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 : 3301087 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 : ['2717511'] with mtr_portfolio_ids : ['21986466'] and first list_photo_ids : [] new path : /proc/3301087/ 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 , BFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 8 ; length of list_pids : 8 ; length of list_args : 8 time to download the photos : 1.4338366985321045 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : 0 number of steps : 10 step1:mask_detect Wed Apr 2 20:30:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10814 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-02 20:30:35.720596: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-02 20:30:35.747262: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-02 20:30:35.749437: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f2a84000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-02 20:30:35.749499: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-02 20:30:35.754040: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-02 20:30:36.000349: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7175160 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-02 20:30:36.000422: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-02 20:30:36.002049: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-02 20:30:36.002539: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-02 20:30:36.005683: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-02 20:30:36.008580: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-02 20:30:36.008999: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-02 20:30:36.011679: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-02 20:30:36.013032: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-02 20:30:36.029123: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-02 20:30:36.032322: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-02 20:30:36.032577: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-02 20:30:36.033350: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-02 20:30:36.033366: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-02 20:30:36.033375: I tensorflow/core/common_runt2025-04-02 20:30:45.535009: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-02 20:30:45.751344: 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 : 8 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 45 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 28 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 39 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 48 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 57 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 57 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 95 Detection mask done ! Trying to reset tf kernel 3301797 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5525 tf kernel not reseted sub process len(results) : 8 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 8 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 : 10814 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl2847 Catched exception ! Connect or reconnect ! thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] time for calcul the mask position with numpy : 0.001123189926147461 nb_pixel_total : 39585 time to create 1 rle with old method : 0.04333329200744629 length of segment : 220 time for calcul the mask position with numpy : 0.004439592361450195 nb_pixel_total : 206102 time to create 1 rle with new method : 0.011600494384765625 length of segment : 617 time for calcul the mask position with numpy : 0.0007476806640625 nb_pixel_total : 38519 time to create 1 rle with old method : 0.04131197929382324 length of segment : 252 time for calcul the mask position with numpy : 0.0003192424774169922 nb_pixel_total : 16505 time to create 1 rle with old method : 0.01837754249572754 length of segment : 187 time for calcul the mask position with numpy : 0.0014278888702392578 nb_pixel_total : 94390 time to create 1 rle with old method : 0.09867453575134277 length of segment : 435 time for calcul the mask position with numpy : 0.002104043960571289 nb_pixel_total : 118841 time to create 1 rle with old method : 0.12689709663391113 length of segment : 458 time for calcul the mask position with numpy : 0.0008983612060546875 nb_pixel_total : 51402 time to create 1 rle with old method : 0.05560111999511719 length of segment : 289 time for calcul the mask position with numpy : 0.002681732177734375 nb_pixel_total : 160601 time to create 1 rle with new method : 0.012604236602783203 length of segment : 669 time for calcul the mask position with numpy : 0.001531362533569336 nb_pixel_total : 94202 time to create 1 rle with old method : 0.1119084358215332 length of segment : 333 time for calcul the mask position with numpy : 0.0007038116455078125 nb_pixel_total : 34905 time to create 1 rle with old method : 0.0421602725982666 length of segment : 225 time for calcul the mask position with numpy : 0.012703180313110352 nb_pixel_total : 342376 time to create 1 rle with new method : 0.030718564987182617 length of segment : 726 time for calcul the mask position with numpy : 0.00042700767517089844 nb_pixel_total : 6375 time to create 1 rle with old method : 0.007318258285522461 length of segment : 69 time for calcul the mask position with numpy : 0.001184225082397461 nb_pixel_total : 55610 time to create 1 rle with old method : 0.0638127326965332 length of segment : 289 time for calcul the mask position with numpy : 0.0013883113861083984 nb_pixel_total : 20448 time to create 1 rle with old method : 0.023191213607788086 length of segment : 243 time for calcul the mask position with numpy : 0.002067089080810547 nb_pixel_total : 57224 time to create 1 rle with old method : 0.06437826156616211 length of segment : 261 time for calcul the mask position with numpy : 0.0025603771209716797 nb_pixel_total : 47554 time to create 1 rle with old method : 0.05426931381225586 length of segment : 392 time for calcul the mask position with numpy : 0.0006053447723388672 nb_pixel_total : 10677 time to create 1 rle with old method : 0.012346029281616211 length of segment : 180 time for calcul the mask position with numpy : 0.0013287067413330078 nb_pixel_total : 30376 time to create 1 rle with old method : 0.03506159782409668 length of segment : 303 time for calcul the mask position with numpy : 0.0010602474212646484 nb_pixel_total : 21094 time to create 1 rle with old method : 0.024481534957885742 length of segment : 206 time for calcul the mask position with numpy : 0.0007228851318359375 nb_pixel_total : 13202 time to create 1 rle with old method : 0.01523590087890625 length of segment : 203 time for calcul the mask position with numpy : 0.0016765594482421875 nb_pixel_total : 32785 time to create 1 rle with old method : 0.050438880920410156 length of segment : 142 time for calcul the mask position with numpy : 0.0008120536804199219 nb_pixel_total : 15554 time to create 1 rle with old method : 0.017966508865356445 length of segment : 145 time for calcul the mask position with numpy : 0.007993221282958984 nb_pixel_total : 233021 time to create 1 rle with new method : 0.014163732528686523 length of segment : 510 time for calcul the mask position with numpy : 0.0049724578857421875 nb_pixel_total : 136540 time to create 1 rle with old method : 0.17792916297912598 length of segment : 482 time for calcul the mask position with numpy : 0.0018689632415771484 nb_pixel_total : 68307 time to create 1 rle with old method : 0.07876086235046387 length of segment : 222 time for calcul the mask position with numpy : 0.0014796257019042969 nb_pixel_total : 52316 time to create 1 rle with old method : 0.06297183036804199 length of segment : 280 time for calcul the mask position with numpy : 0.0007724761962890625 nb_pixel_total : 20011 time to create 1 rle with old method : 0.02158975601196289 length of segment : 235 time for calcul the mask position with numpy : 0.005234241485595703 nb_pixel_total : 198790 time to create 1 rle with new method : 0.010969161987304688 length of segment : 777 time for calcul the mask position with numpy : 0.0012052059173583984 nb_pixel_total : 37178 time to create 1 rle with old method : 0.04144477844238281 length of segment : 244 time for calcul the mask position with numpy : 0.0007894039154052734 nb_pixel_total : 25081 time to create 1 rle with old method : 0.027483701705932617 length of segment : 296 time for calcul the mask position with numpy : 0.0006020069122314453 nb_pixel_total : 13578 time to create 1 rle with old method : 0.0169522762298584 length of segment : 137 time for calcul the mask position with numpy : 0.00058746337890625 nb_pixel_total : 14676 time to create 1 rle with old method : 0.017932891845703125 length of segment : 182 time for calcul the mask position with numpy : 0.001043558120727539 nb_pixel_total : 36106 time to create 1 rle with old method : 0.04303765296936035 length of segment : 195 time for calcul the mask position with numpy : 0.002270221710205078 nb_pixel_total : 57562 time to create 1 rle with old method : 0.08083510398864746 length of segment : 250 time for calcul the mask position with numpy : 0.0006818771362304688 nb_pixel_total : 23911 time to create 1 rle with old method : 0.030340194702148438 length of segment : 180 time for calcul the mask position with numpy : 0.0007307529449462891 nb_pixel_total : 13156 time to create 1 rle with old method : 0.017543792724609375 length of segment : 171 time for calcul the mask position with numpy : 0.00045180320739746094 nb_pixel_total : 8349 time to create 1 rle with old method : 0.01233220100402832 length of segment : 165 time for calcul the mask position with numpy : 0.007389068603515625 nb_pixel_total : 224991 time to create 1 rle with new method : 0.013693809509277344 length of segment : 491 time for calcul the mask position with numpy : 0.0008156299591064453 nb_pixel_total : 12933 time to create 1 rle with old method : 0.016124725341796875 length of segment : 85 time for calcul the mask position with numpy : 0.00047016143798828125 nb_pixel_total : 12563 time to create 1 rle with old method : 0.015828371047973633 length of segment : 156 time for calcul the mask position with numpy : 0.0009462833404541016 nb_pixel_total : 21942 time to create 1 rle with old method : 0.02767181396484375 length of segment : 153 time for calcul the mask position with numpy : 0.2658500671386719 nb_pixel_total : 1547109 time to create 1 rle with new method : 0.3073868751525879 length of segment : 1546 time for calcul the mask position with numpy : 0.0003840923309326172 nb_pixel_total : 14320 time to create 1 rle with old method : 0.015872716903686523 length of segment : 129 time for calcul the mask position with numpy : 0.0033142566680908203 nb_pixel_total : 102976 time to create 1 rle with old method : 0.11345887184143066 length of segment : 493 time for calcul the mask position with numpy : 0.00023484230041503906 nb_pixel_total : 6977 time to create 1 rle with old method : 0.007706165313720703 length of segment : 105 time for calcul the mask position with numpy : 0.0014274120330810547 nb_pixel_total : 71304 time to create 1 rle with old method : 0.07724738121032715 length of segment : 256 time for calcul the mask position with numpy : 0.0007498264312744141 nb_pixel_total : 42900 time to create 1 rle with old method : 0.04888796806335449 length of segment : 143 time for calcul the mask position with numpy : 0.0010867118835449219 nb_pixel_total : 25538 time to create 1 rle with old method : 0.02884817123413086 length of segment : 196 time for calcul the mask position with numpy : 0.0011169910430908203 nb_pixel_total : 34906 time to create 1 rle with old method : 0.04157423973083496 length of segment : 306 time for calcul the mask position with numpy : 0.007600545883178711 nb_pixel_total : 32823 time to create 1 rle with old method : 0.03850126266479492 length of segment : 369 time for calcul the mask position with numpy : 0.0027434825897216797 nb_pixel_total : 66878 time to create 1 rle with old method : 0.07449483871459961 length of segment : 247 time for calcul the mask position with numpy : 0.0030965805053710938 nb_pixel_total : 85080 time to create 1 rle with old method : 0.09698271751403809 length of segment : 287 time for calcul the mask position with numpy : 0.0005724430084228516 nb_pixel_total : 9029 time to create 1 rle with old method : 0.010492801666259766 length of segment : 130 time for calcul the mask position with numpy : 0.0038590431213378906 nb_pixel_total : 85675 time to create 1 rle with old method : 0.09442806243896484 length of segment : 315 time for calcul the mask position with numpy : 0.000484466552734375 nb_pixel_total : 8191 time to create 1 rle with old method : 0.0096282958984375 length of segment : 96 time for calcul the mask position with numpy : 0.0029211044311523438 nb_pixel_total : 81794 time to create 1 rle with old method : 0.08935904502868652 length of segment : 531 time for calcul the mask position with numpy : 0.0015339851379394531 nb_pixel_total : 33173 time to create 1 rle with old method : 0.03705906867980957 length of segment : 214 time for calcul the mask position with numpy : 0.0013399124145507812 nb_pixel_total : 25193 time to create 1 rle with old method : 0.028948307037353516 length of segment : 242 time for calcul the mask position with numpy : 0.008765220642089844 nb_pixel_total : 214148 time to create 1 rle with new method : 0.011729001998901367 length of segment : 469 time for calcul the mask position with numpy : 0.0005362033843994141 nb_pixel_total : 9954 time to create 1 rle with old method : 0.011403560638427734 length of segment : 117 time for calcul the mask position with numpy : 0.0062503814697265625 nb_pixel_total : 139992 time to create 1 rle with old method : 0.15660452842712402 length of segment : 460 time for calcul the mask position with numpy : 0.0029821395874023438 nb_pixel_total : 84451 time to create 1 rle with old method : 0.09338760375976562 length of segment : 271 time for calcul the mask position with numpy : 0.0013239383697509766 nb_pixel_total : 31364 time to create 1 rle with old method : 0.04399275779724121 length of segment : 320 time for calcul the mask position with numpy : 0.010971546173095703 nb_pixel_total : 124339 time to create 1 rle with old method : 0.1594986915588379 length of segment : 508 time for calcul the mask position with numpy : 0.0018703937530517578 nb_pixel_total : 32320 time to create 1 rle with old method : 0.04272031784057617 length of segment : 364 time for calcul the mask position with numpy : 0.0004487037658691406 nb_pixel_total : 12183 time to create 1 rle with old method : 0.016496896743774414 length of segment : 134 time for calcul the mask position with numpy : 0.002349376678466797 nb_pixel_total : 35147 time to create 1 rle with old method : 0.03990674018859863 length of segment : 304 time for calcul the mask position with numpy : 0.004536628723144531 nb_pixel_total : 105073 time to create 1 rle with old method : 0.11627030372619629 length of segment : 432 time for calcul the mask position with numpy : 0.001963376998901367 nb_pixel_total : 48219 time to create 1 rle with old method : 0.05365180969238281 length of segment : 130 time for calcul the mask position with numpy : 0.0009911060333251953 nb_pixel_total : 16964 time to create 1 rle with old method : 0.01865077018737793 length of segment : 166 time for calcul the mask position with numpy : 0.0026404857635498047 nb_pixel_total : 55957 time to create 1 rle with old method : 0.06119680404663086 length of segment : 374 time for calcul the mask position with numpy : 0.0033338069915771484 nb_pixel_total : 89055 time to create 1 rle with old method : 0.09620785713195801 length of segment : 458 time for calcul the mask position with numpy : 0.0010797977447509766 nb_pixel_total : 22263 time to create 1 rle with old method : 0.02474832534790039 length of segment : 186 time for calcul the mask position with numpy : 0.009272336959838867 nb_pixel_total : 231555 time to create 1 rle with new method : 0.015592098236083984 length of segment : 565 time for calcul the mask position with numpy : 0.0010693073272705078 nb_pixel_total : 21761 time to create 1 rle with old method : 0.02550053596496582 length of segment : 263 time for calcul the mask position with numpy : 0.0007493495941162109 nb_pixel_total : 19059 time to create 1 rle with old method : 0.02101278305053711 length of segment : 277 time for calcul the mask position with numpy : 0.010453939437866211 nb_pixel_total : 404826 time to create 1 rle with new method : 0.011904001235961914 length of segment : 870 time for calcul the mask position with numpy : 0.0034470558166503906 nb_pixel_total : 67815 time to create 1 rle with old method : 0.0742034912109375 length of segment : 430 time for calcul the mask position with numpy : 0.003618478775024414 nb_pixel_total : 92514 time to create 1 rle with old method : 0.10543942451477051 length of segment : 371 time for calcul the mask position with numpy : 0.004554033279418945 nb_pixel_total : 113705 time to create 1 rle with old method : 0.12415480613708496 length of segment : 539 time for calcul the mask position with numpy : 0.004841804504394531 nb_pixel_total : 102308 time to create 1 rle with old method : 0.1313011646270752 length of segment : 226 time for calcul the mask position with numpy : 0.00031447410583496094 nb_pixel_total : 7467 time to create 1 rle with old method : 0.008413553237915039 length of segment : 102 time for calcul the mask position with numpy : 0.0003705024719238281 nb_pixel_total : 8367 time to create 1 rle with old method : 0.009802579879760742 length of segment : 103 time for calcul the mask position with numpy : 0.0023725032806396484 nb_pixel_total : 50780 time to create 1 rle with old method : 0.057801008224487305 length of segment : 295 time for calcul the mask position with numpy : 0.0007641315460205078 nb_pixel_total : 16201 time to create 1 rle with old method : 0.01775646209716797 length of segment : 181 time for calcul the mask position with numpy : 0.00503849983215332 nb_pixel_total : 145541 time to create 1 rle with old method : 0.1904771327972412 length of segment : 638 time for calcul the mask position with numpy : 0.0017247200012207031 