python /home/admin/mtr/script_for_cron.py -j python_test3 -m 12 -a ' --short_python3 -v ' -s python_test3 -M 0 -S 0 -U 100,100,120
 import MySQLdb succeeded 
Import error (python version)
python version = 3
warning , we can't find thcl infos in json_data
warning , we can't find pdt infos in json_data
 list_job_run_as_list :  ['mask_detection', 'datou', 'CacheModelData_queries', 'CachePhotoData_queries', 'test_fork', 'prepare_maskdata', 'portfolio_queries', 'sla_mensuel']
python version used : 3
 liste_fichiers : [('tests/mask_test', True, 'Test mask-detection ', 'mask_detection'), ('tests/datou_test', True, 'Datou All Test', 'datou', 'all'), ('mtr/database_queries/CacheModelData_queries', True, 'Test Cache Model Data', 'CacheModelData_queries'), ('tests/cache_photo_data_test', True, 'Test local_cache_photo ', 'CachePhotoData_queries'), ('mtr/mask_rcnn/prepare_maskdata', True, 'test prepare mask data', 'prepare_maskdata', 'all'), ('mtr/database_queries/portfolio_queries', True, 'test portfolio queries', 'portfolio_queries'), ('prod/memo/memo', True, 'SLA Mensuel', 'sla_mensuel', 'all')]


#&_# BEGIN OF TEST : tests/mask_test #&_# 


/home/admin/workarea/git/Velours/python/tests/mask_test.py
<module 'tests.mask_test' from '/home/admin/workarea/git/Velours/python/tests/mask_test.py'>
Test mask-detection 
python version used : 3

############################### TEST memory used  ################################
free memory at begining : 
begin to check gpu status 
inside check gpu memory
l 3610 free memory gpu now : 10582

run mask_detect

Inside batchDatouExec : verbose : False
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
List Step Type Loaded in datou : mask_detect
list_input_json : []
origin
BFwe have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
time to download the photos : 0.20770692825317383
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 : False
number of steps :  1
step1:mask_detect
Mon Sep 29 11:40:52 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 : 10582  max_wait_temp :  1 max_wait :  0
 gpu_flag :  0
/home/admin/workarea/git/Velours/python/tests/python_tests.py:11: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
  import imp
2025-09-29 11:40:56.602392: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2025-09-29 11:40:56.632495: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3492910000 Hz
2025-09-29 11:40:56.634681: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f189c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2025-09-29 11:40:56.634732: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2025-09-29 11:40:56.638672: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2025-09-29 11:40:56.790225: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x205d6390 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2025-09-29 11:40:56.790293: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5
2025-09-29 11:40:56.792237: 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
2025-09-29 11:40:56.793039: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-09-29 11:40:56.829499: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-09-29 11:40:56.843028: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-09-29 11:40:56.844507: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-09-29 11:40:56.881357: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-09-29 11:40:56.886368: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-09-29 11:40:56.944695: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-09-29 11:40:56.946674: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-09-29 11:40:56.947361: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-09-29 11:40:56.948402: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-09-29 11:40:56.948425: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-09-29 11:40:56.948437: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-09-29 11:40:56.950546: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9801 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.

Inside mask_sub_process
Inside mask_detect 
About to load cache.load_thcl_param 
To do loadFromThcl(), then load ParamDescType : thcl454
 thcls : [{'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}]
 thcl {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}
 Update svm_hashtag_type_desc : 3473
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21))
{'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0}
list_class_names : 
['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush']

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                           mask_coco_origin
NUM_CLASSES                    81
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
2025-09-29 11:40:58.771685: 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
2025-09-29 11:40:58.771799: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-09-29 11:40:58.771816: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-09-29 11:40:58.771831: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-09-29 11:40:58.771844: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-09-29 11:40:58.771858: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-09-29 11:40:58.771884: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-09-29 11:40:58.771899: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-09-29 11:40:58.773125: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-09-29 11:40:58.774445: 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
2025-09-29 11:40:58.774477: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-09-29 11:40:58.774493: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-09-29 11:40:58.774507: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-09-29 11:40:58.774521: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-09-29 11:40:58.774535: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-09-29 11:40:58.774549: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-09-29 11:40:58.774567: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-09-29 11:40:58.775758: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-09-29 11:40:58.775799: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-09-29 11:40:58.775807: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-09-29 11:40:58.775814: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-09-29 11:40:58.777051: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9801 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.
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 : mask_coco_origin
model_type : mask_rcnn
list file need :
['mask_model.h5']
file exist in s3 :
['mask_model.h5']
file manque in s3 : 
[]
2025-09-29 11:41:07.354123: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-09-29 11:41:07.534528: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
local folder  : /data/models_weight/mask_coco_origin


/data/models_weight/mask_coco_origin/mask_model.h5
size_local : 257557808 size in s3 : 257557808
create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17
mask_model.h5 already exist and didn't need to update
 list_images length : 1
NEW PHOTO
Processing 1 images
image                    shape: (480, 640, 3)         min:    0.00000  max:  255.00000
molded_images            shape: (1, 640, 640, 3)      min: -123.70000  max:  151.10000
image_metas              shape: (1, 89)               min:    0.00000  max:  640.00000
nb d'objets trouves : 5
Detection mask done !
Trying to reset tf kernel
2886627
begin to check gpu status 
inside check gpu memory
l 3610 free memory gpu now : 5290
tf kernel not reseted
sub process len(results) : 1 len(list_Values) 0
None
max_time_sub_proc : 3600
parent process len(results) : 1 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 : 10582
list_Values should be empty []
To do loadFromThcl(), then load ParamDescType : thcl454
Catched exception !
Connect or reconnect !
 thcls : [{'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}]
 thcl {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}
 Update svm_hashtag_type_desc : 3473
['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush']
time for calcul the mask position with numpy : 0.0004851818084716797
nb_pixel_total : 15553
time to create 1 rle with old method : 0.018749237060546875
length of segment :  256
time for calcul the mask position with numpy : 0.002541780471801758
nb_pixel_total : 145335
time to create 1 rle with old method : 0.15823650360107422
length of segment :  371
time for calcul the mask position with numpy : 0.00020360946655273438
nb_pixel_total : 14255
time to create 1 rle with old method : 0.015423297882080078
length of segment :  151
time for calcul the mask position with numpy : 9.870529174804688e-05
nb_pixel_total : 5613
time to create 1 rle with old method : 0.006399869918823242
length of segment :  48
time for calcul the mask position with numpy : 4.935264587402344e-05
nb_pixel_total : 1824
time to create 1 rle with old method : 0.0021800994873046875
length of segment :  39

time spent for convertir_results : 2.223416328430176

time spend for datou_step_exec : 22.48952341079712
time spend to save output : 3.7670135498046875e-05
total time spend for step 1 : 22.489561080932617

caffe_path_current : 
About to save ! 1
Inside saveOutput : final : True verbose : False
eke 12-6-18 : saveMask need to be cleaned for new output !
Number saved : None
batch 1
Loaded 3395 chid ids of type : 445
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 1
time used for this insertion : 0.03849506378173828

save missing photos in datou_result :
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {'957285035': [[(957285035, 492601069, 445, 0, 186, 22, 282, 0.99548846, [(140, 26, 6), (135, 27, 15), (133, 28, 18), (131, 29, 22), (126, 30, 28), (10, 31, 1), (120, 31, 35), (8, 32, 13), (26, 32, 4), (115, 32, 41), (7, 33, 52), (109, 33, 48), (6, 34, 70), (103, 34, 55), (5, 35, 154), (4, 36, 155), (3, 37, 156), (3, 38, 156), (3, 39, 156), (2, 40, 157), (2, 41, 157), (2, 42, 157), (2, 43, 157), (2, 44, 157), (2, 45, 157), (1, 46, 158), (1, 47, 158), (1, 48, 158), (1, 49, 157), (1, 50, 157), (1, 51, 156), (1, 52, 156), (1, 53, 155), (1, 54, 154), (1, 55, 152), (1, 56, 149), (1, 57, 145), (1, 58, 141), (1, 59, 136), (1, 60, 133), (1, 61, 130), (1, 62, 127), (1, 63, 126), (1, 64, 124), (1, 65, 123), (1, 66, 121), (1, 67, 120), (1, 68, 118), (1, 69, 117), (1, 70, 116), (1, 71, 115), (1, 72, 114), (1, 73, 113), (1, 74, 112), (1, 75, 111), (1, 76, 110), (1, 77, 108), (1, 78, 108), (1, 79, 107), (1, 80, 106), (1, 81, 105), (2, 82, 104), (2, 83, 103), (2, 84, 103), (2, 85, 102), (2, 86, 102), (2, 87, 101), (2, 88, 100), (2, 89, 99), (2, 90, 99), (2, 91, 98), (2, 92, 97), (2, 93, 96), (2, 94, 95), (2, 95, 93), (2, 96, 91), (2, 97, 90), (2, 98, 89), (2, 99, 87), (2, 100, 86), (2, 101, 86), (2, 102, 85), (2, 103, 84), (2, 104, 83), (2, 105, 83), (2, 106, 82), (2, 107, 81), (2, 108, 80), (2, 109, 80), (2, 110, 79), (2, 111, 78), (2, 112, 77), (2, 113, 76), (1, 114, 76), (1, 115, 75), (1, 116, 74), (1, 117, 73), (1, 118, 72), (1, 119, 71), (1, 120, 71), (1, 121, 70), (1, 122, 69), (1, 123, 69), (1, 124, 68), (1, 125, 68), (1, 126, 67), (1, 127, 67), (1, 128, 66), (1, 129, 66), (1, 130, 66), (1, 131, 65), (1, 132, 65), (1, 133, 64), (1, 134, 63), (1, 135, 63), (1, 136, 62), (1, 137, 61), (1, 138, 60), (1, 139, 60), (1, 140, 59), (1, 141, 58), (1, 142, 58), (1, 143, 57), (1, 144, 56), (1, 145, 56), (1, 146, 55), (1, 147, 54), (1, 148, 54), (1, 149, 53), (1, 150, 52), (1, 151, 52), (1, 152, 51), (1, 153, 50), (1, 154, 49), (1, 155, 48), (1, 156, 47), (1, 157, 46), (1, 158, 45), (1, 159, 45), (1, 160, 44), (1, 161, 43), (1, 162, 42), (1, 163, 41), (1, 164, 41), (1, 165, 40), (1, 166, 40), (1, 167, 39), (1, 168, 38), (1, 169, 37), (1, 170, 36), (1, 171, 35), (1, 172, 34), (1, 173, 34), (1, 174, 33), (1, 175, 33), (1, 176, 32), (1, 177, 32), (1, 178, 32), (1, 179, 32), (1, 180, 31), (1, 181, 31), (1, 182, 31), (1, 183, 30), (1, 184, 30), (1, 185, 30), (1, 186, 29), (1, 187, 29), (1, 188, 29), (1, 189, 28), (1, 190, 28), (1, 191, 27), (1, 192, 27), (1, 193, 26), (1, 194, 26), (1, 195, 26), (1, 196, 26), (1, 197, 26), (1, 198, 26), (1, 199, 26), (1, 200, 25), (1, 201, 25), (1, 202, 25), (1, 203, 25), (1, 204, 25), (1, 205, 25), (1, 206, 25), (1, 207, 25), (1, 208, 25), (1, 209, 25), (1, 210, 25), (1, 211, 25), (1, 212, 25), (1, 213, 25), (1, 214, 25), (1, 215, 25), (1, 216, 25), (1, 217, 25), (1, 218, 25), (1, 219, 25), (1, 220, 24), (1, 221, 24), (1, 222, 24), (1, 223, 24), (1, 224, 24), (1, 225, 24), (1, 226, 25), (1, 227, 25), (1, 228, 25), (2, 229, 24), (2, 230, 24), (2, 231, 24), (2, 232, 23), (2, 233, 23), (2, 234, 23), (2, 235, 23), (2, 236, 23), (2, 237, 23), (2, 238, 23), (2, 239, 23), (2, 240, 23), (2, 241, 23), (2, 242, 23), (2, 243, 23), (2, 244, 23), (2, 245, 23), (2, 246, 23), (2, 247, 23), (2, 248, 23), (2, 249, 24), (2, 250, 24), (2, 251, 23), (2, 252, 23), (2, 253, 23), (2, 254, 23), (2, 255, 23), (2, 256, 23), (2, 257, 23), (2, 258, 23), (2, 259, 23), (2, 260, 23), (2, 261, 23), (3, 262, 22), (3, 263, 22), (3, 264, 22), (3, 265, 22), (4, 266, 21), (4, 267, 21), (5, 268, 20), (5, 269, 20), (6, 270, 19), (7, 271, 17), (8, 272, 16), (8, 273, 16), (9, 274, 13), (11, 275, 9), (15, 276, 2)], ['16,276,8,273,2,261,2,229,1,228,1,114,2,113,2,82,1,81,1,46,3,37,8,32,29,32,30,33,58,33,59,34,75,34,76,35,102,35,114,33,120,31,130,30,135,27,145,26,152,29,158,35,158,48,154,54,141,58,128,61,119,67,105,81,103,86,96,94,89,98,81,109,71,119,65,132,60,138,52,150,41,163,40,166,34,172,29,188,26,193,25,200,25,219,24,232,24,270,23,273']), (957285035, 492601069, 445, 29, 591, 24, 419, 0.9923773, [(315, 37, 25), (272, 38, 86), (253, 39, 130), (238, 40, 151), (199, 41, 196), (189, 42, 213), (180, 43, 238), (175, 44, 250), (172, 45, 258), (169, 46, 266), (166, 47, 274), (162, 48, 284), (159, 49, 294), (157, 50, 304), (155, 51, 311), (153, 52, 317), (151, 53, 323), (149, 54, 330), (148, 55, 334), (146, 56, 337), (144, 57, 341), (142, 58, 344), (140, 59, 347), (138, 60, 350), (136, 61, 353), (134, 62, 356), (132, 63, 358), (130, 64, 361), (128, 65, 364), (126, 66, 367), (124, 67, 370), (122, 68, 373), (120, 69, 376), (118, 70, 379), (117, 71, 381), (115, 72, 385), (114, 73, 387), (113, 74, 389), (112, 75, 391), (112, 76, 393), (111, 77, 395), (110, 78, 397), (109, 79, 399), (109, 80, 400), (108, 81, 402), (107, 82, 404), (107, 83, 404), (106, 84, 406), (105, 85, 408), (105, 86, 409), (104, 87, 410), (104, 88, 411), (103, 89, 413), (102, 90, 415), (101, 91, 417), (100, 92, 420), (98, 93, 423), (97, 94, 426), (96, 95, 428), (94, 96, 431), (93, 97, 433), (92, 98, 435), (91, 99, 437), (90, 100, 439), (89, 101, 441), (89, 102, 441), (89, 103, 442), (89, 104, 443), (89, 105, 444), (89, 106, 444), (89, 107, 445), (89, 108, 446), (89, 109, 447), (89, 110, 448), (89, 111, 449), (89, 112, 450), (89, 113, 451), (89, 114, 453), (89, 115, 454), (89, 116, 455), (88, 117, 456), (88, 118, 457), (87, 119, 459), (87, 120, 459), (86, 121, 461), (85, 122, 462), (85, 123, 463), (84, 124, 464), (84, 125, 465), (83, 126, 466), (82, 127, 468), (82, 128, 468), (81, 129, 470), (80, 130, 471), (78, 131, 473), (76, 132, 476), (75, 133, 477), (73, 134, 480), (71, 135, 482), (70, 136, 484), (68, 137, 486), (67, 138, 488), (65, 139, 490), (64, 140, 492), (63, 141, 493), (61, 142, 496), (60, 143, 497), (59, 144, 499), (58, 145, 501), (58, 146, 501), (57, 147, 503), (57, 148, 504), (56, 149, 506), (56, 150, 507), (56, 151, 507), (55, 152, 509), (55, 153, 510), (54, 154, 511), (54, 155, 512), (54, 156, 513), (53, 157, 514), (53, 158, 514), (52, 159, 516), (52, 160, 516), (51, 161, 517), (51, 162, 517), (51, 163, 517), (50, 164, 518), (50, 165, 518), (49, 166, 519), (49, 167, 520), (48, 168, 521), (48, 169, 521), (47, 170, 522), (47, 171, 522), (46, 172, 523), (46, 173, 523), (46, 174, 523), (45, 175, 524), (45, 176, 523), (44, 177, 524), (44, 178, 524), (44, 179, 524), (43, 180, 525), (43, 181, 525), (42, 182, 525), (42, 183, 525), (41, 184, 526), (41, 185, 526), (41, 186, 526), (40, 187, 526), (39, 188, 526), (39, 189, 525), (38, 190, 526), (38, 191, 525), (37, 192, 525), (37, 193, 523), (36, 194, 523), (36, 195, 523), (36, 196, 522), (35, 197, 522), (35, 198, 521), (34, 199, 521), (34, 200, 521), (34, 201, 520), (34, 202, 520), (34, 203, 520), (34, 204, 519), (33, 205, 520), (33, 206, 520), (33, 207, 519), (33, 208, 519), (33, 209, 519), (33, 210, 518), (33, 211, 518), (33, 212, 518), (33, 213, 517), (32, 214, 518), (32, 215, 517), (32, 216, 517), (32, 217, 516), (32, 218, 515), (32, 219, 514), (32, 220, 513), (32, 221, 512), (32, 222, 511), (32, 223, 510), (32, 224, 508), (32, 225, 507), (32, 226, 505), (32, 227, 504), (32, 228, 503), (32, 229, 502), (32, 230, 502), (32, 231, 501), (32, 232, 500), (32, 233, 499), (32, 234, 498), (32, 235, 497), (31, 236, 496), (31, 237, 495), (31, 238, 494), (31, 239, 493), (31, 240, 491), (31, 241, 490), (31, 242, 488), (31, 243, 487), (31, 244, 486), (31, 245, 485), (31, 246, 483), (31, 247, 482), (31, 248, 480), (31, 249, 479), (31, 250, 477), (31, 251, 475), (31, 252, 473), (31, 253, 472), (31, 254, 470), (31, 255, 468), (31, 256, 467), (31, 257, 465), (31, 258, 464), (31, 259, 463), (31, 260, 462), (31, 261, 461), (31, 262, 459), (31, 263, 458), (31, 264, 456), (31, 265, 455), (31, 266, 453), (31, 267, 451), (31, 268, 449), (31, 269, 448), (31, 270, 447), (31, 271, 445), (31, 272, 444), (31, 273, 443), (32, 274, 441), (32, 275, 440), (32, 276, 438), (32, 277, 437), (32, 278, 435), (32, 279, 434), (32, 280, 432), (33, 281, 429), (33, 282, 427), (33, 283, 426), (33, 284, 424), (33, 285, 423), (34, 286, 421), (34, 287, 420), (34, 288, 419), (35, 289, 416), (35, 290, 415), (35, 291, 414), (36, 292, 411), (36, 293, 410), (37, 294, 407), (37, 295, 406), (38, 296, 403), (38, 297, 401), (39, 298, 399), (39, 299, 397), (41, 300, 394), (42, 301, 392), (43, 302, 389), (44, 303, 387), (45, 304, 385), (46, 305, 382), (47, 306, 380), (47, 307, 378), (48, 308, 376), (49, 309, 373), (50, 310, 370), (51, 311, 368), (51, 312, 367), (52, 313, 365), (54, 314, 362), (55, 315, 360), (56, 316, 358), (58, 317, 356), (61, 318, 352), (64, 319, 349), (67, 320, 345), (70, 321, 341), (73, 322, 338), (75, 323, 335), (78, 324, 332), (80, 325, 329), (82, 326, 327), (84, 327, 324), (86, 328, 322), (88, 329, 320), (90, 330, 317), (93, 331, 314), (96, 332, 311), (99, 333, 307), (102, 334, 304), (105, 335, 300), (108, 336, 297), (111, 337, 294), (113, 338, 291), (115, 339, 289), (117, 340, 286), (119, 341, 283), (121, 342, 281), (123, 343, 278), (125, 344, 275), (127, 345, 272), (129, 346, 269), (132, 347, 266), (135, 348, 262), (138, 349, 258), (141, 350, 255), (143, 351, 252), (146, 352, 249), (147, 353, 247), (149, 354, 245), (151, 355, 242), (152, 356, 241), (154, 357, 239), (156, 358, 237), (159, 359, 233), (161, 360, 231), (163, 361, 229), (165, 362, 227), (167, 363, 224), (169, 364, 222), (170, 365, 221), (172, 366, 219), (173, 367, 218), (174, 368, 216), (175, 369, 215), (177, 370, 213), (178, 371, 212), (180, 372, 209), (183, 373, 206), (185, 374, 204), (188, 375, 200), (191, 376, 197), (194, 377, 193), (196, 378, 191), (199, 379, 188), (201, 380, 185), (203, 381, 183), (205, 382, 180), (207, 383, 178), (208, 384, 176), (210, 385, 174), (212, 386, 171), (213, 387, 169), (215, 388, 166), (218, 389, 162), (221, 390, 158), (225, 391, 153), (228, 392, 149), (232, 393, 144), (235, 394, 140), (238, 395, 136), (241, 396, 133), (245, 397, 128), (248, 398, 124), (252, 399, 119), (257, 400, 113), (263, 401, 105), (272, 402, 94), (283, 403, 82), (297, 404, 65), (306, 405, 53), (313, 406, 38), (321, 407, 23)], ['321,407,296,403,263,401,215,388,178,371,168,363,145,351,129,346,110,336,90,330,56,316,39,299,31,273,32,214,36,194,44,179,58,145,79,131,89,116,89,101,104,88,115,72,159,49,180,43,199,41,237,41,272,38,339,37,382,39,402,43,417,43,460,50,481,55,543,116,556,143,566,156,568,175,566,186,554,199,548,216,528,235,496,256,471,275,420,309,413,316,403,339,392,355,383,385,369,400,358,405']), (957285035, 492601069, 445, 485, 636, 23, 174, 0.9711505, [(540, 24, 21), (626, 24, 3), (531, 25, 49), (594, 25, 40), (527, 26, 107), (523, 27, 111), (520, 28, 114), (517, 29, 118), (516, 30, 119), (515, 31, 120), (513, 32, 122), (512, 33, 123), (510, 34, 125), (509, 35, 126), (507, 36, 128), (506, 37, 129), (504, 38, 131), (503, 39, 132), (501, 40, 134), (500, 41, 135), (499, 42, 136), (498, 43, 137), (497, 44, 138), (496, 45, 139), (496, 46, 139), (495, 47, 140), (495, 48, 140), (494, 49, 141), (493, 50, 142), (492, 51, 143), (491, 52, 144), 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['598,172,591,172,590,171,578,168,573,164,573,162,568,152,568,149,566,145,566,136,565,132,561,125,560,121,556,116,547,109,543,108,536,104,531,99,527,97,491,62,490,54,495,48,496,45,501,40,514,32,517,29,531,25,539,25,540,24,560,24,561,25,579,25,580,26,593,26,594,25,633,25,634,29,634,56,635,57,635,111,634,112,634,129,632,134,629,138,623,141,619,145,617,149,611,155,608,161,604,166']), (957285035, 492601069, 445, 280, 481, 2, 55, 0.8298205, [(292, 3, 128), (284, 4, 146), (282, 5, 151), (281, 6, 154), (281, 7, 156), (281, 8, 157), (281, 9, 158), (281, 10, 160), (281, 11, 162), (281, 12, 165), (281, 13, 167), (281, 14, 169), (281, 15, 171), (281, 16, 173), (281, 17, 174), (281, 18, 175), (281, 19, 177), (281, 20, 178), (281, 21, 179), (281, 22, 180), (281, 23, 181), (281, 24, 182), (281, 25, 183), (281, 26, 184), (281, 27, 185), (281, 28, 185), (281, 29, 185), (282, 30, 185), (283, 31, 27), (337, 31, 131), (371, 32, 97), (401, 33, 68), (409, 34, 61), (419, 35, 52), (424, 36, 48), (429, 37, 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free memory after detection : 
begin to check gpu status 
inside check gpu memory
l 3610 free memory gpu now : 10582

############################### TEST detect object  ################################

run mask_detect

Inside batchDatouExec : verbose : False
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
List Step Type Loaded in datou : mask_detect
list_input_json : []
origin
BFwe have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
time to download the photos : 0.1903066635131836
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 : False
number of steps :  1
step1:mask_detect
Mon Sep 29 11:41:17 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 : 10582  max_wait_temp :  1 max_wait :  0
 gpu_flag :  0
2025-09-29 11:41:19.769697: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2025-09-29 11:41:19.796724: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3492910000 Hz
2025-09-29 11:41:19.798847: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f18a0000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2025-09-29 11:41:19.798924: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2025-09-29 11:41:19.802670: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2025-09-29 11:41:19.978738: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x20666ee0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2025-09-29 11:41:19.978803: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5
2025-09-29 11:41:19.980218: 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
2025-09-29 11:41:19.980666: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-09-29 11:41:19.983654: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-09-29 11:41:19.986532: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-09-29 11:41:19.987036: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-09-29 11:41:19.990010: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-09-29 11:41:19.990950: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-09-29 11:41:19.994874: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-09-29 11:41:19.996296: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-09-29 11:41:19.996376: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-09-29 11:41:19.997172: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-09-29 11:41:19.997188: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-09-29 11:41:19.997198: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-09-29 11:41:19.998516: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9801 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.

2025-09-29 11:41:20.088558: 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
2025-09-29 11:41:20.088700: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-09-29 11:41:20.088733: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-09-29 11:41:20.088763: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-09-29 11:41:20.088790: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-09-29 11:41:20.088819: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-09-29 11:41:20.088847: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-09-29 11:41:20.088896: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-09-29 11:41:20.090478: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-09-29 11:41:20.091539: 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
2025-09-29 11:41:20.091568: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-09-29 11:41:20.091584: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-09-29 11:41:20.091598: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-09-29 11:41:20.091612: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-09-29 11:41:20.091627: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-09-29 11:41:20.091641: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-09-29 11:41:20.091655: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-09-29 11:41:20.092852: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-09-29 11:41:20.092885: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-09-29 11:41:20.092893: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-09-29 11:41:20.092900: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-09-29 11:41:20.094090: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9801 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 
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21))
{'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0}
list_class_names : 
['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush']

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                           mask_coco_origin
NUM_CLASSES                    81
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 : mask_coco_origin
model_type : mask_rcnn
list file need :
['mask_model.h5']
file exist in s3 :
['mask_model.h5']
file manque in s3 : 
[]
2025-09-29 11:41:26.440492: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-09-29 11:41:26.589422: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
local folder  : /data/models_weight/mask_coco_origin


/data/models_weight/mask_coco_origin/mask_model.h5
size_local : 257557808 size in s3 : 257557808
create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17
mask_model.h5 already exist and didn't need to update
 list_images length : 1
NEW PHOTO
Processing 1 images
image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000
molded_images            shape: (1, 640, 640, 3)      min: -123.70000  max:  151.10000
image_metas              shape: (1, 89)               min:    0.00000  max: 1280.00000
nb d'objets trouves : 4
Detection mask done !
Trying to reset tf kernel
2886910
begin to check gpu status 
inside check gpu memory
l 3610 free memory gpu now : 5290
tf kernel not reseted
sub process len(results) : 1 len(list_Values) 0
None
max_time_sub_proc : 3600
parent process len(results) : 1 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 : 10582
list_Values should be empty []
['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush']
time for calcul the mask position with numpy : 0.0006618499755859375
nb_pixel_total : 16902
time to create 1 rle with old method : 0.02209329605102539
length of segment :  107
time for calcul the mask position with numpy : 0.03709578514099121
nb_pixel_total : 480745
time to create 1 rle with new method : 0.026641130447387695
length of segment :  632
time for calcul the mask position with numpy : 0.0004227161407470703
nb_pixel_total : 36640
time to create 1 rle with old method : 0.040060997009277344
length of segment :  133
time for calcul the mask position with numpy : 8.535385131835938e-05
nb_pixel_total : 4793
time to create 1 rle with old method : 0.005537748336791992
length of segment :  51

time spent for convertir_results : 0.30234527587890625

time spend for datou_step_exec : 15.248999118804932
time spend to save output : 3.504753112792969e-05
total time spend for step 1 : 15.24903416633606

caffe_path_current : 
About to save ! 1
Inside saveOutput : final : True verbose : False
eke 12-6-18 : saveMask need to be cleaned for new output !
Catched exception !
Connect or reconnect !
Number saved : None
batch 1
Loaded 443 chid ids of type : 445
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 1
time used for this insertion : 0.03972935676574707

save missing photos in datou_result :
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {'917855882': [[(917855882, 492601069, 445, 1092, 1280, 0, 108, 0.9988373, [(1205, 1, 58), (1165, 2, 105), (1159, 3, 113), (1149, 4, 124), (1113, 5, 161), (1100, 6, 174), (1097, 7, 177), (1095, 8, 179), (1095, 9, 179), (1095, 10, 179), (1095, 11, 179), (1095, 12, 179), (1095, 13, 179), (1095, 14, 178), (1095, 15, 178), (1095, 16, 178), (1095, 17, 178), (1095, 18, 177), (1095, 19, 177), (1095, 20, 177), (1095, 21, 177), (1095, 22, 177), (1095, 23, 178), (1095, 24, 178), (1095, 25, 178), (1095, 26, 179), (1095, 27, 179), (1095, 28, 180), (1095, 29, 181), (1095, 30, 182), (1095, 31, 183), (1095, 32, 183), (1095, 33, 184), (1095, 34, 184), (1096, 35, 183), (1096, 36, 183), (1096, 37, 184), (1097, 38, 183), (1097, 39, 183), (1097, 40, 183), (1098, 41, 182), (1098, 42, 182), (1098, 43, 182), (1099, 44, 181), (1099, 45, 181), (1099, 46, 181), (1100, 47, 180), (1100, 48, 180), (1101, 49, 179), (1101, 50, 179), (1102, 51, 178), (1102, 52, 178), (1103, 53, 177), (1103, 54, 177), (1104, 55, 176), (1104, 56, 176), (1104, 57, 176), (1104, 58, 176), (1105, 59, 175), (1105, 60, 175), (1105, 61, 175), (1105, 62, 175), (1105, 63, 175), (1106, 64, 174), (1106, 65, 174), (1106, 66, 174), (1106, 67, 174), (1106, 68, 174), (1106, 69, 174), (1106, 70, 174), (1106, 71, 174), (1106, 72, 174), (1106, 73, 174), (1107, 74, 173), (1107, 75, 173), (1107, 76, 173), (1107, 77, 173), (1107, 78, 173), (1107, 79, 173), (1108, 80, 172), (1108, 81, 172), (1109, 82, 171), (1110, 83, 170), (1110, 84, 170), (1111, 85, 169), (1112, 86, 168), (1113, 87, 166), (1114, 88, 165), (1115, 89, 164), (1117, 90, 162), (1120, 91, 159), (1138, 92, 141), (1146, 93, 133), (1154, 94, 125), (1167, 95, 112), (1177, 96, 102), (1183, 97, 95), (1185, 98, 93), (1187, 99, 90), (1188, 100, 55), (1264, 100, 12), (1190, 101, 50), (1191, 102, 46), (1194, 103, 40), (1197, 104, 34), (1202, 105, 25), (1207, 106, 16)], ['1222,106,1207,106,1206,105,1197,104,1191,102,1182,96,1176,95,1167,95,1166,94,1154,94,1153,93,1146,93,1145,92,1137,91,1120,91,1115,89,1110,84,1107,79,1106,73,1106,64,1104,55,1099,46,1095,34,1095,8,1100,6,1112,6,1113,5,1148,5,1149,4,1158,4,1165,2,1204,2,1205,1,1262,1,1269,2,1273,5,1273,13,1271,18,1271,22,1273,27,1277,31,1279,37,1279,86,1278,87,1278,96,1275,100,1264,100,1263,99,1243,99,1230,104']), (917855882, 492601069, 445, 52, 1128, 16, 668, 0.99774784, [(711, 22, 22), (925, 22, 47), (608, 23, 146), (894, 23, 103), (598, 24, 234), (850, 24, 158), (589, 25, 428), (582, 26, 444), (574, 27, 459), (569, 28, 466), (565, 29, 472), (560, 30, 480), (555, 31, 487), (550, 32, 495), (544, 33, 503), (538, 34, 512), (532, 35, 520), (527, 36, 527), (523, 37, 534), (518, 38, 541), (514, 39, 548), (510, 40, 554), (506, 41, 561), (503, 42, 566), (499, 43, 572), (496, 44, 577), (493, 45, 582), (491, 46, 585), (488, 47, 590), (487, 48, 592), (485, 49, 595), (483, 50, 598), (482, 51, 600), (481, 52, 602), (480, 53, 603), (479, 54, 605), (478, 55, 606), (476, 56, 608), (475, 57, 610), (474, 58, 611), (473, 59, 613), (472, 60, 614), (470, 61, 616), (469, 62, 618), (468, 63, 619), (466, 64, 621), (465, 65, 623), (464, 66, 624), (462, 67, 626), (461, 68, 628), (459, 69, 630), (458, 70, 631), (456, 71, 633), (455, 72, 635), (453, 73, 637), (452, 74, 638), (451, 75, 639), (450, 76, 640), (448, 77, 642), (447, 78, 643), (446, 79, 644), (445, 80, 645), (444, 81, 646), (442, 82, 648), (441, 83, 649), (440, 84, 650), (439, 85, 651), (438, 86, 652), (437, 87, 653), (436, 88, 654), (435, 89, 655), (434, 90, 656), (433, 91, 657), (432, 92, 658), (431, 93, 659), (430, 94, 660), (429, 95, 661), (428, 96, 662), (427, 97, 663), (425, 98, 665), (423, 99, 667), (421, 100, 669), (419, 101, 671), (417, 102, 673), (414, 103, 676), (410, 104, 680), (406, 105, 684), (401, 106, 689), (397, 107, 693), (392, 108, 698), (387, 109, 703), (382, 110, 708), (377, 111, 713), (373, 112, 717), (369, 113, 721), (365, 114, 725), (362, 115, 728), (358, 116, 732), (356, 117, 734), (353, 118, 737), (351, 119, 739), (349, 120, 741), (346, 121, 744), (344, 122, 746), (341, 123, 749), (338, 124, 752), (335, 125, 755), (331, 126, 759), (327, 127, 763), (323, 128, 767), (319, 129, 770), (314, 130, 775), (308, 131, 781), (303, 132, 786), (294, 133, 795), (287, 134, 802), (279, 135, 810), (273, 136, 816), (267, 137, 822), (262, 138, 827), (258, 139, 831), (255, 140, 834), (252, 141, 837), (250, 142, 839), (247, 143, 842), (245, 144, 844), (242, 145, 847), (240, 146, 849), (237, 147, 852), (234, 148, 855), (230, 149, 859), (226, 150, 863), (220, 151, 869), (213, 152, 876), (207, 153, 882), (200, 154, 889), (193, 155, 896), (187, 156, 902), (183, 157, 906), (181, 158, 908), (178, 159, 911), (176, 160, 913), (174, 161, 915), (172, 162, 917), (170, 163, 919), (168, 164, 921), (167, 165, 922), (165, 166, 924), (164, 167, 925), (162, 168, 927), (161, 169, 928), (159, 170, 930), (157, 171, 932), (155, 172, 934), (153, 173, 935), (151, 174, 937), (148, 175, 940), (146, 176, 942), (144, 177, 944), (142, 178, 946), (140, 179, 948), (139, 180, 949), (137, 181, 951), (136, 182, 952), (134, 183, 954), (133, 184, 955), (132, 185, 956), (131, 186, 957), (130, 187, 958), (129, 188, 959), (128, 189, 960), (127, 190, 960), (126, 191, 961), (126, 192, 961), (125, 193, 962), (124, 194, 963), (123, 195, 964), (122, 196, 965), (122, 197, 965), (121, 198, 966), (120, 199, 967), (119, 200, 968), (118, 201, 969), (117, 202, 970), (116, 203, 971), (114, 204, 973), (113, 205, 973), (112, 206, 974), (111, 207, 975), (109, 208, 977), (108, 209, 978), (107, 210, 979), (106, 211, 980), (105, 212, 981), (104, 213, 982), (103, 214, 983), (102, 215, 984), (101, 216, 985), (101, 217, 984), (100, 218, 985), (100, 219, 985), (99, 220, 986), (98, 221, 987), (98, 222, 987), (97, 223, 988), (97, 224, 987), (96, 225, 988), (96, 226, 988), (95, 227, 989), (95, 228, 989), (94, 229, 990), (94, 230, 990), (94, 231, 990), 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['526,649,416,642,341,627,297,609,263,599,220,577,186,566,102,509,91,496,70,447,63,388,65,329,86,265,91,237,101,216,134,183,187,156,225,151,279,135,318,130,358,116,413,104,426,98,448,77,493,45,574,27,608,23,754,24,893,24,925,22,996,23,1032,27,1066,41,1082,52,1089,72,1088,172,1082,237,1045,305,1002,338,950,373,895,423,865,446,851,473,822,505,810,528,786,554,772,586,725,621,683,638,607,649']), (917855882, 492601069, 445, 0, 440, 0, 116, 0.9919475, [(127, 1, 141), (94, 2, 206), (384, 2, 2), (59, 3, 272), (340, 3, 57), (22, 4, 381), (19, 5, 387), (16, 6, 392), (15, 7, 394), (14, 8, 396), (14, 9, 397), (13, 10, 399), (12, 11, 400), (12, 12, 400), (11, 13, 402), (10, 14, 403), (11, 15, 403), (11, 16, 404), (12, 17, 403), (12, 18, 404), (12, 19, 405), (12, 20, 405), (12, 21, 406), (12, 22, 406), (12, 23, 407), (12, 24, 407), (12, 25, 408), (12, 26, 408), (12, 27, 408), (12, 28, 408), (12, 29, 409), (12, 30, 409), (12, 31, 409), (12, 32, 409), (12, 33, 409), (12, 34, 410), (12, 35, 410), (12, 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91, 57), (299, 91, 103), (6, 92, 54), (300, 92, 102), (6, 93, 50), (301, 93, 100), (7, 94, 46), (303, 94, 97), (7, 95, 44), (306, 95, 92), (7, 96, 42), (308, 96, 89), (7, 97, 40), (310, 97, 86), (7, 98, 38), (312, 98, 83), (8, 99, 34), (314, 99, 79), (8, 100, 32), (317, 100, 75), (8, 101, 29), (319, 101, 71), (13, 102, 19), (324, 102, 63), (20, 103, 6), (330, 103, 51), (337, 104, 37), (344, 105, 22), (352, 106, 3)], ['344,105,343,104,319,101,301,93,291,85,279,84,259,85,244,90,205,90,204,89,176,89,161,88,141,85,125,86,98,86,84,85,56,92,36,101,26,102,8,101,6,92,11,80,11,59,12,58,12,17,10,14,16,6,22,4,58,4,59,3,93,3,94,2,126,2,127,1,267,1,268,2,299,2,300,3,396,3,407,6,419,25,422,46,419,54,410,62,403,74,404,85,401,92,394,98,386,102,365,105']), (917855882, 492601069, 445, 390, 550, 0, 54, 0.9390822, [(414, 0, 7), (441, 0, 60), (508, 0, 28), (402, 1, 142), (401, 2, 146), (402, 3, 145), (404, 4, 143), (406, 5, 140), (408, 6, 137), (410, 7, 134), (411, 8, 132), (412, 9, 130), (413, 10, 127), (414, 11, 125), (415, 12, 123), (415, 13, 122), (416, 14, 120), (417, 15, 117), (417, 16, 116), (418, 17, 114), (418, 18, 113), (418, 19, 111), (418, 20, 109), (419, 21, 107), (419, 22, 105), (419, 23, 103), (419, 24, 102), (420, 25, 99), (420, 26, 97), (420, 27, 95), (420, 28, 94), (421, 29, 91), (421, 30, 90), (422, 31, 88), (422, 32, 88), (422, 33, 87), (423, 34, 84), (423, 35, 82), (423, 36, 81), (424, 37, 79), (424, 38, 77), (424, 39, 75), (424, 40, 73), (424, 41, 71), (425, 42, 67), (425, 43, 66), (426, 44, 62), (426, 45, 6), (433, 45, 52), (443, 46, 30), (450, 47, 1)], ['449,46,443,46,442,45,426,45,424,41,424,37,423,36,422,31,420,28,420,25,419,24,419,21,418,20,418,17,417,15,409,6,402,3,402,1,413,1,414,0,420,0,421,1,440,1,441,0,500,0,501,1,507,1,508,0,535,0,536,1,543,1,546,2,546,4,542,8,530,18,527,19,525,21,522,22,520,24,512,28,508,33,505,34,502,37,494,41,492,41,490,43,488,43,484,45,473,45,472,46'])], 'temp/1759138877_2886529_917855882_da0fa7b7e6b5b551fe26c0ba8713276d.jpg']}

############################### TEST POLYGON  ################################
Inside batchDatouExec : verbose : False
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
List Step Type Loaded in datou : mask_detect
list_input_json : []
origin
BFwe have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
time to download the photos : 0.17781758308410645
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 : False
number of steps :  1
step1:mask_detect
Mon Sep 29 11:41:34 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 : 10582  max_wait_temp :  1 max_wait :  0
 gpu_flag :  0
2025-09-29 11:41:36.807704: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2025-09-29 11:41:36.832541: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3492910000 Hz
2025-09-29 11:41:36.834513: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f18a0000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2025-09-29 11:41:36.834560: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2025-09-29 11:41:36.837951: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2025-09-29 11:41:36.981204: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x21626230 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2025-09-29 11:41:36.981264: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5
2025-09-29 11:41:36.982565: 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
2025-09-29 11:41:36.982921: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-09-29 11:41:36.985693: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-09-29 11:41:36.988149: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-09-29 11:41:36.988646: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-09-29 11:41:36.990543: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-09-29 11:41:36.991413: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-09-29 11:41:36.994959: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-09-29 11:41:36.996299: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-09-29 11:41:36.996384: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-09-29 11:41:36.997094: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-09-29 11:41:36.997108: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-09-29 11:41:36.997115: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-09-29 11:41:36.998386: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9801 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.

2025-09-29 11:41:37.079632: 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
2025-09-29 11:41:37.079717: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-09-29 11:41:37.079742: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-09-29 11:41:37.079764: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-09-29 11:41:37.079785: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-09-29 11:41:37.079805: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-09-29 11:41:37.079826: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-09-29 11:41:37.079846: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-09-29 11:41:37.081435: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-09-29 11:41:37.082673: 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
2025-09-29 11:41:37.082721: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-09-29 11:41:37.082743: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-09-29 11:41:37.082763: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-09-29 11:41:37.082784: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-09-29 11:41:37.082804: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-09-29 11:41:37.082823: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-09-29 11:41:37.082844: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-09-29 11:41:37.084443: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-09-29 11:41:37.084476: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-09-29 11:41:37.084486: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-09-29 11:41:37.084495: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-09-29 11:41:37.086108: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9801 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 
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21))
{'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0}
list_class_names : 
['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush']

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                           mask_coco_origin
NUM_CLASSES                    81
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 : mask_coco_origin
model_type : mask_rcnn
list file need :
['mask_model.h5']
file exist in s3 :
['mask_model.h5']
file manque in s3 : 
[]
2025-09-29 11:41:44.090718: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-09-29 11:41:44.261484: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
local folder  : /data/models_weight/mask_coco_origin


/data/models_weight/mask_coco_origin/mask_model.h5
size_local : 257557808 size in s3 : 257557808
create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17
mask_model.h5 already exist and didn't need to update
 list_images length : 1
NEW PHOTO
Processing 1 images
image                    shape: (2448, 2448, 3)       min:    0.00000  max:  255.00000
molded_images            shape: (1, 640, 640, 3)      min: -123.70000  max:  151.10000
image_metas              shape: (1, 89)               min:    0.00000  max: 2448.00000
nb d'objets trouves : 1
Detection mask done !
Trying to reset tf kernel
2887124
begin to check gpu status 
inside check gpu memory
l 3610 free memory gpu now : 5290
tf kernel not reseted
sub process len(results) : 1 len(list_Values) 0
None
max_time_sub_proc : 3600
parent process len(results) : 1 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 : 10582
list_Values should be empty []
['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush']
time for calcul the mask position with numpy : 0.20493769645690918
nb_pixel_total : 3693278
time to create 1 rle with new method : 0.44922828674316406
length of segment :  2042

time spent for convertir_results : 1.5338478088378906

time spend for datou_step_exec : 17.383992671966553
time spend to save output : 2.574920654296875e-05
total time spend for step 1 : 17.384018421173096

caffe_path_current : 
About to save ! 1
Inside saveOutput : final : True verbose : False
eke 12-6-18 : saveMask need to be cleaned for new output !
Catched exception !
Connect or reconnect !
Number saved : None
batch 1
Loaded 724 chid ids of type : 445
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 1
time used for this insertion : 0.04242277145385742

save missing photos in datou_result :
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {'917877156': [[(917877156, 492601069, 445, 7, 2268, 118, 2241, 0.9849803, [(674, 120, 114), (520, 121, 481), (1050, 121, 381), (502, 122, 947), (486, 123, 981), (470, 124, 1015), (455, 125, 1046), (442, 126, 1091), (429, 127, 1136), (417, 128, 1168), (405, 129, 1187), (394, 130, 1205), (383, 131, 1222), (373, 132, 1239), (368, 133, 1250), (366, 134, 1258), (363, 135, 1266), (361, 136, 1274), (359, 137, 1281), (357, 138, 1288), (355, 139, 1295), (352, 140, 1303), (351, 141, 1309), (349, 142, 1315), (347, 143, 1320), (345, 144, 1326), (343, 145, 1331), (342, 146, 1335), (340, 147, 1340), (338, 148, 1345), (337, 149, 1349), (335, 150, 1354), (334, 151, 1358), (332, 152, 1363), (331, 153, 1366), (330, 154, 1370), (328, 155, 1374), (327, 156, 1378), (326, 157, 1381), (325, 158, 1385), (323, 159, 1389), (322, 160, 1393), (321, 161, 1397), (319, 162, 1402), (318, 163, 1406), (317, 164, 1410), (315, 165, 1415), (314, 166, 1419), (312, 167, 1424), (310, 168, 1429), (309, 169, 1434), (307, 170, 1439), (305, 171, 1444), (304, 172, 1448), (302, 173, 1453), (300, 174, 1458), (298, 175, 1463), (296, 176, 1469), (294, 177, 1474), (292, 178, 1480), (289, 179, 1487), (286, 180, 1493), (283, 181, 1500), (280, 182, 1508), (278, 183, 1514), (275, 184, 1521), (272, 185, 1529), (269, 186, 1536), (266, 187, 1544), (263, 188, 1552), (260, 189, 1561), (257, 190, 1569), (254, 191, 1579), (251, 192, 1588), (248, 193, 1597), (245, 194, 1606), (242, 195, 1615), (239, 196, 1624), (237, 197, 1631), (234, 198, 1640), (231, 199, 1648), (228, 200, 1657), (225, 201, 1665), (222, 202, 1673), (219, 203, 1682), (216, 204, 1689), (213, 205, 1694), (210, 206, 1699), (208, 207, 1702), (206, 208, 1706), (204, 209, 1710), (203, 210, 1712), (201, 211, 1716), (199, 212, 1719), (198, 213, 1722), (196, 214, 1725), (195, 215, 1727), (193, 216, 1730), (192, 217, 1733), (191, 218, 1735), (189, 219, 1738), (188, 220, 1740), (187, 221, 1742), (186, 222, 1744), (185, 223, 1746), (183, 224, 1749), (182, 225, 1751), (181, 226, 1753), (180, 227, 1755), (179, 228, 1757), (178, 229, 1759), (177, 230, 1761), (176, 231, 1762), (176, 232, 1763), (175, 233, 1765), (174, 234, 1767), (173, 235, 1768), (172, 236, 1770), (171, 237, 1772), (170, 238, 1774), (169, 239, 1776), (168, 240, 1777), (167, 241, 1779), (166, 242, 1781), (165, 243, 1783), (164, 244, 1785), (163, 245, 1787), (162, 246, 1789), (161, 247, 1791), (159, 248, 1794), (158, 249, 1796), (157, 250, 1798), (156, 251, 1800), (154, 252, 1803), (153, 253, 1805), (152, 254, 1807), (150, 255, 1810), (149, 256, 1812), (148, 257, 1815), (146, 258, 1818), (145, 259, 1820), (143, 260, 1824), (142, 261, 1826), (140, 262, 1829), (138, 263, 1833), (137, 264, 1835), (135, 265, 1839), (133, 266, 1842), (132, 267, 1845), (130, 268, 1849), (128, 269, 1852), (126, 270, 1856), (125, 271, 1859), (124, 272, 1862), (122, 273, 1865), (121, 274, 1868), (120, 275, 1871), (119, 276, 1873), (117, 277, 1877), (116, 278, 1879), (115, 279, 1881), (114, 280, 1884), 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nb pixel non reg : 3692295
nb pixel common : 3690670
proportion of common points : 0.9995598943204701
[('test release memory', 'SUCCESS', True), ('test detect objet', 'SUCCESS', True), ('test polygone', 'SUCCESS', True)]
 res_total : True


#&_# TEST SUCCEEDED #&_# : tests/mask_test #&_# 


/home/admin/workarea/git/Velours/python/tests/python_tests.py refs/heads/migration_mbp_22_09_25_a78d6507d1d43be5b35a5d36f9c95011145ed55a
SQL :INSERT INTO MTRAdmin.monitor_sys (name, type, server, version_code, result_str, result_bool, lien , test_group ,test_name) VALUES ('python_test3','1','marlene','refs/heads/migration_mbp_22_09_25_a78d6507d1d43be5b35a5d36f9c95011145ed55a','{"mask_detection": "success"}','1','http://marlene.fotonower-preprod.com/job/2025/September/29092025/python_test3/','mask_detection','unknown');

#&_# END OF TEST #&_# : tests/mask_test #&_# 





#&_# BEGIN OF TEST : tests/datou_test #&_# 


/home/admin/workarea/git/Velours/python/tests/datou_test.py
<module 'tests.datou_test' from '/home/admin/workarea/git/Velours/python/tests/datou_test.py'>
Datou All Test
python version used : 3

############################### TEST sam  ################################
TEST SAM
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4573

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=4573
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 4573
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=4573
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : sam
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (1189321094) 
 Found this number of photos: 1
##### Call download_photos : nb_thread : 5
begin to download photo : 1189321094
download finish for photo 1189321094
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.17214560508728027
#### fin chargement data
Blocking on flush ?
No conitnuing 
About to test input to load 
we should then remove the video here, and this would fix the bug of datou_current ! 
WARNING : we have an input that is not a photo, we should get rid of it
Calling datou_exec

Inside datou_exec : verbose : True
number of steps :  1
step1:sam
Mon Sep 29 11:41:58 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759138917_2886529_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png': 1189321094}
map_photo_id_path_extension : {1189321094: {'path': 'temp/1759138917_2886529_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png', 'extension': 'png'}}
map_subphoto_mainphoto : {}

Beginning of datou step sam !
pht : 4677
Inside sam : nb paths : 1
(640, 960, 3)
time for calcul the mask position with numpy : 0.0019040107727050781
nb_pixel_total : 11983
time to create 1 rle with old method : 0.013453245162963867
time for calcul the mask position with numpy : 0.0013310909271240234
nb_pixel_total : 7606
time to create 1 rle with old method : 0.00844120979309082
time for calcul the mask position with numpy : 0.0013813972473144531
nb_pixel_total : 16182
time to create 1 rle with old method : 0.017638206481933594
time for calcul the mask position with numpy : 0.0013206005096435547
nb_pixel_total : 3792
time to create 1 rle with old method : 0.004134178161621094
time for calcul the mask position with numpy : 0.00133514404296875
nb_pixel_total : 8647
time to create 1 rle with old method : 0.009434223175048828
time for calcul the mask position with numpy : 0.0013415813446044922
nb_pixel_total : 10774
time to create 1 rle with old method : 0.011439085006713867
time for calcul the mask position with numpy : 0.0013272762298583984
nb_pixel_total : 5631
time to create 1 rle with old method : 0.0061681270599365234
time for calcul the mask position with numpy : 0.0013511180877685547
nb_pixel_total : 14647
time to create 1 rle with old method : 0.01596212387084961
time for calcul the mask position with numpy : 0.001325368881225586
nb_pixel_total : 5391
time to create 1 rle with old method : 0.006058216094970703
time for calcul the mask position with numpy : 0.0018100738525390625
nb_pixel_total : 84089
time to create 1 rle with old method : 0.08979940414428711
time for calcul the mask position with numpy : 0.0013866424560546875
nb_pixel_total : 13913
time to create 1 rle with old method : 0.01532602310180664
time for calcul the mask position with numpy : 0.0013210773468017578
nb_pixel_total : 2784
time to create 1 rle with old method : 0.00311279296875
time for calcul the mask position with numpy : 0.0014314651489257812
nb_pixel_total : 29478
time to create 1 rle with old method : 0.032077789306640625
time for calcul the mask position with numpy : 0.0013053417205810547
nb_pixel_total : 2942
time to create 1 rle with old method : 0.003250598907470703
time for calcul the mask position with numpy : 0.001298666000366211
nb_pixel_total : 3858
time to create 1 rle with old method : 0.0044209957122802734
time for calcul the mask position with numpy : 0.0012972354888916016
nb_pixel_total : 3946
time to create 1 rle with old method : 0.0043828487396240234
time for calcul the mask position with numpy : 0.0012912750244140625
nb_pixel_total : 2377
time to create 1 rle with old method : 0.0027773380279541016
time for calcul the mask position with numpy : 0.0014102458953857422
nb_pixel_total : 6580
time to create 1 rle with old method : 0.007466316223144531
time for calcul the mask position with numpy : 0.0013227462768554688
nb_pixel_total : 1227
time to create 1 rle with old method : 0.0014140605926513672
time for calcul the mask position with numpy : 0.0013158321380615234
nb_pixel_total : 4286
time to create 1 rle with old method : 0.004792213439941406
time for calcul the mask position with numpy : 0.0013549327850341797
nb_pixel_total : 13099
time to create 1 rle with old method : 0.014253377914428711
time for calcul the mask position with numpy : 0.0013391971588134766
nb_pixel_total : 2080
time to create 1 rle with old method : 0.0023009777069091797
time for calcul the mask position with numpy : 0.001524209976196289
nb_pixel_total : 38741
time to create 1 rle with old method : 0.04167366027832031
time for calcul the mask position with numpy : 0.001363992691040039
nb_pixel_total : 1195
time to create 1 rle with old method : 0.0015063285827636719
time for calcul the mask position with numpy : 0.0013129711151123047
nb_pixel_total : 4272
time to create 1 rle with old method : 0.004940986633300781
time for calcul the mask position with numpy : 0.001425027847290039
nb_pixel_total : 3510
time to create 1 rle with old method : 0.0040285587310791016
time for calcul the mask position with numpy : 0.0013756752014160156
nb_pixel_total : 1510
time to create 1 rle with old method : 0.0017027854919433594
time for calcul the mask position with numpy : 0.001316070556640625
nb_pixel_total : 5475
time to create 1 rle with old method : 0.0061130523681640625
time for calcul the mask position with numpy : 0.00130462646484375
nb_pixel_total : 1438
time to create 1 rle with old method : 0.0017750263214111328
time for calcul the mask position with numpy : 0.0013408660888671875
nb_pixel_total : 906
time to create 1 rle with old method : 0.001100301742553711
time for calcul the mask position with numpy : 0.0013377666473388672
nb_pixel_total : 9885
time to create 1 rle with old method : 0.010954141616821289
time for calcul the mask position with numpy : 0.0013020038604736328
nb_pixel_total : 2447
time to create 1 rle with old method : 0.0027310848236083984
time for calcul the mask position with numpy : 0.0013985633850097656
nb_pixel_total : 2727
time to create 1 rle with old method : 0.0032532215118408203
time for calcul the mask position with numpy : 0.0013930797576904297
nb_pixel_total : 16475
time to create 1 rle with old method : 0.01807713508605957
time for calcul the mask position with numpy : 0.0013082027435302734
nb_pixel_total : 2408
time to create 1 rle with old method : 0.0028579235076904297
time for calcul the mask position with numpy : 0.0013453960418701172
nb_pixel_total : 221
time to create 1 rle with old method : 0.00030541419982910156
time for calcul the mask position with numpy : 0.0013048648834228516
nb_pixel_total : 1647
time to create 1 rle with old method : 0.0018813610076904297
time for calcul the mask position with numpy : 0.001409292221069336
nb_pixel_total : 1253
time to create 1 rle with old method : 0.0014801025390625
time for calcul the mask position with numpy : 0.0013115406036376953
nb_pixel_total : 1614
time to create 1 rle with old method : 0.001867532730102539
time for calcul the mask position with numpy : 0.0013189315795898438
nb_pixel_total : 3328
time to create 1 rle with old method : 0.003818988800048828
time for calcul the mask position with numpy : 0.0013055801391601562
nb_pixel_total : 1025
time to create 1 rle with old method : 0.0011525154113769531
time for calcul the mask position with numpy : 0.0012950897216796875
nb_pixel_total : 342
time to create 1 rle with old method : 0.00041413307189941406
time for calcul the mask position with numpy : 0.0013310909271240234
nb_pixel_total : 4140
time to create 1 rle with old method : 0.004581928253173828
time for calcul the mask position with numpy : 0.0013396739959716797
nb_pixel_total : 10574
time to create 1 rle with old method : 0.011612415313720703
time for calcul the mask position with numpy : 0.0013196468353271484
nb_pixel_total : 2766
time to create 1 rle with old method : 0.003133535385131836
time for calcul the mask position with numpy : 0.0013239383697509766
nb_pixel_total : 2399
time to create 1 rle with old method : 0.0029497146606445312
time for calcul the mask position with numpy : 0.0013661384582519531
nb_pixel_total : 4199
time to create 1 rle with old method : 0.004791975021362305
time for calcul the mask position with numpy : 0.0013222694396972656
nb_pixel_total : 858
time to create 1 rle with old method : 0.001157522201538086
time for calcul the mask position with numpy : 0.0013051033020019531
nb_pixel_total : 875
time to create 1 rle with old method : 0.0010828971862792969
time for calcul the mask position with numpy : 0.0013585090637207031
nb_pixel_total : 13053
time to create 1 rle with old method : 0.015039443969726562
time for calcul the mask position with numpy : 0.0013256072998046875
nb_pixel_total : 1673
time to create 1 rle with old method : 0.002089977264404297
time for calcul the mask position with numpy : 0.0013051033020019531
nb_pixel_total : 589
time to create 1 rle with old method : 0.0007472038269042969
time for calcul the mask position with numpy : 0.0014102458953857422
nb_pixel_total : 2167
time to create 1 rle with old method : 0.0024213790893554688
time for calcul the mask position with numpy : 0.0013360977172851562
nb_pixel_total : 8617
time to create 1 rle with old method : 0.009407281875610352
time for calcul the mask position with numpy : 0.0013041496276855469
nb_pixel_total : 2324
time to create 1 rle with old method : 0.002687692642211914
time for calcul the mask position with numpy : 0.0013446807861328125
nb_pixel_total : 1738
time to create 1 rle with old method : 0.0020406246185302734
time for calcul the mask position with numpy : 0.0012898445129394531
nb_pixel_total : 577
time to create 1 rle with old method : 0.0007402896881103516
time for calcul the mask position with numpy : 0.001382589340209961
nb_pixel_total : 299
time to create 1 rle with old method : 0.0004258155822753906
time for calcul the mask position with numpy : 0.0013687610626220703
nb_pixel_total : 16718
time to create 1 rle with old method : 0.019049406051635742
time for calcul the mask position with numpy : 0.0013141632080078125
nb_pixel_total : 4422
time to create 1 rle with old method : 0.0050258636474609375
time for calcul the mask position with numpy : 0.0012919902801513672
nb_pixel_total : 693
time to create 1 rle with old method : 0.0008516311645507812
time for calcul the mask position with numpy : 0.0012841224670410156
nb_pixel_total : 337
time to create 1 rle with old method : 0.0004305839538574219
time for calcul the mask position with numpy : 0.0012869834899902344
nb_pixel_total : 1706
time to create 1 rle with old method : 0.0019614696502685547
time for calcul the mask position with numpy : 0.0012850761413574219
nb_pixel_total : 1056
time to create 1 rle with old method : 0.0012836456298828125
time for calcul the mask position with numpy : 0.0012884140014648438
nb_pixel_total : 1073
time to create 1 rle with old method : 0.0012803077697753906
time for calcul the mask position with numpy : 0.00150299072265625
nb_pixel_total : 27607
time to create 1 rle with old method : 0.029878616333007812
time for calcul the mask position with numpy : 0.0012907981872558594
nb_pixel_total : 587
time to create 1 rle with old method : 0.0007138252258300781
time for calcul the mask position with numpy : 0.001298666000366211
nb_pixel_total : 1616
time to create 1 rle with old method : 0.001905679702758789
time for calcul the mask position with numpy : 0.00130462646484375
nb_pixel_total : 3092
time to create 1 rle with old method : 0.0035479068756103516
time for calcul the mask position with numpy : 0.0012984275817871094
nb_pixel_total : 1319
time to create 1 rle with old method : 0.0015838146209716797
time for calcul the mask position with numpy : 0.001329183578491211
nb_pixel_total : 8498
time to create 1 rle with old method : 0.009273052215576172
time for calcul the mask position with numpy : 0.0012919902801513672
nb_pixel_total : 1486
time to create 1 rle with old method : 0.001699686050415039
time for calcul the mask position with numpy : 0.0012848377227783203
nb_pixel_total : 267
time to create 1 rle with old method : 0.0003228187561035156
time for calcul the mask position with numpy : 0.001329660415649414
nb_pixel_total : 9079
time to create 1 rle with old method : 0.009976387023925781
time for calcul the mask position with numpy : 0.0012934207916259766
nb_pixel_total : 1110
time to create 1 rle with old method : 0.0012793540954589844
time for calcul the mask position with numpy : 0.0013022422790527344
nb_pixel_total : 713
time to create 1 rle with old method : 0.0008885860443115234
time for calcul the mask position with numpy : 0.0013241767883300781
nb_pixel_total : 9502
time to create 1 rle with old method : 0.010409116744995117
time for calcul the mask position with numpy : 0.0012843608856201172
nb_pixel_total : 970
time to create 1 rle with old method : 0.001115560531616211
time for calcul the mask position with numpy : 0.0013685226440429688
nb_pixel_total : 18398
time to create 1 rle with old method : 0.019878149032592773
time for calcul the mask position with numpy : 0.0013041496276855469
nb_pixel_total : 3170
time to create 1 rle with old method : 0.0035610198974609375
time for calcul the mask position with numpy : 0.0012903213500976562
nb_pixel_total : 618
time to create 1 rle with old method : 0.0007488727569580078
time for calcul the mask position with numpy : 0.001291513442993164
nb_pixel_total : 974
time to create 1 rle with old method : 0.0011968612670898438
time for calcul the mask position with numpy : 0.0012917518615722656
nb_pixel_total : 246
time to create 1 rle with old method : 0.00032973289489746094
time for calcul the mask position with numpy : 0.0012905597686767578
nb_pixel_total : 734
time to create 1 rle with old method : 0.0009481906890869141
time for calcul the mask position with numpy : 0.0013651847839355469
nb_pixel_total : 7497
time to create 1 rle with old method : 0.008362531661987305
time for calcul the mask position with numpy : 0.0013201236724853516
nb_pixel_total : 1632
time to create 1 rle with old method : 0.0019314289093017578
time for calcul the mask position with numpy : 0.0013718605041503906
nb_pixel_total : 953
time to create 1 rle with old method : 0.0011518001556396484
time for calcul the mask position with numpy : 0.0013070106506347656
nb_pixel_total : 1442
time to create 1 rle with old method : 0.001806497573852539
time for calcul the mask position with numpy : 0.0012888908386230469
nb_pixel_total : 595
time to create 1 rle with old method : 0.0007560253143310547
time for calcul the mask position with numpy : 0.001383066177368164
nb_pixel_total : 830
time to create 1 rle with old method : 0.0010023117065429688
time for calcul the mask position with numpy : 0.0012922286987304688
nb_pixel_total : 1123
time to create 1 rle with old method : 0.0013959407806396484
time for calcul the mask position with numpy : 0.0012900829315185547
nb_pixel_total : 917
time to create 1 rle with old method : 0.0012273788452148438
time for calcul the mask position with numpy : 0.0013110637664794922
nb_pixel_total : 2204
time to create 1 rle with old method : 0.0026428699493408203
time for calcul the mask position with numpy : 0.0013031959533691406
nb_pixel_total : 335
time to create 1 rle with old method : 0.0004169940948486328
time for calcul the mask position with numpy : 0.0012896060943603516
nb_pixel_total : 884
time to create 1 rle with old method : 0.00106048583984375
time for calcul the mask position with numpy : 0.0013875961303710938
nb_pixel_total : 1203
time to create 1 rle with old method : 0.0014455318450927734
time for calcul the mask position with numpy : 0.0013051033020019531
nb_pixel_total : 1319
time to create 1 rle with old method : 0.0016531944274902344
time for calcul the mask position with numpy : 0.0013420581817626953
nb_pixel_total : 11116
time to create 1 rle with old method : 0.012183666229248047
time for calcul the mask position with numpy : 0.001294851303100586
nb_pixel_total : 480
time to create 1 rle with old method : 0.0005965232849121094
time for calcul the mask position with numpy : 0.0012905597686767578
nb_pixel_total : 889
time to create 1 rle with old method : 0.0010650157928466797
insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s)
batch 1
Loaded 100 chid ids of type : 4677
Number RLEs to save : 9094
INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) 
 first line : ('3980043705', '27', '0', '122')
...
 last line : ('3980043804', '477', '190', '2')
INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) 
 TO DO : save crop sub photo not yet done ! 

After datou_step_exec type output : <class 'dict'>
map_portfolio_photo : len 0 keys : dict_keys([])
Inside saveOutput : final : True verbose : True
 saveOutput not yet implemented for datou_step.type : sam we use saveGeneral 
[1189321094]
 map_info['map_portfolio_photo'] : {}
 final :  True mtd_id 4573 list_pids : [1189321094]
Looping around the photos to save general results
len do output : 1
/1189321094Didn't retrieve data
.Didn't retrieve data
.
before output type
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f196d0a7460>
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f196d0a74c0>
Managing all output in save final without adding information in the mtr_datou_result 
('4573', None, None, None, None, None, None, None, None)
('4573', None, '1189321094', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 3
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('4573', None, '1189321094', 'None', None, None, None, None, None)]
time used for this insertion : 0.039290428161621094
 save_final 

save missing photos in datou_result :

time spend for datou_step_exec : 17.986522436141968
time spend to save output : 0.039475440979003906
total time spend for step 1 : 18.02599787712097

caffe_path_current : 
About to save ! 2
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {'1189321094': [[<mtr.simple_image_editor.image_utils.CHI object at 0x7f190c404dc0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c404df0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c404d90>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c404d00>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c404c70>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c404ca0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c404cd0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb9d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb5b0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb820>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb910>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb7c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb7f0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb6d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb580>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb790>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb670>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb700>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb550>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb6a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb5e0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb4c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb490>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb460>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb430>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb3a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb370>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb340>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb310>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb280>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb250>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb1c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb190>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb100>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb0d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3fb040>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5fd0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5f40>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5f10>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5ee0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5eb0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5e20>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5df0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5dc0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5d90>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5d00>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5cd0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5c40>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5c10>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5b80>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5b50>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5b20>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5af0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5a60>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5a30>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5a00>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f59d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5940>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5910>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f58e0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f58b0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5820>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f57f0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f57c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5790>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5700>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f56d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5640>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5610>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5580>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5550>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5520>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f54f0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5460>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5430>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5400>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f53d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5340>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5310>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f52e0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f52b0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5220>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f51f0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f51c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5190>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5100>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f50d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f50a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3f5070>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3edfa0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3edf70>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3edf40>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3edf10>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3ede80>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3ede50>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3ede20>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3eddf0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3edd60>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3edd30>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c3edd00>], 'temp/1759138917_2886529_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']}
nb_objects detect : 100

############################### TEST frcnn  ################################
test frcnn
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4184

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=4184
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 4184
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=4184
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : frcnn
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (917754606) 
 Found this number of photos: 1
##### Call download_photos : nb_thread : 5
begin to download photo : 917754606
download finish for photo 917754606
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.18735790252685547
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  1
step1:frcnn
Mon Sep 29 11:42:16 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759138936_2886529_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg': 917754606}
map_photo_id_path_extension : {917754606: {'path': 'temp/1759138936_2886529_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou step Faster rcnn !
classes : ['background', 'plaque']
pht : 4370
caffemodel_name (should be vgg16_immat_307 but not used because net loaded outside in the fonction) : {'id': 3375, 'mtr_user_id': 31, 'name': 'detection_plaque_valcor_010622', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,plaque', 'svm_portfolios_learning': '0,0', 'photo_hashtag_type': 4370, 'photo_desc_type': 5676, 'type_classification': 'caffe_faster_rcnn', 'hashtag_id_list': '0,0'}
To loadFromThcl()
model_param file didn't exist 
model_name : detection_plaque_valcor_010622
model_type : caffe_faster_rcnn
list file need :
['caffemodel', 'test.prototxt']
file exist in s3 :
['caffemodel', 'test.prototxt']
file manque in s3 : 
[]
local folder  : /data/models_weight/detection_plaque_valcor_010622


/data/models_weight/detection_plaque_valcor_010622/caffemodel
size_local : 349723073 size in s3 : 349723073
create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:56
caffemodel already exist and didn't need to update


/data/models_weight/detection_plaque_valcor_010622/test.prototxt
size_local : 7163 size in s3 : 7163
create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:55
test.prototxt already exist and didn't need to update
 prototxt : /data/models_weight/detection_plaque_valcor_010622/test.prototxt
 caffemodel : /data/models_weight/detection_plaque_valcor_010622/caffemodel


Loaded network /data/models_weight/detection_plaque_valcor_010622/caffemodel
About to compute detect_faster_rcnn : len(args) : 1
Inside frcnn step exec : nb paths : 1
image_path : temp/1759138936_2886529_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg
image_size (600, 800, 3)
[[[  4   6   6]
  [  5   7   7]
  [  6   8   8]
  ...
  [207 215 214]
  [206 214 213]
  [206 214 213]]

 [[  4   6   6]
  [  5   7   7]
  [  6   8   8]
  ...
  [207 215 214]
  [206 214 213]
  [206 214 213]]

 [[  4   6   6]
  [  5   7   7]
  [  6   8   8]
  ...
  [207 215 214]
  [206 214 213]
  [206 214 213]]

 ...

 [[ 14  16  16]
  [ 13  15  15]
  [ 11  13  13]
  ...
  [198 206 205]
  [198 206 205]
  [198 206 205]]

 [[ 16  18  18]
  [ 14  16  16]
  [ 11  13  13]
  ...
  [206 214 213]
  [206 214 213]
  [206 214 213]]

 [[ 13  15  15]
  [ 12  14  14]
  [  9  11  11]
  ...
  [210 218 217]
  [210 218 217]
  [210 218 217]]]
Detection took 0.073s for 300 object proposals
 c : plaque
 list_crops.shape (72, 5)
 proba :  0.06384062
(374.12692, 293.91928, 430.81015, 317.80862)
 proba :  0.052224305
(382.1776, 297.18826, 552.3599, 344.65805)
 proba :  0.012271235
(345.35678, 272.42987, 468.85764, 320.7243)
We are managing local photo_id
len de result frcnn : 1

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 2.4539802074432373
time spend to save output : 5.626678466796875e-05
total time spend for step 1 : 2.4540364742279053

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
Inside saveFrcnn : final : True verbose : True
threshold to save the result : 0.1
output flattener :  [(0, 493029425, 4370, 374, 430, 293, 317, 0.06384062, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.052224305, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012271235, None)]
Warning : no hashtag_ids to insert in the database
final : True


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 1
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('4184', None, '917754606', '0', 0, '0', 493061979, '0', None)]
time used for this insertion : 0.04436016082763672
[917754606]
 map_info['map_portfolio_photo'] : {}
 final :  True mtd_id 4184 list_pids : [917754606]
Looping around the photos to save general results
len do output : 1
/0
before output type
Managing all output in save final without adding information in the mtr_datou_result 
('4184', None, None, None, None, None, None, None, None)
('4184', None, '917754606', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 1
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('4184', None, '917754606', None, None, None, None, None, None)]
time used for this insertion : 0.03805875778198242
 save_final 

save missing photos in datou_result :
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {0: [[(0, 493029425, 4370, 374, 430, 293, 317, 0.06384062, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.052224305, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012271235, None)], 'temp/1759138936_2886529_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg']}

############################### TEST thcl  ################################
TEST THCL
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=2
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 2
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=2
# 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)
 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 : step 1 thcl is not linked in the step_by_step architecture !
WARNING : step 2 argmax is not linked in the step_by_step architecture !
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 !
DataTypes for each output/input checked !
no param json to modify
List Step Type Loaded in datou : thcl, argmax
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (916235064) 
 Found this number of photos: 1
##### Call download_photos : nb_thread : 5
begin to download photo : 916235064
download finish for photo 916235064
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.614830732345581
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  2
step1:thcl
Mon Sep 29 11:42:19 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759138939_2886529_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064}
map_photo_id_path_extension : {916235064: {'path': 'temp/1759138939_2886529_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou step Thcl !
multi_thcl or not :False
multi_thcl_cond or not :False
dic_thcl :  {'355': 1}
we are using the classfication for only one thcl
355


In convert_file_to_np l 337 : 1
 l343 1
l357 after caffe.io.load_image
dimension du image : (3, (66, 66, 3))
dimension displayed !
time to import caffe and check if the image exist :  0.010289430618286133
time to convert the images to numpy array :  0.0007834434509277344
total time to convert the images to numpy array :  0.011453628540039062
list photo_ids error:
[]
list photo_ids correct :
[916235064]
number of photos to traite : 1
try to delete the photos incorrect in DB

tagging for thcl : 355
To do loadFromThcl(), then load ParamDescType : thcl355
 get_desc_type_from_thcl : type of cat <class 'mtr.database_queries.classification_admin_tools.ClassificationMetaTools'>

            SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists,
            photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at,
            type_classification
            FROM MTRDatou.classification_theme  WHERE  `id` IN (355) 
 thcls : [{'id': 355, 'mtr_user_id': 31, 'name': 'car_360_1027', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'c_elysee_1027_gao__port_506302,mokka_1027_gao__port_506374,captur_1027_gao__port_506399,sorento_1027_gao__port_506192,navara_1027_gao__port_506205,xc90_1027_gao__port_506350,saxo_1027_gao__port_506052,trafic_1027_gao__port_506295,punto_evo_1027_gao__port_506066,5_1027_gao__port_506117,250_1027_gao__port_506065,d_max_1027_gao__port_506125,panamera_1027_gao__port_506387,alhambra_1027_gao__port_506381,x6_1027_gao__port_506349,vitara_1027_gao__port_506328,fiesta_1027_gao__port_506377,qashqai_1027_gao__port_506286,147_1027_gao__port_506124,c5_1027_gao__port_506172,q5_1027_gao__port_506206,giulia_1027_gao__port_506178,karl_1027_gao__port_506371,mehari_1027_gao__port_506076,911_1027_gao__port_506114,508_1027_gao__port_506329,idea_1027_gao__port_506122,megane_1027_gao__port_506220,ghibli_1027_gao__port_506174,touareg_1027_gao__port_506224,i10_1027_gao__port_506232,jumper_1027_gao__port_506234,classe_clk_1027_gao__port_506173,kuga_1027_gao__port_506181,ct_1027_gao__port_506323,leon_1027_gao__port_506326,ds5_1027_gao__port_506376,cordoba_1027_gao__port_506048,classe_cla_1027_gao__port_506400,jumpy_1027_gao__port_506179,avensis_1027_gao__port_506311,juke_1027_gao__port_506325,4008_1027_gao__port_506402,190_series_1027_gao__port_506051,serie_3_1027_gao__port_506294,q7_1027_gao__port_506318,glc_1027_gao__port_506303,grand_vitara_1027_gao__port_506175,s40_1027_gao__port_506099,toledo_1027_gao__port_506061,5008_1027_gao__port_506337,continental_1027_gao__port_506250,coupe_1027_gao__port_506082,iq_1027_gao__port_506166,407_1027_gao__port_506133,touran_1027_gao__port_506308,300c_1027_gao__port_506078,classe_gl_1027_gao__port_506340,vivaro_1027_gao__port_506310,sl_1027_gao__port_506100,elise_1027_gao__port_506121,1007_1027_gao__port_506070,i40_1027_gao__port_506218,bipper_tepee_1027_gao__port_506227,focus_1027_gao__port_506272,primera_1027_gao__port_506147,r4_1027_gao__port_506160,a8_1027_gao__port_506265,boxer_1027_gao__port_506202,s5_1027_gao__port_506222,r21_1027_gao__port_506093,c3_1027_gao__port_506257,santa_fe_1027_gao__port_506208,m4_1027_gao__port_506344,safrane_1027_gao__port_506077,classe_gle_1027_gao__port_506395,0_1027_gao__port_506094,ix35_1027_gao__port_506219,carens_1027_gao__port_506298,classe_a_1027_gao__port_506339,ix20_1027_gao__port_506343,note_1027_gao__port_506365,a5_1027_gao__port_506200,sx4_1027_gao__port_506348,sandero_1027_gao__port_506198,3008_1027_gao__port_506385,q50_1027_gao__port_506239,latitude_1027_gao__port_506236,v40_1027_gao__port_506391,xsara_1027_gao__port_506087,grand_c_max_1027_gao__port_506342,swift_1027_gao__port_506149,serie_1_1027_gao__port_506184,xc70_1027_gao__port_506393,master_1027_gao__port_506203,clio_1027_gao__port_506280,duster_1027_gao__port_506216,traveller_1027_gao__port_506403,tipo_1027_gao__port_506355,rav_4_1027_gao__port_506332,coccinelle_1027_gao__port_506259,spacetourer_1027_gao__port_506401,xe_1027_gao__port_506357,ds3_1027_gao__port_506324,mx_5_1027_gao__port_506098,land_cruiser_1027_gao__port_506315,classe_b_1027_gao__port_506335,806_1027_gao__port_506088,rx_8_1027_gao__port_506046,spark_1027_gao__port_506185,6_1027_gao__port_506171,bravo_1027_gao__port_506080,nx_1027_gao__port_506345,sharan_1027_gao__port_506347,x_type_1027_gao__port_506067,jimny_1027_gao__port_506233,wrangler_1027_gao__port_506225,c_crosser_1027_gao__port_506312,v70_1027_gao__port_506278,classe_e_1027_gao__port_506300,classe_v_1027_gao__port_506258,m3_1027_gao__port_506182,abarth_500_1027_gao__port_506226,serie_6_1027_gao__port_506262,modus_1027_gao__port_506146,3_1027_gao__port_506113,405_1027_gao__port_506108,allroad_1027_gao__port_506297,auris_1027_gao__port_506322,galaxy_1027_gao__port_506143,giulietta_1027_gao__port_506363,106_1027_gao__port_506073,classe_m_1027_gao__port_506154,espace_1027_gao__port_506313,panda_1027_gao__port_506189,rcz_1027_gao__port_506197,4007_1027_gao__port_506162,classe_cl_1027_gao__port_506249,leaf_1027_gao__port_506139,octavia_1027_gao__port_506237,ds4_1027_gao__port_506336,freelander_1027_gao__port_506084,evasion_1027_gao__port_506109,punto_1027_gao__port_506106,2cv_1027_gao__port_506045,x4_1027_gao__port_506392,antara_1027_gao__port_506247,murano_1027_gao__port_506316,alto_1027_gao__port_506201,meriva_1027_gao__port_506353,orlando_1027_gao__port_506305,new_beetle_1027_gao__port_506050,306_1027_gao__port_506145,tiguan_1027_gao__port_506362,s_type_1027_gao__port_506101,c1_1027_gao__port_506128,vectra_1027_gao__port_506044,outlander_1027_gao__port_506317,307_1027_gao__port_506074,a6_s6_1027_gao__port_506134,nemo_combi_1027_gao__port_506196,berlingo_1027_gao__port_506194,partner_1027_gao__port_506285,cayenne_1027_gao__port_506177,quattroporte_1027_gao__port_506240,c_max_1027_gao__port_506282,fabia_1027_gao__port_506396,cx_3_1027_gao__port_506281,x_trail_1027_gao__port_506264,scirocco_1027_gao__port_506276,matiz_1027_gao__port_506144,tigra_1027_gao__port_506069,escort_1027_gao__port_506091,c2_1027_gao__port_506081,mini_1027_gao__port_506168,i30_1027_gao__port_506291,picanto_1027_gao__port_506238,mito_1027_gao__port_506072,impreza_1027_gao__port_506085,kangoo_1027_gao__port_506235,a4_1027_gao__port_506193,cayman_1027_gao__port_506268,sportage_1027_gao__port_506148,up_1027_gao__port_506356,optima_1027_gao__port_506386,defender_1027_gao__port_506229,serie_2_1027_gao__port_506256,edge_1027_gao__port_506187,r19_1027_gao__port_506110,jetta_1027_gao__port_506304,eos_1027_gao__port_506115,accord_1027_gao__port_506214,yaris_1027_gao__port_506334,classe_cls_1027_gao__port_506289,polo_1027_gao__port_506361,serie_4_1027_gao__port_506366,mini_cabriolet_1027_gao__port_506204,prius_1027_gao__port_506190,lodgy_1027_gao__port_506188,serie_7_1027_gao__port_506307,c15_1027_gao__port_506055,kadjar_1027_gao__port_506389,insignia_1027_gao__port_506364,308_1027_gao__port_506279,roomster_1027_gao__port_506241,80_1027_gao__port_506057,309_1027_gao__port_506063,tucson_1027_gao__port_506320,x3_1027_gao__port_506212,xf_1027_gao__port_506263,2008_1027_gao__port_506394,passat_1027_gao__port_506306,compass_1027_gao__port_506260,twingo_1027_gao__port_506309,micra_1027_gao__port_506221,golf_1027_gao__port_506155,soul_1027_gao__port_506176,rapid_1027_gao__port_506398,forester_1027_gao__port_506360,slk_1027_gao__port_506210,forfour_1027_gao__port_506341,serie_5_1027_gao__port_506209,xj_1027_gao__port_506170,pajero_1027_gao__port_506097,agila_1027_gao__port_506119,a6_1027_gao__port_506163,fox_1027_gao__port_506092,boxster_1027_gao__port_506267,altea_1027_gao__port_506246,samurai_1027_gao__port_506047,trax_1027_gao__port_506296,getz_1027_gao__port_506058,cherokee_1027_gao__port_506269,koleos_1027_gao__port_506378,z_series_1027_gao__port_506123,ecosport_1027_gao__port_506271,space_star_1027_gao__port_506277,rs3_sportback_1027_gao__port_506207,civic_1027_gao__port_506141,talisman_1027_gao__port_506390,f_pace_1027_gao__port_506314,classe_c_1027_gao__port_506299,tt_1027_gao__port_506075,pathfinder_1027_gao__port_506183,156_1027_gao__port_506157,cx_5_1027_gao__port_506228,scenic_1027_gao__port_506255,yeti_1027_gao__port_506358,mustang_1027_gao__port_506053,stilo_1027_gao__port_506060,ateca_1027_gao__port_506382,fiorino_1027_gao__port_506217,classe_glk_1027_gao__port_506290,fortwo_1027_gao__port_506230,cruze_1027_gao__port_506186,107_1027_gao__port_506213,aygo_1027_gao__port_506248,rx_1027_gao__port_506354,500_1027_gao__port_506245,bora_1027_gao__port_506104,transit_1027_gao__port_506111,pt_cruiser_1027_gao__port_506054,patrol_1027_gao__port_506068,r8_1027_gao__port_506156,xm_1027_gao__port_506102,s60_1027_gao__port_506191,aveo_1027_gao__port_506158,captiva_1027_gao__port_506159,ax_1027_gao__port_506153,rexton_1027_gao__port_506107,camaro_1027_gao__port_506056,ypsilon_1027_gao__port_506131,delta_1027_gao__port_506165,c4_1027_gao__port_506370,zx_1027_gao__port_506161,verso_1027_gao__port_506242,superb_1027_gao__port_506327,r5_1027_gao__port_506253,caddy_1027_gao__port_506330,x5_1027_gao__port_506243,f_type_1027_gao__port_506231,fusion_1027_gao__port_506096,dokker_1027_gao__port_506331,205_1027_gao__port_506062,macan_1027_gao__port_506195,tourneo_1027_gao__port_506369,108_1027_gao__port_506384,9_3_1027_gao__port_506071,mondeo_1027_gao__port_506116,cr_v_1027_gao__port_506164,c30_1027_gao__port_506090,pulsar_1027_gao__port_506397,ibiza_1027_gao__port_506273,a1_1027_gao__port_506338,matrix_1027_gao__port_506140,carnival_1027_gao__port_506136,xantia_1027_gao__port_506086,terrano_1027_gao__port_506083,q3_1027_gao__port_506275,hr_v_1027_gao__port_506283,expert_1027_gao__port_506142,multivan_1027_gao__port_506383,venga_1027_gao__port_506380,scudo_1027_gao__port_506129,laguna_1027_gao__port_506368,vel_satis_1027_gao__port_506130,b_max_1027_gao__port_506367,ignis_1027_gao__port_506292,159_1027_gao__port_506064,grande_punto_1027_gao__port_506138,logan_1027_gao__port_506167,s_max_1027_gao__port_506223,caravelle_1027_gao__port_506351,adam_1027_gao__port_506079,406_1027_gao__port_506132,q30_1027_gao__port_506293,almera_1027_gao__port_506089,corsa_1027_gao__port_506095,corolla_1027_gao__port_506120,xc60_1027_gao__port_506388,viano_1027_gao__port_506211,pro_cee_d_1027_gao__port_506274,a3_1027_gao__port_506321,v50_1027_gao__port_506150,voyager_1027_gao__port_506169,corvette_1027_gao__port_506049,rio_1027_gao__port_506379,jazz_1027_gao__port_506252,200_1027_gao__port_506112,tts_1027_gao__port_506199,zafira_1027_gao__port_506287,asx_1027_gao__port_506266,607_1027_gao__port_506118,207_1027_gao__port_506103,classe_s_1027_gao__port_506301,c6_1027_gao__port_506105,express_1027_gao__port_506137,classe_gla_1027_gao__port_506352,v60_1027_gao__port_506333,ka_1027_gao__port_506180,range_rover_1027_gao__port_506254,discovery_1027_gao__port_506375,classe_r_1027_gao__port_506270,transporter_1027_gao__port_506319,cee_d_1027_gao__port_506288,zoe_1027_gao__port_506244,i20_1027_gao__port_506284,gtv_1027_gao__port_506059,s4_avant_1027_gao__port_506261,x1_1027_gao__port_506372,autres_1027_gao__port_506127,208_1027_gao__port_506359,c8_1027_gao__po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'svm_portfolios_learning': 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'svm_portfolios_learning': '506302,506374,506399,506192,506205,506350,506052,506295,506066,506117,506065,506125,506387,506381,506349,506328,506377,506286,506124,506172,506206,506178,506371,506076,506114,506329,506122,506220,506174,506224,506232,506234,506173,506181,506323,506326,506376,506048,506400,506179,506311,506325,506402,506051,506294,506318,506303,506175,506099,506061,506337,506250,506082,506166,506133,506308,506078,506340,506310,506100,506121,506070,506218,506227,506272,506147,506160,506265,506202,506222,506093,506257,506208,506344,506077,506395,506094,506219,506298,506339,506343,506365,506200,506348,506198,506385,506239,506236,506391,506087,506342,506149,506184,506393,506203,506280,506216,506403,506355,506332,506259,506401,506357,506324,506098,506315,506335,506088,506046,506185,506171,506080,506345,506347,506067,506233,506225,506312,506278,506300,506258,506182,506226,506262,506146,506113,506108,506297,506322,506143,506363,506073,506154,506313,506189,506197,506162,506249,506139,506237,506336,506084,506109,506106,506045,506392,506247,506316,506201,506353,506305,506050,506145,506362,506101,506128,506044,506317,506074,506134,506196,506194,506285,506177,506240,506282,506396,506281,506264,506276,506144,506069,506091,506081,506168,506291,506238,506072,506085,506235,506193,506268,506148,506356,506386,506229,506256,506187,506110,506304,506115,506214,506334,506289,506361,506366,506204,506190,506188,506307,506055,506389,506364,506279,506241,506057,506063,506320,506212,506263,506394,506306,506260,506309,506221,506155,506176,506398,506360,506210,506341,506209,506170,506097,506119,506163,506092,506267,506246,506047,506296,506058,506269,506378,506123,506271,506277,506207,506141,506390,506314,506299,506075,506183,506157,506228,506255,506358,506053,506060,506382,506217,506290,506230,506186,506213,506248,506354,506245,506104,506111,506054,506068,506156,506102,506191,506158,506159,506153,506107,506056,506131,506165,506370,506161,506242,506327,506253,506330,506243,506231,506096,506331,506062,506195,506369,506384,506071,506116,506164,506090,506397,506273,506338,506140,506136,506086,506083,506275,506283,506142,506383,506380,506129,506368,506130,506367,506292,506064,506138,506167,506223,506351,506079,506132,506293,506089,506095,506120,506388,506211,506274,506321,506150,506169,506049,506379,506252,506112,506199,506287,506266,506118,506103,506301,506105,506137,506352,506333,506180,506254,506375,506270,506319,506288,506244,506284,506059,506261,506372,506127,506359,506135,506215,506151,506251,506152,506126,506373,506346', 'photo_hashtag_type': 332, 'photo_desc_type': 3390, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}
 Update svm_hashtag_type_desc : 3390
 SELECT * FROM MTRDatou.photo_desc_type_params  WHERE id in (3390)
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27))
To loadFromThcl() : net_3390
begin to check gpu status 
inside check gpu memory
l 3637 free memory gpu now : 6482  max_wait_temp :  1 max_wait :  0
 SELECT * FROM MTRDatou.photo_desc_type_params  WHERE id in (3390)
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27))
 param : <mtr.database_queries.descriptor_queries.ParamDescriptorType object at 0x7f186c069310>, param.caffemodel : car_360_1027
None
 mean_file_type : 
 mean_file_path : 
 prototxt_file_path : 
 model : car_360_1027
 Inside get_net  
 Inside get_net before cache_data_model 
model_param file didn't exist 
 Inside get_net before  CDM.load_model_par_type  
model_name : car_360_1027
model_type : caffe
list file need :
['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt']
file exist in s3 :
['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt']
file manque in s3 : 
[]
local folder  : /data/models_weight/car_360_1027


/data/models_weight/car_360_1027/caffemodel
size_local : 542944640 size in s3 : 542944640
create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43
caffemodel already exist and didn't need to update


/data/models_weight/car_360_1027/deploy_conv_normal.prototxt
size_local : 4626 size in s3 : 4626
create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42
deploy_conv_normal.prototxt already exist and didn't need to update


/data/models_weight/car_360_1027/deploy_fc.prototxt
size_local : 1132 size in s3 : 1132
create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43
deploy_fc.prototxt already exist and didn't need to update


/data/models_weight/car_360_1027/deploy.prototxt
size_local : 5654 size in s3 : 5654
create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42
deploy.prototxt already exist and didn't need to update


/data/models_weight/car_360_1027/mean.npy
size_local : 1572944 size in s3 : 1572944
create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:55
mean.npy already exist and didn't need to update


/data/models_weight/car_360_1027/synset_words.txt
size_local : 13687 size in s3 : 13687
create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43
synset_words.txt already exist and didn't need to update
 Inside get_net after CDM.load_model_par_type  
  After if not only_with_local_cache:
/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/:/home/admin/workarea/git/apy/
Here before set mode gpu
Doing nothing but we could set mode gpu
after set mode gpu
 prototxt_filename : /data/models_weight/car_360_1027/deploy.prototxt
 caffemodel_filename : /data/models_weight/car_360_1027/caffemodel
now we set caffe to gpu mode


 before predict
begin to check gpu status 
inside check gpu memory
l 3637 free memory gpu now : 6482  max_wait_temp :  1 max_wait :  0
dict_keys(['pool5', 'prob'])
time used to do the prepocess of the images : 0.022010087966918945
time used to do the prediction : 0.0592803955078125


save descriptor for thcl : 355
(1, 512, 7, 7)
Got the blobs of the net
 to insert :  
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
code_as_byte_string:b'0000000000'|
time to traite the descriptors :  0.048200368881225586
Testing : ['916235064']
 In select_photos_meta_from_ids: 
        SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text
        FROM MTRBack.photos_view
        WHERE photo_id IN (916235064) 
result : {916235064: {'photo_id': 916235064, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2017/10/14/6293d1bb790dc6902450e7c572b7d10b.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': None}}
list_photo_exists : [916235064]
storage_type for insertDescriptorsMulti : 1
To insert : 916235064
Catched exception !
Connect or reconnect !
time to insert the descriptors : 2.3138933181762695






After datou_step_exec type output : <class 'dict'>
map_portfolio_photo : len 0 keys : dict_keys([])
Inside saveOutput : final : False verbose : True
time used to find the portfolios of the photos
select button_legend_list from MTRDatou.classification_theme where id = 355

 SAVE THCL, output : {'916235064': [[('916235064', 'c_elysee_1027_gao__port_506302', 0.0018814747, 332, '355'), ('916235064', 'mokka_1027_gao__port_506374', 0.0011635066, 332, '355'), ('916235064', 'captur_1027_gao__port_506399', 0.00081571686, 332, '355'), ('916235064', 'sorento_1027_gao__port_506192', 0.0011772149, 332, '355'), ('916235064', 'navara_1027_gao__port_506205', 0.0025849906, 332, '355'), ('916235064', 'xc90_1027_gao__port_506350', 0.0041700685, 332, '355'), ('916235064', 'saxo_1027_gao__port_506052', 0.0034809527, 332, '355'), ('916235064', 'trafic_1027_gao__port_506295', 0.0073665273, 332, '355'), ('916235064', 'punto_evo_1027_gao__port_506066', 0.0021886877, 332, '355'), ('916235064', '5_1027_gao__port_506117', 0.00057974854, 332, '355'), ('916235064', '250_1027_gao__port_506065', 0.004591175, 332, '355'), ('916235064', 'd_max_1027_gao__port_506125', 0.0031586671, 332, '355'), ('916235064', 'panamera_1027_gao__port_506387', 0.002250797, 332, '355'), ('916235064', 'alhambra_1027_gao__port_506381', 0.005319882, 332, '355'), ('916235064', 'x6_1027_gao__port_506349', 0.0010998962, 332, '355'), ('916235064', 'vitara_1027_gao__port_506328', 0.0054023983, 332, '355'), ('916235064', 'fiesta_1027_gao__port_506377', 0.00391893, 332, '355'), ('916235064', 'qashqai_1027_gao__port_506286', 0.0014787031, 332, '355'), ('916235064', '147_1027_gao__port_506124', 0.0019778002, 332, '355'), ('916235064', 'c5_1027_gao__port_506172', 0.0012442046, 332, '355'), ('916235064', 'q5_1027_gao__port_506206', 0.0015049708, 332, '355'), ('916235064', 'giulia_1027_gao__port_506178', 0.0021693304, 332, '355'), ('916235064', 'karl_1027_gao__port_506371', 0.0027079245, 332, '355'), ('916235064', 'mehari_1027_gao__port_506076', 0.0047044754, 332, '355'), ('916235064', '911_1027_gao__port_506114', 0.0019420342, 332, '355'), ('916235064', '508_1027_gao__port_506329', 0.0009584829, 332, '355'), ('916235064', 'idea_1027_gao__port_506122', 0.0007699286, 332, '355'), ('916235064', 'megane_1027_gao__port_506220', 0.0019467721, 332, '355'), ('916235064', 'ghibli_1027_gao__port_506174', 0.0013724393, 332, '355'), ('916235064', 'touareg_1027_gao__port_506224', 0.0016201922, 332, '355'), ('916235064', 'i10_1027_gao__port_506232', 0.0013924431, 332, '355'), ('916235064', 'jumper_1027_gao__port_506234', 0.010044087, 332, '355'), ('916235064', 'classe_clk_1027_gao__port_506173', 0.0010793478, 332, '355'), ('916235064', 'kuga_1027_gao__port_506181', 0.0008446879, 332, '355'), ('916235064', 'ct_1027_gao__port_506323', 0.001252033, 332, '355'), ('916235064', 'leon_1027_gao__port_506326', 0.0025843766, 332, '355'), ('916235064', 'ds5_1027_gao__port_506376', 0.0012429748, 332, '355'), ('916235064', 'cordoba_1027_gao__port_506048', 0.0028650318, 332, '355'), ('916235064', 'classe_cla_1027_gao__port_506400', 0.0012948901, 332, '355'), ('916235064', 'jumpy_1027_gao__port_506179', 0.010338424, 332, '355'), ('916235064', 'avensis_1027_gao__port_506311', 0.0018766467, 332, '355'), ('916235064', 'juke_1027_gao__port_506325', 0.0011343288, 332, '355'), ('916235064', '4008_1027_gao__port_506402', 0.0015757158, 332, '355'), ('916235064', '190_series_1027_gao__port_506051', 0.003980868, 332, '355'), ('916235064', 'serie_3_1027_gao__port_506294', 0.0028740875, 332, '355'), ('916235064', 'q7_1027_gao__port_506318', 0.0023353891, 332, '355'), ('916235064', 'glc_1027_gao__port_506303', 0.0012106004, 332, '355'), ('916235064', 'grand_vitara_1027_gao__port_506175', 0.0011447046, 332, '355'), ('916235064', 's40_1027_gao__port_506099', 0.002233793, 332, '355'), ('916235064', 'toledo_1027_gao__port_506061', 0.0017464521, 332, '355'), ('916235064', '5008_1027_gao__port_506337', 0.0046988963, 332, '355'), ('916235064', 'continental_1027_gao__port_506250', 0.0021912765, 332, '355'), ('916235064', 'coupe_1027_gao__port_506082', 0.0022633786, 332, '355'), ('916235064', 'iq_1027_gao__port_506166', 0.0018174057, 332, '355'), ('916235064', '407_1027_gao__port_506133', 0.00090562797, 332, '355'), ('916235064', 'touran_1027_gao__port_506308', 0.0020400204, 332, '355'), ('916235064', '300c_1027_gao__port_506078', 0.002533665, 332, '355'), ('916235064', 'classe_gl_1027_gao__port_506340', 0.004488759, 332, '355'), ('916235064', 'vivaro_1027_gao__port_506310', 0.0034251467, 332, '355'), ('916235064', 'sl_1027_gao__port_506100', 0.0031356607, 332, '355'), ('916235064', 'elise_1027_gao__port_506121', 0.0010256807, 332, '355'), ('916235064', '1007_1027_gao__port_506070', 0.0015353187, 332, '355'), ('916235064', 'i40_1027_gao__port_506218', 0.00059149356, 332, '355'), ('916235064', 'bipper_tepee_1027_gao__port_506227', 0.0040288055, 332, '355'), ('916235064', 'focus_1027_gao__port_506272', 0.0011585428, 332, '355'), ('916235064', 'primera_1027_gao__port_506147', 0.0012158579, 332, '355'), ('916235064', 'r4_1027_gao__port_506160', 0.014968481, 332, '355'), ('916235064', 'a8_1027_gao__port_506265', 0.001132119, 332, '355'), ('916235064', 'boxer_1027_gao__port_506202', 0.0105452975, 332, '355'), ('916235064', 's5_1027_gao__port_506222', 0.0011985902, 332, '355'), ('916235064', 'r21_1027_gao__port_506093', 0.0041861897, 332, '355'), ('916235064', 'c3_1027_gao__port_506257', 0.0023634408, 332, '355'), ('916235064', 'santa_fe_1027_gao__port_506208', 0.0016322527, 332, '355'), ('916235064', 'm4_1027_gao__port_506344', 0.0015568187, 332, '355'), ('916235064', 'safrane_1027_gao__port_506077', 0.0013959856, 332, '355'), ('916235064', 'classe_gle_1027_gao__port_506395', 0.002197858, 332, '355'), ('916235064', '0_1027_gao__port_506094', 0.0088281855, 332, '355'), ('916235064', 'ix35_1027_gao__port_506219', 0.0014614457, 332, '355'), ('916235064', 'carens_1027_gao__port_506298', 0.00088242814, 332, '355'), ('916235064', 'classe_a_1027_gao__port_506339', 0.0024711282, 332, '355'), ('916235064', 'ix20_1027_gao__port_506343', 0.0010092029, 332, '355'), ('916235064', 'note_1027_gao__port_506365', 0.0015960949, 332, '355'), ('916235064', 'a5_1027_gao__port_506200', 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'q30_1027_gao__port_506293', 0.0009715164, 332, '355'), ('916235064', 'almera_1027_gao__port_506089', 0.0010239474, 332, '355'), ('916235064', 'corsa_1027_gao__port_506095', 0.002520234, 332, '355'), ('916235064', 'corolla_1027_gao__port_506120', 0.0026818968, 332, '355'), ('916235064', 'xc60_1027_gao__port_506388', 0.0018984076, 332, '355'), ('916235064', 'viano_1027_gao__port_506211', 0.0026942403, 332, '355'), ('916235064', 'pro_cee_d_1027_gao__port_506274', 0.0008320037, 332, '355'), ('916235064', 'a3_1027_gao__port_506321', 0.0037379197, 332, '355'), ('916235064', 'v50_1027_gao__port_506150', 0.000791931, 332, '355'), ('916235064', 'voyager_1027_gao__port_506169', 0.0030522812, 332, '355'), ('916235064', 'corvette_1027_gao__port_506049', 0.003723655, 332, '355'), ('916235064', 'rio_1027_gao__port_506379', 0.001773906, 332, '355'), ('916235064', 'jazz_1027_gao__port_506252', 0.0015303687, 332, '355'), ('916235064', '200_1027_gao__port_506112', 0.004087317, 332, '355'), ('916235064', 'tts_1027_gao__port_506199', 0.0011863008, 332, '355'), ('916235064', 'zafira_1027_gao__port_506287', 0.002695063, 332, '355'), ('916235064', 'asx_1027_gao__port_506266', 0.0011406713, 332, '355'), ('916235064', '607_1027_gao__port_506118', 0.0012528357, 332, '355'), ('916235064', '207_1027_gao__port_506103', 0.0015147986, 332, '355'), ('916235064', 'classe_s_1027_gao__port_506301', 0.0031655733, 332, '355'), ('916235064', 'c6_1027_gao__port_506105', 0.0017349314, 332, '355'), ('916235064', 'express_1027_gao__port_506137', 0.0167291, 332, '355'), ('916235064', 'classe_gla_1027_gao__port_506352', 0.0018254474, 332, '355'), ('916235064', 'v60_1027_gao__port_506333', 0.002145741, 332, '355'), ('916235064', 'ka_1027_gao__port_506180', 0.0014152406, 332, '355'), ('916235064', 'range_rover_1027_gao__port_506254', 0.002055302, 332, '355'), ('916235064', 'discovery_1027_gao__port_506375', 0.002296343, 332, '355'), ('916235064', 'classe_r_1027_gao__port_506270', 0.0013944008, 332, '355'), ('916235064', 'transporter_1027_gao__port_506319', 0.011968405, 332, '355'), ('916235064', 'cee_d_1027_gao__port_506288', 0.0010547932, 332, '355'), ('916235064', 'zoe_1027_gao__port_506244', 0.0020713692, 332, '355'), ('916235064', 'i20_1027_gao__port_506284', 0.0017869304, 332, '355'), ('916235064', 'gtv_1027_gao__port_506059', 0.0057226177, 332, '355'), ('916235064', 's4_avant_1027_gao__port_506261', 0.0027667396, 332, '355'), ('916235064', 'x1_1027_gao__port_506372', 0.0017144163, 332, '355'), ('916235064', 'autres_1027_gao__port_506127', 0.004825542, 332, '355'), ('916235064', '208_1027_gao__port_506359', 0.001868488, 332, '355'), ('916235064', 'c8_1027_gao__port_506135', 0.0012579024, 332, '355'), ('916235064', 'astra_1027_gao__port_506215', 0.0012625172, 332, '355'), ('916235064', '2_1027_gao__port_506151', 0.00092442916, 332, '355'), ('916235064', 'doblo_1027_gao__port_506251', 0.007465994, 332, '355'), ('916235064', '807_1027_gao__port_506152', 0.00072900864, 332, '355'), ('916235064', '206_1027_gao__port_506126', 0.0010385982, 332, '355'), ('916235064', 'a7_1027_gao__port_506373', 0.0006911588, 332, '355'), ('916235064', 'renegade_1027_gao__port_506346', 0.0021416005, 332, '355')]]}


begin to insert list_values into class_photo_scores : 
length of list_valuse in save_photo_hashtag_id_thcl_score : 0
insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score)
time used for this insertion : 5.0067901611328125e-06

save missing photos in datou_result :

time spend for datou_step_exec : 7.5643157958984375
time spend to save output : 6.222740650177002
total time spend for step 1 : 13.78705644607544

step2:argmax
Mon Sep 29 11:42:33 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759138939_2886529_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064}
map_photo_id_path_extension : {916235064: {'path': 'temp/1759138939_2886529_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou_step Argmax !
calculate argmax for thcl : 355

