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 : 1006925
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 : ['2574458'] with mtr_portfolio_ids : ['20425797'] and first list_photo_ids : []
new path :  /proc/1006925/
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 , 
BFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
length of list_filenames : 9 ; length of list_pids : 9 ; length of list_args : 9
time to download the photos : 2.071324586868286
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
Tue Feb 11 04:30:31 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
Beginning of datou step mask_detect !
save_polygon : True

 begin detect 

begin to check gpu status 
inside check gpu memory
l 3637 free memory gpu now : 10998  max_wait_temp :  1 max_wait :  0
 gpu_flag :  0
2025-02-11 04:30:35.355209: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2025-02-11 04:30:35.387344: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz
2025-02-11 04:30:35.389803: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f85dc000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2025-02-11 04:30:35.389861: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2025-02-11 04:30:35.395486: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2025-02-11 04:30:35.727531: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x42b17a00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2025-02-11 04:30:35.727608: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5
2025-02-11 04:30:35.729754: 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
2025-02-11 04:30:35.731708: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-02-11 04:30:35.763833: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-02-11 04:30:35.783257: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-02-11 04:30:35.787490: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-02-11 04:30:35.819923: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-02-11 04:30:35.824919: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-02-11 04:30:35.881196: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-02-11 04:30:35.883285: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-02-11 04:30:35.883709: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-02-11 04:30:35.885579: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-02-11 04:30:35.885601: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-02-11 04:30:35.885614: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-02-11 04:30:35.887920: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10193 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5)
WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead.

2025-02-11 04:30:36.332665: 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
2025-02-11 04:30:36.332746: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-02-11 04:30:36.332768: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-02-11 04:30:36.332787: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-02-11 04:30:36.332805: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-02-11 04:30:36.332823: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-02-11 04:30:36.332841: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-02-11 04:30:36.332859: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-02-11 04:30:36.334448: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-02-11 04:30:36.335761: 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
2025-02-11 04:30:36.335801: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-02-11 04:30:36.335822: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-02-11 04:30:36.335842: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-02-11 04:30:36.335862: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-02-11 04:30:36.335881: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-02-11 04:30:36.335900: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-02-11 04:30:36.335920: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-02-11 04:30:36.337512: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-02-11 04:30:36.337542: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-02-11 04:30:36.337553: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-02-11 04:30:36.337563: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-02-11 04:30:36.339235: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10193 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5)
Using TensorFlow backend.
WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version.
Instructions for updating:
box_ind is deprecated, use box_indices instead
WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
Inside mask_sub_process
Inside mask_detect 
About to load cache.load_thcl_param 
To do loadFromThcl(), then load ParamDescType : thcl2847
 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}]
 thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}
 Update svm_hashtag_type_desc : 5275
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39))
{'thcl': {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'], 'list_hashtags_csv': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'svm_hashtag_type_desc': 5275, 'photo_desc_type': 5275, 'pb_hashtag_id_or_classifier': 0}
list_class_names : 
['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement']

Configurations:
BACKBONE                       resnet101
BACKBONE_SHAPES                [[160 160]
 [ 80  80]
 [ 40  40]
 [ 20  20]
 [ 10  10]]
BACKBONE_STRIDES               [4, 8, 16, 32, 64]
BATCH_SIZE                     1
BBOX_STD_DEV                   [0.1 0.1 0.2 0.2]
DETECTION_MAX_INSTANCES        100
DETECTION_MIN_CONFIDENCE       0.3
DETECTION_NMS_THRESHOLD        0.3
GPU_COUNT                      1
IMAGES_PER_GPU                 1
IMAGE_MAX_DIM                  640
IMAGE_MIN_DIM                  640
IMAGE_PADDING                  True
IMAGE_SHAPE                    [640 640   3]
LEARNING_MOMENTUM              0.9
LEARNING_RATE                  0.001
LOSS_WEIGHTS                   {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0}
MASK_POOL_SIZE                 14
MASK_SHAPE                     [28, 28]
MAX_GT_INSTANCES               100
MEAN_PIXEL                     [123.7 116.8 103.9]
MINI_MASK_SHAPE                (56, 56)
NAME                           learn_RUBBIA_REFUS_AMIENS_23
NUM_CLASSES                    9
POOL_SIZE                      7
POST_NMS_ROIS_INFERENCE        1000
POST_NMS_ROIS_TRAINING         2000
ROI_POSITIVE_RATIO             0.33
RPN_ANCHOR_RATIOS              [0.5, 1, 2]
RPN_ANCHOR_SCALES              (16, 32, 64, 128, 256)
RPN_ANCHOR_STRIDE              1
RPN_BBOX_STD_DEV               [0.1 0.1 0.2 0.2]
RPN_NMS_THRESHOLD              0.7
RPN_TRAIN_ANCHORS_PER_IMAGE    256
STEPS_PER_EPOCH                1000
TRAIN_ROIS_PER_IMAGE           200
USE_MINI_MASK                  True
USE_RPN_ROIS                   True
VALIDATION_STEPS               50
WEIGHT_DECAY                   0.0001


model_param file didn't exist 
model_name : learn_RUBBIA_REFUS_AMIENS_23
model_type : mask_rcnn
list file need :
['mask_model.h5']
file exist in s3 :
['mask_model.h5']
file manque in s3 : 
[]
2025-02-11 04:30:46.347626: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-02-11 04:30:46.775317: 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 : 9
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 : 27
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 : 18
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 : 49
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 : 70
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 : 70
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 : 54
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 : 52
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
Detection mask done !
Trying to reset tf kernel
1007419
begin to check gpu status 
inside check gpu memory
l 3610 free memory gpu now : 417
tf kernel not reseted
sub process len(results) : 9 len(list_Values) 0
None
max_time_sub_proc : 3600
parent process len(results) : 9 len(list_Values) 0
process is alive
finish correctly or not : True

 after detect 

begin to check gpu status 
inside check gpu memory
l 3610 free memory gpu now : 5706
list_Values should be empty []
To do loadFromThcl(), then load ParamDescType : thcl2847
Catched exception !
Connect or reconnect !
 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}]
 thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}
 Update svm_hashtag_type_desc : 5275
['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement']
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length of segment :  229
time for calcul the mask position with numpy : 0.0003342628479003906
nb_pixel_total : 5164
time to create 1 rle with old method : 0.006207942962646484
length of segment :  93
time for calcul the mask position with numpy : 0.0046977996826171875
nb_pixel_total : 27552
time to create 1 rle with old method : 0.03167605400085449
length of segment :  386
time for calcul the mask position with numpy : 0.005067586898803711
nb_pixel_total : 115619
time to create 1 rle with old method : 0.15258240699768066
length of segment :  493
time for calcul the mask position with numpy : 0.0007450580596923828
nb_pixel_total : 22173
time to create 1 rle with old method : 0.026836395263671875
length of segment :  192
time for calcul the mask position with numpy : 0.0018568038940429688
nb_pixel_total : 33466
time to create 1 rle with old method : 0.03938460350036621
length of segment :  229
time for calcul the mask position with numpy : 0.0010669231414794922
nb_pixel_total : 30350
time to create 1 rle with old method : 0.036728858947753906
length of segment :  279

time spent for convertir_results : 24.436907052993774
Inside saveOutput : final : False verbose : 0
eke 12-6-18 : saveMask need to be cleaned for new output !
Number saved : None
batch 1
Loaded 504 chid ids of type : 3594
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 23448

save missing photos in datou_result :

time spend for datou_step_exec : 127.92172932624817
time spend to save output : 178.6636664867401
total time spend for step 1 : 306.5853958129883

step2:crop_condition
Tue Feb 11 04:35:37 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 : 9 !
batch 1
Loaded 504 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 !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
Next one !
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
 map_result returned by crop_photo_return_map_crop : length : 292
 About to insert : list_path_to_insert length 292 new photo from crops !

About to upload 292 photos
upload in portfolio : 3736932
init cache_photo without model_param  
we have 292 photo to upload
uploaded to storage server : ovh
folder_temporaire : temp/1739244993_1006925
we have uploaded 292 photos in the portfolio 3736932

time of upload the photos Elapsed time : 73.46980881690979

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 !
Next one !
we have both polygon and rles
Next one !
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
 map_result returned by crop_photo_return_map_crop : length : 111
 About to insert : list_path_to_insert length 111 new photo from crops !

About to upload 111 photos
upload in portfolio : 3736932
init cache_photo without model_param  
we have 111 photo to upload
uploaded to storage server : ovh
folder_temporaire : temp/1739245110_1006925
we have uploaded 111 photos in the portfolio 3736932

time of upload the photos Elapsed time : 29.194910764694214

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 !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
 map_result returned by crop_photo_return_map_crop : length : 16
 About to insert : list_path_to_insert length 16 new photo from crops !

About to upload 16 photos
upload in portfolio : 3736932
init cache_photo without model_param  
we have 16 photo to upload
uploaded to storage server : ovh
folder_temporaire : temp/1739245144_1006925
we have uploaded 16 photos in the portfolio 3736932

time of upload the photos Elapsed time : 4.000148296356201

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 !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
 map_result returned by crop_photo_return_map_crop : length : 47
 About to insert : list_path_to_insert length 47 new photo from crops !

About to upload 47 photos
upload in portfolio : 3736932
init cache_photo without model_param  
we have 47 photo to upload
uploaded to storage server : ovh
folder_temporaire : temp/1739245186_1006925
we have uploaded 47 photos in the portfolio 3736932

time of upload the photos Elapsed time : 13.580761194229126

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
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
 map_result returned by crop_photo_return_map_crop : length : 14
 About to insert : list_path_to_insert length 14 new photo from crops !

About to upload 14 photos
upload in portfolio : 3736932
init cache_photo without model_param  
we have 14 photo to upload
uploaded to storage server : ovh
folder_temporaire : temp/1739245207_1006925
we have uploaded 14 photos in the portfolio 3736932

time of upload the photos Elapsed time : 3.356801986694336

we have finished the crop for the class : autre



begin to crop the class : pehd
param for this class : {'min_score': 0.7}
filtre for class : pehd 
hashtag_id of this class : 628944319
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
 map_result returned by crop_photo_return_map_crop : length : 6
 About to insert : list_path_to_insert length 6 new photo from crops !

About to upload 6 photos
upload in portfolio : 3736932
init cache_photo without model_param  
we have 6 photo to upload
uploaded to storage server : ovh
folder_temporaire : temp/1739245216_1006925
we have uploaded 6 photos in the portfolio 3736932

time of upload the photos Elapsed time : 1.648087501525879

we have finished the crop for the class : pehd



begin to crop the class : pet_fonce
param for this class : {'min_score': 0.7}
filtre for class : pet_fonce 
hashtag_id of this class : 2107755900
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
we have both polygon and rles
Next one !
Next one !
 map_result returned by crop_photo_return_map_crop : length : 10
 About to insert : list_path_to_insert length 10 new photo from crops !