nb_pixel_total : 33126 time to create 1 rle with old method : 0.03861546516418457 length of segment : 214 time for calcul the mask position with numpy : 0.006047725677490234 nb_pixel_total : 165702 time to create 1 rle with new method : 0.007423877716064453 length of segment : 382 time for calcul the mask position with numpy : 0.0006060600280761719 nb_pixel_total : 7969 time to create 1 rle with old method : 0.009785890579223633 length of segment : 126 time for calcul the mask position with numpy : 0.0015857219696044922 nb_pixel_total : 32060 time to create 1 rle with old method : 0.03712344169616699 length of segment : 221 time for calcul the mask position with numpy : 0.007497549057006836 nb_pixel_total : 135737 time to create 1 rle with old method : 0.15597224235534668 length of segment : 536 time for calcul the mask position with numpy : 0.001390218734741211 nb_pixel_total : 20600 time to create 1 rle with old method : 0.023708105087280273 length of segment : 327 time for calcul the mask position with numpy : 0.0007991790771484375 nb_pixel_total : 15504 time to create 1 rle with old method : 0.01776266098022461 length of segment : 126 time for calcul the mask position with numpy : 0.003728628158569336 nb_pixel_total : 101158 time to create 1 rle with old method : 0.11696624755859375 length of segment : 377 time for calcul the mask position with numpy : 0.0023441314697265625 nb_pixel_total : 40695 time to create 1 rle with old method : 0.04591178894042969 length of segment : 273 time for calcul the mask position with numpy : 0.0013811588287353516 nb_pixel_total : 26310 time to create 1 rle with old method : 0.03016209602355957 length of segment : 173 time for calcul the mask position with numpy : 0.0022153854370117188 nb_pixel_total : 34885 time to create 1 rle with old method : 0.04033780097961426 length of segment : 226 time for calcul the mask position with numpy : 0.003833293914794922 nb_pixel_total : 63350 time to create 1 rle with old method : 0.08860945701599121 length of segment : 301 time for calcul the mask position with numpy : 0.0010957717895507812 nb_pixel_total : 9568 time to create 1 rle with old method : 0.015568017959594727 length of segment : 255 time for calcul the mask position with numpy : 0.004297733306884766 nb_pixel_total : 117593 time to create 1 rle with old method : 0.1313791275024414 length of segment : 320 time for calcul the mask position with numpy : 0.010302305221557617 nb_pixel_total : 274436 time to create 1 rle with new method : 0.014226436614990234 length of segment : 588 time for calcul the mask position with numpy : 0.0029642581939697266 nb_pixel_total : 83808 time to create 1 rle with old method : 0.09261178970336914 length of segment : 434 time for calcul the mask position with numpy : 0.0018627643585205078 nb_pixel_total : 32498 time to create 1 rle with old method : 0.0372014045715332 length of segment : 421 time for calcul the mask position with numpy : 0.0018832683563232422 nb_pixel_total : 23590 time to create 1 rle with old method : 0.026556968688964844 length of segment : 213 time for calcul the mask position with numpy : 0.0012359619140625 nb_pixel_total : 45334 time to create 1 rle with old method : 0.0514979362487793 length of segment : 327 time for calcul the mask position with numpy : 0.003284931182861328 nb_pixel_total : 86894 time to create 1 rle with old method : 0.09754800796508789 length of segment : 376 time for calcul the mask position with numpy : 0.008958578109741211 nb_pixel_total : 233029 time to create 1 rle with new method : 0.012444496154785156 length of segment : 468 time for calcul the mask position with numpy : 0.00144195556640625 nb_pixel_total : 33072 time to create 1 rle with old method : 0.03626060485839844 length of segment : 284 time for calcul the mask position with numpy : 0.006690502166748047 nb_pixel_total : 213952 time to create 1 rle with new method : 0.009269237518310547 length of segment : 646 time for calcul the mask position with numpy : 0.0003745555877685547 nb_pixel_total : 5277 time to create 1 rle with old method : 0.005971431732177734 length of segment : 95 time for calcul the mask position with numpy : 0.0008146762847900391 nb_pixel_total : 17789 time to create 1 rle with old method : 0.019769668579101562 length of segment : 148 time for calcul the mask position with numpy : 0.0025396347045898438 nb_pixel_total : 45230 time to create 1 rle with old method : 0.050305843353271484 length of segment : 319 time for calcul the mask position with numpy : 0.001653432846069336 nb_pixel_total : 27403 time to create 1 rle with old method : 0.0322568416595459 length of segment : 250 time for calcul the mask position with numpy : 0.001644134521484375 nb_pixel_total : 21700 time to create 1 rle with old method : 0.0251772403717041 length of segment : 193 time for calcul the mask position with numpy : 0.0022258758544921875 nb_pixel_total : 29113 time to create 1 rle with old method : 0.03350019454956055 length of segment : 404 time for calcul the mask position with numpy : 0.0055389404296875 nb_pixel_total : 147712 time to create 1 rle with old method : 0.17778658866882324 length of segment : 466 time for calcul the mask position with numpy : 0.0011258125305175781 nb_pixel_total : 14899 time to create 1 rle with old method : 0.017212629318237305 length of segment : 150 time for calcul the mask position with numpy : 0.004809141159057617 nb_pixel_total : 107722 time to create 1 rle with old method : 0.12831950187683105 length of segment : 508 time for calcul the mask position with numpy : 0.0020051002502441406 nb_pixel_total : 33853 time to create 1 rle with old method : 0.03885698318481445 length of segment : 195 time for calcul the mask position with numpy : 0.0005681514739990234 nb_pixel_total : 9553 time to create 1 rle with old method : 0.011145591735839844 length of segment : 131 time for calcul the mask position with numpy : 0.0005855560302734375 nb_pixel_total : 8473 time to create 1 rle with old method : 0.014761686325073242 length of segment : 118 time for calcul the mask position with numpy : 0.0007379055023193359 nb_pixel_total : 11183 time to create 1 rle with old method : 0.013773202896118164 length of segment : 91 time for calcul the mask position with numpy : 0.0006976127624511719 nb_pixel_total : 18861 time to create 1 rle with old method : 0.021575212478637695 length of segment : 173 time for calcul the mask position with numpy : 0.00039958953857421875 nb_pixel_total : 7336 time to create 1 rle with old method : 0.008590221405029297 length of segment : 134 time for calcul the mask position with numpy : 0.002084493637084961 nb_pixel_total : 37592 time to create 1 rle with old method : 0.04287552833557129 length of segment : 217 time for calcul the mask position with numpy : 0.0006556510925292969 nb_pixel_total : 15493 time to create 1 rle with old method : 0.01789546012878418 length of segment : 136 time for calcul the mask position with numpy : 0.0008397102355957031 nb_pixel_total : 18655 time to create 1 rle with old method : 0.021671772003173828 length of segment : 206 time for calcul the mask position with numpy : 0.001989126205444336 nb_pixel_total : 22786 time to create 1 rle with old method : 0.02613663673400879 length of segment : 303 time for calcul the mask position with numpy : 0.001367330551147461 nb_pixel_total : 31057 time to create 1 rle with old method : 0.0372922420501709 length of segment : 152 time for calcul the mask position with numpy : 0.0009515285491943359 nb_pixel_total : 25828 time to create 1 rle with old method : 0.02998971939086914 length of segment : 184 time for calcul the mask position with numpy : 0.0008418560028076172 nb_pixel_total : 18978 time to create 1 rle with old method : 0.021929502487182617 length of segment : 277 time for calcul the mask position with numpy : 0.0022127628326416016 nb_pixel_total : 36547 time to create 1 rle with old method : 0.0416874885559082 length of segment : 266 time for calcul the mask position with numpy : 0.000978708267211914 nb_pixel_total : 5563 time to create 1 rle with old method : 0.006693840026855469 length of segment : 164 time for calcul the mask position with numpy : 0.0009937286376953125 nb_pixel_total : 22826 time to create 1 rle with old method : 0.02621150016784668 length of segment : 285 time for calcul the mask position with numpy : 0.00036907196044921875 nb_pixel_total : 7521 time to create 1 rle with old method : 0.008812189102172852 length of segment : 86 time for calcul the mask position with numpy : 0.0006992816925048828 nb_pixel_total : 11094 time to create 1 rle with old method : 0.013333320617675781 length of segment : 112 time for calcul the mask position with numpy : 0.0005376338958740234 nb_pixel_total : 12560 time to create 1 rle with old method : 0.014809370040893555 length of segment : 80 time for calcul the mask position with numpy : 0.0005245208740234375 nb_pixel_total : 17806 time to create 1 rle with old method : 0.020659208297729492 length of segment : 179 time for calcul the mask position with numpy : 0.0010402202606201172 nb_pixel_total : 34663 time to create 1 rle with old method : 0.05654096603393555 length of segment : 212 time for calcul the mask position with numpy : 0.002361297607421875 nb_pixel_total : 68296 time to create 1 rle with old method : 0.07891845703125 length of segment : 379 time for calcul the mask position with numpy : 0.0017914772033691406 nb_pixel_total : 36060 time to create 1 rle with old method : 0.0452885627746582 length of segment : 347 time for calcul the mask position with numpy : 0.00020837783813476562 nb_pixel_total : 3772 time to create 1 rle with old method : 0.004587888717651367 length of segment : 51 time for calcul the mask position with numpy : 0.0007236003875732422 nb_pixel_total : 12219 time to create 1 rle with old method : 0.014433145523071289 length of segment : 160 time for calcul the mask position with numpy : 0.0027430057525634766 nb_pixel_total : 43779 time to create 1 rle with old method : 0.050939321517944336 length of segment : 194 time for calcul the mask position with numpy : 0.0015518665313720703 nb_pixel_total : 17553 time to create 1 rle with old method : 0.024920225143432617 length of segment : 193 time for calcul the mask position with numpy : 0.0006928443908691406 nb_pixel_total : 8887 time to create 1 rle with old method : 0.014836549758911133 length of segment : 70 time for calcul the mask position with numpy : 0.0034074783325195312 nb_pixel_total : 33033 time to create 1 rle with old method : 0.03808236122131348 length of segment : 234 time for calcul the mask position with numpy : 0.0008976459503173828 nb_pixel_total : 8515 time to create 1 rle with old method : 0.009714841842651367 length of segment : 161 time for calcul the mask position with numpy : 0.0008177757263183594 nb_pixel_total : 11148 time to create 1 rle with old method : 0.012778759002685547 length of segment : 351 time for calcul the mask position with numpy : 0.0011925697326660156 nb_pixel_total : 16197 time to create 1 rle with old method : 0.018044471740722656 length of segment : 177 time for calcul the mask position with numpy : 0.0008280277252197266 nb_pixel_total : 12659 time to create 1 rle with old method : 0.013869762420654297 length of segment : 168 time for calcul the mask position with numpy : 0.005855083465576172 nb_pixel_total : 98221 time to create 1 rle with old method : 0.11169314384460449 length of segment : 490 time for calcul the mask position with numpy : 0.0005540847778320312 nb_pixel_total : 9844 time to create 1 rle with old method : 0.011172771453857422 length of segment : 76 time for calcul the mask position with numpy : 0.0014674663543701172 nb_pixel_total : 19713 time to create 1 rle with old method : 0.02271437644958496 length of segment : 246 time for calcul the mask position with numpy : 0.002237558364868164 nb_pixel_total : 23399 time to create 1 rle with old method : 0.026835203170776367 length of segment : 292 time for calcul the mask position with numpy : 0.0028917789459228516 nb_pixel_total : 25115 time to create 1 rle with old method : 0.028429508209228516 length of segment : 271 time for calcul the mask position with numpy : 0.0006937980651855469 nb_pixel_total : 12804 time to create 1 rle with old method : 0.014772176742553711 length of segment : 98 time for calcul the mask position with numpy : 0.004108905792236328 nb_pixel_total : 45715 time to create 1 rle with old method : 0.05377006530761719 length of segment : 272 time for calcul the mask position with numpy : 0.0025110244750976562 nb_pixel_total : 33457 time to create 1 rle with old method : 0.0379490852355957 length of segment : 279 time for calcul the mask position with numpy : 0.004572629928588867 nb_pixel_total : 62924 time to create 1 rle with old method : 0.0712134838104248 length of segment : 443 time for calcul the mask position with numpy : 0.0008358955383300781 nb_pixel_total : 14290 time to create 1 rle with old method : 0.017101287841796875 length of segment : 119 time for calcul the mask position with numpy : 0.0044100284576416016 nb_pixel_total : 57642 time to create 1 rle with old method : 0.06978702545166016 length of segment : 378 time for calcul the mask position with numpy : 0.0002739429473876953 nb_pixel_total : 6464 time to create 1 rle with old method : 0.007436275482177734 length of segment : 98 time for calcul the mask position with numpy : 0.0007753372192382812 nb_pixel_total : 15060 time to create 1 rle with old method : 0.0177001953125 length of segment : 172 time for calcul the mask position with numpy : 0.0005941390991210938 nb_pixel_total : 14341 time to create 1 rle with old method : 0.01587224006652832 length of segment : 152 time for calcul the mask position with numpy : 0.0006122589111328125 nb_pixel_total : 11562 time to create 1 rle with old method : 0.013223648071289062 length of segment : 230 time for calcul the mask position with numpy : 0.0008833408355712891 nb_pixel_total : 8131 time to create 1 rle with old method : 0.009438753128051758 length of segment : 218 time for calcul the mask position with numpy : 0.001811981201171875 nb_pixel_total : 26163 time to create 1 rle with old method : 0.03000926971435547 length of segment : 355 time for calcul the mask position with numpy : 0.0007388591766357422 nb_pixel_total : 10903 time to create 1 rle with old method : 0.012856721878051758 length of segment : 99 time for calcul the mask position with numpy : 0.0018639564514160156 nb_pixel_total : 23765 time to create 1 rle with old method : 0.027526378631591797 length of segment : 200 time for calcul the mask position with numpy : 0.003203868865966797 nb_pixel_total : 58143 time to create 1 rle with old method : 0.0671544075012207 length of segment : 387 time for calcul the mask position with numpy : 0.0030570030212402344 nb_pixel_total : 25260 time to create 1 rle with old method : 0.028559207916259766 length of segment : 267 time for calcul the mask position with numpy : 0.0006966590881347656 nb_pixel_total : 6849 time to create 1 rle with old method : 0.007792234420776367 length of segment : 114 time for calcul the mask position with numpy : 0.0051250457763671875 nb_pixel_total : 42650 time to create 1 rle with old method : 0.047693490982055664 length of segment : 391 time for calcul the mask position with numpy : 0.002043485641479492 nb_pixel_total : 29567 time to create 1 rle with old method : 0.0334012508392334 length of segment : 251 time for calcul the mask position with numpy : 0.0005886554718017578 nb_pixel_total : 6644 time to create 1 rle with old method : 0.007916688919067383 length of segment : 117 time for calcul the mask position with numpy : 0.0034117698669433594 nb_pixel_total : 44423 time to create 1 rle with old method : 0.04995846748352051 length of segment : 310 time for calcul the mask position with numpy : 0.000415802001953125 nb_pixel_total : 6044 time to create 1 rle with old method : 0.007223844528198242 length of segment : 95 time for calcul the mask position with numpy : 0.0005297660827636719 nb_pixel_total : 14693 time to create 1 rle with old method : 0.017591238021850586 length of segment : 139 time for calcul the mask position with numpy : 0.0006999969482421875 nb_pixel_total : 11461 time to create 1 rle with old method : 0.019185543060302734 length of segment : 162 time for calcul the mask position with numpy : 0.0007312297821044922 nb_pixel_total : 12259 time to create 1 rle with old method : 0.016937255859375 length of segment : 203 time for calcul the mask position with numpy : 0.0013358592987060547 nb_pixel_total : 19844 time to create 1 rle with old method : 0.022790193557739258 length of segment : 220 time for calcul the mask position with numpy : 0.0014739036560058594 nb_pixel_total : 29827 time to create 1 rle with old method : 0.03297734260559082 length of segment : 200 time for calcul the mask position with numpy : 0.0010411739349365234 nb_pixel_total : 9875 time to create 1 rle with old method : 0.011360406875610352 length of segment : 128 time for calcul the mask position with numpy : 0.002156972885131836 nb_pixel_total : 27297 time to create 1 rle with old method : 0.030451059341430664 length of segment : 260 time for calcul the mask position with numpy : 0.0014064311981201172 nb_pixel_total : 19495 time to create 1 rle with old method : 0.021544933319091797 length of segment : 160 time for calcul the mask position with numpy : 0.0004572868347167969 nb_pixel_total : 5458 time to create 1 rle with old method : 0.00616455078125 length of segment : 129 time for calcul the mask position with numpy : 0.0004761219024658203 nb_pixel_total : 12206 time to create 1 rle with old method : 0.013921022415161133 length of segment : 222 time for calcul the mask position with numpy : 0.001207590103149414 nb_pixel_total : 18007 time to create 1 rle with old method : 0.020468473434448242 length of segment : 122 time for calcul the mask position with numpy : 0.0017170906066894531 nb_pixel_total : 39005 time to create 1 rle with old method : 0.04402971267700195 length of segment : 533 time for calcul the mask position with numpy : 0.0013217926025390625 nb_pixel_total : 14697 time to create 1 rle with old method : 0.01697850227355957 length of segment : 101 time for calcul the mask position with numpy : 0.002744436264038086 