After datou_step_exec type output : <class 'dict'>
map_portfolio_photo : len 0 keys : dict_keys([])
Inside saveOutput : final : True verbose : True
 photo_id : 916235064 output[photo_id] :  [('916235064', 'c15_1027_gao__port_506055', 0.01771489, 332, '355'), 'temp/1759138939_2886529_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg']


begin to insert list_values into photo_hahstag_ids : 
length of list_valuse in save_photo_hashtag_id_type : 1
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s)
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s)
 first line : ('916235064', '2049863950', '332')
...
 last line : ('916235064', '2049863950', '332')
time used for this insertion : 0.03853249549865723


begin to insert list_values into class_photo_scores : 
length of list_valuse in save_photo_hashtag_id_thcl_score : 1
insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score)
time used for this insertion : 0.049599409103393555
len list_finale : 1, len picture : 1


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 1
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('2', None, '916235064', 'c15_1027_gao__port_506055', None, None, '2049863950', '0.01771489', None)]
time used for this insertion : 0.039409637451171875
saving photo_ids in datou_result
photo id not in port


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 0
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : []
time used for this insertion : 3.5762786865234375e-06

save missing photos in datou_result :

time spend for datou_step_exec : 0.00020694732666015625
time spend to save output : 0.12776446342468262
total time spend for step 2 : 0.12797141075134277

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 2
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 2
 output : {'916235064': [('916235064', 'c15_1027_gao__port_506055', 0.01771489, 332, '355'), 'temp/1759138939_2886529_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg']}

############################### TEST tfhub2  ################################
TEST TFHUB2
######################## test with use_multi_inputs=0 ########################
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4567

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=4567
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 4567
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=4567
# 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)
 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 : step 12835 tfhub_classification2 is not linked in the step_by_step architecture !
WARNING : step 12836 argmax is not linked in the step_by_step architecture !
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 !
DataTypes for each output/input checked !
no param json to modify
List Step Type Loaded in datou : tfhub_classification2, argmax
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (1171252784,1171252764,1171252487) 
 Found this number of photos: 3
##### Call download_photos : nb_thread : 5
begin to download photo : 1171252487
begin to download photo : 1171252764
begin to download photo : 1171252784
download finish for photo 1171252784
download finish for photo 1171252764
download finish for photo 1171252487
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3
##### After load_data_input 
time to download the photos : 0.21205592155456543
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  2
step1:tfhub_classification2
Mon Sep 29 11:42:33 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759138953_2886529_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg': 1171252784, 'temp/1759138953_2886529_1171252764_29d5179a892cc50aadc9d67245534b59.jpg': 1171252764, 'temp/1759138953_2886529_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg': 1171252487}
map_photo_id_path_extension : {1171252784: {'path': 'temp/1759138953_2886529_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'extension': 'jpg'}, 1171252764: {'path': 'temp/1759138953_2886529_1171252764_29d5179a892cc50aadc9d67245534b59.jpg', 'extension': 'jpg'}, 1171252487: {'path': 'temp/1759138953_2886529_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou_step TFHub with tf2 !
multi_thcl or not :False
multi_thcl_cond or not :False
dic_thcl :  {'3609': 1}
we are using the classfication for only one thcl
3609


begin to check gpu status 
inside check gpu memory
2025-09-29 11:42:36.872714: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2025-09-29 11:42:36.873375: 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
2025-09-29 11:42:36.873454: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-09-29 11:42:36.873500: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-09-29 11:42:36.890234: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-09-29 11:42:36.890331: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-09-29 11:42:36.919267: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-09-29 11:42:36.923654: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-09-29 11:42:36.974010: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-09-29 11:42:36.975425: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-09-29 11:42:36.976099: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2025-09-29 11:42:37.004583: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3492910000 Hz
2025-09-29 11:42:37.006598: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f1604000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2025-09-29 11:42:37.006645: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2025-09-29 11:42:37.010044: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x9ed018a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2025-09-29 11:42:37.010067: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5
2025-09-29 11:42:37.010978: 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
2025-09-29 11:42:37.011081: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-09-29 11:42:37.011104: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-09-29 11:42:37.011180: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-09-29 11:42:37.011209: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-09-29 11:42:37.011244: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-09-29 11:42:37.011283: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-09-29 11:42:37.011321: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-09-29 11:42:37.012577: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-09-29 11:42:37.012634: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-09-29 11:42:37.012681: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-09-29 11:42:37.012693: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-09-29 11:42:37.012702: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-09-29 11:42:37.013991: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3096 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5)
l 3637 free memory gpu now : 6482  max_wait_temp :  1 max_wait :  5
1 Physical GPUs, 1 Logical GPUs

tagging for thcl : 3609
To do loadFromThcl(), then load ParamDescType : thcl3609
 get_desc_type_from_thcl : type of cat <class 'mtr.database_queries.classification_admin_tools.ClassificationMetaTools'>

            SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists,
            photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at,
            type_classification
            FROM MTRDatou.classification_theme  WHERE  `id` IN (3609) 
 thcls : [{'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'}]
 thcl {'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'}
 Update svm_hashtag_type_desc : 5832
 SELECT * FROM MTRDatou.photo_desc_type_params  WHERE id in (5832)
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5832, 'tfhub_19_06_2023', 1280, 1280, 'tfhub_19_06_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 6, 19, 12, 55, 22), datetime.datetime(2023, 6, 19, 12, 55, 22))
model_name : tfhub_19_06_2023
model_param file didn't exist 
model_name : tfhub_19_06_2023
model_type : tf_classification2
list file need :
['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5']
file exist in s3 :
['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5']
file manque in s3 : 
[]
/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/../../tools/../lib/rpn/proposal_layer.py:28: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
  layer_params = yaml.load(self.param_str_)
local folder  : /data/models_weight/tfhub_19_06_2023


/data/models_weight/tfhub_19_06_2023/Confusion_Matrix.png
size_local : 57753 size in s3 : 57753
create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15
Confusion_Matrix.png already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/Precision_Recall_jrm.jpg
size_local : 79724 size in s3 : 79724
create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20
Precision_Recall_jrm.jpg already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/Precision_Recall_pcm.jpg
size_local : 83556 size in s3 : 83556
create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15
Precision_Recall_pcm.jpg already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/Precision_Recall_pcnc.jpg
size_local : 74107 size in s3 : 74107
create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20
Precision_Recall_pcnc.jpg already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/Precision_Recall_pehd.jpg
size_local : 72705 size in s3 : 72705
create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:20
Precision_Recall_pehd.jpg already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/Precision_Recall_tapis_vide.jpg
size_local : 70874 size in s3 : 70874
create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:15
Precision_Recall_tapis_vide.jpg already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/Result_Summary.txt
size_local : 642 size in s3 : 642
create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22
Result_Summary.txt already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/checkpoint
size_local : 99 size in s3 : 99
create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22
checkpoint already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00000-of-00002
size_local : 216488 size in s3 : 216488
create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22
model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00001-of-00002
size_local : 32279708 size in s3 : 32279708
create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:21
model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.index
size_local : 43546 size in s3 : 43546
create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:22
model_checkpoint.ckpt.index already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/model_weights.h5
size_local : 16499144 size in s3 : 16499144
create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:15
model_weights.h5 already exist and didn't need to update
desc size : 1280
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
module (KerasLayer)          (None, 1280)              4049564   
_________________________________________________________________
tfhub_19_06_2023dense (Dense (None, 5)                 6405      
=================================================================
Total params: 4,055,969
Trainable params: 6,405
Non-trainable params: 4,049,564
_________________________________________________________________
Loading Weights...
time used to create the model : 8.272433757781982
time used to load_weights : 0.1398603916168213

0it [00:00, ?it/s]
3it [00:00, 1064.54it/s]2025-09-29 11:42:47.658882: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
temp/1759138953_2886529_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg
temp/1759138953_2886529_1171252764_29d5179a892cc50aadc9d67245534b59.jpg
temp/1759138953_2886529_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg
Found 3 images belonging to 1 classes.

 begin to do the prediction :
time used to do the prediction : 3.2582826614379883
['temp/image000000000_1759138953_2886529_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'temp/image000000001_1759138953_2886529_1171252764_29d5179a892cc50aadc9d67245534b59.jpg', 'temp/image000000002_1759138953_2886529_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg']
(3,)
(3, 5)
(3, 1280)
shape of features :  (3, 1280)
shape of new features :  (1, 3, 1280)


save descriptor for thcl : 3609
(3, 1280)
Got the blobs of the net
 to insert :  
[0, 6, 0, 0, 1, 0, 0, 1, 0, 0]
code_as_byte_string:b'0006000001'|
Got the blobs of the net
 to insert :  
[0, 6, 0, 1, 0, 0, 0, 1, 0, 0]
code_as_byte_string:b'0006000100'|
Got the blobs of the net
 to insert :  
[0, 9, 0, 0, 0, 0, 1, 0, 0, 0]
code_as_byte_string:b'0009000000'|
time to traite the descriptors :  0.02353501319885254
Testing : ['1171252784', '1171252764', '1171252487']
 In select_photos_meta_from_ids: 
        SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text
        FROM MTRBack.photos_view
        WHERE photo_id IN (1171252784,1171252764,1171252487) 
result : {1171252487: {'photo_id': 1171252487, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/22/5ebdd6b0a6bb39942a3808ed114806de.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_21_55_35_005998m0.jpg 0.4259977941513062 for time 6.000020980834961, id_amount 3 this amount prod time diff : 0.006000020980834961'}, 1171252764: {'photo_id': 1171252764, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/22/29d5179a892cc50aadc9d67245534b59.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_21_55_41_005998m0.jpg 0.4319977941513062 for time 6.0, id_amount 3 this amount prod time diff : 0.006'}, 1171252784: {'photo_id': 1171252784, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/22/5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_21_55_47_006033m0.jpg 0.4379978291988373 for time 6.000035047531128, id_amount 4 this amount prod time diff : 0.006000035047531128'}}
list_photo_exists : [1171252487, 1171252764, 1171252784]
storage_type for insertDescriptorsMulti : 3
To insert : 1171252784
To insert : 1171252764
To insert : 1171252487
time to insert the descriptors : 1.1096954345703125






After datou_step_exec type output : <class 'dict'>
map_portfolio_photo : len 0 keys : dict_keys([])
Inside saveOutput : final : False verbose : True
 saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral 
[1171252784, 1171252764, 1171252487]
 map_info['map_portfolio_photo'] : {}
 final :  False mtd_id 4567 list_pids : [1171252784, 1171252764, 1171252487]
Looping around the photos to save general results
len do output : 3
/1171252784Didn't retrieve data
.
/1171252764Didn't retrieve data
.
/1171252487Didn't retrieve data
.
before output type
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f190c112ac0>
Managing all output in save final without adding information in the mtr_datou_result 
('4567', None, None, None, None, None, None, None, None)
('4567', None, '1171252784', None, None, None, None, None, None)
('4567', None, None, None, None, None, None, None, None)
('4567', None, '1171252764', None, None, None, None, None, None)
('4567', None, None, None, None, None, None, None, None)
('4567', None, '1171252487', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 6
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('4567', None, '1171252784', 'None', None, None, None, None, None), ('4567', None, '1171252764', 'None', None, None, None, None, None), ('4567', None, '1171252487', 'None', None, None, None, None, None)]
time used for this insertion : 0.0396730899810791
 save_final 

save missing photos in datou_result :

time spend for datou_step_exec : 18.14815616607666
time spend to save output : 0.040033578872680664
total time spend for step 1 : 18.18818974494934

step2:argmax
Mon Sep 29 11:42:52 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
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759138953_2886529_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg': 1171252784, 'temp/1759138953_2886529_1171252764_29d5179a892cc50aadc9d67245534b59.jpg': 1171252764, 'temp/1759138953_2886529_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg': 1171252487}
map_photo_id_path_extension : {1171252784: {'path': 'temp/1759138953_2886529_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'extension': 'jpg'}, 1171252764: {'path': 'temp/1759138953_2886529_1171252764_29d5179a892cc50aadc9d67245534b59.jpg', 'extension': 'jpg'}, 1171252487: {'path': 'temp/1759138953_2886529_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou_step Argmax !
calculate argmax for thcl : 3609

After datou_step_exec type output : <class 'dict'>
map_portfolio_photo : len 0 keys : dict_keys([])
Inside saveOutput : final : True verbose : True
 photo_id : 1171252784 output[photo_id] :  [(1171252784, 'jrm', 0.9677485, 4674, '3609'), 'temp/1759138953_2886529_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg']
 photo_id : 1171252764 output[photo_id] :  [(1171252764, 'jrm', 0.98537517, 4674, '3609'), 'temp/1759138953_2886529_1171252764_29d5179a892cc50aadc9d67245534b59.jpg']
 photo_id : 1171252487 output[photo_id] :  [(1171252487, 'jrm', 0.9262805, 4674, '3609'), 'temp/1759138953_2886529_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg']


begin to insert list_values into photo_hahstag_ids : 
length of list_valuse in save_photo_hashtag_id_type : 3
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s)
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s)
 first line : ('1171252784', '495916461', '4674')
...
 last line : ('1171252487', '495916461', '4674')
time used for this insertion : 0.036290645599365234


begin to insert list_values into class_photo_scores : 
length of list_valuse in save_photo_hashtag_id_thcl_score : 3
insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score)
time used for this insertion : 0.04029726982116699
len list_finale : 3, len picture : 3


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 3
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('4567', None, '1171252784', 'jrm', None, None, '495916461', '0.9677485', None), ('4567', None, '1171252764', 'jrm', None, None, '495916461', '0.98537517', None), ('4567', None, '1171252487', 'jrm', None, None, '495916461', '0.9262805', None)]
time used for this insertion : 0.03978705406188965
saving photo_ids in datou_result
photo id not in port
photo id not in port
photo id not in port


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 0
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : []
time used for this insertion : 4.291534423828125e-06

save missing photos in datou_result :

time spend for datou_step_exec : 0.00019288063049316406
time spend to save output : 0.13479971885681152
total time spend for step 2 : 0.1349925994873047

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 2
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 2
 output : {'1171252784': [(1171252784, 'jrm', 0.9677485, 4674, '3609'), 'temp/1759138953_2886529_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'], '1171252764': [(1171252764, 'jrm', 0.98537517, 4674, '3609'), 'temp/1759138953_2886529_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'], '1171252487': [(1171252487, 'jrm', 0.9262805, 4674, '3609'), 'temp/1759138953_2886529_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg']}
--------------------- test with use_multi_inputs=0 is succeded -------------------
######################## test with use_multi_inputs=1 ########################
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4621

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=4621
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 4621
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=4621
# 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)
 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 : step 12927 tfhub_classification2 is not linked in the step_by_step architecture !
WARNING : step 12928 argmax is not linked in the step_by_step architecture !
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 !
DataTypes for each output/input checked !
no param json to modify
List Step Type Loaded in datou : tfhub_classification2, argmax
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (1171291875,1171275372,1171275314) 
 Found this number of photos: 3
##### Call download_photos : nb_thread : 5
begin to download photo : 1171275314
begin to download photo : 1171275372
begin to download photo : 1171291875
download finish for photo 1171275314
download finish for photo 1171291875
download finish for photo 1171275372
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3
##### After load_data_input 
time to download the photos : 0.21578073501586914
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  2
step1:tfhub_classification2
Mon Sep 29 11:42:52 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759138972_2886529_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg': 1171275314, 'temp/1759138972_2886529_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg': 1171291875, 'temp/1759138972_2886529_1171275372_76d81364ff7df843bff095f45c07ba35.jpg': 1171275372}
map_photo_id_path_extension : {1171275314: {'path': 'temp/1759138972_2886529_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'extension': 'jpg'}, 1171291875: {'path': 'temp/1759138972_2886529_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'extension': 'jpg'}, 1171275372: {'path': 'temp/1759138972_2886529_1171275372_76d81364ff7df843bff095f45c07ba35.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou_step TFHub with tf2 !
multi_thcl or not :False
multi_thcl_cond or not :False
dic_thcl :  {'3655': 1}
we are using the classfication for only one thcl
3655


begin to check gpu status 
inside check gpu memory
inside check gpu memory
inside check gpu memory
inside check gpu memory
inside check gpu memory
inside check gpu memory
l 3637 free memory gpu now : 2930  max_wait_temp :  6 max_wait :  5
1 Physical GPUs, 1 Logical GPUs

tagging for thcl : 3655
To do loadFromThcl(), then load ParamDescType : thcl3655
 get_desc_type_from_thcl : type of cat <class 'mtr.database_queries.classification_admin_tools.ClassificationMetaTools'>

            SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists,
            photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at,
            type_classification
            FROM MTRDatou.classification_theme  WHERE  `id` IN (3655) 
 thcls : [{'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'}]
 thcl {'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'}
 Update svm_hashtag_type_desc : 5862
 SELECT * FROM MTRDatou.photo_desc_type_params  WHERE id in (5862)
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5862, 'tfhub_18_7_2023', 1280, 1280, 'tfhub_18_7_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 7, 18, 22, 46, 29), datetime.datetime(2023, 7, 18, 22, 46, 29))
model_name : tfhub_18_7_2023
model_param file didn't exist 
model_name : tfhub_18_7_2023
model_type : tf_classification2
list file need :
['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5']
file exist in s3 :
['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5']
file manque in s3 : 
[]
local folder  : /data/models_weight/tfhub_18_7_2023


/data/models_weight/tfhub_18_7_2023/Confusion_Matrix.png
size_local : 54360 size in s3 : 54360
create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:28
Confusion_Matrix.png already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/Precision_Recall_jrm.jpg
size_local : 72583 size in s3 : 72583
create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23
Precision_Recall_jrm.jpg already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/Precision_Recall_pcm.jpg
size_local : 81681 size in s3 : 81681
create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:17
Precision_Recall_pcm.jpg already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/Precision_Recall_pcnc.jpg
size_local : 79510 size in s3 : 79510
create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23
Precision_Recall_pcnc.jpg already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/Precision_Recall_pehd.jpg
size_local : 59936 size in s3 : 59936
create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23
Precision_Recall_pehd.jpg already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/Precision_Recall_tapis_vide.jpg
size_local : 78974 size in s3 : 78974
create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17
Precision_Recall_tapis_vide.jpg already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/Result_Summary.txt
size_local : 642 size in s3 : 642
create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23
Result_Summary.txt already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/checkpoint
size_local : 99 size in s3 : 99
create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23
checkpoint already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00000-of-00002
size_local : 216529 size in s3 : 216529
create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17
model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00001-of-00002
size_local : 32279748 size in s3 : 32279748
create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19
model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.index
size_local : 43546 size in s3 : 43546
create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19
model_checkpoint.ckpt.index already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/model_weights.h5
size_local : 16500868 size in s3 : 16500868
create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:18
model_weights.h5 already exist and didn't need to update
desc size : 1280
Model: "model"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            
__________________________________________________________________________________________________
input_2 (InputLayer)            [(None, 1)]          0                                            
__________________________________________________________________________________________________
module (KerasLayer)             (None, 1280)         4049564     input_1[0][0]                    
__________________________________________________________________________________________________
concatenate (Concatenate)       (None, 1281)         0           input_2[0][0]                    
                                                                 module[0][0]                     
__________________________________________________________________________________________________
tfhub_18_7_2023dense (Dense)    (None, 5)            6410        concatenate[0][0]                
==================================================================================================
Total params: 4,055,974
Trainable params: 0
Non-trainable params: 4,055,974
__________________________________________________________________________________________________
Loading Weights...
time used to create the model : 6.852008581161499
time used to load_weights : 0.12656259536743164
found 3 data
found 0 labels

 begin to do the prediction :
time used to do the prediction : 0.9876260757446289
['temp/1759138972_2886529_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'temp/1759138972_2886529_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'temp/1759138972_2886529_1171275372_76d81364ff7df843bff095f45c07ba35.jpg']
(3,)
(3, 5)
(3, 1280)
shape of features :  (3, 1280)
shape of new features :  (1, 3, 1280)


save descriptor for thcl : 3655
(3, 1280)
Got the blobs of the net
 to insert :  
[0, 0, 0, 0, 8, 0, 0, 0, 3, 0]
code_as_byte_string:b'0000000008'|
Got the blobs of the net
 to insert :  
[0, 1, 0, 0, 11, 0, 2, 2, 0, 0]
code_as_byte_string:b'000100000b'|
Got the blobs of the net
 to insert :  
[0, 0, 0, 0, 14, 0, 1, 4, 0, 0]
code_as_byte_string:b'000000000e'|
time to traite the descriptors :  0.03486776351928711
Testing : ['1171275314', '1171291875', '1171275372']
 In select_photos_meta_from_ids: 
        SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text
        FROM MTRBack.photos_view
        WHERE photo_id IN (1171275314,1171291875,1171275372) 
result : {1171275314: {'photo_id': 1171275314, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/23/6e0a72c8fa00d5e4b018bd689b547133.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_23_54_22_6187.jpg'}, 1171275372: {'photo_id': 1171275372, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/23/76d81364ff7df843bff095f45c07ba35.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_23_56_46_6098.jpg'}, 1171291875: {'photo_id': 1171291875, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/23/b62cd9e0d976b143f86fe82d072798c0.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_23_59_04_5803.jpg'}}
list_photo_exists : [1171275314, 1171275372, 1171291875]
storage_type for insertDescriptorsMulti : 3
To insert : 1171275314
To insert : 1171291875
To insert : 1171275372
time to insert the descriptors : 1.1975879669189453






After datou_step_exec type output : <class 'dict'>
map_portfolio_photo : len 0 keys : dict_keys([])
Inside saveOutput : final : False verbose : True
 saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral 
[1171275314, 1171291875, 1171275372]
 map_info['map_portfolio_photo'] : {}
 final :  False mtd_id 4621 list_pids : [1171275314, 1171291875, 1171275372]
Looping around the photos to save general results
len do output : 3
/1171275314Didn't retrieve data
.
/1171291875Didn't retrieve data
.
/1171275372Didn't retrieve data
.
before output type
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f190c112cd0>
Managing all output in save final without adding information in the mtr_datou_result 
('4621', None, None, None, None, None, None, None, None)
('4621', None, '1171275314', None, None, None, None, None, None)
('4621', None, None, None, None, None, None, None, None)
('4621', None, '1171291875', None, None, None, None, None, None)
('4621', None, None, None, None, None, None, None, None)
('4621', None, '1171275372', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 6
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('4621', None, '1171275314', 'None', None, None, None, None, None), ('4621', None, '1171291875', 'None', None, None, None, None, None), ('4621', None, '1171275372', 'None', None, None, None, None, None)]
time used for this insertion : 0.039574623107910156
 save_final 

save missing photos in datou_result :

time spend for datou_step_exec : 18.555457830429077
time spend to save output : 0.039897918701171875
total time spend for step 1 : 18.59535574913025

step2:argmax
Mon Sep 29 11:43:11 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759138972_2886529_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg': 1171275314, 'temp/1759138972_2886529_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg': 1171291875, 'temp/1759138972_2886529_1171275372_76d81364ff7df843bff095f45c07ba35.jpg': 1171275372}
map_photo_id_path_extension : {1171275314: {'path': 'temp/1759138972_2886529_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'extension': 'jpg'}, 1171291875: {'path': 'temp/1759138972_2886529_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'extension': 'jpg'}, 1171275372: {'path': 'temp/1759138972_2886529_1171275372_76d81364ff7df843bff095f45c07ba35.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou_step Argmax !
calculate argmax for thcl : 3655

After datou_step_exec type output : <class 'dict'>
map_portfolio_photo : len 0 keys : dict_keys([])
Inside saveOutput : final : True verbose : True
 photo_id : 1171275314 output[photo_id] :  [(1171275314, 'tapis_vide', 0.96514195, 4723, '3655'), 'temp/1759138972_2886529_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg']
 photo_id : 1171291875 output[photo_id] :  [(1171291875, 'tapis_vide', 0.97064173, 4723, '3655'), 'temp/1759138972_2886529_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg']
 photo_id : 1171275372 output[photo_id] :  [(1171275372, 'tapis_vide', 0.96743846, 4723, '3655'), 'temp/1759138972_2886529_1171275372_76d81364ff7df843bff095f45c07ba35.jpg']


begin to insert list_values into photo_hahstag_ids : 
length of list_valuse in save_photo_hashtag_id_type : 3
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s)
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s)
 first line : ('1171275314', '2107748999', '4723')
...
 last line : ('1171275372', '2107748999', '4723')
time used for this insertion : 0.03559613227844238


begin to insert list_values into class_photo_scores : 
length of list_valuse in save_photo_hashtag_id_thcl_score : 3
insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score)
time used for this insertion : 0.03887820243835449
len list_finale : 3, len picture : 3


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 3
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('4621', None, '1171275314', 'tapis_vide', None, None, '2107748999', '0.96514195', None), ('4621', None, '1171291875', 'tapis_vide', None, None, '2107748999', '0.97064173', None), ('4621', None, '1171275372', 'tapis_vide', None, None, '2107748999', '0.96743846', None)]
time used for this insertion : 0.03952670097351074
saving photo_ids in datou_result
photo id not in port
photo id not in port
photo id not in port


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 0
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : []
time used for this insertion : 3.337860107421875e-06

save missing photos in datou_result :

time spend for datou_step_exec : 0.0001399517059326172
time spend to save output : 0.13171172142028809
total time spend for step 2 : 0.1318516731262207

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 2
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 2
 output : {'1171275314': [(1171275314, 'tapis_vide', 0.96514195, 4723, '3655'), 'temp/1759138972_2886529_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'], '1171291875': [(1171291875, 'tapis_vide', 0.97064173, 4723, '3655'), 'temp/1759138972_2886529_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'], '1171275372': [(1171275372, 'tapis_vide', 0.96743846, 4723, '3655'), 'temp/1759138972_2886529_1171275372_76d81364ff7df843bff095f45c07ba35.jpg']}
--------------------- test with use_multi_inputs=1 is succeded -------------------

############################### TEST ordonner  ################################
To do loadFromThcl(), then load ParamDescType : thcl358
 get_desc_type_from_thcl : type of cat <class 'mtr.database_queries.classification_admin_tools.ClassificationMetaTools'>

            SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists,
            photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at,
            type_classification
            FROM MTRDatou.classification_theme  WHERE  `id` IN (358) 
 thcls : [{'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}]
 thcl {'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}
 Update svm_hashtag_type_desc : 3392
['FirstUploadExperveo_vignette__port_505674', 'CAR_EXTERIEUR_Roue__port_503398', 'FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486', 'FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485', 'CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465', 'CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198', 'CAR_EXTERIEUR_Face_avant_axe_droit__port_504451', 'CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235', 'FirstUploadExperveo_vin__port_505675', 'CAR_EXTERIEUR_cote_droite__port_504108', 'CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565', 'FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201', 'cartegrise_orientation__port_505064', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217', 'CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531', 'CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218', 'CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214', 'CAR_EXTERIEUR_Angle_avant_droit__port_504087', 'FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484', 'CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563', 'CAR_EXTERIEUR_Angle_arriere_droit__port_504160', 'CAR_EXTERIEUR_arriere__port_504184', 'CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562', 'INTERIEUR_Compteur_kilometrique__port_503644', 'CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494', 'CAR_EXTERIEUR_Angle_arriere_gauche__port_504170', 'CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226', 'CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202', 'CAR_EXTERIEUR_moteur__port_503704', 'FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487', 'CAR_INTERIEUR_siege_arriere_class_1__port_506551', 'CAR_EXTERIEUR_avant__port_504146', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215', 'CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225', 'CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564', 'FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482', 'CAR_INTERIEUR_coffre__port_503412', 'FirstUploadExperveo_rouetranche__port_505677', 'UploadPhotoImmatBest_class_1__port_505051', 'CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532', 'CAR_EXTERIEUR_angle_avant_gauche__port_504098', 'CAR_EXTERIEUR_face_avant_axe_gauche__port_504236', 'CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540', 'CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233', 'CAR_EXTERIEUR_roue_de_secour__port_503763', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199', 'CAR_EXTERIEUR_cote_gauche__port_504017', 'CAR_INTERIEUR_avant_volant_class_1__port_506503', 'CAR_INTERIEUR_avant_volant_class_2__port_506504', 'CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234']
51
SELECT hashtag_id,hashtag FROM MTRBack.hashtags where hashtag in ('FirstUploadExperveo_vignette__port_505674','CAR_EXTERIEUR_Roue__port_503398','FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486','FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485','CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465','CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198','CAR_EXTERIEUR_Face_avant_axe_droit__port_504451','CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235','FirstUploadExperveo_vin__port_505675','CAR_EXTERIEUR_cote_droite__port_504108','CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565','FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483','CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201','cartegrise_orientation__port_505064','CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217','CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531','CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218','CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214','CAR_EXTERIEUR_Angle_avant_droit__port_504087','FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484','CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563','CAR_EXTERIEUR_Angle_arriere_droit__port_504160','CAR_EXTERIEUR_arriere__port_504184','CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562','INTERIEUR_Compteur_kilometrique__port_503644','CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494','CAR_EXTERIEUR_Angle_arriere_gauche__port_504170','CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226','CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202','CAR_EXTERIEUR_moteur__port_503704','FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487','CAR_INTERIEUR_siege_arriere_class_1__port_506551','CAR_EXTERIEUR_avant__port_504146','CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215','CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225','CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564','FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482','CAR_INTERIEUR_coffre__port_503412','FirstUploadExperveo_rouetranche__port_505677','UploadPhotoImmatBest_class_1__port_505051','CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532','CAR_EXTERIEUR_angle_avant_gauche__port_504098','CAR_EXTERIEUR_face_avant_axe_gauche__port_504236','CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540','CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233','CAR_EXTERIEUR_roue_de_secour__port_503763','CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199','CAR_EXTERIEUR_cote_gauche__port_504017','CAR_INTERIEUR_avant_volant_class_1__port_506503','CAR_INTERIEUR_avant_volant_class_2__port_506504','CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234'); 
51
dict_keys(['cartegrise_orientation__port_505064', 'car_exterieur_angle_arriere_droit_axe_arriere__port_504217', 'car_exterieur_angle_arriere_droit_axe_droit__port_504215', 'car_exterieur_angle_arriere_droit__port_504160', 'car_exterieur_angle_arriere_gauche_axe_arriere__port_504201', 'car_exterieur_angle_arriere_gauche_axe_gauche__port_504199', 'car_exterieur_angle_arriere_gauche__port_504170', 'car_exterieur_angle_avant_droit_axe_arriere__port_504226', 'car_exterieur_angle_avant_droit_axe_droit__port_504225', 'car_exterieur_angle_avant_droit__port_504087', 'car_exterieur_angle_avant_gauche_axe_avant__port_504235', 'car_exterieur_angle_avant_gauche_axe_gauche__port_504234', 'car_exterieur_angle_avant_gauche__port_504098', 'car_exterieur_arriere__port_504184', 'car_exterieur_avant__port_504146', 'car_exterieur_cote_droite__port_504108', 'car_exterieur_cote_droit_axe_arriere__port_504214', 'car_exterieur_cote_droit_axe_avant__port_504465', 'car_exterieur_cote_gauche_axe_arriere__port_504198', 'car_exterieur_cote_gauche_axe_avant__port_504233', 'car_exterieur_cote_gauche__port_504017', 'car_exterieur_face_arriere_axe_droit__port_504218', 'car_exterieur_face_arriere_axe_gauche__port_504202', 'car_exterieur_face_avant_axe_droit__port_504451', 'car_exterieur_face_avant_axe_gauche__port_504236', 'car_exterieur_moteur__port_503704', 'car_exterieur_roue_de_secour__port_503763', 'car_exterieur_roue__port_503398', 'car_interieur_avant_volant_class_1__port_506503', 'car_interieur_avant_volant_class_2__port_506504', 'car_interieur_avant_volant_class_6_boutonrond__port_506562', 'car_interieur_avant_volant_class_6_class_2__port_506563', 'car_interieur_avant_volant_class_6_ecrangrosplan__port_506564', 'car_interieur_avant_volant_class_6_levierdevitesse__port_506565', 'car_interieur_avant_vue-arriere_class_1__port_506531', 'car_interieur_avant_vue-arriere_class_2__port_506532', 'car_interieur_avant_vue_droite_habitacle_class_1__port_506540', 'car_interieur_avant_vue_gauche_habitacle_class_1__port_506494', 'car_interieur_coffre__port_503412', 'car_interieur_siege_arriere_class_1__port_506551', 'firstuploadexperveo_carrosseriegrosplan_carrosserie__port_506483', 'firstuploadexperveo_carrosseriegrosplan_class_6__port_506487', 'firstuploadexperveo_carrosseriegrosplan_morceauderoue__port_506484', 'firstuploadexperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482', 'firstuploadexperveo_carrosseriegrosplan_siegegrosplan__port_506485', 'firstuploadexperveo_carrosseriegrosplan_vindanslamoquette__port_506486', 'firstuploadexperveo_rouetranche__port_505677', 'firstuploadexperveo_vignette__port_505674', 'firstuploadexperveo_vin__port_505675', 'interieur_compteur_kilometrique__port_503644', 'uploadphotoimmatbest_class_1__port_505051'])
select photo_hashtag_type from MTRDatou.classification_theme where id = 358
thcl :  358
photo_hashtag_type :  337

                    SELECT phi.hashtag_id , phi.photo_id FROM MTRBack.photo_hashtag_ids phi,
                    MTRUser.mtr_portfolio_photos mp
                    where phi.type = 337 and phi.photo_id = mp.mtr_photo_id and mp.mtr_portfolio_id =510365;
{510365: [(917973295, 1), (917973297, 1), (917973302, 1), (917973293, 7), (917973296, 11), (917973300, 11), (917973286, 13), (917973289, 13), (917973301, 24), (917973285, 29), (917973290, 29), (917973299, 29), (917973304, 35), (917973287, 36), (917973298, 36), (917973305, 36), (917973292, 37), (917973291, 41), (917973303, 41), (917973294, 42), (917973288, 46)]}

############################### TEST rotate  ################################
test rotate only
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=230

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=230
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 230
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=230
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : rotate
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (917849322) 
 Found this number of photos: 1
##### Call download_photos : nb_thread : 5
begin to download photo : 917849322
download finish for photo 917849322
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.29834938049316406
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  1
step1:rotate
Mon Sep 29 11:43:13 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759138992_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322}
map_photo_id_path_extension : {917849322: {'path': 'temp/1759138992_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou_step_rotate !
We are in a linear step without datou_depend !
rotate photos of 90,180,270 degres
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 917849322) and `type` in (0) 
Loaded 0 chid ids of type : 0
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in ()
 map_chi : {}
 photo_id in download_rotate_and_save : 917849322 list_chi_loc : 0
Use all angle !
Rotation of photo 917849322 of 90 degree
temp/1759138992_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 90
 remove_crop_border : False
version de PIL : 9.5.0
Needs to change image size !
[[ 6.123234e-17  1.000000e+00]
 [-1.000000e+00  6.123234e-17]]
90
[[ 6.123234e-17  1.000000e+00]
 [-1.000000e+00  6.123234e-17]]
 shrink_image : False
 image_rotate :  <PIL.Image.Image image mode=RGB size=2448x3264 at 0x7F1514C4EE80>
 image_path : temp/1759138992_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg
 path_name_rotate : temp/1759138992_2886529_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg
 image_rotate.mode : RGB
Rotation of photo 917849322 of 180 degree
temp/1759138992_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 180
 remove_crop_border : False
version de PIL : 9.5.0
Needs to change image size !
[[-1.0000000e+00  1.2246468e-16]
 [-1.2246468e-16 -1.0000000e+00]]
180
[[-1.0000000e+00  1.2246468e-16]
 [-1.2246468e-16 -1.0000000e+00]]
 shrink_image : False
 image_rotate :  <PIL.Image.Image image mode=RGB size=3264x2448 at 0x7F1514C4EE80>
 image_path : temp/1759138992_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg
 path_name_rotate : temp/1759138992_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg
 image_rotate.mode : RGB
Rotation of photo 917849322 of 270 degree
temp/1759138992_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 270
 remove_crop_border : False
version de PIL : 9.5.0
Needs to change image size !
[[-1.8369702e-16 -1.0000000e+00]
 [ 1.0000000e+00 -1.8369702e-16]]
270
[[-1.8369702e-16 -1.0000000e+00]
 [ 1.0000000e+00 -1.8369702e-16]]
 shrink_image : False
 image_rotate :  <PIL.Image.Image image mode=RGB size=2448x3264 at 0x7F1514C4EE80>
 image_path : temp/1759138992_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg
 path_name_rotate : temp/1759138992_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg
 image_rotate.mode : RGB

About to upload 3 photos
upload in portfolio : 551782
init cache_photo without model_param  
we have 3 photo to upload
uploaded to storage server : ovh
folder_temporaire : temp/1759138993_2886529

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !
we have uploaded 3 photos in the portfolio 551782

time of upload the photos Elapsed time : 1.6139070987701416

 map_filename_photo_id : 3
 map_filename_photo_id : {'temp/1759138992_2886529_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg': 0, 'temp/1759138992_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg': 0, 'temp/1759138992_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg': 0}
Len new_chis : 3
Len list_new_chi_with_photo_id : 0 of type : 0
 list_new_chi_with_photo_id : []

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 1.8464794158935547
time spend to save output : 3.5762786865234375e-05
total time spend for step 1 : 1.84651517868042

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
 saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral 
[917849322]
 map_info['map_portfolio_photo'] : {}
 final :  True mtd_id 230 list_pids : [917849322]
Looping around the photos to save general results
len do output : 1
/0Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
before output type
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f19d514e0d0>
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f1514c53070>
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f1514c530d0>
Managing all output in save final without adding information in the mtr_datou_result 
('230', None, None, None, None, None, None, None, None)
('230', None, '917849322', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 4
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('230', None, '0', 'None', None, None, None, None, None), ('230', None, '917849322', None, None, None, None, None, None)]
time used for this insertion : 0.04045844078063965
 save_final 

save missing photos in datou_result :
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {0: ['917849322', 'temp/1759138992_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg', []]}
ERROR, got 0 upload, expected 3
test rotate conditionnel
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=233

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=233
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 233
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=233
# 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 !
 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 !
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 !
DataTypes for each output/input checked !
no param json to modify
List Step Type Loaded in datou : thcl, argmax, rotate
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (917849322) 
 Found this number of photos: 1
##### Call download_photos : nb_thread : 5
begin to download photo : 917849322
download finish for photo 917849322
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.2321321964263916
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  3
step1:thcl
Mon Sep 29 11:43:15 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759138995_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322}
map_photo_id_path_extension : {917849322: {'path': 'temp/1759138995_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou step Thcl !
multi_thcl or not :False
multi_thcl_cond or not :False
dic_thcl :  {'500': 1}
we are using the classfication for only one thcl
500


In convert_file_to_np l 337 : 1
 l343 1
l357 after caffe.io.load_image
dimension du image : (3, (2448, 3264, 3))
dimension displayed !
time to import caffe and check if the image exist :  0.00020456314086914062
time to convert the images to numpy array :  0.6055967807769775
total time to convert the images to numpy array :  0.6061131954193115
list photo_ids error:
[]
list photo_ids correct :
[917849322]
number of photos to traite : 1
try to delete the photos incorrect in DB

tagging for thcl : 500
To do loadFromThcl(), then load ParamDescType : thcl500
 get_desc_type_from_thcl : type of cat <class 'mtr.database_queries.classification_admin_tools.ClassificationMetaTools'>

            SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists,
            photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at,
            type_classification
            FROM MTRDatou.classification_theme  WHERE  `id` IN (500) 
 thcls : [{'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'}]
 thcl {'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'}
 Update svm_hashtag_type_desc : 3517
 SELECT * FROM MTRDatou.photo_desc_type_params  WHERE id in (3517)
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34))
To loadFromThcl() : net_3517
begin to check gpu status 
inside check gpu memory
l 3637 free memory gpu now : 2930  max_wait_temp :  1 max_wait :  0
 SELECT * FROM MTRDatou.photo_desc_type_params  WHERE id in (3517)
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34))
 param : <mtr.database_queries.descriptor_queries.ParamDescriptorType object at 0x7f1515c36160>, param.caffemodel : orientation_carte_grise_all_2
None
 mean_file_type : 
 mean_file_path : 
 prototxt_file_path : 
 model : orientation_carte_grise_all_2
 Inside get_net  
 Inside get_net before cache_data_model 
model_param file didn't exist 
 Inside get_net before  CDM.load_model_par_type  
model_name : orientation_carte_grise_all_2
model_type : caffe
list file need :
['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt']
file exist in s3 :
['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt']
file manque in s3 : 
[]
local folder  : /data/models_weight/orientation_carte_grise_all_2


/data/models_weight/orientation_carte_grise_all_2/caffemodel
size_local : 537110520 size in s3 : 537110520
create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:17
caffemodel already exist and didn't need to update


/data/models_weight/orientation_carte_grise_all_2/deploy_conv_normal.prototxt
size_local : 4626 size in s3 : 4626
create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16
deploy_conv_normal.prototxt already exist and didn't need to update


/data/models_weight/orientation_carte_grise_all_2/deploy_fc.prototxt
size_local : 1130 size in s3 : 1130
create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16
deploy_fc.prototxt already exist and didn't need to update


/data/models_weight/orientation_carte_grise_all_2/deploy.prototxt
size_local : 5653 size in s3 : 5653
create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16
deploy.prototxt already exist and didn't need to update


/data/models_weight/orientation_carte_grise_all_2/mean.npy
size_local : 1572992 size in s3 : 1572992
create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:31
mean.npy already exist and didn't need to update


/data/models_weight/orientation_carte_grise_all_2/synset_words.txt
size_local : 159 size in s3 : 159
create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16
synset_words.txt already exist and didn't need to update
 Inside get_net after CDM.load_model_par_type  
  After if not only_with_local_cache:
/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/:/home/admin/workarea/git/apy/
Here before set mode gpu
Doing nothing but we could set mode gpu
after set mode gpu
 prototxt_filename : /data/models_weight/orientation_carte_grise_all_2/deploy.prototxt
 caffemodel_filename : /data/models_weight/orientation_carte_grise_all_2/caffemodel
now we set caffe to gpu mode


 before predict
begin to check gpu status 
inside check gpu memory
l 3637 free memory gpu now : 2930  max_wait_temp :  1 max_wait :  0
dict_keys(['pool5', 'prob'])
time used to do the prepocess of the images : 2.265658140182495
time used to do the prediction : 0.11104011535644531


save descriptor for thcl : 500
(1, 512, 7, 7)
Got the blobs of the net
 to insert :  
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
code_as_byte_string:b'0000000000'|
time to traite the descriptors :  0.0508725643157959
Testing : ['917849322']
 In select_photos_meta_from_ids: 
        SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text
        FROM MTRBack.photos_view
        WHERE photo_id IN (917849322) 
result : {917849322: {'photo_id': 917849322, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/9/13/2bd260e91e91df8378dde8bb8b8c4548.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_13092022_12_32_19_5566.jpg'}}
list_photo_exists : [917849322]
storage_type for insertDescriptorsMulti : 1
To insert : 917849322
time to insert the descriptors : 0.9091811180114746






After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 9.35164475440979
time spend to save output : 4.1484832763671875e-05
total time spend for step 1 : 9.351686239242554

step2:argmax
Mon Sep 29 11:43:24 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior 
Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed
complete output_args for input 0 : {'917849322': [[('917849322', 'carteGrisesVerticales__port_549774', 0.99764997, 507, '500'), ('917849322', 'cartegrise_90deg__port_550987', 0.0005036166, 507, '500'), ('917849322', 'cartesGrisesEnvers__port_549765', 0.00036582554, 507, '500'), ('917849322', 'portfolio_270deg__port_550988', 0.0014805314, 507, '500')]]}
 input_args_next_step : {'917849322': ()}
 output_args : {'917849322': [[('917849322', 'carteGrisesVerticales__port_549774', 0.99764997, 507, '500'), ('917849322', 'cartegrise_90deg__port_550987', 0.0005036166, 507, '500'), ('917849322', 'cartesGrisesEnvers__port_549765', 0.00036582554, 507, '500'), ('917849322', 'portfolio_270deg__port_550988', 0.0014805314, 507, '500')]]}
 args : 917849322
 depend.output_id : 0
VR 22-3-18 : For now we do not clean correctly the datou structure 

input_args_next_step, len :1, first value : ([('917849322', 'carteGrisesVerticales__port_549774', 0.99764997, 507, '500'), ('917849322', 'cartegrise_90deg__port_550987', 0.0005036166, 507, '500'), ('917849322', 'cartesGrisesEnvers__port_549765', 0.00036582554, 507, '500'), ('917849322', 'portfolio_270deg__port_550988', 0.0014805314, 507, '500')],)
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759138995_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322}
map_photo_id_path_extension : {917849322: {'path': 'temp/1759138995_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou_step Argmax !
calculate argmax for thcl : 500

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 0.0001590251922607422
time spend to save output : 2.5987625122070312e-05
total time spend for step 2 : 0.0001850128173828125

step3:rotate
Mon Sep 29 11:43:24 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior 
Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed
complete output_args for input 0 : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.99764997, 507, '500'), 'temp/1759138995_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']}
 input_args_next_step : {'917849322': ()}
 output_args : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.99764997, 507, '500'), 'temp/1759138995_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']}
 args : 917849322
 depend.output_id : 1
complete output_args for input 1 : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.99764997, 507, '500'), 'temp/1759138995_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']}
 input_args_next_step : {'917849322': ('temp/1759138995_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg',)}
 output_args : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.99764997, 507, '500'), 'temp/1759138995_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']}
 args : 917849322
 depend.output_id : 0
VR 22-3-18 : For now we do not clean correctly the datou structure 

input_args_next_step, len :1, first value : ('temp/1759138995_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', ('917849322', 'carteGrisesVerticales__port_549774', 0.99764997, 507, '500'))
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759138995_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322}
map_photo_id_path_extension : {917849322: {'path': 'temp/1759138995_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou_step_rotate !
We are in a datou with depends !
angle_condi : {'carteGrisesVerticales__port_549774': 0, 'cartegrise_90deg__port_550987': 270, 'portfolio_270deg__port_550988': 90, 'cartesGrisesEnvers__port_549765': 180}

rotate photos for hashtag carteGrisesVerticales__port_549774 of 0 degres
1 photos founded : [917849322]
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 917849322) and `type` in (0) 
Loaded 0 chid ids of type : 0
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in ()
 map_chi : {}
 photo_id in download_rotate_and_save : 917849322 list_chi_loc : 0
Use all angle !
Rotation of photo 917849322 of 0 degree
temp/1759138995_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 0
 remove_crop_border : False
version de PIL : 9.5.0
Needs to change image size !
[[ 1.  0.]
 [-0.  1.]]
0
[[ 1.  0.]
 [-0.  1.]]
 shrink_image : False
 image_rotate :  <PIL.Image.Image image mode=RGB size=3264x2448 at 0x7F19D514E0D0>
 image_path : temp/1759138995_2886529_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg
 path_name_rotate : temp/1759138995_2886529_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg
 image_rotate.mode : RGB

About to upload 1 photos
upload in portfolio : 551782
init cache_photo without model_param  
we have 1 photo to upload
uploaded to storage server : ovh
folder_temporaire : temp/1759139005_2886529
we have uploaded 1 photos in the portfolio 551782

time of upload the photos Elapsed time : 0.8993887901306152

 map_filename_photo_id : 1
 map_filename_photo_id : {'temp/1759138995_2886529_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg': 0}
Len new_chis : 1
Len list_new_chi_with_photo_id : 0 of type : 0
 list_new_chi_with_photo_id : []

rotate photos for hashtag cartegrise_90deg__port_550987 of 270 degres
0 photos founded : []

rotate photos for hashtag portfolio_270deg__port_550988 of 90 degres
0 photos founded : []

rotate photos for hashtag cartesGrisesEnvers__port_549765 of 180 degres
0 photos founded : []

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 1.0025019645690918
time spend to save output : 6.651878356933594e-05
total time spend for step 3 : 1.0025684833526611

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
 saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral 
[917849322]
 map_info['map_portfolio_photo'] : {}
 final :  True mtd_id 233 list_pids : [917849322]
Looping around the photos to save general results
len do output : 1
/0Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
before output type
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f18a95ba370>
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f18a95ba220>
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f167023e0a0>
Managing all output in save final without adding information in the mtr_datou_result 
('233', None, None, None, None, None, None, None, None)
('233', None, '917849322', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 4
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('233', None, '0', 'None', None, None, None, None, None), ('233', None, '917849322', None, None, None, None, None, None)]
time used for this insertion : 0.03911471366882324
 save_final 

save missing photos in datou_result :
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 3
 output : {0: ['917849322', 'temp/1759138995_2886529_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg', []]}
ERROR rotate FAILED

############################### TEST data_augmentation_ellipse_varroa_tile_rotate  ################################
SELECT id FROM MTRPhoto.crop_hashtag_ids WHERE photo_id=937852786 AND `type`=520
DELETE FROM MTRPhoto.crop_hashtag_ids WHERE id IN (3976486126,3976486127,3976486128,3976486129) 
# 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 !
 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 : step 316 crop is not linked in the step_by_step architecture !
Step 318 rotate have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs !
Step 318 rotate have less outputs used (0) than in the step definition (3) : some outputs may be not used !
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 !
DataTypes for each output/input checked !
Unexpected type seems boolean for variable list_input_json
ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0)
 Tried to parse : 
DATA AUGMENTATION ELLIPSE VARROA TILE ROTATE
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=243

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=243
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 243
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=243
# 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 !
 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 : step 316 crop is not linked in the step_by_step architecture !
Step 318 rotate have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs !
Step 318 rotate have less outputs used (0) than in the step definition (3) : some outputs may be not used !
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 !
DataTypes for each output/input checked !
no param json to modify
List Step Type Loaded in datou : crop, tile, rotate
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (937852786) 
 Found this number of photos: 1
##### Call download_photos : nb_thread : 5
begin to download photo : 937852786
download finish for photo 937852786
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.1579432487487793
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  3
step1:crop
Mon Sep 29 11:43:26 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg': 937852786}
map_photo_id_path_extension : {937852786: {'path': 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou_step Crop !
param_json : {'hashtag_id_ellipse': 2087736828, 'photo_hashtag_type_from_ellipse': 520, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'crop_detect_varroa', 'photo_hashtag_type': 407, 'feed_id_new_photos_not_used': 549103, 'host': 'www.fotonower.com', 'margin': 8, 'upload_type': 'python'}
margin_type : margin
margin_value : [8, 8, 8, 8]


Loading chi in step crop with photo_hashtag_type : 407
Loading chi in step crop for list_pids : 1 !
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 937852786) and `type` in (407) 
Loaded 4 chid ids of type : 407
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (8165075,8165076,8165077,8165078)
+WARNING : Unexpected points, we should remove this data for chi_id : 8165075, for now we just ignore these empty polygon points 
+WARNING : Unexpected points, we should remove this data for chi_id : 8165076, for now we just ignore these empty polygon points 
+WARNING : Unexpected points, we should remove this data for chi_id : 8165077, for now we just ignore these empty polygon points 
+WARNING : Unexpected points, we should remove this data for chi_id : 8165078, for now we just ignore these empty polygon points 
SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (8165075,8165076,8165077,8165078)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (8165075,8165076,8165077,8165078)
select photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type, id from MTRPhoto.photo_sub_photos  where photo_id in ( 937852786) 
WARNING : margin is only used for type bib !

type of cropped photo chosen : <class 'type'>
we resize croppped photo by 1 on x axis and by 1 on y axis
 new_file_path_bib_crop : temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg
 new_file_path_bib_crop : temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg
 new_file_path_bib_crop : temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg
 new_file_path_bib_crop : temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg
 map_result returned by crop_photo_return_map_crop : length : 4
map_result after crop : {8165075: {'crop': 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg', 'photo_id': 937852786, 'sub_photo_id': -1, 'coordonates': (426, 467, 312, 347), 'sub_photo_infos': (418, 475, 304, 355, 1, 1), 'same_chi': False}, 8165076: {'crop': 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg', 'photo_id': 937852786, 'sub_photo_id': -1, 'coordonates': (411, 445, 443, 480), 'sub_photo_infos': (403, 453, 435, 480, 1, 1), 'same_chi': False}, 8165077: {'crop': 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg', 'photo_id': 937852786, 'sub_photo_id': -1, 'coordonates': (103, 138, 358, 396), 'sub_photo_infos': (95, 146, 350, 404, 1, 1), 'same_chi': False}, 8165078: {'crop': 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg', 'photo_id': 937852786, 'sub_photo_id': -1, 'coordonates': (104, 131, 256, 292), 'sub_photo_infos': (96, 139, 248, 300, 1, 1), 'same_chi': False}}
Here we crop with rles
 About to insert : list_path_to_insert length 4 new photo from crops !

About to upload 4 photos
 Status : 404
 Content : b'<!doctype html>\n<html lang=en>\n<title>404 Not Found</title>\n<h1>Not Found</h1>\n<p>The requested URL was not found on the server. If you entered the URL manually please check your spelling and try again.</p>\n'
 All Response : <Response [404]>
 WARNING : NO PORTFOLIO CREATED portfolio_id 0 used instead of new portfolio
upload in portfolio : 0
init cache_photo without model_param  
we have 4 photo to upload
uploaded to storage server : ovh
folder_temporaire : temp/1759139006_2886529

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (51, 57, 1794, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 54591989, 'jpg', UNHEX('c2d4b7e9a87b733b24c212df7b55bc09'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (45, 50, 1790, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 19136640, 'jpg', UNHEX('73bb947187a7cccf402d3c49d52f0e04'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (54, 51, 1867, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 58485124, 'jpg', UNHEX('cef221ccc8163d341ac8369fab7a6492'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (52, 43, 1418, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 40640667, 'jpg', UNHEX('036d93e6c08ecafaae7820b02e96abeb'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !
we have uploaded 4 photos in the portfolio 0

time of upload the photos Elapsed time : 10.212813377380371

{'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 0}
list_errors : []
map_result_insert : {'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 0}
Now we prepare data that will be used for ellipse search !
 chi_id found to be used  8165075
 path of cropped varroa found to be used to match on an ellipse temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg
 sub_photo_id found to be used  0
 chi_id found to be used  8165076
 path of cropped varroa found to be used to match on an ellipse temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg
 sub_photo_id found to be used  0
 chi_id found to be used  8165077
 path of cropped varroa found to be used to match on an ellipse temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg
 sub_photo_id found to be used  0
 chi_id found to be used  8165078
 path of cropped varroa found to be used to match on an ellipse temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg
 sub_photo_id found to be used  0
insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(8165075, '0', 31), (8165076, '0', 31), (8165077, '0', 31), (8165078, '0', 31)]
map of cropped photos with some data : 
{'0': ['937852786', 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg', (104, 131, 256, 292)]}
About to compute ellipse and record with type : 520
(57, 52)
(52, 43)
[57, 52]
(57, 52)
score : 7970
 strategy_opt : 5|  strategy_opt : [('excentricity', [0.5, 2.0, 0.05]), ('angle', [-90.0, 90.0, 5.0]), ('xc', [10.75, 32.25, 1.34375]), ('yc', [13.0, 39.0, 1.625]), ('radius', [13.0, 39.0, 1.625])]
{0.5: 16167, 0.55: 14430, 0.6000000000000001: 12864, 0.65: 11529, 0.7000000000000001: 10346, 0.75: 9256, 0.8: 8418, 0.8500000000000001: 7609, 0.9: 6862, 0.9500000000000001: 6277, 1.0: 5482, 1.05: 4840, 1.1: 4264, 1.1500000000000001: 3773, 1.2000000000000002: 3275, 1.25: 2948, 1.3: 2727, 1.35: 2514, 1.4000000000000001: 2254, 1.4500000000000002: 2155, 1.5: 1986, 1.55: 1892, 1.6: 1846, 1.6500000000000001: 1782, 1.7000000000000002: 1717, 1.75: 1656, 1.8: 1641, 1.85: 1602, 1.9000000000000001: 1587, 1.9500000000000002: 1576}
[(1.9500000000000002, 1576), (1.9000000000000001, 1587), (1.85, 1602), (1.8, 1641), (1.75, 1656), (1.7000000000000002, 1717), (1.6500000000000001, 1782), (1.6, 1846), (1.55, 1892), (1.5, 1986), (1.4500000000000002, 2155), (1.4000000000000001, 2254), (1.35, 2514), (1.3, 2727), (1.25, 2948), (1.2000000000000002, 3275), (1.1500000000000001, 3773), (1.1, 4264), (1.05, 4840), (1.0, 5482), (0.9500000000000001, 6277), (0.9, 6862), (0.8500000000000001, 7609), (0.8, 8418), (0.75, 9256), (0.7000000000000001, 10346), (0.65, 11529), (0.6000000000000001, 12864), (0.55, 14430), (0.5, 16167)]
 arg_min reach at : 1.9500000000000002 with value = 1576
 | arg_min : 1.9500000000000002 min_score : 1576{-90.0: 2291, -85.0: 2369, -80.0: 2390, -75.0: 2401, -70.0: 2341, -65.0: 2390, -60.0: 2418, -55.0: 2445, -50.0: 2599, -45.0: 2701, -40.0: 2755, -35.0: 2787, -30.0: 2753, -25.0: 2678, -20.0: 2611, -15.0: 2478, -10.0: 2313, -5.0: 2098, 0.0: 1870, 5.0: 1702, 10.0: 1576, 15.0: 1422, 20.0: 1258, 25.0: 1061, 30.0: 872, 35.0: 752, 40.0: 632, 45.0: 677, 50.0: 872, 55.0: 1070, 60.0: 1274, 65.0: 1477, 70.0: 1637, 75.0: 1895, 80.0: 2049, 85.0: 2193}
[(40.0, 632), (45.0, 677), (35.0, 752), (30.0, 872), (50.0, 872), (25.0, 1061), (55.0, 1070), (20.0, 1258), (60.0, 1274), (15.0, 1422), (65.0, 1477), (10.0, 1576), (70.0, 1637), (5.0, 1702), (0.0, 1870), (75.0, 1895), (80.0, 2049), (-5.0, 2098), (85.0, 2193), (-90.0, 2291), (-10.0, 2313), (-70.0, 2341), (-85.0, 2369), (-80.0, 2390), (-65.0, 2390), (-75.0, 2401), (-60.0, 2418), (-55.0, 2445), (-15.0, 2478), (-50.0, 2599), (-20.0, 2611), (-25.0, 2678), (-45.0, 2701), (-30.0, 2753), (-40.0, 2755), (-35.0, 2787)]
 arg_min reach at : 40.0 with value = 632
 | arg_min : 40.0 min_score : 632{10.75: 982, 12.09375: 841, 13.4375: 751, 14.78125: 672, 16.125: 624, 17.46875: 599, 18.8125: 575, 20.15625: 561, 21.5: 632, 22.84375: 895, 24.1875: 1257, 25.53125: 1708, 26.875: 2192, 28.21875: 2686, 29.5625: 3202, 30.90625: 3701}
[(20.15625, 561), (18.8125, 575), (17.46875, 599), (16.125, 624), (21.5, 632), (14.78125, 672), (13.4375, 751), (12.09375, 841), (22.84375, 895), (10.75, 982), (24.1875, 1257), (25.53125, 1708), (26.875, 2192), (28.21875, 2686), (29.5625, 3202), (30.90625, 3701)]
 arg_min reach at : 20.15625 with value = 561
 | arg_min : 20.15625 min_score : 561{13.0: 3254, 14.625: 2871, 16.25: 2436, 17.875: 1977, 19.5: 1474, 21.125: 1071, 22.75: 835, 24.375: 629, 26.0: 561, 27.625: 716, 29.25: 937, 30.875: 1286, 32.5: 1738, 34.125: 2226, 35.75: 2782, 37.375: 3291}
[(26.0, 561), (24.375, 629), (27.625, 716), (22.75, 835), (29.25, 937), (21.125, 1071), (30.875, 1286), (19.5, 1474), (32.5, 1738), (17.875, 1977), (34.125, 2226), (16.25, 2436), (35.75, 2782), (14.625, 2871), (13.0, 3254), (37.375, 3291)]
 arg_min reach at : 26.0 with value = 561
 | arg_min : 26.0 min_score : 561{13.0: 1371, 14.625: 1301, 16.25: 1217, 17.875: 1127, 19.5: 1028, 21.125: 926, 22.75: 811, 24.375: 683, 26.0: 561, 27.625: 624, 29.25: 1026, 30.875: 1508, 32.5: 2147, 34.125: 2981, 35.75: 3949, 37.375: 5069}
[(26.0, 561), (27.625, 624), (24.375, 683), (22.75, 811), (21.125, 926), (29.25, 1026), (19.5, 1028), (17.875, 1127), (16.25, 1217), (14.625, 1301), (13.0, 1371), (30.875, 1508), (32.5, 2147), (34.125, 2981), (35.75, 3949), (37.375, 5069)]
 arg_min reach at : 26.0 with value = 561
 | arg_min : 26.0 min_score : 561{0.5: 13327, 0.55: 12420, 0.6000000000000001: 11540, 0.65: 10560, 0.7000000000000001: 9652, 0.75: 8782, 0.8: 7834, 0.8500000000000001: 7061, 0.9: 6294, 0.9500000000000001: 5633, 1.0: 4916, 1.05: 4424, 1.1: 3865, 1.1500000000000001: 3323, 1.2000000000000002: 2817, 1.25: 2401, 1.3: 1935, 1.35: 1600, 1.4000000000000001: 1218, 1.4500000000000002: 1019, 1.5: 834, 1.55: 693, 1.6: 614, 1.6500000000000001: 568, 1.7000000000000002: 549, 1.75: 529, 1.8: 520, 1.85: 521, 1.9000000000000001: 534, 1.9500000000000002: 561}
[(1.8, 520), (1.85, 521), (1.75, 529), (1.9000000000000001, 534), (1.7000000000000002, 549), (1.9500000000000002, 561), (1.6500000000000001, 568), (1.6, 614), (1.55, 693), (1.5, 834), (1.4500000000000002, 1019), (1.4000000000000001, 1218), (1.35, 1600), (1.3, 1935), (1.25, 2401), (1.2000000000000002, 2817), (1.1500000000000001, 3323), (1.1, 3865), (1.05, 4424), (1.0, 4916), (0.9500000000000001, 5633), (0.9, 6294), (0.8500000000000001, 7061), (0.8, 7834), (0.75, 8782), (0.7000000000000001, 9652), (0.65, 10560), (0.6000000000000001, 11540), (0.55, 12420), (0.5, 13327)]
 arg_min reach at : 1.8 with value = 520
 arg_min : 1.8 min_score : 520{-90.0: 2114, -85.0: 2268, -80.0: 2343, -75.0: 2405, -70.0: 2409, -65.0: 2435, -60.0: 2467, -55.0: 2514, -50.0: 2590, -45.0: 2654, -40.0: 2632, -35.0: 2610, -30.0: 2565, -25.0: 2526, -20.0: 2435, -15.0: 2330, -10.0: 2181, -5.0: 2013, 0.0: 1792, 5.0: 1584, 10.0: 1389, 15.0: 1208, 20.0: 1038, 25.0: 843, 30.0: 673, 35.0: 564, 40.0: 520, 45.0: 597, 50.0: 764, 55.0: 963, 60.0: 1158, 65.0: 1390, 70.0: 1573, 75.0: 1800, 80.0: 1969, 85.0: 2070}
[(40.0, 520), (35.0, 564), (45.0, 597), (30.0, 673), (50.0, 764), (25.0, 843), (55.0, 963), (20.0, 1038), (60.0, 1158), (15.0, 1208), (10.0, 1389), (65.0, 1390), (70.0, 1573), (5.0, 1584), (0.0, 1792), (75.0, 1800), (80.0, 1969), (-5.0, 2013), (85.0, 2070), (-90.0, 2114), (-10.0, 2181), (-85.0, 2268), (-15.0, 2330), (-80.0, 2343), (-75.0, 2405), (-70.0, 2409), (-65.0, 2435), (-20.0, 2435), (-60.0, 2467), (-55.0, 2514), (-25.0, 2526), (-30.0, 2565), (-50.0, 2590), (-35.0, 2610), (-40.0, 2632), (-45.0, 2654)]
 arg_min reach at : 40.0 with value = 520
 arg_min : 40.0 min_score : 520{10.75: 1004, 12.09375: 902, 13.4375: 775, 14.78125: 655, 16.125: 580, 17.46875: 515, 18.8125: 494, 20.15625: 520, 21.5: 692, 22.84375: 1028, 24.1875: 1466, 25.53125: 2001, 26.875: 2490, 28.21875: 3027, 29.5625: 3629, 30.90625: 4163}
[(18.8125, 494), (17.46875, 515), (20.15625, 520), (16.125, 580), (14.78125, 655), (21.5, 692), (13.4375, 775), (12.09375, 902), (10.75, 1004), (22.84375, 1028), (24.1875, 1466), (25.53125, 2001), (26.875, 2490), (28.21875, 3027), (29.5625, 3629), (30.90625, 4163)]
 arg_min reach at : 18.8125 with value = 494
 arg_min : 18.8125 min_score : 494{13.0: 3121, 14.625: 2763, 16.25: 2397, 17.875: 1962, 19.5: 1409, 21.125: 1056, 22.75: 809, 24.375: 589, 26.0: 494, 27.625: 635, 29.25: 987, 30.875: 1406, 32.5: 1937, 34.125: 2442, 35.75: 2987, 37.375: 3537}
[(26.0, 494), (24.375, 589), (27.625, 635), (22.75, 809), (29.25, 987), (21.125, 1056), (30.875, 1406), (19.5, 1409), (32.5, 1937), (17.875, 1962), (16.25, 2397), (34.125, 2442), (14.625, 2763), (35.75, 2987), (13.0, 3121), (37.375, 3537)]
 arg_min reach at : 26.0 with value = 494
 arg_min : 26.0 min_score : 494{13.0: 1347, 14.625: 1266, 16.25: 1182, 17.875: 1086, 19.5: 979, 21.125: 865, 22.75: 737, 24.375: 616, 26.0: 494, 27.625: 559, 29.25: 954, 30.875: 1524, 32.5: 2347, 34.125: 3350, 35.75: 4468, 37.375: 5635}
[(26.0, 494), (27.625, 559), (24.375, 616), (22.75, 737), (21.125, 865), (29.25, 954), (19.5, 979), (17.875, 1086), (16.25, 1182), (14.625, 1266), (13.0, 1347), (30.875, 1524), (32.5, 2347), (34.125, 3350), (35.75, 4468), (37.375, 5635)]
 arg_min reach at : 26.0 with value = 494
 arg_min : 26.0 min_score : 494 yc : 26.0 xc : 18.8125 angle : 40.0 radius : 26.0 excentricity : 1.8
 yc : 26.0 xc : 18.8125 angle : 40.0 radius : 26.0 excentricity : 1.8
 x0 : 426 y1 : 347 width : 41, height : 35, area : 1435, score : 1.0
 x0 : 424 y1 : 344 width : 39, height : 43, area : 1677, score : 1.0
 Now saving polygons points : 1| batch 1
Loaded 1 chid ids of type : 520
 CHI and polygons saved !
['temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_ellipsebest.jpg', 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_varroa_with_ellipsebest.jpg']

About to upload 2 photos
 Status : 404
 Content : b'<!doctype html>\n<html lang=en>\n<title>404 Not Found</title>\n<h1>Not Found</h1>\n<p>The requested URL was not found on the server. If you entered the URL manually please check your spelling and try again.</p>\n'
 All Response : <Response [404]>
 WARNING : NO PORTFOLIO CREATED portfolio_id 0 used instead of new portfolio
upload in portfolio : 0
Result OK !

uploaded one batch 0 Elapsed time : 2.9209144115448


After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 13.704163312911987
time spend to save output : 1.5974044799804688e-05
total time spend for step 1 : 13.704179286956787

step2:tile
Mon Sep 29 11:43:40 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 : ['temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg']
We expect there is only one output and this part is used while all output are not tuple or array
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 

input_args_next_step, len :1, first value : [('temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg',)]
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg': 937852786, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 0}
map_photo_id_path_extension : {937852786: {'path': 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg', 'extension': 'jpg'}, 0: {'path': 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg'}}
map_subphoto_mainphoto : {0: 937852786}
verbose : True
 param_json : {'photo_tile_type': 17, 'whiten': True, 'remove_crop_border': True, 'minimal_size_crop_border': 900, 'stride': 240, 'crop_hashtag_type_tiled': 521, 'ETA': 86400, 'new_width': 480, 'new_height': 480, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'tile_taggage_varroa', 'crop_hashtag_type': 520, 'host': 'www.fotonower.com', 'arg_aux_upload': {'type_upload': 'python'}}
 type(crop_hashtag_type) : <class 'int'>
 type(crop_hashtag_type_tiled) : <class 'int'>
We consider crop_hashtag_type is an integer ! 
  map_chi_type_to_chi_type_cropped : {520: 521}
TO DEPRECATE VR 14-6-18
 map_filenames :  {937852786: 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg'}
 list_pids :   1
 list_pids :   2
 list_subpids to replace list_pids : 0
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 937852786,937852786) and `type` in (520) 
Loaded 1 chid ids of type : 520
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (3980043805)
+SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (3980043805)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (3980043805)
 Status : 404
 Content : b'<!doctype html>\n<html lang=en>\n<title>404 Not Found</title>\n<h1>Not Found</h1>\n<p>The requested URL was not found on the server. If you entered the URL manually please check your spelling and try again.</p>\n'
 All Response : <Response [404]>
 WARNING : NO PORTFOLIO CREATED portfolio_id 0 used instead of new portfolio
 created feed_id_new_photos : 0 with name tile_taggage_varroa
 feed_id_new_photos : 0
 filename : temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg
 photo_id : 937852786
 height_image_input : 480 width_image_input : 480
 new_width : 480 new_height : 480
 stride : 240 stride_relative : 0.1
 chi to copy from the main photo to the tiled photo  input_chi_for_this_image_as_chi :  1
list_bib_to_crops : 1 [(0, 480, 0, 480, 0)]
calcul des nouveaux crops pour le tile x0:0,x1:480,y0:0,y1:480
calcul avec la methode originale
chi selectionnes : [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c02d00>]
 new_crops_tiles : 1 crop_transformed : 1
insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s)
[(937852786, 2090988864, 17, 0, 480, 0, 480, 1.0)]
  list_photo_ids_cropped : [937852786]
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 937852786) and `type` in (17) 
Loaded 1 chid ids of type : 17
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (8165084)
SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (8165084)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (8165084)
treat the image : temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg
, 0
before upload mediasElapsed time : 0.009404420852661133

on upload les photos avec python
init cache_photo without model_param  
we have 1 photo to upload
uploaded to storage server : ovh
folder_temporaire : temp/1759139025_2886529

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139025), 0.0, 0.0, 14, '', 0, 0, '1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg', 0,
480, 480, 0, 1759139025,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (480, 480, 50498, 1386782169, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 23505237, 'jpg', UNHEX('345879d5145933efcce16bd1d96b81a8'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !
we have uploaded 1 photos in the portfolio 0
Importing !

upload mediasElapsed time : 0.6102492809295654

, 0insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(8165084, 1386782169, 0)]
Saving 1 CHIs.
list_chi_tile : ["<class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 1386782169, 'hashtag_id': 2087736828, 'type': 521, 'x0': 424, 'x1': 463, 'y0': 301, 'y1': 344, 'score': 1.0, 'id': 0, 'points': ['446,349,434,346,424,337,417,327,415,318,418,311,427,310,438,313,449,322,456,332,458,341,454,348', '446,349,434,346,424,337,417,327,415,318,418,311,427,310,438,313,449,322,456,332,458,341,454,348'], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', 'sum_segment': 0}"]
insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s)
batch 1
Loaded 1 chid ids of type : 521
INSERT IGNORE INTO MTRPhoto.crop_polygon_points (`crop_hashtag_id`, `points`) VALUES (%s, %s)
Number RLEs to save : 0
INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) 
 TO DO : save crop sub photo not yet done ! 

end of tileElapsed time : 0.8348712921142578

map_pid_results : {'1386782169': ['temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg']}

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 6.109174489974976
time spend to save output : 4.076957702636719e-05
total time spend for step 2 : 6.109215259552002

step3:rotate
Mon Sep 29 11:43:46 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior 
Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed
complete output_args for input 0 : {'1386782169': ['temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg']}
 input_args_next_step : {'1386782169': ()}
 output_args : {'1386782169': ['temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg']}
 args : 1386782169
 depend.output_id : 0
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 

input_args_next_step, len :1, first value : ('temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg',)
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg': 937852786, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg': 1386782169}
map_photo_id_path_extension : {937852786: {'path': 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg', 'extension': 'jpg'}, 0: {'path': 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg'}, 1386782169: {'path': 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg'}}
map_subphoto_mainphoto : {0: 937852786, 1386782169: 937852786}

Beginning of datou_step_rotate !
Warning, new_feed_id is empty !
We are in a datou with depends !
rotate photos of 0,15,30,45,60,75,90,105,120,135,150,165,180,195,210,225,240,255,270,285,300,315,330,345 degres
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 1386782169) and `type` in (521) 
Loaded 1 chid ids of type : 521
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (3980043807)
++WARNING : duplicated polygon, we should remove this data for chi_id : 3980043807. Ignored now
SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (3980043807)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (3980043807)
 map_chi : {1386782169: [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>]}
 Status : 404
 Content : b'<!doctype html>\n<html lang=en>\n<title>404 Not Found</title>\n<h1>Not Found</h1>\n<p>The requested URL was not found on the server. If you entered the URL manually please check your spelling and try again.</p>\n'
 All Response : <Response [404]>
 WARNING : NO PORTFOLIO CREATED portfolio_id 0 used instead of new portfolio
 feed_id_new_photos : 0
 photo_id in download_rotate_and_save : 1386782169 list_chi_loc : 1
Use all angle !
Rotation of photo 1386782169 of 0 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 0
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[ 1.  0.]
 [-0.  1.]]
0
[[ 1.  0.]
 [-0.  1.]]
 shrink_image : True
 len(list_crops) : 1
 crop are not in the shrunk photo ! 
Rotation of photo 1386782169 of 15 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 15
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[ 0.96592583  0.25881905]
 [-0.25881905  0.96592583]]
15
[[ 0.96592583  0.25881905]
 [-0.25881905  0.96592583]]
 shrink_image : True
 len(list_crops) : 1
 crop are not in the shrunk photo ! 
Rotation of photo 1386782169 of 30 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 30
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[ 0.8660254  0.5      ]
 [-0.5        0.8660254]]
30
[[ 0.8660254  0.5      ]
 [-0.5        0.8660254]]
 shrink_image : True
 len(list_crops) : 1
 crop are not in the shrunk photo ! 
Rotation of photo 1386782169 of 45 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 45
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[ 0.70710678  0.70710678]
 [-0.70710678  0.70710678]]
45
[[ 0.70710678  0.70710678]
 [-0.70710678  0.70710678]]
 shrink_image : True
 len(list_crops) : 1
 crop are not in the shrunk photo ! 
Rotation of photo 1386782169 of 60 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 60
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[ 0.5        0.8660254]
 [-0.8660254  0.5      ]]
60
[[ 0.5        0.8660254]
 [-0.8660254  0.5      ]]
 shrink_image : True
 len(list_crops) : 1
time for calcul the mask position with numpy : 0.00034356117248535156
nb_pixel_total : 113
time to create 1 rle with old method : 0.00021147727966308594
On the border
Smaller than minimal size ! 
Rotation of photo 1386782169 of 75 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 75
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[ 0.25881905  0.96592583]
 [-0.96592583  0.25881905]]
75
[[ 0.25881905  0.96592583]
 [-0.96592583  0.25881905]]
 shrink_image : True
 len(list_crops) : 1
time for calcul the mask position with numpy : 0.0003445148468017578
nb_pixel_total : 317
time to create 1 rle with old method : 0.0005645751953125
On the border
Smaller than minimal size ! 
Rotation of photo 1386782169 of 90 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 90
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[ 6.123234e-17  1.000000e+00]
 [-1.000000e+00  6.123234e-17]]
90
[[ 6.123234e-17  1.000000e+00]
 [-1.000000e+00  6.123234e-17]]
 shrink_image : True
 len(list_crops) : 1
 crop are not in the shrunk photo ! 
Rotation of photo 1386782169 of 105 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 105
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[-0.25881905  0.96592583]
 [-0.96592583 -0.25881905]]
105
[[-0.25881905  0.96592583]
 [-0.96592583 -0.25881905]]
 shrink_image : True
 len(list_crops) : 1
 crop are not in the shrunk photo ! 
Rotation of photo 1386782169 of 120 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 120
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[-0.5        0.8660254]
 [-0.8660254 -0.5      ]]
120
[[-0.5        0.8660254]
 [-0.8660254 -0.5      ]]
 shrink_image : True
 len(list_crops) : 1
 crop are not in the shrunk photo ! 
Rotation of photo 1386782169 of 135 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 135
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[-0.70710678  0.70710678]
 [-0.70710678 -0.70710678]]
135
[[-0.70710678  0.70710678]
 [-0.70710678 -0.70710678]]
 shrink_image : True
 len(list_crops) : 1
 crop are not in the shrunk photo ! 
Rotation of photo 1386782169 of 150 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 150
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[-0.8660254  0.5      ]
 [-0.5       -0.8660254]]
150
[[-0.8660254  0.5      ]
 [-0.5       -0.8660254]]
 shrink_image : True
 len(list_crops) : 1
time for calcul the mask position with numpy : 0.0003254413604736328
nb_pixel_total : 135
time to create 1 rle with old method : 0.0002586841583251953
On the border
Smaller than minimal size ! 
Rotation of photo 1386782169 of 165 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 165
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[-0.96592583  0.25881905]
 [-0.25881905 -0.96592583]]
165
[[-0.96592583  0.25881905]
 [-0.25881905 -0.96592583]]
 shrink_image : True
 len(list_crops) : 1
time for calcul the mask position with numpy : 0.0003261566162109375
nb_pixel_total : 317
time to create 1 rle with old method : 0.0004818439483642578
On the border
Smaller than minimal size ! 
Rotation of photo 1386782169 of 180 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 180
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[-1.0000000e+00  1.2246468e-16]
 [-1.2246468e-16 -1.0000000e+00]]
180
[[-1.0000000e+00  1.2246468e-16]
 [-1.2246468e-16 -1.0000000e+00]]
 shrink_image : True
 len(list_crops) : 1
 crop are not in the shrunk photo ! 
Rotation of photo 1386782169 of 195 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 195
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[-0.96592583 -0.25881905]
 [ 0.25881905 -0.96592583]]
195
[[-0.96592583 -0.25881905]
 [ 0.25881905 -0.96592583]]
 shrink_image : True
 len(list_crops) : 1
 crop are not in the shrunk photo ! 
Rotation of photo 1386782169 of 210 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 210
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[-0.8660254 -0.5      ]
 [ 0.5       -0.8660254]]
210
[[-0.8660254 -0.5      ]
 [ 0.5       -0.8660254]]
 shrink_image : True
 len(list_crops) : 1
 crop are not in the shrunk photo ! 
Rotation of photo 1386782169 of 225 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 225
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[-0.70710678 -0.70710678]
 [ 0.70710678 -0.70710678]]
225
[[-0.70710678 -0.70710678]
 [ 0.70710678 -0.70710678]]
 shrink_image : True
 len(list_crops) : 1
 crop are not in the shrunk photo ! 
Rotation of photo 1386782169 of 240 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 240
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[-0.5       -0.8660254]
 [ 0.8660254 -0.5      ]]
240
[[-0.5       -0.8660254]
 [ 0.8660254 -0.5      ]]
 shrink_image : True
 len(list_crops) : 1
time for calcul the mask position with numpy : 0.0003261566162109375
nb_pixel_total : 135
time to create 1 rle with old method : 0.0002460479736328125
On the border
Smaller than minimal size ! 
Rotation of photo 1386782169 of 255 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 255
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[-0.25881905 -0.96592583]
 [ 0.96592583 -0.25881905]]
255
[[-0.25881905 -0.96592583]
 [ 0.96592583 -0.25881905]]
 shrink_image : True
 len(list_crops) : 1
time for calcul the mask position with numpy : 0.0003390312194824219
nb_pixel_total : 280
time to create 1 rle with old method : 0.00042724609375
On the border
Smaller than minimal size ! 
Rotation of photo 1386782169 of 270 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 270
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[-1.8369702e-16 -1.0000000e+00]
 [ 1.0000000e+00 -1.8369702e-16]]
270
[[-1.8369702e-16 -1.0000000e+00]
 [ 1.0000000e+00 -1.8369702e-16]]
 shrink_image : True
 len(list_crops) : 1
 crop are not in the shrunk photo ! 
Rotation of photo 1386782169 of 285 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 285
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[ 0.25881905 -0.96592583]
 [ 0.96592583  0.25881905]]
285
[[ 0.25881905 -0.96592583]
 [ 0.96592583  0.25881905]]
 shrink_image : True
 len(list_crops) : 1
 crop are not in the shrunk photo ! 
Rotation of photo 1386782169 of 300 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 300
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[ 0.5       -0.8660254]
 [ 0.8660254  0.5      ]]
300
[[ 0.5       -0.8660254]
 [ 0.8660254  0.5      ]]
 shrink_image : True
 len(list_crops) : 1
 crop are not in the shrunk photo ! 
Rotation of photo 1386782169 of 315 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 315
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[ 0.70710678 -0.70710678]
 [ 0.70710678  0.70710678]]
315
[[ 0.70710678 -0.70710678]
 [ 0.70710678  0.70710678]]
 shrink_image : True
 len(list_crops) : 1
 crop are not in the shrunk photo ! 
Rotation of photo 1386782169 of 330 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 330
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[ 0.8660254 -0.5      ]
 [ 0.5        0.8660254]]
330
[[ 0.8660254 -0.5      ]
 [ 0.5        0.8660254]]
 shrink_image : True
 len(list_crops) : 1
time for calcul the mask position with numpy : 0.00032448768615722656
nb_pixel_total : 113
time to create 1 rle with old method : 0.00020384788513183594
On the border
Smaller than minimal size ! 
Rotation of photo 1386782169 of 345 degree
temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515b4b700>] 345
 remove_crop_border : True
version de PIL : 9.5.0
Needs to change image size !
[[ 0.96592583 -0.25881905]
 [ 0.25881905  0.96592583]]
345
[[ 0.96592583 -0.25881905]
 [ 0.25881905  0.96592583]]
 shrink_image : True
 len(list_crops) : 1
time for calcul the mask position with numpy : 0.00032520294189453125
nb_pixel_total : 280
time to create 1 rle with old method : 0.0004374980926513672
On the border
Smaller than minimal size ! 

About to upload 24 photos
 Status : 404
 Content : b'<!doctype html>\n<html lang=en>\n<title>404 Not Found</title>\n<h1>Not Found</h1>\n<p>The requested URL was not found on the server. If you entered the URL manually please check your spelling and try again.</p>\n'
 All Response : <Response [404]>
 WARNING : NO PORTFOLIO CREATED portfolio_id 0 used instead of new portfolio
upload in portfolio : 0
init cache_photo without model_param  
we have 24 photo to upload
uploaded to storage server : ovh
folder_temporaire : temp/1759139026_2886529

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !
we have uploaded 24 photos in the portfolio 0

time of upload the photos Elapsed time : 11.872359991073608

 map_filename_photo_id : 24
 map_filename_photo_id : {'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg': 0, 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg': 0}
Len new_chis : 24
Len list_new_chi_with_photo_id : 0 of type : 529
 list_new_chi_with_photo_id : []

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 12.221155643463135
time spend to save output : 3.8623809814453125e-05
total time spend for step 3 : 12.22119426727295

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
 saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral 
[937852786, 937852786, '1386782169']
 map_info['map_portfolio_photo'] : {}
 final :  True mtd_id 243 list_pids : [937852786, 937852786, '1386782169']
Looping around the photos to save general results
len do output : 1
/0Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
before output type
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f1515ba0c40>
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f1515ba0370>
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f1515ba04c0>
Managing all output in save final without adding information in the mtr_datou_result 
('243', None, None, None, None, None, None, None, None)
('243', None, '937852786', None, None, None, None, None, None)
('243', None, None, None, None, None, None, None, None)
('243', None, '937852786', None, None, None, None, None, None)
('243', None, None, None, None, None, None, None, None)
('243', None, '1386782169', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 6
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('243', None, '0', 'None', None, None, None, None, None), ('243', None, '937852786', None, None, None, None, None, None), ('243', None, '1386782169', None, None, None, None, None, None)]
time used for this insertion : 0.04123973846435547
 save_final 

save missing photos in datou_result :
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 3
 output : {0: ['937852786', 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg', []]}
 ret_da : {0: ['937852786', 'temp/1759139006_2886529_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg', []]}
number of sub image is incorrect than we expected , got : 1 ; expected : 24
list chi :  [[]]
ERROR in DATA AUGMENTATION ELLIPSE VARROA TILE ROTATE WRONG SIZE OF DATA : flat_list_chi : 0 vs flat_list_chi_non_reg : 28
ERROR data_augmentation_ellipse_varroa_tile_rotate FAILED

############################### TEST flip  ################################
t
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=571

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=571
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 571
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=571
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : flip
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (911785586) 
 Found this number of photos: 1
##### Call download_photos : nb_thread : 5
begin to download photo : 911785586
download finish for photo 911785586
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.318239688873291
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  1
step1:flip
Mon Sep 29 11:43:59 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139039_2886529_911785586_d8582feabcd359151ff718b5832248c7-big.jpg': 911785586}
map_photo_id_path_extension : {911785586: {'path': 'temp/1759139039_2886529_911785586_d8582feabcd359151ff718b5832248c7-big.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou_step_flip !
We are in a linear step without datou_depend !
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 911785586) and `type` in (741) 
Loaded 6 chid ids of type : 741
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (18344206,18344211,18344210,18344209,18344208,18344207)
+++++WARNING : Unexpected points, we should remove this data for chi_id : 18344210, for now we just ignore these empty polygon points 
+SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (18344206,18344211,18344210,18344209,18344208,18344207)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (18344206,18344211,18344210,18344209,18344208,18344207)
 map_chi_objs : {911785586: [<mtr.simple_image_editor.image_utils.CHI object at 0x7f190c112ca0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f190c112cd0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c36820>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c365b0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c36be0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c36fd0>]}
 photo_id in download_rotate_and_save : 911785586 list_chi_loc : 6
Vertical flip of photo 911785586
version de PIL : 9.5.0
vertically flipped image is saved in temp/1759139039_2886529_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg
Horizontal flip of photo 911785586
version de PIL : 9.5.0
horizontally flipped image is saved in temp/1759139039_2886529_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg

About to upload 2 photos
upload in portfolio : 1090565
init cache_photo without model_param  
we have 2 photo to upload
uploaded to storage server : ovh
folder_temporaire : temp/1759139039_2886529

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !
we have uploaded 2 photos in the portfolio 1090565

time of upload the photos Elapsed time : 1.09610915184021

 map_filename_photo_id : 2
 map_filename_photo_id : {'temp/1759139039_2886529_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg': 0, 'temp/1759139039_2886529_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg': 0}
Len new_chis : 12
Len list_new_chi_with_photo_id : 12 of type : 741
 list_new_chi_with_photo_id : [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44eb0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44a60>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44700>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44af0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44970>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44340>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44370>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44130>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44a90>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44910>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44520>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44df0>]
insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s)
batch 1
Loaded 0 chid ids of type : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 492689227 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 492689227 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493061979 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 494826614 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2096875709 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2096875721 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 492689227 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 492689227 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493061979 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 494826614 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2096875709 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2096875721 on photo_id : 0
Number RLEs to save : 0
 TO DO : save crop sub photo not yet done ! 

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 1.2455968856811523
time spend to save output : 3.814697265625e-05
total time spend for step 1 : 1.2456350326538086

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
 saveOutput not yet implemented for datou_step.type : flip we use saveGeneral 
[911785586]
 map_info['map_portfolio_photo'] : {}
 final :  True mtd_id 571 list_pids : [911785586]
Looping around the photos to save general results
len do output : 1
/0
before output type
Managing all output in save final without adding information in the mtr_datou_result 
('571', None, None, None, None, None, None, None, None)
('571', None, '911785586', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 1
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('571', None, '911785586', None, None, None, None, None, None)]
time used for this insertion : 0.04052424430847168
 save_final 

save missing photos in datou_result :
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {'0': ['911785586', 'temp/1759139039_2886529_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg', [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44eb0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44a60>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44700>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44af0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44970>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44340>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44370>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44130>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44a90>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44910>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44520>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c44df0>]]}
ERROR, got 1 upload, expected 2
ERROR flip FAILED

############################### TEST crop_rles  ################################
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
Unexpected type seems boolean for variable list_input_json
ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0)
 Tried to parse : 
TEST CROP RLES
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=686

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=686
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 686
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=686
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : crop
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (950103132) 
 Found this number of photos: 1
##### Call download_photos : nb_thread : 5
begin to download photo : 950103132
download finish for photo 950103132
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.15990376472473145
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  1
step1:crop
Mon Sep 29 11:44:01 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00.jpg': 950103132}
map_photo_id_path_extension : {950103132: {'path': 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou_step Crop !
param_json : {'photo_hashtag_type': 755, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'feed_id_new_photos': 0, 'host': 'www.fotonower.com', 'crop_type': 'rle', 'margin_relative': 0.1, 'min_score': 0.3, 'upload,type': 'python'}
margin_type : margin_relative
margin_value : [0.1, 0.1, 0.1, 0.1]


Loading chi in step crop with photo_hashtag_type : 755
Loading chi in step crop for list_pids : 1 !
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 950103132) and `type` in (755)  and score>0.3
Loaded 8 chid ids of type : 755
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947670931,1947670932,1947670933,1947670934,1947670935,1947670936,1947670937,1947670938)
++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947670931,1947670932,1947670933,1947670934,1947670935,1947670936,1947670937,1947670938)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947670931,1947670932,1947670933,1947670934,1947670935,1947670936,1947670937,1947670938)
select photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type, id from MTRPhoto.photo_sub_photos  where photo_id in ( 950103132) 
WARNING : margin is only used for type bib !

type of cropped photo chosen : rle
we resize croppped photo by 1 on x axis and by 1 on y axis
we have both polygon and rles
Here we manage rles !
we have both polygon and rles
Here we manage rles !
we have both polygon and rles
Here we manage rles !
we have both polygon and rles
Here we manage rles !
we have both polygon and rles
Here we manage rles !
we have both polygon and rles
Here we manage rles !
we have both polygon and rles
Here we manage rles !
we have both polygon and rles
Here we manage rles !
 map_result returned by crop_photo_return_map_crop : length : 8
map_result after crop : {1947670931: {'crop': 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670931_0.jpg', 'coordonates': (183, 199, 15, 41), 'sub_photo_id': -1, 'same_chi': False}, 1947670932: {'crop': 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670932_0.jpg', 'coordonates': (38, 85, 113, 140), 'sub_photo_id': -1, 'same_chi': False}, 1947670933: {'crop': 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670933_0.jpg', 'coordonates': (168, 194, 141, 151), 'sub_photo_id': -1, 'same_chi': False}, 1947670934: {'crop': 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670934_0.jpg', 'coordonates': (47, 101, 16, 110), 'sub_photo_id': -1, 'same_chi': False}, 1947670935: {'crop': 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670935_0.jpg', 'coordonates': (175, 199, 104, 111), 'sub_photo_id': -1, 'same_chi': False}, 1947670936: {'crop': 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670936_0.jpg', 'coordonates': (86, 130, 184, 196), 'sub_photo_id': -1, 'same_chi': False}, 1947670937: {'crop': 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670937_0.jpg', 'coordonates': (79, 195, 0, 61), 'sub_photo_id': -1, 'same_chi': False}, 1947670938: {'crop': 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670938_0.jpg', 'coordonates': (131, 155, 181, 195), 'sub_photo_id': -1, 'same_chi': False}}
Here we crop with rles
 About to insert : list_path_to_insert length 8 new photo from crops !

About to upload 8 photos
 Status : 404
 Content : b'<!doctype html>\n<html lang=en>\n<title>404 Not Found</title>\n<h1>Not Found</h1>\n<p>The requested URL was not found on the server. If you entered the URL manually please check your spelling and try again.</p>\n'
 All Response : <Response [404]>
 WARNING : NO PORTFOLIO CREATED portfolio_id 0 used instead of new portfolio
upload in portfolio : 0
in upload media
 Upload medias :  ['temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg'] : url : https://www.fotonower.com/api/v1/secured/photo/upload?token=78d09a0790ec6ecbf119343125a81fdc&datou=0
temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg
temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg
temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg
temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg
temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg
temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg
temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg
temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg
after data_to_send, before sending request
after request
<Response [200]>
b'{"map_files_photo_id": {"file0": 1386782163, "file1": 1386782164, "file2": 1386782165, "file3": 1386782166, "file4": 1386782167, "file5": 1386782168, "file6": 1386782169, "file7": 1386782170}}'
Result OK !

uploaded one batch 0 Elapsed time : 5.141423940658569

map_result_insert : {'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg': 1386782163, 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg': 1386782164, 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg': 1386782165, 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg': 1386782166, 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg': 1386782167, 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg': 1386782168, 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg': 1386782169, 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg': 1386782170}
Now we prepare data that will be used for ellipse search !
 chi_id found to be used  1947670931
 path of cropped varroa found to be used to match on an ellipse temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg
 sub_photo_id found to be used  1386782163
 chi_id found to be used  1947670932
 path of cropped varroa found to be used to match on an ellipse temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg
 sub_photo_id found to be used  1386782164
 chi_id found to be used  1947670933
 path of cropped varroa found to be used to match on an ellipse temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg
 sub_photo_id found to be used  1386782165
 chi_id found to be used  1947670934
 path of cropped varroa found to be used to match on an ellipse temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg
 sub_photo_id found to be used  1386782166
 chi_id found to be used  1947670935
 path of cropped varroa found to be used to match on an ellipse temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg
 sub_photo_id found to be used  1386782167
 chi_id found to be used  1947670936
 path of cropped varroa found to be used to match on an ellipse temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg
 sub_photo_id found to be used  1386782168
 chi_id found to be used  1947670937
 path of cropped varroa found to be used to match on an ellipse temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg
 sub_photo_id found to be used  1386782169
 chi_id found to be used  1947670938
 path of cropped varroa found to be used to match on an ellipse temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg
 sub_photo_id found to be used  1386782170
insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(1947670931, '1386782163', 31), (1947670932, '1386782164', 31), (1947670933, '1386782165', 31), (1947670934, '1386782166', 31), (1947670935, '1386782167', 31), (1947670936, '1386782168', 31), (1947670937, '1386782169', 31), (1947670938, '1386782170', 31)]
map of cropped photos with some data : 
{'1386782163': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1386782164': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1386782165': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1386782166': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1386782167': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1386782168': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1386782169': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1386782170': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]}

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 5.301354885101318
time spend to save output : 5.221366882324219e-05
total time spend for step 1 : 5.301407098770142

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
 saveOutput not yet implemented for datou_step.type : crop we use saveGeneral 
[950103132]
 map_info['map_portfolio_photo'] : {}
 final :  True mtd_id 686 list_pids : [950103132]
Looping around the photos to save general results
len do output : 8
/1386782163Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1386782164Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1386782165Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1386782166Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1386782167Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1386782168Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1386782169Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1386782170Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
before output type
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f1515c44af0>
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f1515c44970>
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f1515c44130>
Managing all output in save final without adding information in the mtr_datou_result 
('686', None, None, None, None, None, None, None, None)
('686', None, '950103132', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 25
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('686', None, '1386782163', 'None', None, None, None, None, None), ('686', None, '1386782164', 'None', None, None, None, None, None), ('686', None, '1386782165', 'None', None, None, None, None, None), ('686', None, '1386782166', 'None', None, None, None, None, None), ('686', None, '1386782167', 'None', None, None, None, None, None), ('686', None, '1386782168', 'None', None, None, None, None, None), ('686', None, '1386782169', 'None', None, None, None, None, None), ('686', None, '1386782170', 'None', None, None, None, None, None), ('686', None, '950103132', None, None, None, None, None, None)]
time used for this insertion : 0.04067635536193848
 save_final 

save missing photos in datou_result :
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {'1386782163': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1386782164': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1386782165': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1386782166': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1386782167': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1386782168': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1386782169': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1386782170': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]}
 ret_da : {'1386782163': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1386782164': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1386782165': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1386782166': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1386782167': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1386782168': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1386782169': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1386782170': ['950103132', 'temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]}
8
Found filename_to_hash : temp/1759139040_2886529_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg

############################### TEST angular_coeff  ################################
t
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=852

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=852
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 852
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=852
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : angular_coeff
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (932296368) 
 Found this number of photos: 1
##### Call download_photos : nb_thread : 5
begin to download photo : 932296368
download finish for photo 932296368
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.19237232208251953
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  1
step1:angular_coeff
Mon Sep 29 11:44:06 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139046_2886529_932296368_97c5e7b0f2830e550e2d6eeb248d8006.jpg': 932296368}
map_photo_id_path_extension : {932296368: {'path': 'temp/1759139046_2886529_932296368_97c5e7b0f2830e550e2d6eeb248d8006.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
beginning of step detection filter
 param_json : {'input_type': 846, 'output_type': -1, 'orientation_type': 872, 'ref_crop_type': 846, 'condition_crop': 'car', 'criteria_crop': 'center_rect', 'crops_coeffs': {'CAR_EXTERIEUR_angle_avant_droit.*': {'aile-avant': [[15, 0.0], [240, 0.0], [285, 1.0], [345, 1.0]], 'capot': [[45, 1.0], [60, 0.5], [270, 0.0], [315, 1.0], [360, 1.0]]}}}
angular_coefficients_to_crops
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 932296368) and `type` in (846) 
Loaded 19 chid ids of type : 846
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (769189713,769189714,769189715,769189716,769189717,769189718,769189721,769189723,769189724,769189725,769189727,769189729,769189730,769189732,769189733,769189734,769189737,769189738,769189739)
SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (769189713,769189714,769189715,769189716,769189717,769189718,769189721,769189723,769189724,769189725,769189727,769189729,769189730,769189732,769189733,769189734,769189737,769189738,769189739)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (769189713,769189714,769189715,769189716,769189717,769189718,769189721,769189723,769189724,769189725,769189727,769189729,769189730,769189732,769189733,769189734,769189737,769189738,769189739)
select distinct hashtag_id from MTRBack.photo_hashtag_ids where photo_id in (932296368) and type=872
 treating photo 932296368
select distinct hashtag_id from MTRBack.photo_hashtag_ids where photo_id in (932296368) and type=872

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 0.22046613693237305
time spend to save output : 4.792213439941406e-05
total time spend for step 1 : 0.22051405906677246

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 0
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {932296368: ([(932296368, 2106233860, 846, 1066, 1277, 93, 340, 0.31964028378983567, 0, []), (932296368, 2106233860, 846, 434, 690, 218, 498, 0.7170410105787726, 0, []), (932296368, 503548896, 846, 902, 1111, 466, 576, 0.31724966, 769189715, []), (932296368, 599722655, 846, 523, 1100, 152, 337, 0.98039776, 0, []), (932296368, 492601069, 846, 143, 1190, 90, 695, 0.9696157, 769189717, []), (932296368, 492601069, 846, 0, 408, 246, 719, 0.9431181, 769189718, []), (932296368, 2096875722, 846, 567, 964, 162, 215, 0.55490255, 769189721, []), (932296368, 2096875709, 846, 437, 939, 24, 198, 0.9983077, 769189723, []), (932296368, 2096875709, 846, 1004, 1263, 28, 144, 0.9485744, 769189724, []), (932296368, 624624117, 846, 595, 1122, 331, 640, 0.99100167, 769189725, []), (932296368, 492624020, 846, 585, 874, 308, 393, 0.78697366, 769189727, []), (932296368, 2096875719, 846, 943, 1100, 428, 547, 0.96733797, 769189729, []), (932296368, 492654799, 846, 253, 467, 35, 441, 0.99621326, 769189730, []), (932296368, 492689227, 846, 1118, 1264, 270, 438, 0.9901647, 769189732, []), (932296368, 492689227, 846, 486, 671, 378, 690, 0.98789483, 769189733, []), (932296368, 492689227, 846, 161, 255, 229, 409, 0.70801014, 769189734, []), (932296368, 492925064, 846, 261, 421, 27, 193, 0.92215157, 769189737, []), (932296368, 492925064, 846, 873, 1045, 46, 156, 0.7535122, 769189738, []), (932296368, 492925064, 846, 1090, 1279, 20, 107, 0.45259848, 769189739, [])],)}
test angular coeff is a success !