About to upload 10 photos
upload in portfolio : 3736932
init cache_photo without model_param  
we have 10 photo to upload
uploaded to storage server : ovh
folder_temporaire : temp/1739245221_1006925
we have uploaded 10 photos in the portfolio 3736932

time of upload the photos Elapsed time : 2.792130470275879

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
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 
[1336668708, 1336481138, 1336481135, 1336479370, 1336479334, 1336479330, 1336479326, 1336479304, 1336479300]
Looping around the photos to save general results
len do output : 496
/1336690504Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1336690505Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1336690506Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1336690507Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1336690508Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1336690509Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1336690510Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1336690511Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1336690512Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1336690513Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1336690514Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1336690515Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1336690516Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1336690517Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1336690518Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1336690519Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1336690520Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1336690521Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1336690522Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1336690523Didn't retrieve data
.Didn't retrieve data
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before output type
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f86f23c7940>
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f86f23c7910>
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f86f23c7a00>
Managing all output in save final without adding information in the mtr_datou_result 
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336668708', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336481138', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336481135', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479370', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479334', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479330', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479326', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479304', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479300', None, None, None, None, None, '2574458')


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 1497
time used for this insertion : 0.25382304191589355
 save_final 

save missing photos in datou_result :

time spend for datou_step_exec : 287.59121775627136
time spend to save output : 0.4118044376373291
total time spend for step 2 : 288.0030221939087

