13059139251708984 time for calcul the mask position with numpy : 0.06025862693786621 nb_pixel_total : 1925336 time to create 1 rle with new method : 0.300687313079834 time for calcul the mask position with numpy : 0.006315469741821289 nb_pixel_total : 15591 time to create 1 rle with old method : 0.017071008682250977 time for calcul the mask position with numpy : 0.006850719451904297 nb_pixel_total : 123871 time to create 1 rle with old method : 0.16540145874023438 time for calcul the mask position with numpy : 0.006556034088134766 nb_pixel_total : 8802 time to create 1 rle with old method : 0.010115623474121094 create new chi : 0.5830605030059814 time to delete rle : 0.0003185272216796875 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 2760 TO DO : save crop sub photo not yet done ! save time : 0.23553991317749023 No data in photo_id : 1395872484 No data in photo_id : 1395872476 No data in photo_id : 1395872469 nb_obj : 3 nb_hashtags : 1 time to prepare the origin masks : 0.05887937545776367 time for calcul the mask position with numpy : 0.060689449310302734 nb_pixel_total : 1922205 time to create 1 rle with new method : 0.17807483673095703 time for calcul the mask position with numpy : 0.010518074035644531 nb_pixel_total : 14570 time to create 1 rle with old method : 0.02156829833984375 time for calcul the mask position with numpy : 0.011278152465820312 nb_pixel_total : 87128 time to create 1 rle with old method : 0.10083460807800293 time for calcul the mask position with numpy : 0.011745929718017578 nb_pixel_total : 49697 time to create 1 rle with old method : 0.06463384628295898 create new chi : 0.4708282947540283 time to delete rle : 0.0005311965942382812 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 3056 TO DO : save crop sub photo not yet done ! save time : 0.24413537979125977 No data in photo_id : 1395872461 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.13432002067565918 time for calcul the mask position with numpy : 0.18041181564331055 nb_pixel_total : 2046291 time to create 1 rle with new method : 0.17870616912841797 time for calcul the mask position with numpy : 0.006767749786376953 nb_pixel_total : 14062 time to create 1 rle with old method : 0.01594400405883789 time for calcul the mask position with numpy : 0.006643772125244141 nb_pixel_total : 4509 time to create 1 rle with old method : 0.005652666091918945 time for calcul the mask position with numpy : 0.007428884506225586 nb_pixel_total : 8738 time to create 1 rle with old method : 0.010011434555053711 create new chi : 0.4228026866912842 time to delete rle : 0.0003838539123535156 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 1706 TO DO : save crop sub photo not yet done ! save time : 0.16130757331848145 nb_obj : 3 nb_hashtags : 3 time to prepare the origin masks : 0.1061089038848877 time for calcul the mask position with numpy : 0.07193636894226074 nb_pixel_total : 2050689 time to create 1 rle with new method : 0.13567137718200684 time for calcul the mask position with numpy : 0.006954193115234375 nb_pixel_total : 9661 time to create 1 rle with old method : 0.011627912521362305 time for calcul the mask position with numpy : 0.006451606750488281 nb_pixel_total : 7229 time to create 1 rle with old method : 0.0083465576171875 time for calcul the mask position with numpy : 0.006173849105834961 nb_pixel_total : 6021 time to create 1 rle with old method : 0.006752729415893555 create new chi : 0.2666926383972168 time to delete rle : 0.0003428459167480469 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 1802 TO DO : save crop sub photo not yet done ! save time : 0.1652507781982422 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.050803184509277344 time for calcul the mask position with numpy : 0.016115188598632812 nb_pixel_total : 820231 time to create 1 rle with new method : 0.05385398864746094 time for calcul the mask position with numpy : 0.015651226043701172 nb_pixel_total : 1246586 time to create 1 rle with new method : 0.03372931480407715 time for calcul the mask position with numpy : 0.006653785705566406 nb_pixel_total : 6783 time to create 1 rle with old method : 0.0075800418853759766 create new chi : 0.13420581817626953 time to delete rle : 0.0007138252258300781 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 4059 TO DO : save crop sub photo not yet done ! save time : 0.31270503997802734 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.04280972480773926 time for calcul the mask position with numpy : 0.12336111068725586 nb_pixel_total : 2051783 time to create 1 rle with new method : 0.12726736068725586 time for calcul the mask position with numpy : 0.006942272186279297 nb_pixel_total : 21817 time to create 1 rle with old method : 0.023795604705810547 create new chi : 0.29109621047973633 time to delete rle : 0.0002613067626953125 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1438 TO DO : save crop sub photo not yet done ! save time : 0.14284563064575195 nb_obj : 3 nb_hashtags : 3 time to prepare the origin masks : 0.12625861167907715 time for calcul the mask position with numpy : 0.07020854949951172 nb_pixel_total : 2056147 time to create 1 rle with new method : 0.12537240982055664 time for calcul the mask position with numpy : 0.006342887878417969 nb_pixel_total : 6715 time to create 1 rle with old method : 0.0076487064361572266 time for calcul the mask position with numpy : 0.010837554931640625 nb_pixel_total : 7419 time to create 1 rle with old method : 0.009796142578125 time for calcul the mask position with numpy : 0.012321233749389648 nb_pixel_total : 3319 time to create 1 rle with old method : 0.0037822723388671875 create new chi : 0.2569103240966797 time to delete rle : 0.00041222572326660156 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 1696 TO DO : save crop sub photo not yet done ! save time : 0.17669439315795898 nb_obj : 5 nb_hashtags : 2 time to prepare the origin masks : 0.16031694412231445 time for calcul the mask position with numpy : 0.08274006843566895 nb_pixel_total : 1911119 time to create 1 rle with new method : 0.13544607162475586 time for calcul the mask position with numpy : 0.006766319274902344 nb_pixel_total : 118240 time to create 1 rle with old method : 0.12920761108398438 time for calcul the mask position with numpy : 0.006435394287109375 nb_pixel_total : 10920 time to create 1 rle with old method : 0.012281179428100586 time for calcul the mask position with numpy : 0.006072521209716797 nb_pixel_total : 11134 time to create 1 rle with old method : 0.012157201766967773 time for calcul the mask position with numpy : 0.006276369094848633 nb_pixel_total : 7420 time to create 1 rle with old method : 0.00874185562133789 time for calcul the mask position with numpy : 0.006269931793212891 nb_pixel_total : 14767 time to create 1 rle with old method : 0.015968799591064453 create new chi : 0.43891429901123047 time to delete rle : 0.0005321502685546875 batch 1 Loaded 11 chid ids of type : 3594 +++++Number RLEs to save : 3046 TO DO : save crop sub photo not yet done ! save time : 0.258420467376709 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.048774003982543945 time for calcul the mask position with numpy : 0.017245054244995117 nb_pixel_total : 1337869 time to create 1 rle with new method : 0.08831381797790527 time for calcul the mask position with numpy : 0.013916969299316406 nb_pixel_total : 728054 time to create 1 rle with new method : 0.02837991714477539 time for calcul the mask position with numpy : 0.005939006805419922 nb_pixel_total : 7677 time to create 1 rle with old method : 0.008332490921020508 create new chi : 0.16269659996032715 time to delete rle : 0.0006747245788574219 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 3936 TO DO : save crop sub photo not yet done ! save time : 0.2917921543121338 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.07444453239440918 time for calcul the mask position with numpy : 0.18216586112976074 nb_pixel_total : 2062561 time to create 1 rle with new method : 0.42955899238586426 time for calcul the mask position with numpy : 0.00853586196899414 nb_pixel_total : 4498 time to create 1 rle with old method : 0.005158424377441406 time for calcul the mask position with numpy : 0.007916927337646484 nb_pixel_total : 6541 time to create 1 rle with old method : 0.007415771484375 create new chi : 