nb_pixel_total : 23025 time to create 1 rle with old method : 0.026524066925048828 length of segment : 245 time for calcul the mask position with numpy : 0.0008742809295654297 nb_pixel_total : 14138 time to create 1 rle with old method : 0.016067028045654297 length of segment : 111 time for calcul the mask position with numpy : 0.0020575523376464844 nb_pixel_total : 33668 time to create 1 rle with old method : 0.038103580474853516 length of segment : 216 time for calcul the mask position with numpy : 0.0012958049774169922 nb_pixel_total : 21513 time to create 1 rle with old method : 0.02421879768371582 length of segment : 268 time for calcul the mask position with numpy : 0.0007610321044921875 nb_pixel_total : 12608 time to create 1 rle with old method : 0.014501094818115234 length of segment : 121 time for calcul the mask position with numpy : 0.0008335113525390625 nb_pixel_total : 19023 time to create 1 rle with old method : 0.021813631057739258 length of segment : 245 time for calcul the mask position with numpy : 0.0004336833953857422 nb_pixel_total : 4710 time to create 1 rle with old method : 0.005615711212158203 length of segment : 93 time for calcul the mask position with numpy : 0.0002739429473876953 nb_pixel_total : 7274 time to create 1 rle with old method : 0.008646965026855469 length of segment : 105 time for calcul the mask position with numpy : 0.0023467540740966797 nb_pixel_total : 43067 time to create 1 rle with old method : 0.047682762145996094 length of segment : 211 time for calcul the mask position with numpy : 0.0005147457122802734 nb_pixel_total : 8038 time to create 1 rle with old method : 0.009741783142089844 length of segment : 111 time for calcul the mask position with numpy : 0.0006783008575439453 nb_pixel_total : 12364 time to create 1 rle with old method : 0.01428532600402832 length of segment : 231 time for calcul the mask position with numpy : 0.0003254413604736328 nb_pixel_total : 10089 time to create 1 rle with old method : 0.011516094207763672 length of segment : 132 time for calcul the mask position with numpy : 0.001027822494506836 nb_pixel_total : 16708 time to create 1 rle with old method : 0.01915884017944336 length of segment : 174 time for calcul the mask position with numpy : 0.0016586780548095703 nb_pixel_total : 24433 time to create 1 rle with old method : 0.028342008590698242 length of segment : 192 time for calcul the mask position with numpy : 0.0006699562072753906 nb_pixel_total : 6592 time to create 1 rle with old method : 0.007645130157470703 length of segment : 121 time for calcul the mask position with numpy : 0.0003445148468017578 nb_pixel_total : 7109 time to create 1 rle with old method : 0.008245229721069336 length of segment : 129 time for calcul the mask position with numpy : 0.0008797645568847656 nb_pixel_total : 14087 time to create 1 rle with old method : 0.016091346740722656 length of segment : 87 time for calcul the mask position with numpy : 0.000942230224609375 nb_pixel_total : 10467 time to create 1 rle with old method : 0.011728763580322266 length of segment : 214 time for calcul the mask position with numpy : 0.004575967788696289 nb_pixel_total : 67750 time to create 1 rle with old method : 0.07635211944580078 length of segment : 308 time for calcul the mask position with numpy : 0.0014233589172363281 nb_pixel_total : 23720 time to create 1 rle with old method : 0.027227401733398438 length of segment : 204 time for calcul the mask position with numpy : 0.0024912357330322266 nb_pixel_total : 35044 time to create 1 rle with old method : 0.0392608642578125 length of segment : 334 time for calcul the mask position with numpy : 0.010074377059936523 nb_pixel_total : 184755 time to create 1 rle with new method : 0.010883569717407227 length of segment : 582 time for calcul the mask position with numpy : 0.0018384456634521484 nb_pixel_total : 33270 time to create 1 rle with old method : 0.03806257247924805 length of segment : 236 time for calcul the mask position with numpy : 0.0011072158813476562 nb_pixel_total : 9482 time to create 1 rle with old method : 0.01094198226928711 length of segment : 166 time for calcul the mask position with numpy : 0.0023679733276367188 nb_pixel_total : 28042 time to create 1 rle with old method : 0.0464324951171875 length of segment : 231 time for calcul the mask position with numpy : 0.0004699230194091797 nb_pixel_total : 6756 time to create 1 rle with old method : 0.007906913757324219 length of segment : 72 time for calcul the mask position with numpy : 0.004495143890380859 nb_pixel_total : 61037 time to create 1 rle with old method : 0.06762123107910156 length of segment : 336 time for calcul the mask position with numpy : 0.002740621566772461 nb_pixel_total : 42799 time to create 1 rle with old method : 0.04744458198547363 length of segment : 352 time for calcul the mask position with numpy : 0.0025267601013183594 nb_pixel_total : 37158 time to create 1 rle with old method : 0.04319500923156738 length of segment : 181 time for calcul the mask position with numpy : 0.005800485610961914 nb_pixel_total : 84636 time to create 1 rle with old method : 0.09461855888366699 length of segment : 314 time for calcul the mask position with numpy : 0.009555816650390625 nb_pixel_total : 103042 time to create 1 rle with old method : 0.13592743873596191 length of segment : 634 time for calcul the mask position with numpy : 0.0011348724365234375 nb_pixel_total : 14511 time to create 1 rle with old method : 0.01691746711730957 length of segment : 200 time for calcul the mask position with numpy : 0.0012581348419189453 nb_pixel_total : 15870 time to create 1 rle with old method : 0.026189804077148438 length of segment : 154 time for calcul the mask position with numpy : 0.0013873577117919922 nb_pixel_total : 17716 time to create 1 rle with old method : 0.0241239070892334 length of segment : 163 time for calcul the mask position with numpy : 0.0011444091796875 nb_pixel_total : 19185 time to create 1 rle with old method : 0.022167682647705078 length of segment : 192 time for calcul the mask position with numpy : 0.0013155937194824219 nb_pixel_total : 17420 time to create 1 rle with old method : 0.019820690155029297 length of segment : 196 time for calcul the mask position with numpy : 0.0005834102630615234 nb_pixel_total : 13258 time to create 1 rle with old method : 0.015266895294189453 length of segment : 154 time for calcul the mask position with numpy : 0.001199483871459961 nb_pixel_total : 20706 time to create 1 rle with old method : 0.02405071258544922 length of segment : 151 time for calcul the mask position with numpy : 0.001188516616821289 nb_pixel_total : 15154 time to create 1 rle with old method : 0.017000913619995117 length of segment : 181 time for calcul the mask position with numpy : 0.006234169006347656 nb_pixel_total : 128094 time to create 1 rle with old method : 0.145493745803833 length of segment : 522 time for calcul the mask position with numpy : 0.0005567073822021484 nb_pixel_total : 9813 time to create 1 rle with old method : 0.011355400085449219 length of segment : 108 time for calcul the mask position with numpy : 0.0027883052825927734 nb_pixel_total : 45357 time to create 1 rle with old method : 0.05028891563415527 length of segment : 240 time for calcul the mask position with numpy : 0.00026345252990722656 nb_pixel_total : 9430 time to create 1 rle with old method : 0.010677099227905273 length of segment : 102 time for calcul the mask position with numpy : 0.0030367374420166016 nb_pixel_total : 51174 time to create 1 rle with old method : 0.05617880821228027 length of segment : 357 time for calcul the mask position with numpy : 0.0006515979766845703 nb_pixel_total : 12123 time to create 1 rle with old method : 0.013486623764038086 length of segment : 153 time for calcul the mask position with numpy : 0.0006837844848632812 nb_pixel_total : 13902 time to create 1 rle with old method : 0.01549077033996582 length of segment : 145 time for calcul the mask position with numpy : 0.0009243488311767578 nb_pixel_total : 30009 time to create 1 rle with old method : 0.033039093017578125 length of segment : 235 time for calcul the mask position with numpy : 0.0027136802673339844 nb_pixel_total : 59708 time to create 1 rle with old method : 0.09044766426086426 length of segment : 450 time for calcul the mask position with numpy : 0.0005006790161132812 nb_pixel_total : 16595 time to create 1 rle with old method : 0.020821094512939453 length of segment : 166 time for calcul the mask position with numpy : 0.0011336803436279297 nb_pixel_total : 20021 time to create 1 rle with old method : 0.023145675659179688 length of segment : 240 time for calcul the mask position with numpy : 0.0005593299865722656 nb_pixel_total : 24311 time to create 1 rle with old method : 0.027895689010620117 length of segment : 194 time for calcul the mask position with numpy : 0.0005357265472412109 nb_pixel_total : 12601 time to create 1 rle with old method : 0.014616727828979492 length of segment : 142 time for calcul the mask position with numpy : 0.0045528411865234375 nb_pixel_total : 71145 time to create 1 rle with old method : 0.08236861228942871 length of segment : 413 time for calcul the mask position with numpy : 0.001767873764038086 nb_pixel_total : 32196 time to create 1 rle with old method : 0.03647494316101074 length of segment : 298 time for calcul the mask position with numpy : 0.002722024917602539 nb_pixel_total : 23605 time to create 1 rle with old method : 0.027182340621948242 length of segment : 293 time for calcul the mask position with numpy : 0.0011491775512695312 nb_pixel_total : 17778 time to create 1 rle with old method : 0.02070164680480957 length of segment : 235 time for calcul the mask position with numpy : 0.0014238357543945312 nb_pixel_total : 26600 time to create 1 rle with old method : 0.030189037322998047 length of segment : 223 time for calcul the mask position with numpy : 0.00025081634521484375 nb_pixel_total : 2808 time to create 1 rle with old method : 0.0033905506134033203 length of segment : 49 time for calcul the mask position with numpy : 0.0002200603485107422 nb_pixel_total : 3641 time to create 1 rle with old method : 0.004670143127441406 length of segment : 75 time for calcul the mask position with numpy : 0.0006451606750488281 nb_pixel_total : 9028 time to create 1 rle with old method : 0.010896921157836914 length of segment : 118 time for calcul the mask position with numpy : 0.0024192333221435547 nb_pixel_total : 40382 time to create 1 rle with old method : 0.04601144790649414 length of segment : 236 time for calcul the mask position with numpy : 0.0009725093841552734 nb_pixel_total : 19475 time to create 1 rle with old method : 0.02247762680053711 length of segment : 189 time for calcul the mask position with numpy : 0.002574443817138672 nb_pixel_total : 32149 time to create 1 rle with old method : 0.03644275665283203 length of segment : 317 time for calcul the mask position with numpy : 0.001129150390625 nb_pixel_total : 24684 time to create 1 rle with old method : 0.028107643127441406 length of segment : 190 time for calcul the mask position with numpy : 0.0006041526794433594 nb_pixel_total : 16272 time to create 1 rle with old method : 0.01896214485168457 length of segment : 175 time spent for convertir_results : 20.563812017440796 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 256 chid ids of type : 3594 Number RLEs to save : 66757 save missing photos in datou_result : time spend for datou_step_exec : 103.1739649772644 time spend to save output : 5.852742433547974 total time spend for step 1 : 109.02670741081238 step2:crop_condition Wed Apr 2 20:32:21 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 : 8 ! batch 1 Loaded 256 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : papier param for this class : {'min_score': 0.7} filtre for class : papier hashtag_id of this class : 492668766 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 191 About to insert : list_path_to_insert length 191 new photo from crops ! About to upload 191 photos upload in portfolio : 3736932 init cache_photo without model_param we have 191 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743618776_3301087 we have uploaded 191 photos in the portfolio 3736932 time of upload the photos Elapsed time : 56.114408016204834 we have finished the crop for the class : papier begin to crop the class : carton param for this class : {'min_score': 0.7} filtre for class : carton hashtag_id of this class : 492774966 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 35 About to insert : list_path_to_insert length 35 new photo from crops ! About to upload 35 photos upload in portfolio : 3736932 init cache_photo without model_param we have 35 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743618842_3301087 we have uploaded 35 photos in the portfolio 3736932 time of upload the photos Elapsed time : 9.956620454788208 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/1743618854_3301087 we have uploaded 4 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.2800161838531494 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 ! map_result returned by crop_photo_return_map_crop : length : 18 About to insert : list_path_to_insert length 18 new photo from crops ! About to upload 18 photos upload in portfolio : 3736932 init cache_photo without model_param we have 18 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743618865_3301087 we have uploaded 18 photos in the portfolio 3736932 time of upload the photos Elapsed time : 7.41708517074585 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 7 About to insert : list_path_to_insert length 7 new photo from crops ! About to upload 7 photos upload in portfolio : 3736932 init cache_photo without model_param we have 7 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743618882_3301087 we have uploaded 7 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.0362467765808105 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 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/1743618885_3301087 we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.6235365867614746 we have finished the crop for the class : pet_fonce delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1349567563, 1349566411, 1349566408, 1349566346, 1349566338, 1349566334, 1349566331, 1349566326] Looping around the photos to save general results len do output : 256 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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, '2717511') ('3318', '21986466', '1349567563', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566411', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566408', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566346', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566338', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566334', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566331', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566326', None, None, None, None, None, '2717511') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 776 time used for this insertion : 0.040282487869262695 save_final save missing photos in datou_result : time spend for datou_step_exec : 145.13267421722412 time spend to save output : 0.04546833038330078 total time spend for step 2 : 145.17814254760742 step3:rle_unique_nms_with_priority Wed Apr 2 20:34:46 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 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 256 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 23 nb_hashtags : 4 time to prepare the origin masks : 7.329946756362915 time for calcul the mask position with numpy : 0.469789981842041 nb_pixel_total : 5335312 time to create 1 rle with new method : 0.7054038047790527 time for calcul the mask position with numpy : 0.03514838218688965 nb_pixel_total : 232960 time to create 1 rle with new method : 0.46396660804748535 time for calcul the mask position with numpy : 0.030393123626708984 nb_pixel_total : 14020 time to create 1 rle with old method : 0.015503168106079102 time for calcul the mask position with numpy : 0.023401737213134766 nb_pixel_total : 32785 time to create 1 rle with old method : 0.03658032417297363 time for calcul the mask position with numpy : 0.02564549446105957 nb_pixel_total : 13202 time to create 1 rle with old method : 0.014845609664916992 time for calcul the mask position with numpy : 0.027810096740722656 nb_pixel_total : 21094 time to create 1 rle with old method : 0.024869203567504883 time for calcul the mask position with numpy : 0.03983187675476074 nb_pixel_total : 5551 time to create 1 rle with old method : 0.009330987930297852 time for calcul the mask position with numpy : 0.037911415100097656 nb_pixel_total : 10677 time to create 1 rle with old method : 0.014785051345825195 time for calcul the mask position with numpy : 0.03828024864196777 nb_pixel_total : 47554 time to create 1 rle with old method : 0.05347275733947754 time for calcul the mask position with numpy : 0.035686492919921875 nb_pixel_total : 57224 time to create 1 rle with old method : 0.0623016357421875 time for calcul the mask position with numpy : 0.03371000289916992 nb_pixel_total : 20448 time to create 1 rle with old method : 0.022571325302124023 time for calcul the mask position with numpy : 0.03801298141479492 nb_pixel_total : 55610 time to create 1 rle with old method : 0.07926464080810547 time for calcul the mask position with numpy : 0.034240007400512695 nb_pixel_total : 6375 time to create 1 rle with old method : 0.00724029541015625 time for calcul the mask position with numpy : 0.036295175552368164 nb_pixel_total : 342376 time to create 1 rle with new method : 0.7733826637268066 time for calcul the mask position with numpy : 0.03391695022583008 nb_pixel_total : 34905 time to create 1 rle with old method : 0.038091421127319336 time for calcul the mask position with numpy : 0.034351348876953125 nb_pixel_total : 94202 time to create 1 rle with old method : 0.10387897491455078 time for calcul the mask position with numpy : 0.03512835502624512 nb_pixel_total : 160601 time to create 1 rle with new method : 0.7356283664703369 time for calcul the mask position with numpy : 0.03274416923522949 nb_pixel_total : 51402 time to create 1 rle with old