step3:rle_unique_nms_with_priority
Tue Feb 11 04:40:25 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
complete output_args for input 0
We expect there is only one output and this part is used while all output are not tuple or array
We expect there is only one output and this part is used while all output are not tuple or array
We expect there is only one output and this part is used while all output are not tuple or array
We expect there is only one output and this part is used while all output are not tuple or array
We expect there is only one output and this part is used while all output are not tuple or array
We expect there is only one output and this part is used while all output are not tuple or array
We expect there is only one output and this part is used while all output are not tuple or array
We expect there is only one output and this part is used while all output are not tuple or array
We expect there is only one output and this part is used while all output are not tuple or array
VR 22-3-18 : For now we do not clean correctly the datou structure 
Begin step rle-unique-nms
batch 1
Loaded 504 chid ids of type : 3594
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 10
nb_hashtags :  3
time to prepare the origin masks  : 5.000791072845459
time for calcul the mask position with numpy : 0.5290403366088867
nb_pixel_total : 5829405
time to create 1 rle with new method : 0.8785309791564941
time for calcul the mask position with numpy : 0.0249021053314209
nb_pixel_total : 328765
time to create 1 rle with new method : 0.5666422843933105
time for calcul the mask position with numpy : 0.022695541381835938
nb_pixel_total : 32617
time to create 1 rle with old method : 0.03898215293884277
time for calcul the mask position with numpy : 0.025135040283203125
nb_pixel_total : 15338
time to create 1 rle with old method : 0.0171966552734375
time for calcul the mask position with numpy : 0.026170015335083008
nb_pixel_total : 497186
time to create 1 rle with new method : 0.340681791305542
time for calcul the mask position with numpy : 0.02375173568725586
nb_pixel_total : 11900
time to create 1 rle with old method : 0.013923168182373047
time for calcul the mask position with numpy : 0.02213573455810547
nb_pixel_total : 18464
time to create 1 rle with old method : 0.021559715270996094
time for calcul the mask position with numpy : 0.02268695831298828
nb_pixel_total : 123844
time to create 1 rle with old method : 0.1388399600982666
time for calcul the mask position with numpy : 0.023711204528808594
nb_pixel_total : 21338
time to create 1 rle with old method : 0.024979114532470703
time for calcul the mask position with numpy : 0.035668373107910156
nb_pixel_total : 152299
time to create 1 rle with new method : 0.45125460624694824
time for calcul the mask position with numpy : 0.033936500549316406
nb_pixel_total : 19084
time to create 1 rle with old method : 0.021795034408569336
create new chi : 3.420616626739502
time to delete rle : 0.016525983810424805
batch 1
Loaded 21 chid ids of type : 3594
++++++++++++++++Number RLEs to save : 10098
 TO DO : save crop sub photo not yet done ! 
save time : 1.704993486404419
nb_obj : 15
nb_hashtags :  4
time to prepare the origin masks  : 7.888959884643555
time for calcul the mask position with numpy : 0.43422436714172363
nb_pixel_total : 6149665
time to create 1 rle with new method : 0.7021048069000244
time for calcul the mask position with numpy : 0.029935121536254883
nb_pixel_total : 206880
time to create 1 rle with new method : 0.25480103492736816
time for calcul the mask position with numpy : 0.029071569442749023
nb_pixel_total : 62104
time to create 1 rle with old method : 0.0692758560180664
time for calcul the mask position with numpy : 0.028882741928100586
nb_pixel_total : 19562
time to create 1 rle with old method : 0.02333545684814453
time for calcul the mask position with numpy : 0.028929471969604492
nb_pixel_total : 25865
time to create 1 rle with old method : 0.029070615768432617
time for calcul the mask position with numpy : 0.02878713607788086
nb_pixel_total : 23328
time to create 1 rle with old method : 0.026736736297607422
time for calcul the mask position with numpy : 0.028789997100830078
nb_pixel_total : 17678
time to create 1 rle with old method : 0.01999640464782715
time for calcul the mask position with numpy : 0.028845787048339844
nb_pixel_total : 233
time to create 1 rle with old method : 0.0005128383636474609
time for calcul the mask position with numpy : 0.029109954833984375
nb_pixel_total : 23333
time to create 1 rle with old method : 0.02657794952392578
time for calcul the mask position with numpy : 0.028834104537963867
nb_pixel_total : 27230
time to create 1 rle with old method : 0.03074502944946289
time for calcul the mask position with numpy : 0.029205799102783203
nb_pixel_total : 94557
time to create 1 rle with old method : 0.10622739791870117
time for calcul the mask position with numpy : 0.029155254364013672
nb_pixel_total : 23057
time to create 1 rle with old method : 0.02687239646911621
time for calcul the mask position with numpy : 0.028914451599121094
nb_pixel_total : 12892
time to create 1 rle with old method : 0.018779754638671875
time for calcul the mask position with numpy : 0.029944181442260742
nb_pixel_total : 27115
time to create 1 rle with old method : 0.030292749404907227
time for calcul the mask position with numpy : 0.030379772186279297
nb_pixel_total : 336741
time to create 1 rle with new method : 0.3234996795654297
create new chi : 2.6221072673797607
time to delete rle : 0.0015628337860107422
batch 1
Loaded 30 chid ids of type : 3594
++++++++++++++++++++++++Number RLEs to save : 223
 TO DO : save crop sub photo not yet done ! 
save time : 0.384998083114624
nb_obj : 24
nb_hashtags :  5
time to prepare the origin masks  : 7.682045221328735
time for calcul the mask position with numpy : 0.8627233505249023
nb_pixel_total : 5841345
time to create 1 rle with new method : 0.7501001358032227
time for calcul the mask position with numpy : 0.030988693237304688
nb_pixel_total : 4704
time to create 1 rle with old method : 0.0055387020111083984
time for calcul the mask position with numpy : 0.02875518798828125
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time to create 1 rle with old method : 0.011822223663330078
time for calcul the mask position with numpy : 0.0297701358795166
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time to create 1 rle with old method : 0.1238100528717041
time for calcul the mask position with numpy : 0.028819561004638672
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time to create 1 rle with old method : 0.019688129425048828
time for calcul the mask position with numpy : 0.029237747192382812
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time to create 1 rle with old method : 0.13805270195007324
time for calcul the mask position with numpy : 0.029244661331176758
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time to create 1 rle with old method : 0.05719637870788574
time for calcul the mask position with numpy : 0.029193878173828125
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time to create 1 rle with old method : 0.19733762741088867
time for calcul the mask position with numpy : 0.02922224998474121
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time to create 1 rle with old method : 0.013352155685424805
time for calcul the mask position with numpy : 0.029076814651489258
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time to create 1 rle with old method : 0.006704807281494141
time for calcul the mask position with numpy : 0.03127717971801758
nb_pixel_total : 295724
time to create 1 rle with new method : 0.39836740493774414
time for calcul the mask position with numpy : 0.029144763946533203
nb_pixel_total : 27976
time to create 1 rle with old method : 0.0330202579498291
time for calcul the mask position with numpy : 0.02883768081665039
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time to create 1 rle with old method : 0.029363632202148438
time for calcul the mask position with numpy : 0.027753591537475586
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time to create 1 rle with old method : 0.017657995223999023
time for calcul the mask position with numpy : 0.028742313385009766
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time to create 1 rle with old method : 0.008811473846435547
time for calcul the mask position with numpy : 0.02877974510192871
nb_pixel_total : 25701
time to create 1 rle with old method : 0.03006911277770996
time for calcul the mask position with numpy : 0.028275012969970703
nb_pixel_total : 30777
time to create 1 rle with old method : 0.03448009490966797
time for calcul the mask position with numpy : 0.028827428817749023
nb_pixel_total : 25213
time to create 1 rle with old method : 0.02877521514892578
time for calcul the mask position with numpy : 0.02983546257019043
nb_pixel_total : 43262
time to create 1 rle with old method : 0.04809212684631348
time for calcul the mask position with numpy : 0.02877497673034668
nb_pixel_total : 25760
time to create 1 rle with old method : 0.029241323471069336
time for calcul the mask position with numpy : 0.029115676879882812
nb_pixel_total : 23695
time to create 1 rle with old method : 0.02689051628112793
time for calcul the mask position with numpy : 0.029124021530151367
nb_pixel_total : 97589
time to create 1 rle with old method : 0.10883498191833496
time for calcul the mask position with numpy : 0.02956390380859375
nb_pixel_total : 178584
time to create 1 rle with new method : 0.599755048751831
time for calcul the mask position with numpy : 0.028850317001342773
nb_pixel_total : 10343
time to create 1 rle with old method : 0.011673450469970703
create new chi : 4.377072095870972
time to delete rle : 0.002258777618408203
batch 1
Loaded 47 chid ids of type : 3594
++++++++++++++++++++++++++++++++++++Number RLEs to save : 0
 TO DO : save crop sub photo not yet done ! 
save time : 0.14004039764404297
nb_obj : 40
nb_hashtags :  3
time to prepare the origin masks  : 7.800559997558594
time for calcul the mask position with numpy : 0.6906342506408691
nb_pixel_total : 6075193
time to create 1 rle with new method : 0.49649500846862793
time for calcul the mask position with numpy : 0.02921581268310547
nb_pixel_total : 13234
time to create 1 rle with old method : 0.01590442657470703
time for calcul the mask position with numpy : 0.029802560806274414
nb_pixel_total : 91953
time to create 1 rle with old method : 0.10494422912597656
time for calcul the mask position with numpy : 0.029112577438354492
nb_pixel_total : 20534
time to create 1 rle with old method : 0.023142576217651367
time for calcul the mask position with numpy : 0.029318809509277344
nb_pixel_total : 33643
time to create 1 rle with old method : 0.03798103332519531
time for calcul the mask position with numpy : 0.028879880905151367
nb_pixel_total : 410
time to create 1 rle with old method : 0.0007565021514892578
time for calcul the mask position with numpy : 0.02916550636291504
nb_pixel_total : 886
time to create 1 rle with old method : 0.001379251480102539
time for calcul the mask position with numpy : 0.029423236846923828
nb_pixel_total : 55786
time to create 1 rle with old method : 0.06334090232849121
time for calcul the mask position with numpy : 0.02911686897277832
nb_pixel_total : 27173
time to create 1 rle with old method : 0.030355453491210938
time for calcul the mask position with numpy : 0.028799772262573242
nb_pixel_total : 935
time to create 1 rle with old method : 0.0013899803161621094
time for calcul the mask position with numpy : 0.028848648071289062
nb_pixel_total : 36107
time to create 1 rle with old method : 0.04096198081970215
time for calcul the mask position with numpy : 0.028676986694335938
nb_pixel_total : 260
time to create 1 rle with old method : 0.00048613548278808594
time for calcul the mask position with numpy : 0.029963016510009766
nb_pixel_total : 40416
time to create 1 rle with old method : 0.046712398529052734
time for calcul the mask position with numpy : 0.028774023056030273
nb_pixel_total : 1045
time to create 1 rle with old method : 0.0015881061553955078
time for calcul the mask position with numpy : 0.029157161712646484
nb_pixel_total : 102639
time to create 1 rle with old method : 0.1141366958618164
time for calcul the mask position with numpy : 0.029132366180419922
nb_pixel_total : 35496
time to create 1 rle with old method : 0.04041910171508789
time for calcul the mask position with numpy : 0.029198408126831055
nb_pixel_total : 42739
time to create 1 rle with old method : 0.048351287841796875
time for calcul the mask position with numpy : 0.028771162033081055
nb_pixel_total : 1198
time to create 1 rle with old method : 0.0016705989837646484
time for calcul the mask position with numpy : 0.028635263442993164
nb_pixel_total : 9507
time to create 1 rle with old method : 0.01184988021850586
time for calcul the mask position with numpy : 0.029909610748291016
nb_pixel_total : 185326
time to create 1 rle with new method : 0.4465053081512451
time for calcul the mask position with numpy : 0.02877020835876465
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time to create 1 rle with old method : 0.0017807483673095703
time for calcul the mask position with numpy : 0.0296475887298584
nb_pixel_total : 21804
time to create 1 rle with old method : 0.02512526512145996
time for calcul the mask position with numpy : 0.0288848876953125
nb_pixel_total : 26394
time to create 1 rle with old method : 0.035799264907836914
time for calcul the mask position with numpy : 0.03570199012756348
nb_pixel_total : 17083
time to create 1 rle with old method : 0.03264498710632324
time for calcul the mask position with numpy : 0.03402972221374512
nb_pixel_total : 21386
time to create 1 rle with old method : 0.025283336639404297
time for calcul the mask position with numpy : 0.028794288635253906
nb_pixel_total : 125
time to create 1 rle with old method : 0.0003261566162109375
time for calcul the mask position with numpy : 0.028764963150024414
nb_pixel_total : 9420
time to create 1 rle with old method : 0.010788679122924805
time for calcul the mask position with numpy : 0.02981090545654297
nb_pixel_total : 25142
time to create 1 rle with old method : 0.0287473201751709
time for calcul the mask position with numpy : 0.0289304256439209
nb_pixel_total : 1521
time to create 1 rle with old method : 0.0021293163299560547
time for calcul the mask position with numpy : 0.028876066207885742
nb_pixel_total : 1164
time to create 1 rle with old method : 0.0016112327575683594
time for calcul the mask position with numpy : 0.029242515563964844
nb_pixel_total : 26242
time to create 1 rle with old method : 0.02955007553100586
time for calcul the mask position with numpy : 0.029439926147460938
nb_pixel_total : 12002
time to create 1 rle with old method : 0.014261484146118164
time for calcul the mask position with numpy : 0.029054641723632812
nb_pixel_total : 3442
time to create 1 rle with old method : 0.00461268424987793