0.6548604965209961 time to delete rle : 0.0003998279571533203 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1422 TO DO : save crop sub photo not yet done ! save time : 0.15172529220581055 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.06657719612121582 time for calcul the mask position with numpy : 0.15006685256958008 nb_pixel_total : 2055657 time to create 1 rle with new method : 0.24687957763671875 time for calcul the mask position with numpy : 0.008447408676147461 nb_pixel_total : 10801 time to create 1 rle with old method : 0.019170284271240234 time for calcul the mask position with numpy : 0.0076732635498046875 nb_pixel_total : 7142 time to create 1 rle with old method : 0.014242172241210938 create new chi : 0.46121811866760254 time to delete rle : 0.0008115768432617188 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1554 TO DO : save crop sub photo not yet done ! save time : 0.16271138191223145 No data in photo_id : 1395872281 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.035675764083862305 time for calcul the mask position with numpy : 0.14166903495788574 nb_pixel_total : 2069506 time to create 1 rle with new method : 0.12972021102905273 time for calcul the mask position with numpy : 0.006330728530883789 nb_pixel_total : 4094 time to create 1 rle with old method : 0.004383563995361328 create new chi : 0.285494327545166 time to delete rle : 0.00026535987854003906 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1220 TO DO : save crop sub photo not yet done ! save time : 0.13168954849243164 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03516054153442383 time for calcul the mask position with numpy : 0.02279043197631836 nb_pixel_total : 1960924 time to create 1 rle with new method : 0.20106077194213867 time for calcul the mask position with numpy : 0.00687718391418457 nb_pixel_total : 112676 time to create 1 rle with old method : 0.12262415885925293 create new chi : 0.3624565601348877 time to delete rle : 0.0004036426544189453 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 2084 TO DO : save crop sub photo not yet done ! save time : 0.20623373985290527 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.0331873893737793 time for calcul the mask position with numpy : 0.032927751541137695 nb_pixel_total : 1966492 time to create 1 rle with new method : 0.11702656745910645 time for calcul the mask position with numpy : 0.007088899612426758 nb_pixel_total : 107108 time to create 1 rle with old method : 0.13402676582336426 create new chi : 0.3007993698120117 time to delete rle : 0.0005309581756591797 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 2114 TO DO : save crop sub photo not yet done ! save time : 0.19299101829528809 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.0375669002532959 time for calcul the mask position with numpy : 0.10377264022827148 nb_pixel_total : 2066420 time to create 1 rle with new method : 0.15803837776184082 time for calcul the mask position with numpy : 0.009372472763061523 nb_pixel_total : 7180 time to create 1 rle with old method : 0.012246131896972656 create new chi : 0.29506397247314453 time to delete rle : 0.00045609474182128906 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1270 TO DO : save crop sub photo not yet done ! save time : 0.13510537147521973 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03845334053039551 time for calcul the mask position with numpy : 0.08856773376464844 nb_pixel_total : 2067040 time to create 1 rle with new method : 1.869957685470581 time for calcul the mask position with numpy : 0.006634712219238281 nb_pixel_total : 6560 time to create 1 rle with old method : 0.008710145950317383 create new chi : 1.9845185279846191 time to delete rle : 0.00034427642822265625 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1268 TO DO : save crop sub photo not yet done ! save time : 0.12531805038452148 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.032921552658081055 time for calcul the mask position with numpy : 0.07144904136657715 nb_pixel_total : 2066627 time to create 1 rle with new method : 0.09867644309997559 time for calcul the mask position with numpy : 0.006293535232543945 nb_pixel_total : 6973 time to create 1 rle with old method : 0.0076978206634521484 create new chi : 0.19324541091918945 time to delete rle : 0.0002579689025878906 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1266 TO DO : save crop sub photo not yet done ! save time : 0.13580751419067383 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.04363512992858887 time for calcul the mask position with numpy : 0.26813840866088867 nb_pixel_total : 2051919 time to create 1 rle with new method : 0.08134937286376953 time for calcul the mask position with numpy : 0.0060346126556396484 nb_pixel_total : 17765 time to create 1 rle with old method : 0.019036293029785156 time for calcul the mask position with numpy : 0.0059413909912109375 nb_pixel_total : 3916 time to create 1 rle with old method : 0.004149198532104492 create new chi : 0.39507532119750977 time to delete rle : 0.00029540061950683594 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1572 TO DO : save crop sub photo not yet done ! save time : 0.16319561004638672 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03381776809692383 time for calcul the mask position with numpy : 0.787247896194458 nb_pixel_total : 2070731 time to create 1 rle with new method : 0.09830331802368164 time for calcul the mask position with numpy : 0.005982637405395508 nb_pixel_total : 2869 time to create 1 rle with old method : 0.003095865249633789 create new chi : 0.8981046676635742 time to delete rle : 0.00022459030151367188 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1162 TO DO : save crop sub photo not yet done ! save time : 0.12592315673828125 No data in photo_id : 1395872064 No data in photo_id : 1395872037 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.04299163818359375 time for calcul the mask position with numpy : 0.014158010482788086 nb_pixel_total : 1097715 time to create 1 rle with new method : 0.07618331909179688 time for calcul the mask position with numpy : 0.013388395309448242 nb_pixel_total : 971936 time to create 1 rle with new method : 0.12325286865234375 time for calcul the mask position with numpy : 0.006293058395385742 nb_pixel_total : 3949 time to create 1 rle with old method : 0.004300355911254883 create new chi : 0.24623322486877441 time to delete rle : 0.0007038116455078125 batch 1 Loaded 5 chid ids of type : 3594 ++++Number RLEs to save : 4260 TO DO : save crop sub photo not yet done ! save time : 0.3168017864227295 map_output_result : {1395872581: (0.0, 'Should be the crop_list due to order', 0), 1395872579: (0.0, 'Should be the crop_list due to order', 0), 1395872577: (0.0, 'Should be the crop_list due to order', 0.0), 1395872576: (0.0, 'Should be the crop_list due to order', 0), 1395872575: (0.0, 'Should be the crop_list due to order', 0.0), 1395872574: (0.0, 'Should be the crop_list due to order', 0), 1395872573: (0.0, 'Should be the crop_list due to order', 0), 1395872572: (0.0, 'Should be the crop_list due to order', 0), 1395872570: (0.0, 'Should be the crop_list due to order', 0), 1395872568: (0.0, 'Should be the crop_list due to order', 0), 1395872565: (0.0, 'Should be the crop_list due to order', 0), 1395872512: (0.0, 'Should be the crop_list due to order', 0.0), 1395872487: (0.0, 'Should be the crop_list due to order', 0), 1395872485: (0.0, 'Should be the crop_list due to order', 0), 1395872484: (0.0, 'Should be the crop_list due to order', 0.0), 1395872476: (0.0, 'Should be the crop_list due to order', 0.0), 1395872469: (0.0, 'Should be the crop_list due to order', 0.0), 1395872463: (0.0, 'Should be the crop_list due to order', 0), 1395872461: (0.0, 'Should be the crop_list due to order', 0.0), 1395872459: (0.0, 'Should be the crop_list due to order', 0), 1395872457: (0.0, 'Should be the crop_list due to order', 0), 1395872435: (0.0, 'Should be the crop_list due to order', 0), 1395872405: (0.0, 'Should be the crop_list due to order', 0), 1395872385: (0.0, 'Should be the crop_list due to order', 0), 1395872383: (0.0, 'Should be the crop_list due to order', 0), 1395872380: (0.0, 'Should be the crop_list due to order', 0), 1395872298: (0.0, 'Should be the crop_list due to order', 0), 1395872296: (0.0, 'Should be