method : 0.05610370635986328 time for calcul the mask position with numpy : 0.03139925003051758 nb_pixel_total : 118841 time to create 1 rle with old method : 0.12881851196289062 time for calcul the mask position with numpy : 0.022023677825927734 nb_pixel_total : 94390 time to create 1 rle with old method : 0.10393261909484863 time for calcul the mask position with numpy : 0.021639347076416016 nb_pixel_total : 16505 time to create 1 rle with old method : 0.018263578414916992 time for calcul the mask position with numpy : 0.021947622299194336 nb_pixel_total : 38519 time to create 1 rle with old method : 0.04046940803527832 time for calcul the mask position with numpy : 0.0229189395904541 nb_pixel_total : 206102 time to create 1 rle with new method : 0.5752308368682861 time for calcul the mask position with numpy : 0.021941423416137695 nb_pixel_total : 39585 time to create 1 rle with old method : 0.044938087463378906 create new chi : 5.429289817810059 time to delete rle : 0.021334409713745117 batch 1 Loaded 47 chid ids of type : 3594 ++++++++++++++++++++++++++Number RLEs to save : 16432 TO DO : save crop sub photo not yet done ! save time : 3.4705474376678467 nb_obj : 15 nb_hashtags : 4 time to prepare the origin masks : 6.079016447067261 time for calcul the mask position with numpy : 0.20391607284545898 nb_pixel_total : 6119688 time to create 1 rle with new method : 0.9620082378387451 time for calcul the mask position with numpy : 0.03681612014770508 nb_pixel_total : 224991 time to create 1 rle with new method : 0.45425963401794434 time for calcul the mask position with numpy : 0.03432774543762207 nb_pixel_total : 8349 time to create 1 rle with old method : 0.00911259651184082 time for calcul the mask position with numpy : 0.03290867805480957 nb_pixel_total : 13156 time to create 1 rle with old method : 0.014438629150390625 time for calcul the mask position with numpy : 0.023984432220458984 nb_pixel_total : 23911 time to create 1 rle with old method : 0.025583744049072266 time for calcul the mask position with numpy : 0.022364139556884766 nb_pixel_total : 57562 time to create 1 rle with old method : 0.06267595291137695 time for calcul the mask position with numpy : 0.020886659622192383 nb_pixel_total : 36106 time to create 1 rle with old method : 0.03842878341674805 time for calcul the mask position with numpy : 0.02250194549560547 nb_pixel_total : 14676 time to create 1 rle with old method : 0.01624441146850586 time for calcul the mask position with numpy : 0.02161121368408203 nb_pixel_total : 13578 time to create 1 rle with old method : 0.014809370040893555 time for calcul the mask position with numpy : 0.021422624588012695 nb_pixel_total : 25081 time to create 1 rle with old method : 0.027024269104003906 time for calcul the mask position with numpy : 0.022142410278320312 nb_pixel_total : 37178 time to create 1 rle with old method : 0.039700984954833984 time for calcul the mask position with numpy : 0.022778749465942383 nb_pixel_total : 198790 time to create 1 rle with new method : 0.5802125930786133 time for calcul the mask position with numpy : 0.02019524574279785 nb_pixel_total : 20011 time to create 1 rle with old method : 0.02042365074157715 time for calcul the mask position with numpy : 0.0209352970123291 nb_pixel_total : 52316 time to create 1 rle with old method : 0.05727124214172363 time for calcul the mask position with numpy : 0.02017354965209961 nb_pixel_total : 68307 time to create 1 rle with old method : 0.07133674621582031 time for calcul the mask position with numpy : 0.0208432674407959 nb_pixel_total : 136540 time to create 1 rle with old method : 0.13999462127685547 create new chi : 3.18094539642334 time to delete rle : 0.0012879371643066406 batch 1 Loaded 31 chid ids of type : 3594 ++++++++++++++++++Number RLEs to save : 10768 TO DO : save crop sub photo not yet done ! save time : 0.7633931636810303 nb_obj : 18 nb_hashtags : 4 time to prepare the origin masks : 5.93153977394104 time for calcul the mask position with numpy : 0.47309064865112305 nb_pixel_total : 4808739 time to create 1 rle with new method : 0.8315210342407227 time for calcul the mask position with numpy : 0.020867347717285156 nb_pixel_total : 81794 time to create 1 rle with old method : 0.0893392562866211 time for calcul the mask position with numpy : 0.02213883399963379 nb_pixel_total : 8191 time to create 1 rle with old method : 0.009162187576293945 time for calcul the mask position with numpy : 0.022014617919921875 nb_pixel_total : 85675 time to create 1 rle with old method : 0.09393000602722168 time for calcul the mask position with numpy : 0.02306365966796875 nb_pixel_total : 9029 time to create 1 rle with old method : 0.010143280029296875 time for calcul the mask position with numpy : 0.020694971084594727 nb_pixel_total : 85080 time to create 1 rle with old method : 0.09512114524841309 time for calcul the mask position with numpy : 0.025397777557373047 nb_pixel_total : 66878 time to create 1 rle with old method : 0.08127856254577637 time for calcul the mask position with numpy : 0.0229184627532959 nb_pixel_total : 12058 time to create 1 rle with old method : 0.013396978378295898 time for calcul the mask position with numpy : 0.02149677276611328 nb_pixel_total : 34906 time to create 1 rle with old method : 0.0386049747467041 time for calcul the mask position with numpy : 0.020839929580688477 nb_pixel_total : 24866 time to create 1 rle with old method : 0.026785850524902344 time for calcul the mask position with numpy : 0.020629167556762695 nb_pixel_total : 42900 time to create 1 rle with old method : 0.0469059944152832 time for calcul the mask position with numpy : 0.023629188537597656 nb_pixel_total : 71304 time to create 1 rle with old method : 0.09822773933410645 time for calcul the mask position with numpy : 0.022145986557006836 nb_pixel_total : 6977 time to create 1 rle with old method : 0.007574558258056641 time for calcul the mask position with numpy : 0.021524667739868164 nb_pixel_total : 102976 time to create 1 rle with old method : 0.11010217666625977 time for calcul the mask position with numpy : 0.021046876907348633 nb_pixel_total : 14320 time to create 1 rle with old method : 0.015482187271118164 time for calcul the mask position with numpy : 0.042481422424316406 nb_pixel_total : 1547109 time to create 1 rle with new method : 0.6169061660766602 time for calcul the mask position with numpy : 0.030892610549926758 nb_pixel_total : 21942 time to create 1 rle with old method : 0.024713516235351562 time for calcul the mask position with numpy : 0.03236889839172363 nb_pixel_total : 12563 time to create 1 rle with old method : 0.0140533447265625 time for calcul the mask position with numpy : 0.032858848571777344 nb_pixel_total : 12933 time to create 1 rle with old method : 0.014342069625854492 create new chi : 3.2146248817443848 time to delete rle : 0.0019347667694091797 batch 1 Loaded 37 chid ids of type : 3594 +++++++++++++++++++++++Number RLEs to save : 12683 TO DO : save crop sub photo not yet done ! save time : 0.9793252944946289 nb_obj : 21 nb_hashtags : 4 time to prepare the origin masks : 7.92552375793457 time for calcul the mask position with numpy : 0.3310401439666748 nb_pixel_total : 5293470 time to create 1 rle with new method : 1.0159132480621338 time for calcul the mask position with numpy : 0.02369976043701172 nb_pixel_total : 404826 time to create 1 rle with new method : 0.578789472579956 time for calcul the mask position with numpy : 0.023142337799072266 nb_pixel_total : 19046 time to create 1 rle with old method : 0.031114816665649414 time for calcul the mask position with numpy : 0.032503366470336914 nb_pixel_total : 21761 time to create 1 rle with old method : 0.028583049774169922 time for calcul the mask position with numpy : 0.040145158767700195 nb_pixel_total : 231555 time to create 1 rle with new method : 0.33020734786987305 time for calcul the mask position with numpy : 0.033205270767211914 nb_pixel_total : 22263 time to create 1 rle with old method : 0.02509927749633789 time for calcul the mask position with numpy : 0.022672414779663086 nb_pixel_total : 89055 time to create 1 rle with old method : 0.10761880874633789 time for calcul the mask position with numpy : 0.02180194854736328 nb_pixel_total : 55957 time to create 1 rle with old method : 0.06157231330871582 time for calcul the mask position with numpy : 0.022397756576538086 nb_pixel_total : 16964 time to create 1 rle with old method : 0.019763708114624023 time for calcul the mask position with numpy : 0.021811962127685547 nb_pixel_total : 48219 time to create 1 rle with old method : 0.05518484115600586 time for calcul the mask position with numpy : 0.021282434463500977 nb_pixel_total : 105073 time to create 1 rle with old method : 0.12670207023620605 time for calcul the mask position with numpy : 0.023510456085205078 nb_pixel_total : 35147 time to create 1 rle with old method : 0.06307673454284668 time for calcul the mask position with numpy : 0.029313087463378906 nb_pixel_total : 12183 time to create 1 rle with old method : 0.013559103012084961 time for calcul the mask position with numpy : 0.02297806739807129 nb_pixel_total : 32320 time to create 1 rle with old method : 0.03655648231506348 time for calcul the mask position with numpy : 0.026050329208374023 nb_pixel_total : 124126 time to create 1 rle with old method : 0.1418306827545166 time for calcul the mask position with numpy : 0.023676633834838867 nb_pixel_total : 31364 time to create 1 rle with old method : 0.03433370590209961 time for calcul the mask position with numpy : 0.023113012313842773 nb_pixel_total : 84451 time to create 1 rle with old method : 0.10103130340576172 time for calcul the mask position with numpy : 0.026111125946044922 nb_pixel_total : 139992 time to create 1 rle with old method : 0.17098689079284668 time for calcul the mask position with numpy : 0.02243351936340332 nb_pixel_total : 9954 time to create 1 rle with old method : 0.010909795761108398 time for calcul the mask position with numpy : 0.02490091323852539 nb_pixel_total : 214148 time to create 1 rle with new method : 0.5926315784454346 time for calcul the mask position with numpy : 0.021347761154174805 nb_pixel_total : 25193 time to create 1 rle with old method : 0.02905440330505371 time for calcul the mask position with numpy : 0.022286176681518555 nb_pixel_total : 33173 time to create 1 rle with old method : 0.03709220886230469 create new chi : 4.588061094284058 time to delete rle : 0.00249481201171875 batch 1 Loaded 43 chid ids of type : 3594 +++++++++++++++++++++++++++++++Number RLEs to save : 16352 TO DO : save crop sub photo not yet done ! save time : 1.1884210109710693 nb_obj : 33 nb_hashtags : 4 time to prepare the origin masks : 5.006141424179077 time for calcul the mask position with numpy : 0.31348085403442383 nb_pixel_total : 4652041 time to create 1 rle with new method : 0.8173155784606934 time for calcul the mask position with numpy : 0.03958249092102051 nb_pixel_total : 102308 time to create 1 rle with old method : 0.11326742172241211 time for calcul the mask position with numpy : 0.02877044677734375 nb_pixel_total : 7194 time to create 1 rle with old method : 0.008029460906982422 time for calcul the mask position with numpy : 0.03191494941711426 nb_pixel_total : 135737 time to create 1 rle with old method : 0.17867779731750488 time for calcul the mask position with numpy : 0.03666329383850098 nb_pixel_total : 8367 time to create 1 rle with old method : 0.009346961975097656 time for calcul the mask position with numpy : 0.029433012008666992 nb_pixel_total : 32498 time to create 1 rle with old method : 0.037407636642456055 time for calcul the mask position with numpy : 0.03345799446105957 nb_pixel_total : 26310 time to create 1 rle with old method : 0.04297184944152832 time for calcul the mask position with numpy : 0.0333254337310791 nb_pixel_total : 9568 time to create 1 rle with old method : 0.015590429306030273 time for calcul the mask position with numpy : 0.03350663185119629 nb_pixel_total : 82909 time to create 1 rle with old method : 0.09160995483398438 time for calcul the mask position with numpy : 0.029483318328857422 nb_pixel_total : 34885 time to create 1 rle with old method : 0.03861284255981445 time for calcul the mask position with numpy : 0.03229999542236328 nb_pixel_total : 274345 time to create 1 rle with new method : 1.5461804866790771 time for calcul the mask position with numpy : 0.028650760650634766 nb_pixel_total : 16201 time to create 1 rle with old method : 0.017996788024902344 time for calcul the mask position with numpy : 0.02938246726989746 nb_pixel_total : 67815 time to create 1 rle with old method : 0.07754826545715332 time for calcul the mask position with numpy : 0.03389930725097656 nb_pixel_total : 92514 time to create 1 rle with old method : 0.11333584785461426 time for calcul the mask position with numpy : 0.028187990188598633 nb_pixel_total : 5693 time to create 1 rle with old method : 0.006388187408447266 time for calcul the mask position with numpy : 0.028683185577392578 nb_pixel_total : 40695 time to create 1 rle with old method : 0.043732404708862305 time for calcul the mask position with numpy : 0.0299072265625 nb_pixel_total : 165702 time to create 1 rle with new method : 1.127884864807129 time for calcul the mask position with numpy : 0.02936387062072754 nb_pixel_total : 23590 time to create 1 rle with old method : 0.02626490592956543 time for calcul the mask position with numpy : 0.03006267547607422 nb_pixel_total : 145541 time to create 1 rle with old method : 0.16165661811828613 time for calcul the mask position with numpy : 0.030147790908813477 nb_pixel_total : 113705 time to create 1 rle with old method : 0.1268477439880371 time for calcul the mask position with numpy : 0.030933141708374023 nb_pixel_total : 212567 time to create 1 rle with new method : 0.3364145755767822 time for calcul the mask position with numpy : 0.02936100959777832 nb_pixel_total : 50780 time to create 1 rle with old method : 0.05651688575744629 time for calcul the mask position with numpy : 0.029178619384765625 nb_pixel_total : 33072 time to create 1 rle with old method : 0.036672353744506836 time for calcul the mask position with numpy : 0.03374886512756348 nb_pixel_total : 233029 time to create 1 rle with new method : 2.282301664352417 time for calcul the mask position with numpy : 0.029494047164916992 nb_pixel_total : 63350 time to create 1 rle with old method : 0.07044386863708496 time for calcul the mask position with numpy : 0.02954387664794922 nb_pixel_total : 86894 time to create 1 rle with old method : 0.09690213203430176 time for calcul the mask position with numpy : 0.03300166130065918 nb_pixel_total : 33126 time to create 1 rle with old method : 0.05254530906677246 time for calcul the mask position with numpy : 0.02912163734436035 nb_pixel_total : 7969 time to create 1 rle with old method : 0.008864879608154297 time for calcul the mask position with numpy : 0.0292203426361084 nb_pixel_total : 100801 time to create 1 rle with old method : 0.11109733581542969 time for calcul the mask position with numpy : 0.029250621795654297 nb_pixel_total : 32060 time to create 1 rle with old method : 0.03514385223388672 time for calcul the mask position with numpy : 0.028814315795898438 nb_pixel_total : 5277 time to create 1 rle with old method : 0.0057086944580078125 time for calcul the mask position with numpy : 0.029252290725708008 nb_pixel_total : 20600 time to create 1 rle with old method : 0.02279806137084961 time for calcul the mask position with numpy : 0.029549598693847656 nb_pixel_total : 117593 time to create 1 rle with old method : 0.12880682945251465 time for calcul the mask position with numpy : 0.029043912887573242 nb_pixel_total : 15504 time to create 1 rle with old method : 0.01736903190612793 create new chi : 9.320109605789185 time to delete rle : 0.0056421756744384766 batch 1 Loaded 67 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 22652 TO DO : save crop sub photo not yet done ! save time : 2.3284642696380615 nb_obj : 33 nb_hashtags : 3 time to prepare the origin masks : 3.742100238800049 time for calcul the mask position with numpy : 0.3085780143737793 nb_pixel_total : 6136939 time to create 1 rle with new method : 0.9915273189544678 time for calcul the mask position with numpy : 0.02950143814086914 nb_pixel_total : 31057 time to create 1 rle with old method : 0.0346221923828125 time for calcul the mask position with numpy : 0.028884172439575195 nb_pixel_total : 45230 time to create 1 rle with old method : 0.0485689640045166 time for calcul the mask position with numpy : 0.028064966201782227 nb_pixel_total : 5563 time to create 1 rle with old method : 0.006174325942993164 time for calcul the mask position with numpy : 0.02845025062561035 nb_pixel_total : 9553 time to create 1 rle with old method : 0.011013269424438477 time for calcul the mask position with numpy : 0.030022382736206055 nb_pixel_total : 107722 time to create 1 rle with old method : 0.12070894241333008 time for calcul the mask position with numpy : 0.028802871704101562 nb_pixel_total : 36060 time to create 1 rle with old method : 0.04240775108337402 time for calcul the mask position with numpy : 0.028962373733520508 nb_pixel_total : 11013 time to create 1 rle with old method : 0.012072324752807617 time for calcul the mask position with numpy : 0.028949737548828125 nb_pixel_total : 11183 time to create 1 rle with old method : 0.012456178665161133 time for calcul the mask position with numpy : 0.029126405715942383 nb_pixel_total : 17789 time to create 1 rle with old method : 0.01995706558227539 time for calcul the mask position with numpy : 0.029321908950805664 nb_pixel_total : 22786 time to create 1 rle with old method : 0.025507211685180664 time for calcul the mask position with numpy : 0.02909541130065918 nb_pixel_total : 7521 time to create 1 rle with old method : 0.008430719375610352 time for calcul the mask position with numpy : 0.029316425323486328 nb_pixel_total : 37592 time to create 1 rle with old method : 0.04150867462158203 time for calcul the mask position with numpy : 0.029401540756225586 nb_pixel_total : 17806 time to create 1 rle with old method : 0.020122766494750977 time for calcul the mask position with numpy : 0.029436111450195312 nb_pixel_total : 18655 time to create 1 rle with old method : 0.021106243133544922 time for calcul the mask position with numpy : 0.029779434204101562 nb_pixel_total : 68296 time to create 1 rle with old method : 0.0783090591430664 time for calcul the mask position with numpy : 0.02873086929321289 nb_pixel_total : 29113 time to create 1 rle with old method : 0.03251481056213379 time for calcul the mask position with numpy : 0.028618812561035156 nb_pixel_total : 17904 time to create 1 rle with old method : 0.019820690155029297 time for calcul the mask position with numpy : 0.02844691276550293 nb_pixel_total : 8976 time to create 1 rle with old method : 0.010242938995361328 time for calcul the mask position with numpy : 0.028862714767456055 nb_pixel_total : 25828 time to create 1 rle with old method : 0.0287477970123291 time for calcul the mask position with numpy : 0.028998851776123047 nb_pixel_total : 33853 time to create 1 rle with old method : 0.037783145904541016 time for calcul the mask position with numpy : 0.02917027473449707 nb_pixel_total : 7336 time to create 1 rle with old method : 0.008212566375732422 time for calcul the mask position with numpy : 0.030949831008911133 nb_pixel_total : 18861 time to create 1 rle with old method : 0.03351736068725586 time for calcul the mask position with numpy : 0.03984808921813965 nb_pixel_total : 22826 time to create 1 rle with old method : 0.04176926612854004 time for calcul the mask position with numpy : 0.03761410713195801 nb_pixel_total : 27403 time to create 1 rle with old method : 0.0490262508392334 time for calcul the mask position with numpy : 0.03021097183227539 nb_pixel_total : 12219 time to create 1 rle with old method : 0.014198541641235352 time for calcul the mask position with numpy : 0.03010725975036621 nb_pixel_total : 147712 time to create 1 rle with old method : 0.16238903999328613 time for calcul the mask position with numpy : 0.028759002685546875 nb_pixel_total : 36547 time to create 1 rle with old method : 0.04041767120361328 time for calcul the mask position with numpy : 0.02889084815979004 nb_pixel_total : 8473 time to create 1 rle with old method : 0.009539127349853516 time for calcul the mask position with numpy : 0.02885746955871582 nb_pixel_total : 14899 time to create 1 rle with old method : 0.016678333282470703 time for calcul the mask position with numpy : 0.028995275497436523 nb_pixel_total : 21700 time to create 1 rle with old method : 0.025053977966308594 time for calcul the mask position with numpy : 0.030354022979736328 nb_pixel_total : 3772 time to create 1 rle with old method : 0.004290342330932617 time for calcul the mask position with numpy : 0.0284421443939209 nb_pixel_total : 15493 time to create 1 rle with old method : 0.017309188842773438 time for calcul the mask position with numpy : 0.028378009796142578 nb_pixel_total : 12560 time to create 1 rle with old method : 0.013981819152832031 create new chi : 3.386692523956299 time to delete rle : 0.0025954246520996094 batch 1 Loaded 67 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 16085 TO DO : save crop sub photo not yet done ! save time : 1.2326850891113281 nb_obj : 64 nb_hashtags : 3 time to prepare the origin masks : 4.06869912147522 time for calcul the mask position with numpy : 1.6119024753570557 nb_pixel_total : 5692308 time to create 1 rle with new method : 1.12874174118042 time for calcul the mask position with numpy : 0.031351327896118164 nb_pixel_total : 14138 time to create 1 rle with old method : 0.017207860946655273 time for calcul the mask position with numpy : 0.02950453758239746 nb_pixel_total : 12258 time to create 1 rle with old method : 0.013683795928955078 time for calcul the mask position with numpy : 0.028662681579589844 nb_pixel_total : 8515 time to create 1 rle with old method : 0.009431600570678711 time for calcul the mask position with numpy : 0.028754711151123047 nb_pixel_total : 8131 time to create 1 rle with old method : 0.008841991424560547 time for calcul the mask position with numpy : 0.028310775756835938 nb_pixel_total : 6849 time to create 1 rle with old method : 0.0073969364166259766 time for calcul the mask position with numpy : 0.028580665588378906 nb_pixel_total : 11148 time to create 1 rle with old method : 0.012355804443359375 time for calcul the mask position with numpy : 0.028897523880004883 nb_pixel_total : 25260 time to create 1 rle with old method : 0.02902507781982422 time for calcul the mask position with numpy : 0.030427217483520508 nb_pixel_total : 12659 time to create 1 rle with old method : 0.014240026473999023 time for calcul the mask position with numpy : 0.02970743179321289 nb_pixel_total : 19495 time to create 1 rle with old method : 0.02198052406311035 time for calcul the mask position with numpy : 0.0302431583404541 nb_pixel_total : 57642 time to create 1 rle with old method : 0.06369638442993164 time for calcul the mask position with numpy : 0.029403209686279297 nb_pixel_total : 24433 time to create 1 rle with old method : 0.031120777130126953 time for calcul the mask position with numpy : 0.03907322883605957 nb_pixel_total : 19713 time to create 1 rle with old method : 0.02196049690246582 time for calcul the mask position with numpy : 0.028649568557739258 nb_pixel_total : 12608 time to create 1 rle with old method : 0.013958454132080078 time for calcul the mask position with numpy : 0.02823925018310547 nb_pixel_total : 23025 time to create 1 rle with old method : 0.02568793296813965 time for calcul the mask position with numpy : 0.028717994689941406 nb_pixel_total : 33668 time to create 1 rle with old method : 0.03602147102355957 time for calcul the mask position with numpy : 0.030231475830078125 nb_pixel_total : 26163 time to create 1 rle with old method : 0.02935004234313965 time for calcul the mask position with numpy : 0.029903411865234375 nb_pixel_total : 58143 time to create 1 rle with old method : 0.06529545783996582 time for calcul the mask position with numpy : 0.028288602828979492 nb_pixel_total : 14697 time to create 1 rle with old method : 0.01635456085205078 time for calcul the mask position with numpy : 0.029120683670043945 nb_pixel_total : 16197 time to create 1 rle with old method : 0.018294572830200195 time for calcul the mask position with numpy : 0.03030109405517578 nb_pixel_total : 14258 time to create 1 rle with old method : 0.017394542694091797 time for calcul the mask position with numpy : 0.03192782402038574 nb_pixel_total : 44423 time to create 1 rle with old method : 0.05632758140563965 time for calcul the mask position with numpy : 0.02945423126220703 nb_pixel_total : 33033 time to create 1 rle with old method : 0.04022979736328125 time for calcul the mask position with numpy : 0.030686378479003906 nb_pixel_total : 43067 time to create 1 rle with old method : 0.05275583267211914 time for calcul the mask position with numpy : 0.030375003814697266 nb_pixel_total : 98221 time to create 1 rle with old method : 0.11511111259460449 time for calcul the mask position with numpy : 0.030116796493530273 nb_pixel_total : 45715 time to create 1 rle with old method : 0.052789926528930664 time for calcul the mask position with numpy : 0.02911996841430664 nb_pixel_total : 14672 time to create 1 rle with old method : 0.016543149948120117 time for calcul the mask position with numpy : 0.03227496147155762 nb_pixel_total : 15060 time to create 1 rle with old method : 0.018235445022583008 time for calcul the mask position with numpy : 0.029821395874023438 nb_pixel_total : 29567 time to create 1 rle with old method : 0.03596615791320801 time for calcul the mask position with numpy : 0.0312800407409668 nb_pixel_total : 62924 time to create 1 rle with old method : 0.07072091102600098 time for calcul the mask position with numpy : 0.029398679733276367 nb_pixel_total : 43779 time to create 1 rle with old method : 0.04864358901977539 time for calcul the mask position with numpy : 0.028191566467285156 nb_pixel_total : 27297 time to create 1 rle with old method : 0.02890300750732422 time for calcul the mask position with numpy : 0.028835535049438477 nb_pixel_total : 21513 time to create 1 rle with old method : 0.025632619857788086 time for calcul the mask position with numpy : 0.02864861488342285 nb_pixel_total : 29827 time to create 1 rle with old method : 0.03218698501586914 time for calcul the mask position with numpy : 0.028233766555786133 nb_pixel_total : 8038 time to create 1 rle with old method : 0.010218381881713867 time for calcul the mask position with numpy : 0.02885890007019043 nb_pixel_total : 12804 time to create 1 rle with old method : 0.014206647872924805 time for calcul the mask position with numpy : 0.027912378311157227 nb_pixel_total : 6592 time to create 1 rle with old method : 0.012385129928588867 time for calcul the mask position with numpy : 0.04297804832458496 nb_pixel_total : 7815 time to create 1 rle with old method : 0.008760929107666016 time for calcul the mask position with numpy : 0.028278827667236328 nb_pixel_total : 4710 time to create 1 rle with old method : 0.0052263736724853516 time for calcul the mask position with numpy : 0.02843618392944336 nb_pixel_total : 7109 time to create 1 rle with old method : 0.007989645004272461 time for calcul the mask position with numpy : 0.028490543365478516 nb_pixel_total : 6644 time to create 1 rle with old method : 0.007555484771728516 time for calcul the mask position with numpy : 0.029399633407592773 nb_pixel_total : 12364 time to create 1 rle with old method : 0.013640165328979492 time for calcul the mask position with numpy : 0.02812647819519043 nb_pixel_total : 8887 time to create 1 rle with old method : 0.009428024291992188 time for calcul the mask position with numpy : 0.027562856674194336 nb_pixel_total : 19844 time to create 1 rle with old method : 0.023352384567260742 time for calcul the mask position with numpy : 0.028939485549926758 nb_pixel_total : 16708 time to create 1 rle with old method : 0.018929243087768555 time for calcul the mask position with numpy : 0.031847476959228516 nb_pixel_total : 23036 time to create 1 rle with old method : 0.025460004806518555 time for calcul the mask position with numpy : 0.028789281845092773 nb_pixel_total : 10903 time to create 1 rle with old method : 0.01208186149597168 time for calcul the mask position with numpy : 0.02953648567199707 nb_pixel_total : 6464 time to create 1 rle with old method : 0.007246255874633789 time for calcul the mask position with numpy : 0.06237173080444336 nb_pixel_total : 7220 time to create 1 rle with old method : 0.018787860870361328 time for calcul the mask position with numpy : 0.056337594985961914 nb_pixel_total : 25115 time to create 1 rle with old method : 0.027492046356201172 time for calcul the mask position with numpy : 0.0294039249420166 nb_pixel_total : 5707 time to create 1 rle with old method : 0.0065174102783203125 time for calcul the mask position with numpy : 0.029163360595703125 nb_pixel_total : 23399 time to create 1 rle with old method : 0.026043415069580078 time for calcul the mask position with numpy : 0.028248071670532227 nb_pixel_total : 17553 time to create 1 rle with old method : 0.018754243850708008 time for calcul the mask position with numpy : 0.02728867530822754 nb_pixel_total : 7662 time to create 1 rle with old method : 0.00821685791015625 time for calcul the mask position with numpy : 0.027256488800048828 nb_pixel_total : 19023 time to create 1 rle with old method : 0.02001667022705078 time for calcul the mask position with numpy : 0.028443336486816406 nb_pixel_total : 33457 time to create 1 rle with old method : 0.03504657745361328 time for calcul the mask position with numpy : 0.027710914611816406 nb_pixel_total : 42650 time to create 1 rle with old method : 0.061219215393066406 time for calcul the mask position with numpy : 0.0662984848022461 nb_pixel_total : 10666 time to create 1 rle with old method : 0.021662473678588867 time for calcul the mask position with numpy : 0.0293729305267334 nb_pixel_total : 18007 time to create 1 rle with old method : 0.020531415939331055 time for calcul the mask position with numpy : 0.03464198112487793 nb_pixel_total : 7274 time to create 1 rle with old method : 0.009717464447021484 time for calcul the mask position with numpy : 0.031217336654663086 nb_pixel_total : 11461 time to create 1 rle with old method : 0.013128280639648438 time for calcul the mask position with numpy : 0.03047919273376465 nb_pixel_total : 5458 time to create 1 rle with old method : 0.006905317306518555 time for calcul the mask position with numpy : 0.032230377197265625 nb_pixel_total : 23765 time to create 1 rle with old method : 0.027124881744384766 time for calcul the mask position with numpy : 0.029831409454345703 nb_pixel_total : 9655 time to create 1 rle with old method : 0.010963201522827148 time for calcul the mask position with numpy : 0.029905319213867188 nb_pixel_total : 9844 time to create 1 rle with old method : 0.012171506881713867 create new chi : 6.37256646156311 time to delete rle : 0.006156206130981445 batch 1 Loaded 129 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 27936 TO DO : save crop sub photo not yet done ! save time : 2.489145517349243 nb_obj : 49 nb_hashtags : 5 time to prepare the origin masks : 3.964630126953125 time for calcul the mask position with numpy : 0.2342529296875 nb_pixel_total : 5435954 time to create 1 rle with new method : 0.9492051601409912 time for calcul the mask position with numpy : 0.029102563858032227 nb_pixel_total : 3641 time to create 1 rle with old method : 0.004172801971435547 time for calcul the mask position with numpy : 0.028877973556518555 nb_pixel_total : 13902 time to create 1 rle with old method : 0.015568733215332031 time for calcul the mask position with numpy : 0.02913951873779297 nb_pixel_total : 15870 time to create 1 rle with old method : 0.017887115478515625 time for calcul the mask position with numpy : 0.029099702835083008 nb_pixel_total : 8855 time to create 1 rle with old method : 0.009990692138671875 time for calcul the mask position with numpy : 0.02942514419555664 nb_pixel_total : 33270 time to create 1 rle with old method : 0.05083942413330078 time for calcul the mask position with numpy : 0.03273892402648926 nb_pixel_total : 15154 time to create 1 rle with old method : 0.016981840133666992 time for calcul the mask position with numpy : 0.028961181640625 nb_pixel_total : 17420 time to create 1 rle with old method : 0.021628618240356445 time for calcul the mask position with numpy : 0.02895522117614746 nb_pixel_total : 35044 time to create 1 rle with old method : 0.038919925689697266 time for calcul the mask position with numpy : 0.029458284378051758 nb_pixel_total : 59708 time to create 1 rle with old method : 0.06695222854614258 time for calcul the mask position with numpy : 0.02934885025024414 nb_pixel_total : 40382 time to create 1 rle with old method : 0.044954776763916016 time for calcul the mask position with numpy : 0.028925657272338867 nb_pixel_total : 12123 time to create 1 rle with old method : 0.013138771057128906 time for calcul the mask position with numpy : 0.028393983840942383 nb_pixel_total : 18951 time to create 1 rle with old method : 0.020171165466308594 time for calcul the mask position with numpy : 0.02879786491394043 nb_pixel_total : 20706 time to create 1 rle with old method : 0.022165775299072266 time for calcul the mask position with numpy : 0.0279998779296875 nb_pixel_total : 10467 time to create 1 rle with old method : 0.011302947998046875 time for calcul the mask position with numpy : 0.02752542495727539 nb_pixel_total : 13258 time to create 1 rle with old method : 0.013599157333374023 time for calcul the mask position with numpy : 0.026797056198120117 nb_pixel_total : 12601 time to create 1 rle with old method : 0.013597249984741211 time for calcul the mask position with numpy : 0.028140783309936523 nb_pixel_total : 71145 time to create 1 rle with old method : 0.07510709762573242 time for calcul the mask position with numpy : 0.02827596664428711 nb_pixel_total : 32149 time to create 1 rle with old method : 0.03469705581665039 time for calcul the mask position with numpy : 0.028419971466064453 nb_pixel_total : 12654 time to create 1 rle with old method : 0.013781309127807617 time for calcul the mask position with numpy : 0.02844405174255371 nb_pixel_total : 103042 time to create 1 rle with old method : 0.11098313331604004 time for calcul the mask position with numpy : 0.02786850929260254 nb_pixel_total : 9482 time to create 1 rle with old method : 0.010802030563354492 time for calcul the mask position with numpy : 0.02891254425048828 nb_pixel_total : 24676 time to create 1 rle with old method : 0.027422666549682617 time for calcul the mask position with numpy : 0.02725052833557129 nb_pixel_total : 128094 time to create 1 rle with old method : 0.13272500038146973 time for calcul the mask position with numpy : 0.0274808406829834 nb_pixel_total : 9028 time to create 1 rle with old method : 0.009630680084228516 time for calcul the mask position with numpy : 0.028770923614501953 nb_pixel_total : 17716 time to create 1 rle with old method : 0.019340038299560547 time for calcul the mask position with numpy : 0.02804422378540039 nb_pixel_total : 16595 time to create 1 rle with old method : 0.017883777618408203 time for calcul the mask position with numpy : 0.028464317321777344 nb_pixel_total : 42799 time to create 1 rle with old method : 0.04634714126586914 time for calcul the mask position with numpy : 0.028725385665893555 nb_pixel_total : 23720 time to create 1 rle with old method : 0.025719404220581055 time for calcul the mask position with numpy : 0.028406858444213867 nb_pixel_total : 51174 time to create 1 rle with old method : 0.055814504623413086 time for calcul the mask position with numpy : 0.027862071990966797 nb_pixel_total : 19185 time to create 1 rle with old method : 0.020273208618164062 time for calcul the mask position with numpy : 0.02779865264892578 nb_pixel_total : 32196 time to create 1 rle with old method : 0.03430604934692383 time for calcul the mask position with numpy : 0.028485536575317383 nb_pixel_total : 19475 time to create 1 rle with old method : 0.020735740661621094 time for calcul the mask position with numpy : 0.028911828994750977 nb_pixel_total : 184713 time to create 1 rle with new method : 0.5603342056274414 time for calcul the mask position with numpy : 0.028583049774169922 nb_pixel_total : 14087 time to create 1 rle with old method : 0.0159456729888916 time for calcul the mask position with numpy : 0.029013395309448242 nb_pixel_total : 84636 time to create 1 rle with old method : 0.09527802467346191 time for calcul the mask position with numpy : 0.029139041900634766 nb_pixel_total : 23605 time to create 1 rle with old method : 0.026152610778808594 time for calcul the mask position with numpy : 0.029163599014282227 nb_pixel_total : 17778 time to create 1 rle with old method : 0.019425630569458008 time for calcul the mask position with numpy : 0.02836894989013672 nb_pixel_total : 24311 time to create 1 rle with old method : 0.03380608558654785 time for calcul the mask position with numpy : 0.028700828552246094 nb_pixel_total : 61037 time to create 1 rle with old method : 0.06863641738891602 time for calcul the mask position with numpy : 0.032476186752319336 nb_pixel_total : 28042 time to create 1 rle with old method : 0.030565261840820312 time for calcul the mask position with numpy : 0.028084516525268555 nb_pixel_total : 26600 time to create 1 rle with old method : 0.028089284896850586 time for calcul the mask position with numpy : 0.031235456466674805 nb_pixel_total : 37158 time to create 1 rle with old method : 0.06470346450805664 time for calcul the mask position with numpy : 0.031662940979003906 nb_pixel_total : 646 time to create 1 rle with old method : 0.000789642333984375 time for calcul the mask position with numpy : 0.028909683227539062 nb_pixel_total : 30009 time to create 1 rle with old method : 0.03342175483703613 time for calcul the mask position with numpy : 0.02932572364807129 nb_pixel_total : 45357 time to create 1 rle with old method : 0.054744720458984375 time for calcul the mask position with numpy : 0.030448436737060547 nb_pixel_total : 67750 time to create 1 rle with old method : 0.08908724784851074 time for calcul the mask position with numpy : 0.029553651809692383 nb_pixel_total : 14511 time to create 1 rle with old method : 0.02351856231689453 time for calcul the mask position with numpy : 0.033254146575927734 nb_pixel_total : 2808 time to create 1 rle with old method : 0.004586935043334961 time for calcul the mask position with numpy : 0.03262662887573242 nb_pixel_total : 6756 time to create 1 rle with old method : 0.00751185417175293 create new chi : 4.863446950912476 time to delete rle : 0.0038161277770996094 batch 1 Loaded 99 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 24710 TO DO : save crop sub photo not yet done ! save time : 1.6999971866607666 map_output_result : {1349567563: (0.0, 'Should be the crop_list due to order', 0), 1349566411: (0.0, 'Should be the crop_list due to order', 0), 1349566408: (0.0, 'Should be the crop_list due to order', 0), 1349566346: (0.0, 'Should be the crop_list due to order', 0), 1349566338: (0.0, 'Should be the crop_list due to order', 0), 1349566334: (0.0, 'Should be the crop_list due to order', 0), 1349566331: (0.0, 'Should be the crop_list due to order', 0), 1349566326: (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 [1349567563, 1349566411, 1349566408, 1349566346, 1349566338, 1349566334, 1349566331, 1349566326] Looping around the photos to save general results len do output : 8 /1349567563.Didn't retrieve data . /1349566411.Didn't retrieve data . /1349566408.Didn't retrieve data . /1349566346.Didn't retrieve data . /1349566338.Didn't retrieve data . /1349566334.Didn't retrieve data . /1349566331.Didn't retrieve data . /1349566326.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, '2717511') ('3318', '21986466', '1349567563', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566411', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566408', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566346', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566338', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566334', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566331', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566326', None, None, None, None, None, '2717511') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 24 time used for this insertion : 0.014042854309082031 save_final save missing photos in datou_result : time spend for datou_step_exec : 99.57364416122437 time spend to save output : 0.015068531036376953 total time spend for step 3 : 99.58871269226074 step4:ventilate_hashtags_in_portfolio Wed Apr 2 20:36:25 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 21986466 get user id for portfolio 21986466 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`=21986466 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('flou','environnement','papier','autre','pet_fonce','pet_clair','background','carton','pehd','metal','mal_croppe')) AND mptpi.`min_score`=0.5 To do To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21986466 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('flou','environnement','papier','autre','pet_fonce','pet_clair','background','carton','pehd','metal','mal_croppe')) AND mptpi.`min_score`=0.5 To do Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21986466 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('flou','environnement','papier','autre','pet_fonce','pet_clair','background','carton','pehd','metal','mal_croppe')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/21987151,21987152,21987153,21987154,21987155,21987156,21987157,21987158,21987159,21987160,21987161?tags=flou,environnement,papier,autre,pet_fonce,pet_clair,background,carton,pehd,metal,mal_croppe Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1349567563, 1349566411, 1349566408, 1349566346, 1349566338, 1349566334, 1349566331, 1349566326] Looping around the photos to save general results len do output : 1 /21986466. 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, '2717511') ('3318', '21986466', '1349567563', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566411', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566408', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566346', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566338', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566334', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566331', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566326', None, None, None, None, None, '2717511') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 9 time used for this insertion : 0.018815279006958008 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.685204029083252 time spend to save output : 0.019139528274536133 total time spend for step 4 : 1.704343557357788 step5:final Wed Apr 2 20:36:27 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step final ! Catched exception ! Connect or reconnect ! Inside saveOutput : final : False verbose : 0 original output for save of step final : {1349567563: ('0.22920264061932644',), 1349566411: ('0.22920264061932644',), 1349566408: ('0.22920264061932644',), 1349566346: ('0.22920264061932644',), 1349566338: ('0.22920264061932644',), 1349566334: ('0.22920264061932644',), 1349566331: ('0.22920264061932644',), 1349566326: ('0.22920264061932644',)} new output for save of step final : {1349567563: ('0.22920264061932644',), 1349566411: ('0.22920264061932644',), 1349566408: ('0.22920264061932644',), 1349566346: ('0.22920264061932644',), 1349566338: ('0.22920264061932644',), 1349566334: ('0.22920264061932644',), 1349566331: ('0.22920264061932644',), 1349566326: ('0.22920264061932644',)} [1349567563, 1349566411, 1349566408, 1349566346, 1349566338, 1349566334, 1349566331, 1349566326] Looping around the photos to save general results len do output : 8 /1349567563.Didn't retrieve data . /1349566411.Didn't retrieve data . /1349566408.Didn't retrieve data . /1349566346.Didn't retrieve data . /1349566338.Didn't retrieve data . /1349566334.Didn't retrieve data . /1349566331.Didn't retrieve data . /1349566326.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, '2717511') ('3318', '21986466', '1349567563', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566411', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566408', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566346', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566338', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566334', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566331', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566326', None, None, None, None, None, '2717511') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 24 time used for this insertion : 0.01664900779724121 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.11438632011413574 time spend to save output : 0.017238855361938477 total time spend for step 5 : 0.13162517547607422 step6:blur_detection Wed Apr 2 20:36:27 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection methode: ratio et variance treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984.jpg resize: (2160, 3264) 1349567563 -1.196072475395097 treat image : temp/1743618630_3301087_1349566411_43f30a08cb5cf0dba73c686d15bd2e69.jpg resize: (2160, 3264) 1349566411 -2.6608888224430722 treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef.jpg resize: (2160, 3264) 1349566408 -2.3699837728269837 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb.jpg resize: (2160, 3264) 1349566346 -3.2772942907919393 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428.jpg resize: (2160, 3264) 1349566338 -3.462683244268872 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489.jpg resize: (2160, 3264) 1349566334 -0.042466653215712 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f.jpg resize: (2160, 3264) 1349566331 -4.794675328051784 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f.jpg resize: (2160, 3264) 1349566326 -4.620112719729964 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655639_0.png resize: (614, 479) 1349594084 -1.4232509090854097 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655642_0.png resize: (427, 287) 1349594085 -0.9914082731360225 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655655_0.png resize: (215, 205) 1349594086 -2.0333601639699883 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655640_0.png resize: (252, 220) 1349594087 -1.1570274268674712 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655658_0.png resize: (140, 365) 1349594088 -1.7147126556893988 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655650_0.png resize: (223, 494) 1349594089 -2.1304488212658095 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655647_0.png resize: (208, 273) 1349594090 -1.905470335923423 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655656_0.png resize: (205, 150) 1349594091 -2.567395302987794 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655660_0.png resize: (499, 640) 1349594092 -0.9150666444356821 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655651_0.png resize: (243, 173) 1349594093 0.4817191534082382 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655643_0.png resize: (453, 421) 1349594094 0.5402155688497796 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655648_0.png resize: (593, 936) 1349594095 -0.6431104825193772 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655659_0.png resize: (130, 190) 1349594097 -0.22766734771096803 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655638_0.png resize: (219, 281) 1349594098 -0.5710447240004469 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655657_0.png resize: (203, 88) 1349594099 -0.31832087355985966 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655641_0.png resize: (187, 131) 1349594100 -2.2548919786426516 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655654_0.png resize: (159, 131) 1349594101 -2.5117119199822726 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655653_0.png resize: (255, 332) 1349594102 -1.959191946588139 treat image : temp/1743618630_3301087_1349566411_43f30a08cb5cf0dba73c686d15bd2e69_rle_crop_3744655666_0.png resize: (237, 230) 1349594103 -1.9653023269061773 treat image : temp/1743618630_3301087_1349566411_43f30a08cb5cf0dba73c686d15bd2e69_rle_crop_3744655667_0.png resize: (247, 142) 1349594104 -2.153898575921372 treat image : temp/1743618630_3301087_1349566411_43f30a08cb5cf0dba73c686d15bd2e69_rle_crop_3744655674_0.png resize: (157, 76) 1349594105 -0.7318425061533941 treat image : temp/1743618630_3301087_1349566411_43f30a08cb5cf0dba73c686d15bd2e69_rle_crop_3744655668_0.png resize: (134, 155) 1349594106 -1.9148234962361335 treat image : temp/1743618630_3301087_1349566411_43f30a08cb5cf0dba73c686d15bd2e69_rle_crop_3744655670_0.png resize: (187, 300) 1349594107 -2.2482076070658317 treat image : temp/1743618630_3301087_1349566411_43f30a08cb5cf0dba73c686d15bd2e69_rle_crop_3744655662_0.png resize: (218, 397) 1349594108 -1.1397415298021085 treat image : temp/1743618630_3301087_1349566411_43f30a08cb5cf0dba73c686d15bd2e69_rle_crop_3744655663_0.png resize: (280, 230) 1349594109 -0.3908421039426676 treat image : temp/1743618630_3301087_1349566411_43f30a08cb5cf0dba73c686d15bd2e69_rle_crop_3744655671_0.png resize: (206, 366) 1349594111 -2.1763450481910183 treat image : temp/1743618630_3301087_1349566411_43f30a08cb5cf0dba73c686d15bd2e69_rle_crop_3744655669_0.png resize: (182, 111) 1349594112 -0.8872769509395092 treat image : temp/1743618630_3301087_1349566411_43f30a08cb5cf0dba73c686d15bd2e69_rle_crop_3744655673_0.png resize: (167, 193) 1349594113 -2.724864818442692 treat image : temp/1743618630_3301087_1349566411_43f30a08cb5cf0dba73c686d15bd2e69_rle_crop_3744655665_0.png resize: (568, 561) 1349594114 -3.2200228331852045 treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655689_0.png resize: (204, 734) 1349594116 -1.818105570476529 treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655692_0.png resize: (93, 137) 1349594117 -2.579679619801496 treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655677_0.png resize: (155, 121) 1349594118 -2.013070810011693 treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655680_0.png resize: (117, 148) 1349594119 -1.7680994940164445 treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655691_0.png resize: (315, 454) 1349594120 -4.126389767799179 treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655688_0.png resize: (245, 496) 1349594121 -3.9316910040840987 treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655690_0.png resize: (130, 115) 1349594122 -2.0540571534429795 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655705_0.png resize: (432, 395) 1349594123 -2.6126666378441636 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655702_0.png resize: (229, 225) 1349594124 -3.3879187201401395 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655713_0.png resize: (200, 160) 1349594125 -3.4945475387541363 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655708_0.png resize: (256, 458) 1349594126 -3.155360275624004 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655707_0.png resize: (143, 204) 1349594127 -1.9587226361183367 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655698_0.png resize: (413, 543) 1349594128 -2.531778289406808 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655710_0.png resize: (186, 245) 1349594129 -0.14176710347451116 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655712_0.png resize: (208, 211) 1349594130 -2.587569402208416 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655711_0.png resize: (561, 768) 1349594131 -2.097354342931339 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655717_0.png resize: (537, 271) 1349594132 -0.5521580961664929 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655729_0.png resize: (313, 117) 1349594133 -2.3278696827847836 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655736_0.png resize: (255, 51) 1349594134 -1.8566704521011743 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655716_0.png resize: (351, 391) 1349594135 -2.431399480473336 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655721_0.png resize: (251, 368) 1349594136 -2.189234729178782 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655722_0.png resize: (180, 141) 1349594137 -1.8055942526979376 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655715_0.png resize: (430, 248) 1349594138 -2.1334821846419234 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655747_0.png resize: (95, 76) 1349594139 -0.1086538314980422 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655733_0.png resize: (173, 284) 1349594140 -1.6095167152154497 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655725_0.png resize: (367, 574) 1349594141 -1.3643155892872452 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655724_0.png resize: (214, 236) 1349594142 -3.751999370973052 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655741_0.png resize: (237, 282) 1349594143 -1.4439573470431284 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655726_0.png resize: (124, 121) 1349594144 -3.4666009157195994 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655732_0.png resize: (272, 238) 1349594145 -2.1197581727720465 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655728_0.png resize: (466, 592) 1349594146 -2.203304159637054 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655740_0.png resize: (214, 469) 1349594147 -0.4318912343708796 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655734_0.png resize: (219, 287) 1349594148 -3.8925655294648283 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655723_0.png resize: (526, 390) 1349594149 -3.326822814005425 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655777_0.png resize: (318, 263) 1349594150 -1.0261902386699442 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655749_0.png resize: (295, 330) 1349594151 -1.7855033150757422 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655754_0.png resize: (149, 130) 1349594152 -1.155871964842191 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655780_0.png resize: (156, 149) 1349594153 -2.0515495487557858 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655769_0.png resize: (250, 255) 1349594154 -1.6789156581334146 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655762_0.png resize: (217, 244) 1349594155 -0.0032413266965919093 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655751_0.png resize: (175, 215) 1349594156 -1.5870450378154404 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655768_0.png resize: (208, 169) 1349594157 -1.8744719896577895 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655770_0.png resize: (164, 146) 1349594158 -1.9444018017217735 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655748_0.png resize: (142, 168) 1349594159 -0.8025866489788434 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655752_0.png resize: (255, 175) 1349594160 -1.294451584114987 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655772_0.png resize: (86, 111) 1349594162 0.7677249692581175 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655775_0.png resize: (156, 150) 1349594163 0.31809290890743697 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655776_0.png resize: (212, 262) 1349594164 -1.1076608027856092 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655764_0.png resize: (206, 133) 1349594165 -0.1607998367489735 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655760_0.png resize: (167, 170) 1349594166 -1.664862983155055 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655758_0.png resize: (118, 152) 1349594167 -1.75386915396332 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655771_0.png resize: (264, 145) 1349594168 -1.2706287414668842 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655778_0.png resize: (303, 161) 1349594169 -0.7473812573150407 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655765_0.png resize: (253, 240) 1349594170 -0.28611798633290564 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655757_0.png resize: (131, 105) 1349594171 -0.46348042070759166 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655759_0.png resize: (91, 177) 1349594172 -0.07100161424802408 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655774_0.png resize: (79, 204) 1349594173 -1.2191823853381951 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655773_0.png resize: (112, 179) 1349594174 -0.32020833400965365 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655766_0.png resize: (152, 252) 1349594175 -0.7841040773889775 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655779_0.png resize: (51, 112) 1349594176 -1.6395429475485803 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655761_0.png resize: (134, 73) 1349594177 -1.5623023669178315 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655820_0.png resize: (194, 212) 1349594178 -2.4741416455723857 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655795_0.png resize: (234, 309) 1349594179 -2.7902082327843485 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655810_0.png resize: (112, 87) 1349594180 -2.257961085195951 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655838_0.png resize: (103, 132) 1349594181 -4.228365337651908 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655804_0.png resize: (171, 81) 1349594182 -3.0919366889271385 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655832_0.png resize: (232, 130) 1349594183 -2.3358087857978824 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655808_0.png resize: (257, 290) 1349594184 -2.8928246837743843 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655788_0.png resize: (166, 90) 1349594185 -2.927053161707586 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655799_0.png resize: (343, 242) 1349594187 -2.8346481174457 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655806_0.png resize: (91, 199) 1349594188 -2.4698403687844968 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655826_0.png resize: (121, 196) 1349594190 -1.5310234910748368 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655818_0.png resize: (192, 112) 1349594191 -2.4874174567230396 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655785_0.png resize: (158, 73) 1349594192 -1.2570415047883017 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655786_0.png resize: (254, 72) 1349594193 -4.2246972854828835 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655829_0.png resize: (183, 250) 1349594194 -3.1509814736129256 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655800_0.png resize: (95, 88) 1349594195 -1.3701384849292337 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655822_0.png resize: (218, 204) 1349594196 -3.3203148774044493 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655783_0.png resize: (68, 176) 1349594197 -1.759871308309315 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655807_0.png resize: (187, 187) 1349594199 -2.551027831264649 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655792_0.png resize: (223, 207) 1349594200 -2.8681313209589496 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655836_0.png resize: (105, 96) 1349594201 -1.9298319910174093 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655791_0.png resize: (192, 154) 1349594202 -3.8013622993423 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655834_0.png resize: (204, 164) 1349594203 -2.805110111132679 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655839_0.png resize: (231, 85) 1349594204 -2.1051802393999544 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655811_0.png resize: (335, 246) 1349594205 -1.8846579865838513 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655837_0.png resize: (193, 318) 1349594206 -2.670675219757724 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655825_0.png resize: (143, 217) 1349594207 -3.479268078803336 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655819_0.png resize: (206, 200) 1349594208 -2.6193236093042684 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655823_0.png resize: (160, 177) 1349594209 -2.739534358992891 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655828_0.png resize: (96, 274) 1349594210 -3.8013379210907963 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655840_0.png resize: (120, 131) 1349594211 -3.1946715646922708 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655797_0.png resize: (343, 292) 1349594213 -2.616594090932543 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655793_0.png resize: (233, 230) 1349594214 -2.29960986470124 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655801_0.png resize: (171, 171) 1349594215 -2.6879626625969317 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655817_0.png resize: (131, 186) 1349594216 -3.205166247854291 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655816_0.png resize: (139, 144) 1349594217 -2.812429090430849 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655798_0.png resize: (119, 185) 1349594218 -3.02419425994073 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655814_0.png resize: (275, 262) 1349594219 -3.139998224322183 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655803_0.png resize: (221, 103) 1349594220 -3.105582790573226 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655844_0.png resize: (124, 76) 1349594221 -1.6096229686527335 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655842_0.png resize: (190, 186) 1349594222 -3.6318420538929126 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655796_0.png resize: (262, 201) 1349594223 -2.997738671213984 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655812_0.png resize: (250, 168) 1349594224 -3.6736976540561717 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655827_0.png resize: (279, 283) 1349594225 -2.9570190548348845 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655782_0.png resize: (153, 200) 1349594226 -2.4242733527089455 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655824_0.png resize: (120, 70) 1349594227 -2.4569627013291804 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655813_0.png resize: (86, 137) 1349594228 -2.483705046496469 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655781_0.png resize: (193, 347) 1349594229 -2.536101501778982 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655815_0.png resize: (91, 108) 1349594230 -1.4544151451555836 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655784_0.png resize: (234, 219) 1349594231 -2.591168561688028 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655843_0.png resize: (111, 136) 1349594232 -3.5951358421442445 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655841_0.png resize: (142, 161) 1349594233 -2.7630616630739437 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655821_0.png resize: (128, 176) 1349594234 -1.9529916796800137 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655790_0.png resize: (75, 154) 1349594235 0.9622627172555135 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655835_0.png resize: (92, 108) 1349594236 -3.286989109640717 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655805_0.png resize: (271, 190) 1349594237 -2.6075737019024636 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655809_0.png resize: (275, 192) 1349594239 -4.303465719321395 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655833_0.png resize: (121, 146) 1349594240 -2.986710477523324 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655794_0.png resize: (98, 174) 1349594241 -2.0214118080990717 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655787_0.png resize: (175, 130) 1349594242 -2.8888296270345393 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655802_0.png resize: (152, 141) 1349594243 -4.039782893671996 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655884_0.png resize: (234, 92) 1349594244 -1.1065284849561439 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655852_0.png resize: (166, 75) 1349594245 -1.2053466092420835 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655870_0.png resize: (235, 277) 1349594246 -1.7907193557573446 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655858_0.png resize: (298, 391) 1349594247 -2.2362801798906955 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655857_0.png resize: (161, 372) 1349594248 -2.6360950988847938 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655869_0.png resize: (106, 130) 1349594249 -1.6621578496844964 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655849_0.png resize: (327, 189) 1349594250 -3.0782190385001282 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655845_0.png resize: (86, 237) 1349594251 -2.4694899747532904 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655887_0.png resize: (71, 87) 1349594252 -2.8507144859807028 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655871_0.png resize: (101, 130) 1349594253 -1.5563736177593066 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655877_0.png resize: (162, 125) 1349594254 -1.7762987436273066 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655882_0.png resize: (192, 248) 1349594255 -4.023293626485637 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655865_0.png resize: (154, 169) 1349594256 -2.9310114483188476 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655859_0.png resize: (476, 350) 1349594257 -2.9849294380908122 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655853_0.png resize: (176, 236) 1349594258 -3.38685391903828 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655863_0.png resize: (191, 182) 1349594259 -1.6426406234299262 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655893_0.png resize: (147, 180) 1349594260 -2.930504758651371 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655846_0.png resize: (177, 109) 1349594261 -2.481853781782475 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655880_0.png resize: (142, 140) 1349594262 -1.625348406581762 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655850_0.png resize: (577, 484) 1349594263 -1.7330322399670581 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655861_0.png resize: (144, 189) 1349594265 -0.8363727252284412 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655872_0.png resize: (315, 342) 1349594266 -3.239790251350756 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655875_0.png resize: (235, 227) 1349594267 -2.0660548772952945 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655868_0.png resize: (488, 402) 1349594268 -2.4394274682740362 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655879_0.png resize: (188, 169) 1349594269 -3.801945626105893 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655855_0.png resize: (336, 304) 1349594270 -4.251150253290711 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655848_0.png resize: (184, 179) 1349594271 -1.0654686615410125 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655864_0.png resize: (179, 164) 1349594272 -2.257953743856299 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655873_0.png resize: (153, 100) 1349594273 -2.000553414191626 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655878_0.png resize: (226, 152) 1349594275 -4.056366912217204 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655876_0.png resize: (359, 254) 1349594276 -3.4949468358640954 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655891_0.png resize: (255, 246) 1349594277 -3.160456168111755 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655851_0.png resize: (230, 200) 1349594278 -3.0781529112324173 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655860_0.png resize: (123, 166) 1349594279 -3.7258932242749347 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655867_0.png resize: (181, 142) 1349594280 -3.247077302491162 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655886_0.png resize: (48, 70) 1349594281 2.92035772977956 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655856_0.png resize: (330, 198) 1349594282 -2.80095815686769 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655854_0.png resize: (71, 120) 1349594283 -2.1964578245212487 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655888_0.png resize: (118, 103) 1349594284 -2.3607024640549894 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655874_0.png resize: (145, 125) 1349594285 -1.9642372371190064 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655652_0.png resize: (261, 322) 1349594304 -1.658398430959225 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655649_0.png resize: (69, 126) 1349594305 0.20052125995308745 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655646_0.png resize: (314, 369) 1349594306 -0.8107824454848535 treat image : temp/1743618630_3301087_1349566411_43f30a08cb5cf0dba73c686d15bd2e69_rle_crop_3744655664_0.png resize: (233, 113) 1349594307 -0.533823642299485 treat image : temp/1743618630_3301087_1349566411_43f30a08cb5cf0dba73c686d15bd2e69_rle_crop_3744655661_0.png resize: (448, 601) 1349594308 -1.6565701994963766 treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655693_0.png resize: (455, 241) 1349594309 -1.6846121722655774 treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655684_0.png resize: (134, 377) 1349594310 -1.1647391099473936 treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655676_0.png resize: (84, 254) 1349594311 -2.6119951521239537 treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655683_0.png resize: (230, 510) 1349594312 -2.9013732132864125 treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655681_0.png resize: (459, 424) 1349594313 -2.914446849672446 treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655678_0.png resize: (141, 224) 1349594314 -2.495558189772003 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655695_0.png resize: (241, 146) 1349594315 -1.7923942685957017 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655704_0.png resize: (304, 257) 1349594316 -2.4021817370703302 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655706_0.png resize: (115, 590) 1349594317 -3.4722852317085433 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655697_0.png resize: (109, 134) 1349594318 -1.4773484756465871 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655694_0.png resize: (213, 298) 1349594319 -2.1429193719892172 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655735_0.png resize: (261, 391) 1349594320 -1.5265604752021869 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655730_0.png resize: (126, 203) 1349594321 -1.8479261955728148 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655743_0.png resize: (356, 320) 1349594322 -1.7716412623669118 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655720_0.png resize: (78, 150) 1349594323 -1.3968546670839035 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655719_0.png resize: (96, 135) 1349594325 -2.6860883951715997 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655718_0.png resize: (223, 591) 1349594326 -1.2057223599554014 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655744_0.png resize: (451, 616) 1349594327 -2.243258063137617 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655742_0.png resize: (289, 292) 1349594328 -2.383183716940339 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655727_0.png resize: (189, 250) 1349594329 -1.6860263319366429 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655755_0.png resize: (501, 314) 1349594330 -0.8064468190253702 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655756_0.png resize: (195, 227) 1349594331 -0.07870430224253736 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655767_0.png resize: (176, 239) 1349594332 -1.4531523994651225 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655753_0.png resize: (463, 439) 1349594334 -0.5974274604846361 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655750_0.png resize: (226, 205) 1349594335 -1.2667355436114982 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655831_0.png resize: (216, 227) 1349594336 -2.7506538878796114 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655847_0.png resize: (308, 339) 1349594337 -1.974458367517051 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655883_0.png resize: (293, 243) 1349594338 -2.6893723333290946 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655890_0.png resize: (186, 151) 1349594340 -1.398871962590737 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655862_0.png resize: (163, 132) 1349594341 -1.4530683581168375 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655644_0.png resize: (289, 243) 1349594344 -2.913834507518418 treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655682_0.png resize: (105, 101) 1349594345 -4.0601260603985905 treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655686_0.png resize: (304, 179) 1349594346 -3.791871098813349 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655866_0.png resize: (150, 182) 1349594347 -4.502822845328028 treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655645_0.png resize: (669, 399) 1349594358 -1.725447256614664 treat image : temp/1743618630_3301087_1349566411_43f30a08cb5cf0dba73c686d15bd2e69_rle_crop_3744655672_0.png resize: (180, 188) 1349594359 -3.226104578012459 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655714_0.png resize: (861, 517) 1349594360 -4.109257167014851 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655699_0.png resize: (268, 409) 1349594361 -2.554364963278313 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655701_0.png resize: (446, 709) 1349594364 -1.8890569420756582 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655696_0.png resize: (400, 887) 1349594366 -0.8314262404057745 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655700_0.png resize: (252, 167) 1349594368 -3.5825294740489375 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655709_0.png resize: (378, 344) 1349594369 -2.9606150893420455 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655737_0.png resize: (316, 475) 1349594370 -1.9609996704229455 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655746_0.png resize: (646, 457) 1349594372 -3.6914978999582777 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655738_0.png resize: (573, 712) 1349594373 -3.4987180569659717 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655745_0.png resize: (276, 151) 1349594374 -2.1830803349205663 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655739_0.png resize: (396, 316) 1349594377 -3.554268937293036 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655830_0.png resize: (111, 171) 1349594378 -3.075125264093719 treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f_rle_crop_3744655789_0.png resize: (486, 265) 1349594379 -3.681463735499113 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655881_0.png resize: (370, 268) 1349594381 -2.1106036564337822 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655889_0.png resize: (216, 248) 1349594382 -1.9799173651552284 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655885_0.png resize: (200, 185) 1349594384 -2.6196856609771237 treat image : temp/1743618630_3301087_1349566411_43f30a08cb5cf0dba73c686d15bd2e69_rle_crop_3744655675_0.png resize: (489, 529) 1349594458 -3.112056013209205 treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655679_0.png resize: (1546, 1619) 1349594460 -1.687900439783533 treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655687_0.png resize: (341, 718) 1349594461 -0.28930689507292734 treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655685_0.png resize: (196, 203) 1349594463 -2.6555332923076453 treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655703_0.png resize: (120, 131) 1349594465 -1.8974027547325591 treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655731_0.png resize: (374, 356) 1349594466 -1.9036395735681428 treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655763_0.png resize: (134, 156) 1349594467 -1.1618565550885738 treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655892_0.png resize: (190, 171) 1349594479 -2.4928664808643792 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 : 264 time used for this insertion : 0.028603553771972656 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 264 time used for this insertion : 0.05352044105529785 save missing photos in datou_result : time spend for datou_step_exec : 34.46645927429199 time spend to save output : 0.08882403373718262 total time spend for step 6 : 34.555283308029175 step7:brightness Wed Apr 2 20:37:02 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/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984.jpg treat image : temp/1743618630_3301087_1349566411_43f30a08cb5cf0dba73c686d15bd2e69.jpg treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef.jpg treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb.jpg treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428.jpg treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489.jpg treat image : temp/1743618630_3301087_1349566331_0e527643045057b45d5aaff819ea242f.jpg treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f.jpg treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655639_0.png treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655642_0.png 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temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655682_0.png treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655686_0.png treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655866_0.png treat image : temp/1743618630_3301087_1349567563_03a2e2e01e6bcf88898ca93d833fd984_rle_crop_3744655645_0.png treat image : temp/1743618630_3301087_1349566411_43f30a08cb5cf0dba73c686d15bd2e69_rle_crop_3744655672_0.png treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655714_0.png treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655699_0.png treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655701_0.png treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655696_0.png treat image : 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temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655881_0.png treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655889_0.png treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655885_0.png treat image : temp/1743618630_3301087_1349566411_43f30a08cb5cf0dba73c686d15bd2e69_rle_crop_3744655675_0.png treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655679_0.png treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655687_0.png treat image : temp/1743618630_3301087_1349566408_fc3c8bdde241642f677f0b82315e37ef_rle_crop_3744655685_0.png treat image : temp/1743618630_3301087_1349566346_bc6c062518ab58b97dd4db67932db7fb_rle_crop_3744655703_0.png treat image : temp/1743618630_3301087_1349566338_2bac959dcd464c25e115ddbf582fa428_rle_crop_3744655731_0.png treat image : temp/1743618630_3301087_1349566334_e6ea72f8e4de9cfdfb2b76ad91643489_rle_crop_3744655763_0.png treat image : temp/1743618630_3301087_1349566326_c3bab94bd070d173b7def9edffff256f_rle_crop_3744655892_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 : 264 time used for this insertion : 0.024381637573242188 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 264 time used for this insertion : 0.05843853950500488 save missing photos in datou_result : time spend for datou_step_exec : 8.718028783798218 time spend to save output : 0.08786845207214355 total time spend for step 7 : 8.805897235870361 step8:velours_tree Wed Apr 2 20:37:11 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 : 1.0922210216522217 time spend to save output : 3.600120544433594e-05 total time spend for step 8 : 1.092257022857666 step9:send_mail_cod Wed Apr 2 20:37:12 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 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_P21986466_02-04-2025_20_37_12.pdf 21987151 imagette219871511743619032 21987153 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 .imagette219871531743619032 21987154 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 .imagette219871541743619033 21987155 change filename to text .imagette219871551743619034 21987156 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 .imagette219871561743619034 21987157 imagette219871571743619035 21987158 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 .imagette219871581743619035 21987159 imagette219871591743619037 21987160 change filename to text .change filename to text .change filename to text .change filename to text .imagette219871601743619037 21987161 imagette219871611743619037 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=21986466 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/21987151,21987152,21987153,21987154,21987155,21987156,21987157,21987158,21987159,21987160,21987161?tags=flou,environnement,papier,autre,pet_fonce,pet_clair,background,carton,pehd,metal,mal_croppe args[1349567563] : ((1349567563, -1.196072475395097, 492688767), (1349567563, -0.3152411473904984, 496442774), '0.22920264061932644') We are sending mail with results at report@fotonower.com args[1349566411] : ((1349566411, -2.6608888224430722, 492609224), (1349566411, 0.6679439974199972, 2107752395), '0.22920264061932644') We are sending mail with results at report@fotonower.com args[1349566408] : ((1349566408, -2.3699837728269837, 492609224), (1349566408, -0.13135924450213352, 496442774), '0.22920264061932644') We are sending mail with results at report@fotonower.com args[1349566346] : ((1349566346, -3.2772942907919393, 492609224), (1349566346, -0.23508330730927962, 496442774), '0.22920264061932644') We are sending mail with results at report@fotonower.com args[1349566338] : ((1349566338, -3.462683244268872, 492609224), (1349566338, -0.05759471468523854, 2107752395), '0.22920264061932644') We are sending mail with results at report@fotonower.com args[1349566334] : ((1349566334, -0.042466653215712, 492688767), (1349566334, 0.0633367601304845, 2107752395), '0.22920264061932644') We are sending mail with results at report@fotonower.com args[1349566331] : ((1349566331, -4.794675328051784, 492609224), (1349566331, -0.007903642620766854, 2107752395), '0.22920264061932644') We are sending mail with results at report@fotonower.com args[1349566326] : ((1349566326, -4.620112719729964, 492609224), (1349566326, -0.26252410034279766, 496442774), '0.22920264061932644') We are sending mail with results at report@fotonower.com refus_total : 0.22920264061932644 2022-04-13 10:29:59 0 SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=21986466 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1349566338,1349566346,1349567563,1349566331,1349566326,1349566334,1349566408,1349566411) Found this number of photos: 8 begin to download photo : 1349566338 begin to download photo : 1349567563 begin to download photo : 1349566326 begin to download photo : 1349566408 download finish for photo 1349566338 begin to download photo : 1349566346 download finish for photo 1349566326 begin to download photo : 1349566334 download finish for photo 1349567563 begin to download photo : 1349566331 download finish for photo 1349566408 begin to download photo : 1349566411 download finish for photo 1349566346 download finish for photo 1349566334 download finish for photo 1349566411 download finish for photo 1349566331 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21986466_02-04-2025_20_37_12.pdf results_Auto_P21986466_02-04-2025_20_37_12.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21986466_02-04-2025_20_37_12.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','21986466','results_Auto_P21986466_02-04-2025_20_37_12.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21986466_02-04-2025_20_37_12.pdf','pdf','','1.06','0.22920264061932644') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/21986466

https://www.fotonower.com/image?json=false&list_photos_id=1349567563
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349566411
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349566408
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349566346
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349566338
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349566334
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349566331
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349566326
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 22.92%
Veuillez trouver les photos des contaminants.

exemples de contaminants: papier: https://www.fotonower.com/view/21987153?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/21987154?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/21987155?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/21987156?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/21987158?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/21987160?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21986466_02-04-2025_20_37_12.pdf.

Lien vers velours :https://www.fotonower.com/velours/21987151,21987152,21987153,21987154,21987155,21987156,21987157,21987158,21987159,21987160,21987161?tags=flou,environnement,papier,autre,pet_fonce,pet_clair,background,carton,pehd,metal,mal_croppe.


L'équipe Fotonower 202 b'' Server: nginx Date: Wed, 02 Apr 2025 18:37:20 GMT Content-Length: 0 Connection: close X-Message-Id: Z9PxtO-ZR7uRWKQCDcY19w 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 [1349567563, 1349566411, 1349566408, 1349566346, 1349566338, 1349566334, 1349566331, 1349566326] 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, '2717511') ('3318', '21986466', '1349567563', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566411', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566408', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566346', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566338', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566334', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566331', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566326', None, None, None, None, None, '2717511') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 8 time used for this insertion : 0.013893365859985352 save_final save missing photos in datou_result : time spend for datou_step_exec : 8.538042068481445 time spend to save output : 0.014295339584350586 total time spend for step 9 : 8.552337408065796 step10:split_time_score Wed Apr 2 20:37:20 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 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'}] (('18', 8),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 02042025 21986466 Nombre de photos uploadées : 8 / 23040 (0%) 02042025 21986466 Nombre de photos taguées (types de déchets): 0 / 8 (0%) 02042025 21986466 Nombre de photos taguées (volume) : 0 / 8 (0%) elapsed_time : load_data_split_time_score 1.9073486328125e-06 elapsed_time : order_list_meta_photo_and_scores 6.67572021484375e-06 ???????? elapsed_time : fill_and_build_computed_from_old_data 0.0006210803985595703 elapsed_time : insert_dashboard_record_day_entry 0.02795886993408203 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 : 0.18285560775236032 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21979633_02-04-2025_19_06_04.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21979633 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`=21979633 AND mptpi.`type`=3594 To do Qualite : 0.07143447116002825 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21979679_02-04-2025_18_13_07.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21979679 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21979679 AND mptpi.`type`=3726 To do Qualite : 0.1936615987540849 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21979686_02-04-2025_18_38_16.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21979686 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`=21979686 AND mptpi.`type`=3594 To do Qualite : 0.3020745190437286 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21979695_02-04-2025_18_45_59.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21979695 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`=21979695 AND mptpi.`type`=3594 To do Qualite : 0.1668441074346405 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21979697_02-04-2025_19_37_40.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21979697 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`=21979697 AND mptpi.`type`=3594 To do Qualite : 0.22078829940541034 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21979700_02-04-2025_18_12_37.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21979700 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`=21979700 AND mptpi.`type`=3594 To do Qualite : 0.22814737906479465 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21983711_02-04-2025_19_28_11.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21983711 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`=21983711 AND mptpi.`type`=3594 To do Qualite : 0.22920264061932644 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21986466_02-04-2025_20_37_12.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21986466 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`=21986466 AND mptpi.`type`=3594 To do Qualite : 0.07827723999400463 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21986497_02-04-2025_20_35_47.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21986497 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21986497 AND mptpi.`type`=3726 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'02042025': {'nb_upload': 8, '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 [1349567563, 1349566411, 1349566408, 1349566346, 1349566338, 1349566334, 1349566331, 1349566326] Looping around the photos to save general results len do output : 1 /21986466Didn'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, '2717511') ('3318', '21986466', '1349567563', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566411', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566408', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566346', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566338', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566334', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566331', None, None, None, None, None, '2717511') ('3318', None, None, None, None, None, None, None, '2717511') ('3318', '21986466', '1349566326', None, None, None, None, None, '2717511') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 9 time used for this insertion : 0.013976812362670898 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.7429499626159668 time spend to save output : 0.014147520065307617 total time spend for step 10 : 0.7570974826812744 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 8 set_done_treatment 206.57user 108.37system 6:55.51elapsed 75%CPU (0avgtext+0avgdata 7366088maxresident)k 1079936inputs+165904outputs (17949major+18998643minor)pagefaults 0swaps