time for calcul the mask position with numpy : 0.029286861419677734
nb_pixel_total : 41298
time to create 1 rle with old method : 0.046755313873291016
time for calcul the mask position with numpy : 0.029058218002319336
nb_pixel_total : 12852
time to create 1 rle with old method : 0.015419960021972656
time for calcul the mask position with numpy : 0.028979778289794922
nb_pixel_total : 307
time to create 1 rle with old method : 0.0006148815155029297
time for calcul the mask position with numpy : 0.029232501983642578
nb_pixel_total : 33931
time to create 1 rle with old method : 0.038813114166259766
time for calcul the mask position with numpy : 0.028499841690063477
nb_pixel_total : 157
time to create 1 rle with old method : 0.00040912628173828125
time for calcul the mask position with numpy : 0.028649568557739258
nb_pixel_total : 19924
time to create 1 rle with old method : 0.026450634002685547
time for calcul the mask position with numpy : 0.03351187705993652
nb_pixel_total : 295
time to create 1 rle with old method : 0.0005092620849609375
create new chi : 3.7969183921813965
time to delete rle : 0.0029561519622802734
batch 1
Loaded 118 chid ids of type : 3594
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 8568
 TO DO : save crop sub photo not yet done ! 
save time : 2.7138097286224365
nb_obj : 45
nb_hashtags :  7
time to prepare the origin masks  : 7.842201232910156
time for calcul the mask position with numpy : 0.6305751800537109
nb_pixel_total : 5060877
time to create 1 rle with new method : 0.42018985748291016
time for calcul the mask position with numpy : 0.029645919799804688
nb_pixel_total : 15143
time to create 1 rle with old method : 0.017700910568237305
time for calcul the mask position with numpy : 0.03052353858947754
nb_pixel_total : 120001
time to create 1 rle with old method : 0.13682913780212402
time for calcul the mask position with numpy : 0.03412508964538574
nb_pixel_total : 39259
time to create 1 rle with old method : 0.044173240661621094
time for calcul the mask position with numpy : 0.029032468795776367
nb_pixel_total : 31476
time to create 1 rle with old method : 0.03551816940307617
time for calcul the mask position with numpy : 0.029191017150878906
nb_pixel_total : 11494
time to create 1 rle with old method : 0.013376235961914062
time for calcul the mask position with numpy : 0.02903008460998535
nb_pixel_total : 26431
time to create 1 rle with old method : 0.03055095672607422
time for calcul the mask position with numpy : 0.028984785079956055
nb_pixel_total : 34078
time to create 1 rle with old method : 0.03863716125488281
time for calcul the mask position with numpy : 0.029113054275512695
nb_pixel_total : 23067
time to create 1 rle with old method : 0.026505708694458008
time for calcul the mask position with numpy : 0.028876066207885742
nb_pixel_total : 45604
time to create 1 rle with old method : 0.05110979080200195
time for calcul the mask position with numpy : 0.028676748275756836
nb_pixel_total : 10593
time to create 1 rle with old method : 0.012408018112182617
time for calcul the mask position with numpy : 0.027904272079467773
nb_pixel_total : 161515
time to create 1 rle with new method : 0.6992185115814209
time for calcul the mask position with numpy : 0.0282442569732666
nb_pixel_total : 32044
time to create 1 rle with old method : 0.03594493865966797
time for calcul the mask position with numpy : 0.02807021141052246
nb_pixel_total : 16010
time to create 1 rle with old method : 0.01767706871032715
time for calcul the mask position with numpy : 0.02849888801574707
nb_pixel_total : 87097
time to create 1 rle with old method : 0.09717416763305664
time for calcul the mask position with numpy : 0.03095078468322754
nb_pixel_total : 184599
time to create 1 rle with new method : 0.8797566890716553
time for calcul the mask position with numpy : 0.030172348022460938
nb_pixel_total : 58837
time to create 1 rle with old method : 0.07946610450744629
time for calcul the mask position with numpy : 0.029435396194458008
nb_pixel_total : 26087
time to create 1 rle with old method : 0.02933192253112793
time for calcul the mask position with numpy : 0.02915191650390625
nb_pixel_total : 25099
time to create 1 rle with old method : 0.02970743179321289
time for calcul the mask position with numpy : 0.02851557731628418
nb_pixel_total : 13185
time to create 1 rle with old method : 0.015167951583862305
time for calcul the mask position with numpy : 0.028037071228027344
nb_pixel_total : 21103
time to create 1 rle with old method : 0.02289605140686035
time for calcul the mask position with numpy : 0.02868509292602539
nb_pixel_total : 19434
time to create 1 rle with old method : 0.02285909652709961
time for calcul the mask position with numpy : 0.028339147567749023
nb_pixel_total : 86283
time to create 1 rle with old method : 0.09643054008483887
time for calcul the mask position with numpy : 0.02922368049621582
nb_pixel_total : 33128
time to create 1 rle with old method : 0.03725385665893555
time for calcul the mask position with numpy : 0.029098033905029297
nb_pixel_total : 27598
time to create 1 rle with old method : 0.03280472755432129
time for calcul the mask position with numpy : 0.02924966812133789
nb_pixel_total : 60809
time to create 1 rle with old method : 0.06990194320678711
time for calcul the mask position with numpy : 0.030885696411132812
nb_pixel_total : 38678
time to create 1 rle with old method : 0.045816898345947266
time for calcul the mask position with numpy : 0.03049635887145996
nb_pixel_total : 17050
time to create 1 rle with old method : 0.019962310791015625
time for calcul the mask position with numpy : 0.030876636505126953
nb_pixel_total : 30642
time to create 1 rle with old method : 0.03447413444519043
time for calcul the mask position with numpy : 0.028318405151367188
nb_pixel_total : 11307
time to create 1 rle with old method : 0.012358903884887695
time for calcul the mask position with numpy : 0.028836727142333984
nb_pixel_total : 204743
time to create 1 rle with new method : 0.2465217113494873
time for calcul the mask position with numpy : 0.03016829490661621
nb_pixel_total : 228168
time to create 1 rle with new method : 0.5656676292419434
time for calcul the mask position with numpy : 0.027460336685180664
nb_pixel_total : 909
time to create 1 rle with old method : 0.0015592575073242188
time for calcul the mask position with numpy : 0.027498483657836914
nb_pixel_total : 10476
time to create 1 rle with old method : 0.01183772087097168
time for calcul the mask position with numpy : 0.027281522750854492
nb_pixel_total : 15860
time to create 1 rle with old method : 0.017848491668701172
time for calcul the mask position with numpy : 0.028370141983032227
nb_pixel_total : 3461
time to create 1 rle with old method : 0.004208564758300781
time for calcul the mask position with numpy : 0.028086423873901367
nb_pixel_total : 46126
time to create 1 rle with old method : 0.06602168083190918
time for calcul the mask position with numpy : 0.032898902893066406
nb_pixel_total : 21846
time to create 1 rle with old method : 0.024002552032470703
time for calcul the mask position with numpy : 0.027124404907226562
nb_pixel_total : 17755
time to create 1 rle with old method : 0.018616437911987305
time for calcul the mask position with numpy : 0.027688264846801758
nb_pixel_total : 11077
time to create 1 rle with old method : 0.012488842010498047
time for calcul the mask position with numpy : 0.02801513671875
nb_pixel_total : 24855
time to create 1 rle with old method : 0.028267383575439453
time for calcul the mask position with numpy : 0.0324857234954834
nb_pixel_total : 73
time to create 1 rle with old method : 0.0001926422119140625
time for calcul the mask position with numpy : 0.028155803680419922
nb_pixel_total : 72799
time to create 1 rle with old method : 0.08153176307678223
time for calcul the mask position with numpy : 0.028952836990356445
nb_pixel_total : 14244
time to create 1 rle with old method : 0.016658306121826172
time for calcul the mask position with numpy : 0.029376506805419922
nb_pixel_total : 9320
time to create 1 rle with old method : 0.01063394546508789
create new chi : 6.271449089050293
time to delete rle : 0.0040302276611328125
batch 1
Loaded 93 chid ids of type : 3594
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 1025
 TO DO : save crop sub photo not yet done ! 
save time : 0.5114684104919434
nb_obj : 38
nb_hashtags :  7
time to prepare the origin masks  : 7.834387302398682
time for calcul the mask position with numpy : 0.21099328994750977
nb_pixel_total : 5055474
time to create 1 rle with new method : 0.6884708404541016
time for calcul the mask position with numpy : 0.028948545455932617
nb_pixel_total : 8899
time to create 1 rle with old method : 0.010628700256347656
time for calcul the mask position with numpy : 0.030423641204833984
nb_pixel_total : 337679
time to create 1 rle with new method : 0.5615935325622559
time for calcul the mask position with numpy : 0.029053926467895508
nb_pixel_total : 15084
time to create 1 rle with old method : 0.017418622970581055
time for calcul the mask position with numpy : 0.028835296630859375
nb_pixel_total : 12566
time to create 1 rle with old method : 0.01499032974243164
time for calcul the mask position with numpy : 0.028516054153442383
nb_pixel_total : 138798
time to create 1 rle with old method : 0.1515977382659912
time for calcul the mask position with numpy : 0.027846574783325195
nb_pixel_total : 30251
time to create 1 rle with old method : 0.03500866889953613
time for calcul the mask position with numpy : 0.028834104537963867
nb_pixel_total : 14375
time to create 1 rle with old method : 0.01667332649230957
time for calcul the mask position with numpy : 0.028684616088867188
nb_pixel_total : 20641
time to create 1 rle with old method : 0.023356199264526367
time for calcul the mask position with numpy : 0.028788328170776367
nb_pixel_total : 17747
time to create 1 rle with old method : 0.020716428756713867
time for calcul the mask position with numpy : 0.028780698776245117
nb_pixel_total : 10109
time to create 1 rle with old method : 0.011691808700561523
time for calcul the mask position with numpy : 0.028993844985961914
nb_pixel_total : 203484
time to create 1 rle with new method : 0.5816280841827393
time for calcul the mask position with numpy : 0.028104782104492188
nb_pixel_total : 63843
time to create 1 rle with old method : 0.07067060470581055
time for calcul the mask position with numpy : 0.0282590389251709
nb_pixel_total : 44664
time to create 1 rle with old method : 0.05088520050048828
time for calcul the mask position with numpy : 0.029651641845703125
nb_pixel_total : 9478
time to create 1 rle with old method : 0.01114344596862793
time for calcul the mask position with numpy : 0.027635574340820312
nb_pixel_total : 7294
time to create 1 rle with old method : 0.008938074111938477
time for calcul the mask position with numpy : 0.028241634368896484
nb_pixel_total : 92574
time to create 1 rle with old method : 0.12738800048828125
time for calcul the mask position with numpy : 0.029706716537475586
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time to create 1 rle with old method : 0.00020194053649902344
time for calcul the mask position with numpy : 0.027011632919311523
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time to create 1 rle with old method : 0.032922983169555664
time for calcul the mask position with numpy : 0.02909398078918457
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time to create 1 rle with old method : 0.16591405868530273
time for calcul the mask position with numpy : 0.028043508529663086
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time to create 1 rle with old method : 0.022142410278320312
time for calcul the mask position with numpy : 0.028534650802612305
nb_pixel_total : 49596
time to create 1 rle with old method : 0.05269145965576172
time for calcul the mask position with numpy : 0.02923893928527832
nb_pixel_total : 57627
time to create 1 rle with old method : 0.06288695335388184
time for calcul the mask position with numpy : 0.028197765350341797
nb_pixel_total : 27246
time to create 1 rle with old method : 0.02930307388305664
time for calcul the mask position with numpy : 0.02791428565979004
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time to create 1 rle with old method : 0.0016891956329345703
time for calcul the mask position with numpy : 0.02916884422302246
nb_pixel_total : 21006
time to create 1 rle with old method : 0.023632049560546875
time for calcul the mask position with numpy : 0.029148340225219727
nb_pixel_total : 5777
time to create 1 rle with old method : 0.0067386627197265625
time for calcul the mask position with numpy : 0.029799461364746094
nb_pixel_total : 142533
time to create 1 rle with old method : 0.15758943557739258
time for calcul the mask position with numpy : 0.028795957565307617
nb_pixel_total : 35786
time to create 1 rle with old method : 0.039980411529541016
time for calcul the mask position with numpy : 0.028841495513916016
nb_pixel_total : 159508
time to create 1 rle with new method : 0.595994234085083
time for calcul the mask position with numpy : 0.029012203216552734
nb_pixel_total : 11
time to create 1 rle with old method : 6.031990051269531e-05
time for calcul the mask position with numpy : 0.02964496612548828
nb_pixel_total : 88836
time to create 1 rle with old method : 0.10267090797424316
time for calcul the mask position with numpy : 0.02782297134399414
nb_pixel_total : 2144
time to create 1 rle with old method : 0.0028502941131591797
time for calcul the mask position with numpy : 0.028077363967895508
nb_pixel_total : 120172
time to create 1 rle with old method : 0.13093185424804688
time for calcul the mask position with numpy : 0.027460813522338867
nb_pixel_total : 33585
time to create 1 rle with old method : 0.03630471229553223
time for calcul the mask position with numpy : 0.027151107788085938
nb_pixel_total : 9774
time to create 1 rle with old method : 0.010718107223510742
time for calcul the mask position with numpy : 0.027402162551879883
nb_pixel_total : 13602
time to create 1 rle with old method : 0.014801740646362305
time for calcul the mask position with numpy : 0.0274660587310791
nb_pixel_total : 4674
time to create 1 rle with old method : 0.0051839351654052734
create new chi : 5.290419101715088
time to delete rle : 0.003112316131591797
batch 1
Loaded 76 chid ids of type : 3594
++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 62
 TO DO : save crop sub photo not yet done ! 
save time : 0.24100351333618164
nb_obj : 37
nb_hashtags :  5
time to prepare the origin masks  : 7.6597442626953125
time for calcul the mask position with numpy : 0.29584479331970215
nb_pixel_total : 4893810
time to create 1 rle with new method : 0.2904987335205078
time for calcul the mask position with numpy : 0.029009103775024414
nb_pixel_total : 9780
time to create 1 rle with old method : 0.01170492172241211
time for calcul the mask position with numpy : 0.032073259353637695