the crop_list due to order', 0), 1395872281: (0.0, 'Should be the crop_list due to order', 0.0), 1395872248: (0.0, 'Should be the crop_list due to order', 0), 1395872213: (0.0, 'Should be the crop_list due to order', 0), 1395872185: (0.0, 'Should be the crop_list due to order', 0), 1395872072: (0.0, 'Should be the crop_list due to order', 0), 1395872070: (0.0, 'Should be the crop_list due to order', 0), 1395872068: (0.0, 'Should be the crop_list due to order', 0), 1395872066: (0.0, 'Should be the crop_list due to order', 0), 1395872065: (0.0, 'Should be the crop_list due to order', 0), 1395872064: (0.0, 'Should be the crop_list due to order', 0.0), 1395872037: (0.0, 'Should be the crop_list due to order', 0.0), 1395872035: (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 [1395872581, 1395872579, 1395872577, 1395872576, 1395872575, 1395872574, 1395872573, 1395872572, 1395872570, 1395872568, 1395872565, 1395872512, 1395872487, 1395872485, 1395872484, 1395872476, 1395872469, 1395872463, 1395872461, 1395872459, 1395872457, 1395872435, 1395872405, 1395872385, 1395872383, 1395872380, 1395872298, 1395872296, 1395872281, 1395872248, 1395872213, 1395872185, 1395872072, 1395872070, 1395872068, 1395872066, 1395872065, 1395872064, 1395872037, 1395872035] Looping around the photos to save general results len do output : 40 /1395872581.Didn't retrieve data . /1395872579.Didn't retrieve data . /1395872577.Didn't retrieve data . /1395872576.Didn't retrieve data . /1395872575.Didn't retrieve data . /1395872574.Didn't retrieve data . /1395872573.Didn't retrieve data . /1395872572.Didn't retrieve data . /1395872570.Didn't retrieve data . /1395872568.Didn't retrieve data . /1395872565.Didn't retrieve data . /1395872512.Didn't retrieve data . /1395872487.Didn't retrieve data . /1395872485.Didn't retrieve data . /1395872484.Didn't retrieve data . /1395872476.Didn't retrieve data . /1395872469.Didn't retrieve data . /1395872463.Didn't retrieve data . /1395872461.Didn't retrieve data . /1395872459.Didn't retrieve data . /1395872457.Didn't retrieve data . /1395872435.Didn't retrieve data . /1395872405.Didn't retrieve data . /1395872385.Didn't retrieve data . /1395872383.Didn't retrieve data . /1395872380.Didn't retrieve data . /1395872298.Didn't retrieve data . /1395872296.Didn't retrieve data . /1395872281.Didn't retrieve data . /1395872248.Didn't retrieve data . /1395872213.Didn't retrieve data . /1395872185.Didn't retrieve data . /1395872072.Didn't retrieve data . /1395872070.Didn't retrieve data . /1395872068.Didn't retrieve data . /1395872066.Didn't retrieve data . /1395872065.Didn't retrieve data . /1395872064.Didn't retrieve data . /1395872037.Didn't retrieve data . /1395872035.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, '4102347') ('3318', None, '1395872581', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872579', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872577', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872576', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872575', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872574', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872573', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872572', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872570', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872568', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872565', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872512', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872487', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872485', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872484', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872476', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872469', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872463', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872461', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872459', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872457', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872435', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872405', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872385', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872383', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872380', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872298', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872296', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872281', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872248', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872213', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872185', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872072', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872070', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872068', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872066', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872065', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872064', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872037', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872035', None, None, None, None, None, '4102347') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 120 time used for this insertion : 0.022088050842285156 save_final save missing photos in datou_result : time spend for datou_step_exec : 20.621968746185303 time spend to save output : 0.02340841293334961 total time spend for step 3 : 20.645377159118652 step4:ventilate_hashtags_in_portfolio Mon Nov 24 14:12:35 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 : 28828426 get user id for portfolio 28828426 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`=28828426 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pehd','carton','pet_fonce','background','autre','pet_clair','flou','mal_croppe','papier','environnement','metal')) AND mptpi.`min_score`=0.5 To do 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`=28828426 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pehd','carton','pet_fonce','background','autre','pet_clair','flou','mal_croppe','papier','environnement','metal')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://marlene.fotonower.com/velours/28831321,28831322,28831323,28831324,28831325,28831326,28831327,28831328,28831329,28831330,28831331?tags=metal,mal_croppe,carton,environnement,flou,pet_clair,pet_fonce,background,papier,autre,pehd Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1395872581, 1395872579, 1395872577, 1395872576, 1395872575, 1395872574, 1395872573, 1395872572, 1395872570, 1395872568, 1395872565, 1395872512, 1395872487, 1395872485, 1395872484, 1395872476, 1395872469, 1395872463, 1395872461, 1395872459, 1395872457, 1395872435, 1395872405, 1395872385, 1395872383, 1395872380, 1395872298, 1395872296, 1395872281, 1395872248, 1395872213, 1395872185, 1395872072, 1395872070, 1395872068, 1395872066, 1395872065, 1395872064, 1395872037, 1395872035] Looping around the photos to save general results len do output : 1 /28828426. 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, '4102347') ('3318', None, '1395872581', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872579', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872577', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872576', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872575', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872574', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872573', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872572', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872570', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872568', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872565', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872512', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872487', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872485', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872484', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872476', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872469', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872463', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872461', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872459', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872457', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872435', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872405', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872385', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872383', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872380', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872298', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872296', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872281', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872248', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872213', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872185', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872072', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872070', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872068', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872066', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872065', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872064', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872037', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872035', None, None, None, None, None, '4102347') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 41 time used for this insertion : 0.019964218139648438 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.4933176040649414 time spend to save output : 0.020438432693481445 total time spend for step 4 : 0.5137560367584229 step5:final Mon Nov 24 14:12:35 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 : {1395872581: ('0.10797812982253087',), 1395872579: ('0.10797812982253087',), 1395872577: ('0.10797812982253087',), 1395872576: ('0.10797812982253087',), 1395872575: ('0.10797812982253087',), 1395872574: ('0.10797812982253087',), 1395872573: ('0.10797812982253087',), 1395872572: ('0.10797812982253087',), 1395872570: ('0.10797812982253087',), 1395872568: ('0.10797812982253087',), 1395872565: ('0.10797812982253087',), 1395872512: ('0.10797812982253087',), 1395872487: ('0.10797812982253087',), 1395872485: ('0.10797812982253087',), 1395872484: ('0.10797812982253087',), 1395872476: ('0.10797812982253087',), 1395872469: ('0.10797812982253087',), 1395872463: ('0.10797812982253087',), 1395872461: ('0.10797812982253087',), 1395872459: ('0.10797812982253087',), 1395872457: ('0.10797812982253087',), 1395872435: ('0.10797812982253087',), 1395872405: ('0.10797812982253087',), 1395872385: ('0.10797812982253087',), 1395872383: ('0.10797812982253087',), 1395872380: ('0.10797812982253087',), 1395872298: ('0.10797812982253087',), 1395872296: ('0.10797812982253087',), 1395872281: ('0.10797812982253087',), 1395872248: ('0.10797812982253087',), 1395872213: ('0.10797812982253087',), 1395872185: ('0.10797812982253087',), 1395872072: ('0.10797812982253087',), 1395872070: ('0.10797812982253087',), 1395872068: ('0.10797812982253087',), 1395872066: ('0.10797812982253087',), 1395872065: ('0.10797812982253087',), 1395872064: ('0.10797812982253087',), 1395872037: ('0.10797812982253087',), 1395872035: ('0.10797812982253087',)} new output for save of step final : {1395872581: ('0.10797812982253087',), 1395872579: ('0.10797812982253087',), 1395872577: ('0.10797812982253087',), 1395872576: ('0.10797812982253087',), 1395872575: ('0.10797812982253087',), 1395872574: ('0.10797812982253087',), 1395872573: ('0.10797812982253087',), 1395872572: ('0.10797812982253087',), 1395872570: ('0.10797812982253087',), 1395872568: ('0.10797812982253087',), 1395872565: ('0.10797812982253087',), 1395872512: ('0.10797812982253087',), 1395872487: ('0.10797812982253087',), 1395872485: ('0.10797812982253087',), 1395872484: ('0.10797812982253087',), 1395872476: ('0.10797812982253087',), 1395872469: ('0.10797812982253087',), 1395872463: ('0.10797812982253087',), 1395872461: ('0.10797812982253087',), 1395872459: ('0.10797812982253087',), 1395872457: ('0.10797812982253087',), 1395872435: ('0.10797812982253087',), 1395872405: ('0.10797812982253087',), 1395872385: ('0.10797812982253087',), 1395872383: ('0.10797812982253087',), 1395872380: ('0.10797812982253087',), 1395872298: ('0.10797812982253087',), 1395872296: ('0.10797812982253087',), 1395872281: ('0.10797812982253087',), 1395872248: ('0.10797812982253087',), 1395872213: ('0.10797812982253087',), 1395872185: ('0.10797812982253087',), 1395872072: ('0.10797812982253087',), 1395872070: ('0.10797812982253087',), 1395872068: ('0.10797812982253087',), 1395872066: ('0.10797812982253087',), 1395872065: ('0.10797812982253087',), 1395872064: ('0.10797812982253087',), 1395872037: ('0.10797812982253087',), 1395872035: ('0.10797812982253087',)} [1395872581, 1395872579, 1395872577, 1395872576, 1395872575, 1395872574, 1395872573, 1395872572, 1395872570, 1395872568, 1395872565, 1395872512, 1395872487, 1395872485, 1395872484, 1395872476, 1395872469, 1395872463, 1395872461, 1395872459, 1395872457, 1395872435, 1395872405, 1395872385, 1395872383, 1395872380, 1395872298, 1395872296, 1395872281, 1395872248, 1395872213, 1395872185, 1395872072, 1395872070, 1395872068, 1395872066, 1395872065, 1395872064, 1395872037, 1395872035] Looping around the photos to save general results len do output : 40 /1395872581.Didn't retrieve data . /1395872579.Didn't retrieve data . /1395872577.Didn't retrieve data . /1395872576.Didn't retrieve data . /1395872575.Didn't retrieve data . /1395872574.Didn't retrieve data . /1395872573.Didn't retrieve data . /1395872572.Didn't retrieve data . /1395872570.Didn't retrieve data . /1395872568.Didn't retrieve data . /1395872565.Didn't retrieve data . /1395872512.Didn't retrieve data . /1395872487.Didn't retrieve data . /1395872485.Didn't retrieve data . /1395872484.Didn't retrieve data . /1395872476.Didn't retrieve data . /1395872469.Didn't retrieve data . /1395872463.Didn't retrieve data . /1395872461.Didn't retrieve data . /1395872459.Didn't retrieve data . /1395872457.Didn't retrieve data . /1395872435.Didn't retrieve data . /1395872405.Didn't retrieve data . /1395872385.Didn't retrieve data . /1395872383.Didn't retrieve data . /1395872380.Didn't retrieve data . /1395872298.Didn't retrieve data . /1395872296.Didn't retrieve data . /1395872281.Didn't retrieve data . /1395872248.Didn't retrieve data . /1395872213.Didn't retrieve data . /1395872185.Didn't retrieve data . /1395872072.Didn't retrieve data . /1395872070.Didn't retrieve data . /1395872068.Didn't retrieve data . /1395872066.Didn't retrieve data . /1395872065.Didn't retrieve data . /1395872064.Didn't retrieve data . /1395872037.Didn't retrieve data . /1395872035.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, '4102347') ('3318', None, '1395872581', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872579', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872577', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872576', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872575', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872574', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872573', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872572', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872570', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872568', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872565', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872512', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872487', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872485', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872484', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872476', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872469', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872463', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872461', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872459', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872457', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872435', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872405', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872385', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872383', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872380', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872298', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872296', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872281', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872248', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872213', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872185', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872072', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872070', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872068', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872066', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872065', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872064', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872037', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872035', None, None, None, None, None, '4102347') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 