############################### TEST detection_filter_by_crop  ################################
t
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=708

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=708
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 708
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=708
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : detection_filter_by_crop
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (946711423) 
 Found this number of photos: 1
##### Call download_photos : nb_thread : 5
begin to download photo : 946711423
download finish for photo 946711423
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.15142273902893066
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  1
step1:detection_filter_by_crop
Mon Sep 29 11:44:07 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139047_2886529_946711423_b4bef6b5c6c4b6ffae23f8718c42183c.jpg': 946711423}
map_photo_id_path_extension : {946711423: {'path': 'temp/1759139047_2886529_946711423_b4bef6b5c6c4b6ffae23f8718c42183c.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
beginning of step detection filter
 param_json : {'input_type': 631, 'output_type': -1, 'condition_type': 445, 'condition_crop': 'car', 'criteria_crop': 'center_rect', 'min_surface_ratio': 0.7}
conditional_crop_copy
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 946711423) and `type` in (445) 
Loaded 3 chid ids of type : 445
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947734477,18345275,18345276)
+++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947734477,18345275,18345276)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947734477,18345275,18345276)
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 946711423) and `type` in (631) 
Loaded 35 chid ids of type : 631
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552)
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552)
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 946711423) and `type` in (445) 
Loaded 3 chid ids of type : 445
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947734477,18345275,18345276)
+++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947734477,18345275,18345276)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947734477,18345275,18345276)
 treating photo 946711423
SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552)
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 624624117, 'type': 631, 'x0': 226, 'x1': 569, 'y0': 252, 'y1': 425, 'score': 0.99812776, 'id': 1947740368, 'points': ['395,419,341,419,340,418,316,418,315,417,306,417,
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 162, 'x1': 245, 'y0': 233, 'y1': 396, 'score': 0.99702626, 'id': 1947740369, 'points': ['215,393,206,393,202,390,200,390,192,383,191,380,
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 96, 'x1': 172, 'y0': 39, 'y1': 261, 'score': 0.9928518, 'id': 1947740370, 'points': ['143,252,143,249,141,246,140,246,138,248,138,251,137
   crop not duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 545, 'x1': 612, 'y0': 186, 'y1': 276, 'score': 0.9876676, 'id': 1947740371, 'points': ['584,267,583,266,578,266,574,262,574,259,573,258,5 surface aoutside conditional crop
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2096875719, 'type': 631, 'x0': 468, 'x1': 555, 'y0': 292, 'y1': 365, 'score': 0.9830025, 'id': 1947740372, 'points': ['491,350,489,350,488,349,487,350,483,350,480,348,
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 599722655, 'type': 631, 'x0': 176, 'x1': 535, 'y0': 138, 'y1': 264, 'score': 0.9818268, 'id': 1947740373, 'points': ['453,253,413,253,412,252,387,252,386,250,386,248,3
   crop not duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 53, 'x1': 87, 'y0': 127, 'y1': 212, 'score': 0.9786105, 'id': 1947740374, 'points': ['74,201,69,201,67,199,66,199,65,198,62,192,62,190,61 surface aoutside conditional crop
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492844413, 'type': 631, 'x0': 89, 'x1': 163, 'y0': 93, 'y1': 144, 'score': 0.9772748, 'id': 1947740375, 'points': ['159,142,153,141,151,139,148,138,145,135,141,133,139
   crop not duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 418, 'x1': 522, 'y0': 69, 'y1': 136, 'score': 0.97407305, 'id': 1947740376, 'points': ['510,121,507,121,505,119,501,120,500,119,500,113,4 surface aoutside conditional crop
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2096875709, 'type': 631, 'x0': 185, 'x1': 431, 'y0': 39, 'y1': 136, 'score': 0.97171515, 'id': 1947740377, 'points': ['331,134,287,134,286,133,284,133,283,134,272,134,
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2096875722, 'type': 631, 'x0': 198, 'x1': 395, 'y0': 118, 'y1': 142, 'score': 0.9699756, 'id': 1947740378, 'points': ['328,137,251,137,250,136,249,137,241,137,240,136,
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 499500794, 'type': 631, 'x0': 93, 'x1': 107, 'y0': 127, 'y1': 146, 'score': 0.9574813, 'id': 1947740379, 'points': ['101,143,98,143,95,139,95,131,97,129,100,129,101,13
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 71, 'x1': 125, 'y0': 36, 'y1': 95, 'score': 0.95296955, 'id': 1947740380, 'points': ['104,92,96,92,93,90,91,90,86,86,83,85,83,84,81,82,80
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 101, 'x1': 167, 'y0': 38, 'y1': 127, 'score': 0.9508439, 'id': 1947740381, 'points': ['154,117,152,115,152,112,150,110,148,106,148,104,14
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492624020, 'type': 631, 'x0': 249, 'x1': 400, 'y0': 219, 'y1': 316, 'score': 0.8792459, 'id': 1947740382, 'points': ['395,313,390,313,386,311,384,312,381,312,376,309,3
   crop not duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 540, 'x1': 625, 'y0': 78, 'y1': 221, 'score': 0.87864035, 'id': 1947740383, 'points': ['567,127,566,127,565,126,564,109,562,106,560,106,5 surface aoutside conditional crop
   crop not duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 547, 'x1': 640, 'y0': 79, 'y1': 129, 'score': 0.8165246, 'id': 1947740384, 'points': ['630,96,627,96,627,94,628,92,629,92,631,94,631,95', surface aoutside conditional crop
   crop not duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 360, 'x1': 434, 'y0': 62, 'y1': 116, 'score': 0.74684095, 'id': 1947740385, 'points': ['415,103,413,103,411,101,408,101,405,99,403,99,401 surface aoutside conditional crop
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 503548896, 'type': 631, 'x0': 302, 'x1': 540, 'y0': 339, 'y1': 403, 'score': 0.7406652, 'id': 1947740386, 'points': ['442,401,372,401,372,397,370,395,369,392,366,390,3
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 53, 'x1': 85, 'y0': 75, 'y1': 182, 'score': 0.73015845, 'id': 1947740387, 'points': ['70,147,68,145,65,139,65,137,62,132,61,128,57,126,5
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2096875717, 'type': 631, 'x0': 477, 'x1': 510, 'y0': 220, 'y1': 243, 'score': 0.69028217, 'id': 1947740388, 'points': ['501,241,493,241,489,239,488,237,487,237,480,232
   crop not duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 61, 'x1': 115, 'y0': 42, 'y1': 188, 'score': 0.6900027, 'id': 1947740389, 'points': ['92,47,91,45,92,44,96,45,94,45', '73,141,73,136,72,1 surface aoutside conditional crop
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2096875712, 'type': 631, 'x0': 309, 'x1': 326, 'y0': 382, 'y1': 404, 'score': 0.6633776, 'id': 1947740390, 'points': ['309,383,309,382,311,382', '325,385,324,383,319,3
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2096875719, 'type': 631, 'x0': 427, 'x1': 553, 'y0': 258, 'y1': 315, 'score': 0.6446218, 'id': 1947740391, 'points': ['531,284,526,284,525,283,525,281,523,279,522,280,
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2106233861, 'type': 631, 'x0': 144, 'x1': 267, 'y0': 181, 'y1': 307, 'score': 0.63958377, 'id': 1947740392, 'points': ['212,251,209,251,208,250,203,251,201,250,201,249
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2096875712, 'type': 631, 'x0': 285, 'x1': 433, 'y0': 343, 'y1': 377, 'score': 0.61493844, 'id': 1947740393, 'points': ['431,376,286,376,285,375,285,368,286,367,286,362
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 146, 'x1': 287, 'y0': 140, 'y1': 311, 'score': 0.54784286, 'id': 1947740394, 'points': ['234,254,227,254,221,251,219,248,215,253,212,253
   crop not duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 496, 'x1': 624, 'y0': 141, 'y1': 245, 'score': 0.46262404, 'id': 1947740395, 'points': ['603,176,599,173,595,176,593,176,590,174,589,174 surface aoutside conditional crop
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 495920967, 'type': 631, 'x0': 202, 'x1': 524, 'y0': 112, 'y1': 333, 'score': 0.45109355, 'id': 1947740396, 'points': ['483,289,483,286,482,285,482,283,480,279,480,274,
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2096875722, 'type': 631, 'x0': 433, 'x1': 558, 'y0': 248, 'y1': 286, 'score': 0.44133398, 'id': 1947740397, 'points': ['492,272,474,272,473,271,468,271,465,269,460,269
   crop not duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2106233861, 'type': 631, 'x0': 535, 'x1': 630, 'y0': 138, 'y1': 231, 'score': 0.42747068, 'id': 1947740398, 'points': ['590,171,589,170,585,170,584,171,581,169,579,169 surface aoutside conditional crop
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 399, 'x1': 569, 'y0': 68, 'y1': 251, 'score': 0.41876298, 'id': 1947740399, 'points': [], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', '
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492624020, 'type': 631, 'x0': 420, 'x1': 552, 'y0': 244, 'y1': 293, 'score': 0.35962066, 'id': 1947740400, 'points': ['474,289,453,289,452,288,439,288,437,286,431,286,
   crop duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 503548896, 'type': 631, 'x0': 301, 'x1': 540, 'y0': 339, 'y1': 403, 'score': 0.740756, 'id': 3140491551, 'points': ['442,401,371,401,371,397,366,390,365,386,356,386,35
   crop not duplicated : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 496, 'x1': 624, 'y0': 140, 'y1': 245, 'score': 0.4627206, 'id': 3140491552, 'points': ['595,176,593,176,589,173,586,176,583,176,580,174, surface aoutside conditional crop

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 0.30726003646850586
time spend to save output : 3.62396240234375e-05
total time spend for step 1 : 0.3072962760925293

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 0
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {946711423: ([(946711423, 624624117, 631, 226, 569, 252, 425, 0.99812776, 1947740368, ['395,419,341,419,340,418,316,418,315,417,306,417,305,416,293,415,290,413,284,412,283,411,280,411,272,407,264,405,258,400,254,398,250,394,244,391,242,389,242,386,239,380,240,368,239,367,239,347,238,346,238,331,237,330,237,327,238,326,237,314,239,311,239,308,237,304,238,302,243,298,244,296,244,292,246,291,250,291,251,290,259,290,260,289,264,289,265,288,269,288,271,290,273,294,278,299,280,300,285,300,286,301,293,301,294,302,302,304,305,307,309,308,312,310,314,310,317,312,335,312,336,313,343,313,344,314,370,314,371,315,381,315,382,314,389,313,393,311,405,309,406,308,408,308,412,306,414,304,417,304,421,307,426,308,427,309,433,309,434,310,464,309,467,306,471,304,476,304,477,303,489,303,490,302,494,302,495,301,500,301,501,300,515,300,516,299,519,298,522,292,525,290,533,290,534,291,540,291,541,290,543,290,547,288,550,285,550,285,552,289,552,291,553,292,553,313,552,314,552,324,550,328,550,333,549,334,549,336,544,346,543,353,539,361,532,368,531,368,527,372,519,374,509,379,503,384,499,385,498,386,496,386,492,388,490,390,486,392,484,392,479,396,475,397,474,398,472,398,471,399,469,399,462,403,460,403,459,404,457,404,456,405,454,405,450,407,448,407,443,410,425,413,424,414,422,414,416,417,404,417,403,418,396,418']), (946711423, 492689227, 631, 162, 245, 233, 396, 0.99702626, 1947740369, ['215,393,206,393,202,390,200,390,192,383,191,380,187,375,184,369,184,367,180,360,180,358,179,357,177,349,175,347,174,339,172,336,171,330,170,329,169,324,168,323,168,313,167,312,167,304,166,303,166,298,165,297,165,288,164,287,165,286,165,272,166,271,166,268,167,267,167,263,168,262,169,254,173,249,177,247,178,247,181,251,184,251,184,252,187,255,189,255,193,259,193,261,195,263,195,264,201,270,203,278,207,282,208,289,211,293,211,296,213,299,214,304,215,305,216,312,219,316,219,319,220,320,220,325,222,329,222,335,223,336,223,338,225,342,225,349,226,350,226,359,227,360,227,366,228,367,228,371,231,375,231,382,227,385,226,388,225,389,223,388,219,392,216,392']), (946711423, 492654799, 631, 96, 172, 39, 261, 0.9928518, 1947740370, ['143,252,143,249,141,246,140,246,138,248,138,251,137,250,137,248,135,246,134,246,132,248,127,244,124,244,122,241,122,236,121,235,121,232,118,229,117,225,116,224,116,212,113,209,115,207,116,201,111,194,110,184,106,178,107,154,108,152,112,148,113,144,112,143,112,138,110,136,108,136,107,135,103,128,103,124,102,123,102,121,103,120,103,118,106,115,106,106,107,105,110,104,113,101,117,93,117,71,114,65,116,61,116,59,117,58,117,55,118,54,119,49,122,45,122,44,124,42,150,42,151,43,153,43,153,47,152,48,152,50,154,52,155,56,156,57,156,85,155,86,155,95,154,96,154,98,155,99,155,105,156,106,155,107,155,116,157,120,159,121,159,123,156,127,156,134,157,135,157,138,156,139,156,141,154,145,152,147,150,151,149,159,148,160,148,164,149,165,149,174,148,175,148,197,149,198,149,215,150,216,150,241,149,242,149,245,148,247,146,245,144,247', '122,147,121,138,120,141,119,142,119,144,118,145,121,148']), (946711423, 2096875719, 631, 468, 555, 292, 365, 0.9830025, 1947740372, ['491,350,489,350,488,349,487,350,483,350,480,348,480,341,482,339,482,337,485,334,487,334,491,330,494,330,495,328,498,326,501,326,503,324,507,325,509,323,514,321,516,319,518,321,520,321,521,319,522,319,524,321,527,321,530,317,530,315,531,314,535,313,540,309,543,310,544,311,542,313,542,314,544,316,541,318,541,322,536,322,535,323,533,323,532,322,528,322,527,321,524,321,522,323,518,322,516,324,517,327,516,328,512,327,510,329,512,332,513,332,515,330,516,331,516,333,514,332,511,333,511,336,514,337,516,336,516,339,515,339,513,338,511,340,512,341,512,342,510,343,507,343,502,347,500,347,497,349,492,349', '514,325,515,324,513,322,512,322,511,325,512,326', '522,327,521,327,521,326,522,325']), (946711423, 599722655, 631, 176, 535, 138, 264, 0.9818268, 1947740373, ['453,253,413,253,412,252,387,252,386,250,386,248,383,246,379,245,376,243,361,243,361,240,362,239,359,238,358,237,356,237,355,236,352,236,351,235,333,235,332,234,329,234,329,233,331,231,331,229,329,228,328,224,330,222,330,221,324,218,308,219,307,218,302,218,298,216,288,217,287,218,285,218,283,220,283,221,287,224,295,225,295,225,294,226,289,226,288,227,283,227,282,228,273,228,272,229,271,228,259,228,258,227,254,227,253,226,247,225,247,225,251,221,248,218,243,216,247,213,248,213,249,212,248,211,246,211,245,210,241,210,240,209,237,209,236,208,231,207,230,206,228,202,224,201,223,200,221,200,220,199,214,198,213,195,211,193,208,193,203,189,203,184,201,181,201,176,198,171,199,170,199,158,203,154,205,153,205,151,206,149,209,149,210,148,225,148,226,147,283,147,284,148,287,148,288,147,305,147,306,148,312,148,313,147,354,147,355,146,428,146,429,147,433,147,434,148,437,148,438,149,451,149,457,156,459,162,462,165,464,166,471,166,472,165,477,165,480,167,480,171,486,175,488,175,489,176,502,176,503,178,503,180,509,185,509,189,512,193,512,199,513,200,513,203,514,204,514,210,513,211,514,217,512,221,513,222,513,225,510,229,510,235,507,237,504,238,502,243,490,243,489,244,485,244,484,245,480,245,479,246,463,246,462,247,460,247,458,249,457,252,454,252', '528,212,528,207,526,206,524,203,526,203,527,202,528,202', '299,215,302,212,299,211,298,210,291,210,290,211,281,212,286,215,290,215,291,216', '375,242,376,240,375,238,363,239,368,242,371,242,372,243']), (946711423, 492844413, 631, 89, 163, 93, 144, 0.9772748, 1947740375, ['159,142,153,141,151,139,148,138,145,135,141,133,139,133,138,132,131,132,130,131,125,131,124,130,121,130,120,129,116,129,115,128,112,128,108,126,106,126,100,123,98,121,94,113,94,104,97,101,103,98,105,98,106,97,110,97,111,96,116,96,117,95,132,95,133,96,139,97,141,99,144,100,149,105,150,107,154,108,155,113,157,115,158,115,160,118,160,120,161,121,161,133,160,134,160,140']), (946711423, 2096875709, 631, 185, 431, 39, 136, 0.97171515, 1947740377, ['331,134,287,134,286,133,284,133,283,134,272,134,271,133,264,133,263,134,258,134,257,133,254,133,253,132,236,132,235,131,225,131,224,132,223,131,213,131,212,130,208,130,207,129,204,129,203,128,199,127,193,121,192,117,189,113,189,110,188,109,187,93,186,92,187,91,187,89,186,88,186,65,185,64,186,63,186,61,185,60,185,48,186,47,186,42,187,40,232,40,233,41,248,41,249,42,281,43,282,44,290,44,291,45,300,45,301,46,308,46,309,47,314,47,315,48,322,49,328,53,334,54,336,56,339,57,344,62,349,64,351,66,353,67,356,67,358,69,359,72,363,76,367,78,369,80,379,91,380,93,383,94,390,100,393,101,395,103,396,106,399,109,402,110,406,115,408,115,410,117,410,120,412,123,411,127,409,129,399,129,398,130,395,130,394,131,378,131,377,132,368,132,367,131,346,131,345,132,342,132,341,133,332,133']), (946711423, 2096875722, 631, 198, 395, 118, 142, 0.9699756, 1947740378, ['328,137,251,137,250,136,249,137,241,137,240,136,219,136,218,135,213,135,212,134,206,133,205,132,201,131,200,130,200,122,201,121,205,121,206,122,222,122,226,124,239,124,240,125,369,125,370,124,371,125,389,125,391,127,391,133,390,134,386,134,385,135,380,135,379,134,375,134,374,135,341,135,340,136,329,136']), (946711423, 499500794, 631, 93, 107, 127, 146, 0.9574813, 1947740379, ['101,143,98,143,95,139,95,131,97,129,100,129,101,133,102,134,102,136,103,137,103,140']), (946711423, 492925064, 631, 71, 125, 36, 95, 0.95296955, 1947740380, ['104,92,96,92,93,90,91,90,86,86,83,85,83,84,81,82,80,82,75,77,75,75,74,74,74,66,75,65,75,62,77,60,77,58,80,55,80,54,83,51,83,50,88,45,94,44,95,43,99,43,100,42,113,42,117,45,117,47,116,48,116,51,115,52,114,59,113,60,112,65,111,66,111,69,110,70,110,75,109,76,109,83,108,84,108,86,109,87,108,89']), (946711423, 492925064, 631, 101, 167, 38, 127, 0.9508439, 1947740381, ['154,117,152,115,152,112,150,110,148,106,148,104,145,101,143,100,138,100,137,99,135,99,133,95,131,95,126,93,126,91,128,88,128,83,129,82,129,70,127,68,127,66,128,65,125,61,127,59,127,56,129,52,129,49,130,47,135,42,144,42,148,45,151,49,151,60,152,61,152,75,153,76,153,80,155,83,155,87,156,88,156,105,155,106,156,107,156,110,154,112,156,116', '109,100,108,100,107,99,109,97']), (946711423, 492624020, 631, 249, 400, 219, 316, 0.8792459, 1947740382, ['395,313,390,313,386,311,384,312,381,312,376,309,358,308,357,307,354,307,353,306,350,306,349,307,345,305,343,303,341,304,337,304,334,302,325,302,324,301,315,300,313,298,313,297,310,295,304,295,300,293,295,293,291,288,289,287,283,287,281,285,281,283,278,280,274,280,272,279,272,276,270,273,270,270,269,268,266,265,265,265,264,264,264,262,261,260,260,258,260,256,261,255,259,252,259,248,258,246,255,244,256,241,252,239,251,238,251,226,265,226,266,227,268,227,272,232,276,232,277,233,279,233,281,234,283,237,285,238,290,239,293,241,296,241,304,246,312,247,316,251,318,252,320,252,326,255,328,255,337,260,342,260,343,261,345,261,349,264,351,264,355,266,357,268,364,271,366,273,370,275,374,275,376,276,377,279,379,281,383,282,384,283,386,283,387,286,390,289,390,290,394,294,396,294,398,296,398,308,397,309,397,311']), (946711423, 503548896, 631, 302, 540, 339, 403, 0.7406652, 1947740386, ['442,401,372,401,372,397,370,395,369,392,366,390,367,389,366,386,357,386,354,384,350,384,349,383,320,383,319,382,320,378,318,376,318,374,314,370,309,370,308,369,306,369,305,363,305,357,306,356,306,353,307,353,308,354,313,354,314,355,315,354,320,354,321,353,331,353,332,354,335,354,336,355,339,355,340,356,379,356,380,357,406,357,407,356,409,356,410,357,474,357,475,356,482,356,484,357,485,356,488,356,493,353,501,354,502,353,506,353,507,352,517,352,518,351,522,351,525,347,527,346,530,347,530,349,533,351,530,355,528,355,527,356,515,356,509,359,508,361,505,362,503,365,497,368,494,372,490,373,489,374,492,376,495,376,493,377,488,378,490,380,495,380,497,381,497,381,487,382,485,385,476,387,469,392,466,392,465,393,460,393,456,396,453,397,451,399,443,400', '519,353,518,352,517,353,518,354']), (946711423, 2106233860, 631, 53, 85, 75, 182, 0.73015845, 1947740387, ['70,147,68,145,65,139,65,137,62,132,61,128,57,126,56,124,56,121,54,119,54,110,56,108,56,103,59,100,60,101,61,100,61,96,63,93,63,89,66,84,65,83,65,80,66,78,68,78,68,79,70,80,70,83,74,87,74,90,75,91,75,100,77,102,77,105,75,106,75,125,76,126,77,125,77,125,77,128,76,129,77,131,77,136,75,139,75,143,78,145,76,146,71,146', '61,107,60,106,59,107,60,108', '77,134,76,131,75,134,76,135']), (946711423, 2096875717, 631, 477, 510, 220, 243, 0.69028217, 1947740388, ['501,241,493,241,489,239,488,237,487,237,480,232,479,230,479,226,484,222,487,222,488,223,492,224,496,228,497,228,497,229,502,234,502,235,504,236,504,240,502,240']), (946711423, 2096875712, 631, 309, 326, 382, 404, 0.6633776, 1947740390, ['309,383,309,382,311,382', '325,385,324,383,319,383,318,382,325,382', '320,400,311,400,309,398,309,385,310,386,311,385,315,385,316,384,318,384,319,385,322,385,325,387,325,398,323,398']), (946711423, 2096875719, 631, 427, 553, 258, 315, 0.6446218, 1947740391, ['531,284,526,284,525,283,525,281,523,279,522,280,519,281,521,282,521,283,519,284,518,283,519,281,515,279,516,277,515,276,513,276,512,277,513,279,511,280,507,279,505,276,504,279,497,279,496,278,495,279,485,279,484,280,480,280,479,281,481,283,482,283,482,283,469,283,468,284,438,284,436,283,440,279,446,279,447,278,456,278,458,276,457,275,457,275,467,275,468,274,490,274,491,273,494,273,496,271,496,269,499,268,501,266,503,265,506,265,510,263,514,263,516,261,520,259,544,259,544,259,543,260,545,262,548,262,550,263,550,276,549,277,547,277,540,282,538,282,537,283,532,283']), (946711423, 2106233861, 631, 144, 267, 181, 307, 0.63958377, 1947740392, ['212,251,209,251,208,250,203,251,201,250,201,249,195,243,189,242,188,241,185,241,184,240,182,236,180,236,179,235,173,235,172,234,170,235,164,235,163,234,163,232,162,231,163,217,166,217,168,218,170,215,171,210,172,209,173,209,176,212,178,210,178,208,181,203,186,203,188,201,193,201,194,202,195,201,201,201,202,202,204,202,205,201,209,201,210,202,212,202,215,200,217,200,220,202,221,201,227,201,231,205,231,206,234,209,235,209,235,210,238,213,238,224,234,228,235,232,234,233,228,234,225,237,224,241,222,242,216,242,209,246,211,248,212,248,213,250', '221,228,220,227,219,228,220,229', '224,238,224,237,221,235,217,237,219,239']), (946711423, 2096875712, 631, 285, 433, 343, 377, 0.61493844, 1947740393, ['431,376,286,376,285,375,285,368,286,367,286,362,287,361,287,359,291,359,297,362,306,363,307,364,312,364,313,365,322,366,323,367,331,366,332,368,334,368,335,369,338,368,337,366,336,366,337,365,336,364,327,364,325,361,319,361,317,357,317,356,318,355,325,355,326,353,331,353,333,351,332,350,330,350,328,348,326,348,325,347,319,347,315,345,306,345,305,344,297,344,299,344,300,343,431,343,432,344,432,353,431,354,431,358,430,359,430,365,427,365,425,363,424,363,422,364,421,366,418,366,413,369,404,370,409,371,409,371,399,371,398,372,395,373,419,374,420,373,426,372,428,370,428,367,429,367,430,368,429,369,430,370,430,373,431,374', '381,373,378,372,377,371,356,371,356,371,359,370,347,369,345,367,343,367,342,368,343,369,341,370,354,371,354,371,352,372,353,373,359,373,360,374']), (946711423, 2106233860, 631, 146, 287, 140, 311, 0.54784286, 1947740394, ['234,254,227,254,221,251,219,248,215,253,212,253,210,252,206,247,203,247,198,243,197,243,194,239,189,238,186,236,182,236,181,235,167,235,164,233,164,228,159,227,158,226,158,219,159,218,159,213,162,207,162,205,169,192,169,186,170,185,172,185,177,179,175,175,173,173,177,171,181,171,182,170,184,170,187,167,187,164,188,163,188,161,199,161,202,164,205,165,207,167,209,167,212,165,215,165,216,168,218,170,219,170,221,168,221,164,220,163,220,161,222,161,223,160,230,160,231,159,242,159,244,158,247,161,248,161,247,162,246,168,248,172,248,174,253,176,254,180,253,182,249,182,247,185,249,188,253,188,254,189,254,194,249,194,247,196,247,198,249,200,252,200,253,199,255,202,255,205,254,206,254,208,250,207,249,206,246,209,246,210,249,214,252,212,254,212,254,214,255,215,255,217,254,218,254,221,252,221,249,219,247,221,247,225,249,228,250,228,252,226,253,224,253,224,253,229,252,229,251,228,249,228,247,230,247,233,246,234,246,237,245,238,245,240,243,244,243,247,239,251,237,251', '230,167,229,166,227,167,228,168']), (946711423, 495920967, 631, 202, 524, 112, 333, 0.45109355, 1947740396, ['483,289,483,286,482,285,482,283,480,279,480,274,476,270,472,268,465,268,464,269,459,269,458,268,454,268,453,267,437,267,436,268,428,268,427,269,418,269,417,270,414,270,410,266,410,265,416,262,418,262,421,260,423,260,425,259,426,257,424,255,422,255,419,253,417,253,416,252,412,251,410,250,410,249,412,249,413,248,415,248,416,247,422,246,428,243,429,242,428,241,424,240,423,239,420,239,419,238,390,238,389,237,386,237,385,236,369,236,368,235,363,234,363,233,364,232,364,230,366,226,365,225,357,220,344,220,341,218,339,218,339,218,342,212,342,210,336,207,327,207,326,206,319,206,318,205,314,205,313,204,297,204,291,207,288,210,288,212,291,217,290,220,288,222,284,224,282,224,278,227,273,228,271,230,270,235,265,239,262,236,261,232,263,228,266,226,261,224,256,219,256,210,249,206,242,205,237,202,234,195,226,186,227,184,227,180,228,179,225,175,225,174,222,171,225,165,227,163,229,158,230,157,232,156,235,156,236,155,239,155,240,154,245,154,246,155,254,155,255,156,258,156,259,157,268,157,269,156,272,156,273,155,280,155,281,156,298,156,300,155,301,156,307,156,308,157,311,157,318,152,322,151,323,150,333,150,338,146,339,146,342,143,343,143,346,140,357,140,362,136,366,134,368,134,369,133,373,133,374,132,377,132,378,131,388,131,389,130,410,130,411,131,417,131,428,140,432,142,434,142,435,143,443,145,446,147,448,147,451,154,453,156,457,158,462,159,463,160,466,160,467,161,472,162,474,163,474,164,481,171,489,175,491,175,492,176,494,176,495,177,499,178,500,179,502,184,507,189,517,194,518,195,514,201,514,203,518,207,519,209,518,214,515,218,517,227,515,229,515,231,514,232,515,236,518,239,518,252,519,253,519,263,518,264,518,267,517,269,514,272,512,273,512,274,506,280,500,277,498,273,496,272,493,272,491,274,491,278,490,279,490,281', '312,179,311,178,308,179,309,180', '268,269,264,269,259,266,259,262,261,258,261,250,265,245,269,250,270,257,274,260,278,265,275,267,269,268', '414,281,401,281,414,281']), (946711423, 2096875722, 631, 433, 558, 248, 286, 0.44133398, 1947740397, ['492,272,474,272,473,271,468,271,465,269,460,269,460,268,465,266,467,266,468,265,470,265,471,264,475,264,476,263,479,263,480,262,486,262,487,261,491,261,492,260,495,260,496,259,502,259,506,257,510,257,514,255,517,255,518,254,530,253,531,252,535,252,536,251,538,251,539,252,543,252,544,253,547,253,549,251,553,251,555,253,555,267,552,270,550,270,550,269,548,267,547,267,547,267,548,266,547,265,545,266,540,266,539,264,530,264,529,263,524,263,519,266,513,266,510,268,507,268,506,269,499,270,498,271,493,271', '438,279,435,279,435,273,436,272,448,271,449,272,448,274,443,274,440,277,440,278']), (946711423, 492654799, 631, 399, 569, 68, 251, 0.41876298, 1947740399, []), (946711423, 492624020, 631, 420, 552, 244, 293, 0.35962066, 1947740400, ['474,289,453,289,452,288,439,288,437,286,431,286,427,284,423,284,422,283,422,275,427,275,428,273,430,272,435,272,436,271,438,271,442,269,447,269,450,267,454,267,460,264,464,264,467,262,483,261,484,260,488,260,489,259,494,259,495,258,502,258,503,257,505,257,509,255,512,255,516,252,520,252,521,251,526,250,530,248,534,248,535,247,546,247,547,248,549,248,549,250,550,251,550,266,551,267,551,275,550,276,550,278,549,279,549,281,537,282,535,284,528,284,527,285,504,285,503,286,495,286,492,288,488,287,487,288,475,288']), (946711423, 503548896, 631, 301, 540, 339, 403, 0.740756, 3140491551, ['442,401,371,401,371,397,366,390,365,386,356,386,353,384,348,383,319,383,319,378,314,370,310,370,305,368,304,357,305,353,330,353,339,356,378,356,379,357,474,357,475,356,488,356,493,353,501,354,507,352,517,352,522,351,527,346,530,347,533,351,530,355,527,356,515,356,505,362,503,365,497,368,494,372,489,374,492,376,488,378,490,380,495,380,487,382,485,385,476,387,469,392,461,393,456,395,451,399,447,399', '519,353,518,352,517,353,518,354'])],)}
test detection filter by crop is a success !

############################### TEST detection_filter_by_classif  ################################
t
SELECT id FROM MTRPhoto.crop_hashtag_ids WHERE photo_id=946711423 AND `type`=816
DELETE FROM MTRPhoto.crop_hashtag_ids WHERE id IN (3976487413,3976487412,3976487411,3976487420,3976487419,3976487418,3976487417,3976487416,3976487425,3976487428,3976487414,3976487415,3976487424,3976487423,3976487429,3976487430,3976487431,3976487433,3976487421,3976487427,3976487426,3976487432,3976487422) 
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=672

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=672
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 672
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=672
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : detection_filter_by_classif
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (946711423) 
 Found this number of photos: 1
##### Call download_photos : nb_thread : 5
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
##### After load_data_input 
time to download the photos : 0.017860889434814453
#### fin chargement data
Blocking on flush ?
No conitnuing 
About to test input to load 
Calling datou_exec

Inside datou_exec : verbose : True
number of steps :  1
step1:detection_filter_by_classif
Mon Sep 29 11:44:07 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {}
map_photo_id_path_extension : {}
map_subphoto_mainphoto : {}
beginning of step detection filter with classification results
 param_json : {'input_type': 631, 'output_type': 816, 'condition_type': 872, 'crops_ok': {'CAR_DOCUMENT.*': {}, 'CAR_INTERIEUR.*': {}, 'CAR_EXTERIEUR_angle_avant_droit.*': {'Retroviseur': 2, 'Roue': 2, 'Capot': 1, 'Pare-brise': 1, 'vitre': 10, 'phare': 2, 'Feu-antibrouillard': 2, 'poignee': 2, 'porte': 2, 'calandre': 1, 'logo-marque': 1, 'Plaque-immatriculation': 1, 'Essuie-glace': 1, 'pare-choc': 1, 'toit': 1, 'logo-roue': 1, 'aile-avant': 1}}, 'separation': {'CAR_EXTERIEUR_avant.*': {'pare-choc': ['pare-chocs-avant'], 'phare': ['phare-gauche', 'a-droite-de', 'phare-droit']}, 'CAR_EXTERIEUR_angle_avant_droit.*': {'pare-choc': ['pare-chocs-avant'], 'phare': ['phare-droite', 'a-gauche-de', 'phare-gauche'], 'porte': ['porte-avant', 'a-droite-de', 'porte-arriere']}}}
conditional_crop_by_classif_copy
select distinct hashtag_id from MTRBack.photo_hashtag_ids where photo_id in (946711423) and type=872
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 946711423) and `type` in (631) 
Loaded 35 chid ids of type : 631
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552)
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552)
 treating photo 946711423
select distinct hashtag_id from MTRBack.photo_hashtag_ids where photo_id in (946711423) and type=872
  list of crops kept {'retroviseur': 2, 'roue': 2, 'capot': 1, 'pare-brise': 1, 'vitre': 10, 'phare': 2, 'feu-antibrouillard': 2, 'poignee': 2, 'porte': 2, 'calandre': 1, 'logo-marque': 1, 'plaque-immatriculation': 1, 'essuie-glace': 1, 'pare-choc': 1, 'toit': 1, 'logo-roue': 1, 'aile-avant': 1} for hahstag car_exterieur_angle_avant_droit_merge__port_551052
   crop not duplicated for hashtag aile-arriere : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2106233861, 'type': 631, 'x0': 144, 'x1': 267, 'y0': 181, 'y1': 307, 'score': 0.63958377, 'id': 1947740392, 'points': ['212,251,209,251,208,250,203,251,201,250,201,249
   crop not duplicated for hashtag coffre : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 495920967, 'type': 631, 'x0': 202, 'x1': 524, 'y0': 112, 'y1': 333, 'score': 0.45109355, 'id': 1947740396, 'points': ['483,289,483,286,482,285,482,283,480,279,480,274,
   crop not duplicated for hashtag aile-arriere : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2106233861, 'type': 631, 'x0': 535, 'x1': 630, 'y0': 138, 'y1': 231, 'score': 0.42747068, 'id': 1947740398, 'points': ['590,171,589,170,585,170,584,171,581,169,579,169
   crop duplicated for hashtag retroviseur : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492844413, 'type': 631, 'x0': 89, 'x1': 163, 'y0': 93, 'y1': 144, 'score': 0.9772748, 'id': 1947740375, 'points': ['159,142,153,141,151,139,148,138,145,135,141,133,139
   crop duplicated for hashtag roue : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 162, 'x1': 245, 'y0': 233, 'y1': 396, 'score': 0.99702626, 'id': 1947740369, 'points': ['215,393,206,393,202,390,200,390,192,383,191,380,
   crop duplicated for hashtag roue : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 545, 'x1': 612, 'y0': 186, 'y1': 276, 'score': 0.9876676, 'id': 1947740371, 'points': ['584,267,583,266,578,266,574,262,574,259,573,258,5
   crop not duplicated for hashtag roue : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 53, 'x1': 87, 'y0': 127, 'y1': 212, 'score': 0.9786105, 'id': 1947740374, 'points': ['74,201,69,201,67,199,66,199,65,198,62,192,62,190,61
   crop duplicated for hashtag capot : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 599722655, 'type': 631, 'x0': 176, 'x1': 535, 'y0': 138, 'y1': 264, 'score': 0.9818268, 'id': 1947740373, 'points': ['453,253,413,253,412,252,387,252,386,250,386,248,3
   crop duplicated for hashtag pare-brise : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2096875709, 'type': 631, 'x0': 185, 'x1': 431, 'y0': 39, 'y1': 136, 'score': 0.97171515, 'id': 1947740377, 'points': ['331,134,287,134,286,133,284,133,283,134,272,134,
   crop duplicated for hashtag vitre : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 418, 'x1': 522, 'y0': 69, 'y1': 136, 'score': 0.97407305, 'id': 1947740376, 'points': ['510,121,507,121,505,119,501,120,500,119,500,113,4
   crop duplicated for hashtag vitre : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 71, 'x1': 125, 'y0': 36, 'y1': 95, 'score': 0.95296955, 'id': 1947740380, 'points': ['104,92,96,92,93,90,91,90,86,86,83,85,83,84,81,82,80
   crop duplicated for hashtag vitre : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 101, 'x1': 167, 'y0': 38, 'y1': 127, 'score': 0.9508439, 'id': 1947740381, 'points': ['154,117,152,115,152,112,150,110,148,106,148,104,14
   crop duplicated for hashtag vitre : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 547, 'x1': 640, 'y0': 79, 'y1': 129, 'score': 0.8165246, 'id': 1947740384, 'points': ['630,96,627,96,627,94,628,92,629,92,631,94,631,95',
   crop duplicated for hashtag vitre : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 360, 'x1': 434, 'y0': 62, 'y1': 116, 'score': 0.74684095, 'id': 1947740385, 'points': ['415,103,413,103,411,101,408,101,405,99,403,99,401
   crop duplicated for hashtag phare : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492624020, 'type': 631, 'x0': 249, 'x1': 400, 'y0': 219, 'y1': 316, 'score': 0.8792459, 'id': 1947740382, 'points': ['395,313,390,313,386,311,384,312,381,312,376,309,3
   crop duplicated for hashtag phare : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492624020, 'type': 631, 'x0': 420, 'x1': 552, 'y0': 244, 'y1': 293, 'score': 0.35962066, 'id': 1947740400, 'points': ['474,289,453,289,452,288,439,288,437,286,431,286,
   crop duplicated for hashtag feu-antibrouillard : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2096875712, 'type': 631, 'x0': 309, 'x1': 326, 'y0': 382, 'y1': 404, 'score': 0.6633776, 'id': 1947740390, 'points': ['309,383,309,382,311,382', '325,385,324,383,319,3
   crop duplicated for hashtag feu-antibrouillard : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2096875712, 'type': 631, 'x0': 285, 'x1': 433, 'y0': 343, 'y1': 377, 'score': 0.61493844, 'id': 1947740393, 'points': ['431,376,286,376,285,375,285,368,286,367,286,362
   crop duplicated for hashtag poignee : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 499500794, 'type': 631, 'x0': 93, 'x1': 107, 'y0': 127, 'y1': 146, 'score': 0.9574813, 'id': 1947740379, 'points': ['101,143,98,143,95,139,95,131,97,129,100,129,101,13
   crop duplicated for hashtag porte : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 96, 'x1': 172, 'y0': 39, 'y1': 261, 'score': 0.9928518, 'id': 1947740370, 'points': ['143,252,143,249,141,246,140,246,138,248,138,251,137
   crop duplicated for hashtag porte : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 540, 'x1': 625, 'y0': 78, 'y1': 221, 'score': 0.87864035, 'id': 1947740383, 'points': ['567,127,566,127,565,126,564,109,562,106,560,106,5
   crop not duplicated for hashtag porte : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 61, 'x1': 115, 'y0': 42, 'y1': 188, 'score': 0.6900027, 'id': 1947740389, 'points': ['92,47,91,45,92,44,96,45,94,45', '73,141,73,136,72,1
   crop not duplicated for hashtag porte : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 399, 'x1': 569, 'y0': 68, 'y1': 251, 'score': 0.41876298, 'id': 1947740399, 'points': [], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', '
   crop duplicated for hashtag calandre : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 503548896, 'type': 631, 'x0': 301, 'x1': 540, 'y0': 339, 'y1': 403, 'score': 0.740756, 'id': 3140491551, 'points': ['442,401,371,401,371,397,366,390,365,386,356,386,35
   crop not duplicated for hashtag calandre : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 503548896, 'type': 631, 'x0': 302, 'x1': 540, 'y0': 339, 'y1': 403, 'score': 0.7406652, 'id': 1947740386, 'points': ['442,401,372,401,372,397,370,395,369,392,366,390,3
   crop duplicated for hashtag logo-marque : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2096875717, 'type': 631, 'x0': 477, 'x1': 510, 'y0': 220, 'y1': 243, 'score': 0.69028217, 'id': 1947740388, 'points': ['501,241,493,241,489,239,488,237,487,237,480,232
   crop duplicated for hashtag plaque-immatriculation : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2096875719, 'type': 631, 'x0': 468, 'x1': 555, 'y0': 292, 'y1': 365, 'score': 0.9830025, 'id': 1947740372, 'points': ['491,350,489,350,488,349,487,350,483,350,480,348,
   crop not duplicated for hashtag plaque-immatriculation : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2096875719, 'type': 631, 'x0': 427, 'x1': 553, 'y0': 258, 'y1': 315, 'score': 0.6446218, 'id': 1947740391, 'points': ['531,284,526,284,525,283,525,281,523,279,522,280,
   crop duplicated for hashtag essuie-glace : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2096875722, 'type': 631, 'x0': 198, 'x1': 395, 'y0': 118, 'y1': 142, 'score': 0.9699756, 'id': 1947740378, 'points': ['328,137,251,137,250,136,249,137,241,137,240,136,
   crop not duplicated for hashtag essuie-glace : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2096875722, 'type': 631, 'x0': 433, 'x1': 558, 'y0': 248, 'y1': 286, 'score': 0.44133398, 'id': 1947740397, 'points': ['492,272,474,272,473,271,468,271,465,269,460,269
   crop duplicated for hashtag pare-choc : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 624624117, 'type': 631, 'x0': 226, 'x1': 569, 'y0': 252, 'y1': 425, 'score': 0.99812776, 'id': 1947740368, 'points': ['395,419,341,419,340,418,316,418,315,417,306,417,
   crop duplicated for hashtag aile-avant : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 53, 'x1': 85, 'y0': 75, 'y1': 182, 'score': 0.73015845, 'id': 1947740387, 'points': ['70,147,68,145,65,139,65,137,62,132,61,128,57,126,5
   crop not duplicated for hashtag aile-avant : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 146, 'x1': 287, 'y0': 140, 'y1': 311, 'score': 0.54784286, 'id': 1947740394, 'points': ['234,254,227,254,221,251,219,248,215,253,212,253
   crop not duplicated for hashtag aile-avant : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 496, 'x1': 624, 'y0': 140, 'y1': 245, 'score': 0.4627206, 'id': 3140491552, 'points': ['595,176,593,176,589,173,586,176,583,176,580,174,
   crop not duplicated for hashtag aile-avant : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 496, 'x1': 624, 'y0': 141, 'y1': 245, 'score': 0.46262404, 'id': 1947740395, 'points': ['603,176,599,173,595,176,593,176,590,174,589,174
  list of crops kept {'pare-choc': ('pare-chocs-avant',), 'phare': ('phare-droite', 'a-gauche-de', 'phare-gauche'), 'porte': ('porte-avant', 'a-droite-de', 'porte-arriere')} for hahstag car_exterieur_angle_avant_droit_merge__port_551052
batch 1
Loaded 0 chid ids of type : 0
insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s)
batch 1
Loaded 23 chid ids of type : 816
INSERT IGNORE INTO MTRPhoto.crop_polygon_points (`crop_hashtag_id`, `points`) VALUES (%s, %s)
Number RLEs to save : 1600
INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) 
 first line : ('3980043820', '117', '95', '16')
...
 last line : ('3980043842', '70', '147', '1')
INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) 
 TO DO : save crop sub photo not yet done ! 

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 0.8457436561584473
time spend to save output : 9.584426879882812e-05
total time spend for step 1 : 0.8458395004272461

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 0
After save, about to update current !
test detection filter by classif is a success !

############################### TEST blur_detection  ################################
t
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1243

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=1243
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 1243
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=1243
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : blur_detection
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (930729675) 
 Found this number of photos: 1
##### Call download_photos : nb_thread : 5
begin to download photo : 930729675
download finish for photo 930729675
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.20275020599365234
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  1
step1:blur_detection
Mon Sep 29 11:44:08 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139048_2886529_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg': 930729675}
map_photo_id_path_extension : {930729675: {'path': 'temp/1759139048_2886529_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
inside step blur_detection
methode: ratio et variance
treat image : temp/1759139048_2886529_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg
resize: (600, 800)
930729675 12.961859636534896
score_blur_detection : {930729675: [(930729675, 12.961859636534896, 492688767)]}

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 0.23229289054870605
time spend to save output : 3.910064697265625e-05
total time spend for step 1 : 0.2323319911956787

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 0
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {930729675: [(930729675, 12.961859636534896, 492688767)]}
{930729675: [(930729675, 12.961859636534896, 492688767)]}

############################### TEST detect_point_224x224  ################################
test_detect_point_224x224
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1908

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=1908
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 1908
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=1908
# 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)
 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 : step 4589 thcl is not linked in the step_by_step architecture !
WARNING : step 4590 argmax is not linked in the step_by_step architecture !
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 !
DataTypes for each output/input checked !
no param json to modify
List Step Type Loaded in datou : thcl, argmax
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (987515175,987515176,987515177,987515178,987515179,987515180,987515181,987515182,987515183,987515184,987515185,987515186,987515187,987515188,987515189,987515190,987515192,987515193,987515195,987515196,987515198,987515200,987515201,987515202,987515204,987515205,987515207,987515208,987515209,987515211,987515212,987515213,987515215,987515216,987515217,987515219,987515220,987515222,987515223,987515224,987515226,987515227,987515228,987515230,987515231,987515232,987515233,987515234,987515235,987515236,987515237,987515238,987515239,987515240,987515241,987515242,987515243,987515244,987515245,987515246,987515247,987515248,987515249,987515250) 
 Found this number of photos: 64
##### Call download_photos : nb_thread : 5
maybe url didn't existe for photo_id 987515175 , please check it
begin to download photo : 987515176
begin to download photo : 987515188
begin to download photo : 987515207
begin to download photo : 987515224
begin to download photo : 987515239
download finish for photo 987515224
begin to download photo : 987515226
download finish for photo 987515239
begin to download photo : 987515240
download finish for photo 987515207
begin to download photo : 987515208
download finish for photo 987515188
begin to download photo : 987515189
download finish for photo 987515176
begin to download photo : 987515177
download finish for photo 987515226
begin to download photo : 987515227
download finish for photo 987515240
begin to download photo : 987515241
download finish for photo 987515189
begin to download photo : 987515190
download finish for photo 987515177
begin to download photo : 987515178
download finish for photo 987515208
begin to download photo : 987515209
download finish for photo 987515241
begin to download photo : 987515242
download finish for photo 987515190
begin to download photo : 987515192
download finish for photo 987515227
begin to download photo : 987515228
download finish for photo 987515209
begin to download photo : 987515211
download finish for photo 987515242
begin to download photo : 987515243
download finish for photo 987515192
begin to download photo : 987515193
download finish for photo 987515228
begin to download photo : 987515230
download finish for photo 987515211
maybe url didn't existe for photo_id 987515212 , please check it
begin to download photo : 987515213
download finish for photo 987515243
begin to download photo : 987515244
download finish for photo 987515230
download finish for photo 987515193
begin to download photo : 987515231
maybe url didn't existe for photo_id 987515195 , please check it
begin to download photo : 987515196
download finish for photo 987515213
begin to download photo : 987515215
download finish for photo 987515196
begin to download photo : 987515198
download finish for photo 987515244
begin to download photo : 987515245
download finish for photo 987515231
begin to download photo : 987515232
download finish for photo 987515215
begin to download photo : 987515216
download finish for photo 987515198
begin to download photo : 987515200
download finish for photo 987515216
begin to download photo : 987515217
download finish for photo 987515232
begin to download photo : 987515233
download finish for photo 987515245
begin to download photo : 987515246
download finish for photo 987515217
begin to download photo : 987515219
download finish for photo 987515233
begin to download photo : 987515234
download finish for photo 987515246
begin to download photo : 987515247
download finish for photo 987515200
begin to download photo : 987515201
download finish for photo 987515219
begin to download photo : 987515220
download finish for photo 987515234
begin to download photo : 987515235
download finish for photo 987515247
maybe url didn't existe for photo_id 987515248 , please check it
begin to download photo : 987515249
download finish for photo 987515201
begin to download photo : 987515202
download finish for photo 987515220
begin to download photo : 987515222
download finish for photo 987515235
begin to download photo : 987515236
download finish for photo 987515249
begin to download photo : 987515250
download finish for photo 987515202
begin to download photo : 987515204
download finish for photo 987515222
begin to download photo : 987515223
download finish for photo 987515236
begin to download photo : 987515237
download finish for photo 987515250
download finish for photo 987515204
begin to download photo : 987515205
download finish for photo 987515237
begin to download photo : 987515238
download finish for photo 987515223
download finish for photo 987515205
download finish for photo 987515238
download finish for photo 987515178
begin to download photo : 987515179
download finish for photo 987515179
begin to download photo : 987515180
download finish for photo 987515180
begin to download photo : 987515181
download finish for photo 987515181
begin to download photo : 987515182
download finish for photo 987515182
begin to download photo : 987515183
download finish for photo 987515183
begin to download photo : 987515184
download finish for photo 987515184
begin to download photo : 987515185
download finish for photo 987515185
begin to download photo : 987515186
download finish for photo 987515186
begin to download photo : 987515187
download finish for photo 987515187
we have missing 4 photos in the step downloads : 
photo missing : [987515248, 987515212, 987515195, 987515175]
try to delete the photos missing in DB
HTTP Error 404: NOT FOUND
HTTP Error 404: NOT FOUND
HTTP Error 404: NOT FOUND
HTTP Error 404: NOT FOUND
##### After download_photos 
length of list_filenames : 60 ; length of list_pids : 60 ; length of list_args : 60
##### After load_data_input 
time to download the photos : 12.306588649749756
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  2
step1:thcl
Mon Sep 29 11:44:21 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139049_2886529_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg': 987515239, 'temp/1759139049_2886529_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg': 987515240, 'temp/1759139049_2886529_987515241_073420d938f5f010ffd5b4353c064e09.jpg': 987515241, 'temp/1759139049_2886529_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg': 987515242, 'temp/1759139049_2886529_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg': 987515243, 'temp/1759139049_2886529_987515244_419530eaef5ef868f75c758b94eea4b4.jpg': 987515244, 'temp/1759139049_2886529_987515245_757d9d208d5bd4375c5f21f68b699148.jpg': 987515245, 'temp/1759139049_2886529_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg': 987515246, 'temp/1759139049_2886529_987515247_e47b65403df916ba909bc9c439b0af73.jpg': 987515247, 'temp/1759139049_2886529_987515249_a70ad88462a22fb62a120721a42b2d42.jpg': 987515249, 'temp/1759139049_2886529_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg': 987515250, 'temp/1759139049_2886529_987515207_de216ddb041e249524b0fb2b949064a5.jpg': 987515207, 'temp/1759139049_2886529_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg': 987515208, 'temp/1759139049_2886529_987515209_02dfe1ae39f51994652f4a8538844aea.jpg': 987515209, 'temp/1759139049_2886529_987515211_72cc7664d45bd40477351b9b764f1500.jpg': 987515211, 'temp/1759139049_2886529_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg': 987515213, 'temp/1759139049_2886529_987515215_902ef348a7eebb9a8b87f42927347936.jpg': 987515215, 'temp/1759139049_2886529_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg': 987515216, 'temp/1759139049_2886529_987515217_78877bb2c5760be28518d17f77d1c609.jpg': 987515217, 'temp/1759139049_2886529_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg': 987515219, 'temp/1759139049_2886529_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg': 987515220, 'temp/1759139049_2886529_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg': 987515222, 'temp/1759139049_2886529_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg': 987515223, 'temp/1759139049_2886529_987515188_4116f9906657a69bb76c2fda982037b9.jpg': 987515188, 'temp/1759139049_2886529_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg': 987515189, 'temp/1759139049_2886529_987515190_d56932bfc6ba2a8c974c691108755017.jpg': 987515190, 'temp/1759139049_2886529_987515192_b661073b218f5f056833d6af1c617153.jpg': 987515192, 'temp/1759139049_2886529_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg': 987515193, 'temp/1759139049_2886529_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg': 987515196, 'temp/1759139049_2886529_987515198_599e80f444c876f407e94b533c89360b.jpg': 987515198, 'temp/1759139049_2886529_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg': 987515200, 'temp/1759139049_2886529_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg': 987515201, 'temp/1759139049_2886529_987515202_3314bd90d1404f31b827d8925abf2d62.jpg': 987515202, 'temp/1759139049_2886529_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg': 987515204, 'temp/1759139049_2886529_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg': 987515205, 'temp/1759139049_2886529_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg': 987515224, 'temp/1759139049_2886529_987515226_a18048dca1a77ae086b62cf07759f704.jpg': 987515226, 'temp/1759139049_2886529_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg': 987515227, 'temp/1759139049_2886529_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg': 987515228, 'temp/1759139049_2886529_987515230_846ad925884264181565c81d152a2e94.jpg': 987515230, 'temp/1759139049_2886529_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg': 987515231, 'temp/1759139049_2886529_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg': 987515232, 'temp/1759139049_2886529_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg': 987515233, 'temp/1759139049_2886529_987515234_2eca3480aed0f8b876242675ad99b666.jpg': 987515234, 'temp/1759139049_2886529_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg': 987515235, 'temp/1759139049_2886529_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg': 987515236, 'temp/1759139049_2886529_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg': 987515237, 'temp/1759139049_2886529_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg': 987515238, 'temp/1759139049_2886529_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg': 987515176, 'temp/1759139049_2886529_987515177_4a54e9967227806219ddf45d256539d8.jpg': 987515177, 'temp/1759139049_2886529_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg': 987515178, 'temp/1759139049_2886529_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg': 987515179, 'temp/1759139049_2886529_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg': 987515180, 'temp/1759139049_2886529_987515181_1738c2798fb31152809ecb443ac286d6.jpg': 987515181, 'temp/1759139049_2886529_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg': 987515182, 'temp/1759139049_2886529_987515183_6aab9ca0421398b4899892c10c2594c6.jpg': 987515183, 'temp/1759139049_2886529_987515184_19c8c2177209a285df6014d95fe53f2c.jpg': 987515184, 'temp/1759139049_2886529_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg': 987515185, 'temp/1759139049_2886529_987515186_797def426440b544aa80dbd63a19234a.jpg': 987515186, 'temp/1759139049_2886529_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg': 987515187}
map_photo_id_path_extension : {987515239: {'path': 'temp/1759139049_2886529_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg', 'extension': 'jpg'}, 987515240: {'path': 'temp/1759139049_2886529_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg', 'extension': 'jpg'}, 987515241: {'path': 'temp/1759139049_2886529_987515241_073420d938f5f010ffd5b4353c064e09.jpg', 'extension': 'jpg'}, 987515242: {'path': 'temp/1759139049_2886529_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg', 'extension': 'jpg'}, 987515243: {'path': 'temp/1759139049_2886529_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg', 'extension': 'jpg'}, 987515244: {'path': 'temp/1759139049_2886529_987515244_419530eaef5ef868f75c758b94eea4b4.jpg', 'extension': 'jpg'}, 987515245: {'path': 'temp/1759139049_2886529_987515245_757d9d208d5bd4375c5f21f68b699148.jpg', 'extension': 'jpg'}, 987515246: {'path': 'temp/1759139049_2886529_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg', 'extension': 'jpg'}, 987515247: {'path': 'temp/1759139049_2886529_987515247_e47b65403df916ba909bc9c439b0af73.jpg', 'extension': 'jpg'}, 987515249: {'path': 'temp/1759139049_2886529_987515249_a70ad88462a22fb62a120721a42b2d42.jpg', 'extension': 'jpg'}, 987515250: {'path': 'temp/1759139049_2886529_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg', 'extension': 'jpg'}, 987515207: {'path': 'temp/1759139049_2886529_987515207_de216ddb041e249524b0fb2b949064a5.jpg', 'extension': 'jpg'}, 987515208: {'path': 'temp/1759139049_2886529_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg', 'extension': 'jpg'}, 987515209: {'path': 'temp/1759139049_2886529_987515209_02dfe1ae39f51994652f4a8538844aea.jpg', 'extension': 'jpg'}, 987515211: {'path': 'temp/1759139049_2886529_987515211_72cc7664d45bd40477351b9b764f1500.jpg', 'extension': 'jpg'}, 987515213: {'path': 'temp/1759139049_2886529_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg', 'extension': 'jpg'}, 987515215: {'path': 'temp/1759139049_2886529_987515215_902ef348a7eebb9a8b87f42927347936.jpg', 'extension': 'jpg'}, 987515216: {'path': 'temp/1759139049_2886529_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg', 'extension': 'jpg'}, 987515217: {'path': 'temp/1759139049_2886529_987515217_78877bb2c5760be28518d17f77d1c609.jpg', 'extension': 'jpg'}, 987515219: {'path': 'temp/1759139049_2886529_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg', 'extension': 'jpg'}, 987515220: {'path': 'temp/1759139049_2886529_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg', 'extension': 'jpg'}, 987515222: {'path': 'temp/1759139049_2886529_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg', 'extension': 'jpg'}, 987515223: {'path': 'temp/1759139049_2886529_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg', 'extension': 'jpg'}, 987515188: {'path': 'temp/1759139049_2886529_987515188_4116f9906657a69bb76c2fda982037b9.jpg', 'extension': 'jpg'}, 987515189: {'path': 'temp/1759139049_2886529_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg', 'extension': 'jpg'}, 987515190: {'path': 'temp/1759139049_2886529_987515190_d56932bfc6ba2a8c974c691108755017.jpg', 'extension': 'jpg'}, 987515192: {'path': 'temp/1759139049_2886529_987515192_b661073b218f5f056833d6af1c617153.jpg', 'extension': 'jpg'}, 987515193: {'path': 'temp/1759139049_2886529_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg', 'extension': 'jpg'}, 987515196: {'path': 'temp/1759139049_2886529_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg', 'extension': 'jpg'}, 987515198: {'path': 'temp/1759139049_2886529_987515198_599e80f444c876f407e94b533c89360b.jpg', 'extension': 'jpg'}, 987515200: {'path': 'temp/1759139049_2886529_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg', 'extension': 'jpg'}, 987515201: {'path': 'temp/1759139049_2886529_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg', 'extension': 'jpg'}, 987515202: {'path': 'temp/1759139049_2886529_987515202_3314bd90d1404f31b827d8925abf2d62.jpg', 'extension': 'jpg'}, 987515204: {'path': 'temp/1759139049_2886529_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg', 'extension': 'jpg'}, 987515205: {'path': 'temp/1759139049_2886529_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg', 'extension': 'jpg'}, 987515224: {'path': 'temp/1759139049_2886529_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg', 'extension': 'jpg'}, 987515226: {'path': 'temp/1759139049_2886529_987515226_a18048dca1a77ae086b62cf07759f704.jpg', 'extension': 'jpg'}, 987515227: {'path': 'temp/1759139049_2886529_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg', 'extension': 'jpg'}, 987515228: {'path': 'temp/1759139049_2886529_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg', 'extension': 'jpg'}, 987515230: {'path': 'temp/1759139049_2886529_987515230_846ad925884264181565c81d152a2e94.jpg', 'extension': 'jpg'}, 987515231: {'path': 'temp/1759139049_2886529_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg', 'extension': 'jpg'}, 987515232: {'path': 'temp/1759139049_2886529_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg', 'extension': 'jpg'}, 987515233: {'path': 'temp/1759139049_2886529_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg', 'extension': 'jpg'}, 987515234: {'path': 'temp/1759139049_2886529_987515234_2eca3480aed0f8b876242675ad99b666.jpg', 'extension': 'jpg'}, 987515235: {'path': 'temp/1759139049_2886529_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg', 'extension': 'jpg'}, 987515236: {'path': 'temp/1759139049_2886529_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg', 'extension': 'jpg'}, 987515237: {'path': 'temp/1759139049_2886529_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg', 'extension': 'jpg'}, 987515238: {'path': 'temp/1759139049_2886529_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg', 'extension': 'jpg'}, 987515176: {'path': 'temp/1759139049_2886529_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg', 'extension': 'jpg'}, 987515177: {'path': 'temp/1759139049_2886529_987515177_4a54e9967227806219ddf45d256539d8.jpg', 'extension': 'jpg'}, 987515178: {'path': 'temp/1759139049_2886529_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg', 'extension': 'jpg'}, 987515179: {'path': 'temp/1759139049_2886529_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg', 'extension': 'jpg'}, 987515180: {'path': 'temp/1759139049_2886529_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg', 'extension': 'jpg'}, 987515181: {'path': 'temp/1759139049_2886529_987515181_1738c2798fb31152809ecb443ac286d6.jpg', 'extension': 'jpg'}, 987515182: {'path': 'temp/1759139049_2886529_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg', 'extension': 'jpg'}, 987515183: {'path': 'temp/1759139049_2886529_987515183_6aab9ca0421398b4899892c10c2594c6.jpg', 'extension': 'jpg'}, 987515184: {'path': 'temp/1759139049_2886529_987515184_19c8c2177209a285df6014d95fe53f2c.jpg', 'extension': 'jpg'}, 987515185: {'path': 'temp/1759139049_2886529_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg', 'extension': 'jpg'}, 987515186: {'path': 'temp/1759139049_2886529_987515186_797def426440b544aa80dbd63a19234a.jpg', 'extension': 'jpg'}, 987515187: {'path': 'temp/1759139049_2886529_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou step Thcl !
multi_thcl or not :False
multi_thcl_cond or not :False
dic_thcl :  {'1528': 1}
we are using the classfication for only one thcl
1528


In convert_file_to_np l 337 : 6
 l343 6
In convert_file_to_np l 337 : 6
 l343 6
In convert_file_to_np l 337 : 6
 l343 6
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
In convert_file_to_np l 337 : 6
 l343 6
In convert_file_to_np l 337 : 6
 l343 6
In convert_file_to_np l 337 : 6
 l343 6
In convert_file_to_np l 337 : 6
 l343 6
In convert_file_to_np l 337 : 6
 l343 6
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
In convert_file_to_np l 337 : 6
 l343 6
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
In convert_file_to_np l 337 : 6
 l343 6
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
time to import caffe and check if the image exist :  0.005839109420776367
time to convert the images to numpy array :  0.03221487998962402
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
time to import caffe and check if the image exist :  0.003471851348876953
time to convert the images to numpy array :  0.03661847114562988
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
time to import caffe and check if the image exist :  0.006368160247802734
time to convert the images to numpy array :  0.03490614891052246
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
time to import caffe and check if the image exist :  0.005052804946899414
time to convert the images to numpy array :  0.0351872444152832
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
time to import caffe and check if the image exist :  0.007126569747924805
time to convert the images to numpy array :  0.03418254852294922
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
time to import caffe and check if the image exist :  0.007150888442993164
time to convert the images to numpy array :  0.03513383865356445
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
time to import caffe and check if the image exist :  0.00668644905090332
time to convert the images to numpy array :  0.036493778228759766
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
time to import caffe and check if the image exist :  0.010774374008178711
time to convert the images to numpy array :  0.03213787078857422
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
time to import caffe and check if the image exist :  0.007506847381591797
time to convert the images to numpy array :  0.0351407527923584
l357 after caffe.io.load_image
dimension du image : (3, (224, 224, 3))
dimension displayed !
time to import caffe and check if the image exist :  0.01711440086364746
time to convert the images to numpy array :  0.0278627872467041
222222	2
222
233
33333333	3
3total time to convert the images to numpy array :  0.046668291091918945
list photo_ids error:
[]
list photo_ids correct :
[987515245, 987515246, 987515247, 987515249, 987515250, 987515207, 987515239, 987515240, 987515241, 987515242, 987515243, 987515244, 987515208, 987515209, 987515211, 987515213, 987515215, 987515216, 987515176, 987515177, 987515178, 987515179, 987515180, 987515181, 987515226, 987515227, 987515228, 987515230, 987515231, 987515232, 987515189, 987515190, 987515192, 987515193, 987515196, 987515198, 987515200, 987515201, 987515202, 987515204, 987515205, 987515224, 987515233, 987515234, 987515235, 987515236, 987515237, 987515238, 987515182, 987515183, 987515184, 987515185, 987515186, 987515187, 987515217, 987515219, 987515220, 987515222, 987515223, 987515188]
number of photos to traite : 60
try to delete the photos incorrect in DB

tagging for thcl : 1528
To do loadFromThcl(), then load ParamDescType : thcl1528
 get_desc_type_from_thcl : type of cat <class 'mtr.database_queries.classification_admin_tools.ClassificationMetaTools'>

            SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists,
            photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at,
            type_classification
            FROM MTRDatou.classification_theme  WHERE  `id` IN (1528) 
 thcls : [{'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'}]
 thcl {'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'}
 Update svm_hashtag_type_desc : 4421
 SELECT * FROM MTRDatou.photo_desc_type_params  WHERE id in (4421)
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4421, 'learn_refus_upm_blanches_1924', 16384, 25088, 'learn_refus_upm_blanches_1924', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2019, 10, 22, 17, 39, 25), datetime.datetime(2019, 10, 22, 17, 39, 25))
To loadFromThcl() : net_4421
begin to check gpu status 
inside check gpu memory
l 3637 free memory gpu now : 2930  max_wait_temp :  1 max_wait :  0
 SELECT * FROM MTRDatou.photo_desc_type_params  WHERE id in (4421)
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4421, 'learn_refus_upm_blanches_1924', 16384, 25088, 'learn_refus_upm_blanches_1924', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2019, 10, 22, 17, 39, 25), datetime.datetime(2019, 10, 22, 17, 39, 25))
 param : <mtr.database_queries.descriptor_queries.ParamDescriptorType object at 0x7f1515b5c100>, param.caffemodel : learn_refus_upm_blanches_1924
None
 mean_file_type : 
 mean_file_path : 
 prototxt_file_path : 
 model : learn_refus_upm_blanches_1924
 Inside get_net  
 Inside get_net before cache_data_model 
model_param file didn't exist 
 Inside get_net before  CDM.load_model_par_type  
model_name : learn_refus_upm_blanches_1924
model_type : caffe
list file need :
['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt']
file exist in s3 :
['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt']
file manque in s3 : 
['deploy_conv_normal.prototxt', 'deploy_fc.prototxt']
local folder  : /data/models_weight/learn_refus_upm_blanches_1924


/data/models_weight/learn_refus_upm_blanches_1924/caffemodel
size_local : 45774543 size in s3 : 45774543
create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04
caffemodel already exist and didn't need to update


/data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt
size_local : 17312 size in s3 : 17312
create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03
deploy.prototxt already exist and didn't need to update


/data/models_weight/learn_refus_upm_blanches_1924/mean.npy
size_local : 1572992 size in s3 : 1572992
create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05
mean.npy already exist and didn't need to update


/data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt
size_local : 218 size in s3 : 218
create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04
synset_words.txt already exist and didn't need to update
 Inside get_net after CDM.load_model_par_type  
  After if not only_with_local_cache:
/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/:/home/admin/workarea/git/apy/
Here before set mode gpu
Doing nothing but we could set mode gpu
after set mode gpu
 prototxt_filename : /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt
 caffemodel_filename : /data/models_weight/learn_refus_upm_blanches_1924/caffemodel
now we set caffe to gpu mode


 before predict
begin to check gpu status 
inside check gpu memory
l 3637 free memory gpu now : 2930  max_wait_temp :  1 max_wait :  0
dict_keys(['res5b', 'prob'])
time used to do the prepocess of the images : 0.04781770706176758
time used to do the prediction : 0.13579726219177246


save descriptor for thcl : 1528
(60, 512, 7, 7)
Got the blobs of the net
 to insert :  
[0, 0, 0, 0, 0, 0, 1, 3, 1, 0]
code_as_byte_string:b'0000000000'|
Got the blobs of the net
 to insert :  
[0, 0, 0, 1, 0, 0, 0, 0, 1, 3]
code_as_byte_string:b'0000000100'|
Got the blobs of the net
 to insert :  
[0, 0, 0, 1, 2, 0, 1, 4, 0, 2]
code_as_byte_string:b'0000000102'|
Got the blobs of the net
 to insert :  
[4, 1, 3, 4, 7, 3, 4, 2, 1, 0]
code_as_byte_string:b'0401030407'|
Got the blobs of the net
 to insert :  
[3, 3, 1, 4, 7, 7, 9, 6, 2, 1]
code_as_byte_string:b'0303010407'|
Got the blobs of the net
 to insert :  
[4, 2, 1, 2, 3, 1, 0, 1, 0, 1]
code_as_byte_string:b'0402010203'|
Got the blobs of the net
 to insert :  
[1, 1, 0, 0, 0, 0, 0, 3, 5, 1]
code_as_byte_string:b'0101000000'|
Got the blobs of the net
 to insert :  
[0, 3, 0, 1, 1, 6, 7, 5, 5, 3]
code_as_byte_string:b'0003000101'|
Got the blobs of the net
 to insert :  
[6, 6, 3, 3, 8, 8, 6, 6, 2, 0]
code_as_byte_string:b'0606030308'|
Got the blobs of the net
 to insert :  
[5, 5, 2, 4, 6, 5, 9, 9, 4, 2]
code_as_byte_string:b'0505020406'|
Got the blobs of the net
 to insert :  
[2, 1, 3, 5, 7, 5, 3, 4, 1, 3]
code_as_byte_string:b'0201030507'|
Got the blobs of the net
 to insert :  
[0, 0, 0, 1, 1, 3, 2, 1, 0, 2]
code_as_byte_string:b'0000000101'|
Got the blobs of the net
 to insert :  
[0, 0, 0, 1, 2, 2, 1, 1, 0, 0]
code_as_byte_string:b'0000000102'|
Got the blobs of the net
 to insert :  
[1, 0, 0, 1, 1, 1, 0, 1, 1, 3]
code_as_byte_string:b'0100000101'|
Got the blobs of the net
 to insert :  
[1, 1, 2, 3, 4, 4, 1, 2, 8, 7]
code_as_byte_string:b'0101020304'|
Got the blobs of the net
 to insert :  
[6, 8, 5, 7, 7, 8, 10, 12, 12, 6]
code_as_byte_string:b'0608050707'|
Got the blobs of the net
 to insert :  
[5, 5, 4, 3, 5, 6, 3, 4, 3, 3]
code_as_byte_string:b'0505040305'|
Got the blobs of the net
 to insert :  
[5, 3, 3, 1, 1, 1, 2, 2, 2, 4]
code_as_byte_string:b'0503030101'|
Got the blobs of the net
 to insert :  
[13, 9, 9, 8, 11, 13, 6, 14, 9, 18]
code_as_byte_string:b'0d0909080b'|
Got the blobs of the net
 to insert :  
[2, 2, 6, 7, 8, 6, 4, 3, 1, 8]
code_as_byte_string:b'0202060708'|
Got the blobs of the net
 to insert :  
[1, 2, 2, 1, 0, 1, 1, 2, 1, 1]
code_as_byte_string:b'0102020100'|
Got the blobs of the net
 to insert :  
[2, 2, 1, 3, 2, 3, 2, 0, 0, 1]
code_as_byte_string:b'0202010302'|
Got the blobs of the net
 to insert :  
[1, 0, 1, 2, 1, 0, 3, 3, 3, 8]
code_as_byte_string:b'0100010201'|
Got the blobs of the net
 to insert :  
[5, 5, 6, 4, 3, 6, 9, 7, 7, 7]
code_as_byte_string:b'0505060403'|
Got the blobs of the net
 to insert :  
[3, 3, 3, 1, 6, 7, 10, 5, 3, 5]
code_as_byte_string:b'0303030106'|
Got the blobs of the net
 to insert :  
[3, 2, 2, 1, 1, 2, 2, 3, 2, 5]
code_as_byte_string:b'0302020101'|
Got the blobs of the net
 to insert :  
[1, 0, 3, 1, 0, 0, 0, 0, 0, 5]
code_as_byte_string:b'0100030100'|
Got the blobs of the net
 to insert :  
[0, 0, 2, 2, 0, 0, 0, 0, 0, 3]
code_as_byte_string:b'0000020200'|
Got the blobs of the net
 to insert :  
[1, 1, 2, 1, 0, 0, 0, 3, 6, 7]
code_as_byte_string:b'0101020100'|
Got the blobs of the net
 to insert :  
[1, 3, 3, 3, 0, 1, 5, 6, 7, 4]
code_as_byte_string:b'0103030300'|
Got the blobs of the net
 to insert :  
[3, 2, 1, 4, 5, 7, 6, 5, 6, 5]
code_as_byte_string:b'0302010405'|
Got the blobs of the net
 to insert :  
[3, 4, 5, 5, 9, 9, 9, 9, 11, 7]
code_as_byte_string:b'0304050509'|
Got the blobs of the net
 to insert :  
[5, 3, 3, 6, 8, 12, 9, 9, 3, 5]
code_as_byte_string:b'0503030608'|
Got the blobs of the net
 to insert :  
[6, 3, 3, 0, 1, 2, 2, 5, 2, 4]
code_as_byte_string:b'0603030001'|
Got the blobs of the net
 to insert :  
[0, 0, 0, 0, 0, 2, 5, 1, 0, 0]
code_as_byte_string:b'0000000000'|
Got the blobs of the net
 to insert :  
[1, 1, 0, 0, 1, 2, 0, 0, 0, 1]
code_as_byte_string:b'0101000001'|
Got the blobs of the net
 to insert :  
[0, 1, 0, 1, 3, 4, 2, 1, 3, 5]
code_as_byte_string:b'0001000103'|
Got the blobs of the net
 to insert :  
[3, 3, 3, 8, 7, 5, 5, 4, 5, 3]
code_as_byte_string:b'0303030807'|
Got the blobs of the net
 to insert :  
[2, 5, 4, 6, 9, 9, 10, 9, 11, 5]
code_as_byte_string:b'0205040609'|
Got the blobs of the net
 to insert :  
[5, 2, 3, 7, 5, 7, 6, 2, 4, 3]
code_as_byte_string:b'0502030705'|
Got the blobs of the net
 to insert :  
[5, 7, 3, 2, 1, 2, 2, 2, 0, 0]
code_as_byte_string:b'0507030201'|
Got the blobs of the net
 to insert :  
[7, 7, 3, 8, 9, 6, 7, 10, 11, 6]
code_as_byte_string:b'0707030809'|
Got the blobs of the net
 to insert :  
[5, 7, 3, 2, 5, 9, 10, 3, 5, 0]
code_as_byte_string:b'0507030205'|
Got the blobs of the net
 to insert :  
[6, 8, 3, 6, 6, 6, 5, 10, 10, 3]
code_as_byte_string:b'0608030606'|
Got the blobs of the net
 to insert :  
[1, 2, 1, 5, 7, 10, 8, 2, 2, 1]
code_as_byte_string:b'0102010507'|
Got the blobs of the net
 to insert :  
[0, 0, 0, 1, 5, 4, 6, 3, 1, 3]
code_as_byte_string:b'0000000105'|
Got the blobs of the net
 to insert :  
[0, 2, 1, 0, 0, 0, 0, 0, 4, 2]
code_as_byte_string:b'0002010000'|
Got the blobs of the net
 to insert :  
[0, 2, 0, 1, 1, 0, 0, 0, 1, 3]
code_as_byte_string:b'0002000101'|
Got the blobs of the net
 to insert :  
[4, 6, 6, 4, 7, 8, 8, 8, 12, 8]
code_as_byte_string:b'0406060407'|
Got the blobs of the net
 to insert :  
[11, 8, 5, 9, 12, 14, 13, 14, 12, 6]
code_as_byte_string:b'0b0805090c'|
Got the blobs of the net
 to insert :  
[8, 7, 6, 4, 2, 1, 2, 4, 4, 4]
code_as_byte_string:b'0807060402'|
Got the blobs of the net
 to insert :  
[2, 3, 5, 5, 2, 2, 3, 0, 1, 4]
code_as_byte_string:b'0203050502'|
Got the blobs of the net
 to insert :  
[0, 0, 0, 1, 1, 1, 3, 3, 0, 0]
code_as_byte_string:b'0000000101'|
Got the blobs of the net
 to insert :  
[3, 1, 1, 1, 1, 1, 2, 1, 1, 2]
code_as_byte_string:b'0301010101'|
Got the blobs of the net
 to insert :  
[2, 1, 2, 2, 2, 0, 0, 1, 0, 3]
code_as_byte_string:b'0201020202'|
Got the blobs of the net
 to insert :  
[0, 0, 2, 3, 3, 1, 0, 0, 0, 0]
code_as_byte_string:b'0000020303'|
Got the blobs of the net
 to insert :  
[1, 0, 0, 1, 0, 0, 0, 3, 4, 5]
code_as_byte_string:b'0100000100'|
Got the blobs of the net
 to insert :  
[0, 1, 1, 4, 2, 1, 3, 7, 9, 9]
code_as_byte_string:b'0001010402'|
Got the blobs of the net
 to insert :  
[7, 10, 10, 2, 4, 7, 8, 5, 5, 2]
code_as_byte_string:b'070a0a0204'|
Got the blobs of the net
 to insert :  
[1, 1, 1, 2, 0, 3, 4, 3, 4, 5]
code_as_byte_string:b'0101010200'|
time to traite the descriptors :  2.6940672397613525
Testing : ['987515245', '987515246', '987515247', '987515249', '987515250', '987515207', '987515239', '987515240', '987515241', '987515242', '987515243', '987515244', '987515208', '987515209', '987515211', '987515213', '987515215', '987515216', '987515176', '987515177', '987515178', '987515179', '987515180', '987515181', '987515226', '987515227', '987515228', '987515230', '987515231', '987515232', '987515189', '987515190', '987515192', '987515193', '987515196', '987515198', '987515200', '987515201', '987515202', '987515204', '987515205', '987515224', '987515233', '987515234', '987515235', '987515236', '987515237', '987515238', '987515182', '987515183', '987515184', '987515185', '987515186', '987515187', '987515217', '987515219', '987515220', '987515222', '987515223', '987515188']
 In select_photos_meta_from_ids: 
        SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text
        FROM MTRBack.photos_view
        WHERE photo_id IN (987515245,987515246,987515247,987515249,987515250,987515207,987515239,987515240,987515241,987515242,987515243,987515244,987515208,987515209,987515211,987515213,987515215,987515216,987515176,987515177,987515178,987515179,987515180,987515181,987515226,987515227,987515228,987515230,987515231,987515232,987515189,987515190,987515192,987515193,987515196,987515198,987515200,987515201,987515202,987515204,987515205,987515224,987515233,987515234,987515235,987515236,987515237,987515238,987515182,987515183,987515184,987515185,987515186,987515187,987515217,987515219,987515220,987515222,987515223,987515188) 
result : {987515176: {'photo_id': 987515176, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/8b398cba2f448622cd9657f5eb3f9796.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_144.jpg'}, 987515177: {'photo_id': 987515177, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/4a54e9967227806219ddf45d256539d8.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_176.jpg'}, 987515178: {'photo_id': 987515178, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/298b3d2bfe0fda6787b59a78e2e68867.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_208.jpg'}, 987515179: {'photo_id': 987515179, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/f7d4d1757a470f4c96dc3541eac88b9e.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_240.jpg'}, 987515180: {'photo_id': 987515180, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/776a5d7d8486ee2961bbe3a0d90f95b5.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_272.jpg'}, 987515181: {'photo_id': 987515181, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/1738c2798fb31152809ecb443ac286d6.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_304.jpg'}, 987515182: {'photo_id': 987515182, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/fe7f29bf6d13e08c3e985f91b5232178.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_336.jpg'}, 987515183: {'photo_id': 987515183, 'url': 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storage_type for insertDescriptorsMulti : 1
To insert : 987515245
To insert : 987515246
To insert : 987515247
To insert : 987515249
To insert : 987515250
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To insert : 987515188
time to insert the descriptors : 36.4974627494812






After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 43.03557896614075
time spend to save output : 7.200241088867188e-05
total time spend for step 1 : 43.035650968551636

step2:argmax
Mon Sep 29 11:45:04 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
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139049_2886529_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg': 987515239, 'temp/1759139049_2886529_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg': 987515240, 'temp/1759139049_2886529_987515241_073420d938f5f010ffd5b4353c064e09.jpg': 987515241, 'temp/1759139049_2886529_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg': 987515242, 'temp/1759139049_2886529_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg': 987515243, 'temp/1759139049_2886529_987515244_419530eaef5ef868f75c758b94eea4b4.jpg': 987515244, 'temp/1759139049_2886529_987515245_757d9d208d5bd4375c5f21f68b699148.jpg': 987515245, 'temp/1759139049_2886529_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg': 987515246, 'temp/1759139049_2886529_987515247_e47b65403df916ba909bc9c439b0af73.jpg': 987515247, 'temp/1759139049_2886529_987515249_a70ad88462a22fb62a120721a42b2d42.jpg': 987515249, 'temp/1759139049_2886529_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg': 987515250, 'temp/1759139049_2886529_987515207_de216ddb041e249524b0fb2b949064a5.jpg': 987515207, 'temp/1759139049_2886529_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg': 987515208, 'temp/1759139049_2886529_987515209_02dfe1ae39f51994652f4a8538844aea.jpg': 987515209, 'temp/1759139049_2886529_987515211_72cc7664d45bd40477351b9b764f1500.jpg': 987515211, 'temp/1759139049_2886529_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg': 987515213, 'temp/1759139049_2886529_987515215_902ef348a7eebb9a8b87f42927347936.jpg': 987515215, 'temp/1759139049_2886529_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg': 987515216, 'temp/1759139049_2886529_987515217_78877bb2c5760be28518d17f77d1c609.jpg': 987515217, 'temp/1759139049_2886529_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg': 987515219, 'temp/1759139049_2886529_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg': 987515220, 'temp/1759139049_2886529_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg': 987515222, 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'temp/1759139049_2886529_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg': 987515204, 'temp/1759139049_2886529_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg': 987515205, 'temp/1759139049_2886529_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg': 987515224, 'temp/1759139049_2886529_987515226_a18048dca1a77ae086b62cf07759f704.jpg': 987515226, 'temp/1759139049_2886529_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg': 987515227, 'temp/1759139049_2886529_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg': 987515228, 'temp/1759139049_2886529_987515230_846ad925884264181565c81d152a2e94.jpg': 987515230, 'temp/1759139049_2886529_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg': 987515231, 'temp/1759139049_2886529_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg': 987515232, 'temp/1759139049_2886529_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg': 987515233, 'temp/1759139049_2886529_987515234_2eca3480aed0f8b876242675ad99b666.jpg': 987515234, 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map_photo_id_path_extension : {987515239: {'path': 'temp/1759139049_2886529_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg', 'extension': 'jpg'}, 987515240: {'path': 'temp/1759139049_2886529_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg', 'extension': 'jpg'}, 987515241: {'path': 'temp/1759139049_2886529_987515241_073420d938f5f010ffd5b4353c064e09.jpg', 'extension': 'jpg'}, 987515242: {'path': 'temp/1759139049_2886529_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg', 'extension': 'jpg'}, 987515243: {'path': 'temp/1759139049_2886529_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg', 'extension': 'jpg'}, 987515244: {'path': 'temp/1759139049_2886529_987515244_419530eaef5ef868f75c758b94eea4b4.jpg', 'extension': 'jpg'}, 987515245: {'path': 'temp/1759139049_2886529_987515245_757d9d208d5bd4375c5f21f68b699148.jpg', 'extension': 'jpg'}, 987515246: {'path': 'temp/1759139049_2886529_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg', 'extension': 'jpg'}, 987515247: {'path': 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'extension': 'jpg'}, 987515216: {'path': 'temp/1759139049_2886529_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg', 'extension': 'jpg'}, 987515217: {'path': 'temp/1759139049_2886529_987515217_78877bb2c5760be28518d17f77d1c609.jpg', 'extension': 'jpg'}, 987515219: {'path': 'temp/1759139049_2886529_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg', 'extension': 'jpg'}, 987515220: {'path': 'temp/1759139049_2886529_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg', 'extension': 'jpg'}, 987515222: {'path': 'temp/1759139049_2886529_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg', 'extension': 'jpg'}, 987515223: {'path': 'temp/1759139049_2886529_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg', 'extension': 'jpg'}, 987515188: {'path': 'temp/1759139049_2886529_987515188_4116f9906657a69bb76c2fda982037b9.jpg', 'extension': 'jpg'}, 987515189: {'path': 'temp/1759139049_2886529_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg', 'extension': 'jpg'}, 987515190: {'path': 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'extension': 'jpg'}, 987515205: {'path': 'temp/1759139049_2886529_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg', 'extension': 'jpg'}, 987515224: {'path': 'temp/1759139049_2886529_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg', 'extension': 'jpg'}, 987515226: {'path': 'temp/1759139049_2886529_987515226_a18048dca1a77ae086b62cf07759f704.jpg', 'extension': 'jpg'}, 987515227: {'path': 'temp/1759139049_2886529_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg', 'extension': 'jpg'}, 987515228: {'path': 'temp/1759139049_2886529_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg', 'extension': 'jpg'}, 987515230: {'path': 'temp/1759139049_2886529_987515230_846ad925884264181565c81d152a2e94.jpg', 'extension': 'jpg'}, 987515231: {'path': 'temp/1759139049_2886529_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg', 'extension': 'jpg'}, 987515232: {'path': 'temp/1759139049_2886529_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg', 'extension': 'jpg'}, 987515233: {'path': 'temp/1759139049_2886529_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg', 'extension': 'jpg'}, 987515234: {'path': 'temp/1759139049_2886529_987515234_2eca3480aed0f8b876242675ad99b666.jpg', 'extension': 'jpg'}, 987515235: {'path': 'temp/1759139049_2886529_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg', 'extension': 'jpg'}, 987515236: {'path': 'temp/1759139049_2886529_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg', 'extension': 'jpg'}, 987515237: {'path': 'temp/1759139049_2886529_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg', 'extension': 'jpg'}, 987515238: {'path': 'temp/1759139049_2886529_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg', 'extension': 'jpg'}, 987515176: {'path': 'temp/1759139049_2886529_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg', 'extension': 'jpg'}, 987515177: {'path': 'temp/1759139049_2886529_987515177_4a54e9967227806219ddf45d256539d8.jpg', 'extension': 'jpg'}, 987515178: {'path': 'temp/1759139049_2886529_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg', 'extension': 'jpg'}, 987515179: {'path': 'temp/1759139049_2886529_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg', 'extension': 'jpg'}, 987515180: {'path': 'temp/1759139049_2886529_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg', 'extension': 'jpg'}, 987515181: {'path': 'temp/1759139049_2886529_987515181_1738c2798fb31152809ecb443ac286d6.jpg', 'extension': 'jpg'}, 987515182: {'path': 'temp/1759139049_2886529_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg', 'extension': 'jpg'}, 987515183: {'path': 'temp/1759139049_2886529_987515183_6aab9ca0421398b4899892c10c2594c6.jpg', 'extension': 'jpg'}, 987515184: {'path': 'temp/1759139049_2886529_987515184_19c8c2177209a285df6014d95fe53f2c.jpg', 'extension': 'jpg'}, 987515185: {'path': 'temp/1759139049_2886529_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg', 'extension': 'jpg'}, 987515186: {'path': 'temp/1759139049_2886529_987515186_797def426440b544aa80dbd63a19234a.jpg', 'extension': 'jpg'}, 987515187: {'path': 'temp/1759139049_2886529_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou_step Argmax !
calculate argmax for thcl : 1528

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 0.0007169246673583984
time spend to save output : 6.794929504394531e-05
total time spend for step 2 : 0.0007848739624023438

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 0
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 2
 output : {'987515245': [('987515245', 'Carton', 0.8659642, 1927, '1528'), 'temp/1759139049_2886529_987515245_757d9d208d5bd4375c5f21f68b699148.jpg'], '987515246': [('987515246', 'Carton', 0.99923337, 1927, '1528'), 'temp/1759139049_2886529_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg'], '987515247': [('987515247', 'Carton', 0.99966764, 1927, '1528'), 'temp/1759139049_2886529_987515247_e47b65403df916ba909bc9c439b0af73.jpg'], '987515249': [('987515249', 'Carton', 0.98130333, 1927, '1528'), 'temp/1759139049_2886529_987515249_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515250': [('987515250', 'Carton', 0.98077893, 1927, '1528'), 'temp/1759139049_2886529_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg'], '987515207': [('987515207', 'Papier_Magazine', 0.8741562, 1927, '1528'), 'temp/1759139049_2886529_987515207_de216ddb041e249524b0fb2b949064a5.jpg'], '987515239': [('987515239', 'Carton', 0.99978334, 1927, '1528'), 'temp/1759139049_2886529_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg'], '987515240': [('987515240', 'Carton', 0.99952054, 1927, '1528'), 'temp/1759139049_2886529_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg'], '987515241': [('987515241', 'Carton', 0.9821129, 1927, '1528'), 'temp/1759139049_2886529_987515241_073420d938f5f010ffd5b4353c064e09.jpg'], '987515242': [('987515242', 'Carton', 0.9358889, 1927, '1528'), 'temp/1759139049_2886529_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg'], '987515243': [('987515243', 'Papier_Magazine', 0.8743089, 1927, '1528'), 'temp/1759139049_2886529_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg'], '987515244': [('987515244', 'Papier_Magazine', 0.81725824, 1927, '1528'), 'temp/1759139049_2886529_987515244_419530eaef5ef868f75c758b94eea4b4.jpg'], '987515208': [('987515208', 'Carton', 0.99172616, 1927, '1528'), 'temp/1759139049_2886529_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg'], '987515209': [('987515209', 'Carton', 0.96768725, 1927, '1528'), 'temp/1759139049_2886529_987515209_02dfe1ae39f51994652f4a8538844aea.jpg'], '987515211': [('987515211', 'Carton', 0.9734698, 1927, '1528'), 'temp/1759139049_2886529_987515211_72cc7664d45bd40477351b9b764f1500.jpg'], '987515213': [('987515213', 'Carton', 0.986937, 1927, '1528'), 'temp/1759139049_2886529_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515215': [('987515215', 'Papier_Magazine', 0.9939276, 1927, '1528'), 'temp/1759139049_2886529_987515215_902ef348a7eebb9a8b87f42927347936.jpg'], '987515216': [('987515216', 'Papier_Magazine', 0.9774646, 1927, '1528'), 'temp/1759139049_2886529_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg'], '987515176': [('987515176', 'Papier_Magazine', 0.9998142, 1927, '1528'), 'temp/1759139049_2886529_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515177': [('987515177', 'Papier_Magazine', 0.97719795, 1927, '1528'), 'temp/1759139049_2886529_987515177_4a54e9967227806219ddf45d256539d8.jpg'], '987515178': [('987515178', 'Carton', 0.85884994, 1927, '1528'), 'temp/1759139049_2886529_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg'], '987515179': [('987515179', 'Carton', 0.92734283, 1927, '1528'), 'temp/1759139049_2886529_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg'], '987515180': [('987515180', 'Carton', 0.9899907, 1927, '1528'), 'temp/1759139049_2886529_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg'], '987515181': [('987515181', 'Carton', 0.9977805, 1927, '1528'), 'temp/1759139049_2886529_987515181_1738c2798fb31152809ecb443ac286d6.jpg'], '987515226': [('987515226', 'Papier_Magazine', 0.9869784, 1927, '1528'), 'temp/1759139049_2886529_987515226_a18048dca1a77ae086b62cf07759f704.jpg'], '987515227': [('987515227', 'Papier_Magazine', 0.9005324, 1927, '1528'), 'temp/1759139049_2886529_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg'], '987515228': [('987515228', 'Papier_Magazine', 0.5220881, 1927, '1528'), 'temp/1759139049_2886529_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg'], '987515230': [('987515230', 'Carton', 0.9994055, 1927, '1528'), 'temp/1759139049_2886529_987515230_846ad925884264181565c81d152a2e94.jpg'], '987515231': [('987515231', 'Carton', 0.999421, 1927, '1528'), 'temp/1759139049_2886529_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg'], '987515232': [('987515232', 'Carton', 0.9992449, 1927, '1528'), 'temp/1759139049_2886529_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg'], '987515189': [('987515189', 'Carton', 0.9977896, 1927, '1528'), 'temp/1759139049_2886529_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg'], '987515190': [('987515190', 'Carton', 0.9763498, 1927, '1528'), 'temp/1759139049_2886529_987515190_d56932bfc6ba2a8c974c691108755017.jpg'], '987515192': [('987515192', 'Papier_Magazine', 0.99991155, 1927, '1528'), 'temp/1759139049_2886529_987515192_b661073b218f5f056833d6af1c617153.jpg'], '987515193': [('987515193', 'Papier_Magazine', 0.9993968, 1927, '1528'), 'temp/1759139049_2886529_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg'], '987515196': [('987515196', 'Carton', 0.9846452, 1927, '1528'), 'temp/1759139049_2886529_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515198': [('987515198', 'Carton', 0.96616066, 1927, '1528'), 'temp/1759139049_2886529_987515198_599e80f444c876f407e94b533c89360b.jpg'], '987515200': [('987515200', 'Carton', 0.9859138, 1927, '1528'), 'temp/1759139049_2886529_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg'], '987515201': [('987515201', 'Carton', 0.9954585, 1927, '1528'), 'temp/1759139049_2886529_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg'], '987515202': [('987515202', 'Carton', 0.9911123, 1927, '1528'), 'temp/1759139049_2886529_987515202_3314bd90d1404f31b827d8925abf2d62.jpg'], '987515204': [('987515204', 'Papier_Magazine', 0.9951074, 1927, '1528'), 'temp/1759139049_2886529_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg'], '987515205': [('987515205', 'Papier_Magazine', 0.99084514, 1927, '1528'), 'temp/1759139049_2886529_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg'], '987515224': [('987515224', 'Carton', 0.9086194, 1927, '1528'), 'temp/1759139049_2886529_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg'], '987515233': [('987515233', 'Carton', 0.983497, 1927, '1528'), 'temp/1759139049_2886529_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg'], '987515234': [('987515234', 'Carton', 0.9447632, 1927, '1528'), 'temp/1759139049_2886529_987515234_2eca3480aed0f8b876242675ad99b666.jpg'], '987515235': [('987515235', 'Papier_Magazine', 0.89202553, 1927, '1528'), 'temp/1759139049_2886529_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg'], '987515236': [('987515236', 'Papier_Magazine', 0.5364688, 1927, '1528'), 'temp/1759139049_2886529_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg'], '987515237': [('987515237', 'Carton', 0.7700794, 1927, '1528'), 'temp/1759139049_2886529_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg'], '987515238': [('987515238', 'Carton', 0.99957436, 1927, '1528'), 'temp/1759139049_2886529_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg'], '987515182': [('987515182', 'Carton', 0.992432, 1927, '1528'), 'temp/1759139049_2886529_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg'], '987515183': [('987515183', 'Papier_Magazine', 0.99999225, 1927, '1528'), 'temp/1759139049_2886529_987515183_6aab9ca0421398b4899892c10c2594c6.jpg'], '987515184': [('987515184', 'Papier_Magazine', 0.9997321, 1927, '1528'), 'temp/1759139049_2886529_987515184_19c8c2177209a285df6014d95fe53f2c.jpg'], '987515185': [('987515185', 'Papier_Magazine', 0.7974504, 1927, '1528'), 'temp/1759139049_2886529_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg'], '987515186': [('987515186', 'Carton', 0.9847932, 1927, '1528'), 'temp/1759139049_2886529_987515186_797def426440b544aa80dbd63a19234a.jpg'], '987515187': [('987515187', 'Carton', 0.98113394, 1927, '1528'), 'temp/1759139049_2886529_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg'], '987515217': [('987515217', 'Carton', 0.5291969, 1927, '1528'), 'temp/1759139049_2886529_987515217_78877bb2c5760be28518d17f77d1c609.jpg'], '987515219': [('987515219', 'Carton', 0.99936837, 1927, '1528'), 'temp/1759139049_2886529_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg'], '987515220': [('987515220', 'Carton', 0.9963806, 1927, '1528'), 'temp/1759139049_2886529_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg'], '987515222': [('987515222', 'Carton', 0.9974712, 1927, '1528'), 'temp/1759139049_2886529_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg'], '987515223': [('987515223', 'Carton', 0.99208224, 1927, '1528'), 'temp/1759139049_2886529_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg'], '987515188': [('987515188', 'Carton', 0.995654, 1927, '1528'), 'temp/1759139049_2886529_987515188_4116f9906657a69bb76c2fda982037b9.jpg']}
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1879

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=1879
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 1879
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=1879
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : detect_points
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (987515173) 
 Found this number of photos: 1
##### Call download_photos : nb_thread : 5
begin to download photo : 987515173
download finish for photo 987515173
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.14049363136291504
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  1
step1:detect_points
Mon Sep 29 11:45:04 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139104_2886529_987515173_91fa471b1a04f95b356afdbaf021f623.jpg': 987515173}
map_photo_id_path_extension : {987515173: {'path': 'temp/1759139104_2886529_987515173_91fa471b1a04f95b356afdbaf021f623.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou step predict points !
Inside try reload !
classes : ['Autre_Environement', 'Carton', 'Kraft', 'Lointain_Papier_Magazine', 'Metal', 'Papier_Magazine', 'Plastique', 'Sol_Environement', 'Teint_Dans_La_Masse', 'autre_refus']
pht : 1927
model_name : learn_refus_upm_blanches_1924
{'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'}
gpu_mode in detect_points : 1
To load net FromThcl()
model_param file didn't exist 
model_name : learn_refus_upm_blanches_1924
model_type : caffe
list file need :
['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt']
file exist in s3 :
['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt']
file manque in s3 : 
['deploy_conv_normal.prototxt', 'deploy_fc.prototxt']
local folder  : /data/models_weight/learn_refus_upm_blanches_1924


/data/models_weight/learn_refus_upm_blanches_1924/caffemodel
size_local : 45774543 size in s3 : 45774543
create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04
caffemodel already exist and didn't need to update


/data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt
size_local : 17312 size in s3 : 17312
create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03
deploy.prototxt already exist and didn't need to update


/data/models_weight/learn_refus_upm_blanches_1924/mean.npy
size_local : 1572992 size in s3 : 1572992
create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05
mean.npy already exist and didn't need to update


/data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt
size_local : 218 size in s3 : 218
create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04
synset_words.txt already exist and didn't need to update
reshape net's input to : (224, 224)
origin shape : (10, 3, 224, 224)
after reshape : (1, 3, 224, 224)
[('data', (1, 3, 224, 224)), ('conv1', (1, 64, 112, 112)), ('pool1', (1, 64, 56, 56)), ('pool1_pool1_0_split_0', (1, 64, 56, 56)), ('pool1_pool1_0_split_1', (1, 64, 56, 56)), ('res2a_branch1', (1, 64, 56, 56)), ('res2a_branch2a', (1, 64, 56, 56)), ('res2a_branch2b', (1, 64, 56, 56)), ('res2a', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_0', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_1', (1, 64, 56, 56)), ('res2b_branch2a', (1, 64, 56, 56)), ('res2b_branch2b', (1, 64, 56, 56)), ('res2b', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_0', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_1', (1, 64, 56, 56)), ('res3a_branch1', (1, 128, 28, 28)), ('res3a_branch2a', (1, 128, 28, 28)), ('res3a_branch2b', (1, 128, 28, 28)), ('res3a', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_0', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_1', (1, 128, 28, 28)), ('res3b_branch2a', (1, 128, 28, 28)), ('res3b_branch2b', (1, 128, 28, 28)), ('res3b', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_0', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_1', (1, 128, 28, 28)), ('res4a_branch1', (1, 256, 14, 14)), ('res4a_branch2a', (1, 256, 14, 14)), ('res4a_branch2b', (1, 256, 14, 14)), ('res4a', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_0', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_1', (1, 256, 14, 14)), ('res4b_branch2a', (1, 256, 14, 14)), ('res4b_branch2b', (1, 256, 14, 14)), ('res4b', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_0', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_1', (1, 256, 14, 14)), ('res5a_branch1', (1, 512, 7, 7)), ('res5a_branch2a', (1, 512, 7, 7)), ('res5a_branch2b', (1, 512, 7, 7)), ('res5a', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_0', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_1', (1, 512, 7, 7)), ('res5b_branch2a', (1, 512, 7, 7)), ('res5b_branch2b', (1, 512, 7, 7)), ('res5b', (1, 512, 7, 7)), ('fc2019-10-22_15-02-46', (1, 10, 1, 1)), ('prob', (1, 10, 1, 1))]
set image transformer : 
About to compute detect the points : len(args) : 1
Inside predict_points step exec : nb paths : 1
treate image : temp/1759139104_2886529_987515173_91fa471b1a04f95b356afdbaf021f623.jpg

size of numpy array img : 2408584
scale method : caffe/skimage 
size of numpy array img_scale : 2408584 (448, 448, 3)
nb_h 8 nb_w 8 size of sub images : (224, 224, 3)
size of caffe_input : 38535320 (64, 3, 224, 224)
time to do the preprocess : 0.04212617874145508
time to do a prediction : 0.23969602584838867
dict_keys(['prob'])
shape of output 
(64, 10, 1, 1)
shape of the out_put heatmap
(10, 8, 8)
number of sub_photos  vertical and horizon
8 8
size of heatmap : (8,8)
size of heatmap : (8,8)
size of heatmap : (8,8)
size of heatmap : (8,8)
size of heatmap : (8,8)
size of heatmap : (8,8)
size of heatmap : (8,8)
size of heatmap : (8,8)
size of heatmap : (8,8)
size of heatmap : (8,8)

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 1.5878944396972656
time spend to save output : 4.124641418457031e-05
total time spend for step 1 : 1.5879356861114502