nb_pixel_total : 121513
time to create 1 rle with old method : 0.13567090034484863
time for calcul the mask position with numpy : 0.02878284454345703
nb_pixel_total : 11035
time to create 1 rle with old method : 0.012814760208129883
time for calcul the mask position with numpy : 0.028943300247192383
nb_pixel_total : 5611
time to create 1 rle with old method : 0.0067615509033203125
time for calcul the mask position with numpy : 0.028941869735717773
nb_pixel_total : 10794
time to create 1 rle with old method : 0.012305021286010742
time for calcul the mask position with numpy : 0.028791427612304688
nb_pixel_total : 71794
time to create 1 rle with old method : 0.1057896614074707
time for calcul the mask position with numpy : 0.029076099395751953
nb_pixel_total : 17635
time to create 1 rle with old method : 0.020632505416870117
time for calcul the mask position with numpy : 0.029082059860229492
nb_pixel_total : 2485
time to create 1 rle with old method : 0.003671884536743164
time for calcul the mask position with numpy : 0.030565738677978516
nb_pixel_total : 232517
time to create 1 rle with new method : 0.2946619987487793
time for calcul the mask position with numpy : 0.029485464096069336
nb_pixel_total : 45412
time to create 1 rle with old method : 0.05089068412780762
time for calcul the mask position with numpy : 0.029673337936401367
nb_pixel_total : 51452
time to create 1 rle with old method : 0.05855584144592285
time for calcul the mask position with numpy : 0.02887272834777832
nb_pixel_total : 6268
time to create 1 rle with old method : 0.007304191589355469
time for calcul the mask position with numpy : 0.029140233993530273
nb_pixel_total : 26770
time to create 1 rle with old method : 0.030823945999145508
time for calcul the mask position with numpy : 0.029542922973632812
nb_pixel_total : 187094
time to create 1 rle with new method : 0.5568397045135498
time for calcul the mask position with numpy : 0.02817988395690918
nb_pixel_total : 1407
time to create 1 rle with old method : 0.0021376609802246094
time for calcul the mask position with numpy : 0.02866983413696289
nb_pixel_total : 78459
time to create 1 rle with old method : 0.09759116172790527
time for calcul the mask position with numpy : 0.03093099594116211
nb_pixel_total : 68737
time to create 1 rle with old method : 0.07731842994689941
time for calcul the mask position with numpy : 0.028900861740112305
nb_pixel_total : 965
time to create 1 rle with old method : 0.0014643669128417969
time for calcul the mask position with numpy : 0.028326988220214844
nb_pixel_total : 16485
time to create 1 rle with old method : 0.02201557159423828
time for calcul the mask position with numpy : 0.02899169921875
nb_pixel_total : 21703
time to create 1 rle with old method : 0.023908615112304688
time for calcul the mask position with numpy : 0.028895854949951172
nb_pixel_total : 43891
time to create 1 rle with old method : 0.05155158042907715
time for calcul the mask position with numpy : 0.033152103424072266
nb_pixel_total : 565821
time to create 1 rle with new method : 0.33342504501342773
time for calcul the mask position with numpy : 0.0289762020111084
nb_pixel_total : 45783
time to create 1 rle with old method : 0.05055999755859375
time for calcul the mask position with numpy : 0.02773761749267578
nb_pixel_total : 24738
time to create 1 rle with old method : 0.02708911895751953
time for calcul the mask position with numpy : 0.02809000015258789
nb_pixel_total : 16344
time to create 1 rle with old method : 0.02100372314453125
time for calcul the mask position with numpy : 0.02909255027770996
nb_pixel_total : 78017
time to create 1 rle with old method : 0.08725666999816895
time for calcul the mask position with numpy : 0.028736352920532227
nb_pixel_total : 7864
time to create 1 rle with old method : 0.009405851364135742
time for calcul the mask position with numpy : 0.027997732162475586
nb_pixel_total : 22144
time to create 1 rle with old method : 0.024885177612304688
time for calcul the mask position with numpy : 0.028272628784179688
nb_pixel_total : 80039
time to create 1 rle with old method : 0.09151196479797363
time for calcul the mask position with numpy : 0.029520034790039062
nb_pixel_total : 117388
time to create 1 rle with old method : 0.12965917587280273
time for calcul the mask position with numpy : 0.027621746063232422
nb_pixel_total : 181
time to create 1 rle with old method : 0.0007832050323486328
time for calcul the mask position with numpy : 0.027792930603027344
nb_pixel_total : 9975
time to create 1 rle with old method : 0.011480331420898438
time for calcul the mask position with numpy : 0.028280019760131836
nb_pixel_total : 20479
time to create 1 rle with old method : 0.023331642150878906
time for calcul the mask position with numpy : 0.028844356536865234
nb_pixel_total : 24781
time to create 1 rle with old method : 0.02871251106262207
time for calcul the mask position with numpy : 0.028108835220336914
nb_pixel_total : 76479
time to create 1 rle with old method : 0.08504891395568848
time for calcul the mask position with numpy : 0.028436660766601562
nb_pixel_total : 34590
time to create 1 rle with old method : 0.04017496109008789
create new chi : 4.308079481124878
time to delete rle : 0.003204822540283203
batch 1
Loaded 78 chid ids of type : 3594
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 2066
 TO DO : save crop sub photo not yet done ! 
save time : 1.3758814334869385
nb_obj : 27
nb_hashtags :  5
time to prepare the origin masks  : 7.572856187820435
time for calcul the mask position with numpy : 0.24870824813842773
nb_pixel_total : 4571593
time to create 1 rle with new method : 0.47560858726501465
time for calcul the mask position with numpy : 0.029025554656982422
nb_pixel_total : 4823
time to create 1 rle with old method : 0.005571126937866211
time for calcul the mask position with numpy : 0.029607534408569336
nb_pixel_total : 3097
time to create 1 rle with old method : 0.0052623748779296875
time for calcul the mask position with numpy : 0.03210759162902832
nb_pixel_total : 115080
time to create 1 rle with old method : 0.13336420059204102
time for calcul the mask position with numpy : 0.03076624870300293
nb_pixel_total : 79677
time to create 1 rle with old method : 0.10035204887390137
time for calcul the mask position with numpy : 0.03054523468017578
nb_pixel_total : 87540
time to create 1 rle with old method : 0.1007230281829834
time for calcul the mask position with numpy : 0.028932571411132812
nb_pixel_total : 9025
time to create 1 rle with old method : 0.010367631912231445
time for calcul the mask position with numpy : 0.031325340270996094
nb_pixel_total : 22204
time to create 1 rle with old method : 0.032010793685913086
time for calcul the mask position with numpy : 0.03561735153198242
nb_pixel_total : 477768
time to create 1 rle with new method : 0.5250425338745117
time for calcul the mask position with numpy : 0.03463912010192871
nb_pixel_total : 642592
time to create 1 rle with new method : 0.4368321895599365
time for calcul the mask position with numpy : 0.03096461296081543
nb_pixel_total : 32081
time to create 1 rle with old method : 0.03899216651916504
time for calcul the mask position with numpy : 0.02889084815979004
nb_pixel_total : 12678
time to create 1 rle with old method : 0.014867544174194336
time for calcul the mask position with numpy : 0.028772830963134766
nb_pixel_total : 10464
time to create 1 rle with old method : 0.011693716049194336
time for calcul the mask position with numpy : 0.02907109260559082
nb_pixel_total : 64186
time to create 1 rle with old method : 0.07235431671142578
time for calcul the mask position with numpy : 0.02888774871826172
nb_pixel_total : 8476
time to create 1 rle with old method : 0.01240229606628418
time for calcul the mask position with numpy : 0.03306150436401367
nb_pixel_total : 8740
time to create 1 rle with old method : 0.014454364776611328
time for calcul the mask position with numpy : 0.03522610664367676
nb_pixel_total : 45151
time to create 1 rle with old method : 0.05195760726928711
time for calcul the mask position with numpy : 0.029554128646850586
nb_pixel_total : 174902
time to create 1 rle with new method : 0.7756237983703613
time for calcul the mask position with numpy : 0.029131650924682617
nb_pixel_total : 35495
time to create 1 rle with old method : 0.04002881050109863
time for calcul the mask position with numpy : 0.029316425323486328
nb_pixel_total : 295
time to create 1 rle with old method : 0.000484466552734375
time for calcul the mask position with numpy : 0.02903270721435547
nb_pixel_total : 5575
time to create 1 rle with old method : 0.006697416305541992
time for calcul the mask position with numpy : 0.029003143310546875
nb_pixel_total : 3433
time to create 1 rle with old method : 0.0041408538818359375
time for calcul the mask position with numpy : 0.02929401397705078
nb_pixel_total : 101106
time to create 1 rle with old method : 0.11249613761901855
time for calcul the mask position with numpy : 0.029494762420654297
nb_pixel_total : 12103
time to create 1 rle with old method : 0.013812541961669922
time for calcul the mask position with numpy : 0.031168460845947266
nb_pixel_total : 298351
time to create 1 rle with new method : 0.37809181213378906
time for calcul the mask position with numpy : 0.0295717716217041
nb_pixel_total : 147075
time to create 1 rle with old method : 0.16591691970825195
time for calcul the mask position with numpy : 0.029359102249145508
nb_pixel_total : 76730
time to create 1 rle with old method : 0.0860741138458252
create new chi : 4.812708139419556
time to delete rle : 0.003042936325073242
batch 1
Loaded 54 chid ids of type : 3594
++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0
 TO DO : save crop sub photo not yet done ! 
save time : 0.15845322608947754
nb_obj : 20
nb_hashtags :  4
time to prepare the origin masks  : 7.870358228683472
time for calcul the mask position with numpy : 0.31603407859802246
nb_pixel_total : 6218718
time to create 1 rle with new method : 0.6419346332550049
time for calcul the mask position with numpy : 0.02906966209411621
nb_pixel_total : 6540
time to create 1 rle with old method : 0.007465362548828125
time for calcul the mask position with numpy : 0.028818845748901367
nb_pixel_total : 5162
time to create 1 rle with old method : 0.006145954132080078
time for calcul the mask position with numpy : 0.029066085815429688
nb_pixel_total : 17322
time to create 1 rle with old method : 0.019578933715820312
time for calcul the mask position with numpy : 0.030304908752441406
nb_pixel_total : 115603
time to create 1 rle with old method : 0.130631685256958
time for calcul the mask position with numpy : 0.02916431427001953
nb_pixel_total : 14930
time to create 1 rle with old method : 0.016695737838745117
time for calcul the mask position with numpy : 0.02908158302307129
nb_pixel_total : 20681
time to create 1 rle with old method : 0.023433923721313477
time for calcul the mask position with numpy : 0.02956223487854004
nb_pixel_total : 27953
time to create 1 rle with old method : 0.03191781044006348
time for calcul the mask position with numpy : 0.03038811683654785
nb_pixel_total : 95719
time to create 1 rle with old method : 0.10905909538269043
time for calcul the mask position with numpy : 0.029180288314819336
nb_pixel_total : 4509
time to create 1 rle with old method : 0.005497455596923828
time for calcul the mask position with numpy : 0.029571056365966797
nb_pixel_total : 50428
time to create 1 rle with old method : 0.05568575859069824
time for calcul the mask position with numpy : 0.0268857479095459
nb_pixel_total : 33464
time to create 1 rle with old method : 0.03574943542480469
time for calcul the mask position with numpy : 0.027774810791015625
nb_pixel_total : 15139
time to create 1 rle with old method : 0.01612687110900879
time for calcul the mask position with numpy : 0.02707195281982422
nb_pixel_total : 27540
time to create 1 rle with old method : 0.03122687339782715
time for calcul the mask position with numpy : 0.02880239486694336
nb_pixel_total : 12781
time to create 1 rle with old method : 0.0147247314453125
time for calcul the mask position with numpy : 0.027364253997802734
nb_pixel_total : 116
time to create 1 rle with old method : 0.000217437744140625
time for calcul the mask position with numpy : 0.026824235916137695
nb_pixel_total : 24150
time to create 1 rle with old method : 0.026388883590698242
time for calcul the mask position with numpy : 0.028913021087646484
nb_pixel_total : 12380
time to create 1 rle with old method : 0.014011383056640625
time for calcul the mask position with numpy : 0.03166317939758301
nb_pixel_total : 320314
time to create 1 rle with new method : 0.41454315185546875
time for calcul the mask position with numpy : 0.028340578079223633
nb_pixel_total : 26791
time to create 1 rle with old method : 0.029027223587036133
create new chi : 2.5808615684509277
time to delete rle : 0.001626729965209961
batch 1
Loaded 40 chid ids of type : 3594
++++++++++++++++++++++++++++Number RLEs to save : 65
 TO DO : save crop sub photo not yet done ! 
save time : 0.1444861888885498
 map_output_result : {1336668708: (0.0, 'Should be the crop_list due to order', 0.0), 1336481138: (0.0, 'Should be the crop_list due to order', 0.0), 1336481135: (0.0, 'Should be the crop_list due to order', 0.0), 1336479370: (0.0, 'Should be the crop_list due to order', 0.0), 1336479334: (0.0, 'Should be the crop_list due to order', 0.0), 1336479330: (0.0, 'Should be the crop_list due to order', 0.0), 1336479326: (0.0, 'Should be the crop_list due to order', 0.0), 1336479304: (0.0, 'Should be the crop_list due to order', 0.0), 1336479300: (0.0, 'Should be the crop_list due to order', 0.0)}
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 
[1336668708, 1336481138, 1336481135, 1336479370, 1336479334, 1336479330, 1336479326, 1336479304, 1336479300]
Looping around the photos to save general results
len do output : 9
/1336668708.Didn't retrieve data
.
/1336481138.Didn't retrieve data
.
/1336481135.Didn't retrieve data
.
/1336479370.Didn't retrieve data
.
/1336479334.Didn't retrieve data
.
/1336479330.Didn't retrieve data
.
/1336479326.Didn't retrieve data
.
/1336479304.Didn't retrieve data
.
/1336479300.Didn't retrieve data
.
before output type
Used above
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f86f23c76d0>
Managing all output in save final without adding information in the mtr_datou_result 
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336668708', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336481138', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336481135', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479370', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479334', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479330', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479326', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479304', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479300', None, None, None, None, None, '2574458')