120 time used for this insertion : 0.022870302200317383 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.17490720748901367 time spend to save output : 0.024488210678100586 total time spend for step 5 : 0.19939541816711426 step6:blur_detection Mon Nov 24 14:12:35 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/1763989828_3692814_1395872581_2f19095de53eb044e4eb0526ae3879e4.jpg resize: (1080, 1920) 1395872581 -4.162985618886324 treat image : temp/1763989828_3692814_1395872579_3979aa494baf1b8d98d7ff95564ba374.jpg resize: (1080, 1920) 1395872579 -1.7797441996902523 treat image : temp/1763989828_3692814_1395872577_773c0848d3eeff327f0d9a337708d6a0.jpg resize: (1080, 1920) 1395872577 2.3878070747129256 treat image : temp/1763989828_3692814_1395872576_a4f9d62300e8f0e83406d230e7279fac.jpg resize: (1080, 1920) 1395872576 -2.0213361665572 treat image : temp/1763989828_3692814_1395872575_2045914658e308f0a56f1d3a3d792cbf.jpg resize: (1080, 1920) 1395872575 -0.2742509773009542 treat image : temp/1763989828_3692814_1395872574_8e1c7ced7093a59e325368bd88eb6eea.jpg resize: (1080, 1920) 1395872574 -2.3314684285280065 treat image : temp/1763989828_3692814_1395872573_7ed280bdd0e60327716c8321af406bea.jpg resize: (1080, 1920) 1395872573 -1.850016952677111 treat image : temp/1763989828_3692814_1395872572_9ee42c5520f538cb0f9761665b267b2e.jpg resize: (1080, 1920) 1395872572 -1.9864385509345712 treat image : temp/1763989828_3692814_1395872570_b5afc6b02cc1a0ae7962af9168cd2b84.jpg resize: (1080, 1920) 1395872570 -0.8182140151115446 treat image : temp/1763989828_3692814_1395872568_dfbbbd723dfcb3e84c0d8bb3b88cadb6.jpg resize: (1080, 1920) 1395872568 -1.4694026060605108 treat image : temp/1763989828_3692814_1395872565_543a2c2022d4688ef97d64d9d34b7e81.jpg resize: (1080, 1920) 1395872565 -2.110218786086752 treat image : temp/1763989828_3692814_1395872512_de5c2399f4844a1d24d4d31607796ed4.jpg resize: (1080, 1920) 1395872512 -1.4414754161887575 treat image : temp/1763989828_3692814_1395872487_fd17c6be5782fec25fe42247e8070d45.jpg resize: (1080, 1920) 1395872487 -1.8073298138652614 treat image : temp/1763989828_3692814_1395872485_16f3defcbbf58ce749793790755c60ed.jpg resize: (1080, 1920) 1395872485 -2.246722175807204 treat image : temp/1763989828_3692814_1395872484_d241b4f22a492db08d6d82c67826a755.jpg resize: (1080, 1920) 1395872484 -0.7464007171137959 treat image : temp/1763989828_3692814_1395872476_82d6fed19e73ac77619dd77875f9d04b.jpg resize: (1080, 1920) 1395872476 -1.5574637700199845 treat image : temp/1763989828_3692814_1395872469_d1d19ea66e354dcea91c343ea06b8dc6.jpg resize: (1080, 1920) 1395872469 -1.3799272774866125 treat image : temp/1763989828_3692814_1395872463_a041d104d258cf1753a4f0fad9bbee6e.jpg resize: (1080, 1920) 1395872463 -2.3172253763001027 treat image : temp/1763989828_3692814_1395872461_b8098a64c9d0032f3d7cb8c16abae7f6.jpg resize: (1080, 1920) 1395872461 -1.6991742138997123 treat image : temp/1763989828_3692814_1395872459_ee29e632dec6210b25fae73d2121c706.jpg resize: (1080, 1920) 1395872459 -0.4604198664516323 treat image : temp/1763989828_3692814_1395872457_afd1ace169b3c6fd5c2db37bc0375596.jpg resize: (1080, 1920) 1395872457 -2.3042304263708493 treat image : temp/1763989828_3692814_1395872435_7b9892492896ad4a272ddcf9008cbf76.jpg resize: (1080, 1920) 1395872435 -0.8825557606293813 treat image : temp/1763989828_3692814_1395872405_e0772a99928e5b17d7bfce0a03dc4054.jpg resize: (1080, 1920) 1395872405 -2.163583759628403 treat image : temp/1763989828_3692814_1395872385_36aff5158e6066a17050b839c05ec728.jpg resize: (1080, 1920) 1395872385 -2.169676960415709 treat image : temp/1763989828_3692814_1395872383_345b49333ee83a9ef167100b6a5a33bc.jpg resize: (1080, 1920) 1395872383 -0.32431891563694315 treat image : temp/1763989828_3692814_1395872380_851ad8630341ca23ff1980b323891d8a.jpg resize: (1080, 1920) 1395872380 1.488714395714221 treat image : temp/1763989828_3692814_1395872298_644addd48cdbd0d3470b7831d29dd666.jpg resize: (1080, 1920) 1395872298 -1.9107834767627336 treat image : temp/1763989828_3692814_1395872296_ad2911233becf91ea6fad2f58d0f8c12.jpg resize: (1080, 1920) 1395872296 -2.02723077188188 treat image : temp/1763989828_3692814_1395872281_a7250858ddbbd54493546a2ed9023958.jpg resize: (1080, 1920) 1395872281 -1.6447168685736828 treat image : temp/1763989828_3692814_1395872248_7eacf15e24c40dd2dde9a6d7cba90ebf.jpg resize: (1080, 1920) 1395872248 -2.124544959460348 treat image : temp/1763989828_3692814_1395872213_418119d17f09b71fd77a0dfc274e863a.jpg resize: (1080, 1920) 1395872213 -2.4530742886605488 treat image : temp/1763989828_3692814_1395872185_1f57192815d5efa759e21c5ed39c4bf6.jpg resize: (1080, 1920) 1395872185 -2.060721805995839 treat image : temp/1763989828_3692814_1395872072_89c82f9dd784691fe0ba37eccbb79676.jpg resize: (1080, 1920) 1395872072 -2.1027128753964632 treat image : temp/1763989828_3692814_1395872070_723c25bda3efb2fd3f11cc4cdd55eb6f.jpg resize: (1080, 1920) 1395872070 -1.974770071944301 treat image : temp/1763989828_3692814_1395872068_0675e7c7a1abfb4e696aab122424356f.jpg resize: (1080, 1920) 1395872068 -0.2638832843168115 treat image : temp/1763989828_3692814_1395872066_873f649ad41fd420d511839f9101c67f.jpg resize: (1080, 1920) 1395872066 -1.9122697040745376 treat image : temp/1763989828_3692814_1395872065_35452833fd59bd99313b7d388a5685a5.jpg resize: (1080, 1920) 1395872065 -2.2108715160828516 treat image : temp/1763989828_3692814_1395872064_309d9403d967c796570089637f2feb75.jpg resize: (1080, 1920) 1395872064 -1.5826571483820264 treat image : temp/1763989828_3692814_1395872037_0c82f3851b60210748fc31c7d29a4e3d.jpg resize: (1080, 1920) 1395872037 -2.260899491916398 treat image : temp/1763989828_3692814_1395872035_b790a9166126391af82e3ca94b6fde3a.jpg resize: (1080, 1920) 1395872035 -2.496706052882009 treat image : temp/1763989828_3692814_1395872579_3979aa494baf1b8d98d7ff95564ba374_rle_crop_4043702973_0.png resize: (106, 103) 1395902737 -0.27561089301733704 treat image : temp/1763989828_3692814_1395872579_3979aa494baf1b8d98d7ff95564ba374_rle_crop_4043702974_0.png resize: (73, 144) 1395902738 -2.647218413968801 treat image : temp/1763989828_3692814_1395872570_b5afc6b02cc1a0ae7962af9168cd2b84_rle_crop_4043702984_0.png resize: (58, 168) 1395902741 -0.9051095573928906 treat image : temp/1763989828_3692814_1395872568_dfbbbd723dfcb3e84c0d8bb3b88cadb6_rle_crop_4043702987_0.png resize: (149, 153) 1395902742 -1.56808194039373 treat image : temp/1763989828_3692814_1395872485_16f3defcbbf58ce749793790755c60ed_rle_crop_4043702991_0.png resize: (102, 116) 1395902744 -2.2658321241777917 treat image : temp/1763989828_3692814_1395872457_afd1ace169b3c6fd5c2db37bc0375596_rle_crop_4043703002_0.png resize: (177, 106) 1395902745 -1.8480654132205485 treat image : temp/1763989828_3692814_1395872385_36aff5158e6066a17050b839c05ec728_rle_crop_4043703008_0.png resize: (110, 90) 1395902746 -2.7162972545453576 treat image : temp/1763989828_3692814_1395872383_345b49333ee83a9ef167100b6a5a33bc_rle_crop_4043703010_0.png resize: (111, 88) 1395902748 -0.4013289998815101 treat image : temp/1763989828_3692814_1395872380_851ad8630341ca23ff1980b323891d8a_rle_crop_4043703014_0.png resize: (116, 90) 1395902749 -0.8132001799654585 treat image : temp/1763989828_3692814_1395872380_851ad8630341ca23ff1980b323891d8a_rle_crop_4043703015_0.png resize: (1014, 1241) 1395902750 -0.2097677521665834 treat image : temp/1763989828_3692814_1395872296_ad2911233becf91ea6fad2f58d0f8c12_rle_crop_4043703019_0.png resize: (130, 106) 1395902752 -0.044968221784315664 