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 0
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {987515173: [(987515173, 1982, 'Autre_Environement', 112, -1, 112, -1, 6.243469283240266e-12), (987515173, 1982, 'Autre_Environement', 144, -1, 112, -1, 2.4543542545552022e-11), (987515173, 1982, 'Autre_Environement', 176, -1, 112, -1, 1.070731325825136e-08), (987515173, 1982, 'Autre_Environement', 208, -1, 112, -1, 4.451845256880915e-07), (987515173, 1982, 'Autre_Environement', 240, -1, 112, -1, 1.920181148307165e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 112, -1, 3.767997259274125e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 112, -1, 0.0001228788314620033), (987515173, 1982, 'Autre_Environement', 336, -1, 112, -1, 2.9437218472594395e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 144, -1, 2.3670880366921665e-08), (987515173, 1982, 'Autre_Environement', 144, -1, 144, -1, 2.2125481891066556e-08), (987515173, 1982, 'Autre_Environement', 176, -1, 144, -1, 1.380304439635438e-07), (987515173, 1982, 'Autre_Environement', 208, -1, 144, -1, 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(987515173, 1982, 'autre_refus', 176, -1, 240, -1, 6.490321538876742e-05), (987515173, 1982, 'autre_refus', 208, -1, 240, -1, 2.5311741410405375e-05), (987515173, 1982, 'autre_refus', 240, -1, 240, -1, 7.278987322933972e-05), (987515173, 1982, 'autre_refus', 272, -1, 240, -1, 0.00013978003698866814), (987515173, 1982, 'autre_refus', 304, -1, 240, -1, 8.991508366307244e-05), (987515173, 1982, 'autre_refus', 336, -1, 240, -1, 8.178226562449709e-05), (987515173, 1982, 'autre_refus', 112, -1, 272, -1, 0.0002686335355974734), (987515173, 1982, 'autre_refus', 144, -1, 272, -1, 0.0001114915867219679), (987515173, 1982, 'autre_refus', 176, -1, 272, -1, 0.00012486553168855608), (987515173, 1982, 'autre_refus', 208, -1, 272, -1, 5.116977990837768e-05), (987515173, 1982, 'autre_refus', 240, -1, 272, -1, 2.9186268875491805e-05), (987515173, 1982, 'autre_refus', 272, -1, 272, -1, 4.273717786418274e-05), (987515173, 1982, 'autre_refus', 304, -1, 272, -1, 6.827771721873432e-05), (987515173, 1982, 'autre_refus', 336, -1, 272, -1, 0.0001424249931005761), (987515173, 1982, 'autre_refus', 112, -1, 304, -1, 0.0001158312734332867), (987515173, 1982, 'autre_refus', 144, -1, 304, -1, 0.00021712372836191207), (987515173, 1982, 'autre_refus', 176, -1, 304, -1, 0.0004263451264705509), (987515173, 1982, 'autre_refus', 208, -1, 304, -1, 0.00042696608579717577), (987515173, 1982, 'autre_refus', 240, -1, 304, -1, 6.567915988853201e-05), (987515173, 1982, 'autre_refus', 272, -1, 304, -1, 3.1706040317658335e-05), (987515173, 1982, 'autre_refus', 304, -1, 304, -1, 1.1751332749554422e-05), (987515173, 1982, 'autre_refus', 336, -1, 304, -1, 1.8792985429172404e-05), (987515173, 1982, 'autre_refus', 112, -1, 336, -1, 0.0002474906505085528), (987515173, 1982, 'autre_refus', 144, -1, 336, -1, 0.00047082972014322877), (987515173, 1982, 'autre_refus', 176, -1, 336, -1, 0.000336277240421623), (987515173, 1982, 'autre_refus', 208, -1, 336, -1, 0.00023709746892564), (987515173, 1982, 'autre_refus', 240, -1, 336, -1, 0.00010709973867051303), (987515173, 1982, 'autre_refus', 272, -1, 336, -1, 9.583005885360762e-05), (987515173, 1982, 'autre_refus', 304, -1, 336, -1, 0.0001312946405960247), (987515173, 1982, 'autre_refus', 336, -1, 336, -1, 0.0007318240241147578)]}

result thcl :
{'987515245': [('987515245', 'Carton', 0.8659642, 1927, '1528'), 'temp/1759139049_2886529_987515245_757d9d208d5bd4375c5f21f68b699148.jpg'], '987515246': [('987515246', 'Carton', 0.99923337, 1927, '1528'), 'temp/1759139049_2886529_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg'], '987515247': [('987515247', 'Carton', 0.99966764, 1927, '1528'), 'temp/1759139049_2886529_987515247_e47b65403df916ba909bc9c439b0af73.jpg'], '987515249': [('987515249', 'Carton', 0.98130333, 1927, '1528'), 'temp/1759139049_2886529_987515249_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515250': [('987515250', 'Carton', 0.98077893, 1927, '1528'), 'temp/1759139049_2886529_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg'], '987515207': [('987515207', 'Papier_Magazine', 0.8741562, 1927, '1528'), 'temp/1759139049_2886529_987515207_de216ddb041e249524b0fb2b949064a5.jpg'], '987515239': [('987515239', 'Carton', 0.99978334, 1927, '1528'), 'temp/1759139049_2886529_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg'], '987515240': [('987515240', 'Carton', 0.99952054, 1927, '1528'), 'temp/1759139049_2886529_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg'], '987515241': [('987515241', 'Carton', 0.9821129, 1927, '1528'), 'temp/1759139049_2886529_987515241_073420d938f5f010ffd5b4353c064e09.jpg'], '987515242': [('987515242', 'Carton', 0.9358889, 1927, '1528'), 'temp/1759139049_2886529_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg'], '987515243': [('987515243', 'Papier_Magazine', 0.8743089, 1927, '1528'), 'temp/1759139049_2886529_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg'], '987515244': [('987515244', 'Papier_Magazine', 0.81725824, 1927, '1528'), 'temp/1759139049_2886529_987515244_419530eaef5ef868f75c758b94eea4b4.jpg'], '987515208': [('987515208', 'Carton', 0.99172616, 1927, '1528'), 'temp/1759139049_2886529_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg'], '987515209': [('987515209', 'Carton', 0.96768725, 1927, '1528'), 'temp/1759139049_2886529_987515209_02dfe1ae39f51994652f4a8538844aea.jpg'], '987515211': [('987515211', 'Carton', 0.9734698, 1927, '1528'), 'temp/1759139049_2886529_987515211_72cc7664d45bd40477351b9b764f1500.jpg'], '987515213': [('987515213', 'Carton', 0.986937, 1927, '1528'), 'temp/1759139049_2886529_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515215': [('987515215', 'Papier_Magazine', 0.9939276, 1927, '1528'), 'temp/1759139049_2886529_987515215_902ef348a7eebb9a8b87f42927347936.jpg'], '987515216': [('987515216', 'Papier_Magazine', 0.9774646, 1927, '1528'), 'temp/1759139049_2886529_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg'], '987515176': [('987515176', 'Papier_Magazine', 0.9998142, 1927, '1528'), 'temp/1759139049_2886529_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515177': [('987515177', 'Papier_Magazine', 0.97719795, 1927, '1528'), 'temp/1759139049_2886529_987515177_4a54e9967227806219ddf45d256539d8.jpg'], '987515178': [('987515178', 'Carton', 0.85884994, 1927, '1528'), 'temp/1759139049_2886529_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg'], '987515179': [('987515179', 'Carton', 0.92734283, 1927, '1528'), 'temp/1759139049_2886529_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg'], '987515180': [('987515180', 'Carton', 0.9899907, 1927, '1528'), 'temp/1759139049_2886529_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg'], '987515181': [('987515181', 'Carton', 0.9977805, 1927, '1528'), 'temp/1759139049_2886529_987515181_1738c2798fb31152809ecb443ac286d6.jpg'], '987515226': [('987515226', 'Papier_Magazine', 0.9869784, 1927, '1528'), 'temp/1759139049_2886529_987515226_a18048dca1a77ae086b62cf07759f704.jpg'], '987515227': [('987515227', 'Papier_Magazine', 0.9005324, 1927, '1528'), 'temp/1759139049_2886529_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg'], '987515228': [('987515228', 'Papier_Magazine', 0.5220881, 1927, '1528'), 'temp/1759139049_2886529_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg'], '987515230': [('987515230', 'Carton', 0.9994055, 1927, '1528'), 'temp/1759139049_2886529_987515230_846ad925884264181565c81d152a2e94.jpg'], '987515231': [('987515231', 'Carton', 0.999421, 1927, '1528'), 'temp/1759139049_2886529_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg'], '987515232': [('987515232', 'Carton', 0.9992449, 1927, '1528'), 'temp/1759139049_2886529_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg'], '987515189': [('987515189', 'Carton', 0.9977896, 1927, '1528'), 'temp/1759139049_2886529_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg'], '987515190': [('987515190', 'Carton', 0.9763498, 1927, '1528'), 'temp/1759139049_2886529_987515190_d56932bfc6ba2a8c974c691108755017.jpg'], '987515192': [('987515192', 'Papier_Magazine', 0.99991155, 1927, '1528'), 'temp/1759139049_2886529_987515192_b661073b218f5f056833d6af1c617153.jpg'], '987515193': [('987515193', 'Papier_Magazine', 0.9993968, 1927, '1528'), 'temp/1759139049_2886529_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg'], '987515196': [('987515196', 'Carton', 0.9846452, 1927, '1528'), 'temp/1759139049_2886529_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515198': [('987515198', 'Carton', 0.96616066, 1927, '1528'), 'temp/1759139049_2886529_987515198_599e80f444c876f407e94b533c89360b.jpg'], '987515200': [('987515200', 'Carton', 0.9859138, 1927, '1528'), 'temp/1759139049_2886529_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg'], '987515201': [('987515201', 'Carton', 0.9954585, 1927, '1528'), 'temp/1759139049_2886529_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg'], '987515202': [('987515202', 'Carton', 0.9911123, 1927, '1528'), 'temp/1759139049_2886529_987515202_3314bd90d1404f31b827d8925abf2d62.jpg'], '987515204': [('987515204', 'Papier_Magazine', 0.9951074, 1927, '1528'), 'temp/1759139049_2886529_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg'], '987515205': [('987515205', 'Papier_Magazine', 0.99084514, 1927, '1528'), 'temp/1759139049_2886529_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg'], '987515224': [('987515224', 'Carton', 0.9086194, 1927, '1528'), 'temp/1759139049_2886529_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg'], '987515233': [('987515233', 'Carton', 0.983497, 1927, '1528'), 'temp/1759139049_2886529_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg'], '987515234': [('987515234', 'Carton', 0.9447632, 1927, '1528'), 'temp/1759139049_2886529_987515234_2eca3480aed0f8b876242675ad99b666.jpg'], '987515235': [('987515235', 'Papier_Magazine', 0.89202553, 1927, '1528'), 'temp/1759139049_2886529_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg'], '987515236': [('987515236', 'Papier_Magazine', 0.5364688, 1927, '1528'), 'temp/1759139049_2886529_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg'], '987515237': [('987515237', 'Carton', 0.7700794, 1927, '1528'), 'temp/1759139049_2886529_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg'], '987515238': [('987515238', 'Carton', 0.99957436, 1927, '1528'), 'temp/1759139049_2886529_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg'], '987515182': [('987515182', 'Carton', 0.992432, 1927, '1528'), 'temp/1759139049_2886529_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg'], '987515183': [('987515183', 'Papier_Magazine', 0.99999225, 1927, '1528'), 'temp/1759139049_2886529_987515183_6aab9ca0421398b4899892c10c2594c6.jpg'], '987515184': [('987515184', 'Papier_Magazine', 0.9997321, 1927, '1528'), 'temp/1759139049_2886529_987515184_19c8c2177209a285df6014d95fe53f2c.jpg'], '987515185': [('987515185', 'Papier_Magazine', 0.7974504, 1927, '1528'), 'temp/1759139049_2886529_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg'], '987515186': [('987515186', 'Carton', 0.9847932, 1927, '1528'), 'temp/1759139049_2886529_987515186_797def426440b544aa80dbd63a19234a.jpg'], '987515187': [('987515187', 'Carton', 0.98113394, 1927, '1528'), 'temp/1759139049_2886529_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg'], '987515217': [('987515217', 'Carton', 0.5291969, 1927, '1528'), 'temp/1759139049_2886529_987515217_78877bb2c5760be28518d17f77d1c609.jpg'], '987515219': [('987515219', 'Carton', 0.99936837, 1927, '1528'), 'temp/1759139049_2886529_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg'], '987515220': [('987515220', 'Carton', 0.9963806, 1927, '1528'), 'temp/1759139049_2886529_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg'], '987515222': [('987515222', 'Carton', 0.9974712, 1927, '1528'), 'temp/1759139049_2886529_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg'], '987515223': [('987515223', 'Carton', 0.99208224, 1927, '1528'), 'temp/1759139049_2886529_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg'], '987515188': [('987515188', 'Carton', 0.995654, 1927, '1528'), 'temp/1759139049_2886529_987515188_4116f9906657a69bb76c2fda982037b9.jpg']}

result detect_point :
{987515173: [(987515173, 1982, 'Autre_Environement', 112, -1, 112, -1, 6.243469283240266e-12), (987515173, 1982, 'Autre_Environement', 144, -1, 112, -1, 2.4543542545552022e-11), (987515173, 1982, 'Autre_Environement', 176, -1, 112, -1, 1.070731325825136e-08), (987515173, 1982, 'Autre_Environement', 208, -1, 112, -1, 4.451845256880915e-07), (987515173, 1982, 'Autre_Environement', 240, -1, 112, -1, 1.920181148307165e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 112, -1, 3.767997259274125e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 112, -1, 0.0001228788314620033), (987515173, 1982, 'Autre_Environement', 336, -1, 112, -1, 2.9437218472594395e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 144, -1, 2.3670880366921665e-08), (987515173, 1982, 'Autre_Environement', 144, -1, 144, -1, 2.2125481891066556e-08), (987515173, 1982, 'Autre_Environement', 176, -1, 144, -1, 1.380304439635438e-07), (987515173, 1982, 'Autre_Environement', 208, -1, 144, -1, 1.4776207990507828e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 144, -1, 1.126756160374498e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 144, -1, 0.00015826585877221078), (987515173, 1982, 'Autre_Environement', 304, -1, 144, -1, 0.00044310177327133715), (987515173, 1982, 'Autre_Environement', 336, -1, 144, -1, 6.534564454341307e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 176, -1, 1.3311206430444145e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 176, -1, 1.62173239459662e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 176, -1, 2.518604560464155e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 176, -1, 1.6157548543560551e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 176, -1, 6.242257313715527e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 176, -1, 8.784146484686062e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 176, -1, 0.0003260589437559247), (987515173, 1982, 'Autre_Environement', 336, -1, 176, -1, 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-1, 336, -1, 0.00010709973867051303), (987515173, 1982, 'autre_refus', 272, -1, 336, -1, 9.583005885360762e-05), (987515173, 1982, 'autre_refus', 304, -1, 336, -1, 0.0001312946405960247), (987515173, 1982, 'autre_refus', 336, -1, 336, -1, 0.0007318240241147578)]}

############################### TEST certificat_qualite_papier  ################################
TEST certificat qualite papier
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1848

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=1848
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 1848
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=1848
# 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 !
 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 !
Step 4442 tile have less inputs used (1) than in the step definition (3) : maybe we manage optionnal inputs !
WARNING : number of outputs for step 4441 detect_points is not consistent : 2 used against 1 in the step definition !
WARNING : number of inputs for step 4443 count_percent_refus is not consistent : 4 used against 3 in the step definition !
Step 4444 send_mail_dechet 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 : output 1 of step 4440 have datatype=1 whereas input 0 of step 4443 have datatype=2
WARNING : type of output 1 of step 4441 doesn't seem to be define in the database(
WARNING : type of input 4 of step 4443 doesn't seem to be define in the database(
DataTypes for each output/input checked !
no param json to modify
List Step Type Loaded in datou : init_dechet, tile, detect_points, count_percent_refus, brightness, blur_detection, send_mail_dechet
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (1902940) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
Catched exception !
Connect or reconnect !
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (1902940)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 987321136, 'portfolio_id': 1902940}]
 map_portfolio_id_photo_id: {1902940: [987321136]}
##### Call download_photos : nb_thread : 5
begin to download photo : 987321136
download finish for photo 987321136
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.44301390647888184
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  7
step1:init_dechet
Mon Sep 29 11:45:06 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136}
map_photo_id_path_extension : {987321136: {'path': 'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
debut step init detect dechets
input :  temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg
ON MODIFIE NB AVEC LE INPUT
map photo id path extension :  temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg
scale : 0.9481481481481482
FIN step init dechet

After datou_step_exec type output : <class 'dict'>
map_portfolio_photo : len 1 keys : dict_keys([1902940])
Inside saveOutput : final : False verbose : True
 saveOutput not yet implemented for datou_step.type : init_dechet we use saveGeneral 
[987321136]
 map_info['map_portfolio_photo'] : {1902940: [987321136]}
 final :  False mtd_id 1848 list_pids : [987321136]
Looping around the photos to save general results
len do output : 1
/987321136Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
before output type
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f190c4d3d30>
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f17237d1d00>
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f16a7e36b50>
Managing all output in save final without adding information in the mtr_datou_result 
('1848', None, None, None, None, None, None, None, None)
('1848', '1902940', '987321136', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 4
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('1848', '1902940', '987321136', 'None', None, None, None, None, None)]
time used for this insertion : 0.0441441535949707
 save_final 

save missing photos in datou_result :

time spend for datou_step_exec : 0.0002396106719970703
time spend to save output : 0.044373273849487305
total time spend for step 1 : 0.044612884521484375

step2:tile
Mon Sep 29 11:45:06 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior 
Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed
complete output_args for input 0 : {'987321136': ['temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]}
 input_args_next_step : {'987321136': ()}
 output_args : {'987321136': ['temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]}
 args : 987321136
 depend.output_id : 0
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 

input_args_next_step, len :1, first value : ('temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg',)
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136}
map_photo_id_path_extension : {987321136: {'path': 'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {0: 987321136}
verbose : True
 param_json : {'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'tile_correct_upm', 'ETA': 86400, 'new_width': 1500, 'new_height': 20000, 'host': 'www.fotonower.com', 'protocol': 'https', 'photo_tile_type': 1522, 'option_bande': 'True'}
 type(crop_hashtag_type) : <class 'int'>
 type(crop_hashtag_type_tiled) : <class 'int'>
We consider crop_hashtag_type is an integer ! 
  map_chi_type_to_chi_type_cropped : {406: 410}
 map_filenames :  {987321136: 'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg'}
 list_pids :   1
 list_pids :   2
 list_subpids to replace list_pids : 1
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 987321136,987321136,987321136) and `type` in (406) 
Loaded 0 chid ids of type : 0
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in ()
 Status : 404
 Content : b'<!doctype html>\n<html lang=en>\n<title>404 Not Found</title>\n<h1>Not Found</h1>\n<p>The requested URL was not found on the server. If you entered the URL manually please check your spelling and try again.</p>\n'
 All Response : <Response [404]>
 WARNING : NO PORTFOLIO CREATED portfolio_id 0 used instead of new portfolio
 created feed_id_new_photos : 0 with name tile_correct_upm
 feed_id_new_photos : 0
 filename : temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg
 photo_id : 987321136
 height_image_input : 439 width_image_input : 562
 new_width : 1500 new_height : 20000
 stride : 0 stride_relative : 0.1
 chi to copy from the main photo to the tiled photo  input_chi_for_this_image_as_chi :  0
list_bib_to_crops : 1 [(0, 562, 0, 439, 0)]
calcul des nouveaux crops pour le tile x0:0,x1:562,y0:0,y1:439
chi selectionnes : []
 new_crops_tiles : 1 crop_transformed : 0
insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s)
[(987321136, 2090988864, 1522, 0, 562, 0, 439, 1.0)]
  list_photo_ids_cropped : [987321136]
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 987321136) and `type` in (1522) 
Loaded 1 chid ids of type : 1522
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1608847328)
SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1608847328)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1608847328)
treat the image : temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg
, 0
before upload mediasElapsed time : 0.01120138168334961


About to upload 1 photos
 Status : 404
 Content : b'<!doctype html>\n<html lang=en>\n<title>404 Not Found</title>\n<h1>Not Found</h1>\n<p>The requested URL was not found on the server. If you entered the URL manually please check your spelling and try again.</p>\n'
 All Response : <Response [404]>
 WARNING : NO PORTFOLIO CREATED portfolio_id 0 used instead of new portfolio
upload in portfolio : 0
Result OK !

uploaded one batch 0 Elapsed time : 1.1720547676086426


upload mediasElapsed time : 1.1833033561706543

, 0insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(1608847328, 1386782171, 0)]
Saving 0 CHIs.
list_chi_tile : []

end of tileElapsed time : 1.2191264629364014

map_pid_results : {'1386782171': ['temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']}

After datou_step_exec type output : <class 'dict'>
map_portfolio_photo : len 1 keys : dict_keys([1902940])
Inside saveOutput : final : False verbose : True
 saveOutput not yet implemented for datou_step.type : tile we use saveGeneral 
[987321136, 987321136, '1386782171']
 map_info['map_portfolio_photo'] : {1902940: [987321136]}
 final :  False mtd_id 1848 list_pids : [987321136, 987321136, '1386782171']
Looping around the photos to save general results
len do output : 1
/1386782171Didn't retrieve data
.
before output type
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f1514c53400>
Managing all output in save final without adding information in the mtr_datou_result 
('1848', None, None, None, None, None, None, None, None)
('1848', '1902940', '987321136', None, None, None, None, None, None)
('1848', None, None, None, None, None, None, None, None)
('1848', '1902940', '987321136', None, None, None, None, None, None)
('1848', None, None, None, None, None, None, None, None)
('1848', None, '1386782171', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 4
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('1848', None, '1386782171', 'None', None, None, None, None, None), ('1848', '1902940', '987321136', None, None, None, None, None, None)]
time used for this insertion : 0.041651010513305664
 save_final 

save missing photos in datou_result :

time spend for datou_step_exec : 6.4464967250823975
time spend to save output : 0.041818857192993164
total time spend for step 2 : 6.488315582275391

step3:detect_points
Mon Sep 29 11:45:13 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 : {'1386782171': ['temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']}
 input_args_next_step : {'1386782171': ()}
 output_args : {'1386782171': ['temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']}
 args : 1386782171
 depend.output_id : 0
complete output_args for input 1 : {'987321136': ['temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]}
 input_args_next_step : {'1386782171': ('temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',), '987321136': ()}
 output_args : {'987321136': ['temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]}
 args : 987321136
 depend.output_id : 2
VR 22-3-18 : For now we do not clean correctly the datou structure 

input_args_next_step, len :2, first value : ('temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',)
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1386782171}
map_photo_id_path_extension : {987321136: {'path': 'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1386782171: {'path': 'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}}
map_subphoto_mainphoto : {0: 987321136, 1386782171: 987321136}

Beginning of datou step predict points !
Inside try reload !
classes : ['Autre_Environement', 'Carton', 'Kraft', 'Lointain_Papier_Magazine', 'Metal', 'Papier_Magazine', 'Plastique', 'Sol_Environement', 'Teint_Dans_La_Masse', 'autre_refus']
pht : 1927
model_name : learn_refus_upm_blanches_1924
{'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'}
gpu_mode in detect_points : False
To load net FromThcl()
model_param file didn't exist 
model_name : learn_refus_upm_blanches_1924
model_type : caffe
list file need :
['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt']
file exist in s3 :
['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt']
file manque in s3 : 
['deploy_conv_normal.prototxt', 'deploy_fc.prototxt']
local folder  : /data/models_weight/learn_refus_upm_blanches_1924


/data/models_weight/learn_refus_upm_blanches_1924/caffemodel
size_local : 45774543 size in s3 : 45774543
create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04
caffemodel already exist and didn't need to update


/data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt
size_local : 17312 size in s3 : 17312
create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03
deploy.prototxt already exist and didn't need to update


/data/models_weight/learn_refus_upm_blanches_1924/mean.npy
size_local : 1572992 size in s3 : 1572992
create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05
mean.npy already exist and didn't need to update


/data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt
size_local : 218 size in s3 : 218
create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04
synset_words.txt already exist and didn't need to update
reshape net's input to : (224, 224)
origin shape : (10, 3, 224, 224)
after reshape : (1, 3, 224, 224)
[('data', (1, 3, 224, 224)), ('conv1', (1, 64, 112, 112)), ('pool1', (1, 64, 56, 56)), ('pool1_pool1_0_split_0', (1, 64, 56, 56)), ('pool1_pool1_0_split_1', (1, 64, 56, 56)), ('res2a_branch1', (1, 64, 56, 56)), ('res2a_branch2a', (1, 64, 56, 56)), ('res2a_branch2b', (1, 64, 56, 56)), ('res2a', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_0', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_1', (1, 64, 56, 56)), ('res2b_branch2a', (1, 64, 56, 56)), ('res2b_branch2b', (1, 64, 56, 56)), ('res2b', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_0', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_1', (1, 64, 56, 56)), ('res3a_branch1', (1, 128, 28, 28)), ('res3a_branch2a', (1, 128, 28, 28)), ('res3a_branch2b', (1, 128, 28, 28)), ('res3a', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_0', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_1', (1, 128, 28, 28)), ('res3b_branch2a', (1, 128, 28, 28)), ('res3b_branch2b', (1, 128, 28, 28)), ('res3b', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_0', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_1', (1, 128, 28, 28)), ('res4a_branch1', (1, 256, 14, 14)), ('res4a_branch2a', (1, 256, 14, 14)), ('res4a_branch2b', (1, 256, 14, 14)), ('res4a', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_0', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_1', (1, 256, 14, 14)), ('res4b_branch2a', (1, 256, 14, 14)), ('res4b_branch2b', (1, 256, 14, 14)), ('res4b', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_0', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_1', (1, 256, 14, 14)), ('res5a_branch1', (1, 512, 7, 7)), ('res5a_branch2a', (1, 512, 7, 7)), ('res5a_branch2b', (1, 512, 7, 7)), ('res5a', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_0', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_1', (1, 512, 7, 7)), ('res5b_branch2a', (1, 512, 7, 7)), ('res5b_branch2b', (1, 512, 7, 7)), ('res5b', (1, 512, 7, 7)), ('fc2019-10-22_15-02-46', (1, 10, 1, 1)), ('prob', (1, 10, 1, 1))]
set image transformer : 
About to compute detect the points : len(args) : 2
Inside predict_points step exec : nb paths : 1
treate image : temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea_0.jpg

size of numpy array img : 2960752
scale method : caffe/skimage 
size of numpy array img_scale : 2655880 (416, 532, 3)
nb_h 7 nb_w 11 size of sub images : (224, 224, 3)
size of caffe_input : 46362776 (77, 3, 224, 224)
time to do the preprocess : 0.03963661193847656
time to do a prediction : 13.429848194122314
dict_keys(['prob'])
shape of output 
(77, 10, 1, 1)
shape of the out_put heatmap
(10, 7, 11)
number of sub_photos  vertical and horizon
7 11
size of heatmap : (7,11)
size of heatmap : (7,11)
size of heatmap : (7,11)
size of heatmap : (7,11)
size of heatmap : (7,11)
size of heatmap : (7,11)
size of heatmap : (7,11)
size of heatmap : (7,11)
size of heatmap : (7,11)
size of heatmap : (7,11)

After datou_step_exec type output : <class 'dict'>
map_portfolio_photo : len 1 keys : dict_keys([1902940])
Inside saveOutput : final : False verbose : True
Inside savePoints : final : False verbose : True
threshold to save the result : 0.05
maximun points to save in the table mtr_datou_result for each class : 100
output flattener 5 example :  {1386782171: [(1386782171, 1945, 'Autre_Environement', 185, -1, 118, -1, 1.8337410438107327e-05), (1386782171, 1945, 'Autre_Environement', 320, -1, 118, -1, 0.0001291327498620376), (1386782171, 1945, 'Autre_Environement', 388, -1, 151, -1, 1.3445685908664018e-05), (1386782171, 1945, 'Autre_Environement', 286, -1, 185, -1, 0.00013466527161654085), (1386782171, 1945, 'Autre_Environement', 118, -1, 219, -1, 9.86390750767896e-06), (1386782171, 1945, 'Autre_Environement', 219, -1, 219, -1, 0.00038494516047649086), (1386782171, 1945, 'Autre_Environement', 354, -1, 219, -1, 0.0003431888180784881), (1386782171, 1945, 'Autre_Environement', 421, -1, 253, -1, 1.494663450785083e-07), (1386782171, 1945, 'Autre_Environement', 185, -1, 286, -1, 1.814520658172114e-07), (1386782171, 1945, 'Autre_Environement', 320, -1, 286, -1, 4.115241154067917e-06), (1386782171, 1945, 'Autre_Environement', 118, -1, 320, -1, 2.5274063397695556e-10), (1386782171, 1945, 'Autre_Environement', 253, -1, 320, -1, 1.0447603199237321e-10), (1386782171, 1945, 'Autre_Environement', 388, -1, 320, -1, 4.5192118136583304e-07), (1386782171, 1945, 'Carton', 151, -1, 118, -1, 0.9897055625915527), (1386782171, 1945, 'Carton', 286, -1, 118, -1, 0.8210961222648621), (1386782171, 1945, 'Carton', 421, -1, 118, -1, 0.4851985573768616), (1386782171, 1945, 'Carton', 219, -1, 151, -1, 0.9841372966766357), (1386782171, 1945, 'Carton', 118, -1, 185, -1, 0.9978604912757874), (1386782171, 1945, 'Carton', 185, -1, 219, -1, 0.7996194958686829), (1386782171, 1945, 'Carton', 354, -1, 219, -1, 0.13047637045383453), (1386782171, 1945, 'Carton', 286, -1, 253, -1, 0.05070793256163597), (1386782171, 1945, 'Carton', 118, -1, 286, -1, 0.31989040970802307), (1386782171, 1945, 'Carton', 219, -1, 286, -1, 0.0034983144141733646), (1386782171, 1945, 'Carton', 421, -1, 286, -1, 0.01660206913948059), (1386782171, 1945, 'Carton', 354, -1, 320, -1, 0.0016258988762274384), (1386782171, 1945, 'Kraft', 185, -1, 118, -1, 0.004793279338628054), (1386782171, 1945, 'Kraft', 286, -1, 118, -1, 0.0023909551091492176), (1386782171, 1945, 'Kraft', 421, -1, 118, -1, 0.002102428814396262), (1386782171, 1945, 'Kraft', 354, -1, 151, -1, 0.05212335288524628), (1386782171, 1945, 'Kraft', 118, -1, 185, -1, 0.0017166697653010488), (1386782171, 1945, 'Kraft', 253, -1, 185, -1, 0.04599893465638161), (1386782171, 1945, 'Kraft', 185, -1, 219, -1, 0.021099306643009186), (1386782171, 1945, 'Kraft', 388, -1, 219, -1, 6.061792737455107e-05), (1386782171, 1945, 'Kraft', 286, -1, 253, -1, 0.0037522290367633104), (1386782171, 1945, 'Kraft', 118, -1, 286, -1, 0.010319208726286888), (1386782171, 1945, 'Kraft', 219, -1, 286, -1, 5.351284926291555e-05), (1386782171, 1945, 'Kraft', 421, -1, 286, -1, 9.467339623370208e-06), (1386782171, 1945, 'Kraft', 320, -1, 320, -1, 0.00017124204896390438), (1386782171, 1945, 'Lointain_Papier_Magazine', 185, -1, 118, -1, 2.352082447032444e-06), (1386782171, 1945, 'Lointain_Papier_Magazine', 320, -1, 118, -1, 1.991412864299491e-05), (1386782171, 1945, 'Lointain_Papier_Magazine', 253, -1, 151, -1, 1.1851832823595032e-05), (1386782171, 1945, 'Lointain_Papier_Magazine', 354, -1, 185, -1, 8.877575601218268e-05), (1386782171, 1945, 'Lointain_Papier_Magazine', 118, -1, 219, -1, 2.1015976017224602e-06), (1386782171, 1945, 'Lointain_Papier_Magazine', 219, -1, 219, -1, 7.86672972026281e-05), (1386782171, 1945, 'Lointain_Papier_Magazine', 421, -1, 219, -1, 5.019426794206083e-07), (1386782171, 1945, 'Lointain_Papier_Magazine', 320, -1, 253, -1, 8.220934978453442e-05), (1386782171, 1945, 'Lointain_Papier_Magazine', 185, -1, 286, -1, 3.6812457437918056e-07), (1386782171, 1945, 'Lointain_Papier_Magazine', 388, -1, 286, -1, 3.3782250739022857e-06), (1386782171, 1945, 'Lointain_Papier_Magazine', 118, -1, 320, -1, 3.5824958555252806e-09), (1386782171, 1945, 'Lointain_Papier_Magazine', 286, -1, 320, -1, 1.2866975396264024e-07), (1386782171, 1945, 'Metal', 185, -1, 118, -1, 7.473808364011347e-05), (1386782171, 1945, 'Metal', 286, -1, 118, -1, 3.432366065680981e-05), (1386782171, 1945, 'Metal', 118, -1, 151, -1, 1.1519473446242046e-06), (1386782171, 1945, 'Metal', 354, -1, 151, -1, 0.001217490527778864), (1386782171, 1945, 'Metal', 253, -1, 185, -1, 0.001836584648117423), (1386782171, 1945, 'Metal', 421, -1, 185, -1, 3.084278432652354e-05), (1386782171, 1945, 'Metal', 185, -1, 219, -1, 0.0011127882171422243), (1386782171, 1945, 'Metal', 118, -1, 253, -1, 2.7215228328714147e-05), (1386782171, 1945, 'Metal', 354, -1, 253, -1, 0.0005037166411057115), (1386782171, 1945, 'Metal', 219, -1, 286, -1, 1.657771281315945e-05), (1386782171, 1945, 'Metal', 421, -1, 286, -1, 2.2439740860136226e-05), (1386782171, 1945, 'Metal', 151, -1, 320, -1, 1.393576087860282e-10), (1386782171, 1945, 'Metal', 320, -1, 320, -1, 3.1560623028781265e-05), (1386782171, 1945, 'Papier_Magazine', 118, -1, 118, -1, 0.001665871823206544), (1386782171, 1945, 'Papier_Magazine', 253, -1, 118, -1, 0.28050497174263), (1386782171, 1945, 'Papier_Magazine', 185, -1, 151, -1, 0.003942570183426142), (1386782171, 1945, 'Papier_Magazine', 421, -1, 151, -1, 0.9828073978424072), (1386782171, 1945, 'Papier_Magazine', 354, -1, 185, -1, 0.7690255045890808), (1386782171, 1945, 'Papier_Magazine', 286, -1, 219, -1, 0.9288092851638794), (1386782171, 1945, 'Papier_Magazine', 118, -1, 253, -1, 0.04313919320702553), (1386782171, 1945, 'Papier_Magazine', 219, -1, 253, -1, 0.8978631496429443), (1386782171, 1945, 'Papier_Magazine', 388, -1, 253, -1, 0.9886879324913025), (1386782171, 1945, 'Papier_Magazine', 151, -1, 320, -1, 0.9999996423721313), (1386782171, 1945, 'Papier_Magazine', 253, -1, 320, -1, 0.9999960660934448), (1386782171, 1945, 'Papier_Magazine', 354, -1, 320, -1, 0.9942159056663513), (1386782171, 1945, 'Plastique', 118, -1, 118, -1, 1.236031312146224e-05), (1386782171, 1945, 'Plastique', 219, -1, 118, -1, 0.00030184732167981565), (1386782171, 1945, 'Plastique', 320, -1, 118, -1, 0.0002493929350748658), (1386782171, 1945, 'Plastique', 253, -1, 185, -1, 0.007031592074781656), (1386782171, 1945, 'Plastique', 354, -1, 185, -1, 0.032503753900527954), (1386782171, 1945, 'Plastique', 185, -1, 219, -1, 0.050375860184431076), (1386782171, 1945, 'Plastique', 421, -1, 219, -1, 0.00012226666149217635), (1386782171, 1945, 'Plastique', 118, -1, 253, -1, 0.003930176142603159), (1386782171, 1945, 'Plastique', 286, -1, 253, -1, 0.0025490771513432264), (1386782171, 1945, 'Plastique', 219, -1, 286, -1, 6.120907346485183e-05), (1386782171, 1945, 'Plastique', 354, -1, 286, -1, 0.00538312504068017), (1386782171, 1945, 'Plastique', 151, -1, 320, -1, 1.8926894773674263e-10), (1386782171, 1945, 'Plastique', 421, -1, 320, -1, 0.00020204381144139916), (1386782171, 1945, 'Sol_Environement', 185, -1, 118, -1, 9.372543900099117e-06), (1386782171, 1945, 'Sol_Environement', 320, -1, 118, -1, 2.7338848667568527e-05), (1386782171, 1945, 'Sol_Environement', 118, -1, 151, -1, 2.5881729470711434e-07), (1386782171, 1945, 'Sol_Environement', 253, -1, 185, -1, 0.00011454988998593763), (1386782171, 1945, 'Sol_Environement', 354, -1, 185, -1, 0.00020838991622440517), (1386782171, 1945, 'Sol_Environement', 185, -1, 219, -1, 9.349620813736692e-05), (1386782171, 1945, 'Sol_Environement', 421, -1, 219, -1, 1.1348908657282664e-07), (1386782171, 1945, 'Sol_Environement', 118, -1, 253, -1, 7.004572921687213e-07), (1386782171, 1945, 'Sol_Environement', 320, -1, 253, -1, 5.724640504922718e-05), (1386782171, 1945, 'Sol_Environement', 219, -1, 286, -1, 3.869550369017816e-08), (1386782171, 1945, 'Sol_Environement', 388, -1, 286, -1, 6.792529802623903e-06), (1386782171, 1945, 'Sol_Environement', 151, -1, 320, -1, 2.6225014184994705e-14), (1386782171, 1945, 'Sol_Environement', 286, -1, 320, -1, 1.5886031690115487e-07), (1386782171, 1945, 'Teint_Dans_La_Masse', 185, -1, 118, -1, 0.002272234996780753), (1386782171, 1945, 'Teint_Dans_La_Masse', 286, -1, 118, -1, 0.001813237671740353), (1386782171, 1945, 'Teint_Dans_La_Masse', 388, -1, 118, -1, 0.04538803920149803), (1386782171, 1945, 'Teint_Dans_La_Masse', 118, -1, 151, -1, 0.00010940170614048839), (1386782171, 1945, 'Teint_Dans_La_Masse', 253, -1, 185, -1, 0.0056123328395187855), (1386782171, 1945, 'Teint_Dans_La_Masse', 354, -1, 185, -1, 0.15166763961315155), (1386782171, 1945, 'Teint_Dans_La_Masse', 185, -1, 219, -1, 0.0013750138459727168), (1386782171, 1945, 'Teint_Dans_La_Masse', 118, -1, 253, -1, 6.252125604078174e-05), (1386782171, 1945, 'Teint_Dans_La_Masse', 286, -1, 253, -1, 0.0018639108166098595), (1386782171, 1945, 'Teint_Dans_La_Masse', 388, -1, 253, -1, 0.001438076258637011), (1386782171, 1945, 'Teint_Dans_La_Masse', 219, -1, 286, -1, 1.016586884361459e-05), (1386782171, 1945, 'Teint_Dans_La_Masse', 151, -1, 320, -1, 3.425693932967988e-07), (1386782171, 1945, 'Teint_Dans_La_Masse', 320, -1, 320, -1, 0.0020001486409455538), (1386782171, 1945, 'Teint_Dans_La_Masse', 421, -1, 320, -1, 8.311669807881117e-05), (1386782171, 1945, 'autre_refus', 185, -1, 118, -1, 0.028516046702861786), (1386782171, 1945, 'autre_refus', 354, -1, 118, -1, 0.0014720888575538993), (1386782171, 1945, 'autre_refus', 118, -1, 151, -1, 3.824138184427284e-05), (1386782171, 1945, 'autre_refus', 253, -1, 185, -1, 0.07516990602016449), (1386782171, 1945, 'autre_refus', 185, -1, 219, -1, 0.010234796442091465), (1386782171, 1945, 'autre_refus', 354, -1, 219, -1, 0.04460597038269043), (1386782171, 1945, 'autre_refus', 118, -1, 253, -1, 0.00015751634782645851), (1386782171, 1945, 'autre_refus', 286, -1, 253, -1, 0.01393966656178236), (1386782171, 1945, 'autre_refus', 219, -1, 286, -1, 4.3672833271557465e-05), (1386782171, 1945, 'autre_refus', 388, -1, 286, -1, 0.0030952864326536655), (1386782171, 1945, 'autre_refus', 151, -1, 320, -1, 1.5080686699420198e-10), (1386782171, 1945, 'autre_refus', 320, -1, 320, -1, 0.0030849112663418055)]}
hashtag or score ? = 0.9897055625915527 
hashtag or score ? = 0.8210961222648621 
hashtag or score ? = 0.4851985573768616 
hashtag or score ? = 0.9841372966766357 
hashtag or score ? = 0.9978604912757874 
hashtag or score ? = 0.7996194958686829 
hashtag or score ? = 0.13047637045383453 
hashtag or score ? = 0.05070793256163597 
hashtag or score ? = 0.31989040970802307 
hashtag or score ? = 0.05212335288524628 
hashtag or score ? = 0.28050497174263 
hashtag or score ? = 0.9828073978424072 
hashtag or score ? = 0.7690255045890808 
hashtag or score ? = 0.9288092851638794 
hashtag or score ? = 0.8978631496429443 
hashtag or score ? = 0.9886879324913025 
hashtag or score ? = 0.9999996423721313 
hashtag or score ? = 0.9999960660934448 
hashtag or score ? = 0.9942159056663513 
hashtag or score ? = 0.050375860184431076 
hashtag or score ? = 0.15166763961315155 
hashtag or score ? = 0.07516990602016449 
insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s)
[('1386782171', '492774966', '1945', '151', '-1', '118', '-1', '0.9897055625915527'), ('1386782171', '492774966', '1945', '286', '-1', '118', '-1', '0.8210961222648621'), ('1386782171', '492774966', '1945', '421', '-1', '118', '-1', '0.4851985573768616'), ('1386782171', '492774966', '1945', '219', '-1', '151', '-1', '0.9841372966766357'), ('1386782171', '492774966', '1945', '118', '-1', '185', '-1', '0.9978604912757874'), ('1386782171', '492774966', '1945', '185', '-1', '219', '-1', '0.7996194958686829'), ('1386782171', '492774966', '1945', '354', '-1', '219', '-1', '0.13047637045383453'), ('1386782171', '492774966', '1945', '286', '-1', '253', '-1', '0.05070793256163597'), ('1386782171', '492774966', '1945', '118', '-1', '286', '-1', '0.31989040970802307'), ('1386782171', '493202403', '1945', '354', '-1', '151', '-1', '0.05212335288524628'), ('1386782171', '2107752386', '1945', '253', '-1', '118', '-1', '0.28050497174263'), ('1386782171', '2107752386', '1945', '421', '-1', '151', '-1', '0.9828073978424072'), ('1386782171', '2107752386', '1945', '354', '-1', '185', '-1', '0.7690255045890808'), ('1386782171', '2107752386', '1945', '286', '-1', '219', '-1', '0.9288092851638794'), ('1386782171', '2107752386', '1945', '219', '-1', '253', '-1', '0.8978631496429443'), ('1386782171', '2107752386', '1945', '388', '-1', '253', '-1', '0.9886879324913025'), ('1386782171', '2107752386', '1945', '151', '-1', '320', '-1', '0.9999996423721313'), ('1386782171', '2107752386', '1945', '253', '-1', '320', '-1', '0.9999960660934448'), ('1386782171', '2107752386', '1945', '354', '-1', '320', '-1', '0.9942159056663513'), ('1386782171', '492725882', '1945', '185', '-1', '219', '-1', '0.050375860184431076'), ('1386782171', '2107752385', '1945', '354', '-1', '185', '-1', '0.15166763961315155'), ('1386782171', '2107752406', '1945', '253', '-1', '185', '-1', '0.07516990602016449')]
final : False

save missing photos in datou_result :

time spend for datou_step_exec : 14.52649474143982
time spend to save output : 0.20894765853881836
total time spend for step 3 : 14.735442399978638

step4:count_percent_refus
Mon Sep 29 11:45:28 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior 
Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed
complete output_args for input 0 : {'987321136': ['temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]}
 input_args_next_step : {'987321136': ()}
 output_args : {'987321136': ['temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]}
 args : 987321136
 depend.output_id : 1
complete output_args for input 1 : {'1386782171': ['temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']}
 input_args_next_step : {'987321136': (987321136,), '1386782171': ()}
 output_args : {'1386782171': ['temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']}
 args : 1386782171
 depend.output_id : 0
complete output_args for input 2 : {'987321136': ['temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]}
 input_args_next_step : {'987321136': (987321136,), '1386782171': ('temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',)}
 output_args : {'987321136': ['temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]}
 args : 987321136
 depend.output_id : 2
VR 22-3-18 : For now we do not clean correctly the datou structure 

input_args_next_step, len :2, first value : (987321136, 0.9481481481481482)
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1386782171}
map_photo_id_path_extension : {987321136: {'path': 'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1386782171: {'path': 'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}}
map_subphoto_mainphoto : {0: 987321136, 1386782171: 987321136}
debut step count percent refus
args : {'987321136': (987321136, 0.9481481481481482), '1386782171': ('temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',)}
(987321136, 0.9481481481481482)
('temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',)
on trouve le portfolio_id = 1902940
list_photo : [987321136]
list_photo_correc : [1386782171]
debut step count percent refus
Treating photo_id : 987321136
Calcul du count_res
count res : None
no objects is detected in this image for crop_hashtag_type = 1945 





After datou_step_exec type output : <class 'dict'>
map_portfolio_photo : len 1 keys : dict_keys([1902940])
Inside saveOutput : final : False verbose : True
map_info[mapportfolio_photo] : {1902940: [987321136]}
dans le for
photo id  : 987321136
output[photo_id] : [({}, [1386782171], {'refus_total': 0}, {'refus_total': 0}, 1902940)]


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 2
insert into MTRLabel.upm_carac (ol,type_carac,portfolio_id,value,material,hashtag_type) values (0,'qualipapia_surface',1902940,0,'refus_total',1945) on duplicate key update value= 0
 list_values : [['0', 'qualipapia_surface', 1902940, 0, 'refus_total', 1945]]
insert into MTRLabel.upm_carac (ol,type_carac,portfolio_id,value,material,hashtag_type) values (0,'qualipapia_gravi',1902940,0,'refus_total',1945) on duplicate key update value= 0
 list_values : [['0', 'qualipapia_surface', 1902940, 0, 'refus_total', 1945], ['0', 'qualipapia_gravi', 1902940, 0, 'refus_total', 1945]]
time used for this insertion : 0.07799983024597168

save missing photos in datou_result :

time spend for datou_step_exec : 0.021202802658081055
time spend to save output : 0.07817649841308594
total time spend for step 4 : 0.09937930107116699

step5:brightness
Mon Sep 29 11:45:28 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior 
Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed
complete output_args for input 0 : {'987321136': ['temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]}
 input_args_next_step : {'987321136': ()}
 output_args : {'987321136': ['temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]}
 args : 987321136
 depend.output_id : 0
VR 22-3-18 : For now we do not clean correctly the datou structure 

input_args_next_step, len :1, first value : ('temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg',)
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1386782171}
map_photo_id_path_extension : {987321136: {'path': 'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1386782171: {'path': 'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}}
map_subphoto_mainphoto : {0: 987321136, 1386782171: 987321136}
inside step calcul brightness
treat image : temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg
pour la photo_id : -0.39870825574700136, le score de luminosite est de 987321136
brightness_score : {987321136: [(987321136, -0.39870825574700136, 496442774)]}

After datou_step_exec type output : <class 'dict'>
map_portfolio_photo : len 1 keys : dict_keys([1902940])
Inside saveOutput : final : False verbose : True
select photo_hashtag_type from MTRDatou.classification_theme where id = 1154


begin to insert list_values into class_photo_scores : 
length of list_valuse in save_photo_hashtag_id_thcl_score : 1
insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score)
time used for this insertion : 0.034322500228881836


begin to insert list_values into photo_hahstag_ids : 
length of list_valuse in save_photo_hashtag_id_type : 1
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s)
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s)
 first line : ('987321136', '496442774', '1426')
...
 last line : ('987321136', '496442774', '1426')
time used for this insertion : 0.700432300567627

save missing photos in datou_result :

time spend for datou_step_exec : 0.12198591232299805
time spend to save output : 0.7521705627441406
total time spend for step 5 : 0.8741564750671387

step6:blur_detection
Mon Sep 29 11:45:29 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior 
Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed
complete output_args for input 0 : {'987321136': ['temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]}
 input_args_next_step : {'987321136': ()}
 output_args : {'987321136': ['temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]}
 args : 987321136
 depend.output_id : 0
VR 22-3-18 : For now we do not clean correctly the datou structure 

input_args_next_step, len :1, first value : ('temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg',)
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1386782171}
map_photo_id_path_extension : {987321136: {'path': 'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1386782171: {'path': 'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}}
map_subphoto_mainphoto : {0: 987321136, 1386782171: 987321136}
inside step blur_detection
score_blur_detection : {}
methode: ratio et variance
treat image : temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg
resize: (439, 562)
987321136 -5.392404060312662
score_blur_detection : {987321136: [(987321136, -5.392404060312662, 492609224)]}

After datou_step_exec type output : <class 'dict'>
map_portfolio_photo : len 1 keys : dict_keys([1902940])
Inside saveOutput : final : False verbose : True
select photo_hashtag_type from MTRDatou.classification_theme where id = 1055


begin to insert list_values into class_photo_scores : 
length of list_valuse in save_photo_hashtag_id_thcl_score : 1
insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score)
time used for this insertion : 0.03542137145996094


begin to insert list_values into photo_hahstag_ids : 
length of list_valuse in save_photo_hashtag_id_type : 1
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s)
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s)
 first line : ('987321136', '492609224', '1294')
...
 last line : ('987321136', '492609224', '1294')
time used for this insertion : 0.03450441360473633

save missing photos in datou_result :

time spend for datou_step_exec : 0.1062319278717041
time spend to save output : 0.08740735054016113
total time spend for step 6 : 0.19363927841186523

step7:send_mail_dechet
Mon Sep 29 11:45:29 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior 
Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed
complete output_args for input 0 : {987321136: [(987321136, -5.392404060312662, 492609224)]}
 input_args_next_step : {987321136: ()}
 output_args : {987321136: [(987321136, -5.392404060312662, 492609224)]}
 args : 987321136
 depend.output_id : 0
complete output_args for input 1 : {987321136: [(987321136, -0.39870825574700136, 496442774)]}
 input_args_next_step : {987321136: ((987321136, -5.392404060312662, 492609224),)}
 output_args : {987321136: [(987321136, -0.39870825574700136, 496442774)]}
 args : 987321136
 depend.output_id : 0
complete output_args for input 2 : {987321136: [({}, [1386782171], {'refus_total': 0}, {'refus_total': 0}, 1902940)]}
 input_args_next_step : {987321136: ((987321136, -5.392404060312662, 492609224), (987321136, -0.39870825574700136, 496442774))}
 output_args : {987321136: [({}, [1386782171], {'refus_total': 0}, {'refus_total': 0}, 1902940)]}
 args : 987321136
 depend.output_id : 0
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 

input_args_next_step, len :1, first value : ((987321136, -5.392404060312662, 492609224), (987321136, -0.39870825574700136, 496442774), ({}, [1386782171], {'refus_total': 0}, {'refus_total': 0}, 1902940))
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1386782171}
map_photo_id_path_extension : {987321136: {'path': 'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1386782171: {'path': 'temp/1759139106_2886529_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}}
map_subphoto_mainphoto : {0: 987321136, 1386782171: 987321136}
dans la step send mail dechet
 list_name : ['one', 'sample', 'debug', 'board', 'détect', 'port']
corps du mail : 
<strong> La photo est  trop sombre et nette, merci de reprendre la photo <strong><br/><strong><a href='https://www.fotonower.com/image?json=false&list_photos_id=987321136' > Lien affichage photo</a></strong><br/><br/><br/><strong> Dans ces conditions de prise de photo, les r&eacute;sultats sur le tas sont les suivants : 
                                                            <br/> Le pourcentage de mati&egrave;re impropre est de 0 %.<br/> <br/>Pour plus de d&eacute;tails: <strong><br/><br/><strong> Teint Dans La Masse: 0%. <strong><br/><br/><strong> carton: 0%. <strong><br/><br/><strong> metal: 0%. <strong><br/><br/><strong> plastique: 0%. <strong><br/><br/>
senders@fotonower.com
retour de l'envoi du mail : None

After datou_step_exec type output : <class 'dict'>
map_portfolio_photo : len 1 keys : dict_keys([1902940])
Inside saveOutput : final : True verbose : True
 saveOutput not yet implemented for datou_step.type : send_mail_dechet we use saveGeneral 
[987321136, 987321136, '1386782171']
 map_info['map_portfolio_photo'] : {1902940: [987321136]}
 final :  True mtd_id 1848 list_pids : [987321136, 987321136, '1386782171']
Looping around the photos to save general results
len do output : 1
/987321136.
before output type
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f1514c535e0>
Managing all output in save final without adding information in the mtr_datou_result 
('1848', None, None, None, None, None, None, None, None)
('1848', '1902940', '987321136', None, None, None, None, None, None)
('1848', None, None, None, None, None, None, None, None)
('1848', '1902940', '987321136', None, None, None, None, None, None)
('1848', None, None, None, None, None, None, None, None)
('1848', None, '1386782171', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 4
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('1848', '1902940', '987321136', "{'refus_total': 0}", None, None, None, None, None), ('1848', None, '1386782171', None, None, None, None, None, None)]
time used for this insertion : 0.039313554763793945
 save_final 

save missing photos in datou_result :

time spend for datou_step_exec : 0.6206319332122803
time spend to save output : 0.03960275650024414
total time spend for step 7 : 0.6602346897125244

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 2
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 7
 output : {987321136: (-110, -0.39870825574700136, -5.392404060312662, 0, 0, {}, {'refus_total': 0}, {'refus_total': 0}, 0.0)}
ERROR
expected : {987321136: (-110, -0.39870825574700136, -5.392404060312662, 30.0, 61.64383561643836, {'carton': 3, 'Papier_Magazine': 7}, {'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}, {'refus_total': 61.64383561643836, 'carton': 61.64383561643836, 'Papier_Magazine': 38.35616438356164})}
got : {987321136: (-110, -0.39870825574700136, -5.392404060312662, 0, 0, {}, {'refus_total': 0}, {'refus_total': 0}, 0.0)}
ERROR certificat_qualite_papier FAILED

############################### TEST image_temperature_detection  ################################
t
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1807

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=1807
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 1807
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=1807
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : image_temperature_detection
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (984484223) 
 Found this number of photos: 1
##### Call download_photos : nb_thread : 5
begin to download photo : 984484223
download finish for photo 984484223
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.19229626655578613
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  1
step1:image_temperature_detection
Mon Sep 29 11:45:30 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139130_2886529_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg': 984484223}
map_photo_id_path_extension : {984484223: {'path': 'temp/1759139130_2886529_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
inside step blanche_jaune_detection
treat image : temp/1759139130_2886529_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg
984484223 1.004309911525615

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 0.2143404483795166
time spend to save output : 5.745887756347656e-05
total time spend for step 1 : 0.21439790725708008

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 0
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {984484223: [(984484223, 1.004309911525615, 492630606)]}
{984484223: [(984484223, 1.004309911525615, 492630606)]}

############################### TEST broca  ################################
t
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4041

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=4041
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 4041
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=4041
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : split_time_score
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (5205529) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (5205529)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 1064921404, 'portfolio_id': 5205529}, {'photo_id': 1064921402, 'portfolio_id': 5205529}, {'photo_id': 1064921401, 'portfolio_id': 5205529}, {'photo_id': 1064921201, 'portfolio_id': 5205529}, {'photo_id': 1064921196, 'portfolio_id': 5205529}, {'photo_id': 1064919876, 'portfolio_id': 5205529}, {'photo_id': 1064919873, 'portfolio_id': 5205529}, {'photo_id': 1064919869, 'portfolio_id': 5205529}, {'photo_id': 1064919862, 'portfolio_id': 5205529}, {'photo_id': 1064919858, 'portfolio_id': 5205529}, {'photo_id': 1064919856, 'portfolio_id': 5205529}, {'photo_id': 1064919752, 'portfolio_id': 5205529}, {'photo_id': 1064919748, 'portfolio_id': 5205529}, {'photo_id': 1064919745, 'portfolio_id': 5205529}, {'photo_id': 1064919741, 'portfolio_id': 5205529}, {'photo_id': 1064919737, 'portfolio_id': 5205529}, {'photo_id': 1064919730, 'portfolio_id': 5205529}, {'photo_id': 1064919660, 'portfolio_id': 5205529}]
 map_portfolio_id_photo_id: {5205529: [1064921404, 1064921402, 1064921401, 1064921201, 1064921196, 1064919876, 1064919873, 1064919869, 1064919862, 1064919858, 1064919856, 1064919752, 1064919748, 1064919745, 1064919741, 1064919737, 1064919730, 1064919660]}
##### Call download_photos : nb_thread : 5
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
##### After load_data_input 
time to download the photos : 0.05383133888244629
#### fin chargement data
Blocking on flush ?
No conitnuing 
About to test input to load 
Calling datou_exec

Inside datou_exec : verbose : True
number of steps :  1
step1:split_time_score
Mon Sep 29 11:45:30 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {}
map_photo_id_path_extension : {}
map_subphoto_mainphoto : {}
split portfolio by speed
calcul order for each photo with time
calcul time for a portfolio
query : SELECT photo_id, text FROM MTRBack.photos_view where photo_id in
                 (SELECT mtr_photo_id FROM MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 5205529);
                
result : ((1064919660, 'image_01122021_10_11_30_014389.jpg'), (1064919730, 'image_01122021_10_12_17_665202.jpg'), (1064919737, 'image_01122021_10_11_40_031052.jpg'), (1064919741, 'image_01122021_10_11_34_021658.jpg'), (1064919745, 'image_01122021_10_11_32_018001.jpg'), (1064919748, 'image_01122021_10_12_27_027057.jpg'), (1064919752, 'image_01122021_10_12_24_005017.jpg'), (1064919856, 'image_01122021_10_13_13_399843.jpg'), (1064919858, 'image_01122021_10_13_04_729164.jpg'), (1064919862, 'image_01122021_10_12_56_581019.jpg'), (1064919869, 'image_01122021_10_12_29_030603.jpg'), (1064919873, 'image_01122021_10_13_30_005720.jpg'), (1064919876, 'image_01122021_10_13_22_147712.jpg'), (1064921196, 'image_01122021_10_16_18_114975.jpg'), (1064921201, 'image_01122021_10_16_14_925132.jpg'), (1064921401, 'image_01122021_10_16_57_981306.jpg'), (1064921402, 'image_01122021_10_16_53_913663.jpg'), (1064921404, 'image_01122021_10_16_47_889875.jpg'))

            INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) 
            VALUES (%s, %s, %s) on duplicate key update `order`=VALUES(`order`);
 first line : (5205529, 1064919660, 1098136690)
...
 last line : (5205529, 1064921404, 1098137007)
2021-12-01 10:11:30
2021-12-01 10:11:32
2021-12-01 10:11:30
2021-12-01 10:11:34
2021-12-01 10:11:32
2021-12-01 10:11:40
2021-12-01 10:11:34
2021-12-01 10:12:17
2021-12-01 10:11:40
2021-12-01 10:12:24
2021-12-01 10:12:17
2021-12-01 10:12:27
2021-12-01 10:12:24
2021-12-01 10:12:29
2021-12-01 10:12:27
2021-12-01 10:12:56
2021-12-01 10:12:29
2021-12-01 10:13:04
2021-12-01 10:12:56
2021-12-01 10:13:13
2021-12-01 10:13:04
2021-12-01 10:13:04
distance 1.4513659170185111
2021-12-01 10:13:13
2021-12-01 10:13:22
2021-12-01 10:13:13
2021-12-01 10:13:30
2021-12-01 10:13:22
2021-12-01 10:16:14
2021-12-01 10:13:30
2021-12-01 10:13:30
distance 8.382409567451603
2021-12-01 10:16:14
2021-12-01 10:16:18
2021-12-01 10:16:14
2021-12-01 10:16:47
2021-12-01 10:16:18
2021-12-01 10:16:53
2021-12-01 10:16:47
2021-12-01 10:16:47
distance 8.03396608896571
2021-12-01 10:16:53
2021-12-01 10:16:57
2021-12-01 10:16:53
dict_time_useful: {0: [1098136690, 1098136784, 48.864288393888884, 2.19199505125, [datetime.datetime(2021, 12, 1, 10, 11, 30), datetime.datetime(2021, 12, 1, 10, 13, 4), 94]], 1: [1098136974, 1098137007, 48.86291258986111, 2.19361357125, [datetime.datetime(2021, 12, 1, 10, 16, 14), datetime.datetime(2021, 12, 1, 10, 16, 47), 33]]}
len of dic_time_useful : 2
get gps info of PAV

                SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV;
                
get gps info of PAV

                SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV WHERE type_pav = "CS";
                
get gps info of PAV

                SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV WHERE type_pav = "OM";
                
select cs_nb_photo / nb_photo, om_nb_photo / nb_photo
            from (select sum(1) as nb_photo,sum(if (tags= "[CS]",1,0)) as cs_nb_photo, sum(if (tags= "[OM]",1,0)) as om_nb_photo
            from MTRBack.photos_view where photo_id in
            ()) t1;
select cs_nb_photo / nb_photo, om_nb_photo / nb_photo
            from (select sum(1) as nb_photo,sum(if (tags= "[CS]",1,0)) as cs_nb_photo, sum(if (tags= "[OM]",1,0)) as om_nb_photo
            from MTRBack.photos_view where photo_id in
            (1064919660, 1064919745, 1064919741, 1064919737, 1064919730, 1064919752, 1064919748, 1064919869, 1064919862, 1064919858)) t1;
distance: RUEIL14CS [48.864288393888884, 2.19199505125] 16.57008455321128
(27327420, 48.864288393888884, 2.19199505125, 10, 1064919752, [datetime.datetime(2021, 12, 1, 10, 11, 30), datetime.datetime(2021, 12, 1, 10, 13, 4), 94.0], 5205529)

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 0.4475536346435547
time spend to save output : 7.2479248046875e-05
total time spend for step 1 : 0.44762611389160156

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 0
After save, about to update current !
{15: [(27327420, 48.864288393888884, 2.19199505125, 10, 1064919752, [datetime.datetime(2021, 12, 1, 10, 11, 30), datetime.datetime(2021, 12, 1, 10, 13, 4), 94.0], 5205529)]}
résultat du premier test BROCA :  True True

############################### TEST crop_conditional  ################################
t
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=719

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=719
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 719
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=719
# 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)
 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 : step 1335 frcnn is not linked in the step_by_step architecture !
WARNING : step 1336 crop_condition is not linked in the step_by_step architecture !
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 !
DataTypes for each output/input checked !
no param json to modify
List Step Type Loaded in datou : frcnn, crop_condition
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (1981316) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (1981316)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 950003838, 'portfolio_id': 1981316}, {'photo_id': 950003813, 'portfolio_id': 1981316}, {'photo_id': 950003812, 'portfolio_id': 1981316}, {'photo_id': 950003696, 'portfolio_id': 1981316}, {'photo_id': 950003695, 'portfolio_id': 1981316}, {'photo_id': 926687666, 'portfolio_id': 1981316}]
 map_portfolio_id_photo_id: {1981316: [950003838, 950003813, 950003812, 950003696, 950003695, 926687666]}
##### Call download_photos : nb_thread : 5
begin to download photo : 950003838
begin to download photo : 950003812
begin to download photo : 950003695
download finish for photo 950003838
begin to download photo : 950003813
download finish for photo 950003695
begin to download photo : 926687666
download finish for photo 950003813
download finish for photo 950003812
begin to download photo : 950003696
download finish for photo 926687666
download finish for photo 950003696
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 6 ; length of list_pids : 6 ; length of list_args : 6
##### After load_data_input 
time to download the photos : 0.532522439956665
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  2
step1:frcnn
Mon Sep 29 11:45:31 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139131_2886529_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg': 950003838, 'temp/1759139131_2886529_950003813_e28be02dfcce79cce594a390a9911a0a.jpg': 950003813, 'temp/1759139131_2886529_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg': 950003695, 'temp/1759139131_2886529_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg': 926687666, 'temp/1759139131_2886529_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg': 950003812, 'temp/1759139131_2886529_950003696_11e3a77b72af4b332d366d98984039c7.jpg': 950003696}
map_photo_id_path_extension : {950003838: {'path': 'temp/1759139131_2886529_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg', 'extension': 'jpg'}, 950003813: {'path': 'temp/1759139131_2886529_950003813_e28be02dfcce79cce594a390a9911a0a.jpg', 'extension': 'jpg'}, 950003695: {'path': 'temp/1759139131_2886529_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg', 'extension': 'jpg'}, 926687666: {'path': 'temp/1759139131_2886529_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg', 'extension': 'jpg'}, 950003812: {'path': 'temp/1759139131_2886529_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg', 'extension': 'jpg'}, 950003696: {'path': 'temp/1759139131_2886529_950003696_11e3a77b72af4b332d366d98984039c7.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou step Faster rcnn !
Inside try reload !
classes : ['background', 'retroviseur', 'roue', 'capot', 'pare-brise', 'vitre', 'phare', 'feu-antibrouillard', 'feu-arriere', 'poignee', 'porte', 'radiateur', 'logo-marque', 'cache-reservoir', 'plaque-immatriculation', 'pot-echappement', 'info-modele', 'essuie-glace', 'pare-choc', 'coffre', 'carrosserie-autre', 'toit', 'logo-roue', 'aile-avant', 'aile-arriere', 'autre']
pht : 757
caffemodel_name (should be vgg16_immat_307 but not used because net loaded outside in the fonction) : {'id': 685, 'mtr_user_id': 31, 'name': 'learn_piece_voiture_0808_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,retroviseur,roue,capot,pare-brise,vitre,phare,feu-antibrouillard,feu-arriere,poignee,porte,radiateur,logo-marque,cache-reservoir,plaque-immatriculation,pot-echappement,info-modele,essuie-glace,pare-choc,coffre,carrosserie-autre,toit,logo-roue,aile-avant,aile-arriere,autre', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 757, 'photo_desc_type': 3800, 'type_classification': 'caffe_faster_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}
To loadFromThcl()
model_param file didn't exist 
model_name : learn_piece_voiture_0808_v2
model_type : caffe_faster_rcnn
list file need :
['caffemodel', 'test.prototxt']
file exist in s3 :
['caffemodel', 'test.prototxt']
file manque in s3 : 
[]
local folder  : /data/models_weight/learn_piece_voiture_0808_v2


/data/models_weight/learn_piece_voiture_0808_v2/caffemodel
size_local : 350215080 size in s3 : 350215080
create time local : 2021-08-09 05:30:22 create time in s3 : 2021-08-06 19:24:16
caffemodel already exist and didn't need to update


/data/models_weight/learn_piece_voiture_0808_v2/test.prototxt
size_local : 7166 size in s3 : 7166
create time local : 2021-08-09 05:30:22 create time in s3 : 2021-08-06 19:24:16
test.prototxt already exist and didn't need to update
 prototxt : /data/models_weight/learn_piece_voiture_0808_v2/test.prototxt
 caffemodel : /data/models_weight/learn_piece_voiture_0808_v2/caffemodel


Loaded network /data/models_weight/learn_piece_voiture_0808_v2/caffemodel
About to compute detect_faster_rcnn : len(args) : 6
Inside frcnn step exec : nb paths : 6
image_path : temp/1759139131_2886529_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg
image_size (294, 285, 3)
[[[ 29  29  29]
  [ 29  29  29]
  [ 30  30  30]
  ...
  [182 172 165]
  [141 131 124]
  [103  94  90]]

 [[ 29  29  29]
  [ 29  29  29]
  [ 31  31  31]
  ...
  [231 220 212]
  [202 193 184]
  [164 154 147]]

 [[ 30  30  30]
  [ 27  27  27]
  [ 26  26  26]
  ...
  [223 211 199]
  [229 219 209]
  [228 217 209]]

 ...

 [[ 22  27  25]
  [ 16  21  19]
  [ 11  16  14]
  ...
  [166 145 123]
  [168 147 125]
  [170 149 127]]

 [[ 20  25  23]
  [ 17  22  20]
  [ 15  20  18]
  ...
  [163 142 120]
  [165 144 122]
  [166 145 123]]

 [[ 13  18  16]
  [ 17  22  20]
  [ 20  25  23]
  ...
  [162 141 119]
  [163 142 120]
  [163 142 121]]]
Detection took 0.096s for 300 object proposals
 c : aile-arriere
 list_crops.shape (29, 5)
 proba :  0.052101057
(104.29451, 64.89099, 245.01936, 262.59637)
 proba :  0.011475281
(156.04788, 154.27737, 276.97534, 234.77933)
 proba :  0.010073056
(133.89511, 191.03188, 199.79599, 262.3471)
 c : aile-avant
 list_crops.shape (30, 5)
 proba :  0.16407228
(93.402054, 110.006165, 261.18292, 244.45493)
 proba :  0.011733195
(153.09209, 215.95578, 224.00688, 288.55374)
 c : autre
 list_crops.shape (31, 5)
 c : cache-reservoir
 list_crops.shape (30, 5)
 proba :  0.01678945
(12.162453, 54.5274, 94.045586, 89.234406)
 c : capot
 list_crops.shape (24, 5)
 proba :  0.101510495
(69.293976, 0.0, 280.00598, 54.91256)
 proba :  0.08461438
(113.760345, 84.13607, 262.94397, 231.19208)
 proba :  0.040664755
(6.7407303, 36.862602, 96.11417, 92.56629)
 c : carrosserie-autre
 list_crops.shape (27, 5)
 c : coffre
 list_crops.shape (23, 5)
 proba :  0.047639668
(53.503517, 0.0, 284.0, 92.37465)
 proba :  0.032653026
(10.159981, 31.720573, 95.703926, 100.61201)
 proba :  0.012154086
(235.39413, 155.04279, 284.0, 216.00586)
 c : essuie-glace
 list_crops.shape (31, 5)
 proba :  0.8093958
(149.03622, 63.964462, 284.0, 87.6147)
 proba :  0.034230918
(13.640854, 56.0708, 92.06974, 89.41579)
 proba :  0.020681867
(234.30276, 175.75844, 284.0, 231.14769)
 c : feu-antibrouillard
 list_crops.shape (33, 5)
 proba :  0.055683397
(27.496872, 50.041313, 86.94722, 89.45695)
 proba :  0.018116158
(229.45758, 178.31223, 279.93976, 220.47144)
 proba :  0.010316243
(206.26123, 260.46265, 253.12054, 293.0)
 c : feu-arriere
 list_crops.shape (34, 5)
 proba :  0.06828879
(27.727652, 49.23965, 86.89518, 88.09209)
 proba :  0.024006763
(231.78537, 177.13234, 279.96072, 219.16951)
 c : info-modele
 list_crops.shape (34, 5)
 proba :  0.01589902
(27.181133, 48.777905, 87.64096, 89.7207)
 c : logo-marque
 list_crops.shape (32, 5)
 proba :  0.01061919
(206.26945, 219.39413, 262.95123, 293.0)
 c : logo-roue
 list_crops.shape (30, 5)
 c : pare-brise
 list_crops.shape (24, 5)
 proba :  0.38028947
(84.4493, 0.0, 284.0, 80.62873)
 proba :  0.06983021
(21.856312, 45.92962, 86.83165, 90.58248)
 proba :  0.0109228445
(162.29507, 212.503, 227.28122, 293.0)
 proba :  0.010167604
(238.71837, 156.77808, 284.0, 216.58752)
 c : pare-choc
 list_crops.shape (20, 5)
 proba :  0.15661198
(75.04137, 12.039188, 284.0, 83.47003)
 proba :  0.039174642
(173.63222, 206.89325, 284.0, 287.13312)
 proba :  0.029327964
(3.5351562, 39.444653, 99.71047, 103.58821)
 proba :  0.021781225
(236.45975, 158.82751, 284.0, 219.93918)
 c : phare
 list_crops.shape (33, 5)
 proba :  0.29552427
(16.086155, 55.130524, 94.49304, 86.99965)
 proba :  0.017161757
(85.86915, 17.956541, 272.2637, 68.4394)
 proba :  0.013771662
(241.58144, 159.2385, 284.0, 213.74527)
 proba :  0.01159632
(207.10732, 255.5554, 262.25818, 293.0)
 c : plaque-immatriculation
 list_crops.shape (38, 5)
 proba :  0.13104978
(235.82082, 174.31978, 284.0, 225.2173)
 proba :  0.03965043
(30.735971, 49.492218, 81.558716, 87.70564)
 c : poignee
 list_crops.shape (27, 5)
 c : porte
 list_crops.shape (24, 5)
 proba :  0.59997207
(23.917767, 0.0, 103.23056, 219.55112)
 proba :  0.028165007
(2.5911083, 0.0, 48.603127, 196.39362)
 proba :  0.012119058
(103.532074, 0.101112366, 235.17035, 252.1578)
 c : pot-echappement
 list_crops.shape (30, 5)
 proba :  0.021320507
(228.94226, 176.45409, 280.0206, 221.25743)
 proba :  0.013256265
(208.91542, 255.16562, 263.81223, 293.0)
 proba :  0.0132405935
(26.967428, 48.15625, 87.17846, 89.945724)
 c : radiateur
 list_crops.shape (30, 5)
 c : retroviseur
 list_crops.shape (32, 5)
 proba :  0.037684474
(14.681717, 54.76489, 94.673355, 89.58731)
 proba :  0.0122295795
(210.45583, 255.86855, 263.11145, 293.0)
 proba :  0.010143459
(231.19038, 176.60564, 279.5738, 220.0613)
 c : roue
 list_crops.shape (34, 5)
 proba :  0.81207114
(146.79677, 218.47359, 218.13591, 292.0255)
 proba :  0.27979472
(135.77394, 224.51434, 169.61382, 293.0)
 proba :  0.044861495
(29.877554, 37.664856, 91.5169, 83.93822)
 proba :  0.025318865
(7.384058, 0.09282303, 44.25522, 34.974136)
 proba :  0.01880419
(234.26022, 172.02496, 277.35846, 219.31018)
 proba :  0.011281469
(206.81548, 251.68932, 258.8708, 293.0)
 c : toit
 list_crops.shape (29, 5)
 c : vitre
 list_crops.shape (32, 5)
 proba :  0.14835674
(152.67307, 220.60892, 208.97354, 293.0)
 proba :  0.034728136
(235.49028, 173.76106, 284.0, 228.07799)
 proba :  0.030400075
(138.02519, 226.1509, 164.9868, 293.0)
 proba :  0.01884916
(18.03931, 51.71546, 93.86099, 87.63729)
We are managing local photo_id
image_path : temp/1759139131_2886529_950003813_e28be02dfcce79cce594a390a9911a0a.jpg
image_size (254, 229, 3)
[[[202 190 186]
  [205 193 189]
  [205 194 190]
  ...
  [ 81  70  56]
  [ 80  69  55]
  [ 78  67  53]]

 [[198 187 183]
  [200 189 185]
  [198 189 185]
  ...
  [ 50  41  28]
  [ 44  36  23]
  [ 45  36  23]]

 [[192 187 184]
  [191 186 183]
  [191 186 183]
  ...
  [ 36  30  23]
  [ 32  29  21]
  [ 33  27  20]]

 ...

 [[187 186 190]
  [186 185 189]
  [188 184 189]
  ...
  [ 43  38  35]
  [ 37  33  28]
  [ 33  28  25]]

 [[184 185 189]
  [183 184 188]
  [184 183 187]
  ...
  [ 28  23  22]
  [ 29  24  21]
  [ 33  28  27]]

 [[181 185 186]
  [180 184 185]
  [182 184 185]
  ...
  [ 23  15  16]
  [ 22  14  14]
  [ 24  16  17]]]
Detection took 0.048s for 300 object proposals
 c : aile-arriere
 list_crops.shape (29, 5)
 proba :  0.06769978
(98.06995, 150.88159, 151.9384, 223.72458)
 proba :  0.040915307
(33.360146, 90.13586, 179.12817, 182.83856)
 proba :  0.016991066
(79.35014, 122.72186, 111.49391, 230.36667)
 c : aile-avant
 list_crops.shape (30, 5)
 proba :  0.04859715
(43.715195, 17.110703, 139.32309, 207.18213)
 proba :  0.010095888
(62.041306, 104.14572, 183.72145, 182.30844)
 c : autre
 list_crops.shape (32, 5)
 c : cache-reservoir
 list_crops.shape (33, 5)
 c : capot
 list_crops.shape (22, 5)
 proba :  0.09680849
(38.040413, 75.42136, 207.15137, 166.659)
 c : carrosserie-autre
 list_crops.shape (24, 5)
 c : coffre
 list_crops.shape (21, 5)
 proba :  0.06442346
(165.4743, 115.30337, 226.40012, 165.47672)
 c : essuie-glace
 list_crops.shape (34, 5)
 proba :  0.38615882
(88.2684, 10.88359, 179.58322, 31.356243)
 proba :  0.04239051
(133.07735, 1.8786793, 227.74226, 26.53015)
 proba :  0.024032628
(169.25418, 117.38375, 218.91626, 165.43361)
 proba :  0.0108511085
(61.552063, 1.9377966, 117.81749, 33.00331)
 c : feu-antibrouillard
 list_crops.shape (32, 5)
 proba :  0.019598678
(168.9241, 117.95303, 219.53162, 165.85635)
 c : feu-arriere
 list_crops.shape (30, 5)
 proba :  0.022235306
(171.07289, 119.67639, 221.01033, 164.20016)
 c : info-modele
 list_crops.shape (31, 5)
 proba :  0.010200868
(168.52167, 116.05185, 219.95456, 166.43402)
 c : logo-marque
 list_crops.shape (34, 5)
 proba :  0.015149175
(154.45757, 207.1086, 180.1255, 244.77007)
 c : logo-roue
 list_crops.shape (30, 5)
 c : pare-brise
 list_crops.shape (27, 5)
 proba :  0.04709346
(168.76746, 117.313225, 222.8883, 165.84207)
 proba :  0.013604795
(53.460136, 0.0, 178.92007, 22.404406)
 proba :  0.012160936
(15.941781, 0.0, 44.897884, 48.296783)
 c : pare-choc
 list_crops.shape (23, 5)
 proba :  0.08984564
(166.06454, 118.159744, 226.88153, 168.71364)
 proba :  0.012222795
(108.19558, 130.85999, 228.0, 249.22641)
 c : phare
 list_crops.shape (37, 5)
 proba :  0.013690291
(172.01407, 118.39627, 223.30869, 162.23984)
 c : plaque-immatriculation
 list_crops.shape (38, 5)
 proba :  0.051211476
(173.59421, 120.67732, 218.5257, 161.78459)
 c : poignee
 list_crops.shape (31, 5)
 c : porte
 list_crops.shape (25, 5)
 proba :  0.039294552
(4.7749786, 0.0, 38.134384, 169.70985)
 proba :  0.014844884
(170.32451, 112.397705, 220.70692, 170.46423)
 proba :  0.013462718
(19.337929, 0.0, 92.45093, 176.19347)
 c : pot-echappement
 list_crops.shape (31, 5)
 proba :  0.02178788
(168.56729, 115.56244, 219.41681, 166.48639)
 proba :  0.016472416
(154.5291, 206.32008, 179.71355, 244.10432)
 c : radiateur
 list_crops.shape (30, 5)
 c : retroviseur
 list_crops.shape (34, 5)
 proba :  0.0101215625
(169.53499, 116.208374, 219.60542, 166.28891)
 c : roue
 list_crops.shape (36, 5)
 proba :  0.10635568
(83.72146, 166.04689, 133.41345, 253.0)
 proba :  0.05047885
(73.95674, 164.67119, 104.23909, 236.14717)
 proba :  0.032672763
(18.135805, 0.0, 45.997948, 33.787838)
 c : toit
 list_crops.shape (33, 5)
 c : vitre
 list_crops.shape (34, 5)
 proba :  0.028287828
(20.599869, 0.0, 45.4946, 43.05677)
 proba :  0.01535073
(170.87395, 118.458275, 221.03477, 164.49481)
We are managing local photo_id
image_path : temp/1759139131_2886529_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg
image_size (2160, 3840, 3)
[[[111 118  91]
  [113 120  93]
  [115 120  93]
  ...
  [ 23  40  37]
  [ 23  40  37]
  [ 24  41  38]]

 [[111 118  91]
  [112 119  92]
  [115 120  93]
  ...
  [ 23  40  37]
  [ 23  40  37]
  [ 23  40  37]]

 [[113 118  91]
  [114 119  92]
  [115 120  93]
  ...
  [ 22  39  36]
  [ 23  40  37]
  [ 23  40  37]]

 ...

 [[120 125  94]
  [119 124  93]
  [118 123  92]
  ...
  [ 22  36  34]
  [ 22  36  34]
  [ 23  37  35]]

 [[119 124  93]
  [119 124  93]
  [118 123  92]
  ...
  [ 22  36  34]
  [ 22  36  34]
  [ 22  36  34]]

 [[118 123  91]
  [117 122  90]
  [117 122  91]
  ...
  [ 22  36  34]
  [ 22  36  34]
  [ 22  36  34]]]
Detection took 0.361s for 300 object proposals
 c : aile-arriere
 list_crops.shape (45, 5)
 proba :  0.016385674
(3266.7014, 1031.8988, 3839.0, 1714.3202)
 proba :  0.01607687
(16.382446, 491.1, 406.7691, 799.84863)
 proba :  0.012597493
(1997.6558, 259.48102, 2506.4473, 848.9449)
 proba :  0.011020511
(51.39702, 1690.1293, 479.94672, 1966.6415)
 c : aile-avant
 list_crops.shape (41, 5)
 c : autre
 list_crops.shape (46, 5)
 c : cache-reservoir
 list_crops.shape (46, 5)
 c : capot
 list_crops.shape (38, 5)
 c : carrosserie-autre
 list_crops.shape (44, 5)
 c : coffre
 list_crops.shape (31, 5)
 c : essuie-glace
 list_crops.shape (49, 5)
 c : feu-antibrouillard
 list_crops.shape (44, 5)
 proba :  0.014232675
(3282.5754, 1219.772, 3793.55, 1821.4832)
 proba :  0.012606514
(27.949982, 498.89944, 410.00748, 789.6398)
 c : feu-arriere
 list_crops.shape (44, 5)
 proba :  0.07466645
(7.596161, 454.2948, 376.21793, 791.58606)
 proba :  0.025204647
(3284.6807, 1151.9535, 3782.7202, 1834.2433)
 proba :  0.011052162
(17.656769, 1586.6855, 282.00922, 1995.7561)
 c : info-modele
 list_crops.shape (44, 5)
 proba :  0.018716244
(35.900528, 482.7196, 414.19495, 791.46704)
 c : logo-marque
 list_crops.shape (46, 5)
 proba :  0.017159525
(41.253403, 486.3765, 409.73438, 792.71924)
 c : logo-roue
 list_crops.shape (45, 5)
 c : pare-brise
 list_crops.shape (38, 5)
 proba :  0.016600598
(24.733917, 0.0, 388.85248, 684.5045)
 c : pare-choc
 list_crops.shape (35, 5)
 c : phare
 list_crops.shape (48, 5)
 c : plaque-immatriculation
 list_crops.shape (47, 5)
 proba :  0.025557986
(26.288696, 479.65546, 404.9155, 771.0745)
 proba :  0.01656252
(28.251373, 1620.7368, 299.37173, 1974.6028)
 proba :  0.014695855
(18.400024, 3.5821686, 386.72763, 255.02428)
 c : poignee
 list_crops.shape (43, 5)
 c : porte
 list_crops.shape (40, 5)
 proba :  0.11929406
(1872.7751, 10.208618, 2442.97, 862.23987)
 proba :  0.11467305
(2136.5088, 52.951324, 2855.984, 815.2816)
 proba :  0.024791533
(3234.8313, 69.63132, 3823.1155, 847.8651)
 proba :  0.013810306
(109.56169, 1835.4792, 459.8427, 2159.0)
 proba :  0.011386464
(3315.017, 1115.6047, 3779.021, 1883.898)
 proba :  0.011383693
(1348.9751, 1049.3472, 1927.9502, 1777.0051)
 proba :  0.010760124
(2525.7002, 168.77847, 3531.495, 924.14233)
 c : pot-echappement
 list_crops.shape (43, 5)
 proba :  0.033424407
(5.113983, 1746.7181, 343.44214, 2019.2855)
 proba :  0.010658947
(117.196396, 1862.9636, 459.03406, 2155.1729)
 c : radiateur
 list_crops.shape (43, 5)
 c : retroviseur
 list_crops.shape (47, 5)
 proba :  0.018040141
(3296.5908, 1199.999, 3788.0034, 1822.636)
 proba :  0.013667158
(13.038925, 1748.9756, 340.38232, 2015.769)
 proba :  0.012990922
(124.44516, 1867.747, 456.3184, 2150.5205)
 c : roue
 list_crops.shape (45, 5)
 proba :  0.5839697
(3132.7424, 1107.5459, 3839.0, 1925.3467)
 proba :  0.045883857
(3481.3098, 1409.8964, 3814.2937, 1997.3822)
 proba :  0.03729999
(38.598343, 1751.9738, 339.52893, 2013.6791)
 proba :  0.029186202
(3244.3757, 40.53366, 3721.5994, 739.2029)
 proba :  0.01818595
(3167.0613, 383.71185, 3484.2517, 1016.36115)
 proba :  0.012426742
(229.0701, 0.0, 685.79126, 557.6382)
 proba :  0.0103601515
(2689.2131, 224.94803, 3238.7922, 934.28503)
 c : toit
 list_crops.shape (48, 5)
 c : vitre
 list_crops.shape (44, 5)
 proba :  0.018342316
(23.036057, 0.0, 379.9082, 299.26862)
 proba :  0.014216091
(3321.2346, 1194.3083, 3770.2102, 1805.9631)
We are managing local photo_id
image_path : temp/1759139131_2886529_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg
image_size (480, 640, 3)
[[[36 41 44]
  [36 41 44]
  [35 40 43]
  ...
  [ 8 10 10]
  [ 8 10 10]
  [ 8 10 10]]

 [[37 42 45]
  [36 41 44]
  [35 40 43]
  ...
  [ 5  7  7]
  [ 5  7  7]
  [ 5  7  7]]

 [[37 42 45]
  [36 41 44]
  [35 40 43]
  ...
  [ 3  5  5]
  [ 4  6  6]
  [ 4  6  6]]

 ...

 [[42 47 50]
  [41 46 49]
  [40 45 48]
  ...
  [ 8 10 10]
  [ 8 10 10]
  [ 8 10 10]]

 [[41 46 49]
  [41 46 49]
  [40 45 48]
  ...
  [ 0  2  2]
  [10 12 12]
  [22 24 24]]

 [[40 45 48]
  [40 45 48]
  [40 45 48]
  ...
  [10 12 12]
  [17 19 19]
  [26 28 28]]]
Detection took 0.039s for 300 object proposals
 c : aile-arriere
 list_crops.shape (32, 5)
 proba :  0.12727997
(160.85567, 172.85123, 306.1952, 321.39285)
 proba :  0.011540753
(538.53314, 190.83588, 613.96564, 298.88864)
 proba :  0.010252577
(197.33014, 160.36838, 490.47406, 311.2708)
 c : aile-avant
 list_crops.shape (40, 5)
 proba :  0.94816476
(161.7869, 149.53821, 330.65045, 343.41766)
 proba :  0.09029217
(20.761242, 105.881454, 54.77528, 195.6285)
 proba :  0.02217671
(152.8124, 143.30527, 217.16658, 306.26077)
 proba :  0.013754741
(320.70102, 330.90732, 439.32962, 414.44547)
 proba :  0.011395621
(557.7669, 199.66882, 608.6683, 270.50723)
 c : autre
 list_crops.shape (36, 5)
 proba :  0.011427441
(458.26993, 15.298817, 521.1441, 98.993256)
 c : cache-reservoir
 list_crops.shape (37, 5)
 proba :  0.0297497
(470.56097, 21.58107, 531.46155, 89.20072)
 proba :  0.025542783
(368.27322, 265.85745, 446.90042, 331.26797)
 proba :  0.014927516
(353.33173, 360.48917, 412.48853, 423.13065)
 proba :  0.014713539
(451.1127, 57.61358, 523.15924, 125.931816)
 c : capot
 list_crops.shape (33, 5)
 proba :  0.9944299
(211.27588, 115.14263, 555.77313, 300.73563)
 proba :  0.23367602
(65.79123, 24.148949, 285.7335, 46.316803)
 proba :  0.034848165
(343.23734, 279.91083, 508.2054, 431.91907)
 proba :  0.017473638
(89.42523, 37.517593, 357.51498, 63.62851)
 proba :  0.010149338
(424.37628, 246.66846, 564.62085, 357.00574)
 c : carrosserie-autre
 list_crops.shape (35, 5)
 proba :  0.028550778
(454.54358, 25.081802, 523.52234, 121.41107)
 c : coffre
 list_crops.shape (31, 5)
 proba :  0.12970288
(446.42908, 19.303154, 528.3397, 117.90945)
 proba :  0.102553196
(139.89659, 45.983246, 419.52136, 192.6684)
 proba :  0.030856097
(269.2923, 162.00162, 553.9398, 348.94415)
 c : essuie-glace
 list_crops.shape (40, 5)
 proba :  0.7894473
(203.86427, 108.230804, 416.75763, 147.35837)
 proba :  0.14472052
(369.06165, 267.5311, 445.03522, 332.9595)
 proba :  0.09796674
(391.05417, 261.1866, 527.01013, 318.12888)
 proba :  0.07720064
(470.47235, 22.207699, 532.0703, 88.02234)
 proba :  0.035417624
(354.3854, 360.35913, 414.2043, 422.9771)
 proba :  0.014149095
(114.83726, 22.671062, 335.36823, 42.910023)
 proba :  0.012226184
(254.5795, 94.722336, 456.819, 288.55945)
 proba :  0.010929902
(353.8385, 285.5669, 484.78717, 414.69403)
 c : feu-antibrouillard
 list_crops.shape (39, 5)
 proba :  0.21194312
(341.78375, 363.48306, 404.84973, 430.65848)
 proba :  0.05586707
(368.66183, 268.62842, 446.44467, 330.09308)
 proba :  0.025233986
(470.423, 21.905354, 531.3917, 89.25714)
 proba :  0.023561409
(389.44293, 264.04523, 529.7855, 316.9583)
 proba :  0.016472949
(451.4887, 58.84314, 522.9121, 125.20781)
 proba :  0.010561558
(467.00644, 343.55118, 573.7671, 420.20358)
 c : feu-arriere
 list_crops.shape (39, 5)
 proba :  0.10164532
(329.66953, 259.26025, 456.71756, 315.55084)
 proba :  0.04224622
(454.30338, 28.764324, 522.9122, 104.09799)
 proba :  0.025246693
(333.51105, 352.84525, 405.06036, 425.97482)
 proba :  0.010688383
(475.5662, 341.54065, 575.4463, 420.15326)
 c : info-modele
 list_crops.shape (37, 5)
 proba :  0.07046481
(470.4835, 21.811588, 532.03894, 89.57268)
 proba :  0.038773675
(353.82886, 361.54083, 412.9958, 422.8529)
 proba :  0.033351302
(368.7074, 266.81424, 448.2433, 331.63028)
 proba :  0.02305354
(450.84296, 57.473644, 524.0715, 126.34062)
 proba :  0.015798539
(390.13696, 260.84967, 533.1667, 317.53296)
 c : logo-marque
 list_crops.shape (39, 5)
 proba :  0.11936508
(352.9692, 361.31387, 412.53116, 422.70078)
 proba :  0.032974973
(470.55072, 20.776169, 530.95544, 88.80192)
 proba :  0.027512077
(370.81335, 264.58487, 447.208, 329.64212)
 proba :  0.025027672
(397.46735, 260.82416, 526.30554, 316.55)
 proba :  0.014329763
(584.1553, 0.74874496, 639.0, 70.73624)
 c : logo-roue
 list_crops.shape (37, 5)
 proba :  0.014609004
(353.32263, 360.72873, 413.24713, 423.6066)
 proba :  0.013224482
(470.33865, 21.597965, 532.06165, 89.581345)
 c : pare-brise
 list_crops.shape (34, 5)
 proba :  0.956977
(141.09102, 42.465473, 444.09357, 147.77126)
 proba :  0.10513123
(319.455, 22.013908, 424.0322, 129.13104)
 proba :  0.06655454
(453.85483, 19.660042, 523.2704, 91.5455)
 proba :  0.050991844
(287.21155, 164.05992, 547.3199, 296.73663)
 proba :  0.023291035
(95.59753, 40.14828, 158.49222, 162.53923)
 c : pare-choc
 list_crops.shape (29, 5)
 proba :  0.9453789
(272.89798, 257.50354, 580.2355, 444.44427)
 proba :  0.21966992
(233.49039, 224.18689, 397.76492, 411.5475)
 proba :  0.028825402
(487.97964, 17.56393, 613.42053, 120.57449)
 proba :  0.028744599
(435.95575, 307.78705, 588.67755, 426.57855)
 c : phare
 list_crops.shape (38, 5)
 proba :  0.7775868
(326.83813, 251.89113, 477.77557, 312.8628)
 proba :  0.07698164
(328.2212, 359.63788, 410.24237, 426.9674)
 proba :  0.038827475
(292.8092, 225.13826, 466.7426, 392.3429)
 proba :  0.03310783
(538.239, 197.21587, 600.21655, 303.4421)
 proba :  0.026036022
(466.645, 20.837364, 531.73236, 84.11888)
 proba :  0.018858653
(305.60803, 209.47296, 597.7098, 307.1176)
 proba :  0.014257981
(478.92255, 344.12985, 578.63165, 416.75455)
 proba :  0.0112901125
(96.66904, 66.98663, 163.91147, 147.69226)
 c : plaque-immatriculation
 list_crops.shape (38, 5)
 proba :  0.23006861
(518.55493, 294.16425, 582.33777, 390.75763)
 proba :  0.033766154
(438.44427, 271.7624, 568.41986, 386.5594)
 proba :  0.025870364
(347.18515, 259.77985, 452.87912, 315.94495)
 proba :  0.021144737
(470.3828, 23.619102, 531.60767, 85.37939)
 c : poignee
 list_crops.shape (35, 5)
 proba :  0.05437758
(583.3567, 0.030948639, 639.0, 70.98572)
 proba :  0.034033507
(470.4478, 21.595158, 531.67084, 89.7744)
 proba :  0.02638052
(353.17117, 360.95795, 412.89438, 423.59967)
 proba :  0.024486203
(368.05402, 266.12305, 448.0559, 332.26898)
 c : porte
 list_crops.shape (28, 5)
 proba :  0.9854092
(78.164444, 43.0784, 169.65366, 306.44266)
 proba :  0.96724796
(33.645004, 44.178917, 90.650925, 241.29367)
 proba :  0.09155692
(452.37, 19.606403, 521.0885, 113.535934)
 proba :  0.03417806
(158.71034, 46.13974, 421.33344, 212.15817)
 proba :  0.012424165
(436.9651, 223.37784, 575.0623, 403.32742)
 c : pot-echappement
 list_crops.shape (37, 5)
 proba :  0.05553766
(353.8215, 360.70197, 412.23117, 423.33868)
 proba :  0.018787522
(458.05484, 14.54628, 521.22046, 99.6706)
 proba :  0.012938654
(584.3939, 0.0, 639.0, 71.1006)
 proba :  0.011320226
(466.14532, 340.34045, 575.404, 420.87988)
 c : radiateur
 list_crops.shape (38, 5)
 c : retroviseur
 list_crops.shape (39, 5)
 proba :  0.2380269
(452.17804, 56.800533, 522.1683, 124.1259)
 proba :  0.2077851
(471.1477, 21.537247, 531.21704, 89.27232)
 proba :  0.19335103
(369.90198, 266.02124, 447.44684, 332.58807)
 proba :  0.045619205
(342.49414, 362.4577, 404.128, 431.03387)
 proba :  0.026579065
(176.92041, 115.949295, 409.84918, 150.13213)
 proba :  0.021461492
(584.3822, 0.0, 639.0, 70.68931)
 proba :  0.013626498
(392.93713, 259.88358, 530.606, 318.64255)
 proba :  0.010495504
(101.94349, 73.78749, 164.67831, 158.12735)
 c : roue
 list_crops.shape (38, 5)
 proba :  0.9417777
(187.78546, 271.2436, 306.51035, 426.32977)
 proba :  0.13538726
(334.87302, 358.32193, 400.07718, 428.798)
 proba :  0.04979097
(538.71124, 250.9977, 599.993, 395.27863)
 proba :  0.03739416
(12.769402, 136.75432, 64.1616, 244.91664)
 proba :  0.03481427
(549.3052, 192.38966, 605.7947, 305.71478)
 proba :  0.015743054
(479.68817, 335.91656, 572.4849, 421.2669)
 c : toit
 list_crops.shape (34, 5)
 proba :  0.7919592
(83.63237, 31.277132, 341.39325, 55.134727)
 c : vitre
 list_crops.shape (37, 5)
 proba :  0.9804717
(95.83032, 50.27418, 162.35254, 142.97362)
 proba :  0.84250766
(42.275692, 43.049828, 91.28232, 114.95818)
 proba :  0.07669096
(277.41418, 28.772747, 404.65918, 139.554)
 proba :  0.064472936
(455.88452, 20.459667, 518.84143, 90.143845)
 proba :  0.027514279
(148.30208, 51.20498, 356.0141, 112.50843)
 proba :  0.013178587
(336.19415, 352.38318, 408.2209, 424.2326)
 proba :  0.012460459
(364.92896, 257.46826, 445.52588, 324.04065)
We are managing local photo_id
image_path : temp/1759139131_2886529_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg
image_size (480, 614, 3)
[[[ 44  44  44]
  [ 49  51  51]
  [ 42  44  44]
  ...
  [  8  10  10]
  [  8  10  10]
  [  8  10  10]]

 [[ 43  43  43]
  [ 36  38  38]
  [ 39  41  41]
  ...
  [  5   7   7]
  [  5   7   7]
  [  5   7   7]]

 [[ 70  70  70]
  [ 40  42  42]
  [ 41  43  43]
  ...
  [  4   6   6]
  [  4   6   6]
  [  4   6   6]]

 ...

 [[103 101 101]
  [110 108 108]
  [ 61  59  59]
  ...
  [  8  10  10]
  [  8  10  10]
  [  8  10  10]]

 [[ 98  96  96]
  [115 113 113]
  [ 73  71  71]
  ...
  [  0   2   2]
  [ 11  13  13]
  [ 21  23  23]]

 [[ 92  90  90]
  [114 112 112]
  [ 87  82  83]
  ...
  [ 10  12  12]
  [ 18  20  20]
  [ 25  27  27]]]
Detection took 0.029s for 300 object proposals
 c : aile-arriere
 list_crops.shape (30, 5)
 proba :  0.13238606
(133.29764, 172.39062, 280.82468, 321.49603)
 proba :  0.0132743325
(153.19507, 232.50717, 369.76257, 359.78293)
 c : aile-avant
 list_crops.shape (35, 5)
 proba :  0.9504051
(133.96959, 146.1612, 305.77856, 344.49078)
 proba :  0.020750698
(128.14926, 143.01114, 188.89828, 305.02676)
 proba :  0.0149299875
(294.2448, 331.83795, 412.49908, 415.33014)
 proba :  0.011201359
(533.0577, 198.27582, 583.4974, 268.4578)
 proba :  0.0100036245
(0.0, 114.11074, 47.583683, 234.02557)
 c : autre
 list_crops.shape (36, 5)
 c : cache-reservoir
 list_crops.shape (35, 5)
 proba :  0.025676342
(426.6967, 57.941082, 496.61887, 125.314804)
 proba :  0.025273357
(342.52127, 266.6149, 421.3428, 330.55453)
 proba :  0.017259292
(306.41132, 352.42938, 382.60596, 422.0016)
 c : capot
 list_crops.shape (32, 5)
 proba :  0.99047124
(196.7523, 110.37572, 532.50287, 291.96515)
 proba :  0.22531527
(40.248833, 25.965935, 253.16254, 48.717834)
 proba :  0.058448747
(318.88684, 286.61407, 481.59192, 427.89557)
 proba :  0.017970476
(64.42467, 38.41082, 331.29297, 64.35663)
 proba :  0.0106379995
(116.1494, 36.753815, 368.63306, 155.92609)
 c : carrosserie-autre
 list_crops.shape (29, 5)
 proba :  0.03030178
(429.34747, 22.06366, 497.81714, 122.4805)
 c : coffre
 list_crops.shape (29, 5)
 proba :  0.12298184
(421.33826, 15.522415, 501.77417, 119.22189)
 proba :  0.10120624
(112.74597, 46.271683, 395.26865, 193.28415)
 proba :  0.062826924
(247.78348, 171.97949, 517.5148, 335.8561)
 c : essuie-glace
 list_crops.shape (37, 5)
 proba :  0.7717582
(178.34856, 109.068245, 389.55005, 147.69456)
 proba :  0.14845324
(343.28012, 268.2906, 419.46072, 332.34344)
 proba :  0.1008002
(364.45322, 261.52002, 501.23288, 317.86377)
 proba :  0.047921862
(446.58145, 21.590965, 508.3306, 88.696365)
 proba :  0.03781656
(328.75992, 360.46216, 389.8561, 423.33447)
 proba :  0.014705681
(85.60243, 24.015614, 308.10565, 43.34746)
 proba :  0.013853137
(267.03647, 240.33759, 412.96115, 348.48694)
 proba :  0.012840888
(427.07162, 58.160645, 496.35245, 124.40587)
 c : feu-antibrouillard
 list_crops.shape (37, 5)
 proba :  0.21098839
(307.2801, 354.3132, 381.79572, 421.6105)
 proba :  0.056849264
(342.86545, 269.365, 420.86002, 329.4411)
 proba :  0.028137324
(427.11954, 58.821865, 496.1492, 124.55365)
 proba :  0.023515057
(362.92813, 264.44644, 504.09073, 316.8403)
 c : feu-arriere
 list_crops.shape (34, 5)
 proba :  0.09635113
(303.15067, 259.64264, 431.2908, 316.4461)
 proba :  0.041332528
(428.95193, 25.621414, 497.4836, 104.48793)
 proba :  0.03447116
(305.4742, 351.55878, 379.51144, 423.9406)
 c : info-modele
 list_crops.shape (36, 5)
 proba :  0.042865787
(306.73895, 353.51407, 383.17584, 421.5607)
 proba :  0.03521399
(426.51993, 57.957302, 497.4398, 125.615204)
 proba :  0.033362743
(342.96036, 267.55933, 422.6783, 330.89514)
 proba :  0.016011834
(363.58472, 261.26318, 507.5719, 317.39294)
 proba :  0.010807748
(445.90033, 19.326908, 507.9972, 91.462494)
 c : logo-marque
 list_crops.shape (36, 5)
 proba :  0.10544256
(327.43323, 361.3277, 387.95428, 423.03418)
 proba :  0.028344005
(344.96402, 265.39145, 421.62723, 329.04562)
 proba :  0.02536678
(370.85934, 261.33887, 500.61594, 316.38672)
 proba :  0.017483598
(427.5979, 57.651665, 496.78857, 125.72637)
 c : logo-roue
 list_crops.shape (34, 5)
 proba :  0.014208436
(327.66238, 360.7206, 388.80002, 424.04056)
 c : pare-brise
 list_crops.shape (30, 5)
 proba :  0.9546375
(113.60887, 42.32615, 417.92194, 147.55469)
 proba :  0.10699732
(294.67838, 24.328117, 399.7062, 129.18164)
 proba :  0.054093387
(258.4785, 161.26163, 521.5884, 295.0536)
 proba :  0.027621351
(424.6269, 20.86715, 499.5703, 97.22992)
 proba :  0.017619265
(72.590256, 41.742165, 127.25363, 168.7923)
 c : pare-choc
 list_crops.shape (25, 5)
 proba :  0.94856405
(247.4653, 265.47186, 552.93787, 445.2744)
 proba :  0.09526285
(194.32153, 225.23355, 348.43445, 419.22714)
 proba :  0.019610401
(422.4503, 329.61017, 549.5318, 424.8125)
 proba :  0.014918259
(455.54254, 26.13042, 592.816, 120.21477)
 c : phare
 list_crops.shape (35, 5)
 proba :  0.67815113
(318.1397, 264.6474, 489.91937, 310.62482)
 proba :  0.40323994
(261.64148, 234.11752, 408.5141, 331.0247)
 proba :  0.0958962
(301.10437, 356.4165, 385.49823, 425.23486)
 proba :  0.014655572
(525.66724, 198.54651, 575.2174, 289.94983)
 proba :  0.013315895
(427.9977, 23.336235, 501.29965, 100.650444)
 proba :  0.0114958575
(277.7342, 207.81781, 557.26544, 296.8563)
 c : plaque-immatriculation
 list_crops.shape (36, 5)
 proba :  0.190535
(491.398, 294.22546, 563.8239, 390.17822)
 proba :  0.057779536
(439.51474, 291.40826, 531.0498, 407.167)
 proba :  0.022952951
(298.98138, 257.41574, 447.536, 319.17215)
 proba :  0.01134066
(309.8407, 356.20618, 386.15698, 421.9347)
 c : poignee
 list_crops.shape (34, 5)
 proba :  0.025672395
(327.5086, 360.97192, 388.47107, 424.01862)
 proba :  0.024166234
(342.31418, 266.88937, 422.49307, 331.527)
 proba :  0.021987375
(426.4223, 57.757767, 497.0791, 125.79884)
 proba :  0.013409357
(559.0071, 0.020671844, 613.0, 71.29187)
 c : porte
 list_crops.shape (27, 5)
 proba :  0.9922963
(51.415756, 40.436356, 152.79599, 306.3874)
 proba :  0.9720328
(4.56773, 51.351334, 72.87575, 242.47539)
 proba :  0.058688473
(425.32236, 19.076637, 501.12173, 130.88055)
 proba :  0.030509418
(132.09253, 46.46412, 397.1621, 213.5802)
 proba :  0.0145429885
(360.29984, 244.2825, 551.6094, 395.6216)
 c : pot-echappement
 list_crops.shape (36, 5)
 proba :  0.050583676
(328.16983, 360.69727, 387.76804, 423.7301)
 proba :  0.020144936
(427.1537, 57.467968, 496.29352, 125.84367)
 c : radiateur
 list_crops.shape (35, 5)
 c : retroviseur
 list_crops.shape (35, 5)
 proba :  0.43162438
(427.40536, 56.78142, 495.9445, 123.67093)
 proba :  0.18573771
(344.11066, 266.8142, 421.86267, 331.86877)
 proba :  0.11660608
(446.86667, 19.438538, 506.4644, 90.949104)
 proba :  0.048026774
(316.7069, 363.19226, 377.6394, 430.79218)
 proba :  0.027535662
(150.80496, 116.120346, 381.59518, 150.22108)
 proba :  0.012777196
(366.31216, 260.31354, 504.92654, 318.49066)
 proba :  0.011191188
(77.328415, 81.656784, 130.17664, 174.47661)
 c : roue
 list_crops.shape (37, 5)
 proba :  0.9375667
(166.73473, 261.12903, 275.2209, 420.81024)
 proba :  0.14077246
(307.79626, 351.35687, 385.35028, 430.9638)
 proba :  0.11402865
(4.555645, 161.28665, 50.79914, 253.06851)
 proba :  0.045011047
(513.9556, 252.55704, 573.9212, 393.48148)
 proba :  0.0371069
(526.66644, 191.77794, 581.60114, 303.90466)
 c : toit
 list_crops.shape (33, 5)
 proba :  0.796433
(58.20294, 31.455034, 316.0078, 55.599693)
 c : vitre
 list_crops.shape (33, 5)
 proba :  0.9803719
(69.39578, 49.350883, 135.83386, 142.4219)
 proba :  0.8937898
(14.668798, 41.21952, 66.17401, 115.17721)
 proba :  0.11751009
(205.48586, 48.102623, 379.274, 127.905785)
 proba :  0.043761622
(428.12982, 21.900127, 499.2107, 97.15698)
 proba :  0.017622946
(308.7075, 352.47733, 385.68686, 423.2488)
 proba :  0.012128547
(338.9495, 258.53644, 419.88425, 323.73804)
We are managing local photo_id
image_path : temp/1759139131_2886529_950003696_11e3a77b72af4b332d366d98984039c7.jpg
image_size (2160, 3264, 3)
[[[168 165 161]
  [168 165 161]
  [168 165 161]
  ...
  [ 47  59  63]
  [ 48  60  64]
  [ 48  60  64]]

 [[168 165 161]
  [168 165 161]
  [168 165 161]
  ...
  [ 47  59  63]
  [ 47  59  63]
  [ 48  60  64]]

 [[168 165 161]
  [168 165 161]
  [168 165 161]
  ...
  [ 47  59  63]
  [ 47  59  63]
  [ 47  59  63]]

 ...