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 27
time used for this insertion : 0.019599199295043945
 save_final 

save missing photos in datou_result :

time spend for datou_step_exec : 115.744313955307
time spend to save output : 0.02016282081604004
total time spend for step 3 : 115.76447677612305

step4:ventilate_hashtags_in_portfolio
Tue Feb 11 04:42:21 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 !
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 : 20425797
get user id for portfolio 20425797
 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`=20425797 AND  mptpi.`type`=3594 AND  mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('autre','papier','mal_croppe','flou','pehd','pet_fonce','metal','carton','background','pet_clair','environnement'))  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`=20425797 AND  mptpi.`type`=3594 AND  mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('autre','papier','mal_croppe','flou','pehd','pet_fonce','metal','carton','background','pet_clair','environnement'))  AND  mptpi.`min_score`=0.5
To do
To do !
Use context local managing function !
 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id,
        mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc,
        h.hashtag
        FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h  WHERE  h.hashtag_id=mptpi.hashtag_id  AND  mptpi.`mtr_portfolio_id_1`=20425797 AND  mptpi.`type`=3594 AND  mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('autre','papier','mal_croppe','flou','pehd','pet_fonce','metal','carton','background','pet_clair','environnement'))  AND  mptpi.`min_score`=0.5
To do


lien utilise dans velours :  https://www.fotonower.com/velours/20426626,20426627,20426628,20426629,20426630,20426631,20426632,20426633,20426634,20426635,20426636?tags=autre,papier,mal_croppe,flou,pehd,pet_fonce,metal,carton,background,pet_clair,environnement
Inside saveOutput : final : False verbose : 0
 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral 
[1336668708, 1336481138, 1336481135, 1336479370, 1336479334, 1336479330, 1336479326, 1336479304, 1336479300]
Looping around the photos to save general results
len do output : 1
/20425797.
before output type
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f86f23c78e0>
Managing all output in save final without adding information in the mtr_datou_result 
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336668708', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336481138', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336481135', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479370', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479334', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479330', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479326', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479304', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479300', None, None, None, None, None, '2574458')


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 10
time used for this insertion : 0.1185452938079834
 save_final 

save missing photos in datou_result :

time spend for datou_step_exec : 3.807027816772461
time spend to save output : 0.11883974075317383
total time spend for step 4 : 3.9258675575256348

step5:final
Tue Feb 11 04:42:25 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 !
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 : {1336668708: ('0.2167945110142823',), 1336481138: ('0.2167945110142823',), 1336481135: ('0.2167945110142823',), 1336479370: ('0.2167945110142823',), 1336479334: ('0.2167945110142823',), 1336479330: ('0.2167945110142823',), 1336479326: ('0.2167945110142823',), 1336479304: ('0.2167945110142823',), 1336479300: ('0.2167945110142823',)}
new output for save of step final : {1336668708: ('0.2167945110142823',), 1336481138: ('0.2167945110142823',), 1336481135: ('0.2167945110142823',), 1336479370: ('0.2167945110142823',), 1336479334: ('0.2167945110142823',), 1336479330: ('0.2167945110142823',), 1336479326: ('0.2167945110142823',), 1336479304: ('0.2167945110142823',), 1336479300: ('0.2167945110142823',)}
[1336668708, 1336481138, 1336481135, 1336479370, 1336479334, 1336479330, 1336479326, 1336479304, 1336479300]
Looping around the photos to save general results
len do output : 9
/1336668708.Didn't retrieve data
.
/1336481138.Didn't retrieve data
.
/1336481135.Didn't retrieve data
.
/1336479370.Didn't retrieve data
.
/1336479334.Didn't retrieve data
.
/1336479330.Didn't retrieve data
.
/1336479326.Didn't retrieve data
.
/1336479304.Didn't retrieve data
.
/1336479300.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, '2574458')
('3318', '20425797', '1336668708', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336481138', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336481135', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479370', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479334', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479330', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479326', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479304', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479300', None, None, None, None, None, '2574458')


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 27
time used for this insertion : 0.015229463577270508
 save_final 

save missing photos in datou_result :

time spend for datou_step_exec : 0.40017080307006836
time spend to save output : 0.0157930850982666
total time spend for step 5 : 0.41596388816833496