treat image : temp/1763989828_3692814_1395872072_89c82f9dd784691fe0ba37eccbb79676_rle_crop_4043703023_0.png resize: (95, 89) 1395902753 2.6843223586646494 treat image : temp/1763989828_3692814_1395872068_0675e7c7a1abfb4e696aab122424356f_rle_crop_4043703025_0.png resize: (93, 92) 1395902754 0.9040757698931676 treat image : temp/1763989828_3692814_1395872035_b790a9166126391af82e3ca94b6fde3a_rle_crop_4043703029_0.png resize: (107, 57) 1395902756 -3.4676876259892717 treat image : temp/1763989828_3692814_1395872574_8e1c7ced7093a59e325368bd88eb6eea_rle_crop_4043702978_0.png resize: (118, 77) 1395902765 -1.018989123171669 treat image : temp/1763989828_3692814_1395872574_8e1c7ced7093a59e325368bd88eb6eea_rle_crop_4043702979_0.png resize: (95, 82) 1395902766 0.5003520005182104 treat image : temp/1763989828_3692814_1395872459_ee29e632dec6210b25fae73d2121c706_rle_crop_4043702997_0.png resize: (135, 91) 1395902768 -1.2372661791708364 treat image : temp/1763989828_3692814_1395872457_afd1ace169b3c6fd5c2db37bc0375596_rle_crop_4043703001_0.png resize: (102, 87) 1395902769 1.0319823744411114 treat image : temp/1763989828_3692814_1395872385_36aff5158e6066a17050b839c05ec728_rle_crop_4043703007_0.png resize: (111, 83) 1395902770 0.5624498716295819 treat image : temp/1763989828_3692814_1395872298_644addd48cdbd0d3470b7831d29dd666_rle_crop_4043703016_0.png resize: (96, 87) 1395902771 0.41412005619374953 treat image : temp/1763989828_3692814_1395872296_ad2911233becf91ea6fad2f58d0f8c12_rle_crop_4043703018_0.png resize: (106, 89) 1395902772 -0.3727124598521711 treat image : temp/1763989828_3692814_1395872213_418119d17f09b71fd77a0dfc274e863a_rle_crop_4043703021_0.png resize: (393, 576) 1395902773 -2.003374342659729 treat image : temp/1763989828_3692814_1395872070_723c25bda3efb2fd3f11cc4cdd55eb6f_rle_crop_4043703024_0.png resize: (94, 87) 1395902774 0.7385177064813899 treat image : temp/1763989828_3692814_1395872579_3979aa494baf1b8d98d7ff95564ba374_rle_crop_4043702975_0.png resize: (34, 104) 1395902776 -2.390357030332867 treat image : temp/1763989828_3692814_1395872573_7ed280bdd0e60327716c8321af406bea_rle_crop_4043702982_0.png resize: (32, 131) 1395902777 -4.766727012501731 treat image : temp/1763989828_3692814_1395872248_7eacf15e24c40dd2dde9a6d7cba90ebf_rle_crop_4043703020_0.png resize: (70, 70) 1395902778 -3.4704758944035445 treat image : temp/1763989828_3692814_1395872065_35452833fd59bd99313b7d388a5685a5_rle_crop_4043703028_0.png resize: (40, 93) 1395902779 -3.9916942598987943 treat image : temp/1763989828_3692814_1395872581_2f19095de53eb044e4eb0526ae3879e4_rle_crop_4043702971_0.png resize: (76, 152) 1395903062 -2.3447844267133595 treat image : temp/1763989828_3692814_1395872581_2f19095de53eb044e4eb0526ae3879e4_rle_crop_4043702972_0.png resize: (546, 377) 1395903063 0.07459794662029136 treat image : temp/1763989828_3692814_1395872579_3979aa494baf1b8d98d7ff95564ba374_rle_crop_4043702976_0.png resize: (80, 78) 1395903064 -2.9714489635330965 treat image : temp/1763989828_3692814_1395872576_a4f9d62300e8f0e83406d230e7279fac_rle_crop_4043702977_0.png resize: (999, 1642) 1395903065 -2.2295542042508734 treat image : 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insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 100 time used for this insertion : 0.017978429794311523 save missing photos in datou_result : time spend for datou_step_exec : 33.669049978256226 time spend to save output : 0.04291701316833496 total time spend for step 6 : 33.71196699142456 step7:brightness Mon Nov 24 14:13:09 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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/1763989828_3692814_1395872581_2f19095de53eb044e4eb0526ae3879e4.jpg treat image : temp/1763989828_3692814_1395872579_3979aa494baf1b8d98d7ff95564ba374.jpg treat image : temp/1763989828_3692814_1395872577_773c0848d3eeff327f0d9a337708d6a0.jpg treat image : temp/1763989828_3692814_1395872576_a4f9d62300e8f0e83406d230e7279fac.jpg treat image : temp/1763989828_3692814_1395872575_2045914658e308f0a56f1d3a3d792cbf.jpg treat image : temp/1763989828_3692814_1395872574_8e1c7ced7093a59e325368bd88eb6eea.jpg treat image : temp/1763989828_3692814_1395872573_7ed280bdd0e60327716c8321af406bea.jpg treat image : temp/1763989828_3692814_1395872572_9ee42c5520f538cb0f9761665b267b2e.jpg treat image : temp/1763989828_3692814_1395872570_b5afc6b02cc1a0ae7962af9168cd2b84.jpg treat image : temp/1763989828_3692814_1395872568_dfbbbd723dfcb3e84c0d8bb3b88cadb6.jpg treat image : 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temp/1763989828_3692814_1395872435_7b9892492896ad4a272ddcf9008cbf76_rle_crop_4043703004_0.png treat image : temp/1763989828_3692814_1395872405_e0772a99928e5b17d7bfce0a03dc4054_rle_crop_4043703005_0.png treat image : temp/1763989828_3692814_1395872383_345b49333ee83a9ef167100b6a5a33bc_rle_crop_4043703009_0.png treat image : temp/1763989828_3692814_1395872383_345b49333ee83a9ef167100b6a5a33bc_rle_crop_4043703011_0.png treat image : temp/1763989828_3692814_1395872383_345b49333ee83a9ef167100b6a5a33bc_rle_crop_4043703012_0.png treat image : temp/1763989828_3692814_1395872383_345b49333ee83a9ef167100b6a5a33bc_rle_crop_4043703013_0.png treat image : temp/1763989828_3692814_1395872298_644addd48cdbd0d3470b7831d29dd666_rle_crop_4043703017_0.png treat image : temp/1763989828_3692814_1395872185_1f57192815d5efa759e21c5ed39c4bf6_rle_crop_4043703022_0.png treat image : temp/1763989828_3692814_1395872066_873f649ad41fd420d511839f9101c67f_rle_crop_4043703026_0.png treat image : temp/1763989828_3692814_1395872066_873f649ad41fd420d511839f9101c67f_rle_crop_4043703027_0.png treat image : temp/1763989828_3692814_1395872035_b790a9166126391af82e3ca94b6fde3a_rle_crop_4043703030_0.png treat image : temp/1763989828_3692814_1395872385_36aff5158e6066a17050b839c05ec728_rle_crop_4043703006_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 : 100 time used for this insertion : 0.019353151321411133 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 100 time used for this insertion : 0.020601511001586914 save missing photos in datou_result : time spend for datou_step_exec : 10.074187517166138 time spend to save output : 0.04627346992492676 total time spend for step 7 : 10.120460987091064 step8:velours_tree Mon Nov 24 14:13:19 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 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 : 0.216994047164917 time spend to save output : 5.221366882324219e-05 total time spend for step 8 : 0.21704626083374023 step9:send_mail_cod Mon Nov 24 14:13:20 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_P28828426_24-11-2025_14_13_20.pdf 28831321 change filename to text .change filename to text .change filename to text .change filename to text .imagette288313211763990000 28831322 imagette288313221763990000 28831323 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 .imagette288313231763990000 28831325 imagette288313251763990002 28831326 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 .imagette288313261763990002 28831327 imagette288313271763990003 28831328 imagette288313281763990003 28831329 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 .imagette288313291763990003 28831330 change filename to text .change filename to text .change filename to text .imagette288313301763990005 28831331 imagette288313311763990005 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=28828426 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://marlene.fotonower.com/velours/28831321,28831322,28831323,28831324,28831325,28831326,28831327,28831328,28831329,28831330,28831331?tags=metal,mal_croppe,carton,environnement,flou,pet_clair,pet_fonce,background,papier,autre,pehd args[1395872581] : ((1395872581, -4.162985618886324, 492609224), (1395872581, 0.49036933580803105, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872579] : ((1395872579, -1.7797441996902523, 492688767), (1395872579, 0.3136334796789782, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872577] : ((1395872577, 2.3878070747129256, 492688767), (1395872577, 0.5726821790844556, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872576] : ((1395872576, -2.0213361665572, 492609224), (1395872576, 0.4950728788711972, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872575] : ((1395872575, -0.2742509773009542, 492688767), (1395872575, 