 [[167 164 160]
  [167 164 160]
  [167 164 160]
  ...
  [ 44  59  61]
  [ 44  59  61]
  [ 44  59  61]]

 [[165 162 158]
  [165 162 158]
  [165 162 158]
  ...
  [ 45  60  62]
  [ 45  60  62]
  [ 45  60  62]]

 [[164 161 157]
  [164 161 157]
  [164 161 157]
  ...
  [ 45  60  62]
  [ 45  60  62]
  [ 45  60  62]]]
Detection took 0.320s for 300 object proposals
 c : aile-arriere
 list_crops.shape (52, 5)
 proba :  0.020721454
(396.22726, 192.55933, 703.2368, 482.50916)
 proba :  0.013662253
(2606.483, 1489.7395, 2954.395, 2131.3933)
 proba :  0.010280708
(2270.1772, 1651.4272, 2706.3877, 2138.8582)
 proba :  0.010018195
(2922.7334, 683.2467, 3263.0, 1188.8965)
 c : aile-avant
 list_crops.shape (55, 5)
 proba :  0.025830148
(2616.2046, 1475.3556, 2938.8389, 2144.033)
 c : autre
 list_crops.shape (53, 5)
 proba :  0.013096917
(405.69366, 192.22087, 689.09064, 476.50568)
 c : cache-reservoir
 list_crops.shape (50, 5)
 proba :  0.029477792
(2629.4736, 1461.0215, 2954.7153, 2148.8484)
 proba :  0.02454188
(299.21423, 89.51262, 716.66113, 498.37213)
 proba :  0.020119106
(2232.0503, 676.7728, 2590.5142, 1171.2032)
 proba :  0.01879109
(2298.3872, 1682.8022, 2783.6611, 2156.6445)
 proba :  0.012400566
(2317.1357, 1436.0569, 2614.4824, 1924.958)
 proba :  0.012134863
(2925.148, 677.27856, 3258.8936, 1209.0471)
 c : capot
 list_crops.shape (44, 5)
 proba :  0.018010484
(2645.9841, 973.04395, 3214.7268, 1642.1047)
 proba :  0.016425382
(77.751465, 795.9664, 694.0035, 1481.1052)
 c : carrosserie-autre
 list_crops.shape (45, 5)
 proba :  0.029315153
(251.71573, 0.0, 712.3502, 634.9923)
 proba :  0.013689
(2284.6982, 1569.4862, 2687.3687, 2159.0)
 c : coffre
 list_crops.shape (42, 5)
 proba :  0.13925129
(283.04462, 0.0, 631.68304, 633.3989)
 proba :  0.013911806
(1720.5857, 507.76276, 2227.843, 1191.3557)
 c : essuie-glace
 list_crops.shape (56, 5)
 proba :  0.018926991
(320.8921, 110.23695, 601.00183, 462.48242)
 proba :  0.013812622
(2187.6118, 749.4845, 2497.3213, 1214.441)
 proba :  0.01355684
(2629.3284, 1462.9336, 2950.865, 2139.649)
 proba :  0.013343284
(74.38069, 721.1932, 391.73853, 1112.9583)
 c : feu-antibrouillard
 list_crops.shape (52, 5)
 proba :  0.06085765
(372.09866, 242.48941, 759.4324, 515.92285)
 proba :  0.018312275
(2928.38, 694.2862, 3257.6504, 1206.6543)
 proba :  0.018102653
(2299.543, 1693.8586, 2784.125, 2158.6943)
 proba :  0.015857436
(2230.3674, 698.557, 2591.1687, 1175.2931)
 proba :  0.014355835
(2630.749, 1480.9924, 2954.4731, 2151.7527)
 proba :  0.011737955
(383.55545, 1437.1191, 937.1355, 1845.1282)
 c : feu-arriere
 list_crops.shape (48, 5)
 proba :  0.646685
(354.849, 213.38072, 730.16077, 532.05273)
 proba :  0.12032653
(2225.6616, 661.20886, 2605.2173, 1235.8998)
 proba :  0.0794024
(256.18494, 24.241379, 540.2457, 478.12366)
 proba :  0.060690556
(1807.4268, 656.7417, 2155.9038, 1147.3506)
 proba :  0.033559434
(2308.1853, 1667.5708, 2786.7566, 2159.0)
 proba :  0.032267246
(2930.4373, 638.04956, 3237.765, 1243.203)
 proba :  0.018535657
(619.976, 619.2589, 953.7485, 1223.5769)
 proba :  0.017300386
(2630.6836, 1496.9255, 2955.379, 2159.0)
 proba :  0.016657727
(2317.2383, 1411.0991, 2618.2651, 1961.699)
 proba :  0.01582057
(770.6253, 939.7085, 1053.5684, 1456.145)
 proba :  0.010595245
(2489.4504, 424.4911, 2939.1892, 1234.4261)
 proba :  0.010264929
(828.0394, 1.9259338, 1174.95, 369.90378)
 proba :  0.0100936415
(14.197006, 430.89093, 165.10475, 734.03253)
 c : info-modele
 list_crops.shape (49, 5)
 proba :  0.0701014
(403.7279, 194.21564, 693.0525, 475.90448)
 proba :  0.022200912
(2299.2551, 1684.2521, 2787.114, 2154.6646)
 proba :  0.019055918
(2232.6091, 682.5511, 2592.737, 1170.8303)
 proba :  0.017019358
(2630.5815, 1468.2206, 2959.7207, 2151.3472)
 proba :  0.01088155
(1.7524414, 575.7819, 261.89774, 885.70917)
 c : logo-marque
 list_crops.shape (51, 5)
 proba :  0.05188664
(408.49487, 200.25813, 688.2726, 481.55768)
 proba :  0.034075845
(2304.7786, 1690.4406, 2793.8875, 2159.0)
 proba :  0.018232925
(2631.4717, 1489.2543, 2967.995, 2159.0)
 proba :  0.013038151
(2236.69, 683.9109, 2594.5933, 1180.4825)
 proba :  0.011358671
(2319.9644, 1440.4111, 2621.3867, 1933.8379)
 c : logo-roue
 list_crops.shape (52, 5)
 proba :  0.030219503
(402.78033, 191.0682, 692.75385, 478.26633)
 proba :  0.02090027
(2230.9316, 677.76373, 2592.7188, 1173.9944)
 proba :  0.018413354
(2512.7617, 492.69, 2833.5469, 843.9925)
 proba :  0.018071903
(2298.6846, 1681.3522, 2786.085, 2156.1255)
 proba :  0.013549487
(2610.3862, 558.7892, 3007.562, 1178.0306)
 proba :  0.013279355
(1762.796, 675.094, 2234.706, 1109.9445)
 proba :  0.0124116335
(2317.8044, 1434.3566, 2615.264, 1928.4266)
 proba :  0.011196565
(2783.5876, 1527.3899, 3036.1252, 2077.7495)
 c : pare-brise
 list_crops.shape (46, 5)
 proba :  0.054985233
(2411.5898, 398.6623, 2957.357, 1171.5542)
 proba :  0.045024693
(2240.3506, 634.9248, 2579.2617, 1217.42)
 proba :  0.026939405
(1424.878, 304.92932, 2074.7832, 1161.5795)
 proba :  0.025700461
(312.55487, 97.24336, 605.92456, 472.6009)
 proba :  0.024183564
(2954.8074, 604.7057, 3255.3665, 1264.5082)
 proba :  0.021959977
(2056.9155, 93.799774, 2392.5625, 713.71985)
 proba :  0.020821696
(323.81387, 1090.698, 787.1581, 1678.9526)
 proba :  0.01857816
(103.62854, 794.42474, 696.19684, 1524.2737)
 proba :  0.017079577
(2701.8223, 949.39465, 3158.2441, 1362.8368)
 proba :  0.011479208
(2240.9016, 3.3528748, 2720.2, 772.5847)
 proba :  0.010908625
(1694.8082, 659.70447, 2162.865, 1116.5951)
 c : pare-choc
 list_crops.shape (38, 5)
 proba :  0.10618855
(347.37756, 1367.4285, 1073.0188, 1857.05)
 proba :  0.01668099
(1310.3904, 1570.4409, 2576.6165, 2051.272)
 proba :  0.0109814145
(0.0, 545.1321, 997.94666, 1254.1384)
 proba :  0.010150352
(2850.2402, 1465.4105, 3233.6216, 2099.7554)
 c : phare
 list_crops.shape (49, 5)
 proba :  0.06943744
(2930.6885, 682.5105, 3257.6636, 1220.2612)
 proba :  0.06410909
(310.48914, 118.323425, 715.9464, 508.53955)
 proba :  0.028809223
(324.56848, 1179.3389, 758.8093, 1634.1963)
 proba :  0.013003921
(2226.5747, 695.25244, 2603.434, 1195.8007)
 proba :  0.011204964
(83.39888, 743.9813, 419.8631, 1117.4813)
 proba :  0.010501359
(2318.0786, 1449.4462, 2631.6821, 1922.1364)
 c : plaque-immatriculation
 list_crops.shape (55, 5)
 proba :  0.057377893
(399.6267, 207.96199, 679.6078, 493.95447)
 proba :  0.04576252
(1744.0807, 665.869, 2221.1506, 1071.6484)
 proba :  0.023891345
(2294.9846, 1697.7516, 2792.1936, 2132.58)
 proba :  0.02344552
(2538.795, 1521.5804, 2898.8691, 2073.3604)
 proba :  0.013999708
(2783.4624, 1564.8615, 3034.8975, 2056.81)
 proba :  0.01354155
(2245.3342, 695.2737, 2573.1614, 1168.4249)
 proba :  0.011307143
(2945.9402, 676.8616, 3251.0232, 1190.98)
 c : poignee
 list_crops.shape (48, 5)
 proba :  0.057069637
(2629.5088, 1457.6094, 2958.767, 2152.966)
 proba :  0.023785664
(2299.0876, 1682.4327, 2785.6086, 2157.393)
 proba :  0.018131746
(2232.1702, 677.8631, 2592.0955, 1173.5793)
 proba :  0.014422471
(403.02313, 191.0307, 692.26654, 478.38263)
 c : porte
 list_crops.shape (42, 5)
 proba :  0.12676841
(2295.5747, 1531.3802, 2685.668, 2159.0)
 proba :  0.044973075
(2857.5034, 1450.7472, 3263.0, 2033.0052)
 proba :  0.04126958
(2466.1416, 1663.9418, 3030.1997, 2159.0)
 proba :  0.028290575
(390.16052, 1195.2063, 897.94543, 1796.6553)
 proba :  0.023547394
(1444.4811, 1494.1017, 2537.7583, 2103.8564)
 proba :  0.017251369
(311.3446, 15.9461975, 609.73755, 659.74097)
 proba :  0.012642643
(2938.5862, 622.9511, 3247.7961, 1300.4812)
 c : pot-echappement
 list_crops.shape (49, 5)
 proba :  0.03307312
(2632.8506, 1456.6238, 2954.791, 2152.6873)
 proba :  0.03093514
(2302.4873, 1680.4291, 2781.5732, 2157.6838)
 proba :  0.015973916
(405.3088, 189.98491, 691.14343, 477.74866)
 proba :  0.011729801
(67.23093, 695.8185, 394.59567, 1122.9188)
 proba :  0.011399458
(1748.3538, 537.04724, 2217.0127, 1143.0487)
 proba :  0.011031573
(2926.5444, 674.94666, 3258.5605, 1211.8741)
 c : radiateur
 list_crops.shape (50, 5)
 c : retroviseur
 list_crops.shape (52, 5)
 proba :  0.08366427
(2235.2058, 682.12427, 2591.2039, 1175.3987)
 proba :  0.0594911
(2321.9434, 1443.8904, 2613.5684, 1922.5833)
 proba :  0.043853644
(2937.0754, 682.24695, 3252.612, 1205.0391)
 proba :  0.042290084
(2632.6492, 1459.1238, 2953.6677, 2146.3127)
 proba :  0.034092788
(71.95578, 699.8297, 393.3188, 1120.5212)
 proba :  0.0318841
(308.78082, 91.36073, 716.15985, 497.187)
 proba :  0.025059877
(2306.4988, 1681.2102, 2777.2517, 2150.37)
 proba :  0.019790618
(2394.738, 1122.3643, 2636.161, 1534.456)
 proba :  0.01827181
(2293.514, 971.51245, 2576.615, 1378.5513)
 proba :  0.01642202
(2956.3335, 1594.2177, 3251.5352, 2041.8783)
 proba :  0.01573016
(10.902863, 573.5206, 252.95627, 882.5061)
 proba :  0.015464128
(2710.9739, 884.67487, 3001.4153, 1337.108)
 proba :  0.014095434
(2619.5144, 564.0205, 2997.3977, 1174.5137)
 proba :  0.013078378
(162.06512, 998.6118, 907.79156, 1740.7048)
 proba :  0.012830089
(2117.2517, 1467.146, 2431.4875, 2134.755)
 proba :  0.0121800145
(2070.727, 153.25638, 2426.667, 695.5758)
 c : roue
 list_crops.shape (48, 5)
 proba :  0.08215192
(2921.2754, 638.34546, 3254.5332, 1258.7112)
 proba :  0.058020525
(2507.5432, 1513.986, 2926.2444, 2083.4536)
 proba :  0.057317752
(2746.1365, 1692.5988, 3205.8547, 2127.352)
 proba :  0.03822828
(2267.4558, 1589.3503, 2719.9382, 2144.4573)
 proba :  0.029192604
(2521.6038, 448.82324, 3069.241, 1139.6517)
 proba :  0.026404217
(416.58606, 1400.4644, 896.4568, 1839.0698)
 proba :  0.022059109
(258.38052, 30.396362, 777.48584, 579.22815)
 proba :  0.018164568
(269.50903, 1082.4207, 809.2012, 1696.925)
 proba :  0.016246164
(814.92395, 0.0, 1180.1229, 343.59937)
 proba :  0.014413888
(1708.762, 636.11633, 2206.222, 1093.4128)
 proba :  0.011406215
(2210.2756, 646.7535, 2617.3347, 1202.6609)
 c : toit
 list_crops.shape (51, 5)
 c : vitre
 list_crops.shape (47, 5)
 proba :  0.17965636
(2201.5273, 706.0793, 2505.8032, 1225.5999)
 proba :  0.1370792
(2332.1963, 1420.2556, 2614.772, 1932.4656)
 proba :  0.092068754
(2941.328, 639.2339, 3238.2415, 1233.3285)
 proba :  0.047084823
(321.5064, 101.23309, 604.4536, 480.10095)
 proba :  0.041127656
(2545.2458, 540.1163, 2972.1326, 1045.7811)
 proba :  0.039572347
(2362.644, 1470.2217, 2901.4219, 2152.238)
 proba :  0.030482478
(2784.0974, 1531.0524, 3025.3796, 2079.116)
 proba :  0.017642656
(1748.7094, 547.4492, 2193.0366, 1162.627)
 proba :  0.015796063
(2879.6238, 1427.4043, 3199.1814, 2141.84)
 proba :  0.010809961
(2403.907, 706.2804, 2720.5051, 1277.5779)
 proba :  0.010698661
(2683.0664, 868.77826, 2997.1611, 1348.8726)
We are managing local photo_id
len de result frcnn : 6

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 3.439314126968384
time spend to save output : 0.00014328956604003906
total time spend for step 1 : 3.439457416534424

step2:crop_condition
Mon Sep 29 11:45:35 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139131_2886529_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg': 950003838, 'temp/1759139131_2886529_950003813_e28be02dfcce79cce594a390a9911a0a.jpg': 950003813, 'temp/1759139131_2886529_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg': 950003695, 'temp/1759139131_2886529_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg': 926687666, 'temp/1759139131_2886529_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg': 950003812, 'temp/1759139131_2886529_950003696_11e3a77b72af4b332d366d98984039c7.jpg': 950003696}
map_photo_id_path_extension : {950003838: {'path': 'temp/1759139131_2886529_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg', 'extension': 'jpg'}, 950003813: {'path': 'temp/1759139131_2886529_950003813_e28be02dfcce79cce594a390a9911a0a.jpg', 'extension': 'jpg'}, 950003695: {'path': 'temp/1759139131_2886529_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg', 'extension': 'jpg'}, 926687666: {'path': 'temp/1759139131_2886529_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg', 'extension': 'jpg'}, 950003812: {'path': 'temp/1759139131_2886529_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg', 'extension': 'jpg'}, 950003696: {'path': 'temp/1759139131_2886529_950003696_11e3a77b72af4b332d366d98984039c7.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
param_json : {'photo_hashtag_type': 757, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'feed_id_new_photos': 1981313, 'host': 'www.fotonower.com', 'filter': {'phare': {'margin_type': 'margin', 'margin_value': 300, 'feed_id_new_photos': 1097966}, 'aile-avant': {}}, 'crop_type': 'bib', 'margin_type': 'margin_relative', 'margin_value': [0.5, 0.1, 0.5, 0.1], 'min_score': 0.3}
 list_filenames in step crop :  ['temp/1759139131_2886529_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg', 'temp/1759139131_2886529_950003813_e28be02dfcce79cce594a390a9911a0a.jpg', 'temp/1759139131_2886529_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg', 'temp/1759139131_2886529_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg', 'temp/1759139131_2886529_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg', 'temp/1759139131_2886529_950003696_11e3a77b72af4b332d366d98984039c7.jpg']
Loading chi in step crop with photo_hashtag_type : 757
Loading chi in step crop for subpids : 6 !
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 950003838,950003813,950003695,926687666,950003812,950003696) and `type` in (757) 
Loaded 32 chid ids of type : 757
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1655713604,1655713607,1655713608,1655713612,1655713617,1655713619,1655713621,1655713623,1655713624,1655713628,1655713630,1655713631,1655713632,1655713634,1655713635,1655713639,1655713644,1655713646,1655713647,1655713648,1655713650,1655713651,1655713652,1655713655,1655713658,1655713659,1655713660,1655713662,1655713695,1655713696,1655713700,1655713701)
SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1655713604,1655713607,1655713608,1655713612,1655713617,1655713619,1655713621,1655713623,1655713624,1655713628,1655713630,1655713631,1655713632,1655713634,1655713635,1655713639,1655713644,1655713646,1655713647,1655713648,1655713650,1655713651,1655713652,1655713655,1655713658,1655713659,1655713660,1655713662,1655713695,1655713696,1655713700,1655713701)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1655713604,1655713607,1655713608,1655713612,1655713617,1655713619,1655713621,1655713623,1655713624,1655713628,1655713630,1655713631,1655713632,1655713634,1655713635,1655713639,1655713644,1655713646,1655713647,1655713648,1655713650,1655713651,1655713652,1655713655,1655713658,1655713659,1655713660,1655713662,1655713695,1655713696,1655713700,1655713701)
select photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type, id from MTRPhoto.photo_sub_photos  where photo_id in ( 950003838,950003813,950003695,926687666,950003812,950003696) 
select sub_photo_id, crop_hashtag_id from MTRPhoto.crop_sub_photo_ids where sub_photo_id in (971020109,971021726,971022383,1071808962,1071808969,1071808957,1071808960,1071808966) 
map_pid_crop from SQL : 
['[<mtr.simple_image_editor.image_utils.CHI object at 0x7f190c4d3790>]', '[<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515ba16a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515ba1b50>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515ba1160>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515ba1d30>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515ba1460>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515ba1520>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515ba17f0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515ba1550>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515ba1e50>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515ba1670>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515ba1640>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1515ba1ee0>]', '[<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515ba1eb0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f18a991f520>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f18a991fd30>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f18a991f3a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f18a991f3d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f18a991fd90>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f16a819a820>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f16a819a760>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f16a819a730>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f16a819a7f0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f167023e460>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f167023e190>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f167023e0d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f167023e040>]', '[<mtr.simple_image_editor.image_utils.CHI object at 0x7f167023e640>]', '[<mtr.simple_image_editor.image_utils.CHI object at 0x7f167023e520>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f167023e310>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f167023e130>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f167023e0a0>]']

begin to crop the class : phare
param for this class : {'margin_type': 'margin', 'margin_value': 300, 'feed_id_new_photos': 1097966}
filtre for class : phare 
hashtag_id of this class : 492624020
select * from
(select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge,
IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage
from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on
chi.id =css.crop_hashtag_id
where type = 757 and photo_id in (950003838,950003813,950003695,926687666,950003812,950003696) and hashtag_id = 492624020) as a 
where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 
and coverage >= 0 and surface_rectangle >= 0;
        
chi_id interessant : [1655713621]
chi_id interessant : [1655713621, 1655713648]
chi_id interessant : [1655713621, 1655713648, 1655713647]
WARNING : margin is only used for type bib !

type of cropped photo chosen : bib
we resize croppped photo by 1 on x axis and by 1 on y axis
 new_file_path_bib_crop : temp/1759139131_2886529_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713621_0.jpg
 new_file_path_bib_crop : temp/1759139131_2886529_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713647_0.jpg
 new_file_path_bib_crop : temp/1759139131_2886529_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713648_0.jpg
 map_result returned by crop_photo_return_map_crop : length : 3
map_result after crop : {1655713621: {'crop': 'temp/1759139131_2886529_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713621_0.jpg', 'photo_id': 926687666, 'sub_photo_id': 1071808962, 'coordonates': (326, 477, 251, 312), 'sub_photo_infos': (26, 640, 0, 480, 1, 1), 'same_chi': True}, 1655713647: {'crop': 'temp/1759139131_2886529_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713647_0.jpg', 'photo_id': 950003812, 'sub_photo_id': 1071808957, 'coordonates': (318, 489, 264, 310), 'sub_photo_infos': (18, 614, 0, 480, 1, 1), 'same_chi': True}, 1655713648: {'crop': 'temp/1759139131_2886529_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713648_0.jpg', 'photo_id': 950003812, 'sub_photo_id': 1071808960, 'coordonates': (261, 408, 234, 331), 'sub_photo_infos': (0, 614, 0, 480, 1, 1), 'same_chi': True}}
 About to insert : list_path_to_insert length 0 new photo from crops !

About to upload 0 photos
WARNING : list_path_to_insert is empty, cannot upload !
map_result_insert : {}
insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : []
insert ignore into MTRPhoto.photo_sub_photos (photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s) : []
map of cropped photos with some data : 
{1071808962: [926687666, 'temp/1759139131_2886529_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713621_0.jpg', (326, 477, 251, 312)], 1071808957: [950003812, 'temp/1759139131_2886529_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713647_0.jpg', (318, 489, 264, 310)], 1071808960: [950003812, 'temp/1759139131_2886529_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713648_0.jpg', (261, 408, 234, 331)]}
we have finished the crop for the class : phare



begin to crop the class : aile-avant
param for this class : {}
filtre for class : aile-avant 
hashtag_id of this class : 2106233860
select * from
(select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge,
IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage
from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on
chi.id =css.crop_hashtag_id
where type = 757 and photo_id in (950003838,950003813,950003695,926687666,950003812,950003696) and hashtag_id = 2106233860) as a 
where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 
and coverage >= 0 and surface_rectangle >= 0;
        
chi_id interessant : [1655713607]
chi_id interessant : [1655713607, 1655713634]
WARNING : margin is only used for type bib !

type of cropped photo chosen : bib
we resize croppped photo by 1 on x axis and by 1 on y axis
 new_file_path_bib_crop : temp/1759139131_2886529_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713607_0.jpg
now we use margin_relative for the photo_id : 926687666
 new_file_path_bib_crop : temp/1759139131_2886529_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713634_0.jpg
now we use margin_relative for the photo_id : 950003812
 map_result returned by crop_photo_return_map_crop : length : 2
map_result after crop : {1655713607: {'crop': 'temp/1759139131_2886529_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713607_0.jpg', 'photo_id': 926687666, 'sub_photo_id': 1071808969, 'coordonates': (161, 330, 149, 343), 'sub_photo_infos': (64, 349, 65, 359, 1, 1), 'same_chi': True}, 1655713634: {'crop': 'temp/1759139131_2886529_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713634_0.jpg', 'photo_id': 950003812, 'sub_photo_id': 1071808966, 'coordonates': (133, 305, 146, 344), 'sub_photo_infos': (34, 324, 60, 361, 1, 1), 'same_chi': True}}
 About to insert : list_path_to_insert length 0 new photo from crops !

About to upload 0 photos
WARNING : list_path_to_insert is empty, cannot upload !
map_result_insert : {}
insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : []
insert ignore into MTRPhoto.photo_sub_photos (photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s) : []
map of cropped photos with some data : 
{1071808969: [926687666, 'temp/1759139131_2886529_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713607_0.jpg', (161, 330, 149, 343)], 1071808966: [950003812, 'temp/1759139131_2886529_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713634_0.jpg', (133, 305, 146, 344)]}
we have finished the crop for the class : aile-avant


map of total cropped photos with some data : 
{1071808962: [926687666, 'temp/1759139131_2886529_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713621_0.jpg', (326, 477, 251, 312)], 1071808957: [950003812, 'temp/1759139131_2886529_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713647_0.jpg', (318, 489, 264, 310)], 1071808960: [950003812, 'temp/1759139131_2886529_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713648_0.jpg', (261, 408, 234, 331)], 1071808969: [926687666, 'temp/1759139131_2886529_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713607_0.jpg', (161, 330, 149, 343)], 1071808966: [950003812, 'temp/1759139131_2886529_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713634_0.jpg', (133, 305, 146, 344)]}

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 0.5303375720977783
time spend to save output : 9.107589721679688e-05
total time spend for step 2 : 0.5304286479949951

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 0
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 2
 output : {1071808962: [926687666, 'temp/1759139131_2886529_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713621_0.jpg', (326, 477, 251, 312)], 1071808957: [950003812, 'temp/1759139131_2886529_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713647_0.jpg', (318, 489, 264, 310)], 1071808960: [950003812, 'temp/1759139131_2886529_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713648_0.jpg', (261, 408, 234, 331)], 1071808969: [926687666, 'temp/1759139131_2886529_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713607_0.jpg', (161, 330, 149, 343)], 1071808966: [950003812, 'temp/1759139131_2886529_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713634_0.jpg', (133, 305, 146, 344)]}

############################### TEST image_blanchir  ################################
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1818

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=1818
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 1818
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=1818
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : image_blanchir
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (990111206) 
 Found this number of photos: 1
##### Call download_photos : nb_thread : 5
begin to download photo : 990111206
download finish for photo 990111206
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.28084468841552734
#### fin chargement data
Blocking on flush ?
No conitnuing 
About to test input to load 
we should then remove the video here, and this would fix the bug of datou_current ! 
WARNING : we have an input that is not a photo, we should get rid of it
Calling datou_exec

Inside datou_exec : verbose : True
number of steps :  1
step1:image_blanchir
Mon Sep 29 11:45:35 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139135_2886529_990111206_7ca22c7e68dd0a10509c7987af0cf549.png': 990111206}
map_photo_id_path_extension : {990111206: {'path': 'temp/1759139135_2886529_990111206_7ca22c7e68dd0a10509c7987af0cf549.png', 'extension': 'png'}}
map_subphoto_mainphoto : {}
inside step blanchir_image
 Status : 404
 Content : b'<!doctype html>\n<html lang=en>\n<title>404 Not Found</title>\n<h1>Not Found</h1>\n<p>The requested URL was not found on the server. If you entered the URL manually please check your spelling and try again.</p>\n'
 All Response : <Response [404]>
 WARNING : NO PORTFOLIO CREATED portfolio_id 0 used instead of new portfolio
feed_id_new_photos:0
treat image : temp/1759139135_2886529_990111206_7ca22c7e68dd0a10509c7987af0cf549.png
blanchir func
in upload media
 Upload medias :  ['temp/1759139135_2886529_990111206_7ca22c7e68dd0a10509c7987af0cf549.png'] : url : https://www.fotonower.com/api/v1/secured/photo/upload?token=78d09a0790ec6ecbf119343125a81fdc&datou=0
temp/1759139135_2886529_990111206_7ca22c7e68dd0a10509c7987af0cf549.png
after data_to_send, before sending request
after request
<Response [200]>
b'{"map_files_photo_id": {"file0": 1386782172}}'
Result OK !

After datou_step_exec type output : <class 'list'>

time spend for datou_step_exec : 1.3394501209259033
time spend to save output : 1.811981201171875e-05
total time spend for step 1 : 1.339468240737915

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
sauvegarde pour la step blanchir_image


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 1
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [[1818, 0, 990111206, 1, 1, 1, None, 1, None]]
time used for this insertion : 0.04015469551086426

save missing photos in datou_result :
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : [(990111206, 1386782172, 0, 300, 0, 381, 1, 1, 'blanc')]
[(990111206, 1386782172, 0, 300, 0, 381, 1, 1, 'blanc')]
list index out of range
ERROR image_blanchir FAILED

############################### TEST darker_image  ################################
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2085

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=2085
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 2085
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=2085
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : darker_image
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (2077915) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (2077915)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 989962950, 'portfolio_id': 2077915}]
 map_portfolio_id_photo_id: {2077915: [989962950]}
##### Call download_photos : nb_thread : 5
begin to download photo : 989962950
download finish for photo 989962950
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.22981739044189453
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  1
step1:darker_image
Mon Sep 29 11:45:37 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139137_2886529_989962950_4d2e56be59e275c3d57b085a836be0ba.jpg': 989962950}
map_photo_id_path_extension : {989962950: {'path': 'temp/1759139137_2886529_989962950_4d2e56be59e275c3d57b085a836be0ba.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
dans la step darker
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 989962950) and `type` in (2228) 
Loaded 7 chid ids of type : 2228
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1753484977,1753484978,1753484979,1753484980,1753484981,1753484982,1753484983)
+WARNING : Unexpected points, we should remove this data for chi_id : 1753484977, for now we just ignore these empty polygon points 
+WARNING : Unexpected points, we should remove this data for chi_id : 1753484978, for now we just ignore these empty polygon points 
+WARNING : Unexpected points, we should remove this data for chi_id : 1753484979, for now we just ignore these empty polygon points 
+WARNING : Unexpected points, we should remove this data for chi_id : 1753484980, for now we just ignore these empty polygon points 
+WARNING : Unexpected points, we should remove this data for chi_id : 1753484981, for now we just ignore these empty polygon points 
+WARNING : Unexpected points, we should remove this data for chi_id : 1753484982, for now we just ignore these empty polygon points 
+WARNING : Unexpected points, we should remove this data for chi_id : 1753484983, for now we just ignore these empty polygon points 
SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1753484977,1753484978,1753484979,1753484980,1753484981,1753484982,1753484983)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1753484977,1753484978,1753484979,1753484980,1753484981,1753484982,1753484983)
treat image : temp/1759139137_2886529_989962950_4d2e56be59e275c3d57b085a836be0ba.jpg
in upload media
 Upload medias :  ['temp/1759139137_2886529_989962950_4d2e56be59e275c3d57b085a836be0badarker.jpg'] : url : https://www.fotonower.com/api/v1/secured/photo/upload?token=78d09a0790ec6ecbf119343125a81fdc&datou=0
temp/1759139137_2886529_989962950_4d2e56be59e275c3d57b085a836be0badarker.jpg
after data_to_send, before sending request
after request
<Response [200]>
b'{"map_files_photo_id": {"file0": 1386782173}}'
Result OK !
list chi to save
[<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515c51be0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f18a991f3a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f18a991fd90>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f18a991f3d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f167023e460>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f167023e190>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f167023e610>]
insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s)
batch 1
Loaded 0 chid ids of type : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 492774966 on photo_id : 1386782173
ERROR missing MTRPhoto.crop_hashtag_ids : 492774966 on photo_id : 1386782173
ERROR missing MTRPhoto.crop_hashtag_ids : 492725882 on photo_id : 1386782173
ERROR missing MTRPhoto.crop_hashtag_ids : 492725882 on photo_id : 1386782173
ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1386782173
ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1386782173
ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1386782173
Number RLEs to save : 0
 TO DO : save crop sub photo not yet done ! 
crops sauvegardes

After datou_step_exec type output : <class 'list'>

time spend for datou_step_exec : 2.649217128753662
time spend to save output : 4.553794860839844e-05
total time spend for step 1 : 2.6492626667022705

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
sauvegarde pour la step blanchir_image


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 1
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [[2085, 0, 989962950, 1, 1, 1, None, 1, None]]
time used for this insertion : 0.03862476348876953

save missing photos in datou_result :
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : [(989962950, 1386782173, 0, 897, 0, 1431, 1, 1, 'darker')]
[(989962950, 1386782173, 0, 897, 0, 1431, 1, 1, 'darker')]
list index out of range
ERROR darker_image FAILED

############################### TEST img_aug  ################################
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2041

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=2041
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 2041
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=2041
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : data_aug
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (2077915) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (2077915)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 989962950, 'portfolio_id': 2077915}]
 map_portfolio_id_photo_id: {2077915: [989962950]}
##### Call download_photos : nb_thread : 5
begin to download photo : 989962950
download finish for photo 989962950
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.16179656982421875
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  1
step1:data_aug
Mon Sep 29 11:45:40 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139140_2886529_989962950_4d2e56be59e275c3d57b085a836be0ba.jpg': 989962950}
map_photo_id_path_extension : {989962950: {'path': 'temp/1759139140_2886529_989962950_4d2e56be59e275c3d57b085a836be0ba.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
numpy.version est ancienne, on utilise l'ancien bit generator
numpy.version est ancienne, on utilise l'ancien bit generator
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 989962950) and `type` in (2228) 
Loaded 7 chid ids of type : 2228
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1753484977,1753484978,1753484979,1753484980,1753484981,1753484982,1753484983)
+WARNING : Unexpected points, we should remove this data for chi_id : 1753484977, for now we just ignore these empty polygon points 
+WARNING : Unexpected points, we should remove this data for chi_id : 1753484978, for now we just ignore these empty polygon points 
+WARNING : Unexpected points, we should remove this data for chi_id : 1753484979, for now we just ignore these empty polygon points 
+WARNING : Unexpected points, we should remove this data for chi_id : 1753484980, for now we just ignore these empty polygon points 
+WARNING : Unexpected points, we should remove this data for chi_id : 1753484981, for now we just ignore these empty polygon points 
+WARNING : Unexpected points, we should remove this data for chi_id : 1753484982, for now we just ignore these empty polygon points 
+WARNING : Unexpected points, we should remove this data for chi_id : 1753484983, for now we just ignore these empty polygon points 
SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1753484977,1753484978,1753484979,1753484980,1753484981,1753484982,1753484983)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1753484977,1753484978,1753484979,1753484980,1753484981,1753484982,1753484983)


on traite des points
in upload media
 Upload medias :  ['temp/1759139140_2886529_989962950_4d2e56be59e275c3d57b085a836be0ba_aug.jpg'] : url : https://www.fotonower.com/api/v1/secured/photo/upload?token=7ad776945df9e5335881f03fafdabb27&datou=0
temp/1759139140_2886529_989962950_4d2e56be59e275c3d57b085a836be0ba_aug.jpg
after data_to_send, before sending request
after request
<Response [200]>
b'{"map_files_photo_id": {"file0": 1386782174}}'
Result OK !
insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s)
batch 1
Loaded 0 chid ids of type : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 492774966 on photo_id : 1386782174
ERROR missing MTRPhoto.crop_hashtag_ids : 492774966 on photo_id : 1386782174
ERROR missing MTRPhoto.crop_hashtag_ids : 492725882 on photo_id : 1386782174
ERROR missing MTRPhoto.crop_hashtag_ids : 492725882 on photo_id : 1386782174
ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1386782174
ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1386782174
ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1386782174
Number RLEs to save : 0
 TO DO : save crop sub photo not yet done ! 
photo_uploade
augmentation faite pour la photo : 989962950

After datou_step_exec type output : <class 'list'>

time spend for datou_step_exec : 1.5657756328582764
time spend to save output : 4.100799560546875e-05
total time spend for step 1 : 1.5658166408538818

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
sauvegarde pour la step blanchir_image


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 1
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [[2041, 0, 989962950, 1, 1, 1, None, 1, None]]
time used for this insertion : 0.0389552116394043

save missing photos in datou_result :
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : [(989962950, 1386782174, 0, 1431, 0, 897, 1, 1, 'img_aug')]
[(989962950, 1386782174, 0, 1431, 0, 897, 1, 1, 'img_aug')]
list index out of range
ERROR img_aug FAILED

############################### TEST rubbia  ################################
warning , we can't find thcl infos in json_data
warning , we can't find pdt infos in json_data
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=3789

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=3789
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 3789
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=3789
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : split_time_score
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (4599398) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (4599398)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 1049318362, 'portfolio_id': 4599398}, {'photo_id': 1049318360, 'portfolio_id': 4599398}, {'photo_id': 1049318358, 'portfolio_id': 4599398}, {'photo_id': 1049318356, 'portfolio_id': 4599398}, {'photo_id': 1049318342, 'portfolio_id': 4599398}, {'photo_id': 1049318339, 'portfolio_id': 4599398}, {'photo_id': 1049318337, 'portfolio_id': 4599398}, {'photo_id': 1049318311, 'portfolio_id': 4599398}, {'photo_id': 1049318310, 'portfolio_id': 4599398}, {'photo_id': 1049318309, 'portfolio_id': 4599398}, {'photo_id': 1049318294, 'portfolio_id': 4599398}, {'photo_id': 1049318293, 'portfolio_id': 4599398}, {'photo_id': 1049318291, 'portfolio_id': 4599398}, {'photo_id': 1049318289, 'portfolio_id': 4599398}, {'photo_id': 1049318288, 'portfolio_id': 4599398}, {'photo_id': 1049318287, 'portfolio_id': 4599398}, {'photo_id': 1049318279, 'portfolio_id': 4599398}, {'photo_id': 1049318276, 'portfolio_id': 4599398}, {'photo_id': 1049318273, 'portfolio_id': 4599398}, {'photo_id': 1049318271, 'portfolio_id': 4599398}, {'photo_id': 1049318268, 'portfolio_id': 4599398}, {'photo_id': 1049318265, 'portfolio_id': 4599398}, {'photo_id': 1049318260, 'portfolio_id': 4599398}, {'photo_id': 1049318257, 'portfolio_id': 4599398}, {'photo_id': 1049318253, 'portfolio_id': 4599398}, {'photo_id': 1049318250, 'portfolio_id': 4599398}, {'photo_id': 1049318247, 'portfolio_id': 4599398}, {'photo_id': 1049318246, 'portfolio_id': 4599398}, {'photo_id': 1049318222, 'portfolio_id': 4599398}, {'photo_id': 1049318219, 'portfolio_id': 4599398}, {'photo_id': 1049318216, 'portfolio_id': 4599398}, {'photo_id': 1049318214, 'portfolio_id': 4599398}, {'photo_id': 1049318213, 'portfolio_id': 4599398}, {'photo_id': 1049318212, 'portfolio_id': 4599398}, {'photo_id': 1049317554, 'portfolio_id': 4599398}, {'photo_id': 1049317551, 'portfolio_id': 4599398}, {'photo_id': 1049317549, 'portfolio_id': 4599398}, {'photo_id': 1049317546, 'portfolio_id': 4599398}, {'photo_id': 1049317542, 'portfolio_id': 4599398}, {'photo_id': 1049317536, 'portfolio_id': 4599398}, {'photo_id': 1049317526, 'portfolio_id': 4599398}, {'photo_id': 1049317525, 'portfolio_id': 4599398}, {'photo_id': 1049317524, 'portfolio_id': 4599398}, {'photo_id': 1049317522, 'portfolio_id': 4599398}, {'photo_id': 1049317520, 'portfolio_id': 4599398}, {'photo_id': 1049317517, 'portfolio_id': 4599398}, {'photo_id': 1049317497, 'portfolio_id': 4599398}, {'photo_id': 1049317493, 'portfolio_id': 4599398}, {'photo_id': 1049317491, 'portfolio_id': 4599398}, {'photo_id': 1049317489, 'portfolio_id': 4599398}, {'photo_id': 1049317487, 'portfolio_id': 4599398}, {'photo_id': 1049317485, 'portfolio_id': 4599398}, {'photo_id': 1049317468, 'portfolio_id': 4599398}, {'photo_id': 1049317461, 'portfolio_id': 4599398}, {'photo_id': 1049317457, 'portfolio_id': 4599398}, {'photo_id': 1049317453, 'portfolio_id': 4599398}, {'photo_id': 1049317444, 'portfolio_id': 4599398}, {'photo_id': 1049317440, 'portfolio_id': 4599398}, {'photo_id': 1049317359, 'portfolio_id': 4599398}, {'photo_id': 1049317333, 'portfolio_id': 4599398}, {'photo_id': 1049317282, 'portfolio_id': 4599398}, {'photo_id': 1049317225, 'portfolio_id': 4599398}, {'photo_id': 1049317210, 'portfolio_id': 4599398}, {'photo_id': 1049317197, 'portfolio_id': 4599398}, {'photo_id': 1049316790, 'portfolio_id': 4599398}, {'photo_id': 1049316785, 'portfolio_id': 4599398}, {'photo_id': 1049316782, 'portfolio_id': 4599398}, {'photo_id': 1049316778, 'portfolio_id': 4599398}, {'photo_id': 1049316752, 'portfolio_id': 4599398}, {'photo_id': 1049316749, 'portfolio_id': 4599398}, {'photo_id': 1049316610, 'portfolio_id': 4599398}, {'photo_id': 1049316600, 'portfolio_id': 4599398}, {'photo_id': 1049316597, 'portfolio_id': 4599398}, {'photo_id': 1049316594, 'portfolio_id': 4599398}, {'photo_id': 1049316588, 'portfolio_id': 4599398}, {'photo_id': 1049316582, 'portfolio_id': 4599398}, {'photo_id': 1049316545, 'portfolio_id': 4599398}, {'photo_id': 1049316543, 'portfolio_id': 4599398}, {'photo_id': 1049316540, 'portfolio_id': 4599398}, {'photo_id': 1049316537, 'portfolio_id': 4599398}, {'photo_id': 1049316534, 'portfolio_id': 4599398}, {'photo_id': 1049316520, 'portfolio_id': 4599398}, {'photo_id': 1049316338, 'portfolio_id': 4599398}, {'photo_id': 1049316336, 'portfolio_id': 4599398}, {'photo_id': 1049316332, 'portfolio_id': 4599398}, {'photo_id': 1049316331, 'portfolio_id': 4599398}, {'photo_id': 1049316257, 'portfolio_id': 4599398}, {'photo_id': 1049316255, 'portfolio_id': 4599398}, {'photo_id': 1049316222, 'portfolio_id': 4599398}, {'photo_id': 1049316216, 'portfolio_id': 4599398}, {'photo_id': 1049316214, 'portfolio_id': 4599398}, {'photo_id': 1049316212, 'portfolio_id': 4599398}, {'photo_id': 1049316210, 'portfolio_id': 4599398}, {'photo_id': 1049316209, 'portfolio_id': 4599398}, {'photo_id': 1049313025, 'portfolio_id': 4599398}, {'photo_id': 1049312984, 'portfolio_id': 4599398}, {'photo_id': 1049312803, 'portfolio_id': 4599398}, {'photo_id': 1049312588, 'portfolio_id': 4599398}, {'photo_id': 1049312585, 'portfolio_id': 4599398}, {'photo_id': 1049312583, 'portfolio_id': 4599398}, {'photo_id': 1049312579, 'portfolio_id': 4599398}, {'photo_id': 1049312574, 'portfolio_id': 4599398}, {'photo_id': 1049312573, 'portfolio_id': 4599398}, {'photo_id': 1049312571, 'portfolio_id': 4599398}, {'photo_id': 1049312568, 'portfolio_id': 4599398}, {'photo_id': 1049312566, 'portfolio_id': 4599398}, {'photo_id': 1049312562, 'portfolio_id': 4599398}, {'photo_id': 1049312556, 'portfolio_id': 4599398}, {'photo_id': 1049312508, 'portfolio_id': 4599398}, {'photo_id': 1049312489, 'portfolio_id': 4599398}, {'photo_id': 1049312488, 'portfolio_id': 4599398}, {'photo_id': 1049312487, 'portfolio_id': 4599398}, {'photo_id': 1049312485, 'portfolio_id': 4599398}, {'photo_id': 1049312484, 'portfolio_id': 4599398}, {'photo_id': 1049312464, 'portfolio_id': 4599398}, {'photo_id': 1049312463, 'portfolio_id': 4599398}, {'photo_id': 1049312462, 'portfolio_id': 4599398}, {'photo_id': 1049312461, 'portfolio_id': 4599398}, {'photo_id': 1049312460, 'portfolio_id': 4599398}, {'photo_id': 1049312449, 'portfolio_id': 4599398}, {'photo_id': 1049312445, 'portfolio_id': 4599398}, {'photo_id': 1049312444, 'portfolio_id': 4599398}, {'photo_id': 1049312442, 'portfolio_id': 4599398}, {'photo_id': 1049312440, 'portfolio_id': 4599398}, {'photo_id': 1049312438, 'portfolio_id': 4599398}, {'photo_id': 1049312429, 'portfolio_id': 4599398}, {'photo_id': 1049312426, 'portfolio_id': 4599398}, {'photo_id': 1049312424, 'portfolio_id': 4599398}, {'photo_id': 1049312422, 'portfolio_id': 4599398}, {'photo_id': 1049312420, 'portfolio_id': 4599398}, {'photo_id': 1049312409, 'portfolio_id': 4599398}, {'photo_id': 1049312406, 'portfolio_id': 4599398}, {'photo_id': 1049312404, 'portfolio_id': 4599398}, {'photo_id': 1049312363, 'portfolio_id': 4599398}, {'photo_id': 1049312208, 'portfolio_id': 4599398}, {'photo_id': 1049311964, 'portfolio_id': 4599398}, {'photo_id': 1049311963, 'portfolio_id': 4599398}, {'photo_id': 1049311962, 'portfolio_id': 4599398}, {'photo_id': 1049311961, 'portfolio_id': 4599398}, {'photo_id': 1049311960, 'portfolio_id': 4599398}, {'photo_id': 1049311943, 'portfolio_id': 4599398}, {'photo_id': 1049311938, 'portfolio_id': 4599398}, {'photo_id': 1049311937, 'portfolio_id': 4599398}, {'photo_id': 1049311935, 'portfolio_id': 4599398}, {'photo_id': 1049311934, 'portfolio_id': 4599398}, {'photo_id': 1049311932, 'portfolio_id': 4599398}, {'photo_id': 1049311795, 'portfolio_id': 4599398}, {'photo_id': 1049311793, 'portfolio_id': 4599398}, {'photo_id': 1049311791, 'portfolio_id': 4599398}, {'photo_id': 1049311771, 'portfolio_id': 4599398}, {'photo_id': 1049311767, 'portfolio_id': 4599398}, {'photo_id': 1049311267, 'portfolio_id': 4599398}, {'photo_id': 1049311266, 'portfolio_id': 4599398}, {'photo_id': 1049311263, 'portfolio_id': 4599398}, {'photo_id': 1049311252, 'portfolio_id': 4599398}, {'photo_id': 1049311199, 'portfolio_id': 4599398}, {'photo_id': 1049311136, 'portfolio_id': 4599398}, {'photo_id': 1049311073, 'portfolio_id': 4599398}, {'photo_id': 1049311009, 'portfolio_id': 4599398}, {'photo_id': 1049311006, 'portfolio_id': 4599398}, {'photo_id': 1049310994, 'portfolio_id': 4599398}, {'photo_id': 1049310992, 'portfolio_id': 4599398}, {'photo_id': 1049310991, 'portfolio_id': 4599398}, {'photo_id': 1049310984, 'portfolio_id': 4599398}, {'photo_id': 1049310982, 'portfolio_id': 4599398}, {'photo_id': 1049310981, 'portfolio_id': 4599398}, {'photo_id': 1049310919, 'portfolio_id': 4599398}, {'photo_id': 1049310914, 'portfolio_id': 4599398}, {'photo_id': 1049310911, 'portfolio_id': 4599398}, {'photo_id': 1049310909, 'portfolio_id': 4599398}, {'photo_id': 1049310907, 'portfolio_id': 4599398}, {'photo_id': 1049310905, 'portfolio_id': 4599398}, {'photo_id': 1049310165, 'portfolio_id': 4599398}, {'photo_id': 1049310162, 'portfolio_id': 4599398}, {'photo_id': 1049310159, 'portfolio_id': 4599398}, {'photo_id': 1049310145, 'portfolio_id': 4599398}, {'photo_id': 1049310141, 'portfolio_id': 4599398}, {'photo_id': 1049310139, 'portfolio_id': 4599398}, {'photo_id': 1049310138, 'portfolio_id': 4599398}, {'photo_id': 1049310134, 'portfolio_id': 4599398}, {'photo_id': 1049310132, 'portfolio_id': 4599398}, {'photo_id': 1049309737, 'portfolio_id': 4599398}, {'photo_id': 1049309734, 'portfolio_id': 4599398}, {'photo_id': 1049309732, 'portfolio_id': 4599398}, {'photo_id': 1049309706, 'portfolio_id': 4599398}, {'photo_id': 1049309703, 'portfolio_id': 4599398}, {'photo_id': 1049309701, 'portfolio_id': 4599398}, {'photo_id': 1049309686, 'portfolio_id': 4599398}, {'photo_id': 1049309681, 'portfolio_id': 4599398}, {'photo_id': 1049309677, 'portfolio_id': 4599398}, {'photo_id': 1049309675, 'portfolio_id': 4599398}, {'photo_id': 1049309672, 'portfolio_id': 4599398}, {'photo_id': 1049309670, 'portfolio_id': 4599398}, {'photo_id': 1049309658, 'portfolio_id': 4599398}, {'photo_id': 1049309657, 'portfolio_id': 4599398}, {'photo_id': 1049309656, 'portfolio_id': 4599398}, {'photo_id': 1049309655, 'portfolio_id': 4599398}, {'photo_id': 1049309653, 'portfolio_id': 4599398}, {'photo_id': 1049309651, 'portfolio_id': 4599398}, {'photo_id': 1049309605, 'portfolio_id': 4599398}, {'photo_id': 1049309603, 'portfolio_id': 4599398}, {'photo_id': 1049309599, 'portfolio_id': 4599398}, {'photo_id': 1049309597, 'portfolio_id': 4599398}, {'photo_id': 1049309595, 'portfolio_id': 4599398}, {'photo_id': 1049309592, 'portfolio_id': 4599398}, {'photo_id': 1049309385, 'portfolio_id': 4599398}, {'photo_id': 1049309383, 'portfolio_id': 4599398}, {'photo_id': 1049309382, 'portfolio_id': 4599398}, {'photo_id': 1049309381, 'portfolio_id': 4599398}, {'photo_id': 1049309380, 'portfolio_id': 4599398}, {'photo_id': 1049309379, 'portfolio_id': 4599398}, {'photo_id': 1049309345, 'portfolio_id': 4599398}, {'photo_id': 1049308384, 'portfolio_id': 4599398}, {'photo_id': 1049308381, 'portfolio_id': 4599398}, {'photo_id': 1049308376, 'portfolio_id': 4599398}, {'photo_id': 1049308280, 'portfolio_id': 4599398}, {'photo_id': 1049308276, 'portfolio_id': 4599398}, {'photo_id': 1049308275, 'portfolio_id': 4599398}, {'photo_id': 1049308235, 'portfolio_id': 4599398}, {'photo_id': 1049307693, 'portfolio_id': 4599398}, {'photo_id': 1049306823, 'portfolio_id': 4599398}, {'photo_id': 1049306804, 'portfolio_id': 4599398}, {'photo_id': 1049306792, 'portfolio_id': 4599398}, {'photo_id': 1049306791, 'portfolio_id': 4599398}, {'photo_id': 1049306635, 'portfolio_id': 4599398}, {'photo_id': 1049306205, 'portfolio_id': 4599398}, {'photo_id': 1049304810, 'portfolio_id': 4599398}, {'photo_id': 1049303925, 'portfolio_id': 4599398}, {'photo_id': 1049296996, 'portfolio_id': 4599398}, {'photo_id': 1049296121, 'portfolio_id': 4599398}, {'photo_id': 1049294990, 'portfolio_id': 4599398}, {'photo_id': 1049293230, 'portfolio_id': 4599398}]
 map_portfolio_id_photo_id: {4599398: [1049318362, 1049318360, 1049318358, 1049318356, 1049318342, 1049318339, 1049318337, 1049318311, 1049318310, 1049318309, 1049318294, 1049318293, 1049318291, 1049318289, 1049318288, 1049318287, 1049318279, 1049318276, 1049318273, 1049318271, 1049318268, 1049318265, 1049318260, 1049318257, 1049318253, 1049318250, 1049318247, 1049318246, 1049318222, 1049318219, 1049318216, 1049318214, 1049318213, 1049318212, 1049317554, 1049317551, 1049317549, 1049317546, 1049317542, 1049317536, 1049317526, 1049317525, 1049317524, 1049317522, 1049317520, 1049317517, 1049317497, 1049317493, 1049317491, 1049317489, 1049317487, 1049317485, 1049317468, 1049317461, 1049317457, 1049317453, 1049317444, 1049317440, 1049317359, 1049317333, 1049317282, 1049317225, 1049317210, 1049317197, 1049316790, 1049316785, 1049316782, 1049316778, 1049316752, 1049316749, 1049316610, 1049316600, 1049316597, 1049316594, 1049316588, 1049316582, 1049316545, 1049316543, 1049316540, 1049316537, 1049316534, 1049316520, 1049316338, 1049316336, 1049316332, 1049316331, 1049316257, 1049316255, 1049316222, 1049316216, 1049316214, 1049316212, 1049316210, 1049316209, 1049313025, 1049312984, 1049312803, 1049312588, 1049312585, 1049312583, 1049312579, 1049312574, 1049312573, 1049312571, 1049312568, 1049312566, 1049312562, 1049312556, 1049312508, 1049312489, 1049312488, 1049312487, 1049312485, 1049312484, 1049312464, 1049312463, 1049312462, 1049312461, 1049312460, 1049312449, 1049312445, 1049312444, 1049312442, 1049312440, 1049312438, 1049312429, 1049312426, 1049312424, 1049312422, 1049312420, 1049312409, 1049312406, 1049312404, 1049312363, 1049312208, 1049311964, 1049311963, 1049311962, 1049311961, 1049311960, 1049311943, 1049311938, 1049311937, 1049311935, 1049311934, 1049311932, 1049311795, 1049311793, 1049311791, 1049311771, 1049311767, 1049311267, 1049311266, 1049311263, 1049311252, 1049311199, 1049311136, 1049311073, 1049311009, 1049311006, 1049310994, 1049310992, 1049310991, 1049310984, 1049310982, 1049310981, 1049310919, 1049310914, 1049310911, 1049310909, 1049310907, 1049310905, 1049310165, 1049310162, 1049310159, 1049310145, 1049310141, 1049310139, 1049310138, 1049310134, 1049310132, 1049309737, 1049309734, 1049309732, 1049309706, 1049309703, 1049309701, 1049309686, 1049309681, 1049309677, 1049309675, 1049309672, 1049309670, 1049309658, 1049309657, 1049309656, 1049309655, 1049309653, 1049309651, 1049309605, 1049309603, 1049309599, 1049309597, 1049309595, 1049309592, 1049309385, 1049309383, 1049309382, 1049309381, 1049309380, 1049309379, 1049309345, 1049308384, 1049308381, 1049308376, 1049308280, 1049308276, 1049308275, 1049308235, 1049307693, 1049306823, 1049306804, 1049306792, 1049306791, 1049306635, 1049306205, 1049304810, 1049303925, 1049296996, 1049296121, 1049294990, 1049293230]}
##### Call download_photos : nb_thread : 5
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
##### After load_data_input 
time to download the photos : 0.1815495491027832
#### fin chargement data
Blocking on flush ?
No conitnuing 
About to test input to load 
Calling datou_exec

Inside datou_exec : verbose : True
we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save
number of steps :  1
step1:split_time_score
Mon Sep 29 11:45:42 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {}
map_photo_id_path_extension : {}
map_subphoto_mainphoto : {}
begin split time score
2022-04-13 10:29:59 0
SELECT app_name, token FROM MTRUser.mtr_app_api_token WHERE mtr_user_id=739 AND app_name="token_split_time_score" AND expire_at > NOW() 
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'}]
select SUBSTRING(ph.text,16,2) as h, count(*) from MTRUser.mtr_portfolio_photos mpp inner join MTRBack.photos_view ph on mpp.mtr_photo_id = ph.photo_id where mpp.mtr_portfolio_id = 4599398 group by h
(('05', 2), ('07', 25), ('06', 1), ('08', 96), ('09', 44), ('10', 64))

            SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4599398  AND mpp.hide_status=0  ORDER BY mpp.order  LIMIT 0, 100000
SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id
FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1
where mpp1.mtr_photo_id=cps.photo_id
AND h.hashtag_id=cps.hashtag_id
AND mpp1.hide_status=0
AND mpp1.mtr_portfolio_id in (4599398)  AND cps.thcl in (861) order by cps.score desc LIMIT 0, 100000
SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id
FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1
where mpp1.mtr_photo_id=cps.photo_id
AND h.hashtag_id=cps.hashtag_id
AND mpp1.hide_status=0
AND mpp1.mtr_portfolio_id in (4599398)  AND cps.thcl in (758) order by cps.score desc LIMIT 0, 100000
ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {1: 188, 2: 36, 3: 8}
07092021 4599398 Nombre de photos uploadées : 232 / 23040 (1%)
07092021 4599398 Nombre de photos taguées (types de déchets): 232 / 232 (100%)
07092021 4599398 Nombre de photos taguées (volume) : 232 / 232 (100%)
[{'photo_id': 1049296121, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/9/7/0093b445a3c4de9b92562659d544a352.jpg', 'username': None, 'uploaded_at': None, 'text': 'image_07092021_05_47_33_009984m0.jpg 0.002 for time 1, id_amount 3 this amount prod time diff : 0.001'}, {'photo_id': 1049296996, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/9/7/f6ab2ade6ce6c6a1cdc5e37444e94ea8.jpg', 'username': None, 'uploaded_at': None, 'text': 'image_07092021_06_05_54_010003m0.jpg 0.003 for time 1, id_amount 3 this amount prod time diff : 0.001'}, {'photo_id': 1049303925, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/9/7/225c2975cddc22def1487c51a2d2f466.jpg', 'username': None, 'uploaded_at': None, 'text': 'image_07092021_07_00_04_009910m0.jpg 0.004 for time 1, id_amount 3 this amount prod time diff : 0.001'}, {'photo_id': 1049304810, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/9/7/b0506a8333964620f1a3b9eb8688daf7.jpg', 'username': None, 'uploaded_at': None, 'text': 'image_07092021_07_09_13_010022m0.jpg 0.005 for time 1, id_amount 3 this amount prod time diff : 0.001'}]
232
[{'photo_id': 1049318293, 'score': 0.9999683, 'hashtag': '05102018_Papier_non_papier_tres_dense', 'hashtag_id': 2107751016, 'label': 4599398}, {'photo_id': 1049312426, 'score': 0.9998722, 'hashtag': '05102018_Papier_non_papier_tres_dense', 'hashtag_id': 2107751016, 'label': 4599398}, {'photo_id': 1049318291, 'score': 0.9998473, 'hashtag': '05102018_Papier_non_papier_tres_dense', 'hashtag_id': 2107751016, 'label': 4599398}, {'photo_id': 1049318289, 'score': 0.99982566, 'hashtag': '05102018_Papier_non_papier_tres_dense', 'hashtag_id': 2107751016, 'label': 4599398}]
 elapsed_time : load_data_split_time_score 1.1920928955078125e-06
 elapsed_time : order_list_meta_photo_and_scores 9.465217590332031e-05
 elapsed_time : fill_and_build_computed_from_old_data 0.013184785842895508

        INSERT INTO `MTRPhoto`.`dashboard_entry_day`
(`dashboard_place_id`,
`mtr_portfolio_id`,
`date`)
VALUES
(
42, 4599398, '2021-09-07'
) ON DUPLICATE KEY UPDATE mtr_portfolio_id=VALUES(mtr_portfolio_id), updated_at=NOW();

Catched exception !
Connect or reconnect !
SELECT id FROM MTRPhoto.dashboard_entry_day where dashboard_place_id = 42 and date = '2021-09-07' ; 

        INSERT INTO `MTRPhoto`.`dashboard_run_ids`
(`dashboard_entry_day`,
`mtr_user_id`,
`misc_info`)
VALUES
(106260,739,"{}");

Catched exception !
Connect or reconnect !
 elapsed_time : insert_dashboard_record_day_entry 0.6866421699523926

 Creating list_photo_total 

 in select_descriptors : SELECT `photo_id`, `type_store` FROM MTRPhoto.photo_desc_search
        WHERE photo_id IN (1049293230,1049296121,1049296996,1049303925,1049304810,1049306205,1049306635,1049306791,1049294990,1049306792,1049306804,1049306823,1049307693,1049308235,1049308275,1049308276,1049308280,1049308376,1049308381,1049308384,1049309345,1049309379,1049309380,1049309381,1049309382,1049309383,1049309385,1049309592,1049309595,1049309597,1049309599,1049309603,1049309605,1049309651,1049309653,1049309655,1049309656,1049309657,1049309658,1049309670,1049309672,1049309675,1049309677,1049309681,1049309686,1049309701,1049309703,1049309706,1049309732,1049309734,1049309737,1049310132,1049310134,1049310138,1049310139,1049310141,1049310145,1049310159,1049310162,1049310165,1049310905,1049310907,1049310909,1049310911,1049310914,1049310919,1049310981,1049310982,1049310984,1049310991,1049310992,1049310994,1049311006,1049311009,1049311073,1049311136,1049311199,1049311252,1049311263,1049311266,1049311267,1049311767,1049311771,1049311791,1049311793,1049311795,1049311932,1049311934,1049311935,1049311937,1049311938,1049311943,1049311960,1049311961,1049311962,1049311963,1049311964,1049312208,1049312363,1049312404,1049312406,1049312409,1049312420,1049312422,1049312424,1049312426,1049312429,1049312438,1049312440,1049312442,1049312444,1049312445,1049312449,1049312460,1049312461,1049312462,1049312463,1049312464,1049312484,1049312485,1049312487,1049312488,1049312489,1049312508,1049312556,1049312562,1049312566,1049312568,1049312571,1049312573,1049312574,1049312579,1049312583,1049312585,1049312588,1049312803,1049312984,1049313025,1049316209,1049316210,1049316212,1049316214,1049316216,1049316222,1049316255,1049316257,1049316331,1049316332,1049316336,1049316338,1049316520,1049316534,1049316537,1049316540,1049316543,1049316545,1049316582,1049316588,1049316594,1049316597,1049316600,1049316610,1049316749,1049316752,1049316778,1049316782,1049316785,1049316790,1049317197,1049317210,1049317225,1049317282,1049317333,1049317359,1049317440,1049317444,1049317453,1049317457,1049317461,1049317468,1049317485,1049317487,1049317489,1049317491,1049317493,1049317497,1049317517,1049317520,1049317522,1049317524,1049317525,1049317526,1049317536,1049317542,1049317546,1049317549,1049317551,1049317554,1049318212,1049318213,1049318214,1049318216,1049318219,1049318222,1049318246,1049318247,1049318250,1049318253,1049318257,1049318260,1049318265,1049318268,1049318271,1049318273,1049318276,1049318279,1049318287,1049318288,1049318289,1049318291,1049318293,1049318294,1049318309,1049318310,1049318311,1049318337,1049318339,1049318342,1049318356,1049318358,1049318360,1049318362) AND `type`=3963
 elapsed_time : select_descriptors 22.729777574539185
07092021 4599398 Nombre de photos avec descriptors (type 3963) : 232 / 232 (100%)
ERROR : Hum hum, what can we do for different size of descriptors (ignore the difference ) :  0 vs 2048 photo_id : 1049293230 photo_id_prec : 0
0:00:00|ON:0:27:28.999934|OFF:1:46:59.999878|ON:0:00:20.000007|OFF:0:01:51.000162|ON:0:12:18.999909|OFF:0:01:01.000055|ON:0:08:50.000116|OFF:0:00:09.999867|ON:0:00:19.999899|OFF:0:00:09.000058|ON:0:00:29.999860|OFF:0:01:40.000249|ON:0:00:30.999931|OFF:0:07:40.000107|ON:0:00:28.999981|OFF:0:00:09.999968|ON:0:00:10.999986|OFF:0:08:09.999919|ON:0:00:40.000176|OFF:0:01:08.999784|ON:0:00:11.000245|OFF:0:00:39.999921|ON:0:00:19.000004|OFF:0:06:31.000039|ON:0:02:09.999929|OFF:0:01:40.000021|ON:0:00:39.000031|OFF:0:07:10.999966|ON:0:12:30.000101|OFF:0:00:18.999765|ON:0:00:39.999946|OFF:0:00:11.000212|ON:0:00:29.999851|OFF:0:00:20.000150|ON:0:00:30.000042|OFF:0:00:18.999771|ON:0:07:31.000243|OFF:0:00:09.999942|ON:0:00:08.999822|OFF:0:00:11.000172|ON:0:00:39.999914|OFF:0:00:20|ON:0:31:10.000147|OFF:0:12:18.999857|ON:0:01:39.999950|OFF:0:00:19.999947|ON:0:00:21.000213|OFF:0:00:28.999911|ON:0:00:21.000117|OFF:0:00:40.000020|ON:0:10:58.999762|OFF:0:00:41.000023|ON:0:00:09.000008|OFF:0:00:21.000234|ON:0:00:29.999765|OFF:0:00:28.999920|ON:0:00:21.000174|OFF:0:00:30.000078|ON:0:00:29.999938|OFF:0:00:29.999871|ON:0:00:08.999965|OFF:0:09:31.000234|ON:0:00:09.999916|OFF:0:00:20.000049|ON:0:04:09.999926|OFF:0:01:09.000014|ON:0:02:00.999957|OFF:0:00:08.999951|ON:0:00:21.000053|OFF:0:00:18.999927|ON:0:00:39.999997|OFF:0:00:30.000158|ON:
 07092021 Removing 115 photos because of the 'same image' condition
 list_time_on : 36 first ten : [datetime.timedelta(seconds=1648, microseconds=999934), datetime.timedelta(seconds=20, microseconds=7), datetime.timedelta(seconds=738, microseconds=999909), datetime.timedelta(seconds=530, microseconds=116), datetime.timedelta(seconds=19, microseconds=999899), datetime.timedelta(seconds=29, microseconds=999860), datetime.timedelta(seconds=30, microseconds=999931), datetime.timedelta(seconds=28, microseconds=999981), datetime.timedelta(seconds=10, microseconds=999986), datetime.timedelta(seconds=40, microseconds=176)]
 list_time_off : 37 first ten : [datetime.timedelta(0), datetime.timedelta(seconds=6419, microseconds=999878), datetime.timedelta(seconds=111, microseconds=162), datetime.timedelta(seconds=61, microseconds=55), datetime.timedelta(seconds=9, microseconds=999867), datetime.timedelta(seconds=9, microseconds=58), datetime.timedelta(seconds=100, microseconds=249), datetime.timedelta(seconds=460, microseconds=107), datetime.timedelta(seconds=9, microseconds=999968), datetime.timedelta(seconds=489, microseconds=999919)]
Total on : 7859.999814999999
list_time_on
{'nb': 36, 'mean': 218.33332819444445, 'stddev': 429.7602163813713, 'min': 8.999822, 'max': 1870.000147, 'quantil_10': {'min': [9.999916], 'max': [738.999909]}, 'quantil_100': {'min': [8.999822], 'max': [1870.000147]}, 'quantil_1000': {'min': [8.999822], 'max': [1870.000147]}, 'quantil_5000': {'min': [8.999822], 'max': [1870.000147]}, 'quantil_10000': {'min': [8.999822], 'max': [1870.000147]}}


Total off : 10509.0002
list_time_off
{'nb': 37, 'mean': 284.02703243243246, 'stddev': 1039.1357821819204, 'min': 0.0, 'max': 6419.999878, 'quantil_10': {'min': [9.999867], 'max': [489.999919]}, 'quantil_100': {'min': [0.0], 'max': [6419.999878]}, 'quantil_1000': {'min': [0.0], 'max': [6419.999878]}, 'quantil_5000': {'min': [0.0], 'max': [6419.999878]}, 'quantil_10000': {'min': [0.0], 'max': [6419.999878]}}


dist_desc




begin to insert list_values into photo_hahstag_ids : 
length of list_valuse in save_photo_hashtag_id_type : 232
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id)
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id)
 first line : (1049293230, 2107752370, 1539)
...
 last line : (1049318362, 2107752370, 1539)
time used for this insertion : 0.12749505043029785
  photos_removed : len 115
 elapsed_time : remove_photo_duplicate 0.3231394290924072

 Creating list_photo_total 

XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX elapsed_time : count_sum_diff_and_build_graph 0.030716419219970703
 Total photos : 232
..can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
....can't find max_score_info
.....can't find max_score_info
.can't find max_score_info
...Change port : 10 hashtag : 2107750911   photo_id =1049308384 : rungis_plastique_fonce
..can't find max_score_info
...can't find max_score_info
....can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
....can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
....can't find max_score_info
..can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.....can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
..can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
...can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.....Change port : 25 hashtag : 2107750908   photo_id =1049311795 : rungis_papier
..can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
...Change port : 4 hashtag : 2107750911   photo_id =1049311961 : rungis_plastique_fonce
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.Change port : 1 hashtag : 2107750908   photo_id =1049312208 : rungis_papier
.....Change port : 5 hashtag : 2107750911   photo_id =1049312420 : rungis_plastique_fonce
.Change port : 1 hashtag : 2107750908   photo_id =1049312422 : rungis_papier
..can't find max_score_info
.can't find max_score_info
.Change port : 2 hashtag : 2107750911   photo_id =1049312438 : rungis_plastique_fonce
....can't find max_score_info
...can't find max_score_info
.can't find max_score_info
....can't find max_score_info
.can't find max_score_info
....Change port : 12 hashtag : 2107750908   photo_id =1049312556 : rungis_papier
.can't find max_score_info
..can't find max_score_info
.....can't find max_score_info
.can't find max_score_info
...Change port : 8 hashtag : 2107750911   photo_id =1049312984 : rungis_plastique_fonce
...can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.........can't find max_score_info
.can't find max_score_info
...can't find max_score_info
.can't find max_score_info
...can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
...can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.Change port : 17 hashtag : 2107751280   photo_id =1049317359 : rungis_aluminium
.can't find max_score_info
.can't find max_score_info
....can't find max_score_info
.can't find max_score_info
.can't find max_score_info
...can't find max_score_info
.can't find max_score_info
.can't find max_score_info
...Change port : 8 hashtag : 2107750913   photo_id =1049317524 : rungis_tetrapak
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
..can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.Change port : 2 hashtag : 2107750911   photo_id =1049318212 : rungis_plastique_fonce
.can't find max_score_info
.can't find max_score_info
...can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.can't find max_score_info
..........Change port : 12 hashtag : 2107750908   photo_id =1049318287 : rungis_papier
...can't find max_score_info
..Change port : 4 hashtag : 2107750911   photo_id =1049318294 : rungis_plastique_fonce
.can't find max_score_info
.can't find max_score_info
.....can't find max_score_info
.can't find max_score_info
.can't find max_score_info
.
 Total photos : 232
Number of lists : 15
counter photos in port : 117
 hashtag : rungis_aluminium(2107751280) : 8 photos in 1 portfolios !
 hashtag : rungis_carton(2107750907) : 0 photos in 0 portfolios !
 hashtag : rungis_papier(2107750908) : 33 photos in 6 portfolios !
 hashtag : rungis_plastique_clair(2107750909) : 0 photos in 0 portfolios !
 hashtag : rungis_plastique_dur(2107750910) : 0 photos in 0 portfolios !
 hashtag : rungis_plastique_fonce(2107750911) : 74 photos in 7 portfolios !
 hashtag : rungis_tapis_vide(2107750912) : 0 photos in 0 portfolios !
 hashtag : rungis_tetrapak(2107750913) : 2 photos in 1 portfolios !
 elapsed_time : group_photo_by_moyenne_exp 0.007002353668212891
 elapsed_time : compute_and_correct_tag_with_moyenne_mobile 1.9073486328125e-06
today str has not a value , we define it as the date of the first image
todaystr_first : 07092021
attention , prev_timestamp is 0 , we do nothing
*******o**
BIG TIME 550.0000100135803 (11.000229120254517, 2, 0, 0, 0.979349, 0, 0, 0, 0, 0, 0.8199999980926513, 0.0012, 0.0008999778032302856, 2.9, 0.11100016188621521, -0.0) on 3 1049307693 2021-09-07 07:45:54.010041 id_data : 12  

*
BIG TIME 168.99982810020447 (11.000229120254517, 2, 0, 0, 0.979349, 0, 0, 0, 0, 0, 0.8199999980926513, 0.0012, 0.0008999778032302856, 2.9, 0.11100016188621521, -0.0) on 3 1049308235 2021-09-07 07:48:43.009869 id_data : 13  

**
BIG TIME 499.9998118877411 (191.00026988983154, 2, 0, 0, 0.93498826, 0, 0, 0, 0, 0, 0.9018999995946884, 0.002, 0.002999984407424927, 2.9, 0.019000051021575926, -0.01666705330212911) on 7 1049309345 2021-09-07 07:58:43.009858 id_data : 20  

*
BIG TIME 371.0001440048218 (271.00081276893616, 5, 0.24365342, 0, 0, 0, 0, 0.38418204, 0, 0.34177557, 0.9720000085830689, 0.0041, 0.014999971222877502, 2.9, 0.061000473976135255, -0.2516670016447703) on 15 1049310132 2021-09-07 08:09:54.010082 id_data : 51  

*
BIG TIME 461.0001001358032 (301.0006546974182, 0, 0.5752453, 0, 0, 0, 0, 0, 0, 0, 1.0228999980926514, 0.0056, 0.02589997522830963, 2.9, 0.009999968051910401, -0.0) on 18 1049310905 2021-09-07 08:18:54.009936 id_data : 60  

*
BIG TIME 370.0001759529114 (359.0003435611725, 5, 0, 0, 0, 0, 0, 0.85983855, 0, 0, 1.0898999773979188, 0.0078, 0.04109999623298645, 2.9, 0.008999762058258056, -0.0) on 24 1049311767 2021-09-07 08:28:24.010105 id_data : 81  

***
BIG TIME 411.0001149177551 (557.9999935626984, 2, 0, 0, 0.66983944, 0, 0, 0, 0, 0, 1.1558999848842622, 0.0094, 0.050199996829032895, 2.9, 0.00900000500679016, -0.0) on 32 1049312208 2021-09-07 08:40:04.010052 id_data : 97  

*
BIG TIME 549.9999330043793 (557.9999935626984, 2, 0, 0, 0.66983944, 0, 0, 0, 0, 0, 1.1558999848842622, 0.0094, 0.050199996829032895, 2.9, 0.00900000500679016, -0.0) on 32 1049312363 2021-09-07 08:49:14.009985 id_data : 98  

**
BIG TIME 168.99987387657166 (867.0004575252533, 2, 0, 0, 0.5498895, 0, 0, 0, 0, 0.29987606, 1.293000004196167, 0.0123, 0.06009994525909424, 0.9, 0.011000201940536499, -0.0) on 49 1049312508 2021-09-07 08:58:23.009966 id_data : 123  

*
BIG TIME 259.99999809265137 (867.0004575252533, 2, 0, 0, 0.5498895, 0, 0, 0, 0, 0.29987606, 1.293000004196167, 0.0123, 0.06009994525909424, 0.9, 0.011000201940536499, -0.0) on 49 1049312556 2021-09-07 09:02:43.009964 id_data : 124  

*
BIG TIME 190.00016593933105 (929.000762462616, 5, 0, 0, 0.44379362, 0, 0, 0.54574114, 0, 0, 1.3459999933004378, 0.0135, 0.06409994735717774, 2.9, 0.009999895095825195, -0.0) on 55 1049312803 2021-09-07 09:07:34.010149 id_data : 135  

*
BIG TIME 180.0000081062317 (929.000762462616, 5, 0, 0, 0.44379362, 0, 0, 0.54574114, 0, 0, 1.3459999933004378, 0.0135, 0.06409994735717774, 2.9, 0.009999895095825195, -0.0) on 55 1049312984 2021-09-07 09:10:34.010157 id_data : 136  

*
BIG TIME 1480.0000269412994 (939.0006575584412, 5, 0, 0, 0.2945285, 0, 0, 0.48689777, 0, 0.20073189, 1.3838999820947646, 0.0138, 0.06409994735717774, 2.9, 0.018999608993530273, -0.6316664799054463) on 56 1049316209 2021-09-07 09:35:23.009898 id_data : 138  

*
BIG TIME 668.9998891353607 (939.0006575584412, 5, 0, 0, 0.2945285, 0, 0, 0.48689777, 0, 0.20073189, 1.3838999820947646, 0.0138, 0.06409994735717774, 2.9, 0.018999608993530273, -0.6316664799054463) on 56 1049316332 2021-09-07 09:47:53.009987 id_data : 147  

*
BIG TIME 649.9999890327454 (1086.000019311905, 5, 0, 0, 0, 0, 0, 0.69907516, 0, 0.23055789, 1.6300000094890594, 0.0168, 0.08039999685287476, 2.9, 0.01100021505355835, -0.0) on 68 1049317197 2021-09-07 10:02:34.010134 id_data : 168  

*
BIG TIME 540.0001981258392 (1189.0002081394196, 0, 0.8074409, 0, 0, 0, 0, 0, 0, 0, 1.7199999867916107, 0.0194, 0.09519999706745148, 2.9, 0.009999808073043823, -0.0) on 78 1049318212 2021-09-07 10:16:24.010117 id_data : 198  

*
BIG TIME 190.00007104873657 (1199.0000162124634, 5, 0, 0.22708784, 0, 0, 0, 0.7244179, 0, 0, 1.781000003194809, 0.0202, 0.10109996955394746, 2.9, 0.011000241041183472, -0.0) on 79 1049318219 2021-09-07 10:20:04.010153 id_data : 202  

**Count Time bigger than 30s : 31
#Number Photos for regression : 
{'07092021': {2107751280: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 80.99965000152588, 2107751017: 0}, 2107750907: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 49.000049114227295, 2107751017: 0}, 2107750908: {2107751013: 0, 2107751014: 11.000201940536499, 2107751015: 0, 2107751016: 534.9997780323029, 2107751017: 0}, 2107750909: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 0, 2107751017: 0}, 2107750910: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 31.000057697296143, 2107751017: 0}, 2107750911: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 564.0013737678528, 2107751017: 19.999656200408936}, 2107750912: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 0, 2107751017: 0}, 2107750913: {2107751013: 0, 2107751014: 8.999944925308228, 2107751015: 0, 2107751016: 89.99986672401428, 2107751017: 9.999994993209839}}}
07092021|rungis_aluminium, 05102018_papier_non_papier_dense:0
07092021|rungis_aluminium, 05102018_papier_non_papier_peu_dense:0
07092021|rungis_aluminium, 05102018_papier_non_papier_presque_vide:0
07092021|rungis_aluminium, 05102018_papier_non_papier_tres_dense:80.99965000152588
07092021|rungis_aluminium, 05102018_papier_non_papier_tres_peu_dense:0
07092021|rungis_carton, 05102018_papier_non_papier_dense:0
07092021|rungis_carton, 05102018_papier_non_papier_peu_dense:0
07092021|rungis_carton, 05102018_papier_non_papier_presque_vide:0
07092021|rungis_carton, 05102018_papier_non_papier_tres_dense:49.000049114227295
07092021|rungis_carton, 05102018_papier_non_papier_tres_peu_dense:0
07092021|rungis_papier, 05102018_papier_non_papier_dense:0
07092021|rungis_papier, 05102018_papier_non_papier_peu_dense:11.000201940536499
07092021|rungis_papier, 05102018_papier_non_papier_presque_vide:0
07092021|rungis_papier, 05102018_papier_non_papier_tres_dense:534.9997780323029
07092021|rungis_papier, 05102018_papier_non_papier_tres_peu_dense:0
07092021|rungis_plastique_clair, 05102018_papier_non_papier_dense:0
07092021|rungis_plastique_clair, 05102018_papier_non_papier_peu_dense:0
07092021|rungis_plastique_clair, 05102018_papier_non_papier_presque_vide:0
07092021|rungis_plastique_clair, 05102018_papier_non_papier_tres_dense:0
07092021|rungis_plastique_clair, 05102018_papier_non_papier_tres_peu_dense:0
07092021|rungis_plastique_dur, 05102018_papier_non_papier_dense:0
07092021|rungis_plastique_dur, 05102018_papier_non_papier_peu_dense:0
07092021|rungis_plastique_dur, 05102018_papier_non_papier_presque_vide:0
07092021|rungis_plastique_dur, 05102018_papier_non_papier_tres_dense:31.000057697296143
07092021|rungis_plastique_dur, 05102018_papier_non_papier_tres_peu_dense:0
07092021|rungis_plastique_fonce, 05102018_papier_non_papier_dense:0
07092021|rungis_plastique_fonce, 05102018_papier_non_papier_peu_dense:0
07092021|rungis_plastique_fonce, 05102018_papier_non_papier_presque_vide:0
07092021|rungis_plastique_fonce, 05102018_papier_non_papier_tres_dense:564.0013737678528
07092021|rungis_plastique_fonce, 05102018_papier_non_papier_tres_peu_dense:19.999656200408936
07092021|rungis_tapis_vide, 05102018_papier_non_papier_dense:0
07092021|rungis_tapis_vide, 05102018_papier_non_papier_peu_dense:0
07092021|rungis_tapis_vide, 05102018_papier_non_papier_presque_vide:0
07092021|rungis_tapis_vide, 05102018_papier_non_papier_tres_dense:0
07092021|rungis_tapis_vide, 05102018_papier_non_papier_tres_peu_dense:0
07092021|rungis_tetrapak, 05102018_papier_non_papier_dense:0
07092021|rungis_tetrapak, 05102018_papier_non_papier_peu_dense:8.999944925308228
07092021|rungis_tetrapak, 05102018_papier_non_papier_presque_vide:0
07092021|rungis_tetrapak, 05102018_papier_non_papier_tres_dense:89.99986672401428
07092021|rungis_tetrapak, 05102018_papier_non_papier_tres_peu_dense:9.999994993209839
#Number Photos for regression amount gros magasin papier (time_diff then nb_photo) : 
We have not displayed the number of photos removed for one material since Rungis_Papier wasn't in the thcl used !
07092021_time_diff_distrib


 Number amount portfolio for this type of dechet :  aluminium 8
 Status : 404
 Content : b'<!doctype html>\n<html lang=en>\n<title>404 Not Found</title>\n<h1>Not Found</h1>\n<p>The requested URL was not found on the server. If you entered the URL manually please check your spelling and try again.</p>\n'
 All Response : <Response [404]>
 WARNING : NO PORTFOLIO CREATED portfolio_id 0 used instead of new portfolio
Catched exception !
Connect or reconnect !
ERROR in datou_step_exec, will save and exit !
(1062, "Duplicate entry '0-1049310909' for key 'mtr_portfolio_photos.mtr_port_phot_many_to_one'")
  File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2339, in datou_exec
    output = datou_step_exec(sNext, args, cache, context, map_info, verbose, mtr_user_id)
  File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2523, in datou_step_exec
    return lib_process.datou_step_split_time_score(param, json_param, args, context, map_info, verbose, mtr_user_id)
  File "/home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py", line 2295, in datou_step_split_time_score
    one_result = split_port_in_batch_balle(
  File "/home/admin/workarea/git/Velours/python/misc/split_time_score.py", line 687, in split_port_in_batch_balle
    map_sum_diff_per_class_rubbish_and_densite, map_info = count_sum_diff(ds,
  File "/home/admin/workarea/git/Velours/python/mtr/math_fotonower/timeseries/lib_split_time_score.py", line 3799, in count_sum_diff
    context.pq.insert_photos(port_id, map_class_amount_list_portfolio[hashtag_id][hashtag_id_amount])
  File "/home/admin/workarea/git/Velours/python/mtr/database_queries/portfolio_queries.py", line 211, in insert_photos
    self.gq.insert_many(query, tup_port_id_pid)
  File "/home/admin/workarea/git/Velours/python/mtr/database_queries/general_queries.py", line 198, in insert_many
    cursor.executemany(query, args[:limit])
  File "/home/admin/.local/lib/python3.8/site-packages/MySQLdb/cursors.py", line 241, in executemany
    return self._do_execute_many(
  File "/home/admin/.local/lib/python3.8/site-packages/MySQLdb/cursors.py", line 275, in _do_execute_many
    rows += self.execute(sql + postfix)
  File "/home/admin/.local/lib/python3.8/site-packages/MySQLdb/cursors.py", line 179, in execute
    res = self._query(mogrified_query)
  File "/home/admin/.local/lib/python3.8/site-packages/MySQLdb/cursors.py", line 330, in _query
    db.query(q)
  File "/home/admin/.local/lib/python3.8/site-packages/MySQLdb/connections.py", line 280, in query
    _mysql.connection.query(self, query)

[1049318362, 1049318360, 1049318358, 1049318356, 1049318342, 1049318339, 1049318337, 1049318311, 1049318310, 1049318309, 1049318294, 1049318293, 1049318291, 1049318289, 1049318288, 1049318287, 1049318279, 1049318276, 1049318273, 1049318271, 1049318268, 1049318265, 1049318260, 1049318257, 1049318253, 1049318250, 1049318247, 1049318246, 1049318222, 1049318219, 1049318216, 1049318214, 1049318213, 1049318212, 1049317554, 1049317551, 1049317549, 1049317546, 1049317542, 1049317536, 1049317526, 1049317525, 1049317524, 1049317522, 1049317520, 1049317517, 1049317497, 1049317493, 1049317491, 1049317489, 1049317487, 1049317485, 1049317468, 1049317461, 1049317457, 1049317453, 1049317444, 1049317440, 1049317359, 1049317333, 1049317282, 1049317225, 1049317210, 1049317197, 1049316790, 1049316785, 1049316782, 1049316778, 1049316752, 1049316749, 1049316610, 1049316600, 1049316597, 1049316594, 1049316588, 1049316582, 1049316545, 1049316543, 1049316540, 1049316537, 1049316534, 1049316520, 1049316338, 1049316336, 1049316332, 1049316331, 1049316257, 1049316255, 1049316222, 1049316216, 1049316214, 1049316212, 1049316210, 1049316209, 1049313025, 1049312984, 1049312803, 1049312588, 1049312585, 1049312583, 1049312579, 1049312574, 1049312573, 1049312571, 1049312568, 1049312566, 1049312562, 1049312556, 1049312508, 1049312489, 1049312488, 1049312487, 1049312485, 1049312484, 1049312464, 1049312463, 1049312462, 1049312461, 1049312460, 1049312449, 1049312445, 1049312444, 1049312442, 1049312440, 1049312438, 1049312429, 1049312426, 1049312424, 1049312422, 1049312420, 1049312409, 1049312406, 1049312404, 1049312363, 1049312208, 1049311964, 1049311963, 1049311962, 1049311961, 1049311960, 1049311943, 1049311938, 1049311937, 1049311935, 1049311934, 1049311932, 1049311795, 1049311793, 1049311791, 1049311771, 1049311767, 1049311267, 1049311266, 1049311263, 1049311252, 1049311199, 1049311136, 1049311073, 1049311009, 1049311006, 1049310994, 1049310992, 1049310991, 1049310984, 1049310982, 1049310981, 1049310919, 1049310914, 1049310911, 1049310909, 1049310907, 1049310905, 1049310165, 1049310162, 1049310159, 1049310145, 1049310141, 1049310139, 1049310138, 1049310134, 1049310132, 1049309737, 1049309734, 1049309732, 1049309706, 1049309703, 1049309701, 1049309686, 1049309681, 1049309677, 1049309675, 1049309672, 1049309670, 1049309658, 1049309657, 1049309656, 1049309655, 1049309653, 1049309651, 1049309605, 1049309603, 1049309599, 1049309597, 1049309595, 1049309592, 1049309385, 1049309383, 1049309382, 1049309381, 1049309380, 1049309379, 1049309345, 1049308384, 1049308381, 1049308376, 1049308280, 1049308276, 1049308275, 1049308235, 1049307693, 1049306823, 1049306804, 1049306792, 1049306791, 1049306635, 1049306205, 1049304810, 1049303925, 1049296996, 1049296121, 1049294990, 1049293230]
 map_info['map_portfolio_photo'] : {4599398: [1049318362, 1049318360, 1049318358, 1049318356, 1049318342, 1049318339, 1049318337, 1049318311, 1049318310, 1049318309, 1049318294, 1049318293, 1049318291, 1049318289, 1049318288, 1049318287, 1049318279, 1049318276, 1049318273, 1049318271, 1049318268, 1049318265, 1049318260, 1049318257, 1049318253, 1049318250, 1049318247, 1049318246, 1049318222, 1049318219, 1049318216, 1049318214, 1049318213, 1049318212, 1049317554, 1049317551, 1049317549, 1049317546, 1049317542, 1049317536, 1049317526, 1049317525, 1049317524, 1049317522, 1049317520, 1049317517, 1049317497, 1049317493, 1049317491, 1049317489, 1049317487, 1049317485, 1049317468, 1049317461, 1049317457, 1049317453, 1049317444, 1049317440, 1049317359, 1049317333, 1049317282, 1049317225, 1049317210, 1049317197, 1049316790, 1049316785, 1049316782, 1049316778, 1049316752, 1049316749, 1049316610, 1049316600, 1049316597, 1049316594, 1049316588, 1049316582, 1049316545, 1049316543, 1049316540, 1049316537, 1049316534, 1049316520, 1049316338, 1049316336, 1049316332, 1049316331, 1049316257, 1049316255, 1049316222, 1049316216, 1049316214, 1049316212, 1049316210, 1049316209, 1049313025, 1049312984, 1049312803, 1049312588, 1049312585, 1049312583, 1049312579, 1049312574, 1049312573, 1049312571, 1049312568, 1049312566, 1049312562, 1049312556, 1049312508, 1049312489, 1049312488, 1049312487, 1049312485, 1049312484, 1049312464, 1049312463, 1049312462, 1049312461, 1049312460, 1049312449, 1049312445, 1049312444, 1049312442, 1049312440, 1049312438, 1049312429, 1049312426, 1049312424, 1049312422, 1049312420, 1049312409, 1049312406, 1049312404, 1049312363, 1049312208, 1049311964, 1049311963, 1049311962, 1049311961, 1049311960, 1049311943, 1049311938, 1049311937, 1049311935, 1049311934, 1049311932, 1049311795, 1049311793, 1049311791, 1049311771, 1049311767, 1049311267, 1049311266, 1049311263, 1049311252, 1049311199, 1049311136, 1049311073, 1049311009, 1049311006, 1049310994, 1049310992, 1049310991, 1049310984, 1049310982, 1049310981, 1049310919, 1049310914, 1049310911, 1049310909, 1049310907, 1049310905, 1049310165, 1049310162, 1049310159, 1049310145, 1049310141, 1049310139, 1049310138, 1049310134, 1049310132, 1049309737, 1049309734, 1049309732, 1049309706, 1049309703, 1049309701, 1049309686, 1049309681, 1049309677, 1049309675, 1049309672, 1049309670, 1049309658, 1049309657, 1049309656, 1049309655, 1049309653, 1049309651, 1049309605, 1049309603, 1049309599, 1049309597, 1049309595, 1049309592, 1049309385, 1049309383, 1049309382, 1049309381, 1049309380, 1049309379, 1049309345, 1049308384, 1049308381, 1049308376, 1049308280, 1049308276, 1049308275, 1049308235, 1049307693, 1049306823, 1049306804, 1049306792, 1049306791, 1049306635, 1049306205, 1049304810, 1049303925, 1049296996, 1049296121, 1049294990, 1049293230]}
 final :  True mtd_id 3789 list_pids : [1049318362, 1049318360, 1049318358, 1049318356, 1049318342, 1049318339, 1049318337, 1049318311, 1049318310, 1049318309, 1049318294, 1049318293, 1049318291, 1049318289, 1049318288, 1049318287, 1049318279, 1049318276, 1049318273, 1049318271, 1049318268, 1049318265, 1049318260, 1049318257, 1049318253, 1049318250, 1049318247, 1049318246, 1049318222, 1049318219, 1049318216, 1049318214, 1049318213, 1049318212, 1049317554, 1049317551, 1049317549, 1049317546, 1049317542, 1049317536, 1049317526, 1049317525, 1049317524, 1049317522, 1049317520, 1049317517, 1049317497, 1049317493, 1049317491, 1049317489, 1049317487, 1049317485, 1049317468, 1049317461, 1049317457, 1049317453, 1049317444, 1049317440, 1049317359, 1049317333, 1049317282, 1049317225, 1049317210, 1049317197, 1049316790, 1049316785, 1049316782, 1049316778, 1049316752, 1049316749, 1049316610, 1049316600, 1049316597, 1049316594, 1049316588, 1049316582, 1049316545, 1049316543, 1049316540, 1049316537, 1049316534, 1049316520, 1049316338, 1049316336, 1049316332, 1049316331, 1049316257, 1049316255, 1049316222, 1049316216, 1049316214, 1049316212, 1049316210, 1049316209, 1049313025, 1049312984, 1049312803, 1049312588, 1049312585, 1049312583, 1049312579, 1049312574, 1049312573, 1049312571, 1049312568, 1049312566, 1049312562, 1049312556, 1049312508, 1049312489, 1049312488, 1049312487, 1049312485, 1049312484, 1049312464, 1049312463, 1049312462, 1049312461, 1049312460, 1049312449, 1049312445, 1049312444, 1049312442, 1049312440, 1049312438, 1049312429, 1049312426, 1049312424, 1049312422, 1049312420, 1049312409, 1049312406, 1049312404, 1049312363, 1049312208, 1049311964, 1049311963, 1049311962, 1049311961, 1049311960, 1049311943, 1049311938, 1049311937, 1049311935, 1049311934, 1049311932, 1049311795, 1049311793, 1049311791, 1049311771, 1049311767, 1049311267, 1049311266, 1049311263, 1049311252, 1049311199, 1049311136, 1049311073, 1049311009, 1049311006, 1049310994, 1049310992, 1049310991, 1049310984, 1049310982, 1049310981, 1049310919, 1049310914, 1049310911, 1049310909, 1049310907, 1049310905, 1049310165, 1049310162, 1049310159, 1049310145, 1049310141, 1049310139, 1049310138, 1049310134, 1049310132, 1049309737, 1049309734, 1049309732, 1049309706, 1049309703, 1049309701, 1049309686, 1049309681, 1049309677, 1049309675, 1049309672, 1049309670, 1049309658, 1049309657, 1049309656, 1049309655, 1049309653, 1049309651, 1049309605, 1049309603, 1049309599, 1049309597, 1049309595, 1049309592, 1049309385, 1049309383, 1049309382, 1049309381, 1049309380, 1049309379, 1049309345, 1049308384, 1049308381, 1049308376, 1049308280, 1049308276, 1049308275, 1049308235, 1049307693, 1049306823, 1049306804, 1049306792, 1049306791, 1049306635, 1049306205, 1049304810, 1049303925, 1049296996, 1049296121, 1049294990, 1049293230]


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 232
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('3789', '4599398', '1049318362', '[<FrameSummary file /home/admin/workarea/git/Velours/python/tests/python_tests.py, line 245 in <module>>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 3680 in test>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 1379 in test_rubbia_ver2>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 826 in batchDatouExec>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 2356 in datou_exec>, \'(1062, "Duplicate entry \\\'0-1049310909\\\' for key \\\'mtr_portfolio_photos.mtr_port_phot_many_to_one\\\'")\']', '-1', '-1.0', '501120777', '1.0', None), ('3789', '4599398', '1049318360', '[<FrameSummary file /home/admin/workarea/git/Velours/python/tests/python_tests.py, line 245 in <module>>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 3680 in test>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 1379 in test_rubbia_ver2>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 826 in batchDatouExec>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 2356 in datou_exec>, \'(1062, "Duplicate entry \\\'0-1049310909\\\' for key \\\'mtr_portfolio_photos.mtr_port_phot_many_to_one\\\'")\']', '-1', '-1.0', '501120777', '1.0', None), ('3789', '4599398', '1049318358', '[<FrameSummary file /home/admin/workarea/git/Velours/python/tests/python_tests.py, line 245 in <module>>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 3680 in test>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 1379 in test_rubbia_ver2>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 826 in batchDatouExec>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 2356 in datou_exec>, \'(1062, "Duplicate entry \\\'0-1049310909\\\' for key \\\'mtr_portfolio_photos.mtr_port_phot_many_to_one\\\'")\']', '-1', '-1.0', '501120777', '1.0', None), ('3789', '4599398', '1049318356', '[<FrameSummary file /home/admin/workarea/git/Velours/python/tests/python_tests.py, line 245 in <module>>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 3680 in test>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 1379 in test_rubbia_ver2>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 826 in batchDatouExec>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 2356 in datou_exec>, \'(1062, "Duplicate entry \\\'0-1049310909\\\' for key \\\'mtr_portfolio_photos.mtr_port_phot_many_to_one\\\'")\']', '-1', '-1.0', '501120777', '1.0', None), ('3789', '4599398', '1049318342', '[<FrameSummary file /home/admin/workarea/git/Velours/python/tests/python_tests.py, line 245 in <module>>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 3680 in test>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 1379 in test_rubbia_ver2>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 826 in batchDatouExec>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 2356 in datou_exec>, \'(1062, "Duplicate entry \\\'0-1049310909\\\' for key \\\'mtr_portfolio_photos.mtr_port_phot_many_to_one\\\'")\']', '-1', '-1.0', '501120777', '1.0', None), ('3789', '4599398', '1049318339', '[<FrameSummary file /home/admin/workarea/git/Velours/python/tests/python_tests.py, line 245 in <module>>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 3680 in test>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 1379 in test_rubbia_ver2>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 826 in batchDatouExec>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 2356 in datou_exec>, \'(1062, "Duplicate entry \\\'0-1049310909\\\' for key \\\'mtr_portfolio_photos.mtr_port_phot_many_to_one\\\'")\']', '-1', '-1.0', '501120777', '1.0', None), ('3789', '4599398', '1049318337', '[<FrameSummary file /home/admin/workarea/git/Velours/python/tests/python_tests.py, line 245 in <module>>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 3680 in test>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 1379 in test_rubbia_ver2>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 826 in batchDatouExec>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 2356 in datou_exec>, \'(1062, "Duplicate entry \\\'0-1049310909\\\' for key \\\'mtr_portfolio_photos.mtr_port_phot_many_to_one\\\'")\']', '-1', '-1.0', '501120777', '1.0', None), ('3789', '4599398', '1049318311', '[<FrameSummary file /home/admin/workarea/git/Velours/python/tests/python_tests.py, line 245 in <module>>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 3680 in test>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 1379 in test_rubbia_ver2>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 826 in batchDatouExec>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 2356 in datou_exec>, \'(1062, "Duplicate entry \\\'0-1049310909\\\' for key \\\'mtr_portfolio_photos.mtr_port_phot_many_to_one\\\'")\']', '-1', '-1.0', '501120777', '1.0', None), ('3789', '4599398', '1049318310', '[<FrameSummary file /home/admin/workarea/git/Velours/python/tests/python_tests.py, line 245 in <module>>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 3680 in test>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 1379 in test_rubbia_ver2>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 826 in batchDatouExec>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 2356 in datou_exec>, \'(1062, "Duplicate entry \\\'0-1049310909\\\' for key \\\'mtr_portfolio_photos.mtr_port_phot_many_to_one\\\'")\']', '-1', '-1.0', '501120777', '1.0', None), ('3789', '4599398', '1049318309', '[<FrameSummary file /home/admin/workarea/git/Velours/python/tests/python_tests.py, line 245 in <module>>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 3680 in test>, <FrameSummary file 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/home/admin/workarea/git/Velours/python/tests/datou_test.py, line 1379 in test_rubbia_ver2>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 826 in batchDatouExec>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 2356 in datou_exec>, \'(1062, "Duplicate entry \\\'0-1049310909\\\' for key \\\'mtr_portfolio_photos.mtr_port_phot_many_to_one\\\'")\']', '-1', '-1.0', '501120777', '1.0', None), ('3789', '4599398', '1049294990', '[<FrameSummary file /home/admin/workarea/git/Velours/python/tests/python_tests.py, line 245 in <module>>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 3680 in test>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 1379 in test_rubbia_ver2>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 826 in batchDatouExec>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 2356 in datou_exec>, \'(1062, "Duplicate entry \\\'0-1049310909\\\' for key \\\'mtr_portfolio_photos.mtr_port_phot_many_to_one\\\'")\']', '-1', '-1.0', '501120777', '1.0', None), ('3789', '4599398', '1049293230', '[<FrameSummary file /home/admin/workarea/git/Velours/python/tests/python_tests.py, line 245 in <module>>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 3680 in test>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 1379 in test_rubbia_ver2>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 826 in batchDatouExec>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 2356 in datou_exec>, \'(1062, "Duplicate entry \\\'0-1049310909\\\' for key \\\'mtr_portfolio_photos.mtr_port_phot_many_to_one\\\'")\']', '-1', '-1.0', '501120777', '1.0', None)]
time used for this insertion : 0.19744181632995605
 save_final 
ERROR in last step split_time_score, (1062, "Duplicate entry '0-1049310909' for key 'mtr_portfolio_photos.mtr_port_phot_many_to_one'")

time spend for datou_step_exec : 24.47737693786621
time spend to save output : 0.20619988441467285
total time spend for step 0 : 24.683576822280884
caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
ouput is None
saving photo_ids in datou_result