step6:blur_detection
Tue Feb 11 04:42:26 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/1739244629_1006925_1336668708_4207748f530d44e4cbd56787ad72c2a4.jpg
resize: (2160, 3264)
1336668708 -6.465719008329033
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812.jpg
resize: (2160, 3264)
1336481138 -3.6216588990620755
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f.jpg
resize: (2160, 3264)
1336481135 -3.2805075710323726
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9.jpg
resize: (2160, 3264)
1336479370 -5.956194130360916
treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1.jpg
resize: (2160, 3264)
1336479334 -5.803463342883308
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb.jpg
resize: (2160, 3264)
1336479330 -5.473470039444218
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576.jpg
resize: (2160, 3264)
1336479326 -5.457906414752434
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8.jpg
resize: (2160, 3264)
1336479304 -4.4935584207898875
treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74.jpg
resize: (2160, 3264)
1336479300 -5.166787030595441
treat image : temp/1739244629_1006925_1336668708_4207748f530d44e4cbd56787ad72c2a4_rle_crop_3669159970_0.png
resize: (238, 136)
1336690504 -4.451974907232176
treat image : temp/1739244629_1006925_1336668708_4207748f530d44e4cbd56787ad72c2a4_rle_crop_3669159973_0.png
resize: (400, 534)
1336690505 -4.521498757784432
treat image : temp/1739244629_1006925_1336668708_4207748f530d44e4cbd56787ad72c2a4_rle_crop_3669159978_0.png
resize: (236, 221)
1336690506 -5.062556313582427
treat image : temp/1739244629_1006925_1336668708_4207748f530d44e4cbd56787ad72c2a4_rle_crop_3669159974_0.png
resize: (129, 173)
1336690507 -3.001577809754463
treat image : temp/1739244629_1006925_1336668708_4207748f530d44e4cbd56787ad72c2a4_rle_crop_3669159975_0.png
resize: (147, 147)
1336690508 -3.6026154618723845
treat image : temp/1739244629_1006925_1336668708_4207748f530d44e4cbd56787ad72c2a4_rle_crop_3669159977_0.png
resize: (248, 187)
1336690509 -3.657246809876368
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668207094_0.png
resize: (246, 125)
1336690510 -1.532047365429908
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668208346_0.png
resize: (246, 124)
1336690511 -0.47410350143190716
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668207093_0.png
resize: (411, 231)
1336690512 -2.500634839117895
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668208347_0.png
resize: (411, 231)
1336690513 -2.500634839117895
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668207089_0.png
resize: (206, 153)
1336690514 -1.8638239866881967
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668207088_0.png
resize: (166, 220)
1336690515 -2.303641683859809
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668208351_0.png
resize: (206, 153)
1336690516 -1.8638239866881967
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668208352_0.png
resize: (166, 220)
1336690517 -2.303641683859809
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668207099_0.png
resize: (207, 218)
1336690518 -3.8772741762807716
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668208341_0.png
resize: (207, 218)
1336690519 -3.8772741762807716
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668207096_0.png
resize: (223, 192)
1336690520 -1.5430232338763503
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668208344_0.png
resize: (223, 192)
1336690521 -1.5430232338763503
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3669159980_0.png
resize: (223, 281)
1336690522 -2.190100561032352
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668207091_0.png
resize: (226, 282)
1336690523 -2.2450256749648805
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668208349_0.png
resize: (226, 282)
1336690524 -2.2450256749648805
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668207097_0.png
resize: (275, 327)
1336690525 -2.196447832689816
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668208343_0.png
resize: (275, 327)
1336690526 -2.196447832689816
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3669159981_0.png
resize: (495, 253)
1336690527 -4.852480380102981
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668207095_0.png
resize: (495, 250)
1336690528 -4.853067217953412
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668208345_0.png
resize: (495, 250)
1336690529 -4.853067217953412
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668207098_0.png
resize: (138, 122)
1336690530 -4.503689356580331
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668208342_0.png
resize: (138, 122)
1336690531 -4.503689356580331
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207119_0.png
resize: (120, 275)
1336690532 -2.634766412949896
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208404_0.png
resize: (120, 275)
1336690533 -2.634766412949896
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207110_0.png
resize: (224, 78)
1336690534 -2.125892357122949
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208413_0.png
resize: (224, 78)
1336690535 -2.125892357122949
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207111_0.png
resize: (106, 59)
1336690536 -2.379888864628039
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208412_0.png
resize: (106, 59)
1336690537 -2.379888864628039
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207103_0.png
resize: (674, 308)
1336690538 -3.665526011462777
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208420_0.png
resize: (674, 308)
1336690539 -3.665526011462777
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207118_0.png
resize: (203, 191)
1336690540 -1.7076512008773106
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208405_0.png
resize: (203, 191)
1336690541 -1.7076512008773106
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208421_0.png
resize: (87, 116)
1336690542 0.7913620495676796
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207102_0.png
resize: (87, 116)
1336690543 0.7913620495676796
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207116_0.png
resize: (249, 135)
1336690544 -2.36137586755622
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208407_0.png
resize: (249, 135)
1336690545 -2.37134920053418
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207120_0.png
resize: (134, 164)
1336690546 -3.7748898423693604
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208403_0.png
resize: (134, 128)
1336690547 -3.1896879860091443
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207121_0.png
resize: (119, 160)
1336690548 -2.407865501820965
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208402_0.png
resize: (119, 160)
1336690549 -2.407865501820965
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160004_0.png
resize: (178, 388)
1336690550 -4.079388833884749
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669159999_0.png
resize: (252, 186)
1336690551 -4.4916197646044385
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668207125_0.png
resize: (194, 245)
1336690552 -4.533754636413141
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668208496_0.png
resize: (194, 245)
1336690553 -4.533754636413141
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669159996_0.png
resize: (193, 247)
1336690554 -4.485784133869231
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669159995_0.png
resize: (305, 110)
1336690555 -3.037055899227204
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160016_0.png
resize: (310, 346)
1336690556 -3.070996042525783
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669159986_0.png
resize: (307, 314)
1336690557 -4.082392356371568
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668207126_0.png
resize: (295, 303)
1336690558 -4.090557899388361
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668208495_0.png
resize: (295, 303)
1336690559 -4.090557899388361
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668207123_0.png
resize: (250, 214)
1336690560 -3.587020468410617
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669159983_0.png
resize: (254, 216)
1336690561 -3.5530782030875083
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668208498_0.png
resize: (250, 214)
1336690562 -3.587020468410617
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668207135_0.png
resize: (247, 214)
1336690563 -3.269162570869079
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668208486_0.png
resize: (247, 214)
1336690564 -3.269162570869079
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669159987_0.png
resize: (281, 199)
1336690565 -3.4991919165846546
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160003_0.png
resize: (283, 450)
1336690566 -4.234498534130452
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160009_0.png
resize: (161, 139)
1336690567 -3.2812343415775516
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668207136_0.png
resize: (167, 135)
1336690568 -3.2744236352004763
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668208485_0.png
resize: (167, 135)
1336690570 -3.2744236352004763
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668207143_0.png
resize: (380, 443)
1336690571 -4.642084930348841
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668208478_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160005_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668207141_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669159998_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668208480_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669159984_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668207127_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668208494_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668208497_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668207124_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669159982_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160013_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668207138_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669159992_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668208483_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160008_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160007_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668207130_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160022_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668208491_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160001_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668207145_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668208476_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669159988_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668207137_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668208484_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668207144_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668208477_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669159997_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668207134_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160006_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669159991_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668208487_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160002_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669159985_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669159990_0.png
resize: (191, 222)
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668208481_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668207140_0.png
resize: (207, 211)
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160018_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160000_0.png
resize: (243, 162)
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668208479_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668207142_0.png
resize: (243, 153)
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160010_0.png
resize: (142, 155)
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160020_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160021_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669159994_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668207131_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668208490_0.png
resize: (238, 215)
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669159993_0.png
resize: (262, 270)
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668207132_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668208489_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669159989_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668207128_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3668208493_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160017_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207172_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208618_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207151_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208632_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207156_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208602_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207148_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208606_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207179_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208614_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207160_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208603_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207161_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208604_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207186_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208611_0.png
resize: (317, 460)
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207152_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208617_0.png
resize: (165, 307)
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207187_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208607_0.png
resize: (188, 157)
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207162_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208600_0.png
resize: (441, 293)
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207184_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207158_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208605_0.png
resize: (273, 178)
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208631_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207181_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208601_0.png
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207211_0.png
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207208_0.png
resize: (351, 145)
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208897_0.png
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207220_0.png
resize: (593, 781)
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208910_0.png
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207215_0.png
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208882_0.png
resize: (593, 781)
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208894_0.png
resize: (89, 154)
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207200_0.png
resize: (225, 353)
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208901_0.png
resize: (225, 353)
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207204_0.png
resize: (124, 115)
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208913_0.png
resize: (124, 114)
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207217_0.png
resize: (217, 236)
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208907_0.png
resize: (217, 236)
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207213_0.png
resize: (98, 77)
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208915_0.png
resize: (98, 77)
1336690672 -3.3145696382906706
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207205_0.png
resize: (115, 206)
1336690673 -3.6873132910050512
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208914_0.png
resize: (115, 206)
1336690674 -3.6873132910050512
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207189_0.png
resize: (343, 274)
1336690675 -2.35358205852497
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208886_0.png
resize: (343, 274)
1336690676 -2.35358205852497
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208884_0.png
resize: (114, 151)
1336690677 -3.8632405760972435
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207207_0.png
resize: (114, 151)
1336690678 -3.8632405760972435
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208887_0.png
resize: (161, 156)
1336690679 -4.597711650228035
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207195_0.png
resize: (161, 156)
1336690680 -4.597711650228035
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207202_0.png
resize: (121, 106)
1336690681 -2.9425670075856916
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208890_0.png
resize: (121, 106)
1336690682 -2.9425670075856916
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207198_0.png
resize: (213, 204)
1336690683 -3.4907247925960294
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208888_0.png
resize: (189, 204)
1336690684 -3.481708598766841
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207196_0.png
resize: (160, 161)
1336690685 -3.2064588796280007
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208889_0.png
resize: (160, 161)
1336690686 -3.2064588796280007
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3669160030_0.png
resize: (457, 301)
1336690687 -3.372308041825184
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207193_0.png
resize: (462, 301)
1336690688 -3.3546353181371464
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208896_0.png
resize: (462, 301)
1336690689 -3.3546353181371464
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207210_0.png
resize: (113, 196)
1336690690 -4.00744963606435
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208883_0.png
resize: (113, 196)
1336690691 -4.00744963606435
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207214_0.png
resize: (225, 234)
1336690692 -3.8252727050760424
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208899_0.png
resize: (225, 234)
1336690693 -3.739374664683379
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207203_0.png
resize: (94, 79)
1336690694 -4.096946288630265
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208905_0.png
resize: (94, 79)
1336690695 -4.096946288630265
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207218_0.png
resize: (283, 136)
1336690696 -3.637736980595311
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208902_0.png
resize: (283, 136)
1336690697 -3.637736980595311
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207241_0.png
resize: (145, 216)
1336690698 -2.857730856697997
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209020_0.png
resize: (145, 216)
1336690699 -2.857730856697997
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207236_0.png
resize: (235, 80)
1336690700 -2.5744419617302254
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209004_0.png
resize: (235, 80)
1336690701 -2.5744419617302254
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207240_0.png
resize: (194, 115)
1336690702 -3.2172083427774503
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209021_0.png
resize: (194, 115)
1336690703 -3.2172083427774503
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207235_0.png
resize: (338, 392)
1336690704 -3.6169191452228335
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209025_0.png
resize: (338, 392)
1336690705 -3.6169191452228335
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207232_0.png
resize: (288, 419)
1336690706 -4.667304841199143
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209022_0.png
resize: (288, 419)
1336690707 -4.667304841199143
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207239_0.png
resize: (304, 317)
1336690708 -3.59147829371406
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3669160036_0.png
resize: (284, 341)
1336690709 -3.1874244683825723
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207245_0.png
resize: (280, 344)
1336690710 -3.1212311209832215
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209017_0.png
resize: (304, 317)
1336690711 -3.59147829371406
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209031_0.png
resize: (252, 311)
1336690712 -3.165684796892661
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207226_0.png
resize: (157, 295)
1336690713 -2.8919782159510325
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209029_0.png
resize: (157, 295)
1336690714 -2.8919782159510325
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3669160037_0.png
resize: (164, 186)
1336690715 -1.9992843825545323
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207246_0.png
resize: (164, 185)
1336690716 -1.9914705052498414
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207242_0.png
resize: (361, 482)
1336690717 -4.353702634446416
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207225_0.png
resize: (148, 381)
1336690718 -0.937587662843161
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209019_0.png
resize: (148, 381)
1336690719 -0.937587662843161
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209028_0.png
resize: (164, 185)
1336690720 -1.9914705052498414
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209001_0.png
resize: (361, 482)
1336690721 -4.353702634446416
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209013_0.png
resize: (372, 330)
1336690722 -3.7447563233465195
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207224_0.png
resize: (372, 330)
1336690723 -3.7447563233465195
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207251_0.png
resize: (175, 212)
1336690725 -1.4653480446316944
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209015_0.png
resize: (175, 212)
1336690726 -1.4653480446316944
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207223_0.png
resize: (266, 286)
1336690727 -3.1692326570012925
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209008_0.png
resize: (266, 286)
1336690728 -3.1692326570012925
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207237_0.png
resize: (177, 207)
1336690729 -1.216528957142477
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209011_0.png
resize: (177, 207)
1336690730 -1.216528957142477
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207227_0.png
resize: (276, 131)
1336690731 -2.5937576419371107
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209002_0.png
resize: (276, 131)
1336690732 -2.5937576419371107
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207252_0.png
resize: (108, 263)
1336690733 -3.7266473777950453
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209006_0.png
resize: (108, 263)
1336690734 -3.7266473777950453
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209000_0.png
resize: (65, 183)
1336690736 -2.053001644165713
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207231_0.png
resize: (65, 183)
1336690737 -2.053001644165713
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207228_0.png
resize: (177, 209)
1336690738 -2.5031521314441467
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209016_0.png
resize: (177, 209)
1336690739 -2.5031521314441467
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207238_0.png
resize: (77, 95)
1336690740 -1.640834904739852
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209010_0.png
resize: (77, 95)
1336690741 -1.640834904739852
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207248_0.png
resize: (644, 194)
1336690742 -2.481683949825629
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209005_0.png
resize: (644, 194)
1336690743 -2.481683949825629
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207243_0.png
resize: (108, 81)
1336690744 -2.660847271254963
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209003_0.png
resize: (108, 81)
1336690745 -2.660847271254963
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668209902_0.png
resize: (369, 127)
1336690746 -3.4845000605984158
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207258_0.png
resize: (369, 127)
1336690747 -3.4845000605984158
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668209907_0.png
resize: (118, 149)
1336690748 -1.3232285695618125
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207262_0.png
resize: (118, 149)
1336690749 -1.3232285695618125
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207276_0.png
resize: (369, 591)
1336690750 -3.5913764060997044
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668209909_0.png
resize: (369, 591)
1336690751 -3.5913764060997044
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207263_0.png
resize: (355, 452)
1336690752 -4.217455277593087
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668209906_0.png
resize: (355, 452)
1336690753 -4.217455277593087
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207273_0.png
resize: (244, 691)
1336690754 -2.136362338795981
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668209910_0.png
resize: (244, 691)
1336690755 -2.136362338795981
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207268_0.png
resize: (52, 117)
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treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668209885_0.png
resize: (52, 117)
1336690757 -3.388080436574522
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207261_0.png
resize: (155, 108)
1336690758 -1.6367416353914561
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668209895_0.png
resize: (155, 108)
1336690759 -1.6367416353914561
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3669160039_0.png
resize: (166, 132)
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treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207280_0.png
resize: (165, 133)
1336690761 -1.8962976310582533
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668209890_0.png
resize: (165, 133)
1336690762 -1.4630528718860125
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207270_0.png
resize: (388, 185)
1336690763 -2.995404271790614
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668209894_0.png
resize: (388, 185)
1336690764 -2.995404271790614
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207269_0.png
resize: (67, 118)
1336690765 -3.2774232556266316
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668209904_0.png
resize: (67, 118)
1336690766 -3.2774232556266316
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207257_0.png
resize: (515, 383)
1336690767 -1.7061435752030474
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668209887_0.png
resize: (515, 383)
1336690768 -1.7061435752030474
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207256_0.png
resize: (298, 274)
1336690769 -3.4912166950554457
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668209900_0.png
resize: (298, 274)
1336690770 -3.4912166950554457
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207267_0.png
resize: (147, 137)
1336690771 -3.6968517858249257
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668209896_0.png
resize: (147, 137)
1336690772 -3.6968517858249257
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668209898_0.png
resize: (143, 82)
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treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207275_0.png
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resize: (133, 72)
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treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668209929_0.png
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treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668207289_0.png
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treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668207290_0.png
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treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668209928_0.png
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treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668207296_0.png
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treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668209922_0.png
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treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668207287_0.png
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treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668209931_0.png
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treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668207281_0.png
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treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668209937_0.png
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treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668207283_0.png
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treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668209935_0.png
resize: (61, 125)
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treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668207295_0.png
resize: (490, 306)
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treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668209923_0.png
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treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668207294_0.png
resize: (294, 391)
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treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668209924_0.png
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treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668209925_0.png
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treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668207293_0.png
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treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668207292_0.png
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treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668209926_0.png
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treat image : temp/1739244629_1006925_1336668708_4207748f530d44e4cbd56787ad72c2a4_rle_crop_3669159972_0.png
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treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668208348_0.png
resize: (309, 220)
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treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668207090_0.png
resize: (587, 755)
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resize: (587, 755)
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treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208416_0.png
resize: (196, 203)
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treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207107_0.png
resize: (196, 203)
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treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207100_0.png
resize: (309, 247)
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treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208423_0.png
resize: (309, 247)
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treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207112_0.png
resize: (319, 194)
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treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208411_0.png
resize: (319, 194)
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treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207115_0.png
resize: (161, 134)
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treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208408_0.png
resize: (161, 134)
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160023_0.png
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160019_0.png
resize: (829, 438)
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treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160012_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207180_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208610_0.png
resize: (303, 81)
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207167_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208613_0.png
resize: (311, 312)
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207153_0.png
resize: (331, 673)
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207174_0.png
resize: (629, 560)
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208608_0.png
resize: (331, 673)
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207150_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208612_0.png
resize: (629, 560)
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208638_0.png
resize: (119, 263)
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207154_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208639_0.png
resize: (60, 260)
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207149_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208633_0.png
resize: (123, 266)
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207169_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208625_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207183_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208621_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207159_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207147_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208624_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208627_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207173_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208623_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208598_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207164_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207176_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208626_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207146_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208628_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3669160024_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207165_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208620_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207166_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3669160028_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208629_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207157_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208619_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207155_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208615_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207175_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208630_0.png
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207199_0.png
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208909_0.png
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207188_0.png
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208881_0.png
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207197_0.png
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208904_0.png
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207201_0.png
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208906_0.png
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207216_0.png
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208895_0.png
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207194_0.png
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208885_0.png
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207192_0.png
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208892_0.png
resize: (339, 229)
1336690891 -1.8097972449279525
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207221_0.png
resize: (238, 151)
1336690892 -3.4356472029267224
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208903_0.png
resize: (232, 151)
1336690893 -2.5856919417337947
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207250_0.png
resize: (105, 116)
1336690894 -2.0728466371567302
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209023_0.png
resize: (105, 116)
1336690895 -2.0728466371567302
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207233_0.png
resize: (813, 1041)
1336690896 -2.4176596356834894
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3669160033_0.png
resize: (817, 1037)
1336690897 -2.448529078717486
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209018_0.png
resize: (813, 1041)
1336690898 -2.4176596356834894
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3669160032_0.png
resize: (349, 721)
1336690899 -3.6689874756549177
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207230_0.png
resize: (346, 716)
1336690900 -3.6494744237342225
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209026_0.png
resize: (346, 716)
1336690901 -3.6494744237342225
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207264_0.png
resize: (307, 311)
1336690902 -2.4063437387437214
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668209897_0.png
resize: (307, 311)
1336690903 -2.4063437387437214
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207272_0.png
resize: (164, 196)
1336690904 -4.108616537276291
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668209891_0.png
resize: (164, 196)
1336690905 -4.108616537276291
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207255_0.png
resize: (683, 422)
1336690906 -3.020841693515458
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668209901_0.png
resize: (683, 422)
1336690907 -3.020841693515458
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207279_0.png
resize: (1089, 882)
1336690908 -3.680865900819819
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668209893_0.png
resize: (1089, 882)
1336690909 -3.680865900819819
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207266_0.png
resize: (360, 313)
1336690910 -1.147833918772742
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668209888_0.png
resize: (360, 313)
1336690911 -1.147833918772742
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207271_0.png
resize: (32, 140)
1336690912 -2.0269297112660243
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668209886_0.png
resize: (32, 140)
1336690913 -2.0269297112660243
treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668207286_0.png
resize: (187, 244)
1336690914 -2.6482851710296362
treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668209932_0.png
resize: (187, 244)
1336690915 -2.6482851710296362
treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668207288_0.png
resize: (193, 101)
1336690916 -3.1623992451635976
treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668209930_0.png
resize: (193, 101)
1336690917 -3.1623992451635976
treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668207291_0.png
resize: (207, 88)
1336690918 -2.673989546968418
treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3669160040_0.png
resize: (208, 89)
1336690919 -2.7772344964003737
treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668209927_0.png
resize: (207, 88)
1336690920 -2.673989546968418
treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668207284_0.png
resize: (248, 293)
1336690921 -3.224260110459567
treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668209934_0.png
resize: (248, 293)
1336690922 -3.224260110459567
treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668207285_0.png
resize: (165, 243)
1336690924 -3.831256535920725
treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668209933_0.png
resize: (165, 243)
1336690925 -3.831256535920725
treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3669160041_0.png
resize: (232, 259)
1336690926 -4.321867696114944
treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668207298_0.png
resize: (161, 245)
1336690927 -4.151913347822525
treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668209920_0.png
resize: (161, 245)
1336690928 -4.17619324677434
treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668207297_0.png
resize: (212, 298)
1336690929 -3.368877503673692
treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668209921_0.png
resize: (212, 298)
1336690930 -3.368877503673692
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207122_0.png
resize: (110, 104)
1336690931 -3.4565736176678246
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208401_0.png
resize: (110, 104)
1336690932 -3.4565736176678246
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207117_0.png
resize: (289, 147)
1336690933 -2.66891647765611
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208406_0.png
resize: (289, 147)
1336690934 -2.66891647765611
treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207185_0.png
resize: (278, 281)
1336690935 -4.615196125315108
treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208622_0.png
resize: (278, 281)
1336690936 -4.615196125315108
treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207168_0.png
resize: (121, 215)
1336690937 -4.310211682608983
treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3669160026_0.png
resize: (120, 215)
1336690938 -4.311587733907506
treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208634_0.png
resize: (121, 215)
1336690939 -4.310211682608983
treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207170_0.png
resize: (195, 238)
1336690940 -4.167189570674544
treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208609_0.png
resize: (195, 238)
1336690941 -4.167189570674544
treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3669160025_0.png
resize: (130, 108)
1336690942 -3.6173103438519307
treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207163_0.png
resize: (130, 109)
1336690943 -3.583766560362418
treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208635_0.png
resize: (130, 109)
1336690944 -3.583766560362418
treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207171_0.png
resize: (139, 124)
1336690945 -4.512249726020575
treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208616_0.png
resize: (139, 124)
1336690946 -4.512249726020575
treat image : temp/1739244629_1006925_1336668708_4207748f530d44e4cbd56787ad72c2a4_rle_crop_3669159976_0.png
resize: (857, 1018)
1336690958 -5.245836411345304
treat image : temp/1739244629_1006925_1336668708_4207748f530d44e4cbd56787ad72c2a4_rle_crop_3669159979_0.png
resize: (476, 1173)
1336690959 -1.1016928424314232
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668207087_0.png
resize: (439, 1143)
1336690960 -0.9645697464891143
treat image : temp/1739244629_1006925_1336481138_87edf9f37a07e4b6d6a4394ac0147812_rle_crop_3668208353_0.png
resize: (439, 1143)
1336690961 -0.9645697464891143
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207106_0.png
resize: (287, 704)
1336690962 -3.182089366050085
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208417_0.png
resize: (287, 704)
1336690963 -3.182089366050085
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207104_0.png
resize: (603, 639)
1336690964 -4.341351047581742
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208419_0.png
resize: (603, 639)
1336690965 -4.341351047581742
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208414_0.png
resize: (190, 576)
1336690966 -1.7732023896120879
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207109_0.png
resize: (190, 576)
1336690967 -1.7732023896120879
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207114_0.png
resize: (322, 990)
1336690968 -1.9058768634957088
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208409_0.png
resize: (322, 990)
1336690969 -1.9058768634957088
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207101_0.png
resize: (259, 144)
1336690970 -3.2728371491325223
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208422_0.png
resize: (259, 144)
1336690971 -3.2728371491325223
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207108_0.png
resize: (209, 216)
1336690972 -2.5568547182737347
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208415_0.png
resize: (209, 216)
1336690973 -2.5568547182737347
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207105_0.png
resize: (232, 322)
1336690974 -3.4875262547560815
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208418_0.png
resize: (232, 322)
1336690975 -3.4875262547560815
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668207113_0.png
resize: (306, 205)
1336690976 -2.0716957980721764
treat image : temp/1739244629_1006925_1336481135_419fd5f370d291d5e9bd1e34231a199f_rle_crop_3668208410_0.png
resize: (306, 205)
1336690977 -2.0716957980721764
treat image : temp/1739244629_1006925_1336479370_78141e34d83c5a69b67aaaa7ac4fcdf9_rle_crop_3669160011_0.png
resize: (331, 435)
1336690978 -3.4683134062732432
treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668207177_0.png
resize: (276, 636)
1336690979 -2.39584468319667
treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3668208599_0.png
resize: (276, 636)
1336690980 -2.39584468319667
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3669160031_0.png
resize: (184, 355)
1336690981 -3.3292668348855345
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207206_0.png
resize: (184, 345)
1336690982 -3.3071963620228564
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208900_0.png
resize: (184, 345)
1336690983 -3.3071963620228564
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207190_0.png
resize: (334, 575)
1336690984 -1.4670496699569702
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208911_0.png
resize: (334, 575)
1336690985 -1.4670496699569702
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3669160038_0.png
resize: (517, 584)
1336690986 -4.393802950251096
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207253_0.png
resize: (515, 583)
1336690987 -4.3685941942914015
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209012_0.png
resize: (515, 583)
1336690988 -4.363855023720786
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3669160035_0.png
resize: (318, 306)
1336690989 -2.807421771731844
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207244_0.png
resize: (311, 308)
1336690990 -2.7687717319581373
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209014_0.png
resize: (311, 308)
1336690991 -2.7687717319581373
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207247_0.png
resize: (499, 664)
1336690992 -3.5237190484656575
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3669160034_0.png
resize: (493, 675)
1336690993 -3.56157796482332
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209007_0.png
resize: (499, 664)
1336690994 -3.5237190484656575
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207229_0.png
resize: (266, 477)
1336690995 -2.797092260671159
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209030_0.png
resize: (266, 477)
1336690996 -2.797092260671159
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207249_0.png
resize: (272, 251)
1336690997 -2.2120647975549783
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209009_0.png
resize: (272, 251)
1336690998 -2.241162681705183
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207278_0.png
resize: (881, 772)
1336690999 -3.2420802979952694
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668209892_0.png
resize: (881, 772)
1336691000 -3.2420802979952694
treat image : temp/1739244629_1006925_1336479304_4bcdb4b619c6566674d76d2b91949de8_rle_crop_3668207259_0.png
resize: (397, 1218)
1336691001 -1.4788117340699276
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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 : 505
time used for this insertion : 0.05228781700134277