0.46112591313987705, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872574] : ((1395872574, -2.3314684285280065, 492609224), (1395872574, 0.605238476774946, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872573] : ((1395872573, -1.850016952677111, 492688767), (1395872573, 0.5030619222499582, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872572] : ((1395872572, -1.9864385509345712, 492688767), (1395872572, 0.563327680189365, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872570] : ((1395872570, -0.8182140151115446, 492688767), (1395872570, 0.3089740698711832, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872568] : ((1395872568, -1.4694026060605108, 492688767), (1395872568, 0.43474018735602726, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872565] : ((1395872565, -2.110218786086752, 492609224), (1395872565, 0.5270062858863753, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872512] : ((1395872512, -1.4414754161887575, 492688767), (1395872512, 0.5710676520402728, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872487] : ((1395872487, -1.8073298138652614, 492688767), (1395872487, 0.6585998203222958, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872485] : ((1395872485, -2.246722175807204, 492609224), (1395872485, 0.44728121965645334, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872484] : ((1395872484, -0.7464007171137959, 492688767), (1395872484, 0.6154545706324459, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872476] : ((1395872476, -1.5574637700199845, 492688767), (1395872476, 0.6063973274486468, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872469] : ((1395872469, -1.3799272774866125, 492688767), (1395872469, 0.7446408974437589, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872463] : ((1395872463, -2.3172253763001027, 492609224), (1395872463, 0.8235445289727151, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872461] : ((1395872461, -1.6991742138997123, 492688767), (1395872461, 0.7245490172574417, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872459] : ((1395872459, -0.4604198664516323, 492688767), (1395872459, 0.5355285422158211, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872457] : ((1395872457, -2.3042304263708493, 492609224), (1395872457, 0.5092514120596553, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872435] : ((1395872435, -0.8825557606293813, 492688767), (1395872435, 0.4049281193786778, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872405] : ((1395872405, -2.163583759628403, 492609224), (1395872405, 0.6390758840012334, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872385] : ((1395872385, -2.169676960415709, 492609224), (1395872385, 0.5214696116669097, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872383] : ((1395872383, -0.32431891563694315, 492688767), (1395872383, 0.29850616584162465, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872380] : ((1395872380, 1.488714395714221, 492688767), (1395872380, 0.3553034953154276, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872298] : ((1395872298, -1.9107834767627336, 492688767), (1395872298, 0.7615452359657833, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872296] : ((1395872296, -2.02723077188188, 492609224), (1395872296, 0.7920274271451614, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872281] : ((1395872281, -1.6447168685736828, 492688767), (1395872281, 0.6717290809446854, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872248] : ((1395872248, -2.124544959460348, 492609224), (1395872248, 0.7317512113574818, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872213] : ((1395872213, -2.4530742886605488, 492609224), (1395872213, 0.4120913823745469, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872185] : ((1395872185, -2.060721805995839, 492609224), (1395872185, 0.4992363795899776, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872072] : ((1395872072, -2.1027128753964632, 492609224), (1395872072, 0.5939749655663004, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872070] : ((1395872070, -1.974770071944301, 492688767), (1395872070, 0.5204333782130056, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872068] : ((1395872068, -0.2638832843168115, 492688767), (1395872068, 0.4542601309448662, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872066] : ((1395872066, -1.9122697040745376, 492688767), (1395872066, 0.6160760571327095, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872065] : ((1395872065, -2.2108715160828516, 492609224), (1395872065, 0.6949653211369602, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872064] : ((1395872064, -1.5826571483820264, 492688767), (1395872064, 0.7009844202870088, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872037] : ((1395872037, -2.260899491916398, 492609224), (1395872037, 0.6645900768273978, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com args[1395872035] : ((1395872035, -2.496706052882009, 492609224), (1395872035, 0.6897709492060392, 2107752395), '0.10797812982253087') We are sending mail with results at report@fotonower.com refus_total : 0.10797812982253087 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=28828426 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_P28828426_24-11-2025_14_13_20.pdf results_Auto_P28828426_24-11-2025_14_13_20.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28828426_24-11-2025_14_13_20.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','28828426','results_Auto_P28828426_24-11-2025_14_13_20.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28828426_24-11-2025_14_13_20.pdf','pdf','','0.92','0.10797812982253087') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/28828426

https://www.fotonower.com/image?json=false&list_photos_id=1395872581
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872579
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872577
La photo est trop floue, merci de reprendre une photo.(avec le score = 2.3878070747129256)
https://www.fotonower.com/image?json=false&list_photos_id=1395872576
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872575
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872574
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872573
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872572
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872570
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872568
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872565
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872512
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872487
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872485
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872484
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872476
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872469
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872463
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872461
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872459
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872457
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872435
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872405
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872385
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872383
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872380
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.488714395714221)
https://www.fotonower.com/image?json=false&list_photos_id=1395872298
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872296
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872281
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872248
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872213
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872185
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872072
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872070
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872068
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872066
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872065
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872064
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872037
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872035
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 10.80%
Veuillez trouver les photos des contaminants.