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 232
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
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'0', '492764225', '0', None), ('3789', '4599398', '1049311964', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311963', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311962', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311961', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311960', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311943', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311938', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311937', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311935', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311934', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311932', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311795', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311793', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311791', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311771', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311767', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311267', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311266', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311263', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311252', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311199', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311136', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311073', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311009', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049311006', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049310994', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049310992', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049310991', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049310984', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049310982', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049310981', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049310919', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049310914', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049310911', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049310909', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049310907', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049310905', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049310165', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049310162', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049310159', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049310145', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049310141', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049310139', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049310138', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049310134', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049310132', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309737', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309734', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309732', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309706', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309703', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309701', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309686', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309681', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309677', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309675', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309672', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309670', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309658', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309657', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309656', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309655', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309653', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309651', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309605', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309603', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309599', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309597', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309595', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309592', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309385', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309383', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309382', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309381', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309380', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309379', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049309345', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049308384', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049308381', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049308376', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049308280', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049308276', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049308275', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049308235', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049307693', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049306823', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049306804', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049306792', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049306791', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049306635', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049306205', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049304810', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049303925', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049296996', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049296121', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049294990', '0', '0', '0', '492764225', '0', None), ('3789', '4599398', '1049293230', '0', '0', '0', '492764225', '0', None)]
time used for this insertion : 0.05840635299682617
After save, about to update current !
Result test rubbia : None|
Result context_with_local_rubbia.cache_model_config.map_io test rubbia : {'input': {}, 'output': {}}|
got : None
expected : {'4599398': ([[0, 7, 8, 9, 11, 12, 13, 14, 17, 18], [19, 20, 22, 23, 25, 26, 27, 38, 39, 40, 51, 52, 53, 55, 60, 61, 62, 63, 71, 76, 77, 81, 82, 83, 84], [85, 86, 91, 92], [93], [97, 98, 99, 100, 101], [102], [103, 104], [107, 108, 109, 110, 112, 113, 116, 117, 118, 121, 122, 123], [124, 126, 128, 129, 130, 131, 134, 135], [136, 137, 138, 147, 148, 149, 150, 151, 152, 153, 154, 157, 158, 161, 162, 167, 168], [173, 176, 177, 178, 182, 183, 187, 188], [189, 193], [198, 201, 202, 207, 208, 209, 210, 211, 212, 213, 214, 215], [216, 217, 218, 220], [221, 224, 225, 226, 227, 231]], {'rungis_aluminium': [(10, 11)], 'rungis_carton': [], 'rungis_papier': [(0, 1), (2, 3), (4, 5), (6, 7), (8, 9), (13, 14)], 'rungis_plastique_clair': [], 'rungis_plastique_dur': [], 'rungis_plastique_fonce': [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (12, 13), (14, 15)], 'rungis_tapis_vide': [], 'rungis_tetrapak': [(11, 12)]}, {13545772: {'list_of_photos': [1049293230, 1049306791, 1049294990, 1049306792, 1049306823, 1049307693, 1049308235, 1049308275, 1049308376, 1049308381], 'hashtag': 'papier'}, 13545774: {'list_of_photos': [1049308384, 1049309345, 1049309380, 1049309381, 1049309383, 1049309385, 1049309592, 1049309658, 1049309670, 1049309672, 1049310132, 1049310134, 1049310138, 1049310141, 1049310905, 1049310907, 1049310909, 1049310911, 1049310994, 1049311199, 1049311252, 1049311767, 1049311771, 1049311791, 1049311793], 'hashtag': 'plastique_fonce'}, 13545777: {'list_of_photos': [1049311795, 1049311932, 1049311943, 1049311960], 'hashtag': 'papier'}, 5570414: {'list_of_photos': [1049311961], 'hashtag': 'plastique_fonce'}, 13545779: {'list_of_photos': [1049312208, 1049312363, 1049312404, 1049312406, 1049312409], 'hashtag': 'papier'}, 13545780: {'list_of_photos': [1049312420], 'hashtag': 'plastique_fonce'}, 13545783: {'list_of_photos': [1049312422, 1049312424], 'hashtag': 'papier'}, 13545785: {'list_of_photos': [1049312438, 1049312440, 1049312442, 1049312444, 1049312449, 1049312460, 1049312463, 1049312464, 1049312484, 1049312488, 1049312489, 1049312508], 'hashtag': 'plastique_fonce'}, 13545787: {'list_of_photos': [1049312556, 1049312566, 1049312571, 1049312573, 1049312574, 1049312579, 1049312588, 1049312803], 'hashtag': 'papier'}, 13545788: {'list_of_photos': [1049312984, 1049313025, 1049316209, 1049316332, 1049316336, 1049316338, 1049316520, 1049316534, 1049316537, 1049316540, 1049316543, 1049316588, 1049316594, 1049316610, 1049316749, 1049316790, 1049317197], 'hashtag': 'plastique_fonce'}, 13543473: {'list_of_photos': [1049317359, 1049317453, 1049317457, 1049317461, 1049317489, 1049317491, 1049317520, 1049317522], 'hashtag': 'aluminium'}, 13543474: {'list_of_photos': [1049317524, 1049317542], 'hashtag': 'tetrapak'}, 13543475: {'list_of_photos': [1049318212, 1049318216, 1049318219, 1049318253, 1049318257, 1049318260, 1049318265, 1049318268, 1049318271, 1049318273, 1049318276, 1049318279], 'hashtag': 'plastique_fonce'}, 13543476: {'list_of_photos': [1049318287, 1049318288, 1049318289, 1049318293], 'hashtag': 'papier'}, 13543477: {'list_of_photos': [1049318294, 1049318311, 1049318337, 1049318339, 1049318342, 1049318362], 'hashtag': 'plastique_fonce'}}, {2107751280: 8, 2107750907: 0, 2107750908: 33, 2107750909: 0, 2107750910: 0, 2107750911: 74, 2107750912: 0, 2107750913: 2}, {'amount_uploaded_and_tagged': {'07092021': {'nb_upload': 232, 'nb_taggue_class': 232, 'nb_taggue_densite': 232, 'nb_descriptors': 232, 'number_port': 15, 'count_photo_in_port': 117, 'nb_port_per_class': {'rungis_aluminium': {'nb_photos': 8, 'nb_portfolios': 1}, 'rungis_carton': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_papier': {'nb_photos': 33, 'nb_portfolios': 6}, 'rungis_plastique_clair': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_dur': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_fonce': {'nb_photos': 74, 'nb_portfolios': 7}, 'rungis_tapis_vide': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_tetrapak': {'nb_photos': 2, 'nb_portfolios': 1}}}}, 'map_all_result_after_group_moy_exp': {'number_port': 15, 'count_photo_in_port': 117, 'nb_port_per_class': {'rungis_aluminium': {'nb_photos': 8, 'nb_portfolios': 1}, 'rungis_carton': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_papier': {'nb_photos': 33, 'nb_portfolios': 6}, 'rungis_plastique_clair': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_dur': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_fonce': {'nb_photos': 74, 'nb_portfolios': 7}, 'rungis_tapis_vide': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_tetrapak': {'nb_photos': 2, 'nb_portfolios': 1}}}, 'map_info_after_moyenne_mobile': {'07092021': {'distrib_time_diff': {'nb': 207, 'mean': 12.512076641626404, 'stddev': 12.296218880583977, 'min': 0.0, 'max': 119.00011491775513, 'quantil_10': {'min': [8.999778032302856], 'max': [11.000291109085083]}, 'quantil_100': {'min': [8.999709129333496], 'max': [61.00024700164795]}, 'quantil_1000': {'min': [0.0], 'max': [119.00011491775513]}, 'quantil_5000': {'min': [0.0], 'max': [119.00011491775513]}, 'quantil_10000': {'min': [0.0], 'max': [119.00011491775513]}}, 'time_diff': {'rungis_aluminium': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 80.99965000152588, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_carton': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 49.000049114227295, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_papier': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 11.000201940536499, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 534.9997780323029, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_clair': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_dur': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 31.000057697296143, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_fonce': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 564.0013737678528, '05102018_papier_non_papier_tres_peu_dense': 19.999656200408936}, 'rungis_tapis_vide': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_tetrapak': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 8.999944925308228, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 89.99986672401428, '05102018_papier_non_papier_tres_peu_dense': 9.999994993209839}}, 'time_diff_removed': {'rungis_aluminium': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 11.000208854675293, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 158.99993062019348, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_carton': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 101.00013995170593, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_papier': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 18.999826192855835, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 168.99926328659058, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_clair': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_dur': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_fonce': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 21.00012707710266, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 489.9993727207184, '05102018_papier_non_papier_tres_peu_dense': 11.000169038772583}, 'rungis_tapis_vide': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_tetrapak': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 38.99988508224487, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 170.0003685951233, '05102018_papier_non_papier_tres_peu_dense': 0}}, 'nb_photos': {'rungis_aluminium': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 8, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_carton': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 6, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_papier': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 1, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 28, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_clair': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_dur': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 2, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_fonce': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 40, '05102018_papier_non_papier_tres_peu_dense': 2}, 'rungis_tapis_vide': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_tetrapak': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 1, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 9, '05102018_papier_non_papier_tres_peu_dense': 1}}, 'nb_photos_removed': {'rungis_aluminium': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 1, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 16, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_carton': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 5, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_papier': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 2, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 16, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_clair': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_dur': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_fonce': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 1, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 46, '05102018_papier_non_papier_tres_peu_dense': 1}, 'rungis_tapis_vide': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_tetrapak': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 4, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 17, '05102018_papier_non_papier_tres_peu_dense': 0}}}}, 'map_amount_per_hashtag': {'rungis_aluminium': [(10, 11)], 'rungis_carton': [], 'rungis_papier': [(0, 1), (2, 3), (4, 5), (6, 7), (8, 9), (13, 14)], 'rungis_plastique_clair': [], 'rungis_plastique_dur': [], 'rungis_plastique_fonce': [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (12, 13), (14, 15)], 'rungis_tapis_vide': [], 'rungis_tetrapak': [(11, 12)]}, 'count': {'rungis_aluminium': [(10, 11)], 'rungis_carton': [], 'rungis_papier': [(0, 1), (2, 3), (4, 5), (6, 7), (8, 9), (13, 14)], 'rungis_plastique_clair': [], 'rungis_plastique_dur': [], 'rungis_plastique_fonce': [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (12, 13), (14, 15)], 'rungis_tapis_vide': [], 'rungis_tetrapak': [(11, 12)]}})}
ERROR rubbia FAILED

############################### TEST rubbia_split_dark  ################################
warning , we can't find thcl infos in json_data
warning , we can't find pdt infos in json_data
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=3787

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=3787
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 3787
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=3787
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : split_time_score_with_photo
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (4608689) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (4608689)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 1055013727, 'portfolio_id': 4608689}, {'photo_id': 1055013724, 'portfolio_id': 4608689}, {'photo_id': 1055013693, 'portfolio_id': 4608689}, {'photo_id': 1055012727, 'portfolio_id': 4608689}, {'photo_id': 1055012722, 'portfolio_id': 4608689}, {'photo_id': 1055012686, 'portfolio_id': 4608689}, {'photo_id': 1055012684, 'portfolio_id': 4608689}, {'photo_id': 1055011740, 'portfolio_id': 4608689}, {'photo_id': 1055011733, 'portfolio_id': 4608689}, {'photo_id': 1055011726, 'portfolio_id': 4608689}, {'photo_id': 1055011459, 'portfolio_id': 4608689}, {'photo_id': 1055011454, 'portfolio_id': 4608689}, {'photo_id': 1055011441, 'portfolio_id': 4608689}, {'photo_id': 1055011086, 'portfolio_id': 4608689}, {'photo_id': 1055011076, 'portfolio_id': 4608689}, {'photo_id': 1055011074, 'portfolio_id': 4608689}, {'photo_id': 1055011072, 'portfolio_id': 4608689}, {'photo_id': 1055010743, 'portfolio_id': 4608689}, {'photo_id': 1055010739, 'portfolio_id': 4608689}, {'photo_id': 1055010737, 'portfolio_id': 4608689}, {'photo_id': 1055010730, 'portfolio_id': 4608689}, {'photo_id': 1055010725, 'portfolio_id': 4608689}, {'photo_id': 1055010723, 'portfolio_id': 4608689}, {'photo_id': 1055010143, 'portfolio_id': 4608689}, {'photo_id': 1055008638, 'portfolio_id': 4608689}, {'photo_id': 1055008599, 'portfolio_id': 4608689}, {'photo_id': 1055008597, 'portfolio_id': 4608689}, {'photo_id': 1055008184, 'portfolio_id': 4608689}, {'photo_id': 1055008181, 'portfolio_id': 4608689}, {'photo_id': 1055007992, 'portfolio_id': 4608689}, {'photo_id': 1055007953, 'portfolio_id': 4608689}, {'photo_id': 1055007950, 'portfolio_id': 4608689}, {'photo_id': 1055004798, 'portfolio_id': 4608689}, {'photo_id': 1055004627, 'portfolio_id': 4608689}, {'photo_id': 1055004608, 'portfolio_id': 4608689}, {'photo_id': 1055004600, 'portfolio_id': 4608689}, {'photo_id': 1055004278, 'portfolio_id': 4608689}, {'photo_id': 1055004217, 'portfolio_id': 4608689}, {'photo_id': 1055003679, 'portfolio_id': 4608689}, {'photo_id': 1055003357, 'portfolio_id': 4608689}, {'photo_id': 1055003348, 'portfolio_id': 4608689}, {'photo_id': 1055003292, 'portfolio_id': 4608689}, {'photo_id': 1055003278, 'portfolio_id': 4608689}, {'photo_id': 1055003266, 'portfolio_id': 4608689}, {'photo_id': 1055003261, 'portfolio_id': 4608689}, {'photo_id': 1055003259, 'portfolio_id': 4608689}, {'photo_id': 1055003249, 'portfolio_id': 4608689}, {'photo_id': 1055003202, 'portfolio_id': 4608689}, {'photo_id': 1055003198, 'portfolio_id': 4608689}, {'photo_id': 1055003197, 'portfolio_id': 4608689}, {'photo_id': 1055003185, 'portfolio_id': 4608689}, {'photo_id': 1055003134, 'portfolio_id': 4608689}, {'photo_id': 1055003131, 'portfolio_id': 4608689}, {'photo_id': 1055002045, 'portfolio_id': 4608689}, {'photo_id': 1055001545, 'portfolio_id': 4608689}, {'photo_id': 1055001542, 'portfolio_id': 4608689}, {'photo_id': 1055001092, 'portfolio_id': 4608689}, {'photo_id': 1055001085, 'portfolio_id': 4608689}, {'photo_id': 1055000228, 'portfolio_id': 4608689}, {'photo_id': 1055000070, 'portfolio_id': 4608689}, {'photo_id': 1055000068, 'portfolio_id': 4608689}, {'photo_id': 1055000063, 'portfolio_id': 4608689}, {'photo_id': 1055000059, 'portfolio_id': 4608689}, {'photo_id': 1055000055, 'portfolio_id': 4608689}]
 map_portfolio_id_photo_id: {4608689: [1055013727, 1055013724, 1055013693, 1055012727, 1055012722, 1055012686, 1055012684, 1055011740, 1055011733, 1055011726, 1055011459, 1055011454, 1055011441, 1055011086, 1055011076, 1055011074, 1055011072, 1055010743, 1055010739, 1055010737, 1055010730, 1055010725, 1055010723, 1055010143, 1055008638, 1055008599, 1055008597, 1055008184, 1055008181, 1055007992, 1055007953, 1055007950, 1055004798, 1055004627, 1055004608, 1055004600, 1055004278, 1055004217, 1055003679, 1055003357, 1055003348, 1055003292, 1055003278, 1055003266, 1055003261, 1055003259, 1055003249, 1055003202, 1055003198, 1055003197, 1055003185, 1055003134, 1055003131, 1055002045, 1055001545, 1055001542, 1055001092, 1055001085, 1055000228, 1055000070, 1055000068, 1055000063, 1055000059, 1055000055]}
##### Call download_photos : nb_thread : 5
begin to download photo : 1055013727
begin to download photo : 1055011086
begin to download photo : 1055003357
begin to download photo : 1055003131
begin to download photo : 1055008597
download finish for photo 1055011086
begin to download photo : 1055011076
download finish for photo 1055003357
begin to download photo : 1055003348
download finish for photo 1055008597
begin to download photo : 1055008184
download finish for photo 1055003131
begin to download photo : 1055002045
download finish for photo 1055013727
begin to download photo : 1055013724
download finish for photo 1055011076
begin to download photo : 1055011074
download finish for photo 1055003348
begin to download photo : 1055003292
download finish for photo 1055002045
begin to download photo : 1055001545
download finish for photo 1055008184
begin to download photo : 1055008181
download finish for photo 1055013724
begin to download photo : 1055013693
download finish for photo 1055003292
begin to download photo : 1055003278
download finish for photo 1055001545
begin to download photo : 1055001542
download finish for photo 1055008181
begin to download photo : 1055007992
download finish for photo 1055003278
begin to download photo : 1055003266
download finish for photo 1055013693
begin to download photo : 1055012727
download finish for photo 1055001542
begin to download photo : 1055001092
download finish for photo 1055007992
begin to download photo : 1055007953
download finish for photo 1055011074
begin to download photo : 1055011072
download finish for photo 1055003266
begin to download photo : 1055003261
download finish for photo 1055001092
begin to download photo : 1055001085
download finish for photo 1055012727
begin to download photo : 1055012722
download finish for photo 1055011072
begin to download photo : 1055010743
download finish for photo 1055003261
begin to download photo : 1055003259
download finish for photo 1055001085
begin to download photo : 1055000228
download finish for photo 1055010743
begin to download photo : 1055010739
download finish for photo 1055012722
begin to download photo : 1055012686
download finish for photo 1055003259
begin to download photo : 1055003249
download finish for photo 1055000228
begin to download photo : 1055000070
download finish for photo 1055012686
begin to download photo : 1055012684
download finish for photo 1055010739
begin to download photo : 1055010737
download finish for photo 1055003249
begin to download photo : 1055003202
download finish for photo 1055007953
begin to download photo : 1055007950
download finish for photo 1055010737
begin to download photo : 1055010730
download finish for photo 1055012684
begin to download photo : 1055011740
download finish for photo 1055000070
begin to download photo : 1055000068
download finish for photo 1055003202
begin to download photo : 1055003198
download finish for photo 1055010730
begin to download photo : 1055010725
download finish for photo 1055007950
begin to download photo : 1055004798
download finish for photo 1055011740
begin to download photo : 1055011733
download finish for photo 1055000068
begin to download photo : 1055000063
download finish for photo 1055003198
begin to download photo : 1055003197
download finish for photo 1055010725
begin to download photo : 1055010723
download finish for photo 1055011733
begin to download photo : 1055011726
download finish for photo 1055003197
begin to download photo : 1055003185
download finish for photo 1055010723
begin to download photo : 1055010143
download finish for photo 1055003185
begin to download photo : 1055003134
download finish for photo 1055000063
begin to download photo : 1055000059
download finish for photo 1055011726
begin to download photo : 1055011459
download finish for photo 1055010143
begin to download photo : 1055008638
download finish for photo 1055000059
begin to download photo : 1055000055
download finish for photo 1055003134
download finish for photo 1055008638
begin to download photo : 1055008599
download finish for photo 1055011459
begin to download photo : 1055011454
download finish for photo 1055000055
download finish for photo 1055008599
download finish for photo 1055011454
begin to download photo : 1055011441
download finish for photo 1055011441
download finish for photo 1055004798
begin to download photo : 1055004627
download finish for photo 1055004627
begin to download photo : 1055004608
download finish for photo 1055004608
begin to download photo : 1055004600
download finish for photo 1055004600
begin to download photo : 1055004278
download finish for photo 1055004278
begin to download photo : 1055004217
download finish for photo 1055004217
begin to download photo : 1055003679
download finish for photo 1055003679
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 64 ; length of list_pids : 64 ; length of list_args : 64
##### After load_data_input 
time to download the photos : 5.960857152938843
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save
number of steps :  1
step1:split_time_score_with_photo
Mon Sep 29 11:46:13 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139168_2886529_1055003357_724bbfa472d57dcea0975679f4a393ff.jpg': 1055003357, 'temp/1759139168_2886529_1055003348_588ecea5e15961fe1292f72d4cf33b3c.jpg': 1055003348, 'temp/1759139168_2886529_1055003292_4ad5363c1776217234fa6acc8a9fbd85.jpg': 1055003292, 'temp/1759139168_2886529_1055003278_1e53f6abe2076619fbf0cb6255fca71b.jpg': 1055003278, 'temp/1759139168_2886529_1055003266_eae639e1ccc20bd1a6a2f69e66c41382.jpg': 1055003266, 'temp/1759139168_2886529_1055003261_fb67a67f6cc7a87b0af2a03906a24b7d.jpg': 1055003261, 'temp/1759139168_2886529_1055003259_cc27886462be43921344678712ff777f.jpg': 1055003259, 'temp/1759139168_2886529_1055003249_a3800e1944260107f11d988a743bd54f.jpg': 1055003249, 'temp/1759139168_2886529_1055003202_39321c32c32d09c1ffaa48a867a690fe.jpg': 1055003202, 'temp/1759139168_2886529_1055003198_3f7cf97aea8a045db187250a09903f94.jpg': 1055003198, 'temp/1759139168_2886529_1055003197_8735b1a92ec92b085fcf6d4191102ae4.jpg': 1055003197, 'temp/1759139168_2886529_1055003185_f94dcbfb2802c930ee56eb9d1c9e9420.jpg': 1055003185, 'temp/1759139168_2886529_1055003134_b26c74c56f9d8d7a5c9ccd672aa5722e.jpg': 1055003134, 'temp/1759139168_2886529_1055003131_12bcf047c16351b2a2b41b2ba70db7b7.jpg': 1055003131, 'temp/1759139168_2886529_1055002045_390fce166d8a4e346d18cfdb695bd3f8.jpg': 1055002045, 'temp/1759139168_2886529_1055001545_292c889b214383c9eff957d779217aeb.jpg': 1055001545, 'temp/1759139168_2886529_1055001542_5e1a9c0f7788cfc726846cda743dd454.jpg': 1055001542, 'temp/1759139168_2886529_1055001092_fd1729b4879040e36f41b3af545fd28c.jpg': 1055001092, 'temp/1759139168_2886529_1055001085_586137b302133d7442a9621d01dfd9ee.jpg': 1055001085, 'temp/1759139168_2886529_1055000228_6046bacdc732ec23020791db686fd31b.jpg': 1055000228, 'temp/1759139168_2886529_1055000070_639c1516bf67f61fa16e61a34313e98b.jpg': 1055000070, 'temp/1759139168_2886529_1055000068_fa025777da94026b7a3688fce1c4c657.jpg': 1055000068, 'temp/1759139168_2886529_1055000063_44f2dd3c2dff30aa3bc9da4b89a2daf0.jpg': 1055000063, 'temp/1759139168_2886529_1055000059_4b09d365e4e3dcefaeb8b421405350f9.jpg': 1055000059, 'temp/1759139168_2886529_1055000055_c9e450bd4b6e2cb9cfa8e540e915987c.jpg': 1055000055, 'temp/1759139168_2886529_1055011086_bdfadbfd9b854d0e57bff554442c9bcd.jpg': 1055011086, 'temp/1759139168_2886529_1055011076_8f47b07eac25de1c6c004fe4d3d508be.jpg': 1055011076, 'temp/1759139168_2886529_1055011074_af75a035dcd1829a43a1e1de711c1c5c.jpg': 1055011074, 'temp/1759139168_2886529_1055011072_37a292bbe61b6a71ca20b3b88d1105ca.jpg': 1055011072, 'temp/1759139168_2886529_1055010743_8e677a02379626daa3d4ee7356ad894e.jpg': 1055010743, 'temp/1759139168_2886529_1055010739_e4eec41f6a47dc3e97c189a1e5caa46f.jpg': 1055010739, 'temp/1759139168_2886529_1055010737_350522f4866a7dcde0258de8aa920041.jpg': 1055010737, 'temp/1759139168_2886529_1055010730_1cfbca5001cab6e5d12df6b4b06e86c5.jpg': 1055010730, 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map_photo_id_path_extension : {1055003357: {'path': 'temp/1759139168_2886529_1055003357_724bbfa472d57dcea0975679f4a393ff.jpg', 'extension': 'jpg'}, 1055003348: {'path': 'temp/1759139168_2886529_1055003348_588ecea5e15961fe1292f72d4cf33b3c.jpg', 'extension': 'jpg'}, 1055003292: {'path': 'temp/1759139168_2886529_1055003292_4ad5363c1776217234fa6acc8a9fbd85.jpg', 'extension': 'jpg'}, 1055003278: {'path': 'temp/1759139168_2886529_1055003278_1e53f6abe2076619fbf0cb6255fca71b.jpg', 'extension': 'jpg'}, 1055003266: {'path': 'temp/1759139168_2886529_1055003266_eae639e1ccc20bd1a6a2f69e66c41382.jpg', 'extension': 'jpg'}, 1055003261: {'path': 'temp/1759139168_2886529_1055003261_fb67a67f6cc7a87b0af2a03906a24b7d.jpg', 'extension': 'jpg'}, 1055003259: {'path': 'temp/1759139168_2886529_1055003259_cc27886462be43921344678712ff777f.jpg', 'extension': 'jpg'}, 1055003249: {'path': 'temp/1759139168_2886529_1055003249_a3800e1944260107f11d988a743bd54f.jpg', 'extension': 'jpg'}, 1055003202: {'path': 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'temp/1759139168_2886529_1055007950_3aea8b8cb1f8c6e4b87714e0d17e12c5.jpg', 'extension': 'jpg'}, 1055004798: {'path': 'temp/1759139168_2886529_1055004798_c38e078097a0afb8565867dfa05724d7.jpg', 'extension': 'jpg'}, 1055004627: {'path': 'temp/1759139168_2886529_1055004627_9afc9aa5c93533104d86b96e0b5de9d0.jpg', 'extension': 'jpg'}, 1055004608: {'path': 'temp/1759139168_2886529_1055004608_8f10a6ef80cc1cb72e52bb22cdd7bac9.jpg', 'extension': 'jpg'}, 1055004600: {'path': 'temp/1759139168_2886529_1055004600_d545fe61ea780a6598c9d5febba7d576.jpg', 'extension': 'jpg'}, 1055004278: {'path': 'temp/1759139168_2886529_1055004278_7d9a1423053759339afb51f45a1b5d84.jpg', 'extension': 'jpg'}, 1055004217: {'path': 'temp/1759139168_2886529_1055004217_63353acd8a59167cd1364f16c9c06813.jpg', 'extension': 'jpg'}, 1055003679: {'path': 'temp/1759139168_2886529_1055003679_9231db8e9237505d07f9358467266703.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
----- Debut du copier-coller des param necessaire pour fonction main de STS -----
SELECT app_name, token FROM MTRUser.mtr_app_api_token WHERE mtr_user_id=739 AND app_name="token_split_time_score" AND expire_at > NOW() 
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'}]
select SUBSTRING(ph.text,16,2) as h, count(*) from MTRUser.mtr_portfolio_photos mpp inner join MTRBack.photos_view ph on mpp.mtr_photo_id = ph.photo_id where mpp.mtr_portfolio_id = 4608689 group by h
(('18', 4), ('19', 5), ('20', 5), ('24', 8), ('26', 6), ('17', 1), ('27', 9), ('51', 7), ('28', 2), ('21', 4), ('52', 2), ('25', 6), ('50', 3), ('22', 2))

            SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4608689  AND mpp.hide_status=0  ORDER BY mpp.order  LIMIT 0, 100000
SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id
FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1
where mpp1.mtr_photo_id=cps.photo_id
AND h.hashtag_id=cps.hashtag_id
AND mpp1.hide_status=0
AND mpp1.mtr_portfolio_id in (4608689)  AND cps.thcl in (861) order by cps.score desc LIMIT 0, 100000
SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id
FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1
where mpp1.mtr_photo_id=cps.photo_id
AND h.hashtag_id=cps.hashtag_id
AND mpp1.hide_status=0
AND mpp1.mtr_portfolio_id in (4608689)  AND cps.thcl in (758) order by cps.score desc LIMIT 0, 100000
ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {}
06102021 4608689 Nombre de photos uploadées : 64 / 23040 (0%)
06102021 4608689 Nombre de photos taguées (types de déchets): 0 / 64 (0%)
06102021 4608689 Nombre de photos taguées (volume) : 0 / 64 (0%)
[{'photo_id': 1055000228, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/10/7/6046bacdc732ec23020791db686fd31b.jpg', 'username': None, 'uploaded_at': None, 'text': 'IMG_20211006_101733.jpg', 'path': 'temp/1759139168_2886529_1055000228_6046bacdc732ec23020791db686fd31b.jpg', 'black': False}, {'photo_id': 1055007950, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/10/7/3aea8b8cb1f8c6e4b87714e0d17e12c5.jpg', 'username': None, 'uploaded_at': None, 'text': 'IMG_20211006_101843.jpg', 'path': 'temp/1759139168_2886529_1055007950_3aea8b8cb1f8c6e4b87714e0d17e12c5.jpg', 'black': False}, {'photo_id': 1055003348, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/10/7/588ecea5e15961fe1292f72d4cf33b3c.jpg', 'username': None, 'uploaded_at': None, 'text': 'IMG_20211006_101853.jpg', 'path': 'temp/1759139168_2886529_1055003348_588ecea5e15961fe1292f72d4cf33b3c.jpg', 'black': False}, {'photo_id': 1055000055, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/10/7/c9e450bd4b6e2cb9cfa8e540e915987c.jpg', 'username': None, 'uploaded_at': None, 'text': 'IMG_20211006_101804.jpg', 'path': 'temp/1759139168_2886529_1055000055_c9e450bd4b6e2cb9cfa8e540e915987c.jpg', 'black': False}]
0
[]
 elapsed_time : load_data_split_time_score 9.059906005859375e-06
 elapsed_time : order_list_meta_photo_and_scores 1.1205673217773438e-05
???????????????????????????????????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.002605915069580078

        INSERT INTO `MTRPhoto`.`dashboard_entry_day`
(`dashboard_place_id`,
`mtr_portfolio_id`,
`date`)
VALUES
(
41, 4608689, '2021-10-06'
) ON DUPLICATE KEY UPDATE mtr_portfolio_id=VALUES(mtr_portfolio_id), updated_at=NOW();

Catched exception !
Connect or reconnect !
SELECT id FROM MTRPhoto.dashboard_entry_day where dashboard_place_id = 41 and date = '2021-10-06' ; 

        INSERT INTO `MTRPhoto`.`dashboard_run_ids`
(`dashboard_entry_day`,
`mtr_user_id`,
`misc_info`)
VALUES
(106232,739,"{}");

Catched exception !
Connect or reconnect !
 elapsed_time : insert_dashboard_record_day_entry 0.6606912612915039
***** BEGIN SPLIT BY DARK *****
To DO 08/10/21
Let s Iterate on Ds Object and set black attribute == >


begin to insert list_values into photo_hahstag_ids : 
length of list_valuse in save_photo_hashtag_id_type : 1
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id)
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id)
 first line : (1055001085, 492584841, 4330)
...
 last line : (1055001085, 492584841, 4330)
time used for this insertion : 0.03604888916015625


begin to insert list_values into photo_hahstag_ids : 
length of list_valuse in save_photo_hashtag_id_type : 2
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id)
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id)
 first line : (1055001085, 492584841, 4330)
...
 last line : (1055008638, 492584841, 4330)
time used for this insertion : 0.03436732292175293


begin to insert list_values into photo_hahstag_ids : 
length of list_valuse in save_photo_hashtag_id_type : 3
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id)
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id)
 first line : (1055001085, 492584841, 4330)
...
 last line : (1055010730, 492584841, 4330)
time used for this insertion : 0.034645795822143555


begin to insert list_values into photo_hahstag_ids : 
length of list_valuse in save_photo_hashtag_id_type : 4
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id)
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id)
 first line : (1055001085, 492584841, 4330)
...
 last line : (1055011086, 492584841, 4330)
time used for this insertion : 0.034574031829833984