begin to insert list_values into photo_hahstag_ids : 
length of list_valuse in save_photo_hashtag_id_type : 505
time used for this insertion : 0.23502802848815918

save missing photos in datou_result :

time spend for datou_step_exec : 46.38949179649353
time spend to save output : 0.29398036003112793
total time spend for step 6 : 46.68347215652466

step7:brightness
Tue Feb 11 04:43:12 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
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
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treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209009_0.png
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treat image : temp/1739244629_1006925_1336479300_d1b53981269b482314c0339cc389ff74_rle_crop_3668209936_0.png
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treat image : temp/1739244629_1006925_1336479334_057eed9347dcfef6752da9f6c2a395f1_rle_crop_3669160027_0.png
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treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668207209_0.png
treat image : temp/1739244629_1006925_1336479330_b1602767a991d63e1963f29e4ec6b4fb_rle_crop_3668208912_0.png
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207254_0.png
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209027_0.png
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668207234_0.png
treat image : temp/1739244629_1006925_1336479326_491c12648409b3d995c6003fdc3b6576_rle_crop_3668209024_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 : 505
time used for this insertion : 0.7296042442321777


begin to insert list_values into photo_hahstag_ids : 
length of list_valuse in save_photo_hashtag_id_type : 505
time used for this insertion : 0.10171651840209961

save missing photos in datou_result :

time spend for datou_step_exec : 13.524592876434326
time spend to save output : 0.8371458053588867
total time spend for step 7 : 14.361738681793213

step8:velours_tree
Tue Feb 11 04:43:27 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
complete output_args for input 0
VR 22-3-18 : For now we do not clean correctly the datou structure 
can't find the photo_desc_type
Inside saveOutput : final : False verbose : 0
ouput is None
No outpout to save, returning out of save general

time spend for datou_step_exec : 0.36383843421936035
time spend to save output : 0.0001914501190185547
total time spend for step 8 : 0.3640298843383789

step9:send_mail_cod
Tue Feb 11 04:43:27 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
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_P20425797_11-02-2025_04_43_27.pdf
20426626
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SELECT h.hashtag,pcr.value
                            FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h 
                            where pcr.portfolio_id=20425797 and hashtag_type = 3594
                            and pcr.hashtag_id = h.hashtag_id;
                         
velour_link :  https://www.fotonower.com/velours/20426626,20426627,20426628,20426629,20426630,20426631,20426632,20426633,20426634,20426635,20426636?tags=autre,papier,mal_croppe,flou,pehd,pet_fonce,metal,carton,background,pet_clair,environnement
args[1336668708] : ((1336668708, -6.465719008329033, 492609224), (1336668708, -0.11651129026529211, 496442774), '0.2167945110142823')
apple ((1336668708, -6.465719008329033, 492609224), (1336668708, -0.11651129026529211, 496442774), '0.2167945110142823')
We are sending mail with results at report@fotonower.com
args[1336481138] : ((1336481138, -3.6216588990620755, 492609224), (1336481138, 0.5574465342190864, 2107752395), '0.2167945110142823')
apple ((1336481138, -3.6216588990620755, 492609224), (1336481138, 0.5574465342190864, 2107752395), '0.2167945110142823')
We are sending mail with results at report@fotonower.com
args[1336481135] : ((1336481135, -3.2805075710323726, 492609224), (1336481135, -0.31493610966723573, 496442774), '0.2167945110142823')
apple ((1336481135, -3.2805075710323726, 492609224), (1336481135, -0.31493610966723573, 496442774), '0.2167945110142823')
We are sending mail with results at report@fotonower.com
args[1336479370] : ((1336479370, -5.956194130360916, 492609224), (1336479370, -0.13543067140706389, 496442774), '0.2167945110142823')
apple ((1336479370, -5.956194130360916, 492609224), (1336479370, -0.13543067140706389, 496442774), '0.2167945110142823')
We are sending mail with results at report@fotonower.com
args[1336479334] : ((1336479334, -5.803463342883308, 492609224), (1336479334, -0.426534367383438, 496442774), '0.2167945110142823')
apple ((1336479334, -5.803463342883308, 492609224), (1336479334, -0.426534367383438, 496442774), '0.2167945110142823')
We are sending mail with results at report@fotonower.com
args[1336479330] : ((1336479330, -5.473470039444218, 492609224), (1336479330, -0.45281595776702727, 496442774), '0.2167945110142823')
apple ((1336479330, -5.473470039444218, 492609224), (1336479330, -0.45281595776702727, 496442774), '0.2167945110142823')
We are sending mail with results at report@fotonower.com
args[1336479326] : ((1336479326, -5.457906414752434, 492609224), (1336479326, -0.17631521228813588, 496442774), '0.2167945110142823')
apple ((1336479326, -5.457906414752434, 492609224), (1336479326, -0.17631521228813588, 496442774), '0.2167945110142823')
We are sending mail with results at report@fotonower.com
args[1336479304] : ((1336479304, -4.4935584207898875, 492609224), (1336479304, -0.33385590256214304, 496442774), '0.2167945110142823')
apple ((1336479304, -4.4935584207898875, 492609224), (1336479304, -0.33385590256214304, 496442774), '0.2167945110142823')
We are sending mail with results at report@fotonower.com
args[1336479300] : ((1336479300, -5.166787030595441, 492609224), (1336479300, -0.16770651856291632, 496442774), '0.2167945110142823')
apple ((1336479300, -5.166787030595441, 492609224), (1336479300, -0.16770651856291632, 496442774), '0.2167945110142823')
We are sending mail with results at report@fotonower.com
refus_total : 0.2167945110142823
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=20425797  AND mpp.hide_status=0  ORDER BY mpp.order  LIMIT 0, 1000

        SELECT photo_id, url
        FROM MTRBack.photos ph
        WHERE photo_id IN (1336479330,1336479334,1336479370,1336479300,1336479304,1336479326,1336481135,1336481138,1336668708) 
 Found this number of photos: 9
begin to download photo : 1336479330
begin to download photo : 1336479370
begin to download photo : 1336479304
begin to download photo : 1336481135
begin to download photo : 1336668708
download finish for photo 1336481135
begin to download photo : 1336481138
download finish for photo 1336479304
begin to download photo : 1336479326
download finish for photo 1336668708
download finish for photo 1336479330
begin to download photo : 1336479334
download finish for photo 1336479370
begin to download photo : 1336479300
download finish for photo 1336481138
download finish for photo 1336479326
download finish for photo 1336479300
download finish for photo 1336479334
start upload file to ovh
https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20425797_11-02-2025_04_43_27.pdf
results_Auto_P20425797_11-02-2025_04_43_27.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20425797_11-02-2025_04_43_27.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','20425797','results_Auto_P20425797_11-02-2025_04_43_27.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20425797_11-02-2025_04_43_27.pdf','pdf','','0.93','0.2167945110142823') 
                
message_in_mail: Bonjour,<br/>Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/20425797 <br/><br/>https://www.fotonower.com/image?json=false&list_photos_id=1336668708<br/>Bravo, la photo est bien prise. <br/>https://www.fotonower.com/image?json=false&list_photos_id=1336481138<br/>Bravo, la photo est bien prise. <br/>https://www.fotonower.com/image?json=false&list_photos_id=1336481135<br/>Bravo, la photo est bien prise. <br/>https://www.fotonower.com/image?json=false&list_photos_id=1336479370<br/>Bravo, la photo est bien prise. <br/>https://www.fotonower.com/image?json=false&list_photos_id=1336479334<br/>Bravo, la photo est bien prise. <br/>https://www.fotonower.com/image?json=false&list_photos_id=1336479330<br/>Bravo, la photo est bien prise. <br/>https://www.fotonower.com/image?json=false&list_photos_id=1336479326<br/>Bravo, la photo est bien prise. <br/>https://www.fotonower.com/image?json=false&list_photos_id=1336479304<br/>Bravo, la photo est bien prise. <br/>https://www.fotonower.com/image?json=false&list_photos_id=1336479300<br/>Bravo, la photo est bien prise. <br/><br/>Dans ces conditions,le taux de refus est: 21.68%<br/>Veuillez trouver les photos des contaminants.<br/><br/>exemples de contaminants: autre: https://www.fotonower.com/view/20426626?limit=200<br/>exemples de contaminants: papier: https://www.fotonower.com/view/20426627?limit=200<br/>exemples de contaminants: pehd: https://www.fotonower.com/view/20426630?limit=200<br/>exemples de contaminants: pet_fonce: https://www.fotonower.com/view/20426631?limit=200<br/>exemples de contaminants: metal: https://www.fotonower.com/view/20426632?limit=200<br/>exemples de contaminants: carton: https://www.fotonower.com/view/20426633?limit=200<br/>exemples de contaminants: pet_clair: https://www.fotonower.com/view/20426635?limit=200<br/>Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20425797_11-02-2025_04_43_27.pdf.<br/><br/>Lien vers velours :https://www.fotonower.com/velours/20426626,20426627,20426628,20426629,20426630,20426631,20426632,20426633,20426634,20426635,20426636?tags=autre,papier,mal_croppe,flou,pehd,pet_fonce,metal,carton,background,pet_clair,environnement.<br/><br/><br/>L'équipe Fotonower
202
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Inside saveOutput : final : False verbose : 0
 saveOutput not yet implemented for datou_step.type : send_mail_cod we use saveGeneral 
[1336668708, 1336481138, 1336481135, 1336479370, 1336479334, 1336479330, 1336479326, 1336479304, 1336479300]
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, '2574458')
('3318', '20425797', '1336668708', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336481138', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336481135', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479370', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479334', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479330', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479326', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479304', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479300', None, None, None, None, None, '2574458')


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 9
time used for this insertion : 0.016634225845336914
 save_final 

save missing photos in datou_result :

time spend for datou_step_exec : 12.073395252227783
time spend to save output : 0.016918420791625977
total time spend for step 9 : 12.09031367301941

step10:split_time_score
Tue Feb 11 04:43:39 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior 
Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed
We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input !
complete output_args for input 1
VR 22-3-18 : For now we do not clean correctly the datou structure 
begin split time score
Catched exception !
Connect or reconnect !
TODO : Insert select and so on
Begin split_port_in_batch_balle
thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}]
thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}]
(('12', 9),)
ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {}
10022025 20425797 Nombre de photos uploadées : 9 / 23040 (0%)
10022025 20425797 Nombre de photos taguées (types de déchets): 0 / 9 (0%)
10022025 20425797 Nombre de photos taguées (volume) : 0 / 9 (0%)
 elapsed_time : load_data_split_time_score 2.86102294921875e-06
 elapsed_time : order_list_meta_photo_and_scores 7.867813110351562e-06
????????? elapsed_time : fill_and_build_computed_from_old_data 0.0005578994750976562
 elapsed_time : insert_dashboard_record_day_entry 0.03513956069946289
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 : 0.20770710642474585
find url: 
select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20423267 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`=20423267 AND  mptpi.`type`=3594
To do
Qualite : 0.08877007922187305
find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20425795_11-02-2025_03_40_47.pdf
select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20425795 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`=20425795 AND  mptpi.`type`=3726
To do
Qualite : 0.2167945110142823
find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20425797_11-02-2025_04_43_27.pdf
select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20425797 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`=20425797 AND  mptpi.`type`=3594
To do
Qualite : 0.23261406703885257
find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20424999_11-02-2025_02_41_12.pdf
select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20424999 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`=20424999 AND  mptpi.`type`=3594
To do
Qualite : 0.19914179581765348
find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20424312_11-02-2025_01_42_58.pdf
select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20424312 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`=20424312 AND  mptpi.`type`=3594
To do
Qualite : 0.2535467764217956
find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20425002_11-02-2025_02_29_22.pdf
select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20425002 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`=20425002 AND  mptpi.`type`=3594
To do
Qualite : 0.08109472723835193
find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20423281_11-02-2025_00_24_28.pdf
select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20423281 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`=20423281 AND  mptpi.`type`=3726
To do
Qualite : 0.18296439199232933
find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20423284_11-02-2025_00_27_40.pdf
select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20423284 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`=20423284 AND  mptpi.`type`=3594
To do
Qualite : 0.19405417404230213
find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20423288_11-02-2025_00_19_07.pdf
select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20423288 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`=20423288 AND  mptpi.`type`=3594
To do
 NUMBER BATCH : 0
# DISPLAY ALL COLLECTED DATA : 
{'10022025': {'nb_upload': 9, '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 
[1336668708, 1336481138, 1336481135, 1336479370, 1336479334, 1336479330, 1336479326, 1336479304, 1336479300]
Looping around the photos to save general results
len do output : 1
/20425797Didn't retrieve data
.
before output type
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f86f23c7a30>
Managing all output in save final without adding information in the mtr_datou_result 
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336668708', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336481138', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336481135', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479370', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479334', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479330', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479326', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479304', None, None, None, None, None, '2574458')
('3318', None, None, None, None, None, None, None, '2574458')
('3318', '20425797', '1336479300', None, None, None, None, None, '2574458')


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 10
time used for this insertion : 0.04544544219970703
 save_final 

save missing photos in datou_result :

time spend for datou_step_exec : 1.6036505699157715
time spend to save output : 0.045720577239990234
total time spend for step 10 : 1.6493711471557617

caffe_path_current : 
About to save ! 2
After save, about to update current !
ret : 2
len(input) + len(total_photo_id_missing) : 9
 set_done_treatment 
352.80user 117.88system 13:16.95elapsed 59%CPU (0avgtext+0avgdata 6449148maxresident)k
3951936inputs+354632outputs (57936major+22103796minor)pagefaults 0swaps