exemples de contaminants: metal: https://www.fotonower.com/view/28831321?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/28831323?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/28831326?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/28831329?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/28831330?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28828426_24-11-2025_14_13_20.pdf.

Lien vers velours :https://marlene.fotonower.com/velours/28831321,28831322,28831323,28831324,28831325,28831326,28831327,28831328,28831329,28831330,28831331?tags=metal,mal_croppe,carton,environnement,flou,pet_clair,pet_fonce,background,papier,autre,pehd.


L'équipe Fotonower 202 b'' Date: Mon, 24 Nov 2025 13:13:28 GMT Content-Length: 0 Connection: close Server: nginx X-Message-Id: 84UaQnTqTmWg_Pt6TkR0Hg 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 [1395872581, 1395872579, 1395872577, 1395872576, 1395872575, 1395872574, 1395872573, 1395872572, 1395872570, 1395872568, 1395872565, 1395872512, 1395872487, 1395872485, 1395872484, 1395872476, 1395872469, 1395872463, 1395872461, 1395872459, 1395872457, 1395872435, 1395872405, 1395872385, 1395872383, 1395872380, 1395872298, 1395872296, 1395872281, 1395872248, 1395872213, 1395872185, 1395872072, 1395872070, 1395872068, 1395872066, 1395872065, 1395872064, 1395872037, 1395872035] 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, '4102347') ('3318', None, '1395872581', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872579', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872577', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872576', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872575', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872574', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872573', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872572', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872570', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872568', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872565', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872512', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872487', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872485', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872484', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872476', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872469', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872463', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872461', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872459', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872457', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872435', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872405', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872385', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872383', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872380', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872298', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872296', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872281', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872248', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872213', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872185', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872072', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872070', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872068', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872066', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872065', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872064', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872037', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872035', None, None, None, None, None, '4102347') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 40 time used for this insertion : 0.02272176742553711 save_final save missing photos in datou_result : time spend for datou_step_exec : 8.651643514633179 time spend to save output : 0.023213624954223633 total time spend for step 9 : 8.674857139587402 step10:split_time_score Mon Nov 24 14:13:28 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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'}] (('09', 87),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 24112025 28828426 Nombre de photos uploadées : 87 / 23040 (0%) 24112025 28828426 Nombre de photos taguées (types de déchets): 0 / 87 (0%) 24112025 28828426 Nombre de photos taguées (volume) : 0 / 87 (0%) elapsed_time : load_data_split_time_score 1.430511474609375e-06 elapsed_time : order_list_meta_photo_and_scores 6.67572021484375e-06 ??????????????????????????????????????????????????????????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.003917694091796875 Catched exception ! Connect or reconnect ! Catched exception ! Connect or reconnect ! elapsed_time : insert_dashboard_record_day_entry 0.26707983016967773 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 : 0.1292190272955247 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28828426_24-11-2025_14_13_20.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28828426 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`=28828426 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28828428 order by id desc limit 1 Qualite : 0.07008904145622898 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28828431_24-11-2025_13_52_11.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28828431 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`=28828431 AND mptpi.`type`=3594 To do Qualite : 0.25237177426268853 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28828433_24-11-2025_12_21_46.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28828433 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`=28828433 AND mptpi.`type`=3594 To do Qualite : 0.021958550347222217 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28828435_24-11-2025_12_41_57.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28828435 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`=28828435 AND mptpi.`type`=3594 To do Qualite : 0.05147855581275721 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28828444_24-11-2025_12_11_42.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28828444 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`=28828444 AND mptpi.`type`=3594 To do Qualite : 0.030636240206552704 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28829905_24-11-2025_13_41_32.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28829905 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`=28829905 AND mptpi.`type`=3594 To do Qualite : 0.03575169994212963 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28829907_24-11-2025_13_23_01.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28829907 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`=28829907 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'24112025': {'nb_upload': 87, '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 [1395872581, 1395872579, 1395872577, 1395872576, 1395872575, 1395872574, 1395872573, 1395872572, 1395872570, 1395872568, 1395872565, 1395872512, 1395872487, 1395872485, 1395872484, 1395872476, 1395872469, 1395872463, 1395872461, 1395872459, 1395872457, 1395872435, 1395872405, 1395872385, 1395872383, 1395872380, 1395872298, 1395872296, 1395872281, 1395872248, 1395872213, 1395872185, 1395872072, 1395872070, 1395872068, 1395872066, 1395872065, 1395872064, 1395872037, 1395872035] Looping around the photos to save general results len do output : 1 /28828426Didn'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, '4102347') ('3318', None, '1395872581', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872579', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872577', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872576', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872575', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872574', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872573', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872572', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872570', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872568', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872565', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872512', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872487', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872485', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872484', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872476', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872469', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872463', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872461', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872459', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872457', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872435', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872405', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872385', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872383', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872380', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872298', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872296', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872281', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872248', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872213', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872185', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872072', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872070', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872068', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872066', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872065', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872064', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872037', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', None, '1395872035', None, None, None, None, None, '4102347') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 41 time used for this insertion : 0.02042102813720703 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.180593490600586 time spend to save output : 0.020806550979614258 total time spend for step 10 : 2.2014000415802 caffe_path_current : About to save ! 2 After save, about to update current ! update_current_state 109.68user 41.58system 3:04.79elapsed 81%CPU (0avgtext+0avgdata 2701008maxresident)k 2663944inputs+72336outputs (15000major+3194608minor)pagefaults 0swaps