begin to insert list_values into photo_hahstag_ids : 
length of list_valuse in save_photo_hashtag_id_type : 5
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id)
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) on DUPLICATE KEY UPDATE hashtag_id=VALUES(hashtag_id)
 first line : (1055001085, 492584841, 4330)
...
 last line : (1055012686, 492584841, 4330)
time used for this insertion : 0.03560304641723633
 elapsed_time : SPLIT_BY_DARK 0.19668340682983398
***** END SPLIT BY DARK *****
((1055001085,), (1055008638,), (1055010730,), (1055011086,), (1055012686,))
***** BEGIN SPLIT TIME *****
[12, 20, 29, 38, 51]
````````````````````````````````````````````````````````````````list printed:  [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]]
forced_hashtag:  jrm
force hashtag to jrm
 elapsed_time : SPLIT_TIME 0.01842474937438965
***** END SPLIT TIME *****
 NUMBER BATCH : 7
 list_ponderation used : [0.001, 0.001, 0.001, 0.001, 0.001] , list_hashtag_class_create_as_list : ['refus', 'jrm']
Listed one port to create portfolio : jrm_diff_batch__06102021_10_17_33_000000
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
Nombres de balles jrm_diff_batch__06102021_10_17_33_000000 : 0 duration : 172.0
update_text_in_photos list_photo_id_text len : 0 , 100 first caracter of query  INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update 
<map object at 0x7f1594723280>
None
result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 172.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_101733.jpg'}
 Production hashtag (incorrect ponderation at 20-10-18) : 0
Listed one port to create portfolio : jrm_diff_batch__06102021_10_20_49_000000
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
Nombres de balles jrm_diff_batch__06102021_10_20_49_000000 : 0 duration : 72.0
update_text_in_photos list_photo_id_text len : 0 , 100 first caracter of query  INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update 
<map object at 0x7f15947230a0>
None
result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 72.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_102049.jpg'}
 Production hashtag (incorrect ponderation at 20-10-18) : 0
Listed one port to create portfolio : jrm_diff_batch__06102021_10_24_04_000000
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
Nombres de balles jrm_diff_batch__06102021_10_24_04_000000 : 0 duration : 52.0
update_text_in_photos list_photo_id_text len : 0 , 100 first caracter of query  INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update 
<map object at 0x7f1594723250>
None
result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 52.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_102404.jpg'}
 Production hashtag (incorrect ponderation at 20-10-18) : 0
Listed one port to create portfolio : jrm_diff_batch__06102021_10_25_25_000000
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
Nombres de balles jrm_diff_batch__06102021_10_25_25_000000 : 0 duration : 53.0
update_text_in_photos list_photo_id_text len : 0 , 100 first caracter of query  INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update 
<map object at 0x7f15947232b0>
None
result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 53.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_102525.jpg'}
 Production hashtag (incorrect ponderation at 20-10-18) : 0
Listed one port to create portfolio : jrm_diff_batch__06102021_10_26_45_000000
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
Nombres de balles jrm_diff_batch__06102021_10_26_45_000000 : 0 duration : 81.0
update_text_in_photos list_photo_id_text len : 0 , 100 first caracter of query  INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update 
<map object at 0x7f1594723340>
None
result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 81.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_102645.jpg'}
 Production hashtag (incorrect ponderation at 20-10-18) : 0
Empty batch, bug or could have been filtered !
Listed one port to create portfolio : jrm_diff_batch__06102021_14_50_39_000000
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
Nombres de balles jrm_diff_batch__06102021_14_50_39_000000 : 0 duration : 102.0
update_text_in_photos list_photo_id_text len : 0 , 100 first caracter of query  INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update 
<map object at 0x7f15947233a0>
None
result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 102.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_145039.jpg'}
 Production hashtag (incorrect ponderation at 20-10-18) : 0
We have rejected 0 photos because of the batch_size condition !
 NUMBER BATCH list_of_portfolios_to_create : 6
list_same_port_ids : [4938484]


 find same portfolio which already exist 4938484 , we will use it
INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 4938484 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) +  100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4938484 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`);
0
list_same_port_ids : [4938485]


 find same portfolio which already exist 4938485 , we will use it
INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 4938485 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) +  100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4938485 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`);
0
list_same_port_ids : [4938486]


 find same portfolio which already exist 4938486 , we will use it
INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 4938486 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) +  100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4938486 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`);
0
list_same_port_ids : [4938487]


 find same portfolio which already exist 4938487 , we will use it
INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 4938487 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) +  100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4938487 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`);
0
list_same_port_ids : [4938488]


 find same portfolio which already exist 4938488 , we will use it
INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 4938488 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) +  100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4938488 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`);
0
list_same_port_ids : [4756245]


 find same portfolio which already exist 4756245 , we will use it
INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 4756245 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) +  100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4756245 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`);
0
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                FROM MTRPhoto.mtr_datou_result
                WHERE mtr_portfolio_id = 4938484
                AND result is not null
                and result<>"None"
                and result<>0.0
                AND mtd_id=3543
                ORDER BY id desc LIMIT 1
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                    FROM MTRPhoto.mtr_datou_result
                    WHERE mtr_portfolio_id = 4938484
                    AND result is not null
                    and result<>"None"
                    and result=0.0
                    AND mtd_id=3543
                    ORDER BY created_at desc LIMIT 1
 list_result_datou : []
select url from MTRUser.mtr_files where mtd_id = 3543 and mtr_portfolio_id = 4938484 order by file_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 !
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 8564 mask_detect is not consistent : 4 used against 2 in the step definition !
WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition !
WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition !
Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used !
Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used !
WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition !
WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition !
Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs !
WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition !
WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition !
Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs !
Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used !
Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs !
Step 9126 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 !
WARNING : type of output 2 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 2 of step 8567 doesn't seem to be define in the database(
WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5
WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7
WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1
WARNING : type of output 2 of step 8568 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8569 doesn't seem to be define in the database(
We ignore checkConsistencyTypeOutputInput for datou_step final !
WARNING : type of output 3 of step 8568 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8571 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8571 doesn't seem to be define in the database(
WARNING : type of input 3 of step 8570 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 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6
WARNING : type of input 2 of step 9453 doesn't seem to be define in the database(
WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None
WARNING : type of output 3 of step 9453 doesn't seem to be define in the database(
WARNING : type of input 2 of step 8571 doesn't seem to be define in the database(
WARNING : type of output 3 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8572 doesn't seem to be define in the database(
WARNING : type of output 3 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8573 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8572 doesn't seem to be define in the database(
WARNING : type of input 3 of step 8567 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8573 doesn't seem to be define in the database(
WARNING : type of input 4 of step 8567 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`=4938484 AND  mptpi.`type`=4038
To do

        INSERT INTO `MTRPhoto`.`dashboard_results`
(`dashboard_run_id`,
`hashtag`,
`date_debut`,
`date_fin`,
`nombre_balle`,
`mtr_portfolio_id`,
`qualite`,
`datou_result_id_qualite`,
`completion_percentage`,
`completion_json`)
VALUES
(2369497, '_______jrm',
'2021-10-06 10:17:33', '2021-10-06 10:20:25',
12, 4938484,
-1, -1,
'-1', "{'max_time_prod_two_photos': 0, 'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P4938484_21-11-2024_00_58_17.pdf', 'cartonnette': {'hashtag': 'cartonnette', 'sub_port_id': 4938531, 'pht': 4038}, 'Carton_brun': {'hashtag': 'Carton_brun', 'sub_port_id': 4938532, 'pht': 4038}, 'Carton_gris': {'hashtag': 'Carton_gris', 'sub_port_id': 4938533, 'pht': 4038}, 'Teint_Dans_La_Masse': {'hashtag': 'Teint_Dans_La_Masse', 'sub_port_id': 4938534, 'pht': 4038}, 'metal': {'hashtag': 'metal', 'sub_port_id': 4938535, 'pht': 4038}, 'plastique': {'hashtag': 'plastique', 'sub_port_id': 4938536, 'pht': 4038}, 'autre_refus': {'hashtag': 'autre_refus', 'sub_port_id': 4938537, 'pht': 4038}, 'papier': {'hashtag': 'papier', 'sub_port_id': 4938538, 'pht': 4038}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 4938539, 'pht': 4038}, 'kraft': {'hashtag': 'kraft', 'sub_port_id': 4938540, 'pht': 4038}}"
);
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                FROM MTRPhoto.mtr_datou_result
                WHERE mtr_portfolio_id = 4938485
                AND result is not null
                and result<>"None"
                and result<>0.0
                AND mtd_id=3543
                ORDER BY id desc LIMIT 1
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                    FROM MTRPhoto.mtr_datou_result
                    WHERE mtr_portfolio_id = 4938485
                    AND result is not null
                    and result<>"None"
                    and result=0.0
                    AND mtd_id=3543
                    ORDER BY created_at desc LIMIT 1
 list_result_datou : []
select url from MTRUser.mtr_files where mtd_id = 3543 and mtr_portfolio_id = 4938485 order by file_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 !
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 8564 mask_detect is not consistent : 4 used against 2 in the step definition !
WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition !
WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition !
Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used !
Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used !
WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition !
WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition !
Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs !
WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition !
WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition !
Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs !
Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used !
Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs !
Step 9126 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 !
WARNING : type of output 2 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 2 of step 8567 doesn't seem to be define in the database(
WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5
WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7
WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1
WARNING : type of output 2 of step 8568 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8569 doesn't seem to be define in the database(
We ignore checkConsistencyTypeOutputInput for datou_step final !
WARNING : type of output 3 of step 8568 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8571 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8571 doesn't seem to be define in the database(
WARNING : type of input 3 of step 8570 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 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6
WARNING : type of input 2 of step 9453 doesn't seem to be define in the database(
WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None
WARNING : type of output 3 of step 9453 doesn't seem to be define in the database(
WARNING : type of input 2 of step 8571 doesn't seem to be define in the database(
WARNING : type of output 3 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8572 doesn't seem to be define in the database(
WARNING : type of output 3 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8573 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8572 doesn't seem to be define in the database(
WARNING : type of input 3 of step 8567 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8573 doesn't seem to be define in the database(
WARNING : type of input 4 of step 8567 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`=4938485 AND  mptpi.`type`=4038
To do

        INSERT INTO `MTRPhoto`.`dashboard_results`
(`dashboard_run_id`,
`hashtag`,
`date_debut`,
`date_fin`,
`nombre_balle`,
`mtr_portfolio_id`,
`qualite`,
`datou_result_id_qualite`,
`completion_percentage`,
`completion_json`)
VALUES
(2369497, '_______jrm',
'2021-10-06 10:20:49', '2021-10-06 10:22:01',
7, 4938485,
-1, -1,
'-1', "{'max_time_prod_two_photos': 0, 'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P4938485_21-11-2024_01_05_11.pdf', 'autre_refus': {'hashtag': 'autre_refus', 'sub_port_id': 4938520, 'pht': 4038}, 'Carton_gris': {'hashtag': 'Carton_gris', 'sub_port_id': 4938521, 'pht': 4038}, 'Carton_brun': {'hashtag': 'Carton_brun', 'sub_port_id': 4938522, 'pht': 4038}, 'metal': {'hashtag': 'metal', 'sub_port_id': 4938523, 'pht': 4038}, 'kraft': {'hashtag': 'kraft', 'sub_port_id': 4938524, 'pht': 4038}, 'Teint_Dans_La_Masse': {'hashtag': 'Teint_Dans_La_Masse', 'sub_port_id': 4938525, 'pht': 4038}, 'plastique': {'hashtag': 'plastique', 'sub_port_id': 4938526, 'pht': 4038}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 4938527, 'pht': 4038}, 'papier': {'hashtag': 'papier', 'sub_port_id': 4938528, 'pht': 4038}, 'cartonnette': {'hashtag': 'cartonnette', 'sub_port_id': 4938529, 'pht': 4038}}"
);
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                FROM MTRPhoto.mtr_datou_result
                WHERE mtr_portfolio_id = 4938486
                AND result is not null
                and result<>"None"
                and result<>0.0
                AND mtd_id=3543
                ORDER BY id desc LIMIT 1
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                    FROM MTRPhoto.mtr_datou_result
                    WHERE mtr_portfolio_id = 4938486
                    AND result is not null
                    and result<>"None"
                    and result=0.0
                    AND mtd_id=3543
                    ORDER BY created_at desc LIMIT 1
 list_result_datou : []
select url from MTRUser.mtr_files where mtd_id = 3543 and mtr_portfolio_id = 4938486 order by file_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 !
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 8564 mask_detect is not consistent : 4 used against 2 in the step definition !
WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition !
WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition !
Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used !
Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used !
WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition !
WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition !
Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs !
WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition !
WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition !
Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs !
Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used !
Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs !
Step 9126 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 !
WARNING : type of output 2 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 2 of step 8567 doesn't seem to be define in the database(
WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5
WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7
WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1
WARNING : type of output 2 of step 8568 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8569 doesn't seem to be define in the database(
We ignore checkConsistencyTypeOutputInput for datou_step final !
WARNING : type of output 3 of step 8568 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8571 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8571 doesn't seem to be define in the database(
WARNING : type of input 3 of step 8570 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 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6
WARNING : type of input 2 of step 9453 doesn't seem to be define in the database(
WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None
WARNING : type of output 3 of step 9453 doesn't seem to be define in the database(
WARNING : type of input 2 of step 8571 doesn't seem to be define in the database(
WARNING : type of output 3 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8572 doesn't seem to be define in the database(
WARNING : type of output 3 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8573 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8572 doesn't seem to be define in the database(
WARNING : type of input 3 of step 8567 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8573 doesn't seem to be define in the database(
WARNING : type of input 4 of step 8567 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`=4938486 AND  mptpi.`type`=4038
To do

        INSERT INTO `MTRPhoto`.`dashboard_results`
(`dashboard_run_id`,
`hashtag`,
`date_debut`,
`date_fin`,
`nombre_balle`,
`mtr_portfolio_id`,
`qualite`,
`datou_result_id_qualite`,
`completion_percentage`,
`completion_json`)
VALUES
(2369497, '_______jrm',
'2021-10-06 10:24:04', '2021-10-06 10:24:56',
8, 4938486,
-1, -1,
'-1', "{'max_time_prod_two_photos': 0, 'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P4938486_21-11-2024_01_17_15.pdf', 'Carton_gris': {'hashtag': 'Carton_gris', 'sub_port_id': 4938542, 'pht': 4038}, 'plastique': {'hashtag': 'plastique', 'sub_port_id': 4938543, 'pht': 4038}, 'autre_refus': {'hashtag': 'autre_refus', 'sub_port_id': 4938544, 'pht': 4038}, 'Teint_Dans_La_Masse': {'hashtag': 'Teint_Dans_La_Masse', 'sub_port_id': 4938545, 'pht': 4038}, 'metal': {'hashtag': 'metal', 'sub_port_id': 4938546, 'pht': 4038}, 'Carton_brun': {'hashtag': 'Carton_brun', 'sub_port_id': 4938547, 'pht': 4038}, 'cartonnette': {'hashtag': 'cartonnette', 'sub_port_id': 4938548, 'pht': 4038}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 4938549, 'pht': 4038}, 'papier': {'hashtag': 'papier', 'sub_port_id': 4938550, 'pht': 4038}, 'kraft': {'hashtag': 'kraft', 'sub_port_id': 4938551, 'pht': 4038}}"
);
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                FROM MTRPhoto.mtr_datou_result
                WHERE mtr_portfolio_id = 4938487
                AND result is not null
                and result<>"None"
                and result<>0.0
                AND mtd_id=3543
                ORDER BY id desc LIMIT 1
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                    FROM MTRPhoto.mtr_datou_result
                    WHERE mtr_portfolio_id = 4938487
                    AND result is not null
                    and result<>"None"
                    and result=0.0
                    AND mtd_id=3543
                    ORDER BY created_at desc LIMIT 1
 list_result_datou : []
select url from MTRUser.mtr_files where mtd_id = 3543 and mtr_portfolio_id = 4938487 order by file_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 !
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 8564 mask_detect is not consistent : 4 used against 2 in the step definition !
WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition !
WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition !
Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used !
Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used !
WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition !
WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition !
Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs !
WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition !
WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition !
Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs !
Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used !
Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs !
Step 9126 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 !
WARNING : type of output 2 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 2 of step 8567 doesn't seem to be define in the database(
WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5
WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7
WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1
WARNING : type of output 2 of step 8568 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8569 doesn't seem to be define in the database(
We ignore checkConsistencyTypeOutputInput for datou_step final !
WARNING : type of output 3 of step 8568 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8571 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8571 doesn't seem to be define in the database(
WARNING : type of input 3 of step 8570 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 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6
WARNING : type of input 2 of step 9453 doesn't seem to be define in the database(
WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None
WARNING : type of output 3 of step 9453 doesn't seem to be define in the database(
WARNING : type of input 2 of step 8571 doesn't seem to be define in the database(
WARNING : type of output 3 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8572 doesn't seem to be define in the database(
WARNING : type of output 3 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8573 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8572 doesn't seem to be define in the database(
WARNING : type of input 3 of step 8567 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8573 doesn't seem to be define in the database(
WARNING : type of input 4 of step 8567 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`=4938487 AND  mptpi.`type`=4038
To do

        INSERT INTO `MTRPhoto`.`dashboard_results`
(`dashboard_run_id`,
`hashtag`,
`date_debut`,
`date_fin`,
`nombre_balle`,
`mtr_portfolio_id`,
`qualite`,
`datou_result_id_qualite`,
`completion_percentage`,
`completion_json`)
VALUES
(2369497, '_______jrm',
'2021-10-06 10:25:25', '2021-10-06 10:26:18',
8, 4938487,
-1, -1,
'-1', "{'max_time_prod_two_photos': 0, 'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P4938487_21-11-2024_01_27_52.pdf', 'Carton_brun': {'hashtag': 'Carton_brun', 'sub_port_id': 4938552, 'pht': 4038}, 'autre_refus': {'hashtag': 'autre_refus', 'sub_port_id': 4938553, 'pht': 4038}, 'Carton_gris': {'hashtag': 'Carton_gris', 'sub_port_id': 4938554, 'pht': 4038}, 'plastique': {'hashtag': 'plastique', 'sub_port_id': 4938555, 'pht': 4038}, 'papier': {'hashtag': 'papier', 'sub_port_id': 4938556, 'pht': 4038}, 'metal': {'hashtag': 'metal', 'sub_port_id': 4938557, 'pht': 4038}, 'Teint_Dans_La_Masse': {'hashtag': 'Teint_Dans_La_Masse', 'sub_port_id': 4938558, 'pht': 4038}, 'cartonnette': {'hashtag': 'cartonnette', 'sub_port_id': 4938559, 'pht': 4038}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 4938560, 'pht': 4038}, 'kraft': {'hashtag': 'kraft', 'sub_port_id': 4938561, 'pht': 4038}}"
);
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                FROM MTRPhoto.mtr_datou_result
                WHERE mtr_portfolio_id = 4938488
                AND result is not null
                and result<>"None"
                and result<>0.0
                AND mtd_id=3543
                ORDER BY id desc LIMIT 1
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                    FROM MTRPhoto.mtr_datou_result
                    WHERE mtr_portfolio_id = 4938488
                    AND result is not null
                    and result<>"None"
                    and result=0.0
                    AND mtd_id=3543
                    ORDER BY created_at desc LIMIT 1
 list_result_datou : []
select url from MTRUser.mtr_files where mtd_id = 3543 and mtr_portfolio_id = 4938488 order by file_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 !
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 8564 mask_detect is not consistent : 4 used against 2 in the step definition !
WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition !
WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition !
Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used !
Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used !
WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition !
WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition !
Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs !
WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition !
WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition !
Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs !
Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used !
Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs !
Step 9126 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 !
WARNING : type of output 2 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 2 of step 8567 doesn't seem to be define in the database(
WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5
WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7
WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1
WARNING : type of output 2 of step 8568 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8569 doesn't seem to be define in the database(
We ignore checkConsistencyTypeOutputInput for datou_step final !
WARNING : type of output 3 of step 8568 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8571 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8571 doesn't seem to be define in the database(
WARNING : type of input 3 of step 8570 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 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6
WARNING : type of input 2 of step 9453 doesn't seem to be define in the database(
WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None
WARNING : type of output 3 of step 9453 doesn't seem to be define in the database(
WARNING : type of input 2 of step 8571 doesn't seem to be define in the database(
WARNING : type of output 3 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8572 doesn't seem to be define in the database(
WARNING : type of output 3 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8573 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8572 doesn't seem to be define in the database(
WARNING : type of input 3 of step 8567 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8573 doesn't seem to be define in the database(
WARNING : type of input 4 of step 8567 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`=4938488 AND  mptpi.`type`=4038
To do

        INSERT INTO `MTRPhoto`.`dashboard_results`
(`dashboard_run_id`,
`hashtag`,
`date_debut`,
`date_fin`,
`nombre_balle`,
`mtr_portfolio_id`,
`qualite`,
`datou_result_id_qualite`,
`completion_percentage`,
`completion_json`)
VALUES
(2369497, '_______jrm',
'2021-10-06 10:26:45', '2021-10-06 10:28:06',
12, 4938488,
-1, -1,
'-1', "{'max_time_prod_two_photos': 0, 'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P4938488_21-11-2024_01_43_46.pdf', 'Carton_gris': {'hashtag': 'Carton_gris', 'sub_port_id': 4938797, 'pht': 4038}, 'papier': {'hashtag': 'papier', 'sub_port_id': 4938798, 'pht': 4038}, 'cartonnette': {'hashtag': 'cartonnette', 'sub_port_id': 4938799, 'pht': 4038}, 'plastique': {'hashtag': 'plastique', 'sub_port_id': 4938800, 'pht': 4038}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 4938801, 'pht': 4038}, 'Carton_brun': {'hashtag': 'Carton_brun', 'sub_port_id': 4938802, 'pht': 4038}, 'metal': {'hashtag': 'metal', 'sub_port_id': 4938803, 'pht': 4038}, 'Teint_Dans_La_Masse': {'hashtag': 'Teint_Dans_La_Masse', 'sub_port_id': 4938804, 'pht': 4038}, 'autre_refus': {'hashtag': 'autre_refus', 'sub_port_id': 4938805, 'pht': 4038}, 'kraft': {'hashtag': 'kraft', 'sub_port_id': 4938806, 'pht': 4038}}"
);
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                FROM MTRPhoto.mtr_datou_result
                WHERE mtr_portfolio_id = 4756245
                AND result is not null
                and result<>"None"
                and result<>0.0
                AND mtd_id=3543
                ORDER BY id desc LIMIT 1
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                    FROM MTRPhoto.mtr_datou_result
                    WHERE mtr_portfolio_id = 4756245
                    AND result is not null
                    and result<>"None"
                    and result=0.0
                    AND mtd_id=3543
                    ORDER BY created_at desc LIMIT 1
 list_result_datou : []
select url from MTRUser.mtr_files where mtd_id = 3543 and mtr_portfolio_id = 4756245 order by file_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 !
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 8564 mask_detect is not consistent : 4 used against 2 in the step definition !
WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition !
WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition !
Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used !
Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used !
WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition !
WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition !
Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs !
WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition !
WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition !
Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs !
Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used !
Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs !
Step 9126 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 !
WARNING : type of output 2 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 2 of step 8567 doesn't seem to be define in the database(
WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5
WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7
WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1
WARNING : type of output 2 of step 8568 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8569 doesn't seem to be define in the database(
We ignore checkConsistencyTypeOutputInput for datou_step final !
WARNING : type of output 3 of step 8568 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8571 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8571 doesn't seem to be define in the database(
WARNING : type of input 3 of step 8570 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 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6
WARNING : type of input 2 of step 9453 doesn't seem to be define in the database(
WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None
WARNING : type of output 3 of step 9453 doesn't seem to be define in the database(
WARNING : type of input 2 of step 8571 doesn't seem to be define in the database(
WARNING : type of output 3 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8572 doesn't seem to be define in the database(
WARNING : type of output 3 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8573 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8572 doesn't seem to be define in the database(
WARNING : type of input 3 of step 8567 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8573 doesn't seem to be define in the database(
WARNING : type of input 4 of step 8567 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`=4756245 AND  mptpi.`type`=4038
To do

        INSERT INTO `MTRPhoto`.`dashboard_results`
(`dashboard_run_id`,
`hashtag`,
`date_debut`,
`date_fin`,
`nombre_balle`,
`mtr_portfolio_id`,
`qualite`,
`datou_result_id_qualite`,
`completion_percentage`,
`completion_json`)
VALUES
(2369497, '_______jrm',
'2021-10-06 14:50:39', '2021-10-06 14:52:21',
12, 4756245,
-1, -1,
'-1', "{'max_time_prod_two_photos': 0, 'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P4756245_21-11-2024_01_56_10.pdf', 'kraft': {'hashtag': 'kraft', 'sub_port_id': 4756351, 'pht': 4038}, 'metal': {'hashtag': 'metal', 'sub_port_id': 4756352, 'pht': 4038}, 'Teint_Dans_La_Masse': {'hashtag': 'Teint_Dans_La_Masse', 'sub_port_id': 4756353, 'pht': 4038}, 'Carton_gris': {'hashtag': 'Carton_gris', 'sub_port_id': 4756354, 'pht': 4038}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 4756355, 'pht': 4038}, 'plastique': {'hashtag': 'plastique', 'sub_port_id': 4756356, 'pht': 4038}, 'cartonnette': {'hashtag': 'cartonnette', 'sub_port_id': 4756357, 'pht': 4038}, 'Carton_brun': {'hashtag': 'Carton_brun', 'sub_port_id': 4756358, 'pht': 4038}, 'papier': {'hashtag': 'papier', 'sub_port_id': 4756359, 'pht': 4038}, 'autre_refus': {'hashtag': 'autre_refus', 'sub_port_id': 4756360, 'pht': 4038}}"
);
 elapsed_time : count_nb_balles_and_create_portfolio 2.431732177734375
# DISPLAY ALL COLLECTED DATA : 
{'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}
------ Fin du Copier-Coller ------
---------- ONE RESULT ---------
([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]], {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, {4938484: {'list_of_photos': [1055000228, 1055000055, 1055003357, 1055007950, 1055003348, 1055007953, 1055000059, 1055007992, 1055008181, 1055003197, 1055003198, 1055008184], 'hashtag': 'jrm'}, 4938485: {'list_of_photos': [1055000063, 1055004600, 1055008597, 1055003134, 1055008599, 1055003679, 1055004627], 'hashtag': 'jrm'}, 4938486: {'list_of_photos': [1055004217, 1055010143, 1055004278, 1055010723, 1055003131, 1055003202, 1055010725, 1055000068], 'hashtag': 'jrm'}, 4938487: {'list_of_photos': [1055010737, 1055010739, 1055003278, 1055010743, 1055011072, 1055011074, 1055011076, 1055000070], 'hashtag': 'jrm'}, 4938488: {'list_of_photos': [1055011441, 1055011454, 1055003185, 1055011459, 1055001092, 1055001542, 1055003292, 1055011726, 1055011733, 1055011740, 1055012684, 1055002045], 'hashtag': 'jrm'}, 4756245: {'list_of_photos': [1055012722, 1055004798, 1055004608, 1055012727, 1055013693, 1055013724, 1055003249, 1055001545, 1055003259, 1055013727, 1055003266, 1055003261], 'hashtag': 'jrm'}}, {2107757407: 59}, {'amount_uploaded_and_tagged': {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}})
---------- END de ONE RESULT ----------
Suppression des photos Telecharges

After datou_step_exec type output : <class 'tuple'>

time spend for datou_step_exec : 8.891447305679321
time spend to save output : 0.00013780593872070312
total time spend for step 1 : 8.891585111618042

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
 saveOutput not yet implemented for datou_step.type : split_time_score_with_photo we use saveGeneral 
[1055003357, 1055003348, 1055003292, 1055003278, 1055003266, 1055003261, 1055003259, 1055003249, 1055003202, 1055003198, 1055003197, 1055003185, 1055003134, 1055003131, 1055002045, 1055001545, 1055001542, 1055001092, 1055001085, 1055000228, 1055000070, 1055000068, 1055000063, 1055000059, 1055000055, 1055011086, 1055011076, 1055011074, 1055011072, 1055010743, 1055010739, 1055010737, 1055010730, 1055010725, 1055010723, 1055010143, 1055008638, 1055008599, 1055013727, 1055013724, 1055013693, 1055012727, 1055012722, 1055012686, 1055012684, 1055011740, 1055011733, 1055011726, 1055011459, 1055011454, 1055011441, 1055008597, 1055008184, 1055008181, 1055007992, 1055007953, 1055007950, 1055004798, 1055004627, 1055004608, 1055004600, 1055004278, 1055004217, 1055003679]
 map_info['map_portfolio_photo'] : {4608689: [1055013727, 1055013724, 1055013693, 1055012727, 1055012722, 1055012686, 1055012684, 1055011740, 1055011733, 1055011726, 1055011459, 1055011454, 1055011441, 1055011086, 1055011076, 1055011074, 1055011072, 1055010743, 1055010739, 1055010737, 1055010730, 1055010725, 1055010723, 1055010143, 1055008638, 1055008599, 1055008597, 1055008184, 1055008181, 1055007992, 1055007953, 1055007950, 1055004798, 1055004627, 1055004608, 1055004600, 1055004278, 1055004217, 1055003679, 1055003357, 1055003348, 1055003292, 1055003278, 1055003266, 1055003261, 1055003259, 1055003249, 1055003202, 1055003198, 1055003197, 1055003185, 1055003134, 1055003131, 1055002045, 1055001545, 1055001542, 1055001092, 1055001085, 1055000228, 1055000070, 1055000068, 1055000063, 1055000059, 1055000055]}
 final :  True mtd_id 3787 list_pids : [1055003357, 1055003348, 1055003292, 1055003278, 1055003266, 1055003261, 1055003259, 1055003249, 1055003202, 1055003198, 1055003197, 1055003185, 1055003134, 1055003131, 1055002045, 1055001545, 1055001542, 1055001092, 1055001085, 1055000228, 1055000070, 1055000068, 1055000063, 1055000059, 1055000055, 1055011086, 1055011076, 1055011074, 1055011072, 1055010743, 1055010739, 1055010737, 1055010730, 1055010725, 1055010723, 1055010143, 1055008638, 1055008599, 1055013727, 1055013724, 1055013693, 1055012727, 1055012722, 1055012686, 1055012684, 1055011740, 1055011733, 1055011726, 1055011459, 1055011454, 1055011441, 1055008597, 1055008184, 1055008181, 1055007992, 1055007953, 1055007950, 1055004798, 1055004627, 1055004608, 1055004600, 1055004278, 1055004217, 1055003679]
Looping around the photos to save general results
len do output : 5
/[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]]
/{'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}
/{4938484: {'list_of_photos': [1055000228, 1055000055, 1055003357, 1055007950, 1055003348, 1055007953, 1055000059, 1055007992, 1055008181, 1055003197, 1055003198, 1055008184], 'hashtag': 'jrm'}, 4938485: {'list_of_photos': [1055000063, 1055004600, 1055008597, 1055003134, 1055008599, 1055003679, 1055004627], 'hashtag': 'jrm'}, 4938486: {'list_of_photos': [1055004217, 1055010143, 1055004278, 1055010723, 1055003131, 1055003202, 1055010725, 1055000068], 'hashtag': 'jrm'}, 4938487: {'list_of_photos': [1055010737, 1055010739, 1055003278, 1055010743, 1055011072, 1055011074, 1055011076, 1055000070], 'hashtag': 'jrm'}, 4938488: {'list_of_photos': [1055011441, 1055011454, 1055003185, 1055011459, 1055001092, 1055001542, 1055003292, 1055011726, 1055011733, 1055011740, 1055012684, 1055002045], 'hashtag': 'jrm'}, 4756245: {'list_of_photos': [1055012722, 1055004798, 1055004608, 1055012727, 1055013693, 1055013724, 1055003249, 1055001545, 1055003259, 1055013727, 1055003266, 1055003261], 'hashtag': 'jrm'}}
/{2107757407: 59}
/{'amount_uploaded_and_tagged': {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}}
before output type
Managing all output in save final without adding information in the mtr_datou_result 
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055003357', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055003348', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055003292', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055003278', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055003266', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055003261', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055003259', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055003249', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055003202', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055003198', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055003197', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055003185', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055003134', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055003131', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055002045', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055001545', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055001542', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055001092', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055001085', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055000228', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055000070', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055000068', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055000063', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055000059', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055000055', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055011086', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055011076', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055011074', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055011072', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055010743', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055010739', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055010737', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055010730', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055010725', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055010723', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055010143', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055008638', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055008599', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055013727', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055013724', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055013693', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055012727', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055012722', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055012686', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055012684', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055011740', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055011733', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055011726', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055011459', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055011454', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055011441', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055008597', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055008184', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055008181', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055007992', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055007953', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055007950', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055004798', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055004627', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055004608', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055004600', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055004278', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055004217', None, None, None, None, None, None)
('3787', None, None, None, None, None, None, None, None)
('3787', '4608689', '1055003679', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 64
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('3787', '4608689', '1055003357', None, None, None, None, None, None), ('3787', '4608689', '1055003348', None, None, None, None, None, None), ('3787', '4608689', '1055003292', None, None, None, None, None, None), ('3787', '4608689', '1055003278', None, None, None, None, None, None), ('3787', '4608689', '1055003266', None, None, None, None, None, None), ('3787', '4608689', '1055003261', None, None, None, None, None, None), ('3787', '4608689', '1055003259', None, None, None, None, None, None), ('3787', '4608689', '1055003249', None, None, None, None, None, None), ('3787', '4608689', '1055003202', None, None, None, None, None, None), ('3787', '4608689', '1055003198', None, None, None, None, None, None), ('3787', '4608689', '1055003197', None, None, None, None, None, None), ('3787', '4608689', '1055003185', None, None, None, None, None, None), ('3787', '4608689', '1055003134', None, None, None, None, None, None), ('3787', '4608689', '1055003131', None, None, None, None, None, None), ('3787', '4608689', '1055002045', None, None, None, None, None, None), ('3787', '4608689', '1055001545', None, None, None, None, None, None), ('3787', '4608689', '1055001542', None, None, None, None, None, None), ('3787', '4608689', '1055001092', None, None, None, None, None, None), ('3787', '4608689', '1055001085', None, None, None, None, None, None), ('3787', '4608689', '1055000228', None, None, None, None, None, None), ('3787', '4608689', '1055000070', None, None, None, None, None, None), ('3787', '4608689', '1055000068', None, None, None, None, None, None), ('3787', '4608689', '1055000063', None, None, None, None, None, None), ('3787', '4608689', '1055000059', None, None, None, None, None, None), ('3787', '4608689', '1055000055', None, None, None, None, None, None), ('3787', '4608689', '1055011086', None, None, None, None, None, None), ('3787', '4608689', '1055011076', None, None, None, None, None, None), ('3787', '4608689', '1055011074', None, None, None, None, None, None), ('3787', '4608689', '1055011072', None, None, None, None, None, None), ('3787', '4608689', '1055010743', None, None, None, None, None, None), ('3787', '4608689', '1055010739', None, None, None, None, None, None), ('3787', '4608689', '1055010737', None, None, None, None, None, None), ('3787', '4608689', '1055010730', None, None, None, None, None, None), ('3787', '4608689', '1055010725', None, None, None, None, None, None), ('3787', '4608689', '1055010723', None, None, None, None, None, None), ('3787', '4608689', '1055010143', None, None, None, None, None, None), ('3787', '4608689', '1055008638', None, None, None, None, None, None), ('3787', '4608689', '1055008599', None, None, None, None, None, None), ('3787', '4608689', '1055013727', None, None, None, None, None, None), ('3787', '4608689', '1055013724', None, None, None, None, None, None), ('3787', '4608689', '1055013693', None, None, None, None, None, None), ('3787', '4608689', '1055012727', None, None, None, None, None, None), ('3787', '4608689', '1055012722', None, None, None, None, None, None), ('3787', '4608689', '1055012686', None, None, None, None, None, None), ('3787', '4608689', '1055012684', None, None, None, None, None, None), ('3787', '4608689', '1055011740', None, None, None, None, None, None), ('3787', '4608689', '1055011733', None, None, None, None, None, None), ('3787', '4608689', '1055011726', None, None, None, None, None, None), ('3787', '4608689', '1055011459', None, None, None, None, None, None), ('3787', '4608689', '1055011454', None, None, None, None, None, None), ('3787', '4608689', '1055011441', None, None, None, None, None, None), ('3787', '4608689', '1055008597', None, None, None, None, None, None), ('3787', '4608689', '1055008184', None, None, None, None, None, None), ('3787', '4608689', '1055008181', None, None, None, None, None, None), ('3787', '4608689', '1055007992', None, None, None, None, None, None), ('3787', '4608689', '1055007953', None, None, None, None, None, None), ('3787', '4608689', '1055007950', None, None, None, None, None, None), ('3787', '4608689', '1055004798', None, None, None, None, None, None), ('3787', '4608689', '1055004627', None, None, None, None, None, None), ('3787', '4608689', '1055004608', None, None, None, None, None, None), ('3787', '4608689', '1055004600', None, None, None, None, None, None), ('3787', '4608689', '1055004278', None, None, None, None, None, None), ('3787', '4608689', '1055004217', None, None, None, None, None, None), ('3787', '4608689', '1055003679', None, None, None, None, None, None)]
time used for this insertion : 0.051982879638671875
 save_final 

save missing photos in datou_result :
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : ([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]], {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, {4938484: {'list_of_photos': [1055000228, 1055000055, 1055003357, 1055007950, 1055003348, 1055007953, 1055000059, 1055007992, 1055008181, 1055003197, 1055003198, 1055008184], 'hashtag': 'jrm'}, 4938485: {'list_of_photos': [1055000063, 1055004600, 1055008597, 1055003134, 1055008599, 1055003679, 1055004627], 'hashtag': 'jrm'}, 4938486: {'list_of_photos': [1055004217, 1055010143, 1055004278, 1055010723, 1055003131, 1055003202, 1055010725, 1055000068], 'hashtag': 'jrm'}, 4938487: {'list_of_photos': [1055010737, 1055010739, 1055003278, 1055010743, 1055011072, 1055011074, 1055011076, 1055000070], 'hashtag': 'jrm'}, 4938488: {'list_of_photos': [1055011441, 1055011454, 1055003185, 1055011459, 1055001092, 1055001542, 1055003292, 1055011726, 1055011733, 1055011740, 1055012684, 1055002045], 'hashtag': 'jrm'}, 4756245: {'list_of_photos': [1055012722, 1055004798, 1055004608, 1055012727, 1055013693, 1055013724, 1055003249, 1055001545, 1055003259, 1055013727, 1055003266, 1055003261], 'hashtag': 'jrm'}}, {2107757407: 59}, {'amount_uploaded_and_tagged': {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}})
Result test split dark : ([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]], {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, {4938484: {'list_of_photos': [1055000228, 1055000055, 1055003357, 1055007950, 1055003348, 1055007953, 1055000059, 1055007992, 1055008181, 1055003197, 1055003198, 1055008184], 'hashtag': 'jrm'}, 4938485: {'list_of_photos': [1055000063, 1055004600, 1055008597, 1055003134, 1055008599, 1055003679, 1055004627], 'hashtag': 'jrm'}, 4938486: {'list_of_photos': [1055004217, 1055010143, 1055004278, 1055010723, 1055003131, 1055003202, 1055010725, 1055000068], 'hashtag': 'jrm'}, 4938487: {'list_of_photos': [1055010737, 1055010739, 1055003278, 1055010743, 1055011072, 1055011074, 1055011076, 1055000070], 'hashtag': 'jrm'}, 4938488: {'list_of_photos': [1055011441, 1055011454, 1055003185, 1055011459, 1055001092, 1055001542, 1055003292, 1055011726, 1055011733, 1055011740, 1055012684, 1055002045], 'hashtag': 'jrm'}, 4756245: {'list_of_photos': [1055012722, 1055004798, 1055004608, 1055012727, 1055013693, 1055013724, 1055003249, 1055001545, 1055003259, 1055013727, 1055003266, 1055003261], 'hashtag': 'jrm'}}, {2107757407: 59}, {'amount_uploaded_and_tagged': {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}})|

############################### TEST rubbia_append  ################################
warning , we can't find thcl infos in json_data
warning , we can't find pdt infos in json_data
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=3856

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=3856
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 3856
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=3856
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : split_time_score
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (4599006) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (4505992) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (4599006,4505992)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 1054572537, 'portfolio_id': 4599006}, {'photo_id': 1054572534, 'portfolio_id': 4599006}, {'photo_id': 1054572532, 'portfolio_id': 4599006}, {'photo_id': 1051605195, 'portfolio_id': 4505992}]
 map_portfolio_id_photo_id: {4599006: [1054572537, 1054572534, 1054572532], 4505992: [1051605195]}
##### Call download_photos : nb_thread : 5
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
##### After load_data_input 
time to download the photos : 0.08602476119995117
#### fin chargement data
Blocking on flush ?
No conitnuing 
About to test input to load 
Calling datou_exec

Inside datou_exec : verbose : True
we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save
number of steps :  1
step1:split_time_score
Mon Sep 29 11:46:23 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {}
map_photo_id_path_extension : {}
map_subphoto_mainphoto : {}
SELECT app_name, token FROM MTRUser.mtr_app_api_token WHERE mtr_user_id=739 AND app_name="token_split_time_score" AND expire_at > NOW() 
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'}]
select SUBSTRING(ph.text,16,2) as h, count(*) from MTRUser.mtr_portfolio_photos mpp inner join MTRBack.photos_view ph on mpp.mtr_photo_id = ph.photo_id where mpp.mtr_portfolio_id = 4599006 group by h
(('30', 1), ('36', 1), ('43', 1))

            SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4599006  AND mpp.hide_status=0  ORDER BY mpp.order  LIMIT 0, 100000
SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id
FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1
where mpp1.mtr_photo_id=cps.photo_id
AND h.hashtag_id=cps.hashtag_id
AND mpp1.hide_status=0
AND mpp1.mtr_portfolio_id in (4599006)  AND cps.thcl in (861) order by cps.score desc LIMIT 0, 100000
SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id
FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1
where mpp1.mtr_photo_id=cps.photo_id
AND h.hashtag_id=cps.hashtag_id
AND mpp1.hide_status=0
AND mpp1.mtr_portfolio_id in (4599006)  AND cps.thcl in (758) order by cps.score desc LIMIT 0, 100000
ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {}
21092021 4599006 Nombre de photos uploadées : 3 / 23040 (0%)
21092021 4599006 Nombre de photos taguées (types de déchets): 0 / 3 (0%)
21092021 4599006 Nombre de photos taguées (volume) : 0 / 3 (0%)
[{'photo_id': 1054572532, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/10/5/4298b86687a1b085f5190a510e3d99e9.jpg', 'username': None, 'uploaded_at': None, 'text': 'IMG_20210921_163030.jpg'}, {'photo_id': 1054572534, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/10/5/c726e7bbaa3020412b11d5d8e4d3e9aa.jpg', 'username': None, 'uploaded_at': None, 'text': 'IMG_20210921_163648.jpg'}, {'photo_id': 1054572537, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/10/5/e7bb2e829c5bfe305d429b7dfb818d83.jpg', 'username': None, 'uploaded_at': None, 'text': 'IMG_20210921_164310.jpg'}]
0
[]
 elapsed_time : load_data_split_time_score 5.4836273193359375e-06
 elapsed_time : order_list_meta_photo_and_scores 9.5367431640625e-06
??? elapsed_time : fill_and_build_computed_from_old_data 0.0002474784851074219

        INSERT INTO `MTRPhoto`.`dashboard_entry_day`
(`dashboard_place_id`,
`mtr_portfolio_id`,
`date`)
VALUES
(
13, 4599006, '2021-09-21'
) ON DUPLICATE KEY UPDATE  updated_at=NOW();

Catched exception !
Connect or reconnect !
SELECT id FROM MTRPhoto.dashboard_entry_day where dashboard_place_id = 13 and date = '2021-09-21' ; 

        INSERT INTO `MTRPhoto`.`dashboard_run_ids`
(`dashboard_entry_day`,
`mtr_user_id`,
`misc_info`)
VALUES
(145759,739,"{}");

Catched exception !
Connect or reconnect !
 elapsed_time : insert_dashboard_record_day_entry 0.6527016162872314
---------- APPEND TASK BEGIN ----------

        INSERT INTO `MTRPhoto`.`dashboard_results`
(`dashboard_run_id`,
`hashtag`,
`date_debut`,
`date_fin`,
`nombre_balle`,
`mtr_portfolio_id`,
`qualite`,
`datou_result_id_qualite`,
`completion_percentage`,
`completion_json`)
VALUES
(152846, '_______Plastique_fonce',
'2021-09-21 16:30:30', '2021-09-21 16:43:10',
0, 4599006,
0, 0,
'0', "{}"
);
---------- APPEND TASK END ----------
We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps 

        SELECT
        dri.dashboard_entry_day,
        dr.dashboard_run_id,
        dr.mtr_portfolio_id,
        dr.id,
        dr.hashtag,
        dr.completion_json
        FROM MTRPhoto.dashboard_results dr,
        MTRPhoto.dashboard_run_ids dri,
        MTRPhoto.dashboard_entry_day ded
        WHERE ded.id= 145759
        AND ded.last_run_id=dri.id
        AND dri.dashboard_entry_day=ded.id
        AND dri.id=dr.dashboard_run_id
        
[{'dashboat_entry_day': 145759, 'dashboard_run_id': 152846, 'mtr_portfolio_id': 4599006, 'dashboard_result_id': 15786640, 'hashtag': '_______Plastique_fonce', 'completion_json': '{}'}]
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                FROM MTRPhoto.mtr_datou_result
                WHERE mtr_portfolio_id = 4599006
                AND result is not null
                and result<>"None"
                and result<>0.0
                AND mtd_id=3759
                ORDER BY id desc LIMIT 1
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                    FROM MTRPhoto.mtr_datou_result
                    WHERE mtr_portfolio_id = 4599006
                    AND result is not null
                    and result<>"None"
                    and result=0.0
                    AND mtd_id=3759
                    ORDER BY created_at desc LIMIT 1
 list_result_datou : []
select url from MTRUser.mtr_files where mtd_id = 3759 and mtr_portfolio_id = 4599006 order by file_id desc limit 1
select url from MTRUser.mtr_files where text like '%4599006%' order by file_id desc limit 1
find url: 
select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 4599006 order by id desc limit 1
update  MTRPhoto.dashboard_results set completion_json  = "{'url_report': ''}" 
                where mtr_portfolio_id = 4599006 and dashboard_run_id = 152846
SELECT mtr_photo_id FROM MTRUser.mtr_portfolio_photos
WHERE mtr_portfolio_id IN (4599006)
 result : ((1054572532,), (1054572534,), (1054572537,))
 NUMBER BATCH : 0
# DISPLAY ALL COLLECTED DATA : 
{'21092021': {'nb_upload': 3, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}
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'}]
select SUBSTRING(ph.text,16,2) as h, count(*) from MTRUser.mtr_portfolio_photos mpp inner join MTRBack.photos_view ph on mpp.mtr_photo_id = ph.photo_id where mpp.mtr_portfolio_id = 4505992 group by h
(('12', 1), (None, 3))

            SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4505992  AND mpp.hide_status=0  ORDER BY mpp.order  LIMIT 0, 100000
SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id
FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1
where mpp1.mtr_photo_id=cps.photo_id
AND h.hashtag_id=cps.hashtag_id
AND mpp1.hide_status=0
AND mpp1.mtr_portfolio_id in (4505992)  AND cps.thcl in (861) order by cps.score desc LIMIT 0, 100000
SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id
FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1
where mpp1.mtr_photo_id=cps.photo_id
AND h.hashtag_id=cps.hashtag_id
AND mpp1.hide_status=0
AND mpp1.mtr_portfolio_id in (4505992)  AND cps.thcl in (758) order by cps.score desc LIMIT 0, 100000
ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {}
21092021 4505992 Nombre de photos uploadées : 1 / 23040 (0%)
21092021 4505992 Nombre de photos taguées (types de déchets): 0 / 1 (0%)
21092021 4505992 Nombre de photos taguées (volume) : 0 / 1 (0%)
[{'photo_id': 1051605195, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/9/21/4cd66aff3fb05d55424e3583f716f45c.jpg', 'username': None, 'uploaded_at': None, 'text': 'IMG_20210921_111217.jpg'}]
0
[]
 elapsed_time : load_data_split_time_score 3.337860107421875e-06
 elapsed_time : order_list_meta_photo_and_scores 8.58306884765625e-06
? elapsed_time : fill_and_build_computed_from_old_data 0.00018525123596191406

        INSERT INTO `MTRPhoto`.`dashboard_entry_day`
(`dashboard_place_id`,
`mtr_portfolio_id`,
`date`)
VALUES
(
13, 4505992, '2021-09-21'
) ON DUPLICATE KEY UPDATE  updated_at=NOW();

Catched exception !
Connect or reconnect !

        INSERT INTO `MTRPhoto`.`dashboard_run_ids`
(`dashboard_entry_day`,
`mtr_user_id`,
`misc_info`)
VALUES
(145759,739,"{}");

Catched exception !
Connect or reconnect !
 elapsed_time : insert_dashboard_record_day_entry 0.648906946182251
---------- APPEND TASK BEGIN ----------

        INSERT INTO `MTRPhoto`.`dashboard_results`
(`dashboard_run_id`,
`hashtag`,
`date_debut`,
`date_fin`,
`nombre_balle`,
`mtr_portfolio_id`,
`qualite`,
`datou_result_id_qualite`,
`completion_percentage`,
`completion_json`)
VALUES
(152846, '_______Plastique_fonce',
'2021-09-21 11:12:17', '2021-09-21 11:12:17',
0, 4505992,
0, 0,
'0', "{}"
);
---------- APPEND TASK END ----------
We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps 

        SELECT
        dri.dashboard_entry_day,
        dr.dashboard_run_id,
        dr.mtr_portfolio_id,
        dr.id,
        dr.hashtag,
        dr.completion_json
        FROM MTRPhoto.dashboard_results dr,
        MTRPhoto.dashboard_run_ids dri,
        MTRPhoto.dashboard_entry_day ded
        WHERE ded.id= 145759
        AND ded.last_run_id=dri.id
        AND dri.dashboard_entry_day=ded.id
        AND dri.id=dr.dashboard_run_id
        
[{'dashboat_entry_day': 145759, 'dashboard_run_id': 152846, 'mtr_portfolio_id': 4599006, 'dashboard_result_id': 15786640, 'hashtag': '_______Plastique_fonce', 'completion_json': "{'url_report': ''}"}, {'dashboat_entry_day': 145759, 'dashboard_run_id': 152846, 'mtr_portfolio_id': 4505992, 'dashboard_result_id': 15786641, 'hashtag': '_______Plastique_fonce', 'completion_json': '{}'}]
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                FROM MTRPhoto.mtr_datou_result
                WHERE mtr_portfolio_id = 4599006
                AND result is not null
                and result<>"None"
                and result<>0.0
                AND mtd_id=3759
                ORDER BY id desc LIMIT 1
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                    FROM MTRPhoto.mtr_datou_result
                    WHERE mtr_portfolio_id = 4599006
                    AND result is not null
                    and result<>"None"
                    and result=0.0
                    AND mtd_id=3759
                    ORDER BY created_at desc LIMIT 1
 list_result_datou : []
select url from MTRUser.mtr_files where mtd_id = 3759 and mtr_portfolio_id = 4599006 order by file_id desc limit 1
select url from MTRUser.mtr_files where text like '%4599006%' order by file_id desc limit 1
find url: 
select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 4599006 order by id desc limit 1
update  MTRPhoto.dashboard_results set completion_json  = "{'url_report': ''}" 
                where mtr_portfolio_id = 4599006 and dashboard_run_id = 152846
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                FROM MTRPhoto.mtr_datou_result
                WHERE mtr_portfolio_id = 4505992
                AND result is not null
                and result<>"None"
                and result<>0.0
                AND mtd_id=3759
                ORDER BY id desc LIMIT 1
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                    FROM MTRPhoto.mtr_datou_result
                    WHERE mtr_portfolio_id = 4505992
                    AND result is not null
                    and result<>"None"
                    and result=0.0
                    AND mtd_id=3759
                    ORDER BY created_at desc LIMIT 1
 list_result_datou : []
select url from MTRUser.mtr_files where mtd_id = 3759 and mtr_portfolio_id = 4505992 order by file_id desc limit 1
select url from MTRUser.mtr_files where text like '%4505992%' order by file_id desc limit 1
find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_COD_P4505992_21-09-2021.pdf
select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 4505992 order by id desc limit 1
update  MTRPhoto.dashboard_results set completion_json  = "{'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_COD_P4505992_21-09-2021.pdf'}" 
                where mtr_portfolio_id = 4505992 and dashboard_run_id = 152846
SELECT mtr_photo_id FROM MTRUser.mtr_portfolio_photos
WHERE mtr_portfolio_id IN (4599006,4505992)
 result : ((1051605195,), (1051615607,), (1051615609,), (1051615610,), (1054572532,), (1054572534,), (1054572537,))
 NUMBER BATCH : 0
# DISPLAY ALL COLLECTED DATA : 
{'21092021': {'nb_upload': 1, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 4.080095291137695
time spend to save output : 6.461143493652344e-05
total time spend for step 1 : 4.080159902572632

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
 saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral 
[1054572537, 1054572534, 1054572532, 1051605195]
 map_info['map_portfolio_photo'] : {4599006: [1054572537, 1054572534, 1054572532], 4505992: [1051605195]}
 final :  True mtd_id 3856 list_pids : [1054572537, 1054572534, 1054572532, 1051605195]
Looping around the photos to save general results
len do output : 2
/4599006Didn't retrieve data
.
/4505992Didn't retrieve data
.
before output type
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f159471d310>
Managing all output in save final without adding information in the mtr_datou_result 
('3856', None, None, None, None, None, None, None, None)
('3856', '4599006', '1054572537', None, None, None, None, None, None)
('3856', None, None, None, None, None, None, None, None)
('3856', '4599006', '1054572534', None, None, None, None, None, None)
('3856', None, None, None, None, None, None, None, None)
('3856', '4599006', '1054572532', None, None, None, None, None, None)
('3856', None, None, None, None, None, None, None, None)
('3856', '4505992', '1051605195', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 6
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('3856', None, '4599006', 'None', None, None, None, None, None), ('3856', None, '4505992', 'None', None, None, None, None, None), ('3856', '4599006', '1054572537', None, None, None, None, None, None), ('3856', '4599006', '1054572534', None, None, None, None, None, None), ('3856', '4599006', '1054572532', None, None, None, None, None, None), ('3856', '4505992', '1051605195', None, None, None, None, None, None)]
time used for this insertion : 0.04066801071166992
 save_final 

save missing photos in datou_result :
After save, about to update current !

        SELECT dashboard_run_id FROM MTRPhoto.dashboard_results where mtr_portfolio_id = 4599006
        

############################### TEST rubbia_horaire  ################################
warning , we can't find thcl infos in json_data
warning , we can't find pdt infos in json_data
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=3181

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=3181
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 3181
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=3181
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : split_time_score
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (3609515) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (3609515)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 1014106095, 'portfolio_id': 3609515}, {'photo_id': 1014106094, 'portfolio_id': 3609515}, {'photo_id': 1014106093, 'portfolio_id': 3609515}, {'photo_id': 1014105800, 'portfolio_id': 3609515}, {'photo_id': 1014105799, 'portfolio_id': 3609515}, {'photo_id': 1014105798, 'portfolio_id': 3609515}, {'photo_id': 1014105797, 'portfolio_id': 3609515}, {'photo_id': 1014105796, 'portfolio_id': 3609515}, {'photo_id': 1014105795, 'portfolio_id': 3609515}, {'photo_id': 1014105791, 'portfolio_id': 3609515}, {'photo_id': 1014105790, 'portfolio_id': 3609515}, {'photo_id': 1014105786, 'portfolio_id': 3609515}, {'photo_id': 1014105785, 'portfolio_id': 3609515}, {'photo_id': 1014105784, 'portfolio_id': 3609515}, {'photo_id': 1014105783, 'portfolio_id': 3609515}, {'photo_id': 1014105782, 'portfolio_id': 3609515}, {'photo_id': 1014105781, 'portfolio_id': 3609515}, {'photo_id': 1014105778, 'portfolio_id': 3609515}, {'photo_id': 1014105777, 'portfolio_id': 3609515}, {'photo_id': 1014099035, 'portfolio_id': 3609515}, {'photo_id': 1014098602, 'portfolio_id': 3609515}, {'photo_id': 1014098236, 'portfolio_id': 3609515}, {'photo_id': 1014097924, 'portfolio_id': 3609515}, {'photo_id': 1014097580, 'portfolio_id': 3609515}, {'photo_id': 1014097499, 'portfolio_id': 3609515}, {'photo_id': 1014097497, 'portfolio_id': 3609515}, {'photo_id': 1014097492, 'portfolio_id': 3609515}, {'photo_id': 1014054235, 'portfolio_id': 3609515}, {'photo_id': 1014054234, 'portfolio_id': 3609515}, {'photo_id': 1014054233, 'portfolio_id': 3609515}, {'photo_id': 1014054232, 'portfolio_id': 3609515}, {'photo_id': 1014054231, 'portfolio_id': 3609515}, {'photo_id': 1014054230, 'portfolio_id': 3609515}]
 map_portfolio_id_photo_id: {3609515: [1014106095, 1014106094, 1014106093, 1014105800, 1014105799, 1014105798, 1014105797, 1014105796, 1014105795, 1014105791, 1014105790, 1014105786, 1014105785, 1014105784, 1014105783, 1014105782, 1014105781, 1014105778, 1014105777, 1014099035, 1014098602, 1014098236, 1014097924, 1014097580, 1014097499, 1014097497, 1014097492, 1014054235, 1014054234, 1014054233, 1014054232, 1014054231, 1014054230]}
##### Call download_photos : nb_thread : 5
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
##### After load_data_input 
time to download the photos : 0.057576894760131836
#### fin chargement data
Blocking on flush ?
No conitnuing 
About to test input to load 
Calling datou_exec

Inside datou_exec : verbose : True
we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save
number of steps :  1
step1:split_time_score
Mon Sep 29 11:46:27 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {}
map_photo_id_path_extension : {}
map_subphoto_mainphoto : {}
SELECT app_name, token FROM MTRUser.mtr_app_api_token WHERE mtr_user_id=739 AND app_name="token_split_time_score" AND expire_at > NOW() 
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'}]
select SUBSTRING(ph.text,16,2) as h, count(*) from MTRUser.mtr_portfolio_photos mpp inner join MTRBack.photos_view ph on mpp.mtr_photo_id = ph.photo_id where mpp.mtr_portfolio_id = 3609515 group by h
()

            SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=3609515  AND mpp.hide_status=0  ORDER BY mpp.order  LIMIT 0, 100000
SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id
FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1
where mpp1.mtr_photo_id=cps.photo_id
AND h.hashtag_id=cps.hashtag_id
AND mpp1.hide_status=0
AND mpp1.mtr_portfolio_id in (3609515)  AND cps.thcl in (861) order by cps.score desc LIMIT 0, 100000
SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id
FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1
where mpp1.mtr_photo_id=cps.photo_id
AND h.hashtag_id=cps.hashtag_id
AND mpp1.hide_status=0
AND mpp1.mtr_portfolio_id in (3609515)  AND cps.thcl in (758) order by cps.score desc LIMIT 0, 100000
ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {}
ERROR in datou_step_exec, will save and exit !
list index out of range
  File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2339, in datou_exec
    output = datou_step_exec(sNext, args, cache, context, map_info, verbose, mtr_user_id)
  File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2523, in datou_step_exec
    return lib_process.datou_step_split_time_score(param, json_param, args, context, map_info, verbose, mtr_user_id)
  File "/home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py", line 2295, in datou_step_split_time_score
    one_result = split_port_in_batch_balle(
  File "/home/admin/workarea/git/Velours/python/misc/split_time_score.py", line 336, in split_port_in_batch_balle
    meta_photos, scores, densite, result_taggue_densite = load_data_split_time_score(int(mtr_portfolio), thcl, thcl_volume, context, limit,
  File "/home/admin/workarea/git/Velours/python/mtr/math_fotonower/timeseries/lib_split_time_score.py", line 2604, in load_data_split_time_score
    current_datetime, mean_pixel = parse_text_to_date(meta_photos[0]["text"])

[]
 map_info['map_portfolio_photo'] : {3609515: [1014106095, 1014106094, 1014106093, 1014105800, 1014105799, 1014105798, 1014105797, 1014105796, 1014105795, 1014105791, 1014105790, 1014105786, 1014105785, 1014105784, 1014105783, 1014105782, 1014105781, 1014105778, 1014105777, 1014099035, 1014098602, 1014098236, 1014097924, 1014097580, 1014097499, 1014097497, 1014097492, 1014054235, 1014054234, 1014054233, 1014054232, 1014054231, 1014054230]}
 final :  True mtd_id 3181 list_pids : []


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 0
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : []
time used for this insertion : 4.291534423828125e-06
 save_final 
ERROR in last step split_time_score, list index out of range

time spend for datou_step_exec : 0.17853784561157227
time spend to save output : 0.0006601810455322266
total time spend for step 0 : 0.1791980266571045
caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
ouput is None
saving photo_ids in datou_result


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 0
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : []
time used for this insertion : 3.0994415283203125e-06
After save, about to update current !
None
argument of type 'NoneType' is not iterable
ERROR rubbia_horaire FAILED

############################### TEST rle_unique_nms_with_priority  ################################
t
SELECT id FROM MTRPhoto.crop_hashtag_ids WHERE photo_id=998957128 AND `type`=2805
DELETE FROM MTRPhoto.crop_hashtag_ids WHERE id IN (3976481548,3976481549,3976481550,3976481551,3976481552,3976481553,3976481554,3976481555,3976481556,3976481557) 
SELECT id FROM MTRPhoto.crop_hashtag_ids WHERE photo_id=996751167 AND `type`=2805
DELETE FROM MTRPhoto.crop_hashtag_ids WHERE id IN (3976481694,3976481667,3976481695,3976481674,3976481657,3976481671,3976481654,3976481685,3976481673,3976481664,3976481692,3976481676,3976481681,3976481682,3976481687,3976481684,3976481660,3976481689,3976481665,3976481668,3976481683,3976481691,3976481670,3976481675,3976481666,3976481656,3976481680,3976481686,3976481658,3976481653,3976481662,3976481655,3976481672,3976481690,3976481677,3976481663,3976481679,3976481678,3976481661,3976481688,3976481693,3976481659,3976481669,3976481652) 
DELETE FROM MTRPhoto.photo_carac_ratio WHERE hashtag_type=2805;
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2548

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=2548
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 2548
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=2548
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : rle_unique_nms_with_priority
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (998957128) 
 Found this number of photos: 1
##### Call download_photos : nb_thread : 5
begin to download photo : 998957128
download finish for photo 998957128
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.19850826263427734
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  1
step1:rle_unique_nms_with_priority
Mon Sep 29 11:46:28 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139188_2886529_998957128_f45017ffedbd1f7cb33fed47ac33648c.jpg': 998957128}
map_photo_id_path_extension : {998957128: {'path': 'temp/1759139188_2886529_998957128_f45017ffedbd1f7cb33fed47ac33648c.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
Begin step rle-unique-nms
on traite la photo : temp/1759139188_2886529_998957128_f45017ffedbd1f7cb33fed47ac33648c.jpg
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 998957128) and `type` in (2804) and hashtag_id in (2107754127,2107755994) 
Loaded 10 chid ids of type : 2804
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1872612725,1872612726,1872612727,1872612728,1872612729,1872612730,1872612731,1872612732,1872612733,1872612734)
++WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612725. Ignored now
++WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612726. Ignored now
++WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612727. Ignored now
++WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612728. Ignored now
++WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612729. Ignored now
++WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612730. Ignored now
++WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612731. Ignored now
++WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612732. Ignored now
++WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612733. Ignored now
++WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
+WARNING : duplicated polygon, we should remove this data for chi_id : 1872612734. Ignored now
SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1872612725,1872612726,1872612727,1872612728,1872612729,1872612730,1872612731,1872612732,1872612733,1872612734)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1872612725,1872612726,1872612727,1872612728,1872612729,1872612730,1872612731,1872612732,1872612733,1872612734)
nb_obj : 10
nb_hashtags :  2
time to prepare the origin masks  : 0.7641124725341797
time for calcul the mask position with numpy : 0.003518819808959961
nb_pixel_total : 217207
time to create 1 rle with new method : 0.0331273078918457
time for calcul the mask position with numpy : 0.0025162696838378906
nb_pixel_total : 1008
time to create 1 rle with old method : 0.0010559558868408203
time for calcul the mask position with numpy : 0.002466917037963867
nb_pixel_total : 751
time to create 1 rle with old method : 0.0008795261383056641
time for calcul the mask position with numpy : 0.0024628639221191406
nb_pixel_total : 722
time to create 1 rle with old method : 0.0008561611175537109
time for calcul the mask position with numpy : 0.0024347305297851562
nb_pixel_total : 2949
time to create 1 rle with old method : 0.003221750259399414
time for calcul the mask position with numpy : 0.0024492740631103516
nb_pixel_total : 497
time to create 1 rle with old method : 0.00052642822265625
time for calcul the mask position with numpy : 0.004166841506958008
nb_pixel_total : 1086
time to create 1 rle with old method : 0.00122833251953125
time for calcul the mask position with numpy : 0.0024785995483398438
nb_pixel_total : 1924
time to create 1 rle with old method : 0.00217437744140625
time for calcul the mask position with numpy : 0.0026140213012695312
nb_pixel_total : 413
time to create 1 rle with old method : 0.00047206878662109375
time for calcul the mask position with numpy : 0.0024650096893310547
nb_pixel_total : 526
time to create 1 rle with old method : 0.0005595684051513672
create new chi : 0.07196640968322754
 proportion hashtag : balle_pet_clair 0.23568467881944444
 proportion hashtag : contaminant_du_pet_clair 0.010716145833333333
time to delete rle : 0.043889760971069336
insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s)
batch 1
Loaded 10 chid ids of type : 2805
Number RLEs to save : 1674
INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) 
 first line : ('3980043890', '538', '174', '1')
...
 last line : ('3980043899', '691', '285', '1')
INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) 
 TO DO : save crop sub photo not yet done ! 
save time : 0.3582141399383545
INSERT IGNORE INTO MTRPhoto.photo_carac_ratio (`photo_id`, `hashtag_type`, `hashtag_id`, `ratio`)
                            VALUES (%s, %s, %s, %s) on duplicate key update `ratio` = VALUES(`ratio`)
 map_output_result : {998957128: (0.0, 'Should be the crop_list due to order', 0)}
End step rle-unique-nms

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 1.529590368270874
time spend to save output : 8.034706115722656e-05
total time spend for step 1 : 1.5296707153320312

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 0
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {998957128: (0.0, 'Should be the crop_list due to order', 0)}
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2573

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=2573
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 2573
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=2573
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : rle_unique_nms_with_priority
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (1066511071) 
 Found this number of photos: 1
##### Call download_photos : nb_thread : 5
begin to download photo : 1066511071
download finish for photo 1066511071
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.21546244621276855
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  1
step1:rle_unique_nms_with_priority
Mon Sep 29 11:46:30 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139190_2886529_1066511071_cca6b29b0253f105b76231d9c28fabe3.jpg': 1066511071}
map_photo_id_path_extension : {1066511071: {'path': 'temp/1759139190_2886529_1066511071_cca6b29b0253f105b76231d9c28fabe3.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
Begin step rle-unique-nms
on traite la photo : temp/1759139190_2886529_1066511071_cca6b29b0253f105b76231d9c28fabe3.jpg
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 1066511071) and `type` in (4169) and hashtag_id in (492774966,492668766,538914404) 
Loaded 10 chid ids of type : 4169
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (2256126232,2256126224,2256126221,2256126220,2256126223,2256126225,2256126229,2256126228,2256126222,2256126218)
SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (2256126232,2256126224,2256126221,2256126220,2256126223,2256126225,2256126229,2256126228,2256126222,2256126218)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (2256126232,2256126224,2256126221,2256126220,2256126223,2256126225,2256126229,2256126228,2256126222,2256126218)
seulement à utiliser dans la step consolidation
insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s)
batch 1
Loaded 10 chid ids of type : 2805
Number RLEs to save : 0
 TO DO : save crop sub photo not yet done ! 
save time : 0.10424613952636719
INSERT IGNORE INTO MTRPhoto.photo_carac_ratio (`photo_id`, `hashtag_type`, `hashtag_id`, `ratio`)
                            VALUES (%s, %s, %s, %s) on duplicate key update `ratio` = VALUES(`ratio`)
 map_output_result : {1066511071: (0.0, 'Should be the crop_list due to order', 0)}
End step rle-unique-nms

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 0.8277866840362549
time spend to save output : 6.103515625e-05
total time spend for step 1 : 0.8278477191925049

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 0
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {1066511071: (0.0, 'Should be the crop_list due to order', 0)}
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2574

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=2574
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 2574
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=2574
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : rle_unique_nms_with_priority
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (996751167) 
 Found this number of photos: 1
##### Call download_photos : nb_thread : 5
begin to download photo : 996751167
download finish for photo 996751167
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.25272035598754883
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  1
step1:rle_unique_nms_with_priority
Mon Sep 29 11:46:31 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139191_2886529_996751167_a4a67aed9b2473876b59443347f3912e.jpg': 996751167}
map_photo_id_path_extension : {996751167: {'path': 'temp/1759139191_2886529_996751167_a4a67aed9b2473876b59443347f3912e.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
Begin step rle-unique-nms
on traite la photo : temp/1759139191_2886529_996751167_a4a67aed9b2473876b59443347f3912e.jpg
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 996751167) and `type` in (2596)  and score>0.5and hashtag_id in (492622729,501120777) 
Loaded 91 chid ids of type : 2596
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1855152024,1947270239,1947270255,1947270247,1855152035,1947270267,1947270236,1855152019,1855152044,1947270245,1855152040,1855152025,1947270251,1855152027,1947270250,1855152020,1947270237,1855152029,1947270241,1855152058,1947270274,1855152062,1855152061,1947270275,1947270277,1855152055,1855152052,1947270261,1855152046,1947270265,1855152021,1947270238,1947270269,1855152045,1947270276,1947270257,1855152054,1855152031,1947270263,1855152023,1855156849,1947270240,1855152042,1947270249,1855152047,1947270273,1947270258,1855152050,1947270270,1855152034,1855152039,1947270259,1855152063,1947270272,1855152038,1947270252,1855152026,1947270242,1855152049,1947270256,1855152048,1947270262,1855152056,1947270266,1947270271,1855152037,1855156850,1947270253,1855152033,1947270246,1855152030,1855152041,1947270260,1855152057,1855152036,1947270268,1855152043,1855152065,1855152064,1947270254,1855152060,1855152032,1947270243,1855152059,1855152051,1855152053,1947270264,1855152022,1947270248,1947270244,1855152028)
+++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1855152024,1947270239,1947270255,1947270247,1855152035,1947270267,1947270236,1855152019,1855152044,1947270245,1855152040,1855152025,1947270251,1855152027,1947270250,1855152020,1947270237,1855152029,1947270241,1855152058,1947270274,1855152062,1855152061,1947270275,1947270277,1855152055,1855152052,1947270261,1855152046,1947270265,1855152021,1947270238,1947270269,1855152045,1947270276,1947270257,1855152054,1855152031,1947270263,1855152023,1855156849,1947270240,1855152042,1947270249,1855152047,1947270273,1947270258,1855152050,1947270270,1855152034,1855152039,1947270259,1855152063,1947270272,1855152038,1947270252,1855152026,1947270242,1855152049,1947270256,1855152048,1947270262,1855152056,1947270266,1947270271,1855152037,1855156850,1947270253,1855152033,1947270246,1855152030,1855152041,1947270260,1855152057,1855152036,1947270268,1855152043,1855152065,1855152064,1947270254,1855152060,1855152032,1947270243,1855152059,1855152051,1855152053,1947270264,1855152022,1947270248,1947270244,1855152028)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1855152024,1947270239,1947270255,1947270247,1855152035,1947270267,1947270236,1855152019,1855152044,1947270245,1855152040,1855152025,1947270251,1855152027,1947270250,1855152020,1947270237,1855152029,1947270241,1855152058,1947270274,1855152062,1855152061,1947270275,1947270277,1855152055,1855152052,1947270261,1855152046,1947270265,1855152021,1947270238,1947270269,1855152045,1947270276,1947270257,1855152054,1855152031,1947270263,1855152023,1855156849,1947270240,1855152042,1947270249,1855152047,1947270273,1947270258,1855152050,1947270270,1855152034,1855152039,1947270259,1855152063,1947270272,1855152038,1947270252,1855152026,1947270242,1855152049,1947270256,1855152048,1947270262,1855152056,1947270266,1947270271,1855152037,1855156850,1947270253,1855152033,1947270246,1855152030,1855152041,1947270260,1855152057,1855152036,1947270268,1855152043,1855152065,1855152064,1947270254,1855152060,1855152032,1947270243,1855152059,1855152051,1855152053,1947270264,1855152022,1947270248,1947270244,1855152028)
nb_obj : 43
nb_hashtags :  2
time to prepare the origin masks  : 13.17108941078186
time for calcul the mask position with numpy : 0.3883967399597168
nb_pixel_total : 5233657
time to create 1 rle with new method : 0.6746129989624023
time for calcul the mask position with numpy : 0.032061100006103516
nb_pixel_total : 11972
time to create 1 rle with old method : 0.013089179992675781
time for calcul the mask position with numpy : 0.031278371810913086
nb_pixel_total : 15054
time to create 1 rle with old method : 0.015891075134277344
time for calcul the mask position with numpy : 0.029822587966918945
nb_pixel_total : 13954
time to create 1 rle with old method : 0.014528274536132812
time for calcul the mask position with numpy : 0.030031681060791016
nb_pixel_total : 4888
time to create 1 rle with old method : 0.0054056644439697266
time for calcul the mask position with numpy : 0.04375457763671875
nb_pixel_total : 1188492
time to create 1 rle with new method : 0.5138294696807861
time for calcul the mask position with numpy : 0.0321965217590332
nb_pixel_total : 184585
time to create 1 rle with new method : 0.5899879932403564
time for calcul the mask position with numpy : 0.03094029426574707
nb_pixel_total : 18620
time to create 1 rle with old method : 0.019719600677490234
time for calcul the mask position with numpy : 0.03105783462524414
nb_pixel_total : 62945
time to create 1 rle with old method : 0.06627202033996582
time for calcul the mask position with numpy : 0.03117966651916504
nb_pixel_total : 9427
time to create 1 rle with old method : 0.009897947311401367
time for calcul the mask position with numpy : 0.030805349349975586
nb_pixel_total : 9081
time to create 1 rle with old method : 0.009389162063598633
time for calcul the mask position with numpy : 0.030392885208129883
nb_pixel_total : 15987
time to create 1 rle with old method : 0.01661539077758789
time for calcul the mask position with numpy : 0.031064748764038086
nb_pixel_total : 33276
time to create 1 rle with old method : 0.03429388999938965
time for calcul the mask position with numpy : 0.030660390853881836
nb_pixel_total : 17533
time to create 1 rle with old method : 0.018715858459472656
time for calcul the mask position with numpy : 0.030439138412475586
nb_pixel_total : 4876
time to create 1 rle with old method : 0.005187034606933594
time for calcul the mask position with numpy : 0.030884265899658203
nb_pixel_total : 25226
time to create 1 rle with old method : 0.026737689971923828
time for calcul the mask position with numpy : 0.03155040740966797
nb_pixel_total : 30773
time to create 1 rle with old method : 0.032581329345703125
time for calcul the mask position with numpy : 0.03159976005554199
nb_pixel_total : 65671
time to create 1 rle with old method : 0.06731271743774414
time for calcul the mask position with numpy : 0.03077530860900879
nb_pixel_total : 12230
time to create 1 rle with old method : 0.013271808624267578
time for calcul the mask position with numpy : 0.03193020820617676
nb_pixel_total : 29560
time to create 1 rle with old method : 0.03250837326049805
time for calcul the mask position with numpy : 0.03139805793762207
nb_pixel_total : 14310
time to create 1 rle with old method : 0.01567220687866211
time for calcul the mask position with numpy : 0.03094625473022461
nb_pixel_total : 15117
time to create 1 rle with old method : 0.016222238540649414
time for calcul the mask position with numpy : 0.033532142639160156
nb_pixel_total : 301487
time to create 1 rle with new method : 0.601841926574707
time for calcul the mask position with numpy : 0.031706809997558594
nb_pixel_total : 29821
time to create 1 rle with old method : 0.031571388244628906
time for calcul the mask position with numpy : 0.031366586685180664
nb_pixel_total : 40299
time to create 1 rle with old method : 0.04321146011352539
time for calcul the mask position with numpy : 0.031114578247070312
nb_pixel_total : 12680
time to create 1 rle with old method : 0.013965368270874023
time for calcul the mask position with numpy : 0.03129696846008301
nb_pixel_total : 9449
time to create 1 rle with old method : 0.010225772857666016
time for calcul the mask position with numpy : 0.03099846839904785
nb_pixel_total : 15168
time to create 1 rle with old method : 0.016408681869506836
time for calcul the mask position with numpy : 0.03125715255737305
nb_pixel_total : 11140
time to create 1 rle with old method : 0.01195073127746582
time for calcul the mask position with numpy : 0.03045034408569336
nb_pixel_total : 29065
time to create 1 rle with old method : 0.03070664405822754
time for calcul the mask position with numpy : 0.030910968780517578
nb_pixel_total : 22774
time to create 1 rle with old method : 0.02412891387939453
time for calcul the mask position with numpy : 0.03081059455871582
nb_pixel_total : 13880
time to create 1 rle with old method : 0.014797687530517578
time for calcul the mask position with numpy : 0.03191542625427246
nb_pixel_total : 155366
time to create 1 rle with new method : 0.3892533779144287
time for calcul the mask position with numpy : 0.03176426887512207
nb_pixel_total : 63941
time to create 1 rle with old method : 0.06923675537109375
time for calcul the mask position with numpy : 0.030894041061401367
nb_pixel_total : 7836
time to create 1 rle with old method : 0.008371591567993164
time for calcul the mask position with numpy : 0.03095388412475586
nb_pixel_total : 7460
time to create 1 rle with old method : 0.007903099060058594
time for calcul the mask position with numpy : 0.03184175491333008
nb_pixel_total : 44600
time to create 1 rle with old method : 0.047516822814941406
time for calcul the mask position with numpy : 0.03127264976501465
nb_pixel_total : 11879
time to create 1 rle with old method : 0.013081073760986328
time for calcul the mask position with numpy : 0.0316472053527832
nb_pixel_total : 44195
time to create 1 rle with old method : 0.04853248596191406
time for calcul the mask position with numpy : 0.031543731689453125
nb_pixel_total : 23652
time to create 1 rle with old method : 0.025446653366088867
time for calcul the mask position with numpy : 0.030961036682128906
nb_pixel_total : 30006
time to create 1 rle with old method : 0.03280329704284668
time for calcul the mask position with numpy : 0.03146529197692871
nb_pixel_total : 15880
time to create 1 rle with old method : 0.01704120635986328
time for calcul the mask position with numpy : 0.031423091888427734
nb_pixel_total : 29845
time to create 1 rle with old method : 0.03307223320007324
time for calcul the mask position with numpy : 0.0333554744720459
nb_pixel_total : 144263
time to create 1 rle with old method : 0.1532609462738037
create new chi : 5.7198779582977295
 proportion hashtag : error 0.14340033061450744
 proportion hashtag : environment 0.209023722085841
 proportion hashtag : pet_fonce 0.6475759472996516
time to delete rle : 0.414201021194458
insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s)
batch 1
Loaded 44 chid ids of type : 2805
Number RLEs to save : 27884
INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) 
 first line : ('3980043918', '0', '0', '3280')
...
 last line : ('3980043961', '2459', '2462', '1')
INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) 
 TO DO : save crop sub photo not yet done ! 
save time : 2.884206533432007
INSERT IGNORE INTO MTRPhoto.photo_carac_ratio (`photo_id`, `hashtag_type`, `hashtag_id`, `ratio`)
                            VALUES (%s, %s, %s, %s) on duplicate key update `ratio` = VALUES(`ratio`)
 map_output_result : {996751167: (1.0, 'Should be the crop_list due to order', 1.0)}
End step rle-unique-nms

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 22.858287811279297
time spend to save output : 0.00014901161193847656
total time spend for step 1 : 22.858436822891235

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 0
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {996751167: (1.0, 'Should be the crop_list due to order', 1.0)}
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 998957128,996751167) and `type` in (2805) 
Loaded 54 chid ids of type : 2805
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (3980043890,3980043891,3980043892,3980043893,3980043894,3980043895,3980043896,3980043897,3980043898,3980043899,3980043918,3980043919,3980043920,3980043921,3980043922,3980043923,3980043924,3980043925,3980043926,3980043927,3980043928,3980043929,3980043930,3980043931,3980043932,3980043933,3980043934,3980043935,3980043936,3980043937,3980043938,3980043939,3980043940,3980043941,3980043942,3980043943,3980043944,3980043945,3980043946,3980043947,3980043948,3980043949,3980043950,3980043951,3980043952,3980043953,3980043954,3980043955,3980043956,3980043957,3980043958,3980043959,3980043960,3980043961)
SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (3980043890,3980043891,3980043892,3980043893,3980043894,3980043895,3980043896,3980043897,3980043898,3980043899,3980043918,3980043919,3980043920,3980043921,3980043922,3980043923,3980043924,3980043925,3980043926,3980043927,3980043928,3980043929,3980043930,3980043931,3980043932,3980043933,3980043934,3980043935,3980043936,3980043937,3980043938,3980043939,3980043940,3980043941,3980043942,3980043943,3980043944,3980043945,3980043946,3980043947,3980043948,3980043949,3980043950,3980043951,3980043952,3980043953,3980043954,3980043955,3980043956,3980043957,3980043958,3980043959,3980043960,3980043961)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (3980043890,3980043891,3980043892,3980043893,3980043894,3980043895,3980043896,3980043897,3980043898,3980043899,3980043918,3980043919,3980043920,3980043921,3980043922,3980043923,3980043924,3980043925,3980043926,3980043927,3980043928,3980043929,3980043930,3980043931,3980043932,3980043933,3980043934,3980043935,3980043936,3980043937,3980043938,3980043939,3980043940,3980043941,3980043942,3980043943,3980043944,3980043945,3980043946,3980043947,3980043948,3980043949,3980043950,3980043951,3980043952,3980043953,3980043954,3980043955,3980043956,3980043957,3980043958,3980043959,3980043960,3980043961)
SELECT * FROM MTRPhoto.photo_carac_ratio WHERE hashtag_type=2805;

############################### TEST random_deformation  ################################
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2896

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=2896
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 2896
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=2896
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : random_deformation
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (3288640) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (3288640)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 1006293201, 'portfolio_id': 3288640}]
 map_portfolio_id_photo_id: {3288640: [1006293201]}
##### Call download_photos : nb_thread : 5
begin to download photo : 1006293201
download finish for photo 1006293201
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.19000625610351562
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  1
step1:random_deformation
Mon Sep 29 11:46:56 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139215_2886529_1006293201_ac133c4479fdab9f9d690f3bcbac83df.jpg': 1006293201}
map_photo_id_path_extension : {1006293201: {'path': 'temp/1759139215_2886529_1006293201_ac133c4479fdab9f9d690f3bcbac83df.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

beginning of datou step random deformation
get user info for portfolio 3288640
on traite la photo : 1006293201

About to upload 4 photos
upload in portfolio : 3287159
init cache_photo without model_param  
we have 4 photo to upload
uploaded to storage server : ovh
folder_temporaire : temp/1759139218_2886529

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
 This is a hack !
we have uploaded 4 photos in the portfolio 3287159

time of upload the photos Elapsed time : 2.205873489379883


After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 4.01595401763916
time spend to save output : 3.1948089599609375e-05
total time spend for step 1 : 4.01598596572876

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
 saveOutput not yet implemented for datou_step.type : random_deformation we use saveGeneral 
[1006293201]
 map_info['map_portfolio_photo'] : {3288640: [1006293201]}
 final :  True mtd_id 2896 list_pids : [1006293201]
Looping around the photos to save general results
len do output : 1
/0Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
before output type
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f1514b9c550>
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f1514b9cd60>
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f1514b9c9a0>
Managing all output in save final without adding information in the mtr_datou_result 
('2896', None, None, None, None, None, None, None, None)
('2896', '3288640', '1006293201', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 4
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('2896', None, '0', 'None', None, None, None, None, None), ('2896', '3288640', '1006293201', None, None, None, None, None, None)]
time used for this insertion : 0.039994001388549805
 save_final 

save missing photos in datou_result :
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {0: ['1006293201', 'temp/1006293201_random_deformation_3.png', []]}
ERROR, got 0 upload, expected 4
ERROR random_deformation FAILED

############################### TEST tile  ################################
test tile avec chi rectangles, rles, polygones
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2985

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=2985
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 2985
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=2985
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : tile
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (3341855) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (3341855)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 1008283903, 'portfolio_id': 3341855}]
 map_portfolio_id_photo_id: {3341855: [1008283903]}
##### Call download_photos : nb_thread : 5
begin to download photo : 1008283903
download finish for photo 1008283903
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.30883073806762695
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  1
step1:tile
Mon Sep 29 11:47:00 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53.jpg': 1008283903}
map_photo_id_path_extension : {1008283903: {'path': 'temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
verbose : True
 param_json : {'ETA': 3600, 'new_width': 640, 'new_height': 640, 'token': '5d93a4b2b749464f208c339a1324b78f', 'stride': 0, 'stride_relative': 0, 'portfolio_name': 'results_test_tile', 'crop_hashtag_type_tiled': 3243, 'crop_hashtag_type': 3242, 'arg_aux_upload': {'type_upload': 'python'}, 'host': 'www.fotonower.com'}
 type(crop_hashtag_type) : <class 'int'>
 type(crop_hashtag_type_tiled) : <class 'int'>
We consider crop_hashtag_type is an integer ! 
  map_chi_type_to_chi_type_cropped : {3242: 3243}
TO DEPRECATE VR 14-6-18
 map_filenames :  {1008283903: 'temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53.jpg'}
 list_pids :   1
 list_pids :   2
 list_subpids to replace list_pids : 0
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 1008283903,1008283903) and `type` in (3242) 
Loaded 2 chid ids of type : 3242
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1929991390,1929991392)
++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1929991390,1929991392)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1929991390,1929991392)
 Status : 404
 Content : b'<!doctype html>\n<html lang=en>\n<title>404 Not Found</title>\n<h1>Not Found</h1>\n<p>The requested URL was not found on the server. If you entered the URL manually please check your spelling and try again.</p>\n'
 All Response : <Response [404]>
 WARNING : NO PORTFOLIO CREATED portfolio_id 0 used instead of new portfolio
 created feed_id_new_photos : 0 with name results_test_tile
 feed_id_new_photos : 0
 filename : temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53.jpg
 photo_id : 1008283903
 height_image_input : 2464 width_image_input : 3280
 new_width : 640 new_height : 640
 stride : 0 stride_relative : 0
 chi to copy from the main photo to the tiled photo  input_chi_for_this_image_as_chi :  2
list_bib_to_crops : 24 [(0, 640, 0, 640, 0), (0, 640, 640, 1280, 1), (0, 640, 1280, 1920, 2), (0, 640, 1824, 2464, 3), (640, 1280, 0, 640, 4), (640, 1280, 640, 1280, 5), (640, 1280, 1280, 1920, 6), (640, 1280, 1824, 2464, 7), (1280, 1920, 0, 640, 8), (1280, 1920, 640, 1280, 9), (1280, 1920, 1280, 1920, 10), (1280, 1920, 1824, 2464, 11), (1920, 2560, 0, 640, 12), (1920, 2560, 640, 1280, 13), (1920, 2560, 1280, 1920, 14), (1920, 2560, 1824, 2464, 15), (2560, 3200, 0, 640, 16), (2560, 3200, 640, 1280, 17), (2560, 3200, 1280, 1920, 18), (2560, 3200, 1824, 2464, 19), (2640, 3280, 0, 640, 20), (2640, 3280, 640, 1280, 21), (2640, 3280, 1280, 1920, 22), (2640, 3280, 1824, 2464, 23)]
calcul des nouveaux crops pour le tile x0:0,x1:640,y0:0,y1:640
chi selectionnes : []
calcul des nouveaux crops pour le tile x0:0,x1:640,y0:640,y1:1280
chi selectionnes : []
calcul des nouveaux crops pour le tile x0:0,x1:640,y0:1280,y1:1920
chi selectionnes : []
calcul des nouveaux crops pour le tile x0:0,x1:640,y0:1824,y1:2464
chi selectionnes : []
calcul des nouveaux crops pour le tile x0:640,x1:1280,y0:0,y1:640
chi selectionnes : []
calcul des nouveaux crops pour le tile x0:640,x1:1280,y0:640,y1:1280
chi selectionnes : []
calcul des nouveaux crops pour le tile x0:640,x1:1280,y0:1280,y1:1920
chi selectionnes : []
calcul des nouveaux crops pour le tile x0:640,x1:1280,y0:1824,y1:2464
chi selectionnes : []
calcul des nouveaux crops pour le tile x0:1280,x1:1920,y0:0,y1:640
chi selectionnes : []
calcul des nouveaux crops pour le tile x0:1280,x1:1920,y0:640,y1:1280
calcul avec la methode originale
chi selectionnes : [<mtr.simple_image_editor.image_utils.CHI object at 0x7f151522ffa0>]
calcul des nouveaux crops pour le tile x0:1280,x1:1920,y0:1280,y1:1920
calcul avec la methode originale
chi selectionnes : [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1554252fd0>]
calcul des nouveaux crops pour le tile x0:1280,x1:1920,y0:1824,y1:2464
calcul avec la methode originale
chi selectionnes : [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1515ba1e50>]
calcul des nouveaux crops pour le tile x0:1920,x1:2560,y0:0,y1:640
calcul avec la methode originale
chi selectionnes : [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34550>]
calcul des nouveaux crops pour le tile x0:1920,x1:2560,y0:640,y1:1280
calcul avec la methode originale
chi selectionnes : [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34250>]
calcul des nouveaux crops pour le tile x0:1920,x1:2560,y0:1280,y1:1920
calcul avec la methode originale
chi selectionnes : [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b343a0>]
calcul des nouveaux crops pour le tile x0:1920,x1:2560,y0:1824,y1:2464
calcul avec la methode originale
chi selectionnes : [<mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34820>]
calcul des nouveaux crops pour le tile x0:2560,x1:3200,y0:0,y1:640
chi selectionnes : []
calcul des nouveaux crops pour le tile x0:2560,x1:3200,y0:640,y1:1280
chi selectionnes : []
calcul des nouveaux crops pour le tile x0:2560,x1:3200,y0:1280,y1:1920
chi selectionnes : []
calcul des nouveaux crops pour le tile x0:2560,x1:3200,y0:1824,y1:2464
chi selectionnes : []
calcul des nouveaux crops pour le tile x0:2640,x1:3280,y0:0,y1:640
chi selectionnes : []
calcul des nouveaux crops pour le tile x0:2640,x1:3280,y0:640,y1:1280
chi selectionnes : []
calcul des nouveaux crops pour le tile x0:2640,x1:3280,y0:1280,y1:1920
chi selectionnes : []
calcul des nouveaux crops pour le tile x0:2640,x1:3280,y0:1824,y1:2464
chi selectionnes : []
 new_crops_tiles : 24 crop_transformed : 7
insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s)
[(1008283903, 2090988864, 17, 0, 640, 0, 640, 1.0), (1008283903, 2090988864, 17, 0, 640, 640, 1280, 1.0), (1008283903, 2090988864, 17, 0, 640, 1280, 1920, 1.0), (1008283903, 2090988864, 17, 0, 640, 1824, 2464, 1.0), (1008283903, 2090988864, 17, 640, 1280, 0, 640, 1.0), (1008283903, 2090988864, 17, 640, 1280, 640, 1280, 1.0), (1008283903, 2090988864, 17, 640, 1280, 1280, 1920, 1.0), (1008283903, 2090988864, 17, 640, 1280, 1824, 2464, 1.0), (1008283903, 2090988864, 17, 1280, 1920, 0, 640, 1.0), (1008283903, 2090988864, 17, 1280, 1920, 640, 1280, 1.0), (1008283903, 2090988864, 17, 1280, 1920, 1280, 1920, 1.0), (1008283903, 2090988864, 17, 1280, 1920, 1824, 2464, 1.0), (1008283903, 2090988864, 17, 1920, 2560, 0, 640, 1.0), (1008283903, 2090988864, 17, 1920, 2560, 640, 1280, 1.0), (1008283903, 2090988864, 17, 1920, 2560, 1280, 1920, 1.0), (1008283903, 2090988864, 17, 1920, 2560, 1824, 2464, 1.0), (1008283903, 2090988864, 17, 2560, 3200, 0, 640, 1.0), (1008283903, 2090988864, 17, 2560, 3200, 640, 1280, 1.0), (1008283903, 2090988864, 17, 2560, 3200, 1280, 1920, 1.0), (1008283903, 2090988864, 17, 2560, 3200, 1824, 2464, 1.0), (1008283903, 2090988864, 17, 2640, 3280, 0, 640, 1.0), (1008283903, 2090988864, 17, 2640, 3280, 640, 1280, 1.0), (1008283903, 2090988864, 17, 2640, 3280, 1280, 1920, 1.0), (1008283903, 2090988864, 17, 2640, 3280, 1824, 2464, 1.0)]
  list_photo_ids_cropped : [1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903, 1008283903]
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903,1008283903) and `type` in (17) 
Loaded 24 chid ids of type : 17
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1930014930,1930014931,1930014932,1930014933,1930014934,1930014935,1930014936,1930014937,1930014938,1930014939,1930014940,1930014941,1930014942,1930014943,1930014944,1930014945,1930014946,1930014947,1930014948,1930014949,1930014950,1930014951,1930014952,1930014953)
SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1930014930,1930014931,1930014932,1930014933,1930014934,1930014935,1930014936,1930014937,1930014938,1930014939,1930014940,1930014941,1930014942,1930014943,1930014944,1930014945,1930014946,1930014947,1930014948,1930014949,1930014950,1930014951,1930014952,1930014953)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1930014930,1930014931,1930014932,1930014933,1930014934,1930014935,1930014936,1930014937,1930014938,1930014939,1930014940,1930014941,1930014942,1930014943,1930014944,1930014945,1930014946,1930014947,1930014948,1930014949,1930014950,1930014951,1930014952,1930014953)
treat the image : temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53.jpg
, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23
before upload mediasElapsed time : 0.40241074562072754

on upload les photos avec python
init cache_photo without model_param  
we have 24 photo to upload
uploaded to storage server : ovh
folder_temporaire : temp/1759139226_2886529

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_0.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 157268, 1386782200, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 56320453, 'jpg', UNHEX('d16cfbf93a6864f1bc03962e0e23bcb1'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_1.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 148520, 1386782201, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 2473206, 'jpg', UNHEX('ce2c4f782965b063d46e106986f3b2e4'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_2.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 150434, 1386782202, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 41687275, 'jpg', UNHEX('6dc06165a42b8d0c0252ddaf88e7e6cb'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_3.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 159038, 1386782203, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 21414461, 'jpg', UNHEX('9fc09702addb3a343c682f2f2b2db5c4'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_4.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 140881, 1386782204, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 78321614, 'jpg', UNHEX('38c52b81ea7fbfc09f7a9fe92cb6c8cc'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_5.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 132017, 1386782205, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 68879513, 'jpg', UNHEX('8d019051d3be58f0374749fb06f24661'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_6.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 133779, 1386782206, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 13592930, 'jpg', UNHEX('9238291edac074dfa5e770261348ab94'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_7.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 142456, 1386782207, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 69101781, 'jpg', UNHEX('2e93baee3662924b59de631afb8c583f'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_8.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 138689, 1386782208, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 99337140, 'jpg', UNHEX('ea4002f70a9c5306758d8bd738566106'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_9.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 133572, 1386782209, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 12119952, 'jpg', UNHEX('12a46bd6865754d684eab42f2de71630'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_10.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 128714, 1386782210, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 77267657, 'jpg', UNHEX('a9e59570c3d2a739997b39ea79a20dc6'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_11.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 146231, 1386782211, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 97807770, 'jpg', UNHEX('6514ae9377f60a30175ac1f0b8e66126'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_12.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 148032, 1386782212, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 49933488, 'jpg', UNHEX('c76ce19692a6b6bb66fc05b5e85bb261'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_13.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 140830, 1386782213, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 36235223, 'jpg', UNHEX('8a1ab3fedcce7d87a2e088800cd3a285'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_14.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 127080, 1386782214, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 40003871, 'jpg', UNHEX('a322678f502639c33ff5067f95cf955d'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_15.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 151617, 1386782215, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 6040239, 'jpg', UNHEX('2f0a311219a9488a7480b85a9d340d3b'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_16.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 155573, 1386782216, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 39605437, 'jpg', UNHEX('5771dfcd856014c9506f9ed2ed99a1ad'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_17.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 150612, 1386782217, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 46569102, 'jpg', UNHEX('8f67fbe0b2aebab34cb80ea267c77ca5'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_18.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 154183, 1386782218, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 3384744, 'jpg', UNHEX('95c8a4d815fadce72770e6dd1f261950'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_19.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 160790, 1386782219, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 41496105, 'jpg', UNHEX('334a2ce960b42e11129b215fddfe668c'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_20.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 158111, 1386782220, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 38557538, 'jpg', UNHEX('f8f501b9dd4a98e0a17c364a01b2d488'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_21.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 152143, 1386782221, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 15034528, 'jpg', UNHEX('4d2b7affdc9bdce256bfa64a0655054d'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_22.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 155690, 1386782222, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 68062665, 'jpg', UNHEX('33307fc437c37b583118e7a65c073e4d'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139232), 0.0, 0.0, 14, '', 0, 0, '1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_23.jpg', 0,
640, 640, 0, 1759139232,'0',0)

 batch_size : 0, verbose : True, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
INSERT IGNORE INTO MTRUser.mtr_fotonower_photos_opt (`height`, `width`, `size_object`, `photo_id`, `base_url`, `date`, `chi_id`, `type_extension`, `hash`)
                VALUES (640, 640, 162499, 1386782223, 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com', '2025/9/29', 68762618, 'jpg', UNHEX('695cb643e5515ce4e7575a2c0a2454b9'))
                ON DUPLICATE KEY UPDATE `height`=values(`height`), `width`=values(`width`), `size_object`=values(`size_object`), `photo_id`=values(`photo_id`), `base_url`=values(`base_url`), `date`=values(`date`), `chi_id`=values(`chi_id`), `type_extension`=values(`type_extension`), `hash`=values(`hash`) 
 This is a hack !
we have uploaded 24 photos in the portfolio 0
Importing !

upload mediasElapsed time : 9.221954107284546

, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(1930014930, 1386782200, 0), (1930014931, 1386782201, 0), (1930014932, 1386782202, 0), (1930014933, 1386782203, 0), (1930014934, 1386782204, 0), (1930014935, 1386782205, 0), (1930014936, 1386782206, 0), (1930014937, 1386782207, 0), (1930014938, 1386782208, 0), (1930014939, 1386782209, 0), (1930014940, 1386782210, 0), (1930014941, 1386782211, 0), (1930014942, 1386782212, 0), (1930014943, 1386782213, 0), (1930014944, 1386782214, 0), (1930014945, 1386782215, 0), (1930014946, 1386782216, 0), (1930014947, 1386782217, 0), (1930014948, 1386782218, 0), (1930014949, 1386782219, 0), (1930014950, 1386782220, 0), (1930014951, 1386782221, 0), (1930014952, 1386782222, 0), (1930014953, 1386782223, 0)]
Saving 7 CHIs.
list_chi_tile : ["<class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 1386782209, 'hashtag_id': 511548407, 'type': 3243, 'x0': 180, 'x1': 640, 'y0': 611, 'y1': 640, 'score': 1.0, 'id': 0, 'points': ['311,640,292,640,268,640,240,640,229,640,223,640,188,640,181,640,180,640,186,640,211,640,216,640,211,640,211,640,244,640,242,640,261,640,275,640,298,640,357,640,380,640,409,640,548,640,557,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,635,640,628,640,628,576,621,543,612,490,611,424,621,352,640', '311,640,292,640,268,640,240,640,229,640,223,640,188,640,181,640,180,640,186,640,211,640,216,640,211,640,211,640,244,640,242,640,261,640,275,640,298,640,357,640,380,640,409,640,548,640,557,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,635,640,628,640,628,576,621,543,612,490,611,424,621,352,640'], 'sub_photo_id': 0, 'rles': [[1929991390, 487, 611, 30], [1929991390, 481, 612, 64], [1929991390, 474, 613, 75], [1929991390, 467, 614, 86], [1929991390, 461, 615, 95], [1929991390, 454, 616, 106], [1929991390, 448, 617, 116], [1929991390, 441, 618, 126], [1929991390, 434, 619, 137], [1929991390, 428, 620, 147], [1929991390, 423, 621, 158], [1929991390, 419, 622, 171], [1929991390, 415, 623, 185], [1929991390, 411, 624, 198], [1929991390, 408, 625, 210], [1929991390, 404, 626, 224], [1929991390, 400, 627, 237], [1929991390, 396, 628, 244], [1929991390, 392, 629, 248], [1929991390, 389, 630, 251], [1929991390, 385, 631, 255], [1929991390, 381, 632, 259], [1929991390, 377, 633, 263], [1929991390, 374, 634, 266], [1929991390, 370, 635, 270], [1929991390, 366, 636, 274], [1929991390, 362, 637, 278], [1929991390, 359, 638, 281], [1929991390, 355, 639, 285]], 'hashtag': '', 'sum_segment': 0}", "<class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 1386782210, 'hashtag_id': 511548407, 'type': 3243, 'x0': 180, 'x1': 640, 'y0': 0, 'y1': 640, 'score': 1.0, 'id': 0, 'points': ['311,11,292,25,268,58,240,82,229,100,223,127,188,148,181,171,180,226,186,327,211,393,216,463,211,472,211,487,238,640,244,640,242,640,261,640,275,640,298,640,357,640,380,640,409,640,548,640,557,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,625,640,566,640,542,640,454,640,388,640,346,640,295,640,246,640,125,640,99,640,63,640,28,640,27,640,17,640,8,640,9,640,0,640,0,640,0,640,0,576,0,543,0,490,0,424,0,352,0', '311,11,292,25,268,58,240,82,229,100,223,127,188,148,181,171,180,226,186,327,211,393,216,463,211,472,211,487,238,640,244,640,242,640,261,640,275,640,298,640,357,640,380,640,409,640,548,640,557,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,640,625,640,566,640,542,640,454,640,388,640,346,640,295,640,246,640,125,640,99,640,63,640,28,640,27,640,17,640,8,640,9,640,0,640,0,640,0,640,0,576,0,543,0,490,0,424,0,352,0'], 'sub_photo_id': 0, 'rles': [[1929991390, 351, 0, 289], [1929991390, 347, 1, 293], [1929991390, 344, 2, 296], [1929991390, 340, 3, 300], [1929991390, 336, 4, 304], [1929991390, 332, 5, 308], [1929991390, 328, 6, 312], [1929991390, 325, 7, 315], [1929991390, 321, 8, 319], [1929991390, 317, 9, 323], [1929991390, 313, 10, 327], [1929991390, 311, 11, 329], [1929991390, 309, 12, 331], [1929991390, 308, 13, 332], [1929991390, 307, 14, 333], [1929991390, 305, 15, 335], [1929991390, 304, 16, 336], [1929991390, 303, 17, 337], [1929991390, 301, 18, 339], [1929991390, 300, 19, 340], [1929991390, 299, 20, 341], [1929991390, 297, 21, 343], [1929991390, 296, 22, 344], [1929991390, 295, 23, 345], [1929991390, 293, 24, 347], [1929991390, 292, 25, 348], [1929991390, 291, 26, 349], [1929991390, 291, 27, 349], [1929991390, 290, 28, 350], [1929991390, 289, 29, 351], [1929991390, 288, 30, 352], [1929991390, 288, 31, 352], [1929991390, 287, 32, 353], [1929991390, 286, 33, 354], [1929991390, 285, 34, 355], [1929991390, 285, 35, 355], [1929991390, 284, 36, 356], [1929991390, 283, 37, 357], [1929991390, 283, 38, 357], [1929991390, 282, 39, 358], [1929991390, 281, 40, 359], [1929991390, 280, 41, 360], [1929991390, 280, 42, 360], [1929991390, 279, 43, 361], [1929991390, 278, 44, 362], [1929991390, 277, 45, 363], [1929991390, 277, 46, 363], [1929991390, 276, 47, 364], [1929991390, 275, 48, 365], [1929991390, 275, 49, 365], [1929991390, 274, 50, 366], [1929991390, 273, 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insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s)
batch 1
Loaded 7 chid ids of type : 3243
INSERT IGNORE INTO MTRPhoto.crop_polygon_points (`crop_hashtag_id`, `points`) VALUES (%s, %s)
Number RLEs to save : 2937
INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) 
 first line : ('3980043988', '487', '611', '30')
...
 last line : ('3980043994', '108', '531', '8')
INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) 
 TO DO : save crop sub photo not yet done ! 

end of tileElapsed time : 9.759205102920532

map_pid_results : {'1386782200': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_0.jpg'], '1386782201': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_1.jpg'], '1386782202': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_2.jpg'], '1386782203': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_3.jpg'], '1386782204': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_4.jpg'], '1386782205': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_5.jpg'], '1386782206': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_6.jpg'], '1386782207': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_7.jpg'], '1386782208': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_8.jpg'], '1386782209': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_9.jpg'], '1386782210': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_10.jpg'], '1386782211': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_11.jpg'], '1386782212': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_12.jpg'], '1386782213': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_13.jpg'], '1386782214': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_14.jpg'], '1386782215': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_15.jpg'], '1386782216': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_16.jpg'], '1386782217': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_17.jpg'], '1386782218': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_18.jpg'], '1386782219': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_19.jpg'], '1386782220': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_20.jpg'], '1386782221': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_21.jpg'], '1386782222': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_22.jpg'], '1386782223': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_23.jpg']}

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 15.294426202774048
time spend to save output : 6.580352783203125e-05
total time spend for step 1 : 15.29449200630188

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 0
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {'1386782200': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_0.jpg'], '1386782201': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_1.jpg'], '1386782202': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_2.jpg'], '1386782203': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_3.jpg'], '1386782204': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_4.jpg'], '1386782205': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_5.jpg'], '1386782206': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_6.jpg'], '1386782207': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_7.jpg'], '1386782208': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_8.jpg'], '1386782209': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_9.jpg'], '1386782210': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_10.jpg'], '1386782211': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_11.jpg'], '1386782212': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_12.jpg'], '1386782213': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_13.jpg'], '1386782214': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_14.jpg'], '1386782215': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_15.jpg'], '1386782216': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_16.jpg'], '1386782217': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_17.jpg'], '1386782218': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_18.jpg'], '1386782219': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_19.jpg'], '1386782220': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_20.jpg'], '1386782221': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_21.jpg'], '1386782222': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_22.jpg'], '1386782223': ['temp/1759139220_2886529_1008283903_6d008d31a1477b2e98cbafa96bd48e53_23.jpg']}
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 1386782200,1386782201,1386782202,1386782203,1386782204,1386782205,1386782206,1386782207,1386782208,1386782209,1386782210,1386782211,1386782212,1386782213,1386782214,1386782215,1386782216,1386782217,1386782218,1386782219,1386782220,1386782221,1386782222,1386782223) and `type` in (3243) 
Loaded 7 chid ids of type : 3243
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (3980043988,3980043989,3980043990,3980043991,3980043992,3980043993,3980043994)
++WARNING : duplicated polygon, we should remove this data for chi_id : 3980043988. Ignored now
++WARNING : duplicated polygon, we should remove this data for chi_id : 3980043989. Ignored now
++WARNING : duplicated polygon, we should remove this data for chi_id : 3980043990. Ignored now
++WARNING : duplicated polygon, we should remove this data for chi_id : 3980043991. Ignored now
++WARNING : duplicated polygon, we should remove this data for chi_id : 3980043992. Ignored now
++WARNING : duplicated polygon, we should remove this data for chi_id : 3980043993. Ignored now
++WARNING : duplicated polygon, we should remove this data for chi_id : 3980043994. Ignored now
SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (3980043988,3980043989,3980043990,3980043991,3980043992,3980043993,3980043994)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (3980043988,3980043989,3980043990,3980043991,3980043992,3980043993,3980043994)
fin du test de tile

############################### TEST rotate_chi  ################################
test rotate avec chi rectangles, rles, polygones
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2970

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=2970
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 2970
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=2970
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : rotate
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (3337029) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (3337029)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 1003369118, 'portfolio_id': 3337029}]
 map_portfolio_id_photo_id: {3337029: [1003369118]}
##### Call download_photos : nb_thread : 5
begin to download photo : 1003369118
download finish for photo 1003369118
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.27727413177490234
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  1
step1:rotate
Mon Sep 29 11:47:16 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139236_2886529_1003369118_58171420504d0b5f05a1233b6c515509_65826337.jpg': 1003369118}
map_photo_id_path_extension : {1003369118: {'path': 'temp/1759139236_2886529_1003369118_58171420504d0b5f05a1233b6c515509_65826337.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou_step_rotate !
Warning, new_feed_id is empty !
We are in a linear step without datou_depend !
rotate photos of 0,90,180,270 degres
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 1003369118) and `type` in (3086) 
Loaded 16 chid ids of type : 3086
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1928951187,1928951188,1928951189,1928951190,1928951191,1928951192,1928951193,1928951194,1928951195,1928951196,1928951197,1928951198,1928951199,1928951200,1928951201,1928951202)
++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1928951187,1928951188,1928951189,1928951190,1928951191,1928951192,1928951193,1928951194,1928951195,1928951196,1928951197,1928951198,1928951199,1928951200,1928951201,1928951202)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1928951187,1928951188,1928951189,1928951190,1928951191,1928951192,1928951193,1928951194,1928951195,1928951196,1928951197,1928951198,1928951199,1928951200,1928951201,1928951202)
 map_chi : {1003369118: [<mtr.simple_image_editor.image_utils.CHI object at 0x7f18a991f520>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1554252fd0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1554241ee0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9ca00>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9c9a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9c310>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9cd60>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9c550>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8e80>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a89a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8b50>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8df0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8be0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8eb0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8bb0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8f40>]}
 Status : 404
 Content : b'<!doctype html>\n<html lang=en>\n<title>404 Not Found</title>\n<h1>Not Found</h1>\n<p>The requested URL was not found on the server. If you entered the URL manually please check your spelling and try again.</p>\n'
 All Response : <Response [404]>
 WARNING : NO PORTFOLIO CREATED portfolio_id 0 used instead of new portfolio
 feed_id_new_photos : 0
 photo_id in download_rotate_and_save : 1003369118 list_chi_loc : 16
Use all angle !
Rotation of photo 1003369118 of 0 degree
temp/1759139236_2886529_1003369118_58171420504d0b5f05a1233b6c515509_65826337.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f18a991f520>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1554252fd0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1554241ee0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9ca00>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9c9a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9c310>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9cd60>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9c550>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8e80>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a89a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8b50>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8df0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8be0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8eb0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8bb0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8f40>] 0
 remove_crop_border : False
version de PIL : 9.5.0
[[ 1.  0.]
 [-0.  1.]]
0
[[ 1.  0.]
 [-0.  1.]]
 shrink_image : False
 len(list_crops) : 16
time for calcul the mask position with numpy : 0.009865522384643555
nb_pixel_total : 110633
time to create 1 rle with old method : 0.11519432067871094
.time for calcul the mask position with numpy : 0.008472681045532227
nb_pixel_total : 15826
time to create 1 rle with old method : 0.016757726669311523
.time for calcul the mask position with numpy : 0.008480548858642578
nb_pixel_total : 5286
time to create 1 rle with old method : 0.0056476593017578125
.time for calcul the mask position with numpy : 0.008479833602905273
nb_pixel_total : 1633
time to create 1 rle with old method : 0.001756429672241211
.time for calcul the mask position with numpy : 0.00920867919921875
nb_pixel_total : 105533
time to create 1 rle with old method : 0.11032676696777344
.time for calcul the mask position with numpy : 0.00849151611328125
nb_pixel_total : 4393
time to create 1 rle with old method : 0.004702329635620117
.time for calcul the mask position with numpy : 0.00840139389038086
nb_pixel_total : 632
time to create 1 rle with old method : 0.0007102489471435547
.time for calcul the mask position with numpy : 0.008897542953491211
nb_pixel_total : 62627
time to create 1 rle with old method : 0.06630635261535645
.time for calcul the mask position with numpy : 0.00857996940612793
nb_pixel_total : 33681
time to create 1 rle with old method : 0.035240888595581055
.time for calcul the mask position with numpy : 0.008637666702270508
nb_pixel_total : 37724
time to create 1 rle with old method : 0.03943586349487305
.time for calcul the mask position with numpy : 0.009545326232910156
nb_pixel_total : 48775
time to create 1 rle with old method : 0.05366253852844238
.time for calcul the mask position with numpy : 0.041528940200805664
nb_pixel_total : 1171703
time to create 1 rle with new method : 0.20432567596435547
.time for calcul the mask position with numpy : 0.009046077728271484
nb_pixel_total : 2310
time to create 1 rle with old method : 0.002721548080444336
.time for calcul the mask position with numpy : 0.008368253707885742
nb_pixel_total : 2256
time to create 1 rle with old method : 0.00247955322265625
.time for calcul the mask position with numpy : 0.008878231048583984
nb_pixel_total : 3112
time to create 1 rle with old method : 0.0032777786254882812
.time for calcul the mask position with numpy : 0.009493112564086914
nb_pixel_total : 1662
time to create 1 rle with old method : 0.0019490718841552734
.len(list_crops_rotate) : 16
 list_crops_rotate : <class 'mtr.simple_image_editor.image_utils.CHI'>: {'photo_id': 0, 'hashtag_id': 493012381, 'type': 3230, 'x0': 0, 'x1': 574, 'y0': 1038, 'y1': 1438, 'score': 1.0, 'id': None, 'points': ['1,1038,97,1108,179,1182,245,1230,274,1204,337,1245,310,1279,313,1288,577,1440,-1,1438'], 'sub_photo_id': 0, 'rles': [(-1, 1, 1038, 1), (-1, 1, 1039, 3), (-1, 1, 1040, 4), (-1, 1, 1041, 5), (-1, 1, 1042, 7), (-1, 1, 1043, 8), (-1, 1, 1044, 9), (-1, 1, 1045, 11), (-1, 1, 1046, 12), (-1, 1, 1047, 14), (-1, 1, 1048, 15), (-1, 1, 1049, 16), (-1, 1, 1050, 18), (-1, 1, 1051, 19), (-1, 1, 1052, 20), (-1, 1, 1053, 22), (-1, 1, 1054, 23), (-1, 1, 1055, 25), (-1, 1, 1056, 26), (-1, 1, 1057, 27), (-1, 1, 1058, 29), (-1, 1, 1059, 30), (-1, 1, 1060, 31), (-1, 1, 1061, 33), (-1, 1, 1062, 34), (-1, 1, 1063, 35), (-1, 1, 1064, 37), (-1, 1, 1065, 38), (-1, 1, 1066, 40), (-1, 1, 1067, 41), (-1, 1, 1068, 42), (-1, 1, 1069, 44), (-1, 1, 1070, 45), (-1, 1, 1071, 46), (-1, 1, 1072, 48), (-1, 1, 1073, 49), (-1, 1, 1074, 51), (-1, 1, 1075, 52), (-1, 1, 1076, 53), (-1, 1, 1077, 55), (-1, 1, 1078, 56), (-1, 1, 1079, 57), (-1, 1, 1080, 59), (-1, 1, 1081, 60), (-1, 1, 1082, 62), (-1, 1, 1083, 63), (-1, 1, 1084, 64), (-1, 1, 1085, 66), (-1, 1, 1086, 67), (-1, 1, 1087, 68), (-1, 1, 1088, 70), (-1, 1, 1089, 71), (-1, 1, 1090, 73), (-1, 1, 1091, 74), (-1, 1, 1092, 75), (-1, 1, 1093, 77), (-1, 1, 1094, 78), (-1, 1, 1095, 79), (-1, 1, 1096, 81), (-1, 1, 1097, 82), (-1, 1, 1098, 83), (-1, 1, 1099, 85), (-1, 1, 1100, 86), (-1, 1, 1101, 88), (-1, 1, 1102, 89), (-1, 1, 1103, 90), (-1, 1, 1104, 92), (-1, 1, 1105, 93), (-1, 1, 1106, 94), (-1, 1, 1107, 96), (-1, 1, 1108, 97), (-1, 1, 1109, 98), (-1, 1, 1110, 99), (-1, 1, 1111, 100), (-1, 1, 1112, 101), (-1, 1, 1113, 103), (-1, 1, 1114, 104), (-1, 1, 1115, 105), (-1, 1, 1116, 106), (-1, 1, 1117, 107), (-1, 1, 1118, 108), (-1, 1, 1119, 109), (-1, 1, 1120, 110), (-1, 1, 1121, 111), (-1, 1, 1122, 113), (-1, 1, 1123, 114), (-1, 1, 1124, 115), (-1, 1, 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Rotation of photo 1003369118 of 90 degree
temp/1759139236_2886529_1003369118_58171420504d0b5f05a1233b6c515509_65826337.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f18a991f520>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1554252fd0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1554241ee0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9ca00>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9c9a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9c310>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9cd60>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9c550>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8e80>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a89a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8b50>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8df0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8be0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8eb0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8bb0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8f40>] 90
 remove_crop_border : False
version de PIL : 9.5.0
Needs to change image size !
[[ 6.123234e-17  1.000000e+00]
 [-1.000000e+00  6.123234e-17]]
90
[[ 6.123234e-17  1.000000e+00]
 [-1.000000e+00  6.123234e-17]]
 shrink_image : False
 len(list_crops) : 16
time for calcul the mask position with numpy : 0.009094715118408203
nb_pixel_total : 110633
time to create 1 rle with old method : 0.1177668571472168
.time for calcul the mask position with numpy : 0.009877920150756836
nb_pixel_total : 15826
time to create 1 rle with old method : 0.018098831176757812
.time for calcul the mask position with numpy : 0.00915217399597168
nb_pixel_total : 5286
time to create 1 rle with old method : 0.0062084197998046875
.time for calcul the mask position with numpy : 0.009396076202392578
nb_pixel_total : 1633
time to create 1 rle with old method : 0.0019354820251464844
.time for calcul the mask position with numpy : 0.009822607040405273
nb_pixel_total : 105533
time to create 1 rle with old method : 0.12162303924560547
.time for calcul the mask position with numpy : 0.009259462356567383
nb_pixel_total : 4393
time to create 1 rle with old method : 0.005120277404785156
.time for calcul the mask position with numpy : 0.009397029876708984
nb_pixel_total : 632
time to create 1 rle with old method : 0.0007998943328857422
.time for calcul the mask position with numpy : 0.009948015213012695
nb_pixel_total : 62627
time to create 1 rle with old method : 0.07256412506103516
.time for calcul the mask position with numpy : 0.009037494659423828
nb_pixel_total : 33681
time to create 1 rle with old method : 0.03863382339477539
.time for calcul the mask position with numpy : 0.008866548538208008
nb_pixel_total : 37724
time to create 1 rle with old method : 0.042560577392578125
.time for calcul the mask position with numpy : 0.009513378143310547
nb_pixel_total : 48775
time to create 1 rle with old method : 0.05593729019165039
.time for calcul the mask position with numpy : 0.05355525016784668
nb_pixel_total : 1171703
time to create 1 rle with new method : 0.23333001136779785
.time for calcul the mask position with numpy : 0.009451866149902344
nb_pixel_total : 2310
time to create 1 rle with old method : 0.0028181076049804688
.time for calcul the mask position with numpy : 0.009593725204467773
nb_pixel_total : 2256
time to create 1 rle with old method : 0.0026559829711914062
.time for calcul the mask position with numpy : 0.009206295013427734
nb_pixel_total : 3112
time to create 1 rle with old method : 0.0036482810974121094
.time for calcul the mask position with numpy : 0.009197235107421875
nb_pixel_total : 1662
time to create 1 rle with old method : 0.0019655227661132812
.len(list_crops_rotate) : 16
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Rotation of photo 1003369118 of 180 degree
temp/1759139236_2886529_1003369118_58171420504d0b5f05a1233b6c515509_65826337.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f18a991f520>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1554252fd0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1554241ee0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9ca00>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9c9a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9c310>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9cd60>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9c550>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8e80>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a89a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8b50>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8df0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8be0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8eb0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8bb0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8f40>] 180
 remove_crop_border : False
version de PIL : 9.5.0
[[-1.0000000e+00  1.2246468e-16]
 [-1.2246468e-16 -1.0000000e+00]]
180
[[-1.0000000e+00  1.2246468e-16]
 [-1.2246468e-16 -1.0000000e+00]]
 shrink_image : False
 len(list_crops) : 16
time for calcul the mask position with numpy : 0.00948023796081543
nb_pixel_total : 110633
time to create 1 rle with old method : 0.11826515197753906
.time for calcul the mask position with numpy : 0.008522510528564453
nb_pixel_total : 15826
time to create 1 rle with old method : 0.016746997833251953
.time for calcul the mask position with numpy : 0.008749723434448242
nb_pixel_total : 5286
time to create 1 rle with old method : 0.005886077880859375
.time for calcul the mask position with numpy : 0.008484601974487305
nb_pixel_total : 1633
time to create 1 rle with old method : 0.0019311904907226562
.time for calcul the mask position with numpy : 0.0087890625
nb_pixel_total : 105533
time to create 1 rle with old method : 0.11266851425170898
.time for calcul the mask position with numpy : 0.00829172134399414
nb_pixel_total : 4393
time to create 1 rle with old method : 0.004728794097900391
.time for calcul the mask position with numpy : 0.008479118347167969
nb_pixel_total : 632
time to create 1 rle with old method : 0.0007152557373046875
.time for calcul the mask position with numpy : 0.009238719940185547
nb_pixel_total : 62627
time to create 1 rle with old method : 0.06865429878234863
.time for calcul the mask position with numpy : 0.009380578994750977
nb_pixel_total : 33681
time to create 1 rle with old method : 0.03622722625732422
.time for calcul the mask position with numpy : 0.008676528930664062
nb_pixel_total : 37724
time to create 1 rle with old method : 0.04119992256164551
.time for calcul the mask position with numpy : 0.008838176727294922
nb_pixel_total : 48775
time to create 1 rle with old method : 0.051537513732910156
.time for calcul the mask position with numpy : 0.04431486129760742
nb_pixel_total : 1171703
time to create 1 rle with new method : 0.14052891731262207
.time for calcul the mask position with numpy : 0.010902166366577148
nb_pixel_total : 2310
time to create 1 rle with old method : 0.002781391143798828
.time for calcul the mask position with numpy : 0.010522127151489258
nb_pixel_total : 2256
time to create 1 rle with old method : 0.0027611255645751953
.time for calcul the mask position with numpy : 0.009090662002563477
nb_pixel_total : 3112
time to create 1 rle with old method : 0.003353595733642578
.time for calcul the mask position with numpy : 0.009353399276733398
nb_pixel_total : 1662
time to create 1 rle with old method : 0.0020220279693603516
.len(list_crops_rotate) : 16
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Rotation of photo 1003369118 of 270 degree
temp/1759139236_2886529_1003369118_58171420504d0b5f05a1233b6c515509_65826337.jpg [<mtr.simple_image_editor.image_utils.CHI object at 0x7f18a991f520>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1554252fd0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1554241ee0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9ca00>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9c9a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9c310>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9cd60>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b9c550>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8e80>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a89a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8b50>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8df0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8be0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8eb0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8bb0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8f40>] 270
 remove_crop_border : False
version de PIL : 9.5.0
Needs to change image size !
[[-1.8369702e-16 -1.0000000e+00]
 [ 1.0000000e+00 -1.8369702e-16]]
270
[[-1.8369702e-16 -1.0000000e+00]
 [ 1.0000000e+00 -1.8369702e-16]]
 shrink_image : False
 len(list_crops) : 16
time for calcul the mask position with numpy : 0.008824825286865234
nb_pixel_total : 110633
time to create 1 rle with old method : 0.11821293830871582
.time for calcul the mask position with numpy : 0.008264303207397461
nb_pixel_total : 15826
time to create 1 rle with old method : 0.016320228576660156
.time for calcul the mask position with numpy : 0.009409427642822266
nb_pixel_total : 5286
time to create 1 rle with old method : 0.006102085113525391
.time for calcul the mask position with numpy : 0.011009454727172852
nb_pixel_total : 1633
time to create 1 rle with old method : 0.0022521018981933594
.time for calcul the mask position with numpy : 0.009168863296508789
nb_pixel_total : 105533
time to create 1 rle with old method : 0.10989713668823242
.time for calcul the mask position with numpy : 0.00824117660522461
nb_pixel_total : 4393
time to create 1 rle with old method : 0.004596233367919922
.time for calcul the mask position with numpy : 0.00823354721069336
nb_pixel_total : 632
time to create 1 rle with old method : 0.0007250308990478516
.time for calcul the mask position with numpy : 0.008596420288085938
nb_pixel_total : 62627
time to create 1 rle with old method : 0.06430196762084961
.time for calcul the mask position with numpy : 0.008442878723144531
nb_pixel_total : 33681
time to create 1 rle with old method : 0.03535819053649902
.time for calcul the mask position with numpy : 0.008453607559204102
nb_pixel_total : 37724
time to create 1 rle with old method : 0.038871049880981445
.time for calcul the mask position with numpy : 0.0086212158203125
nb_pixel_total : 48775
time to create 1 rle with old method : 0.04976177215576172
.time for calcul the mask position with numpy : 0.17034482955932617
nb_pixel_total : 1171703
time to create 1 rle with new method : 0.2833833694458008
.time for calcul the mask position with numpy : 0.010002374649047852
nb_pixel_total : 2310
time to create 1 rle with old method : 0.002511739730834961
.time for calcul the mask position with numpy : 0.008336544036865234
nb_pixel_total : 2256
time to create 1 rle with old method : 0.0024025440216064453
.time for calcul the mask position with numpy : 0.00839853286743164
nb_pixel_total : 3112
time to create 1 rle with old method : 0.0032939910888671875
.time for calcul the mask position with numpy : 0.009315252304077148
nb_pixel_total : 1662
time to create 1 rle with old method : 0.0018298625946044922
.len(list_crops_rotate) : 16
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About to upload 4 photos
 Status : 404
 Content : b'<!doctype html>\n<html lang=en>\n<title>404 Not Found</title>\n<h1>Not Found</h1>\n<p>The requested URL was not found on the server. If you entered the URL manually please check your spelling and try again.</p>\n'
 All Response : <Response [404]>
 WARNING : NO PORTFOLIO CREATED portfolio_id 0 used instead of new portfolio
upload in portfolio : 0
init cache_photo without model_param  
we have 4 photo to upload
uploaded to storage server : ovh
folder_temporaire : temp/1759139250_2886529

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !
we have uploaded 4 photos in the portfolio 0

time of upload the photos Elapsed time : 3.4478299617767334

 map_filename_photo_id : 4
 map_filename_photo_id : {'temp/1759139236_2886529_1003369118_58171420504d0b5f05a1233b6c515509_658263370.jpg': 0, 'temp/1759139236_2886529_1003369118_58171420504d0b5f05a1233b6c515509_6582633790.jpg': 0, 'temp/1759139236_2886529_1003369118_58171420504d0b5f05a1233b6c515509_65826337180.jpg': 0, 'temp/1759139236_2886529_1003369118_58171420504d0b5f05a1233b6c515509_65826337270.jpg': 0}
Len new_chis : 4
Len list_new_chi_with_photo_id : 64 of type : 3230
 list_new_chi_with_photo_id : [<mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8760>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8a00>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471da00>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471d0d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471d4c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471d730>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471de50>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471d7c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471dfa0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471d1f0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471ddf0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471da90>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471d640>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471d550>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471deb0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471d430>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471df40>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471dfd0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471dee0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471de80>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471d850>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34760>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34550>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b340d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b344f0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34130>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34280>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34250>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34040>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b345e0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b342b0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34100>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34070>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34400>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34f70>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34820>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34a00>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34850>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b347c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34160>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34340>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b343a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917100>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15179170a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917160>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917190>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15179171c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15179171f0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917070>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917130>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917280>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15179172b0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15179172e0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917310>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917340>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917370>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15179173a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15179173d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917400>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917250>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917460>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917490>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15179174c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15179174f0>]
batch 1
Loaded 0 chid ids of type : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752385 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752385 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752385 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752385 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
Number RLEs to save : 0
 TO DO : save crop sub photo not yet done ! 
batch 1
Loaded 0 chid ids of type : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752385 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752385 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752385 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752385 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
Number RLEs to save : 0
 TO DO : save crop sub photo not yet done ! 
batch 1
Loaded 0 chid ids of type : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752385 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752385 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752385 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752385 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
Number RLEs to save : 0
 TO DO : save crop sub photo not yet done ! 
batch 1
Loaded 0 chid ids of type : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752385 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752385 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752385 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752385 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 2107752406 on photo_id : 0
ERROR missing MTRPhoto.crop_hashtag_ids : 493012381 on photo_id : 0
Number RLEs to save : 0
 TO DO : save crop sub photo not yet done ! 

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 17.25960898399353
time spend to save output : 5.888938903808594e-05
total time spend for step 1 : 17.25966787338257

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 0
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {0: ['1003369118', 'temp/1759139236_2886529_1003369118_58171420504d0b5f05a1233b6c515509_65826337270.jpg', [<mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8760>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15172a8a00>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471da00>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471d0d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471d4c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471d730>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471de50>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471d7c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471dfa0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471d1f0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471ddf0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471da90>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471d640>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471d550>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471deb0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471d430>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471df40>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471dfd0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471dee0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471de80>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f159471d850>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34760>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34550>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b340d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b344f0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34130>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34280>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34250>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34040>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b345e0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b342b0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34100>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34070>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34400>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34f70>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34820>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34a00>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34850>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b347c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34160>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b34340>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1514b343a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917100>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15179170a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917160>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917190>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15179171c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15179171f0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917070>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917130>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917280>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15179172b0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15179172e0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917310>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917340>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917370>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15179173a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15179173d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917400>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917250>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917460>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f1517917490>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15179174c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f15179174f0>]]}
l'image n'a pas le bon nombre de rotation
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 0) and `type` in (3230) 
Loaded 0 chid ids of type : 0
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in ()
le nombre de chi n'est pas bon
fin du test de rotate_chi
ERROR rotate_chi FAILED
Ayatollah of tests excluded it ! (Bon le prochain developpeur qui passe ici peut enlever ayatollah VR 11-2-21)
 name : rubbia_carac_pet_clair_0121 not run because too long 

############################### TEST rubbia_carac_pet_clair_0121_no_cnn  ################################
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2719

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=2719
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 2719
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=2719
# 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 !
 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 6479 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition !
WARNING : number of inputs for step 6480 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 7445 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition !
Step 6509 final have less inputs used (2) than in the step definition (3) : 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 6479 doesn't seem to be define in the database(
WARNING : type of input 1 of step 6480 doesn't seem to be define in the database(
We ignore checkConsistencyTypeOutputInput for datou_step final !
WARNING : type of output 3 of step 6479 doesn't seem to be define in the database(
WARNING : type of input 1 of step 7445 doesn't seem to be define in the database(
WARNING : type of output 1 of step 7445 doesn't seem to be define in the database(
WARNING : type of input 3 of step 6509 doesn't seem to be define in the database(
We ignore checkConsistencyTypeOutputInput for datou_step final !
DataTypes for each output/input checked !
no param json to modify
List Step Type Loaded in datou : merge_mask_thcl_custom, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (3373196) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (3373196)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 1009068724, 'portfolio_id': 3373196}, {'photo_id': 1009068683, 'portfolio_id': 3373196}]
 map_portfolio_id_photo_id: {3373196: [1009068724, 1009068683]}
##### Call download_photos : nb_thread : 5
begin to download photo : 1009068724
begin to download photo : 1009068683
download finish for photo 1009068683
download finish for photo 1009068724
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 2 ; length of list_pids : 2 ; length of list_args : 2
##### After load_data_input 
time to download the photos : 0.3290722370147705
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  4
step1:merge_mask_thcl_custom
Mon Sep 29 11:47:34 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139253_2886529_1009068683_4beb092fd180a8b620f754bb89015722.jpg': 1009068683, 'temp/1759139253_2886529_1009068724_3e705379f97632c4a2fd676e348a335d.jpg': 1009068724}
map_photo_id_path_extension : {1009068683: {'path': 'temp/1759139253_2886529_1009068683_4beb092fd180a8b620f754bb89015722.jpg', 'extension': 'jpg'}, 1009068724: {'path': 'temp/1759139253_2886529_1009068724_3e705379f97632c4a2fd676e348a335d.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
Begin step merge_mask_thcl_custom
args ['temp/1759139253_2886529_1009068683_4beb092fd180a8b620f754bb89015722.jpg', 'temp/1759139253_2886529_1009068724_3e705379f97632c4a2fd676e348a335d.jpg']
processing picture : temp/1759139253_2886529_1009068683_4beb092fd180a8b620f754bb89015722.jpg
processing picture : temp/1759139253_2886529_1009068724_3e705379f97632c4a2fd676e348a335d.jpg
map_subphoto_result {}
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 1009068683,1009068724) and `type` in (2800) 
Loaded 82 chid ids of type : 2800
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1941272807,1941272792,1941272797,1941272801,1941272788,1941272813,1941272802,1941272799,1941272815,1941272809,1941272786,1941272808,1941272791,1941272816,1941272796,1941272811,1941272817,1941272810,1941272814,1941272793,1941272803,1941272804,1941272805,1941272781,1941272789,1941272798,1941272812,1941272800,1941272784,1941272782,1941272794,1941272795,1941272806,1941272785,1941272790,1941272783,1941272787,1941270022,1941270015,1941270036,1941270021,1941270029,1941270018,1941270017,1941270025,1941607383,1941270019,1941270038,1941270040,1941270014,1941270039,1941270020,1941270030,1941270033,1941270028,1941270027,1941270026,1941270032,1941270031,1941270035,1941270037,1941270041,1941270012,1941270013,1941607380,1941607381,2202875936,1941270008,1941270016,1941607382,1941270034,2202875937,1941270010,1941270005,1941270007,1941270024,1941270011,2202875938,1941270023,1941607379,1941270006,1941270009)
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1941272807,1941272792,1941272797,1941272801,1941272788,1941272813,1941272802,1941272799,1941272815,1941272809,1941272786,1941272808,1941272791,1941272816,1941272796,1941272811,1941272817,1941272810,1941272814,1941272793,1941272803,1941272804,1941272805,1941272781,1941272789,1941272798,1941272812,1941272800,1941272784,1941272782,1941272794,1941272795,1941272806,1941272785,1941272790,1941272783,1941272787,1941270022,1941270015,1941270036,1941270021,1941270029,1941270018,1941270017,1941270025,1941607383,1941270019,1941270038,1941270040,1941270014,1941270039,1941270020,1941270030,1941270033,1941270028,1941270027,1941270026,1941270032,1941270031,1941270035,1941270037,1941270041,1941270012,1941270013,1941607380,1941607381,2202875936,1941270008,1941270016,1941607382,1941270034,2202875937,1941270010,1941270005,1941270007,1941270024,1941270011,2202875938,1941270023,1941607379,1941270006,1941270009)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1941272807,1941272792,1941272797,1941272801,1941272788,1941272813,1941272802,1941272799,1941272815,1941272809,1941272786,1941272808,1941272791,1941272816,1941272796,1941272811,1941272817,1941272810,1941272814,1941272793,1941272803,1941272804,1941272805,1941272781,1941272789,1941272798,1941272812,1941272800,1941272784,1941272782,1941272794,1941272795,1941272806,1941272785,1941272790,1941272783,1941272787,1941270022,1941270015,1941270036,1941270021,1941270029,1941270018,1941270017,1941270025,1941607383,1941270019,1941270038,1941270040,1941270014,1941270039,1941270020,1941270030,1941270033,1941270028,1941270027,1941270026,1941270032,1941270031,1941270035,1941270037,1941270041,1941270012,1941270013,1941607380,1941607381,2202875936,1941270008,1941270016,1941607382,1941270034,2202875937,1941270010,1941270005,1941270007,1941270024,1941270011,2202875938,1941270023,1941607379,1941270006,1941270009)
As expected we have just one thcl present
processing picture : 1009068683


begin to find the sub_photo_id : 

        select crop_hashtag_id , sub_photo_id from MTRPhoto.crop_sub_photo_ids where crop_hashtag_id in (1941272807,1941272792,1941272797,1941272801,1941272788,1941272813,1941272802,1941272799,1941272815,1941272809,1941272786,1941272808,1941272791,1941272816,1941272796,1941272811,1941272817,1941272810,1941272814,1941272793,1941272803,1941272804,1941272805,1941272781,1941272789,1941272798,1941272812,1941272800,1941272784,1941272782,1941272794,1941272795,1941272806,1941272785,1941272790,1941272783,1941272787) ;
        
chi 1941272807 saved with no changes
chi 1941272792 saved with no changes
chi 1941272797 saved with no changes
chi 1941272801 saved with no changes
chi 1941272788 saved with no changes
chi 1941272813 saved with no changes
chi 1941272802 saved with no changes
chi 1941272799 saved with no changes
chi 1941272815 saved with no changes
chi 1941272809 saved with no changes
chi 1941272786 saved with no changes
chi 1941272808 saved with no changes
chi 1941272791 saved with no changes
select cps.photo_id, cps.thcl, h.hashtag, avg(cps.score), p.text,cps.hashtag_id FROM MTRPhoto.class_photo_score cps 
          inner join MTRBack.hashtags h on h.hashtag_id = cps.hashtag_id inner join MTRBack.photos_view p on cps.photo_id = p.photo_id
          where cps.photo_id in(1009071263,1009221813,1009235446,1009235446,1009279340,1009279356,1009280654,1009282603,1009285753,1009285765,1009287138,1009288503,1009288546,1009289519,1009290969,1009291015,1009292124,1009293463,1009293494,1009302642,1009302924,1009314298,1009325392,1009325685,1009331808,1009337442,1009343208,1009348717,1009360426,1009360710,1009366170,1009370139,1009378373,1009378413,1009382207,1009387316,1009387465,1009397637,1009397719,1009407253,1009418244,1009418926,1009427413,1009430107,1009431383,1009433078,1009433144,1009434053,1009434384,1009434694,1009435681,1009436481,1009436964,1009437043,1009437239,1009438334,1009438590,1009440272,1009440278,1009442464,1009442509,1009442528,1009444076,1009444130,1009445413,1009445538,1009446902,1009448266,1009448396,1009449789,1009451629,1009451631,1009451750,1009452461,1009452961,1009452979,1009454196,1009454247,1009454337,1009455534,1009457025,1009457032,1009457228,1009460750,1009460967,1009466377,1009466452,1009467101,1009471967,1009472268,1009477695,1009478118,1009481722,1009482987,1009487453,1009487657,1009487950,1009489088,1009489218,1009489308,1009490315,1009490479,1009495060,1009495488,1009498944,1009500499,1009500766,1009506264,1009506270,1009506275,1009506746,1009509720,1009510178,1009511663,1009517339,1009517535,1009517849,1009521436,1009521607,1009525340,1009525354,1009525617,1009528261,1009529525,1009533039,1009533043,1009533518,1009536205,1009540619,1009540774,1009541035,1009543634,1009544885,1009545316,1009549107,1009549847,1009551213,1009552745,1009553914,1009557764,1009558260,1009560600,1009562085,1009566563,1009567165,1009570167,1009571289,1009576217,1009576836,1009579452,1009580609,1009585073,1009585473,1009587840,1009589122,1009593908,1009596967,1009597989,1009602701,1009603252,1009605651,1009606591,1009610545,1009610907,1009612085,1009612622,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,1009612733,100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and thcl in (2444) group by h.hashtag order by cps.photo_id 
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1070420338, 1070483036, 1070525074, 1070555663, 1070594548, 1070637083, 1070680464, 1070724015, 1070790695, 1070943047, 1071200711, 1071239808, 1071464242, 1071636096, 1071703689, 1071767893, 1071811888, 1071860806, 1071928107, 1072017177, 1072118440, 1072207679, 1072297360, 1072393940, 1072508113, 1072582572, 1072632150, 1072728396, 1072839511, 1072917093, 1072972612, 1073027774, 1073056959, 1073111357, 1073142087, 1073196806, 1073244301, 1073366400, 1073503492, 1073644965, 1073725121, 1073807926, 1073882317, 1073948836, 1074028102], saving chi 1941272796 with no changes
select cps.photo_id, cps.thcl, h.hashtag, avg(cps.score), p.text,cps.hashtag_id FROM MTRPhoto.class_photo_score cps 
          inner join MTRBack.hashtags h on h.hashtag_id = cps.hashtag_id inner join MTRBack.photos_view p on cps.photo_id = p.photo_id
          where cps.photo_id in(1009071264,1009221814,1009235447,1009235447,1009279341,1009279357,1009280655,1009282604,1009285754,1009285766,1009287139,1009288504,1009288547,1009289520,1009290970,1009291016,1009292125,1009293464,1009293495,1009302643,1009302925,1009314299,1009325393,1009325686,1009331809,1009337443,1009343209,1009348718,1009360428,1009360711,1009366171,1009370140,1009378374,1009378414,1009382208,1009387317,1009387466,1009397639,1009397720,1009407254,1009418245,1009418927,1009427414,1009430108,1009431384,1009433079,1009433145,1009434054,1009434385,1009434695,1009435682,1009436482,1009436965,1009437044,1009437240,1009438336,1009438591,1009440273,1009440279,1009442465,1009442510,1009442529,1009444077,1009444131,1009445414,1009445539,1009446903,1009448267,1009448397,1009449790,1009451630,1009451632,1009451751,1009452462,1009452962,1009452980,1009454197,1009454248,1009454338,1009455535,1009457026,1009457033,1009457229,1009460751,1009460968,1009466378,1009466453,1009467102,1009471968,1009472269,1009477696,1009478120,1009481723,1009482988,1009487454,1009487658,1009487951,1009489089,1009489219,1009489309,1009490316,1009490480,1009495061,1009495489,1009498945,1009500500,1009500767,1009506265,1009506271,1009506276,1009506747,1009509721,1009510179,1009511664,1009517341,1009517536,1009517850,1009521437,1009521608,1009525341,1009525355,1009525618,1009528262,1009529526,1009533040,1009533045,1009533519,1009536206,1009540620,1009540775,1009541036,1009543635,1009544886,1009545317,1009549108,1009549848,1009551214,1009552746,1009553915,1009557765,1009558261,1009560601,1009562086,1009566564,1009567166,1009570168,1009571290,1009576219,1009576837,1009579453,1009580610,1009585074,1009585474,1009587841,1009589123,1009593909,1009596968,1009597990,1009602702,1009603253,1009605652,1009606592,1009610546,1009610908,1009612086,1009612623,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,1009612734,100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and thcl in (2444) group by h.hashtag order by cps.photo_id 
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1011608665, 1011609106, 1011609106, 1011609106, 1011611085, 1011611085, 1011611085, 1011624741, 1011624741, 1011624741, 1011634035, 1011634035, 1011634035, 1011634235, 1011640646, 1011640646, 1011640646, 1011644745, 1011644745, 1011644745, 1011645812, 1011651414, 1011651414, 1011651414, 1011654363, 1011654363, 1011654363, 1011654529, 1011657333, 1011657333, 1011657333, 1011662363, 1011662620, 1011662620, 1011662620, 1011663477, 1011663477, 1011663477, 1011668057, 1011668057, 1011668057, 1011670371, 1011670371, 1011670371, 1011670718, 1011673657, 1011673657, 1011673657, 1011677581, 1011677581, 1011677581, 1011678721, 1011683362, 1011685865, 1011685865, 1011685865, 1011686080, 1011688984, 1011688984, 1011688984, 1011693939, 1011694365, 1011694365, 1011694365, 1011695467, 1011695467, 1011695467, 1011699770, 1011699770, 1011699770, 1011702349, 1011702349, 1011702349, 1011702506, 1011705894, 1011705894, 1011705894, 1011709563, 1011709563, 1011709563, 1011710768, 1011715539, 1011715539, 1011715539, 1011717331, 1011717331, 1011717331, 1011717620, 1011720876, 1011720876, 1011720876, 1011729849, 1011730060, 1011732069, 1011732069, 1011732069, 1011745378, 1011745378, 1011745378, 1011749567, 1011749567, 1011749567, 1011749859, 1011754366, 1011754366, 1011754366, 1011760639, 1011760639, 1011760639, 1011762811, 1011771577, 1011771577, 1011771577, 1011775543, 1011776021, 1011781062, 1011781062, 1011781062, 1011796255, 1011796700, 1011796700, 1011796700, 1011799476, 1011799476, 1011799476, 1011808999, 1011808999, 1011808999, 1011814283, 1011821501, 1011821501, 1011821501, 1011832262, 1011832262, 1011832262, 1011835745, 1011848233, 1011848233, 1011848233, 1011854320, 1011854320, 1011854320, 1011854964, 1011864241, 1011864241, 1011864241, 1011898789, 1011899408, 1011899408, 1011899408, 1011931980, 1011944951, 1011965699, 1011996124, 1012057223, 1012102670, 1012119522, 1012135861, 1012160795, 1012200246, 1012235646, 1012274414, 1012311641, 1012339066, 1012349231, 1012361133, 1012373393, 1012384519, 1012398136, 1012427066, 1012513578, 1012578257, 1012607735, 1012648311, 1012685279, 1012707038, 1012743151, 1012781189, 1012813613, 1012833334, 1012850053, 1012869003, 1012888264, 1012906667, 1012936420, 1012962397, 1012994406, 1013020658, 1013050398, 1013079711, 1013109509, 1013134909, 1013164473, 1013191882, 1013222543, 1013249567, 1013282216, 1013324565, 1013355534, 1013384425, 1013419595, 1013448998, 1013485943, 1013512099, 1013537851, 1013559475, 1013586243, 1013611685, 1013640150, 1013670298, 1013697357, 1013725232, 1013753992, 1013783576, 1013815405, 1013840143, 1013868400, 1013894813, 1013925416, 1013949398, 1013978232, 1014004244, 1014031973, 1014060888, 1014089824, 1014119985, 1014151376, 1014181976, 1014218097, 1014249256, 1014284657, 1014318636, 1014358904, 1014399313, 1014444628, 1014481401, 1014495903, 1014509029, 1014535926, 1014563944, 1014589269, 1014610199, 1014629936, 1014653259, 1014670096, 1014686436, 1014702095, 1014722036, 1014741623, 1014798047, 1014814530, 1014852881, 1014876922, 1014904378, 1014932043, 1014973933, 1014997292, 1015026129, 1015061978, 1015087313, 1015113514, 1015135856, 1015159054, 1015185817, 1015228383, 1015250486, 1015271021, 1015289448, 1015309870, 1015328276, 1015341944, 1015364705, 1015387463, 1015408729, 1015431748, 1015447243, 1015472225, 1015491232, 1015505922, 1015524805, 1015545381, 1015569538, 1015593875, 1015618672, 1015640097, 1015678519, 1015699354, 1015725539, 1015785034, 1015803325, 1015824163, 1015858643, 1015891613, 1015922243, 1015951187, 1015979959, 1016008982, 1016031645, 1016057738, 1016079047, 1016104692, 1016129809, 1016151056, 1016165405, 1016177371, 1016191026, 1016200476, 1016250155, 1056228412, 1056230996, 1056231397, 1056270609, 1056338912, 1056384137, 1056391585, 1056418914, 1056453295, 1056513350, 1056583091, 1056651952, 1056690746, 1056711158, 1056736177, 1056748728, 1056756884, 1056764901, 1056772439, 1056784479, 1056800792, 1056808382, 1056818153, 1056830022, 1056830678, 1056830946, 1056831075, 1056831555, 1056831853, 1056837632, 1056856208, 1056920474, 1056980403, 1057041126, 1057088055, 1057121113, 1057151347, 1057220554, 1057280510, 1057341344, 1057404848, 1057405391, 1057407764, 1057472517, 1057500274, 1057540057, 1057593136, 1057647552, 1057687881, 1057710263, 1057732746, 1057755506, 1057764772, 1057772059, 1057778724, 1057785418, 1057793605, 1057801263, 1057807127, 1057813942, 1057816040, 1057829284, 1057848915, 1057892842, 1057897023, 1057932761, 1058003328, 1058069211, 1058128315, 1058168236, 1058219003, 1058252320, 1058301783, 1058344222, 1058357004, 1058360842, 1058373429, 1058397928, 1058419190, 1058452274, 1058454550, 1058497068, 1058498210, 1058498416, 1058498769, 1058521793, 1058551448, 1058552625, 1058555681, 1058558896, 1058563074, 1058588011, 1058598164, 1058621588, 1058654998, 1058676197, 1058707464, 1058970481, 1059006625, 1059046567, 1059191655, 1059236198, 1059303270, 1059358500, 1059421121, 1059465153, 1059519818, 1059570915, 1059621391, 1059666511, 1059721384, 1059764186, 1059769186, 1059785231, 1059879566, 1059945492, 1060020930, 1060078212, 1060220740, 1060379664, 1060464894, 1060518327, 1060593809, 1060658651, 1060713775, 1060747610, 1060788855, 1060803883, 1060857838, 1060887243, 1060936295, 1060966200, 1061000349, 1061056463, 1061100125, 1061192547, 1061226358, 1061229429, 1061238743, 1061286704, 1061337834, 1061360124, 1061417198, 1061498967, 1061555749, 1061610190, 1061671175, 1061719301, 1061764001, 1061784993, 1061817863, 1061883305, 1061935818, 1062011813, 1062066028, 1062133128, 1062316228, 1062405205, 1062458592, 1062503694, 1062550083, 1062590646, 1062648862, 1062695436, 1062723339, 1062756110, 1062784498, 1062812431, 1062854623, 1062915393, 1062972090, 1063010695, 1063064213, 1063123897, 1063221075, 1063265471, 1063308573, 1063509638, 1063558462, 1063625397, 1063754715, 1063792622, 1063859382, 1064104829, 1064194328, 1064238849, 1064271158, 1064300596, 1064328045, 1064355808, 1064384232, 1064413864, 1064442500, 1064475864, 1064569992, 1064629409, 1064679938, 1064718117, 1064843967, 1064885495, 1064925687, 1064980982, 1065025224, 1065066422, 1065122257, 1065166126, 1065199555, 1065231173, 1065248309, 1065277475, 1065295449, 1065311549, 1065327185, 1065338942, 1065347317, 1065358525, 1065423045, 1065464751, 1065551061, 1065640478, 1065683384, 1065752107, 1065853215, 1065872619, 1065886168, 1065906998, 1065929865, 1065949959, 1065970150, 1065989092, 1066036682, 1066082793, 1066127199, 1066177815, 1066234445, 1066280976, 1066333749, 1066370056, 1066403948, 1066434332, 1066459555, 1066470976, 1066476565, 1066483570, 1066495990, 1066526110, 1066576209, 1066628894, 1066688039, 1066748032, 1066802951, 1066855082, 1066901149, 1066957296, 1067018961, 1067077308, 1067126794, 1067176865, 1067220037, 1067242692, 1067290907, 1067350627, 1067420131, 1067482003, 1067541097, 1067577648, 1067619699, 1067661436, 1067690192, 1067717415, 1067747139, 1067774113, 1067802801, 1067861252, 1067911432, 1067941479, 1067982048, 1068035446, 1068088472, 1068111527, 1068135669, 1068160647, 1068189420, 1068212314, 1068237226, 1068269604, 1068303915, 1068329047, 1068346740, 1068348692, 1068386858, 1068425049, 1068452885, 1068458894, 1068468413, 1068469246, 1068469820, 1068470402, 1068471259, 1068471944, 1068474410, 1068506227, 1068541459, 1068570958, 1068623506, 1068682679, 1068719360, 1068741810, 1068773097, 1068806814, 1068837185, 1068851169, 1068855958, 1068875460, 1068897958, 1068917464, 1068943962, 1068968311, 1068982665, 1069002727, 1069020499, 1069057621, 1069125045, 1069205889, 1069239241, 1069260807, 1069283982, 1069310063, 1069337747, 1069359154, 1069378207, 1069385924, 1069408086, 1069454245, 1069505571, 1069547705, 1069590741, 1069703345, 1069823741, 1069863128, 1069957905, 1069991321, 1070012156, 1070039930, 1070071216, 1070086448, 1070096251, 1070099004, 1070109609, 1070151843, 1070176008, 1070188631, 1070229603, 1070274898, 1070325005, 1070371825, 1070420339, 1070483037, 1070525075, 1070555664, 1070594549, 1070637084, 1070680465, 1070724016, 1070790696, 1070943048, 1071200712, 1071239809, 1071464244, 1071636097, 1071703690, 1071767894, 1071811889, 1071860807, 1071928108, 1072017178, 1072118442, 1072207680, 1072297361, 1072393941, 1072508114, 1072582573, 1072632151, 1072728397, 1072839512, 1072917094, 1072972613, 1073027775, 1073056960, 1073111358, 1073142089, 1073196807, 1073244303, 1073366401, 1073503493, 1073644966, 1073725122, 1073807927, 1073882318, 1073948837, 1074028103], saving chi 1941272793 with no changes
chi 1941272803 saved with no changes
chi 1941272804 saved with no changes
chi 1941272805 saved with no changes
chi 1941272781 saved with no changes
chi 1941272789 saved with no changes
chi 1941272798 saved with no changes
chi 1941272812 saved with no changes
chi 1941272800 saved with no changes
chi 1941272784 saved with no changes
chi 1941272782 saved with no changes
chi 1941272794 saved with no changes
chi 1941272795 saved with no changes
chi 1941272806 saved with no changes
chi 1941272785 saved with no changes
chi 1941272790 saved with no changes
chi 1941272783 saved with no changes
chi 1941272787 saved with no changes
processing picture : 1009068724


begin to find the sub_photo_id : 

        select crop_hashtag_id , sub_photo_id from MTRPhoto.crop_sub_photo_ids where crop_hashtag_id in (1941270022,1941270015,1941270036,1941270021,1941270029,1941270018,1941270017,1941270025,1941607383,1941270019,1941270038,1941270040,1941270014,1941270039,1941270020,1941270030,1941270033,1941270028,1941270027,1941270026,1941270032,1941270031,1941270035,1941270037,1941270041,1941270012,1941270013,1941607380,1941607381,2202875936,1941270008,1941270016,1941607382,1941270034,2202875937,1941270010,1941270005,1941270007,1941270024,1941270011,2202875938,1941270023,1941607379,1941270006,1941270009) ;
        
chi 1941270022 saved with no changes
chi 1941270015 saved with no changes
chi 1941270036 saved with no changes
chi 1941270021 saved with no changes
chi 1941270029 saved with no changes
chi 1941270018 saved with no changes
chi 1941270017 saved with no changes
chi 1941270025 saved with no changes
chi 1941607383 saved with no changes
chi 1941270019 saved with no changes
chi 1941270038 saved with no changes
chi 1941270040 saved with no changes
chi 1941270014 saved with no changes
chi 1941270039 saved with no changes
chi 1941270020 saved with no changes
select cps.photo_id, cps.thcl, h.hashtag, avg(cps.score), p.text,cps.hashtag_id FROM MTRPhoto.class_photo_score cps 
          inner join MTRBack.hashtags h on h.hashtag_id = cps.hashtag_id inner join MTRBack.photos_view p on cps.photo_id = p.photo_id
          where cps.photo_id in(1009070856,1009221556,1009235000,1009235000,1009235000,1009279342,1009279352,1009280656,1009282608,1009285758,1009285767,1009287134,1009288505,1009288551,1009289512,1009290974,1009291020,1009292129,1009293459,1009293499,1009302644,1009302926,1009314291,1009325388,1009325687,1009331813,1009337444,1009343213,1009348719,1009360418,1009360712,1009366172,1009370141,1009378378,1009378418,1009382212,1009387309,1009387467,1009397623,1009397721,1009407255,1009418249,1009418931,1009427415,1009430103,1009431388,1009433083,1009433140,1009434049,1009434049,1009434049,1009434377,1009434377,1009434377,1009434690,1009434690,1009434690,1009435683,1009436483,1009436969,1009436969,1009436969,1009437039,1009437244,1009438330,1009438586,1009438586,1009438586,1009440274,1009440283,1009440274,1009440274,1009442466,1009442502,1009442533,1009442533,1009442533,1009444071,1009444126,1009444126,1009444126,1009445418,1009445543,1009446898,1009446898,1009446898,1009448271,1009448392,1009448392,1009448392,1009449794,1009449794,1009449794,1009451625,1009451636,1009451746,1009451746,1009451746,1009452466,1009452466,1009452466,1009452957,1009452957,1009452957,1009452981,1009454192,1009454249,1009454339,1009454339,1009454339,1009455527,1009457021,1009457034,1009457224,1009457224,1009457224,1009460746,1009460746,1009460746,1009460963,1009466379,1009466448,1009467103,1009467103,1009467103,1009471963,1009471963,1009471963,1009472264,1009477697,1009478110,1009478110,1009478110,1009481715,1009481715,1009481715,1009482983,1009482983,1009482983,1009487455,1009487650,1009487952,1009487952,1009487952,1009489081,1009489223,1009489310,1009489310,1009489310,1009490311,1009490311,1009490311,1009490481,1009495056,1009495484,1009495484,1009495484,1009498949,1009498949,1009498949,1009500504,1009500504,1009500504,1009500755,1009506260,1009506266,1009506266,1009506280,1009506742,1009506742,1009506742,1009509716,1009510174,1009510174,1009510174,1009511665,1009511665,1009511665,1009517335,1009517537,1009517845,1009517845,1009517845,1009521429,1009521429,1009521429,1009521603,1009525336,1009525350,1009525619,1009525619,1009525619,1009528263,1009529518,1009529518,1009529518,1009533041,1009533047,1009533520,1009533520,1009533520,1009536210,1009536210,1009536210,1009540621,1009540767,1009541040,1009541040,1009541040,1009543630,1009543630,1009543630,1009544881,1009545318,1009545318,1009545318,1009549109,1009549843,1009549843,1009549843,1009551218,1009551218,1009551218,1009552741,1009553916,1009553916,1009553916,1009557766,1009558262,1009558262,1009558262,1009560602,1009560602,1009560602,1009562081,1009562081,1009562081,1009566559,1009567161,1009567161,1009567161,1009570163,1009571294,1009571294,1009571294,1009576213,1009576838,1009576838,1009576838,1009579448,1009579448,1009579448,1009580611,1009580611,1009580611,1009585078,1009585478,1009585478,1009585478,1009587842,1009589124,1009589124,1009589124,1009593904,1009596963,1009597982,1009597982,1009597982,1009602703,1009603248,1009603248,1009603248,1009605647,1009605647,1009605647,1009606587,1009606587,1009606587,1009610541,1009610909,1009610909,1009610909,1009612090,1009612090,1009612090,1009612624,1009612624,1009612624,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,1009612726,100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and thcl in (2444) group by h.hashtag order by cps.photo_id 
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select cps.photo_id, cps.thcl, h.hashtag, avg(cps.score), p.text,cps.hashtag_id FROM MTRPhoto.class_photo_score cps 
          inner join MTRBack.hashtags h on h.hashtag_id = cps.hashtag_id inner join MTRBack.photos_view p on cps.photo_id = p.photo_id
          where cps.photo_id in(1009070857,1009221557,1009235001,1009235001,1009235001,1009279343,1009279353,1009280657,1009282609,1009285759,1009285768,1009287135,1009288506,1009288552,1009289513,1009290975,1009291021,1009292130,1009293460,1009293500,1009302645,1009302927,1009314292,1009325389,1009325688,1009331814,1009337445,1009343214,1009348720,1009360419,1009360713,1009366173,1009370142,1009378379,1009378419,1009382213,1009387310,1009387468,1009397625,1009397722,1009407256,1009418250,1009418932,1009427416,1009430104,1009431389,1009433084,1009433141,1009434050,1009434050,1009434050,1009434378,1009434378,1009434378,1009434691,1009434691,1009434691,1009435684,1009436484,1009436970,1009436970,1009436970,1009437040,1009437245,1009438331,1009438587,1009438587,1009438587,1009440275,1009440284,1009440275,1009440275,1009442467,1009442503,1009442534,1009442534,1009442534,1009444072,1009444127,1009444127,1009444127,1009445419,1009445544,1009446899,1009446899,1009446899,1009448272,1009448393,1009448393,1009448393,1009449795,1009449795,1009449795,1009451626,1009451637,1009451747,1009451747,1009451747,1009452467,1009452467,1009452467,1009452958,1009452958,1009452958,1009452982,1009454193,1009454250,1009454340,1009454340,1009454340,1009455528,1009457022,1009457035,1009457225,1009457225,1009457225,1009460747,1009460747,1009460747,1009460964,1009466380,1009466449,1009467104,1009467104,1009467104,1009471964,1009471964,1009471964,1009472265,1009477698,1009478112,1009478112,1009478112,1009481716,1009481716,1009481716,1009482984,1009482984,1009482984,1009487456,1009487651,1009487953,1009487953,1009487953,1009489082,1009489224,1009489311,1009489311,1009489311,1009490312,1009490312,1009490312,1009490482,1009495057,1009495485,1009495485,1009495485,1009498950,1009498950,1009498950,1009500505,1009500505,1009500505,1009500757,1009506261,1009506267,1009506267,1009506281,1009506743,1009506743,1009506743,1009509717,1009510175,1009510175,1009510175,1009511666,1009511666,1009511666,1009517336,1009517538,1009517846,1009517846,1009517846,1009521430,1009521430,1009521430,1009521604,1009525337,1009525351,1009525620,1009525620,1009525620,1009528264,1009529519,1009529519,1009529519,1009533042,1009533049,1009533521,1009533521,1009533521,1009536211,1009536211,1009536211,1009540622,1009540768,1009541041,1009541041,1009541041,1009543631,1009543631,1009543631,1009544882,1009545319,1009545319,1009545319,1009549110,1009549844,1009549844,1009549844,1009551219,1009551219,1009551219,1009552742,1009553917,1009553917,1009553917,1009557767,1009558263,1009558263,1009558263,1009560603,1009560603,1009560603,1009562082,1009562082,1009562082,1009566560,1009567162,1009567162,1009567162,1009570164,1009571295,1009571295,1009571295,1009576214,1009576839,1009576839,1009576839,1009579449,1009579449,1009579449,1009580612,1009580612,1009580612,1009585079,1009585479,1009585479,1009585479,1009587843,1009589125,1009589125,1009589125,1009593905,1009596964,1009597983,1009597983,1009597983,1009602704,1009603249,1009603249,1009603249,1009605648,1009605648,1009605648,1009606588,1009606588,1009606588,1009610542,1009610910,1009610910,1009610910,1009612091,1009612091,1009612091,1009612625,1009612625,1009612625,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,1009612727,100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and thcl in (2444) group by h.hashtag order by cps.photo_id 
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select cps.photo_id, cps.thcl, h.hashtag, avg(cps.score), p.text,cps.hashtag_id FROM MTRPhoto.class_photo_score cps 
          inner join MTRBack.hashtags h on h.hashtag_id = cps.hashtag_id inner join MTRBack.photos_view p on cps.photo_id = p.photo_id
          where cps.photo_id in(1009070858,1009221558,1009235002,1009235002,1009235002,1009279344,1009279354,1009280658,1009282610,1009285760,1009285769,1009287136,1009288507,1009288553,1009289514,1009290976,1009291022,1009292131,1009293461,1009293501,1009302646,1009302928,1009314293,1009325390,1009325689,1009331815,1009337446,1009343215,1009348721,1009360422,1009360714,1009366174,1009370143,1009378380,1009378420,1009382214,1009387311,1009387469,1009397627,1009397723,1009407257,1009418251,1009418933,1009427417,1009430105,1009431390,1009433085,1009433142,1009434051,1009434051,1009434051,1009434379,1009434379,1009434379,1009434692,1009434692,1009434692,1009435685,1009436485,1009436971,1009436971,1009436971,1009437041,1009437246,1009438332,1009438588,1009438588,1009438588,1009440276,1009440285,1009440276,1009440276,1009442468,1009442504,1009442535,1009442535,1009442535,1009444074,1009444128,1009444128,1009444128,1009445420,1009445545,1009446900,1009446900,1009446900,1009448273,1009448394,1009448394,1009448394,1009449796,1009449796,1009449796,1009451627,1009451638,1009451748,1009451748,1009451748,1009452468,1009452468,1009452468,1009452959,1009452959,1009452959,1009452983,1009454194,1009454251,1009454341,1009454341,1009454341,1009455529,1009457023,1009457036,1009457226,1009457226,1009457226,1009460748,1009460748,1009460748,1009460965,1009466381,1009466450,1009467105,1009467105,1009467105,1009471965,1009471965,1009471965,1009472266,1009477701,1009478114,1009478114,1009478114,1009481717,1009481717,1009481717,1009482985,1009482985,1009482985,1009487457,1009487652,1009487954,1009487954,1009487954,1009489083,1009489225,1009489312,1009489312,1009489312,1009490313,1009490313,1009490313,1009490483,1009495058,1009495486,1009495486,1009495486,1009498951,1009498951,1009498951,1009500506,1009500506,1009500506,1009500759,1009506262,1009506268,1009506268,1009506282,1009506744,1009506744,1009506744,1009509718,1009510176,1009510176,1009510176,1009511667,1009511667,1009511667,1009517337,1009517539,1009517847,1009517847,1009517847,1009521431,1009521431,1009521431,1009521605,1009525338,1009525352,1009525621,1009525621,1009525621,1009528265,1009529520,1009529520,1009529520,1009533044,1009533051,1009533522,1009533522,1009533522,1009536212,1009536212,1009536212,1009540623,1009540769,1009541042,1009541042,1009541042,1009543632,1009543632,1009543632,1009544883,1009545320,1009545320,1009545320,1009549111,1009549845,1009549845,1009549845,1009551220,1009551220,1009551220,1009552743,1009553918,1009553918,1009553918,1009557768,1009558264,1009558264,1009558264,1009560604,1009560604,1009560604,1009562083,1009562083,1009562083,1009566561,1009567163,1009567163,1009567163,1009570165,1009571296,1009571296,1009571296,1009576215,1009576840,1009576840,1009576840,1009579450,1009579450,1009579450,1009580613,1009580613,1009580613,1009585080,1009585480,1009585480,1009585480,1009587844,1009589126,1009589126,1009589126,1009593906,1009596965,1009597984,1009597984,1009597984,1009602705,1009603250,1009603250,1009603250,1009605649,1009605649,1009605649,1009606589,1009606589,1009606589,1009610543,1009610911,1009610911,1009610911,1009612092,1009612092,1009612092,1009612626,1009612626,1009612626,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,1009612728,100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and thcl in (2444) group by h.hashtag order by cps.photo_id 
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select cps.photo_id, cps.thcl, h.hashtag, avg(cps.score), p.text,cps.hashtag_id FROM MTRPhoto.class_photo_score cps 
          inner join MTRBack.hashtags h on h.hashtag_id = cps.hashtag_id inner join MTRBack.photos_view p on cps.photo_id = p.photo_id
          where cps.photo_id in(1009070859,1009221559,1009235003,1009235003,1009235003,1009279345,1009279355,1009280659,1009282611,1009285761,1009285770,1009287137,1009288508,1009288554,1009289515,1009290977,1009291023,1009292132,1009293462,1009293502,1009302647,1009302929,1009314294,1009325391,1009325690,1009331816,1009337447,1009343216,1009348722,1009360424,1009360715,1009366175,1009370144,1009378381,1009378421,1009382215,1009387312,1009387470,1009397629,1009397724,1009407258,1009418252,1009418934,1009427418,1009430106,1009431391,1009433086,1009433143,1009434052,1009434052,1009434052,1009434380,1009434380,1009434380,1009434693,1009434693,1009434693,1009435686,1009436486,1009436972,1009436972,1009436972,1009437042,1009437247,1009438333,1009438589,1009438589,1009438589,1009440277,1009440286,1009440277,1009440277,1009442469,1009442505,1009442536,1009442536,1009442536,1009444075,1009444129,1009444129,1009444129,1009445421,1009445547,1009446901,1009446901,1009446901,1009448274,1009448395,1009448395,1009448395,1009449797,1009449797,1009449797,1009451628,1009451639,1009451749,1009451749,1009451749,1009452469,1009452469,1009452469,1009452960,1009452960,1009452960,1009452984,1009454195,1009454252,1009454342,1009454342,1009454342,1009455530,1009457024,1009457037,1009457227,1009457227,1009457227,1009460749,1009460749,1009460749,1009460966,1009466382,1009466451,1009467106,1009467106,1009467106,1009471966,1009471966,1009471966,1009472267,1009477703,1009478116,1009478116,1009478116,1009481718,1009481718,1009481718,1009482986,1009482986,1009482986,1009487458,1009487653,1009487955,1009487955,1009487955,1009489084,1009489226,1009489313,1009489313,1009489313,1009490314,1009490314,1009490314,1009490484,1009495059,1009495487,1009495487,1009495487,1009498952,1009498952,1009498952,1009500507,1009500507,1009500507,1009500761,1009506263,1009506269,1009506269,1009506283,1009506745,1009506745,1009506745,1009509719,1009510177,1009510177,1009510177,1009511668,1009511668,1009511668,1009517338,1009517540,1009517848,1009517848,1009517848,1009521432,1009521432,1009521432,1009521606,1009525339,1009525353,1009525622,1009525622,1009525622,1009528266,1009529521,1009529521,1009529521,1009533046,1009533053,1009533523,1009533523,1009533523,1009536213,1009536213,1009536213,1009540624,1009540770,1009541043,1009541043,1009541043,1009543633,1009543633,1009543633,1009544884,1009545321,1009545321,1009545321,1009549112,1009549846,1009549846,1009549846,1009551221,1009551221,1009551221,1009552744,1009553919,1009553919,1009553919,1009557769,1009558265,1009558265,1009558265,1009560605,1009560605,1009560605,1009562084,1009562084,1009562084,1009566562,1009567164,1009567164,1009567164,1009570166,1009571297,1009571297,1009571297,1009576216,1009576841,1009576841,1009576841,1009579451,1009579451,1009579451,1009580614,1009580614,1009580614,1009585081,1009585481,1009585481,1009585481,1009587845,1009589127,1009589127,1009589127,1009593907,1009596966,1009597985,1009597985,1009597985,1009602706,1009603251,1009603251,1009603251,1009605650,1009605650,1009605650,1009606590,1009606590,1009606590,1009610544,1009610912,1009610912,1009610912,1009612093,1009612093,1009612093,1009612627,1009612627,1009612627,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,1009612729,100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and thcl in (2444) group by h.hashtag order by cps.photo_id 
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1012850057, 1012869007, 1012888272, 1012906671, 1012936418, 1012962401, 1012994410, 1013020656, 1013050402, 1013079709, 1013109513, 1013134907, 1013164471, 1013191886, 1013222541, 1013249571, 1013282220, 1013324569, 1013355532, 1013384429, 1013419593, 1013448996, 1013485948, 1013512107, 1013537855, 1013559473, 1013586247, 1013611683, 1013640154, 1013670302, 1013697355, 1013725230, 1013753990, 1013783574, 1013815409, 1013840141, 1013868398, 1013894817, 1013925420, 1013949396, 1013978230, 1014004248, 1014031971, 1014060886, 1014089828, 1014119983, 1014151374, 1014181980, 1014218101, 1014249260, 1014284655, 1014318640, 1014358908, 1014399311, 1014444632, 1014481399, 1014495907, 1014509027, 1014535930, 1014563942, 1014589267, 1014610197, 1014629934, 1014653257, 1014670100, 1014686440, 1014702099, 1014722034, 1014741621, 1014798045, 1014814534, 1014852879, 1014876920, 1014904382, 1014932041, 1014973931, 1014997290, 1015026127, 1015061976, 1015087311, 1015113512, 1015135854, 1015159058, 1015185821, 1015228381, 1015250490, 1015271019, 1015289452, 1015309874, 1015328274, 1015341940, 1015364709, 1015387467, 1015408733, 1015431752, 1015447247, 1015472223, 1015491230, 1015505920, 1015524803, 1015545385, 1015569542, 1015593873, 1015618668, 1015640101, 1015678517, 1015699352, 1015725537, 1015785032, 1015803329, 1015824161, 1015858647, 1015891611, 1015922241, 1015951185, 1015979957, 1016008980, 1016031643, 1016057742, 1016079045, 1016104696, 1016129813, 1016151054, 1016165409, 1016177375, 1016191024, 1016200474, 1016250153, 1056228409, 1056230994, 1056231395, 1056270613, 1056338910, 1056384135, 1056391589, 1056418912, 1056453293, 1056513348, 1056583095, 1056651956, 1056690750, 1056711162, 1056736181, 1056748732, 1056756882, 1056764899, 1056772437, 1056784483, 1056800790, 1056808380, 1056818151, 1056830026, 1056830676, 1056830950, 1056831079, 1056831559, 1056831857, 1056837636, 1056856212, 1056920478, 1056980407, 1057041130, 1057088059, 1057121111, 1057151345, 1057220558, 1057280514, 1057341348, 1057404846, 1057405395, 1057407768, 1057472514, 1057500272, 1057540061, 1057593131, 1057647550, 1057687879, 1057710267, 1057732750, 1057755510, 1057764776, 1057772056, 1057778728, 1057785416, 1057793603, 1057801261, 1057807131, 1057813940, 1057816044, 1057829288, 1057848919, 1057892840, 1057897021, 1057932759, 1058003325, 1058069209, 1058128313, 1058168240, 1058219000, 1058252316, 1058301787, 1058344220, 1058357002, 1058360846, 1058373427, 1058397932, 1058419188, 1058452272, 1058454548, 1058497066, 1058498208, 1058498420, 1058498767, 1058521791, 1058551452, 1058552623, 1058555679, 1058558900, 1058563078, 1058588015, 1058598162, 1058621592, 1058655002, 1058676195, 1058707468, 1058970479, 1059006629, 1059046571, 1059191653, 1059236204, 1059303268, 1059358504, 1059421119, 1059465157, 1059519822, 1059570912, 1059621389, 1059666509, 1059721388, 1059764184, 1059769190, 1059785229, 1059879570, 1059945490, 1060020934, 1060078216, 1060220738, 1060379662, 1060464898, 1060518325, 1060593807, 1060658655, 1060713779, 1060747608, 1060788853, 1060803887, 1060857836, 1060887247, 1060936299, 1060966198, 1061000354, 1061056461, 1061100123, 1061192545, 1061226356, 1061229433, 1061238747, 1061286708, 1061337838, 1061360122, 1061417202, 1061498971, 1061555747, 1061610194, 1061671179, 1061719305, 1061763999, 1061784991, 1061817867, 1061883309, 1061935816, 1062011817, 1062066026, 1062133126, 1062316233, 1062405209, 1062458590, 1062503692, 1062550081, 1062590644, 1062648866, 1062695434, 1062723343, 1062756114, 1062784496, 1062812435, 1062854627, 1062915397, 1062972088, 1063010693, 1063064217, 1063123901, 1063221080, 1063265475, 1063308571, 1063509642, 1063558460, 1063625401, 1063754713, 1063792626, 1063859380, 1064104833, 1064194326, 1064238853, 1064271162, 1064300600, 1064328043, 1064355812, 1064384230, 1064413862, 1064442498, 1064475869, 1064569997, 1064629413, 1064679942, 1064718121, 1064843971, 1064885499, 1064925691, 1064980980, 1065025222, 1065066420, 1065122255, 1065166130, 1065199559, 1065231171, 1065248307, 1065277480, 1065295447, 1065311553, 1065327183, 1065338948, 1065347321, 1065358529, 1065423049, 1065464749, 1065551065, 1065640482, 1065683382, 1065752113, 1065853213, 1065872623, 1065886166, 1065906996, 1065929863, 1065949957, 1065970148, 1065989090, 1066036680, 1066082797, 1066127203, 1066177820, 1066234449, 1066280974, 1066333753, 1066370060, 1066403952, 1066434336, 1066459559, 1066470980, 1066476569, 1066483568, 1066495994, 1066526114, 1066576207, 1066628892, 1066688043, 1066748036, 1066802949, 1066855086, 1066901153, 1066957300, 1067018959, 1067077306, 1067126798, 1067176863, 1067220042, 1067242690, 1067290905, 1067350631, 1067420135, 1067482000, 1067541101, 1067577646, 1067619697, 1067661440, 1067690196, 1067717419, 1067747137, 1067774117, 1067802805, 1067861260, 1067911436, 1067941483, 1067982052, 1068035450, 1068088470, 1068111531, 1068135673, 1068160645, 1068189424, 1068212318, 1068237224, 1068269608, 1068303919, 1068329051, 1068346744, 1068348696, 1068386856, 1068425053, 1068452883, 1068458892, 1068468411, 1068469250, 1068469818, 1068470406, 1068471263, 1068471942, 1068474414, 1068506231, 1068541463, 1068570956, 1068623510, 1068682683, 1068719364, 1068741814, 1068773101, 1068806812, 1068837183, 1068851167, 1068855956, 1068875464, 1068897962, 1068917462, 1068943960, 1068968309, 1068982669, 1069002725, 1069020497, 1069057625, 1069125049, 1069205893, 1069239245, 1069260811, 1069283980, 1069310067, 1069337751, 1069359152, 1069378205, 1069385928, 1069408084, 1069454243, 1069505575, 1069547703, 1069590745, 1069703352, 1069823745, 1069863126, 1069957909, 1069991319, 1070012154, 1070039928, 1070071220, 1070086452, 1070096255, 1070099008, 1070109607, 1070151841, 1070176006, 1070188635, 1070229601, 1070274896, 1070325013, 1070371829, 1070420336, 1070483041, 1070525073, 1070555662, 1070594553, 1070637082, 1070680463, 1070724014, 1070790694, 1070943046, 1071200716, 1071239807, 1071464241, 1071636095, 1071703688, 1071767898, 1071811893, 1071860805, 1071928106, 1072017182, 1072118439, 1072207684, 1072297365, 1072393939, 1072508118, 1072582577, 1072632155, 1072728395, 1072839516, 1072917098, 1072972617, 1073027779, 1073056964, 1073111356, 1073142094, 1073196805, 1073244308, 1073366399, 1073503491, 1073644970, 1073725126, 1073807931, 1073882322, 1073948835, 1074028108], saving chi 1941270026 with no changes
chi 1941270032 saved with no changes
chi 1941270031 saved with no changes
chi 1941270035 saved with no changes
chi 1941270037 saved with no changes
chi 1941270041 saved with no changes
chi 1941270012 saved with no changes
chi 1941270013 saved with no changes
chi 1941607380 saved with no changes
chi 1941607381 saved with no changes
chi 2202875936 saved with no changes
chi 1941270008 saved with no changes
chi 1941270016 saved with no changes
chi 1941607382 saved with no changes
chi 1941270034 saved with no changes
chi 2202875937 saved with no changes
chi 1941270010 saved with no changes
chi 1941270005 saved with no changes
chi 1941270007 saved with no changes
chi 1941270024 saved with no changes
chi 1941270011 saved with no changes
chi 2202875938 saved with no changes
chi 1941270023 saved with no changes
chi 1941607379 saved with no changes
chi 1941270006 saved with no changes
chi 1941270009 saved with no changes
76 crops to save
insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s)
batch 1
Loaded 82 chid ids of type : 2913
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0
INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) 
 TO DO : save crop sub photo not yet done ! 
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 1009068683,1009068724) and `type` in (2913) 
Loaded 82 chid ids of type : 2913
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (2099910103,2099910104,2099910105,2099910106,2099910107,2099910108,2099910109,2099910110,2099910111,2099910112,2099910113,2099910114,2099910115,3974615162,3974615163,2099910116,2099910117,2099910118,2099910119,2099910120,2099910121,2099910122,2099910123,2099910124,2099910125,2099910126,2099910127,2099910128,2099910129,2099910130,2099910131,2099910132,2099910133,2099910134,2099910135,2099910136,2099910137,2099910138,2099910139,2099910140,2099910141,2099910142,2099910143,2099910144,2099910145,2099910146,2099910147,2099910148,2099910149,3974615164,3974615165,3974615166,3974615167,2099910150,2099910151,2099910152,2099910153,2099910154,2099910155,2099910156,2099910157,2099910158,2099910159,2099910160,2099910161,2099910162,2202876163,2099910163,2099910164,2099910165,2099910166,2202876164,2099910167,2099910168,2099910169,2099910170,2099910171,2202876165,2099910172,2099910173,2099910174,2099910175)
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (2099910103,2099910104,2099910105,2099910106,2099910107,2099910108,2099910109,2099910110,2099910111,2099910112,2099910113,2099910114,2099910115,3974615162,3974615163,2099910116,2099910117,2099910118,2099910119,2099910120,2099910121,2099910122,2099910123,2099910124,2099910125,2099910126,2099910127,2099910128,2099910129,2099910130,2099910131,2099910132,2099910133,2099910134,2099910135,2099910136,2099910137,2099910138,2099910139,2099910140,2099910141,2099910142,2099910143,2099910144,2099910145,2099910146,2099910147,2099910148,2099910149,3974615164,3974615165,3974615166,3974615167,2099910150,2099910151,2099910152,2099910153,2099910154,2099910155,2099910156,2099910157,2099910158,2099910159,2099910160,2099910161,2099910162,2202876163,2099910163,2099910164,2099910165,2099910166,2202876164,2099910167,2099910168,2099910169,2099910170,2099910171,2202876165,2099910172,2099910173,2099910174,2099910175)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (2099910103,2099910104,2099910105,2099910106,2099910107,2099910108,2099910109,2099910110,2099910111,2099910112,2099910113,2099910114,2099910115,3974615162,3974615163,2099910116,2099910117,2099910118,2099910119,2099910120,2099910121,2099910122,2099910123,2099910124,2099910125,2099910126,2099910127,2099910128,2099910129,2099910130,2099910131,2099910132,2099910133,2099910134,2099910135,2099910136,2099910137,2099910138,2099910139,2099910140,2099910141,2099910142,2099910143,2099910144,2099910145,2099910146,2099910147,2099910148,2099910149,3974615164,3974615165,3974615166,3974615167,2099910150,2099910151,2099910152,2099910153,2099910154,2099910155,2099910156,2099910157,2099910158,2099910159,2099910160,2099910161,2099910162,2202876163,2099910163,2099910164,2099910165,2099910166,2202876164,2099910167,2099910168,2099910169,2099910170,2099910171,2202876165,2099910172,2099910173,2099910174,2099910175)
insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : []
End of step merge_mask_thcl_custom

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 0.910137414932251
time spend to save output : 5.936622619628906e-05
total time spend for step 1 : 0.9101967811584473

step2:rle_unique_nms_with_priority
Mon Sep 29 11:47:34 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 : ['temp/1759139253_2886529_1009068683_4beb092fd180a8b620f754bb89015722.jpg', 'temp/1759139253_2886529_1009068724_3e705379f97632c4a2fd676e348a335d.jpg']
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 

input_args_next_step, len :2, first value : ['temp/1759139253_2886529_1009068683_4beb092fd180a8b620f754bb89015722.jpg']
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139253_2886529_1009068683_4beb092fd180a8b620f754bb89015722.jpg': 1009068683, 'temp/1759139253_2886529_1009068724_3e705379f97632c4a2fd676e348a335d.jpg': 1009068724}
map_photo_id_path_extension : {1009068683: {'path': 'temp/1759139253_2886529_1009068683_4beb092fd180a8b620f754bb89015722.jpg', 'extension': 'jpg'}, 1009068724: {'path': 'temp/1759139253_2886529_1009068724_3e705379f97632c4a2fd676e348a335d.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
Begin step rle-unique-nms
on traite la photo : temp/1759139253_2886529_1009068683_4beb092fd180a8b620f754bb89015722.jpg
on traite la photo : temp/1759139253_2886529_1009068724_3e705379f97632c4a2fd676e348a335d.jpg
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 1009068683,1009068724) and `type` in (2913) and hashtag_id in (2107755846,492636447,492645504,681467679) 
Loaded 43 chid ids of type : 2913
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (2099910116,2099910118,2099910119,2099910120,2099910121,2099910122,2099910123,2099910124,2099910125,2099910126,2099910127,2099910128,2099910129,2099910130,2099910131,2099910132,2099910133,2099910134,2099910154,2099910155,2099910156,2099910157,2099910158,2099910159,2099910160,2099910161,2099910162,2202876163,2099910163,2099910164,2099910165,2099910166,2202876164,2099910167,2099910168,2099910169,2099910170,2099910171,2202876165,2099910172,2099910173,2099910174,2099910175)
+++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (2099910116,2099910118,2099910119,2099910120,2099910121,2099910122,2099910123,2099910124,2099910125,2099910126,2099910127,2099910128,2099910129,2099910130,2099910131,2099910132,2099910133,2099910134,2099910154,2099910155,2099910156,2099910157,2099910158,2099910159,2099910160,2099910161,2099910162,2202876163,2099910163,2099910164,2099910165,2099910166,2202876164,2099910167,2099910168,2099910169,2099910170,2099910171,2202876165,2099910172,2099910173,2099910174,2099910175)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (2099910116,2099910118,2099910119,2099910120,2099910121,2099910122,2099910123,2099910124,2099910125,2099910126,2099910127,2099910128,2099910129,2099910130,2099910131,2099910132,2099910133,2099910134,2099910154,2099910155,2099910156,2099910157,2099910158,2099910159,2099910160,2099910161,2099910162,2202876163,2099910163,2099910164,2099910165,2099910166,2202876164,2099910167,2099910168,2099910169,2099910170,2099910171,2202876165,2099910172,2099910173,2099910174,2099910175)
nb_obj : 0
nb_hashtags :  2
time to prepare the origin masks  : 1.9871962070465088
create new chi : 3.361701965332031e-05
 proportion hashtag : pet_clair 0.5317132844650205
 proportion hashtag : contaminant 0.002766525205761317
time to delete rle : 0.0617673397064209
save time : 2.574920654296875e-05
nb_obj : 0
nb_hashtags :  1
time to prepare the origin masks  : 2.3046340942382812
create new chi : 5.221366882324219e-05
 proportion hashtag : pet_clair 0.5582751414609054
time to delete rle : 0.012509346008300781
save time : 1.5974044799804688e-05
INSERT IGNORE INTO MTRPhoto.photo_carac_ratio (`photo_id`, `hashtag_type`, `hashtag_id`, `ratio`)
                            VALUES (%s, %s, %s, %s) on duplicate key update `ratio` = VALUES(`ratio`)
 map_output_result : {1009068683: (0.002588053987919006, 'Should be the crop_list due to order', 0.005176107975838012), 1009068724: (0.002588053987919006, 'Should be the crop_list due to order', 0.0)}
End step rle-unique-nms

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 4.610712051391602
time spend to save output : 0.00014281272888183594
total time spend for step 2 : 4.610854864120483

step3:ventilate_hashtags_in_portfolio
Mon Sep 29 11:47:39 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 !
VR 22-3-18 : For now we do not clean correctly the datou structure 

input_args_next_step, len :0, first value : []
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139253_2886529_1009068683_4beb092fd180a8b620f754bb89015722.jpg': 1009068683, 'temp/1759139253_2886529_1009068724_3e705379f97632c4a2fd676e348a335d.jpg': 1009068724}
map_photo_id_path_extension : {1009068683: {'path': 'temp/1759139253_2886529_1009068683_4beb092fd180a8b620f754bb89015722.jpg', 'extension': 'jpg'}, 1009068724: {'path': 'temp/1759139253_2886529_1009068724_3e705379f97632c4a2fd676e348a335d.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
beginning of datou step ventilate_hashtags_in_portfolio : To implement !
Iterating over portfolio : 3373196
get user id for portfolio 3373196
get sub ptf for main ptf 3373196, type 2913 and hashtags []
 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`=3373196 AND  mptpi.`type`=2913 AND  mptpi.`min_score`=0.7
 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`=3373196 AND  mptpi.`type`=2913 AND  mptpi.`min_score`=0.7
To do
returned map {'bouchon': {'main_port_id': 3373196, 'sub_port_id': 3373210, 'hashtag': 'bouchon', 'pht': 2913, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 408, 'last_updated_at': datetime.datetime(2025, 9, 26, 9, 10, 12), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 10, 12), 'last_updated_at_asc': datetime.datetime(2021, 1, 26, 13, 9, 50)}, 'contaminant': {'main_port_id': 3373196, 'sub_port_id': 3373211, 'hashtag': 'contaminant', 'pht': 2913, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 409, 'last_updated_at': datetime.datetime(2025, 9, 26, 9, 10, 12), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 10, 12), 'last_updated_at_asc': datetime.datetime(2021, 1, 26, 13, 9, 50)}, 'environement': {'main_port_id': 3373196, 'sub_port_id': 3373212, 'hashtag': 'environement', 'pht': 2913, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 410, 'last_updated_at': datetime.datetime(2025, 9, 26, 9, 10, 12), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 10, 12), 'last_updated_at_asc': datetime.datetime(2021, 1, 26, 13, 9, 50)}, 'etiquette': {'main_port_id': 3373196, 'sub_port_id': 3373213, 'hashtag': 'etiquette', 'pht': 2913, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 411, 'last_updated_at': datetime.datetime(2025, 9, 26, 9, 10, 12), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 10, 12), 'last_updated_at_asc': datetime.datetime(2021, 1, 26, 13, 9, 50)}, 'mal_croppe': {'main_port_id': 3373196, 'sub_port_id': 3373214, 'hashtag': 'mal_croppe', 'pht': 2913, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 412, 'last_updated_at': datetime.datetime(2025, 9, 26, 9, 10, 12), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 10, 12), 'last_updated_at_asc': datetime.datetime(2021, 1, 26, 13, 9, 50)}, 'multiple_objets': {'main_port_id': 3373196, 'sub_port_id': 3373215, 'hashtag': 'multiple_objets', 'pht': 2913, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 413, 'last_updated_at': datetime.datetime(2025, 9, 26, 9, 10, 12), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 10, 12), 'last_updated_at_asc': datetime.datetime(2021, 1, 26, 13, 9, 50)}, 'pet_clair': {'main_port_id': 3373196, 'sub_port_id': 3373216, 'hashtag': 'pet_clair', 'pht': 2913, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 414, 'last_updated_at': datetime.datetime(2025, 9, 26, 9, 10, 12), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 10, 12), 'last_updated_at_asc': datetime.datetime(2021, 1, 26, 13, 9, 50)}}
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 3373210 , sub_photo_id, 31,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=3373196
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=2913
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='bouchon'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 3373211 , sub_photo_id, 31,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=3373196
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=2913
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='contaminant'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 3373212 , sub_photo_id, 31,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=3373196
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=2913
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='environement'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 3373213 , sub_photo_id, 31,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=3373196
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=2913
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='etiquette'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 3373214 , sub_photo_id, 31,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=3373196
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=2913
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='mal_croppe'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 3373215 , sub_photo_id, 31,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=3373196
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=2913
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='multiple_objets'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 3373216 , sub_photo_id, 31,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=3373196
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=2913
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='pet_clair'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
To do !
Use context local managing function !

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 1.9222564697265625
time spend to save output : 6.031990051269531e-05
total time spend for step 3 : 1.9223167896270752

step4:final
Mon Sep 29 11:47:41 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 : {1009068683: (0.002588053987919006, 'Should be the crop_list due to order', 0.005176107975838012), 1009068724: (0.002588053987919006, 'Should be the crop_list due to order', 0.0)}
 input_args_next_step : {1009068683: ()}
 output_args : {1009068683: (0.002588053987919006, 'Should be the crop_list due to order', 0.005176107975838012), 1009068724: (0.002588053987919006, 'Should be the crop_list due to order', 0.0)}
 args : 1009068683
 depend.output_id : 0
 input_args_next_step : {1009068683: (0.002588053987919006,), 1009068724: ()}
 output_args : {1009068683: (0.002588053987919006, 'Should be the crop_list due to order', 0.005176107975838012), 1009068724: (0.002588053987919006, 'Should be the crop_list due to order', 0.0)}
 args : 1009068724
 depend.output_id : 0
VR 22-3-18 : For now we do not clean correctly the datou structure 

input_args_next_step, len :2, first value : (0.002588053987919006,)
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139253_2886529_1009068683_4beb092fd180a8b620f754bb89015722.jpg': 1009068683, 'temp/1759139253_2886529_1009068724_3e705379f97632c4a2fd676e348a335d.jpg': 1009068724}
map_photo_id_path_extension : {1009068683: {'path': 'temp/1759139253_2886529_1009068683_4beb092fd180a8b620f754bb89015722.jpg', 'extension': 'jpg'}, 1009068724: {'path': 'temp/1759139253_2886529_1009068724_3e705379f97632c4a2fd676e348a335d.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou step final !
 query to retrieve results for portfolio 3373196 : SELECT photo_id, hashtag_id, ratio from MTRPhoto.photo_carac_ratio where hashtag_type = 2914  and photo_id in (1009068683,1009068724);
Catched exception !
Connect or reconnect !
insert ignore into MTRUser.portfolio_carac_ratio (portfolio_id, hashtag_type, hashtag_id, value) values (%s,%s,%s,%s) on DUPLICATE KEY UPDATE value=VALUES(value)

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 0.3728749752044678
time spend to save output : 5.078315734863281e-05
total time spend for step 4 : 0.3729257583618164

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
original output for save of step final : {1009068683: ('0.0025316977085267163',), 1009068724: ('0.0025316977085267163',)}
new output for save of step final : {1009068683: ('0.0025316977085267163',), 1009068724: ('0.0025316977085267163',)}
[1009068683, 1009068724]
 map_info['map_portfolio_photo'] : {3373196: [1009068724, 1009068683]}
 final :  True mtd_id 2719 list_pids : [1009068683, 1009068724]
Looping around the photos to save general results
len do output : 2
/1009068683.Didn't retrieve data
.
/1009068724.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 
('2719', None, None, None, None, None, None, None, None)
('2719', '3373196', '1009068683', None, None, None, None, None, None)
('2719', None, None, None, None, None, None, None, None)
('2719', '3373196', '1009068724', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 6
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('2719', '3373196', '1009068683', '0.0025316977085267163', None, None, None, None, None), ('2719', '3373196', '1009068724', '0.0025316977085267163', None, None, None, None, None)]
time used for this insertion : 0.03935074806213379
 save_final 

save missing photos in datou_result :
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 4
 output : {1009068683: ('0.0025316977085267163',), 1009068724: ('0.0025316977085267163',)}
{1009068683: ('0.0025316977085267163',), 1009068724: ('0.0025316977085267163',)}

############################### TEST rubbia_carac_jrm_no_mask_detect  ################################
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=3164

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=3164
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 3164
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=3164
# 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 !
 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 !
Step 7557 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used !
Step 7556 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used !
WARNING : number of outputs for step 7561 merge_mask_and_thcl is not consistent : 3 used against 1 in the step definition !
WARNING : number of inputs for step 7558 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 7560 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition !
Step 7559 final have less inputs used (2) than in the step definition (3) : 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 7560 doesn't seem to be define in the database(
WARNING : type of input 3 of step 7559 doesn't seem to be define in the database(
We ignore checkConsistencyTypeOutputInput for datou_step final !
WARNING : output 0 of step 7556 have datatype=6 whereas input 0 of step 7561 have datatype=20
WARNING : type of output 1 of step 7561 doesn't seem to be define in the database(
WARNING : type of input 1 of step 7558 doesn't seem to be define in the database(
WARNING : type of output 2 of step 7561 doesn't seem to be define in the database(
WARNING : type of input 1 of step 7560 doesn't seem to be define in the database(
DataTypes for each output/input checked !
no param json to modify
List Step Type Loaded in datou : crop_condition, thcl, argmax, merge_mask_and_thcl, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (3364276) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (3364276)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 1008922130, 'portfolio_id': 3364276}, {'photo_id': 1008922101, 'portfolio_id': 3364276}, {'photo_id': 1008922097, 'portfolio_id': 3364276}, {'photo_id': 1008922095, 'portfolio_id': 3364276}, {'photo_id': 1008922073, 'portfolio_id': 3364276}, {'photo_id': 1008922072, 'portfolio_id': 3364276}, {'photo_id': 1008922003, 'portfolio_id': 3364276}, {'photo_id': 1008922002, 'portfolio_id': 3364276}, {'photo_id': 1008921786, 'portfolio_id': 3364276}, {'photo_id': 1008921657, 'portfolio_id': 3364276}, {'photo_id': 1008921656, 'portfolio_id': 3364276}, {'photo_id': 1008921602, 'portfolio_id': 3364276}, {'photo_id': 1008921601, 'portfolio_id': 3364276}, {'photo_id': 1008921600, 'portfolio_id': 3364276}]
 map_portfolio_id_photo_id: {3364276: [1008922130, 1008922101, 1008922097, 1008922095, 1008922073, 1008922072, 1008922003, 1008922002, 1008921786, 1008921657, 1008921656, 1008921602, 1008921601, 1008921600]}
##### Call download_photos : nb_thread : 5
begin to download photo : 1008922130
begin to download photo : 1008922095
begin to download photo : 1008921657
begin to download photo : 1008922003
begin to download photo : 1008921601
download finish for photo 1008922095
begin to download photo : 1008922073
download finish for photo 1008922130
begin to download photo : 1008922101
download finish for photo 1008922003
begin to download photo : 1008922002
download finish for photo 1008921657
begin to download photo : 1008921656
download finish for photo 1008921601
begin to download photo : 1008921600
download finish for photo 1008922002
begin to download photo : 1008921786
download finish for photo 1008922073
begin to download photo : 1008922072
download finish for photo 1008921600
download finish for photo 1008922101
begin to download photo : 1008922097
download finish for photo 1008921786
download finish for photo 1008922072
download finish for photo 1008922097
download finish for photo 1008921656
begin to download photo : 1008921602
download finish for photo 1008921602
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 14 ; length of list_pids : 14 ; length of list_args : 14
##### After load_data_input 
time to download the photos : 1.9648692607879639
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  7
step1:crop_condition
Mon Sep 29 11:47:43 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139262_2886529_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg': 1008921601, 'temp/1759139262_2886529_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg': 1008921600, 'temp/1759139262_2886529_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg': 1008922003, 'temp/1759139262_2886529_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg': 1008922002, 'temp/1759139262_2886529_1008921786_066e79494b5536cc7d2f3293519cd617.jpg': 1008921786, 'temp/1759139262_2886529_1008922095_dc858351f179fe112b62a458b452c880.jpg': 1008922095, 'temp/1759139262_2886529_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg': 1008922073, 'temp/1759139262_2886529_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg': 1008922072, 'temp/1759139262_2886529_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg': 1008922130, 'temp/1759139262_2886529_1008922101_1a025189b8603388d54304e6754e9df9.jpg': 1008922101, 'temp/1759139262_2886529_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg': 1008922097, 'temp/1759139262_2886529_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg': 1008921657, 'temp/1759139262_2886529_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg': 1008921656, 'temp/1759139262_2886529_1008921602_383b6a7758931a2dff9de560df82456c.jpg': 1008921602}
map_photo_id_path_extension : {1008921601: {'path': 'temp/1759139262_2886529_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg', 'extension': 'jpg'}, 1008921600: {'path': 'temp/1759139262_2886529_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg', 'extension': 'jpg'}, 1008922003: {'path': 'temp/1759139262_2886529_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg', 'extension': 'jpg'}, 1008922002: {'path': 'temp/1759139262_2886529_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg', 'extension': 'jpg'}, 1008921786: {'path': 'temp/1759139262_2886529_1008921786_066e79494b5536cc7d2f3293519cd617.jpg', 'extension': 'jpg'}, 1008922095: {'path': 'temp/1759139262_2886529_1008922095_dc858351f179fe112b62a458b452c880.jpg', 'extension': 'jpg'}, 1008922073: {'path': 'temp/1759139262_2886529_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg', 'extension': 'jpg'}, 1008922072: {'path': 'temp/1759139262_2886529_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg', 'extension': 'jpg'}, 1008922130: {'path': 'temp/1759139262_2886529_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg', 'extension': 'jpg'}, 1008922101: {'path': 'temp/1759139262_2886529_1008922101_1a025189b8603388d54304e6754e9df9.jpg', 'extension': 'jpg'}, 1008922097: {'path': 'temp/1759139262_2886529_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg', 'extension': 'jpg'}, 1008921657: {'path': 'temp/1759139262_2886529_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg', 'extension': 'jpg'}, 1008921656: {'path': 'temp/1759139262_2886529_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg', 'extension': 'jpg'}, 1008921602: {'path': 'temp/1759139262_2886529_1008921602_383b6a7758931a2dff9de560df82456c.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
param_json : {'upload_type': 'python', 'photo_hashtag_type': 3336, 'token': '7c6bc181426bc5913186d8428a0fbc15', 'feed_id_new_photos': 3364311, 'crop_type': 'rle_png_transparent', 'host': 'www.fotonower.com', 'filter': {'teint_dans_la_masse': {'min_score': 0.7}, 'autre_refus': {'min_score': 0.7}, 'carton_gris': {'min_score': 0.7}, 'cartonnette': {'min_score': 0.7}, 'carton_brun': {'min_score': 0.7}, 'plastique': {'min_score': 0.7}, 'kraft': {'min_score': 0.7}, 'metal': {'min_score': 0.7}}}
Loading chi in step crop with photo_hashtag_type : 3336
Loading chi in step crop for list_pids : 14 !
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008922130,1008922101,1008922097,1008921657,1008921656,1008921602) and `type` in (3336) 
Loaded 121 chid ids of type : 3336
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (2099961860,2099961861,2099961862,2099961863,2099961864,2099961865,2099961866,2099961867,2099961868,2099961869,2099961870,2099961871,2099961872,2099961873,2099961874,2099961875,2099961876,2099961877,2099961878,2099961879,2099961880,2099961881,2099961882,2099961883,2099961884,2099961885,2099961886,2099961887,2099961888,2099961889,2099961890,2099961891,2099961892,2099961893,2099961894,2099961895,2099961896,2099961897,2099961898,2099961899,2099961900,2099961901,2099961902,2099961903,2099961904,2099961905,2099961906,2099961907,2099961908,2099961909,2099961910,2099961911,2099961912,2099961913,2099961914,2099961915,2099961916,2099961917,2099961918,2099961919,2099961920,2099961921,2099961922,2099961923,2099961924,2099961925,2099961926,2099961927,2099961928,2099961929,2099961930,2099961931,2099961932,2099961933,2099961934,2099961935,2099961936,2099961937,2099961938,2099961939,2099961940,2099961941,2099961942,2099961943,2099961944,2099961945,2099961946,2099961947,2099961948,2099961949,2099961950,2099961951,2099961952,2099961953,2099961954,2099961955,2099961956,2099961957,2099961958,2099961959,2099961960,2099961961,2099961962,2099961963,2099961964,2099961965,2099961966,2099961967,2099961968,2099961969,2099961970,2099961971,2099961972,2099961973,2099961974,2099961975,2099961976,2099961977,2099961978,2099961979,2099961980)
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (2099961860,2099961861,2099961862,2099961863,2099961864,2099961865,2099961866,2099961867,2099961868,2099961869,2099961870,2099961871,2099961872,2099961873,2099961874,2099961875,2099961876,2099961877,2099961878,2099961879,2099961880,2099961881,2099961882,2099961883,2099961884,2099961885,2099961886,2099961887,2099961888,2099961889,2099961890,2099961891,2099961892,2099961893,2099961894,2099961895,2099961896,2099961897,2099961898,2099961899,2099961900,2099961901,2099961902,2099961903,2099961904,2099961905,2099961906,2099961907,2099961908,2099961909,2099961910,2099961911,2099961912,2099961913,2099961914,2099961915,2099961916,2099961917,2099961918,2099961919,2099961920,2099961921,2099961922,2099961923,2099961924,2099961925,2099961926,2099961927,2099961928,2099961929,2099961930,2099961931,2099961932,2099961933,2099961934,2099961935,2099961936,2099961937,2099961938,2099961939,2099961940,2099961941,2099961942,2099961943,2099961944,2099961945,2099961946,2099961947,2099961948,2099961949,2099961950,2099961951,2099961952,2099961953,2099961954,2099961955,2099961956,2099961957,2099961958,2099961959,2099961960,2099961961,2099961962,2099961963,2099961964,2099961965,2099961966,2099961967,2099961968,2099961969,2099961970,2099961971,2099961972,2099961973,2099961974,2099961975,2099961976,2099961977,2099961978,2099961979,2099961980)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (2099961860,2099961861,2099961862,2099961863,2099961864,2099961865,2099961866,2099961867,2099961868,2099961869,2099961870,2099961871,2099961872,2099961873,2099961874,2099961875,2099961876,2099961877,2099961878,2099961879,2099961880,2099961881,2099961882,2099961883,2099961884,2099961885,2099961886,2099961887,2099961888,2099961889,2099961890,2099961891,2099961892,2099961893,2099961894,2099961895,2099961896,2099961897,2099961898,2099961899,2099961900,2099961901,2099961902,2099961903,2099961904,2099961905,2099961906,2099961907,2099961908,2099961909,2099961910,2099961911,2099961912,2099961913,2099961914,2099961915,2099961916,2099961917,2099961918,2099961919,2099961920,2099961921,2099961922,2099961923,2099961924,2099961925,2099961926,2099961927,2099961928,2099961929,2099961930,2099961931,2099961932,2099961933,2099961934,2099961935,2099961936,2099961937,2099961938,2099961939,2099961940,2099961941,2099961942,2099961943,2099961944,2099961945,2099961946,2099961947,2099961948,2099961949,2099961950,2099961951,2099961952,2099961953,2099961954,2099961955,2099961956,2099961957,2099961958,2099961959,2099961960,2099961961,2099961962,2099961963,2099961964,2099961965,2099961966,2099961967,2099961968,2099961969,2099961970,2099961971,2099961972,2099961973,2099961974,2099961975,2099961976,2099961977,2099961978,2099961979,2099961980)
select photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type, id from MTRPhoto.photo_sub_photos  where photo_id in ( 1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008922130,1008922101,1008922097,1008921657,1008921656,1008921602) 

begin to crop the class : teint_dans_la_masse
param for this class : {'min_score': 0.7}
filtre for class : teint_dans_la_masse 
hashtag_id of this class : 2107752385
select * from
(select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge,
IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage
from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on
chi.id =css.crop_hashtag_id
where type = 3336 and photo_id in (1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008922130,1008922101,1008922097,1008921657,1008921656,1008921602) and hashtag_id = 2107752385) as a 
where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 
and coverage >= 0 and surface_rectangle >= 0;
        
chi_id interessant : [2099961965]
chi_id interessant : [2099961965, 2099961952]
chi_id interessant : [2099961965, 2099961952, 2099961898]
chi_id interessant : [2099961965, 2099961952, 2099961898, 2099961863]

type of cropped photo chosen : rle_png_transparent
we resize croppped photo by 1 on x axis and by 1 on y axis
 map_result returned by crop_photo_return_map_crop : length : 0
map_result after crop : {}
 About to insert : list_path_to_insert length 0 new photo from crops !

About to upload 0 photos
WARNING : list_path_to_insert is empty, cannot upload !
map_result_insert : {}
insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : []
insert ignore into MTRPhoto.photo_sub_photos (photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s) : []
map of cropped photos with some data : 
{}
we have finished the crop for the class : teint_dans_la_masse



begin to crop the class : autre_refus
param for this class : {'min_score': 0.7}
filtre for class : autre_refus 
hashtag_id of this class : 2107752406
select * from
(select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge,
IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage
from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on
chi.id =css.crop_hashtag_id
where type = 3336 and photo_id in (1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008922130,1008922101,1008922097,1008921657,1008921656,1008921602) and hashtag_id = 2107752406) as a 
where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 
and coverage >= 0 and surface_rectangle >= 0;
        
chi_id interessant : [2099961980]
chi_id interessant : [2099961980, 2099961933]
chi_id interessant : [2099961980, 2099961933, 2099961880]
chi_id interessant : [2099961980, 2099961933, 2099961880, 2099961870]

type of cropped photo chosen : rle_png_transparent
we resize croppped photo by 1 on x axis and by 1 on y axis
 map_result returned by crop_photo_return_map_crop : length : 0
map_result after crop : {}
 About to insert : list_path_to_insert length 0 new photo from crops !

About to upload 0 photos
WARNING : list_path_to_insert is empty, cannot upload !
map_result_insert : {}
insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : []
insert ignore into MTRPhoto.photo_sub_photos (photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s) : []
map of cropped photos with some data : 
{}
we have finished the crop for the class : autre_refus



begin to crop the class : carton_gris
param for this class : {'min_score': 0.7}
filtre for class : carton_gris 
hashtag_id of this class : 2107753020
select * from
(select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge,
IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage
from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on
chi.id =css.crop_hashtag_id
where type = 3336 and photo_id in (1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008922130,1008922101,1008922097,1008921657,1008921656,1008921602) and hashtag_id = 2107753020) as a 
where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 
and coverage >= 0 and surface_rectangle >= 0;
        
chi_id interessant : [2099961976]
chi_id interessant : [2099961976, 2099961975]
chi_id interessant : [2099961976, 2099961975, 2099961971]
chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964]
chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960]
chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950]
chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949]
chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937]
chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934]
chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915]
chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913]
chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912]
chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912, 2099961910]
chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912, 2099961910, 2099961908]
chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912, 2099961910, 2099961908, 2099961906]
chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912, 2099961910, 2099961908, 2099961906, 2099961903]
chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912, 2099961910, 2099961908, 2099961906, 2099961903, 2099961890]
chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912, 2099961910, 2099961908, 2099961906, 2099961903, 2099961890, 2099961889]
chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912, 2099961910, 2099961908, 2099961906, 2099961903, 2099961890, 2099961889, 2099961882]
chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912, 2099961910, 2099961908, 2099961906, 2099961903, 2099961890, 2099961889, 2099961882, 2099961876]
chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912, 2099961910, 2099961908, 2099961906, 2099961903, 2099961890, 2099961889, 2099961882, 2099961876, 2099961867]
chi_id interessant : [2099961976, 2099961975, 2099961971, 2099961964, 2099961960, 2099961950, 2099961949, 2099961937, 2099961934, 2099961915, 2099961913, 2099961912, 2099961910, 2099961908, 2099961906, 2099961903, 2099961890, 2099961889, 2099961882, 2099961876, 2099961867, 2099961862]

type of cropped photo chosen : rle_png_transparent
we resize croppped photo by 1 on x axis and by 1 on y axis
 map_result returned by crop_photo_return_map_crop : length : 0
map_result after crop : {}
 About to insert : list_path_to_insert length 0 new photo from crops !

About to upload 0 photos
WARNING : list_path_to_insert is empty, cannot upload !
map_result_insert : {}
insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : []
insert ignore into MTRPhoto.photo_sub_photos (photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s) : []
map of cropped photos with some data : 
{}
we have finished the crop for the class : carton_gris



begin to crop the class : cartonnette
param for this class : {'min_score': 0.7}
filtre for class : cartonnette 
hashtag_id of this class : 702398920
select * from
(select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge,
IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage
from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on
chi.id =css.crop_hashtag_id
where type = 3336 and photo_id in (1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008922130,1008922101,1008922097,1008921657,1008921656,1008921602) and hashtag_id = 702398920) as a 
where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 
and coverage >= 0 and surface_rectangle >= 0;
        
chi_id interessant : [2099961953]
chi_id interessant : [2099961953, 2099961954]
chi_id interessant : [2099961953, 2099961954, 2099961944]
chi_id interessant : [2099961953, 2099961954, 2099961944, 2099961945]
chi_id interessant : [2099961953, 2099961954, 2099961944, 2099961945, 2099961922]
chi_id interessant : [2099961953, 2099961954, 2099961944, 2099961945, 2099961922, 2099961932]
chi_id interessant : [2099961953, 2099961954, 2099961944, 2099961945, 2099961922, 2099961932, 2099961892]
chi_id interessant : [2099961953, 2099961954, 2099961944, 2099961945, 2099961922, 2099961932, 2099961892, 2099961877]
chi_id interessant : [2099961953, 2099961954, 2099961944, 2099961945, 2099961922, 2099961932, 2099961892, 2099961877, 2099961871]

type of cropped photo chosen : rle_png_transparent
we resize croppped photo by 1 on x axis and by 1 on y axis
 map_result returned by crop_photo_return_map_crop : length : 0
map_result after crop : {}
 About to insert : list_path_to_insert length 0 new photo from crops !

About to upload 0 photos
WARNING : list_path_to_insert is empty, cannot upload !
map_result_insert : {}
insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : []
insert ignore into MTRPhoto.photo_sub_photos (photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s) : []
map of cropped photos with some data : 
{}
we have finished the crop for the class : cartonnette



begin to crop the class : carton_brun
param for this class : {'min_score': 0.7}
filtre for class : carton_brun 
hashtag_id of this class : 2107753024
select * from
(select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge,
IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage
from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on
chi.id =css.crop_hashtag_id
where type = 3336 and photo_id in (1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008922130,1008922101,1008922097,1008921657,1008921656,1008921602) and hashtag_id = 2107753024) as a 
where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 
and coverage >= 0 and surface_rectangle >= 0;
        
chi_id interessant : [2099961974]
chi_id interessant : [2099961974, 2099961969]
chi_id interessant : [2099961974, 2099961969, 2099961979]
chi_id interessant : [2099961974, 2099961969, 2099961979, 2099961957]
chi_id interessant : [2099961974, 2099961969, 2099961979, 2099961957, 2099961963]
chi_id interessant : [2099961974, 2099961969, 2099961979, 2099961957, 2099961963, 2099961955]
chi_id interessant : [2099961974, 2099961969, 2099961979, 2099961957, 2099961963, 2099961955, 2099961942]
chi_id interessant : [2099961974, 2099961969, 2099961979, 2099961957, 2099961963, 2099961955, 2099961942, 2099961931]
chi_id interessant : [2099961974, 2099961969, 2099961979, 2099961957, 2099961963, 2099961955, 2099961942, 2099961931, 2099961916]
chi_id interessant : [2099961974, 2099961969, 2099961979, 2099961957, 2099961963, 2099961955, 2099961942, 2099961931, 2099961916, 2099961900]
chi_id interessant : [2099961974, 2099961969, 2099961979, 2099961957, 2099961963, 2099961955, 2099961942, 2099961931, 2099961916, 2099961900, 2099961901]
chi_id interessant : [2099961974, 2099961969, 2099961979, 2099961957, 2099961963, 2099961955, 2099961942, 2099961931, 2099961916, 2099961900, 2099961901, 2099961902]
chi_id interessant : [2099961974, 2099961969, 2099961979, 2099961957, 2099961963, 2099961955, 2099961942, 2099961931, 2099961916, 2099961900, 2099961901, 2099961902, 2099961872]

type of cropped photo chosen : rle_png_transparent
we resize croppped photo by 1 on x axis and by 1 on y axis
 map_result returned by crop_photo_return_map_crop : length : 0
map_result after crop : {}
 About to insert : list_path_to_insert length 0 new photo from crops !

About to upload 0 photos
WARNING : list_path_to_insert is empty, cannot upload !
map_result_insert : {}
insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : []
insert ignore into MTRPhoto.photo_sub_photos (photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s) : []
map of cropped photos with some data : 
{}
we have finished the crop for the class : carton_brun



begin to crop the class : plastique
param for this class : {'min_score': 0.7}
filtre for class : plastique 
hashtag_id of this class : 492725882
select * from
(select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge,
IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage
from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on
chi.id =css.crop_hashtag_id
where type = 3336 and photo_id in (1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008922130,1008922101,1008922097,1008921657,1008921656,1008921602) and hashtag_id = 492725882) as a 
where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 
and coverage >= 0 and surface_rectangle >= 0;
        
chi_id interessant : [2099961978]

begin to crop the class : kraft
param for this class : {'min_score': 0.7}
filtre for class : kraft 
hashtag_id of this class : 493202403
select * from
(select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge,
IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage
from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on
chi.id =css.crop_hashtag_id
where type = 3336 and photo_id in (1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008922130,1008922101,1008922097,1008921657,1008921656,1008921602) and hashtag_id = 493202403) as a 
where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 
and coverage >= 0 and surface_rectangle >= 0;
        

begin to crop the class : metal
param for this class : {'min_score': 0.7}
filtre for class : metal 
hashtag_id of this class : 492628673
select * from
(select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge,
IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage
from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on
chi.id =css.crop_hashtag_id
where type = 3336 and photo_id in (1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008922130,1008922101,1008922097,1008921657,1008921656,1008921602) and hashtag_id = 492628673) as a 
where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 
and coverage >= 0 and surface_rectangle >= 0;
        
map of total cropped photos with some data : 
{}

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 9.574563980102539
time spend to save output : 0.0002593994140625
total time spend for step 1 : 9.574823379516602

step2:thcl
Mon Sep 29 11:47:53 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 
No keys ! 

input_args_next_step, len :0
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139262_2886529_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg': 1008921601, 'temp/1759139262_2886529_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg': 1008921600, 'temp/1759139262_2886529_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg': 1008922003, 'temp/1759139262_2886529_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg': 1008922002, 'temp/1759139262_2886529_1008921786_066e79494b5536cc7d2f3293519cd617.jpg': 1008921786, 'temp/1759139262_2886529_1008922095_dc858351f179fe112b62a458b452c880.jpg': 1008922095, 'temp/1759139262_2886529_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg': 1008922073, 'temp/1759139262_2886529_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg': 1008922072, 'temp/1759139262_2886529_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg': 1008922130, 'temp/1759139262_2886529_1008922101_1a025189b8603388d54304e6754e9df9.jpg': 1008922101, 'temp/1759139262_2886529_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg': 1008922097, 'temp/1759139262_2886529_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg': 1008921657, 'temp/1759139262_2886529_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg': 1008921656, 'temp/1759139262_2886529_1008921602_383b6a7758931a2dff9de560df82456c.jpg': 1008921602}
map_photo_id_path_extension : {1008921601: {'path': 'temp/1759139262_2886529_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg', 'extension': 'jpg'}, 1008921600: {'path': 'temp/1759139262_2886529_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg', 'extension': 'jpg'}, 1008922003: {'path': 'temp/1759139262_2886529_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg', 'extension': 'jpg'}, 1008922002: {'path': 'temp/1759139262_2886529_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg', 'extension': 'jpg'}, 1008921786: {'path': 'temp/1759139262_2886529_1008921786_066e79494b5536cc7d2f3293519cd617.jpg', 'extension': 'jpg'}, 1008922095: {'path': 'temp/1759139262_2886529_1008922095_dc858351f179fe112b62a458b452c880.jpg', 'extension': 'jpg'}, 1008922073: {'path': 'temp/1759139262_2886529_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg', 'extension': 'jpg'}, 1008922072: {'path': 'temp/1759139262_2886529_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg', 'extension': 'jpg'}, 1008922130: {'path': 'temp/1759139262_2886529_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg', 'extension': 'jpg'}, 1008922101: {'path': 'temp/1759139262_2886529_1008922101_1a025189b8603388d54304e6754e9df9.jpg', 'extension': 'jpg'}, 1008922097: {'path': 'temp/1759139262_2886529_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg', 'extension': 'jpg'}, 1008921657: {'path': 'temp/1759139262_2886529_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg', 'extension': 'jpg'}, 1008921656: {'path': 'temp/1759139262_2886529_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg', 'extension': 'jpg'}, 1008921602: {'path': 'temp/1759139262_2886529_1008921602_383b6a7758931a2dff9de560df82456c.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou step Thcl !
no input

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 0.0040013790130615234
time spend to save output : 1.9073486328125e-05
total time spend for step 2 : 0.0040204524993896484

step3:argmax
Mon Sep 29 11:47:53 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 
No keys ! 

input_args_next_step, len :0
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139262_2886529_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg': 1008921601, 'temp/1759139262_2886529_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg': 1008921600, 'temp/1759139262_2886529_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg': 1008922003, 'temp/1759139262_2886529_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg': 1008922002, 'temp/1759139262_2886529_1008921786_066e79494b5536cc7d2f3293519cd617.jpg': 1008921786, 'temp/1759139262_2886529_1008922095_dc858351f179fe112b62a458b452c880.jpg': 1008922095, 'temp/1759139262_2886529_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg': 1008922073, 'temp/1759139262_2886529_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg': 1008922072, 'temp/1759139262_2886529_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg': 1008922130, 'temp/1759139262_2886529_1008922101_1a025189b8603388d54304e6754e9df9.jpg': 1008922101, 'temp/1759139262_2886529_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg': 1008922097, 'temp/1759139262_2886529_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg': 1008921657, 'temp/1759139262_2886529_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg': 1008921656, 'temp/1759139262_2886529_1008921602_383b6a7758931a2dff9de560df82456c.jpg': 1008921602}
map_photo_id_path_extension : {1008921601: {'path': 'temp/1759139262_2886529_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg', 'extension': 'jpg'}, 1008921600: {'path': 'temp/1759139262_2886529_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg', 'extension': 'jpg'}, 1008922003: {'path': 'temp/1759139262_2886529_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg', 'extension': 'jpg'}, 1008922002: {'path': 'temp/1759139262_2886529_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg', 'extension': 'jpg'}, 1008921786: {'path': 'temp/1759139262_2886529_1008921786_066e79494b5536cc7d2f3293519cd617.jpg', 'extension': 'jpg'}, 1008922095: {'path': 'temp/1759139262_2886529_1008922095_dc858351f179fe112b62a458b452c880.jpg', 'extension': 'jpg'}, 1008922073: {'path': 'temp/1759139262_2886529_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg', 'extension': 'jpg'}, 1008922072: {'path': 'temp/1759139262_2886529_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg', 'extension': 'jpg'}, 1008922130: {'path': 'temp/1759139262_2886529_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg', 'extension': 'jpg'}, 1008922101: {'path': 'temp/1759139262_2886529_1008922101_1a025189b8603388d54304e6754e9df9.jpg', 'extension': 'jpg'}, 1008922097: {'path': 'temp/1759139262_2886529_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg', 'extension': 'jpg'}, 1008921657: {'path': 'temp/1759139262_2886529_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg', 'extension': 'jpg'}, 1008921656: {'path': 'temp/1759139262_2886529_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg', 'extension': 'jpg'}, 1008921602: {'path': 'temp/1759139262_2886529_1008921602_383b6a7758931a2dff9de560df82456c.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou_step Argmax !
no input

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 4.9114227294921875e-05
time spend to save output : 1.1682510375976562e-05
total time spend for step 3 : 6.079673767089844e-05

step4:merge_mask_and_thcl
Mon Sep 29 11:47:53 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 
No keys ! 

input_args_next_step, len :0
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139262_2886529_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg': 1008921601, 'temp/1759139262_2886529_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg': 1008921600, 'temp/1759139262_2886529_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg': 1008922003, 'temp/1759139262_2886529_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg': 1008922002, 'temp/1759139262_2886529_1008921786_066e79494b5536cc7d2f3293519cd617.jpg': 1008921786, 'temp/1759139262_2886529_1008922095_dc858351f179fe112b62a458b452c880.jpg': 1008922095, 'temp/1759139262_2886529_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg': 1008922073, 'temp/1759139262_2886529_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg': 1008922072, 'temp/1759139262_2886529_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg': 1008922130, 'temp/1759139262_2886529_1008922101_1a025189b8603388d54304e6754e9df9.jpg': 1008922101, 'temp/1759139262_2886529_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg': 1008922097, 'temp/1759139262_2886529_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg': 1008921657, 'temp/1759139262_2886529_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg': 1008921656, 'temp/1759139262_2886529_1008921602_383b6a7758931a2dff9de560df82456c.jpg': 1008921602}
map_photo_id_path_extension : {1008921601: {'path': 'temp/1759139262_2886529_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg', 'extension': 'jpg'}, 1008921600: {'path': 'temp/1759139262_2886529_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg', 'extension': 'jpg'}, 1008922003: {'path': 'temp/1759139262_2886529_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg', 'extension': 'jpg'}, 1008922002: {'path': 'temp/1759139262_2886529_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg', 'extension': 'jpg'}, 1008921786: {'path': 'temp/1759139262_2886529_1008921786_066e79494b5536cc7d2f3293519cd617.jpg', 'extension': 'jpg'}, 1008922095: {'path': 'temp/1759139262_2886529_1008922095_dc858351f179fe112b62a458b452c880.jpg', 'extension': 'jpg'}, 1008922073: {'path': 'temp/1759139262_2886529_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg', 'extension': 'jpg'}, 1008922072: {'path': 'temp/1759139262_2886529_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg', 'extension': 'jpg'}, 1008922130: {'path': 'temp/1759139262_2886529_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg', 'extension': 'jpg'}, 1008922101: {'path': 'temp/1759139262_2886529_1008922101_1a025189b8603388d54304e6754e9df9.jpg', 'extension': 'jpg'}, 1008922097: {'path': 'temp/1759139262_2886529_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg', 'extension': 'jpg'}, 1008921657: {'path': 'temp/1759139262_2886529_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg', 'extension': 'jpg'}, 1008921656: {'path': 'temp/1759139262_2886529_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg', 'extension': 'jpg'}, 1008921602: {'path': 'temp/1759139262_2886529_1008921602_383b6a7758931a2dff9de560df82456c.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
debut de la step merge mask and classif
[] crops sont a sauvegarder

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 5.555152893066406e-05
time spend to save output : 1.5735626220703125e-05
total time spend for step 4 : 7.128715515136719e-05

step5:rle_unique_nms_with_priority
Mon Sep 29 11:47:53 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 : ['temp/1759139262_2886529_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg', 'temp/1759139262_2886529_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg', 'temp/1759139262_2886529_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg', 'temp/1759139262_2886529_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg', 'temp/1759139262_2886529_1008921786_066e79494b5536cc7d2f3293519cd617.jpg', 'temp/1759139262_2886529_1008922095_dc858351f179fe112b62a458b452c880.jpg', 'temp/1759139262_2886529_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg', 'temp/1759139262_2886529_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg', 'temp/1759139262_2886529_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg', 'temp/1759139262_2886529_1008922101_1a025189b8603388d54304e6754e9df9.jpg', 'temp/1759139262_2886529_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg', 'temp/1759139262_2886529_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg', 'temp/1759139262_2886529_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg', 'temp/1759139262_2886529_1008921602_383b6a7758931a2dff9de560df82456c.jpg']
We expect there is only one output and this part is used while all output are not tuple or array
We expect there is only one output and this part is used while all output are not tuple or array
We expect there is only one output and this part is used while all output are not tuple or array
We expect there is only one output and this part is used while all output are not tuple or array
We expect there is only one output and this part is used while all output are not tuple or array
We expect there is only one output and this part is used while all output are not tuple or array
We expect there is only one output and this part is used while all output are not tuple or array
We expect there is only one output and this part is used while all output are not tuple or array
We expect there is only one output and this part is used while all output are not tuple or array
We expect there is only one output and this part is used while all output are not tuple or array
We expect there is only one output and this part is used while all output are not tuple or array
We expect there is only one output and this part is used while all output are not tuple or array
We expect there is only one output and this part is used while all output are not tuple or array
We expect there is only one output and this part is used while all output are not tuple or array
VR 22-3-18 : For now we do not clean correctly the datou structure 

input_args_next_step, len :14, first value : ['temp/1759139262_2886529_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg']
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139262_2886529_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg': 1008921601, 'temp/1759139262_2886529_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg': 1008921600, 'temp/1759139262_2886529_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg': 1008922003, 'temp/1759139262_2886529_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg': 1008922002, 'temp/1759139262_2886529_1008921786_066e79494b5536cc7d2f3293519cd617.jpg': 1008921786, 'temp/1759139262_2886529_1008922095_dc858351f179fe112b62a458b452c880.jpg': 1008922095, 'temp/1759139262_2886529_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg': 1008922073, 'temp/1759139262_2886529_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg': 1008922072, 'temp/1759139262_2886529_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg': 1008922130, 'temp/1759139262_2886529_1008922101_1a025189b8603388d54304e6754e9df9.jpg': 1008922101, 'temp/1759139262_2886529_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg': 1008922097, 'temp/1759139262_2886529_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg': 1008921657, 'temp/1759139262_2886529_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg': 1008921656, 'temp/1759139262_2886529_1008921602_383b6a7758931a2dff9de560df82456c.jpg': 1008921602}
map_photo_id_path_extension : {1008921601: {'path': 'temp/1759139262_2886529_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg', 'extension': 'jpg'}, 1008921600: {'path': 'temp/1759139262_2886529_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg', 'extension': 'jpg'}, 1008922003: {'path': 'temp/1759139262_2886529_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg', 'extension': 'jpg'}, 1008922002: {'path': 'temp/1759139262_2886529_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg', 'extension': 'jpg'}, 1008921786: {'path': 'temp/1759139262_2886529_1008921786_066e79494b5536cc7d2f3293519cd617.jpg', 'extension': 'jpg'}, 1008922095: {'path': 'temp/1759139262_2886529_1008922095_dc858351f179fe112b62a458b452c880.jpg', 'extension': 'jpg'}, 1008922073: {'path': 'temp/1759139262_2886529_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg', 'extension': 'jpg'}, 1008922072: {'path': 'temp/1759139262_2886529_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg', 'extension': 'jpg'}, 1008922130: {'path': 'temp/1759139262_2886529_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg', 'extension': 'jpg'}, 1008922101: {'path': 'temp/1759139262_2886529_1008922101_1a025189b8603388d54304e6754e9df9.jpg', 'extension': 'jpg'}, 1008922097: {'path': 'temp/1759139262_2886529_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg', 'extension': 'jpg'}, 1008921657: {'path': 'temp/1759139262_2886529_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg', 'extension': 'jpg'}, 1008921656: {'path': 'temp/1759139262_2886529_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg', 'extension': 'jpg'}, 1008921602: {'path': 'temp/1759139262_2886529_1008921602_383b6a7758931a2dff9de560df82456c.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
Begin step rle-unique-nms
on traite la photo : temp/1759139262_2886529_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg
on traite la photo : temp/1759139262_2886529_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg
on traite la photo : temp/1759139262_2886529_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg
on traite la photo : temp/1759139262_2886529_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg
on traite la photo : temp/1759139262_2886529_1008921786_066e79494b5536cc7d2f3293519cd617.jpg
on traite la photo : temp/1759139262_2886529_1008922095_dc858351f179fe112b62a458b452c880.jpg
on traite la photo : temp/1759139262_2886529_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg
on traite la photo : temp/1759139262_2886529_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg
on traite la photo : temp/1759139262_2886529_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg
on traite la photo : temp/1759139262_2886529_1008922101_1a025189b8603388d54304e6754e9df9.jpg
on traite la photo : temp/1759139262_2886529_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg
on traite la photo : temp/1759139262_2886529_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg
on traite la photo : temp/1759139262_2886529_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg
on traite la photo : temp/1759139262_2886529_1008921602_383b6a7758931a2dff9de560df82456c.jpg
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 1008921601,1008921600,1008922003,1008922002,1008921786,1008922095,1008922073,1008922072,1008922130,1008922101,1008922097,1008921657,1008921656,1008921602) and `type` in (3418) and hashtag_id in (492668766,538914404) 
Loaded 26 chid ids of type : 3418
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (2099963667,2099963668,2099963669,2099963670,2099963671,2099963672,2099963673,2099963674,2099963675,2099963676,2099963677,2099963678,2099963679,2099963680,2099963681,2099963682,2099963683,2099963684,2099963685,2099963686,2099963687,2099963688,2099963689,2099963690,2099963691,2099963692)
++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (2099963667,2099963668,2099963669,2099963670,2099963671,2099963672,2099963673,2099963674,2099963675,2099963676,2099963677,2099963678,2099963679,2099963680,2099963681,2099963682,2099963683,2099963684,2099963685,2099963686,2099963687,2099963688,2099963689,2099963690,2099963691,2099963692)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (2099963667,2099963668,2099963669,2099963670,2099963671,2099963672,2099963673,2099963674,2099963675,2099963676,2099963677,2099963678,2099963679,2099963680,2099963681,2099963682,2099963683,2099963684,2099963685,2099963686,2099963687,2099963688,2099963689,2099963690,2099963691,2099963692)
nb_obj : 0
nb_hashtags :  2
time to prepare the origin masks  : 2.1112844944000244
create new chi : 0.006040334701538086
 proportion hashtag : refus 0.011822314499524865
 proportion hashtag : papier 0.9824540455733292
time to delete rle : 0.516289472579956
save time : 1.0967254638671875e-05
nb_obj : 0
nb_hashtags :  2
time to prepare the origin masks  : 2.0481998920440674
create new chi : 0.005970954895019531
 proportion hashtag : refus 0.01255295771301869
 proportion hashtag : papier 0.9863322081881533
time to delete rle : 0.5237271785736084
save time : 3.814697265625e-06
nb_obj : 0
nb_hashtags :  1
time to prepare the origin masks  : 1.3516602516174316
create new chi : 1.9550323486328125e-05
 proportion hashtag : refus 0.010260185698447893
 proportion hashtag : papier 0.9897398143015521
time to delete rle : 0.5412719249725342
save time : 4.5299530029296875e-06
nb_obj : 0
nb_hashtags :  1
time to prepare the origin masks  : 1.0080726146697998
create new chi : 0.006369590759277344
 proportion hashtag : papier 0.98686463117675
time to delete rle : 0.5124039649963379
save time : 4.0531158447265625e-06
nb_obj : 0
nb_hashtags :  1
time to prepare the origin masks  : 1.3603980541229248
create new chi : 2.0265579223632812e-05
 proportion hashtag : refus 0.0024127929996832437
 proportion hashtag : papier 0.9975872070003168
time to delete rle : 0.470367431640625
save time : 4.291534423828125e-06
nb_obj : 0
nb_hashtags :  1
time to prepare the origin masks  : 0.9631657600402832
create new chi : 0.006460666656494141
 proportion hashtag : papier 0.9585115913050364
time to delete rle : 0.4873344898223877
save time : 4.0531158447265625e-06
nb_obj : 0
nb_hashtags :  1
time to prepare the origin masks  : 1.361257553100586
create new chi : 2.0742416381835938e-05
 proportion hashtag : refus 0.0034527686490338928
 proportion hashtag : papier 0.9965472313509661
time to delete rle : 0.5174643993377686
save time : 4.291534423828125e-06
nb_obj : 0
nb_hashtags :  1
time to prepare the origin masks  : 1.9093852043151855
create new chi : 4.2438507080078125e-05
 proportion hashtag : refus 0.006595957396262274
 proportion hashtag : papier 0.9934040426037377
time to delete rle : 0.5104689598083496
save time : 2.5272369384765625e-05
nb_obj : 0
nb_hashtags :  1
time to prepare the origin masks  : 1.3329312801361084
create new chi : 2.1219253540039062e-05
 proportion hashtag : refus 0.009172696586949636
 proportion hashtag : papier 0.9908273034130504
time to delete rle : 0.5635333061218262
save time : 1.0967254638671875e-05
nb_obj : 0
nb_hashtags :  2
time to prepare the origin masks  : 1.4553570747375488
create new chi : 0.0060045719146728516
 proportion hashtag : papier 0.9896722560975609
 proportion hashtag : refus 0.001785095620842572
time to delete rle : 0.5141897201538086
save time : 3.814697265625e-06
nb_obj : 0
nb_hashtags :  2
time to prepare the origin masks  : 1.4772100448608398
create new chi : 0.006160736083984375
 proportion hashtag : papier 0.9968715354767184
 proportion hashtag : refus 0.00213167168197656
time to delete rle : 0.4037652015686035
save time : 4.5299530029296875e-06
nb_obj : 0
nb_hashtags :  1
time to prepare the origin masks  : 1.0114428997039795
create new chi : 0.0060617923736572266
 proportion hashtag : papier 0.994315707158695
time to delete rle : 0.33809876441955566
save time : 4.291534423828125e-06
nb_obj : 0
nb_hashtags :  1
time to prepare the origin masks  : 0.8963396549224854
create new chi : 3.075599670410156e-05
 proportion hashtag : refus 0.0018834633354450428
 proportion hashtag : papier 0.9981165366645549
time to delete rle : 0.40843987464904785
save time : 4.76837158203125e-06
nb_obj : 0
nb_hashtags :  1
time to prepare the origin masks  : 1.34914231300354
create new chi : 2.0742416381835938e-05
 proportion hashtag : refus 0.011059624445676274
 proportion hashtag : papier 0.9889403755543237
time to delete rle : 0.542555570602417
save time : 4.5299530029296875e-06
INSERT IGNORE INTO MTRPhoto.photo_carac_ratio (`photo_id`, `hashtag_type`, `hashtag_id`, `ratio`)
                            VALUES (%s, %s, %s, %s) on duplicate key update `ratio` = VALUES(`ratio`)
 map_output_result : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274)}
End step rle-unique-nms

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 28.51320195198059
time spend to save output : 0.00014400482177734375
total time spend for step 5 : 28.513345956802368

step6:ventilate_hashtags_in_portfolio
Mon Sep 29 11:48:22 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 

input_args_next_step, len :0, first value : []
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139262_2886529_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg': 1008921601, 'temp/1759139262_2886529_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg': 1008921600, 'temp/1759139262_2886529_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg': 1008922003, 'temp/1759139262_2886529_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg': 1008922002, 'temp/1759139262_2886529_1008921786_066e79494b5536cc7d2f3293519cd617.jpg': 1008921786, 'temp/1759139262_2886529_1008922095_dc858351f179fe112b62a458b452c880.jpg': 1008922095, 'temp/1759139262_2886529_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg': 1008922073, 'temp/1759139262_2886529_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg': 1008922072, 'temp/1759139262_2886529_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg': 1008922130, 'temp/1759139262_2886529_1008922101_1a025189b8603388d54304e6754e9df9.jpg': 1008922101, 'temp/1759139262_2886529_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg': 1008922097, 'temp/1759139262_2886529_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg': 1008921657, 'temp/1759139262_2886529_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg': 1008921656, 'temp/1759139262_2886529_1008921602_383b6a7758931a2dff9de560df82456c.jpg': 1008921602}
map_photo_id_path_extension : {1008921601: {'path': 'temp/1759139262_2886529_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg', 'extension': 'jpg'}, 1008921600: {'path': 'temp/1759139262_2886529_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg', 'extension': 'jpg'}, 1008922003: {'path': 'temp/1759139262_2886529_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg', 'extension': 'jpg'}, 1008922002: {'path': 'temp/1759139262_2886529_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg', 'extension': 'jpg'}, 1008921786: {'path': 'temp/1759139262_2886529_1008921786_066e79494b5536cc7d2f3293519cd617.jpg', 'extension': 'jpg'}, 1008922095: {'path': 'temp/1759139262_2886529_1008922095_dc858351f179fe112b62a458b452c880.jpg', 'extension': 'jpg'}, 1008922073: {'path': 'temp/1759139262_2886529_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg', 'extension': 'jpg'}, 1008922072: {'path': 'temp/1759139262_2886529_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg', 'extension': 'jpg'}, 1008922130: {'path': 'temp/1759139262_2886529_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg', 'extension': 'jpg'}, 1008922101: {'path': 'temp/1759139262_2886529_1008922101_1a025189b8603388d54304e6754e9df9.jpg', 'extension': 'jpg'}, 1008922097: {'path': 'temp/1759139262_2886529_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg', 'extension': 'jpg'}, 1008921657: {'path': 'temp/1759139262_2886529_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg', 'extension': 'jpg'}, 1008921656: {'path': 'temp/1759139262_2886529_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg', 'extension': 'jpg'}, 1008921602: {'path': 'temp/1759139262_2886529_1008921602_383b6a7758931a2dff9de560df82456c.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
beginning of datou step ventilate_hashtags_in_portfolio : To implement !
To do loadFromThcl(), then load ParamDescType : thcl2456
 get_desc_type_from_thcl : type of cat <class 'mtr.database_queries.classification_admin_tools.ClassificationMetaTools'>

            SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists,
            photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at,
            type_classification
            FROM MTRDatou.classification_theme  WHERE  `id` IN (2456) 
 thcls : [{'id': 2456, 'mtr_user_id': 31, 'name': 'learn_qualipapia_papier_refus_from_vlg_data_aug', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'papier,refus', 'svm_portfolios_learning': '3028087,3028251', 'photo_hashtag_type': 3049, 'photo_desc_type': 4999, 'type_classification': 'caffe', 'hashtag_id_list': '492668766,538914404'}]
 thcl {'id': 2456, 'mtr_user_id': 31, 'name': 'learn_qualipapia_papier_refus_from_vlg_data_aug', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'papier,refus', 'svm_portfolios_learning': '3028087,3028251', 'photo_hashtag_type': 3049, 'photo_desc_type': 4999, 'type_classification': 'caffe', 'hashtag_id_list': '492668766,538914404'}
 Update svm_hashtag_type_desc : 4999
Iterating over portfolio : 3364276
get user id for portfolio 3364276
get sub ptf for main ptf 3535038, type 3418 and hashtags ['papier', 'refus']
 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`=3535038 AND  mptpi.`type`=3418 AND  mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','refus'))  AND  mptpi.`min_score`=0.7
 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`=3535038 AND  mptpi.`type`=3418 AND  mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','refus'))  AND  mptpi.`min_score`=0.7
To do
returned map {'refus': {'main_port_id': 3535038, 'sub_port_id': 3535090, 'hashtag': 'refus', 'pht': 3418, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 8903, 'last_updated_at': datetime.datetime(2025, 9, 26, 9, 10, 43), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 10, 43), 'last_updated_at_asc': datetime.datetime(2021, 3, 5, 16, 19, 20)}, 'papier': {'main_port_id': 3535038, 'sub_port_id': 3535091, 'hashtag': 'papier', 'pht': 3418, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 8904, 'last_updated_at': datetime.datetime(2025, 9, 26, 9, 10, 43), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 10, 43), 'last_updated_at_asc': datetime.datetime(2021, 3, 5, 16, 19, 20)}}
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 3535090 , sub_photo_id, 739,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=3364276
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=3418
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='refus'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 3535091 , sub_photo_id, 739,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=3364276
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=3418
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='papier'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
To do !
Use context local managing function !
get sub ptf for main ptf 3364276, type 3418 and hashtags ['papier', 'refus']
 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`=3364276 AND  mptpi.`type`=3418 AND  mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','refus'))  AND  mptpi.`min_score`=0.7
 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`=3364276 AND  mptpi.`type`=3418 AND  mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','refus'))  AND  mptpi.`min_score`=0.7
To do
returned map {'refus': {'main_port_id': 3364276, 'sub_port_id': 3534897, 'hashtag': 'refus', 'pht': 3418, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 8901, 'last_updated_at': datetime.datetime(2025, 9, 26, 9, 10, 43), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 10, 43), 'last_updated_at_asc': datetime.datetime(2021, 3, 5, 15, 22, 49)}, 'papier': {'main_port_id': 3364276, 'sub_port_id': 3534898, 'hashtag': 'papier', 'pht': 3418, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 8902, 'last_updated_at': datetime.datetime(2025, 9, 26, 9, 10, 43), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 10, 43), 'last_updated_at_asc': datetime.datetime(2021, 3, 5, 15, 22, 49)}}
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 3534897 , sub_photo_id, 739,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=3364276
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=3418
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='refus'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 3534898 , sub_photo_id, 739,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=3364276
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=3418
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='papier'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
To do !
Use context local managing function !

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 0.34989261627197266
time spend to save output : 9.489059448242188e-05
total time spend for step 6 : 0.3499875068664551

step7:final
Mon Sep 29 11:48:22 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 : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274)}
 input_args_next_step : {1008921601: ()}
 output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274)}
 args : 1008921601
 depend.output_id : 0
 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: ()}
 output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274)}
 args : 1008921600
 depend.output_id : 0
 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: ()}
 output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274)}
 args : 1008922003
 depend.output_id : 0
 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: ()}
 output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274)}
 args : 1008922002
 depend.output_id : 0
 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: (0.005230650193135238,), 1008921786: ()}
 output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274)}
 args : 1008921786
 depend.output_id : 0
 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: (0.005230650193135238,), 1008921786: (0.005230650193135238,), 1008922095: ()}
 output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274)}
 args : 1008922095
 depend.output_id : 0
 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: (0.005230650193135238,), 1008921786: (0.005230650193135238,), 1008922095: (0.005230650193135238,), 1008922073: ()}
 output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274)}
 args : 1008922073
 depend.output_id : 0
 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: (0.005230650193135238,), 1008921786: (0.005230650193135238,), 1008922095: (0.005230650193135238,), 1008922073: (0.005230650193135238,), 1008922072: ()}
 output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274)}
 args : 1008922072
 depend.output_id : 0
 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: (0.005230650193135238,), 1008921786: (0.005230650193135238,), 1008922095: (0.005230650193135238,), 1008922073: (0.005230650193135238,), 1008922072: (0.005230650193135238,), 1008922130: ()}
 output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274)}
 args : 1008922130
 depend.output_id : 0
 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: (0.005230650193135238,), 1008921786: (0.005230650193135238,), 1008922095: (0.005230650193135238,), 1008922073: (0.005230650193135238,), 1008922072: (0.005230650193135238,), 1008922130: (0.005230650193135238,), 1008922101: ()}
 output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274)}
 args : 1008922101
 depend.output_id : 0
 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: (0.005230650193135238,), 1008921786: (0.005230650193135238,), 1008922095: (0.005230650193135238,), 1008922073: (0.005230650193135238,), 1008922072: (0.005230650193135238,), 1008922130: (0.005230650193135238,), 1008922101: (0.005230650193135238,), 1008922097: ()}
 output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274)}
 args : 1008922097
 depend.output_id : 0
 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: (0.005230650193135238,), 1008921786: (0.005230650193135238,), 1008922095: (0.005230650193135238,), 1008922073: (0.005230650193135238,), 1008922072: (0.005230650193135238,), 1008922130: (0.005230650193135238,), 1008922101: (0.005230650193135238,), 1008922097: (0.005230650193135238,), 1008921657: ()}
 output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274)}
 args : 1008921657
 depend.output_id : 0
 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: (0.005230650193135238,), 1008921786: (0.005230650193135238,), 1008922095: (0.005230650193135238,), 1008922073: (0.005230650193135238,), 1008922072: (0.005230650193135238,), 1008922130: (0.005230650193135238,), 1008922101: (0.005230650193135238,), 1008922097: (0.005230650193135238,), 1008921657: (0.005230650193135238,), 1008921656: ()}
 output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274)}
 args : 1008921656
 depend.output_id : 0
 input_args_next_step : {1008921601: (0.005230650193135238,), 1008921600: (0.005230650193135238,), 1008922003: (0.005230650193135238,), 1008922002: (0.005230650193135238,), 1008921786: (0.005230650193135238,), 1008922095: (0.005230650193135238,), 1008922073: (0.005230650193135238,), 1008922072: (0.005230650193135238,), 1008922130: (0.005230650193135238,), 1008922101: (0.005230650193135238,), 1008922097: (0.005230650193135238,), 1008921657: (0.005230650193135238,), 1008921656: (0.005230650193135238,), 1008921602: ()}
 output_args : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274)}
 args : 1008921602
 depend.output_id : 0
VR 22-3-18 : For now we do not clean correctly the datou structure 

input_args_next_step, len :14, first value : (0.005230650193135238,)
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139262_2886529_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg': 1008921601, 'temp/1759139262_2886529_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg': 1008921600, 'temp/1759139262_2886529_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg': 1008922003, 'temp/1759139262_2886529_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg': 1008922002, 'temp/1759139262_2886529_1008921786_066e79494b5536cc7d2f3293519cd617.jpg': 1008921786, 'temp/1759139262_2886529_1008922095_dc858351f179fe112b62a458b452c880.jpg': 1008922095, 'temp/1759139262_2886529_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg': 1008922073, 'temp/1759139262_2886529_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg': 1008922072, 'temp/1759139262_2886529_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg': 1008922130, 'temp/1759139262_2886529_1008922101_1a025189b8603388d54304e6754e9df9.jpg': 1008922101, 'temp/1759139262_2886529_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg': 1008922097, 'temp/1759139262_2886529_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg': 1008921657, 'temp/1759139262_2886529_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg': 1008921656, 'temp/1759139262_2886529_1008921602_383b6a7758931a2dff9de560df82456c.jpg': 1008921602}
map_photo_id_path_extension : {1008921601: {'path': 'temp/1759139262_2886529_1008921601_8e77b77d2ac41a1e6ec6af186d0ee0e0.jpg', 'extension': 'jpg'}, 1008921600: {'path': 'temp/1759139262_2886529_1008921600_37a7af01ee43a568dfe3728dcdbe17a4.jpg', 'extension': 'jpg'}, 1008922003: {'path': 'temp/1759139262_2886529_1008922003_b378dd1de9a27a4b0a8bb35c5e0d5b99.jpg', 'extension': 'jpg'}, 1008922002: {'path': 'temp/1759139262_2886529_1008922002_4e629eb338e1fe79a3726963bf6d47dd.jpg', 'extension': 'jpg'}, 1008921786: {'path': 'temp/1759139262_2886529_1008921786_066e79494b5536cc7d2f3293519cd617.jpg', 'extension': 'jpg'}, 1008922095: {'path': 'temp/1759139262_2886529_1008922095_dc858351f179fe112b62a458b452c880.jpg', 'extension': 'jpg'}, 1008922073: {'path': 'temp/1759139262_2886529_1008922073_f4a476ca7bbbdbb6286b3964cb9dfa09.jpg', 'extension': 'jpg'}, 1008922072: {'path': 'temp/1759139262_2886529_1008922072_0b2e247d5ad70c283fb6fd8596c1a378.jpg', 'extension': 'jpg'}, 1008922130: {'path': 'temp/1759139262_2886529_1008922130_4c0c64c8974710b1875fb84156e8457c.jpg', 'extension': 'jpg'}, 1008922101: {'path': 'temp/1759139262_2886529_1008922101_1a025189b8603388d54304e6754e9df9.jpg', 'extension': 'jpg'}, 1008922097: {'path': 'temp/1759139262_2886529_1008922097_6c2f48fc82a23eb61afdc5b17b032e88.jpg', 'extension': 'jpg'}, 1008921657: {'path': 'temp/1759139262_2886529_1008921657_46fce4176daed1c358e2a7c299fc5587.jpg', 'extension': 'jpg'}, 1008921656: {'path': 'temp/1759139262_2886529_1008921656_c93a3fc2cd8645066f3120d727a4ce22.jpg', 'extension': 'jpg'}, 1008921602: {'path': 'temp/1759139262_2886529_1008921602_383b6a7758931a2dff9de560df82456c.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou step final !
 query to retrieve results for portfolio 3364276 : SELECT photo_id, hashtag_id, ratio from MTRPhoto.photo_carac_ratio where hashtag_type = 3418  and photo_id in (1008921602,1008921601,1008921600,1008921657,1008921656,1008921786,1008922003,1008922002,1008922073,1008922072,1008922097,1008922095,1008922101,1008922130);
insert ignore into MTRUser.portfolio_carac_ratio (portfolio_id, hashtag_type, hashtag_id, value) values (%s,%s,%s,%s) on DUPLICATE KEY UPDATE value=VALUES(value)

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 0.072113037109375
time spend to save output : 5.841255187988281e-05
total time spend for step 7 : 0.07217144966125488

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
original output for save of step final : {1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',)}
new output for save of step final : {1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',)}
[1008921601, 1008921600, 1008922003, 1008922002, 1008921786, 1008922095, 1008922073, 1008922072, 1008922130, 1008922101, 1008922097, 1008921657, 1008921656, 1008921602]
 map_info['map_portfolio_photo'] : {3364276: [1008922130, 1008922101, 1008922097, 1008922095, 1008922073, 1008922072, 1008922003, 1008922002, 1008921786, 1008921657, 1008921656, 1008921602, 1008921601, 1008921600]}
 final :  True mtd_id 3164 list_pids : [1008921601, 1008921600, 1008922003, 1008922002, 1008921786, 1008922095, 1008922073, 1008922072, 1008922130, 1008922101, 1008922097, 1008921657, 1008921656, 1008921602]
Looping around the photos to save general results
len do output : 14
/1008921601.Didn't retrieve data
.
/1008921600.Didn't retrieve data
.
/1008922003.Didn't retrieve data
.
/1008922002.Didn't retrieve data
.
/1008921786.Didn't retrieve data
.
/1008922095.Didn't retrieve data
.
/1008922073.Didn't retrieve data
.
/1008922072.Didn't retrieve data
.
/1008922130.Didn't retrieve data
.
/1008922101.Didn't retrieve data
.
/1008922097.Didn't retrieve data
.
/1008921657.Didn't retrieve data
.
/1008921656.Didn't retrieve data
.
/1008921602.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 
('3164', None, None, None, None, None, None, None, None)
('3164', '3364276', '1008921601', None, None, None, None, None, None)
('3164', None, None, None, None, None, None, None, None)
('3164', '3364276', '1008921600', None, None, None, None, None, None)
('3164', None, None, None, None, None, None, None, None)
('3164', '3364276', '1008922003', None, None, None, None, None, None)
('3164', None, None, None, None, None, None, None, None)
('3164', '3364276', '1008922002', None, None, None, None, None, None)
('3164', None, None, None, None, None, None, None, None)
('3164', '3364276', '1008921786', None, None, None, None, None, None)
('3164', None, None, None, None, None, None, None, None)
('3164', '3364276', '1008922095', None, None, None, None, None, None)
('3164', None, None, None, None, None, None, None, None)
('3164', '3364276', '1008922073', None, None, None, None, None, None)
('3164', None, None, None, None, None, None, None, None)
('3164', '3364276', '1008922072', None, None, None, None, None, None)
('3164', None, None, None, None, None, None, None, None)
('3164', '3364276', '1008922130', None, None, None, None, None, None)
('3164', None, None, None, None, None, None, None, None)
('3164', '3364276', '1008922101', None, None, None, None, None, None)
('3164', None, None, None, None, None, None, None, None)
('3164', '3364276', '1008922097', None, None, None, None, None, None)
('3164', None, None, None, None, None, None, None, None)
('3164', '3364276', '1008921657', None, None, None, None, None, None)
('3164', None, None, None, None, None, None, None, None)
('3164', '3364276', '1008921656', None, None, None, None, None, None)
('3164', None, None, None, None, None, None, None, None)
('3164', '3364276', '1008921602', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 42
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('3164', '3364276', '1008921601', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008921600', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008922003', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008922002', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008921786', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008922095', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008922073', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008922072', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008922130', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008922101', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008922097', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008921657', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008921656', '0.005252307643909098', None, None, None, None, None), ('3164', '3364276', '1008921602', '0.005252307643909098', None, None, None, None, None)]
time used for this insertion : 0.04764866828918457
 save_final 

save missing photos in datou_result :
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 7
 output : {1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',)}
{1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',)}

############################### TEST ventilate_hashtags_in_portfolio  ################################
DELETE FROM MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 5486631;
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=3070

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=3070
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 3070
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=3070
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : ventilate_hashtags_in_portfolio
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (5363525) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (5363525)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 1075306598, 'portfolio_id': 5363525}, {'photo_id': 1075306564, 'portfolio_id': 5363525}, {'photo_id': 1075306534, 'portfolio_id': 5363525}, {'photo_id': 1075306522, 'portfolio_id': 5363525}, {'photo_id': 1075304668, 'portfolio_id': 5363525}]
 map_portfolio_id_photo_id: {5363525: [1075306598, 1075306564, 1075306534, 1075306522, 1075304668]}
##### Call download_photos : nb_thread : 5
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
##### After load_data_input 
time to download the photos : 0.052392005920410156
#### fin chargement data
Blocking on flush ?
No conitnuing 
About to test input to load 
Calling datou_exec

Inside datou_exec : verbose : True
number of steps :  1
step1:ventilate_hashtags_in_portfolio
Mon Sep 29 11:48:22 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {}
map_photo_id_path_extension : {}
map_subphoto_mainphoto : {}
beginning of datou step ventilate_hashtags_in_portfolio : To implement !
Iterating over portfolio : 5363525
get user id for portfolio 5363525
get sub ptf for main ptf 5363525, type 4268 and hashtags ['pet_clair', 'error', 'environment']
 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`=5363525 AND  mptpi.`type`=4268 AND  mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','error','environment'))  AND  mptpi.`min_score`=0.3
 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`=5363525 AND  mptpi.`type`=4268 AND  mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','error','environment'))  AND  mptpi.`min_score`=0.3
To do
returned map {'pet_clair': {'main_port_id': 5363525, 'sub_port_id': 5486630, 'hashtag': 'pet_clair', 'pht': 4268, 'min_score': 0.3, 'mtr_user_id': 31, 'id': 1060439, 'last_updated_at': datetime.datetime(2025, 9, 26, 9, 10, 43), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 10, 43), 'last_updated_at_asc': datetime.datetime(2022, 2, 16, 13, 28, 54)}, 'error': {'main_port_id': 5363525, 'sub_port_id': 5486631, 'hashtag': 'error', 'pht': 4268, 'min_score': 0.3, 'mtr_user_id': 31, 'id': 1060440, 'last_updated_at': datetime.datetime(2025, 9, 26, 9, 10, 43), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 10, 43), 'last_updated_at_asc': datetime.datetime(2022, 2, 16, 13, 28, 54)}, 'environment': {'main_port_id': 5363525, 'sub_port_id': 5486632, 'hashtag': 'environment', 'pht': 4268, 'min_score': 0.3, 'mtr_user_id': 31, 'id': 1060441, 'last_updated_at': datetime.datetime(2025, 9, 26, 9, 10, 43), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 10, 43), 'last_updated_at_asc': datetime.datetime(2022, 2, 16, 13, 28, 54)}}
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 5486630 , sub_photo_id, 31,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=5363525
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.3
AND chi.type=4268
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='pet_clair'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 5486631 , sub_photo_id, 31,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=5363525
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.3
AND chi.type=4268
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='error'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 5486632 , sub_photo_id, 31,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=5363525
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.3
AND chi.type=4268
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='environment'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
To do !
Use context local managing function !

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 0.19873690605163574
time spend to save output : 6.842613220214844e-05
total time spend for step 1 : 0.1988053321838379

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral 
[1075306598, 1075306564, 1075306534, 1075306522, 1075304668]
 map_info['map_portfolio_photo'] : {5363525: [1075306598, 1075306564, 1075306534, 1075306522, 1075304668]}
 final :  True mtd_id 3070 list_pids : [1075306598, 1075306564, 1075306534, 1075306522, 1075304668]
Looping around the photos to save general results
len do output : 1
/5363525.
before output type
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f1514b340d0>
Managing all output in save final without adding information in the mtr_datou_result 
('3070', None, None, None, None, None, None, None, None)
('3070', '5363525', '1075306598', None, None, None, None, None, None)
('3070', None, None, None, None, None, None, None, None)
('3070', '5363525', '1075306564', None, None, None, None, None, None)
('3070', None, None, None, None, None, None, None, None)
('3070', '5363525', '1075306534', None, None, None, None, None, None)
('3070', None, None, None, None, None, None, None, None)
('3070', '5363525', '1075306522', None, None, None, None, None, None)
('3070', None, None, None, None, None, None, None, None)
('3070', '5363525', '1075304668', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 6
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('3070', None, '5363525', "{'pet_clair': 5486630, 'error': 5486631, 'environment': 5486632}", None, None, None, None, None), ('3070', '5363525', '1075306598', None, None, None, None, None, None), ('3070', '5363525', '1075306564', None, None, None, None, None, None), ('3070', '5363525', '1075306534', None, None, None, None, None, None), ('3070', '5363525', '1075306522', None, None, None, None, None, None), ('3070', '5363525', '1075304668', None, None, None, None, None, None)]
time used for this insertion : 0.04091763496398926
 save_final 

save missing photos in datou_result :
After save, about to update current !

        SELECT count(*) from MTRUser.mtr_portfolio_photos 
        WHERE mtr_portfolio_id = 5486631 and hide_status = 0
Ayatollah of tests excluded it ! (Bon le prochain developpeur qui passe ici peut enlever ayatollah VR 11-2-21)
 name : merge_qualipapia_like not run because too long 

############################### TEST poly_ro_rle  ################################
test creation de rle a partir de polygon
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=3138

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=3138
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 3138
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=3138
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : poly_to_rle
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (3337029) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (3337029)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 1003369118, 'portfolio_id': 3337029}]
 map_portfolio_id_photo_id: {3337029: [1003369118]}
##### Call download_photos : nb_thread : 5
begin to download photo : 1003369118
download finish for photo 1003369118
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.20653057098388672
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  1
step1:poly_to_rle
Mon Sep 29 11:48:23 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139303_2886529_1003369118_58171420504d0b5f05a1233b6c515509_65826337.jpg': 1003369118}
map_photo_id_path_extension : {1003369118: {'path': 'temp/1759139303_2886529_1003369118_58171420504d0b5f05a1233b6c515509_65826337.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
can't find the hashtag_type_input ,set the output_type same as the input_type
on traite la photo : temp/1759139303_2886529_1003369118_58171420504d0b5f05a1233b6c515509_65826337.jpg
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 1003369118) and `type` in (3391) 
Loaded 16 chid ids of type : 3391
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1967620930,1967620931,1967620932,1967620933,1967620934,1967620935,1967620936,1967620937,1967620938,1967620939,1967620940,1967620941,1967620942,1967620943,1967620944,1967620945)
++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1967620930,1967620931,1967620932,1967620933,1967620934,1967620935,1967620936,1967620937,1967620938,1967620939,1967620940,1967620941,1967620942,1967620943,1967620944,1967620945)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1967620930,1967620931,1967620932,1967620933,1967620934,1967620935,1967620936,1967620937,1967620938,1967620939,1967620940,1967620941,1967620942,1967620943,1967620944,1967620945)
time for calcul the mask position with numpy : 0.007964611053466797
nb_pixel_total : 110633
time to create 1 rle with old method : 0.11430001258850098
time for calcul the mask position with numpy : 0.006824970245361328
nb_pixel_total : 15826
time to create 1 rle with old method : 0.016695499420166016
time for calcul the mask position with numpy : 0.006786346435546875
nb_pixel_total : 5286
time to create 1 rle with old method : 0.006308555603027344
time for calcul the mask position with numpy : 0.0068817138671875
nb_pixel_total : 1633
time to create 1 rle with old method : 0.001967191696166992
time for calcul the mask position with numpy : 0.007177591323852539
nb_pixel_total : 105533
time to create 1 rle with old method : 0.11075067520141602
time for calcul the mask position with numpy : 0.006707429885864258
nb_pixel_total : 4393
time to create 1 rle with old method : 0.004987001419067383
time for calcul the mask position with numpy : 0.006680965423583984
nb_pixel_total : 632
time to create 1 rle with old method : 0.000797271728515625
time for calcul the mask position with numpy : 0.007239341735839844
nb_pixel_total : 62627
time to create 1 rle with old method : 0.06671690940856934
time for calcul the mask position with numpy : 0.006983280181884766
nb_pixel_total : 33681
time to create 1 rle with old method : 0.03553342819213867
time for calcul the mask position with numpy : 0.00704646110534668
nb_pixel_total : 37724
time to create 1 rle with old method : 0.04048562049865723
time for calcul the mask position with numpy : 0.0071070194244384766
nb_pixel_total : 48775
time to create 1 rle with old method : 0.05252385139465332
time for calcul the mask position with numpy : 0.02128124237060547
nb_pixel_total : 1171703
time to create 1 rle with new method : 0.2730426788330078
time for calcul the mask position with numpy : 0.006742954254150391
nb_pixel_total : 2310
time to create 1 rle with old method : 0.0026292800903320312
time for calcul the mask position with numpy : 0.006684303283691406
nb_pixel_total : 2256
time to create 1 rle with old method : 0.0028090476989746094
time for calcul the mask position with numpy : 0.0066912174224853516
nb_pixel_total : 3112
time to create 1 rle with old method : 0.003525972366333008
time for calcul the mask position with numpy : 0.006678342819213867
nb_pixel_total : 1662
time to create 1 rle with old method : 0.0019521713256835938
insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s)
batch 1
Loaded 16 chid ids of type : 3391
++++++++++++++++Number RLEs to save : 0
INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) 
 TO DO : save crop sub photo not yet done ! 

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 1.3975834846496582
time spend to save output : 9.632110595703125e-05
total time spend for step 1 : 1.3976798057556152

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 0
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {1003369118: 'temp/1759139303_2886529_1003369118_58171420504d0b5f05a1233b6c515509_65826337.jpg'}
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 1003369118) and `type` in (3391) 
Loaded 16 chid ids of type : 3391
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1967620930,1967620931,1967620932,1967620933,1967620934,1967620935,1967620936,1967620937,1967620938,1967620939,1967620940,1967620941,1967620942,1967620943,1967620944,1967620945)
++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1967620930,1967620931,1967620932,1967620933,1967620934,1967620935,1967620936,1967620937,1967620938,1967620939,1967620940,1967620941,1967620942,1967620943,1967620944,1967620945)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1967620930,1967620931,1967620932,1967620933,1967620934,1967620935,1967620936,1967620937,1967620938,1967620939,1967620940,1967620941,1967620942,1967620943,1967620944,1967620945)
fin du test de poly_to_rle

############################### TEST cod_sts  ################################
warning , we can't find thcl infos in json_data
warning , we can't find pdt infos in json_data
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=3781

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=3781
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 3781
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=3781
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : split_time_score
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (4453840) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (4453840)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 1050302186, 'portfolio_id': 4453840}, {'photo_id': 1050302153, 'portfolio_id': 4453840}, {'photo_id': 1050302152, 'portfolio_id': 4453840}, {'photo_id': 1050302146, 'portfolio_id': 4453840}, {'photo_id': 1050302113, 'portfolio_id': 4453840}, {'photo_id': 1050302110, 'portfolio_id': 4453840}, {'photo_id': 1050302106, 'portfolio_id': 4453840}]
 map_portfolio_id_photo_id: {4453840: [1050302186, 1050302153, 1050302152, 1050302146, 1050302113, 1050302110, 1050302106]}
##### Call download_photos : nb_thread : 5
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
##### After load_data_input 
time to download the photos : 0.05333852767944336
#### fin chargement data
Blocking on flush ?
No conitnuing 
About to test input to load 
Calling datou_exec

Inside datou_exec : verbose : True
we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save
number of steps :  1
step1:split_time_score
Mon Sep 29 11:48:25 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {}
map_photo_id_path_extension : {}
map_subphoto_mainphoto : {}
SELECT app_name, token FROM MTRUser.mtr_app_api_token WHERE mtr_user_id=739 AND app_name="token_split_time_score" AND expire_at > NOW() 
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'}]
select SUBSTRING(ph.text,16,2) as h, count(*) from MTRUser.mtr_portfolio_photos mpp inner join MTRBack.photos_view ph on mpp.mtr_photo_id = ph.photo_id where mpp.mtr_portfolio_id = 4453840 group by h
(('48', 4), ('42', 3))

            SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4453840  AND mpp.hide_status=0  ORDER BY mpp.order  LIMIT 0, 100000
SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id
FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1
where mpp1.mtr_photo_id=cps.photo_id
AND h.hashtag_id=cps.hashtag_id
AND mpp1.hide_status=0
AND mpp1.mtr_portfolio_id in (4453840)  AND cps.thcl in (861) order by cps.score desc LIMIT 0, 100000
SELECT cps.photo_id, cps.score, h.hashtag, cps.hashtag_id, mpp1.mtr_portfolio_id
FROM MTRPhoto.class_photo_score cps, MTRBack.hashtags h, MTRUser.mtr_portfolio_photos mpp1
where mpp1.mtr_photo_id=cps.photo_id
AND h.hashtag_id=cps.hashtag_id
AND mpp1.hide_status=0
AND mpp1.mtr_portfolio_id in (4453840)  AND cps.thcl in (758) order by cps.score desc LIMIT 0, 100000
ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {}
17082021 4453840 Nombre de photos uploadées : 7 / 23040 (0%)
17082021 4453840 Nombre de photos taguées (types de déchets): 0 / 7 (0%)
17082021 4453840 Nombre de photos taguées (volume) : 0 / 7 (0%)
[{'photo_id': 1050302113, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/9/13/8b089b6e181d22a2bb6883e02fd3a110.jpg', 'username': None, 'uploaded_at': None, 'text': 'IMG_20210817_104213.jpg'}, {'photo_id': 1050302153, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/9/13/fd7af64d2c8fdd6083cf479b54278cc7.jpg', 'username': None, 'uploaded_at': None, 'text': 'IMG_20210817_104213.jpg'}, {'photo_id': 1050302186, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/9/13/c60917e9be6d6a9f1fa465e963bfa064.jpg', 'username': None, 'uploaded_at': None, 'text': 'IMG_20210817_104216.jpg'}, {'photo_id': 1050302110, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2021/9/13/314ca6fa7e24f250d19c3f32c9493132.jpg', 'username': None, 'uploaded_at': None, 'text': 'IMG_20210817_094832.jpg'}]
0
[]
 elapsed_time : load_data_split_time_score 3.0994415283203125e-06
 elapsed_time : order_list_meta_photo_and_scores 8.58306884765625e-06
??????? elapsed_time : fill_and_build_computed_from_old_data 0.00042510032653808594

        INSERT INTO `MTRPhoto`.`dashboard_entry_day`
(`dashboard_place_id`,
`mtr_portfolio_id`,
`date`)
VALUES
(
34, 4453840, '2021-08-17'
) ON DUPLICATE KEY UPDATE mtr_portfolio_id=VALUES(mtr_portfolio_id), updated_at=NOW();

Catched exception !
Connect or reconnect !
SELECT id FROM MTRPhoto.dashboard_entry_day where dashboard_place_id = 34 and date = '2021-08-17' ; 

        INSERT INTO `MTRPhoto`.`dashboard_run_ids`
(`dashboard_entry_day`,
`mtr_user_id`,
`misc_info`)
VALUES
(173725,739,"{}");

Catched exception !
Connect or reconnect !
 elapsed_time : insert_dashboard_record_day_entry 0.6631302833557129
TODO 20-09-21 https://github.com/fotonower/raspi-fotonower-x/issues/253#issuecomment-923099773
TODO 20-9-21
TODO 20-9-21
***** BEGIN SPLIT TIME *****
```````list printed:  [[0, 1, 2, 3], [4, 5, 6]]
forced_hashtag:  jrm
force hashtag to jrm
 elapsed_time : SPLIT_TIME 0.019123077392578125
***** END SPLIT TIME *****
 NUMBER BATCH : 2
 list_ponderation used : [0.001, 0.001, 0.001, 0.001, 0.001] , list_hashtag_class_create_as_list : ['jrm']
Listed one port to create portfolio : jrm_diff_batch__17082021_09_48_08_000000
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
Nombres de balles jrm_diff_batch__17082021_09_48_08_000000 : 0 duration : 24.0
update_text_in_photos list_photo_id_text len : 0 , 100 first caracter of query  INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update 
<map object at 0x7f15152392b0>
None
result_one_balle_Type_jrm:{'day': '17082021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 24.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20210817_094808.jpg'}
 Production hashtag (incorrect ponderation at 20-10-18) : 0
Listed one port to create portfolio : jrm_diff_batch__17082021_10_42_13_000000
ERROR missing amount info
ERROR missing amount info
ERROR missing amount info
Nombres de balles jrm_diff_batch__17082021_10_42_13_000000 : 0 duration : 3.0
update_text_in_photos list_photo_id_text len : 0 , 100 first caracter of query  INSERT IGNORE INTO MTRUser.mtr_photos (`photo_id`, `text`) VALUES ( %s, %s) on duplicate key update 
<map object at 0x7f15152396a0>
None
result_one_balle_Type_jrm:{'day': '17082021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 3.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20210817_104213.jpg'}
 Production hashtag (incorrect ponderation at 20-10-18) : 0
We have rejected 0 photos because of the batch_size condition !
 NUMBER BATCH list_of_portfolios_to_create : 2
list_same_port_ids : [4453926]


 find same portfolio which already exist 4453926 , we will use it
INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 4453926 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) +  100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4453926 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`);
0
list_same_port_ids : [4652336]


 find same portfolio which already exist 4652336 , we will use it
INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) SELECT 4652336 , ph.photo_id,50000000*CAST(SUBSTRING(ph.text, 13, 2) as unsigned integer) + 4000000*CAST(SUBSTRING(ph.text, 9, 2) as unsigned integer) +  100000*CAST(SUBSTRING(ph.text, 7, 2) as unsigned integer) + 60*60*CAST(SUBSTRING(ph.text, 16, 2) as unsigned integer) + 60*CAST(SUBSTRING(ph.text, 19, 2) as unsigned integer)+ CAST(SUBSTRING(ph.text, 22, 2) as unsigned integer) FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4652336 AND mpp.hide_status=0 on duplicate key update `order`=VALUES(`order`);
0
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                FROM MTRPhoto.mtr_datou_result
                WHERE mtr_portfolio_id = 4453926
                AND result is not null
                and result<>"None"
                and result<>0.0
                AND mtd_id=3543
                ORDER BY id desc LIMIT 1
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                    FROM MTRPhoto.mtr_datou_result
                    WHERE mtr_portfolio_id = 4453926
                    AND result is not null
                    and result<>"None"
                    and result=0.0
                    AND mtd_id=3543
                    ORDER BY created_at desc LIMIT 1
 list_result_datou : []
select url from MTRUser.mtr_files where mtd_id = 3543 and mtr_portfolio_id = 4453926 order by file_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 !
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 8564 mask_detect is not consistent : 4 used against 2 in the step definition !
WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition !
WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition !
Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used !
Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used !
WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition !
WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition !
Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs !
WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition !
WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition !
Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs !
Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used !
Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs !
Step 9126 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 !
WARNING : type of output 2 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 2 of step 8567 doesn't seem to be define in the database(
WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5
WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7
WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1
WARNING : type of output 2 of step 8568 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8569 doesn't seem to be define in the database(
We ignore checkConsistencyTypeOutputInput for datou_step final !
WARNING : type of output 3 of step 8568 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8571 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8571 doesn't seem to be define in the database(
WARNING : type of input 3 of step 8570 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 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6
WARNING : type of input 2 of step 9453 doesn't seem to be define in the database(
WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None
WARNING : type of output 3 of step 9453 doesn't seem to be define in the database(
WARNING : type of input 2 of step 8571 doesn't seem to be define in the database(
WARNING : type of output 3 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8572 doesn't seem to be define in the database(
WARNING : type of output 3 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8573 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8572 doesn't seem to be define in the database(
WARNING : type of input 3 of step 8567 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8573 doesn't seem to be define in the database(
WARNING : type of input 4 of step 8567 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`=4453926 AND  mptpi.`type`=4038
To do

        INSERT INTO `MTRPhoto`.`dashboard_results`
(`dashboard_run_id`,
`hashtag`,
`date_debut`,
`date_fin`,
`nombre_balle`,
`mtr_portfolio_id`,
`qualite`,
`datou_result_id_qualite`,
`completion_percentage`,
`completion_json`)
VALUES
(2369503, '_______jrm',
'2021-08-17 09:48:08', '2021-08-17 09:48:32',
4, 4453926,
-1, -1,
'-1', "{'max_time_prod_two_photos': 0, 'url_report': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P4453926_21-11-2024_01_57_11.pdf', 'Teint_Dans_La_Masse': {'hashtag': 'Teint_Dans_La_Masse', 'sub_port_id': 18491127, 'pht': 4038}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 18491128, 'pht': 4038}, 'cartonnette': {'hashtag': 'cartonnette', 'sub_port_id': 18491129, 'pht': 4038}, 'papier': {'hashtag': 'papier', 'sub_port_id': 18491130, 'pht': 4038}, 'plastique': {'hashtag': 'plastique', 'sub_port_id': 18491131, 'pht': 4038}, 'autre_refus': {'hashtag': 'autre_refus', 'sub_port_id': 18491132, 'pht': 4038}, 'Carton_gris': {'hashtag': 'Carton_gris', 'sub_port_id': 18491133, 'pht': 4038}, 'Carton_brun': {'hashtag': 'Carton_brun', 'sub_port_id': 18491134, 'pht': 4038}, 'kraft': {'hashtag': 'kraft', 'sub_port_id': 18491135, 'pht': 4038}, 'metal': {'hashtag': 'metal', 'sub_port_id': 18491136, 'pht': 4038}}"
);
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                FROM MTRPhoto.mtr_datou_result
                WHERE mtr_portfolio_id = 4652336
                AND result is not null
                and result<>"None"
                and result<>0.0
                AND mtd_id=3543
                ORDER BY id desc LIMIT 1
SELECT mtd_id, mtr_current_id, mtr_portfolio_id, created_at, result, result_long, result_double
                    FROM MTRPhoto.mtr_datou_result
                    WHERE mtr_portfolio_id = 4652336
                    AND result is not null
                    and result<>"None"
                    and result=0.0
                    AND mtd_id=3543
                    ORDER BY created_at desc LIMIT 1
 list_result_datou : []
select url from MTRUser.mtr_files where mtd_id = 3543 and mtr_portfolio_id = 4652336 order by file_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 !
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 8564 mask_detect is not consistent : 4 used against 2 in the step definition !
WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition !
WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition !
Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used !
Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used !
WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition !
WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition !
Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs !
WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition !
WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition !
Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs !
Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used !
Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs !
Step 9126 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 !
WARNING : type of output 2 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 2 of step 8567 doesn't seem to be define in the database(
WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5
WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7
WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1
WARNING : type of output 2 of step 8568 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8569 doesn't seem to be define in the database(
We ignore checkConsistencyTypeOutputInput for datou_step final !
WARNING : type of output 3 of step 8568 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8571 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8571 doesn't seem to be define in the database(
WARNING : type of input 3 of step 8570 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 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6
WARNING : type of input 2 of step 9453 doesn't seem to be define in the database(
WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None
WARNING : type of output 3 of step 9453 doesn't seem to be define in the database(
WARNING : type of input 2 of step 8571 doesn't seem to be define in the database(
WARNING : type of output 3 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8572 doesn't seem to be define in the database(
WARNING : type of output 3 of step 8564 doesn't seem to be define in the database(
WARNING : type of input 1 of step 8573 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8572 doesn't seem to be define in the database(
WARNING : type of input 3 of step 8567 doesn't seem to be define in the database(
WARNING : type of output 1 of step 8573 doesn't seem to be define in the database(
WARNING : type of input 4 of step 8567 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`=4652336 AND  mptpi.`type`=4038
To do

        INSERT INTO `MTRPhoto`.`dashboard_results`
(`dashboard_run_id`,
`hashtag`,
`date_debut`,
`date_fin`,
`nombre_balle`,
`mtr_portfolio_id`,
`qualite`,
`datou_result_id_qualite`,
`completion_percentage`,
`completion_json`)
VALUES
(2369503, '_______jrm',
'2021-08-17 10:42:13', '2021-08-17 10:42:16',
3, 4652336,
-1, -1,
'-1', "{'max_time_prod_two_photos': 0, 'autre_refus': {'hashtag': 'autre_refus', 'sub_port_id': 18491234, 'pht': 4038}, 'Carton_gris': {'hashtag': 'Carton_gris', 'sub_port_id': 18491235, 'pht': 4038}, 'cartonnette': {'hashtag': 'cartonnette', 'sub_port_id': 18491236, 'pht': 4038}, 'Carton_brun': {'hashtag': 'Carton_brun', 'sub_port_id': 18491237, 'pht': 4038}, 'plastique': {'hashtag': 'plastique', 'sub_port_id': 18491238, 'pht': 4038}, 'papier': {'hashtag': 'papier', 'sub_port_id': 18491239, 'pht': 4038}, 'Teint_Dans_La_Masse': {'hashtag': 'Teint_Dans_La_Masse', 'sub_port_id': 18491240, 'pht': 4038}, 'metal': {'hashtag': 'metal', 'sub_port_id': 18491241, 'pht': 4038}, 'environnement': {'hashtag': 'environnement', 'sub_port_id': 18491242, 'pht': 4038}, 'kraft': {'hashtag': 'kraft', 'sub_port_id': 18491243, 'pht': 4038}}"
);
 elapsed_time : count_nb_balles_and_create_portfolio 1.6243247985839844
# DISPLAY ALL COLLECTED DATA : 
{'17082021': {'nb_upload': 7, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 2.480832099914551
time spend to save output : 3.814697265625e-05
total time spend for step 1 : 2.480870246887207

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
 saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral 
[1050302186, 1050302153, 1050302152, 1050302146, 1050302113, 1050302110, 1050302106]
 map_info['map_portfolio_photo'] : {4453840: [1050302186, 1050302153, 1050302152, 1050302146, 1050302113, 1050302110, 1050302106]}
 final :  True mtd_id 3781 list_pids : [1050302186, 1050302153, 1050302152, 1050302146, 1050302113, 1050302110, 1050302106]
Looping around the photos to save general results
len do output : 1
/4453840Didn't retrieve data
.
before output type
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f1554241ee0>
Managing all output in save final without adding information in the mtr_datou_result 
('3781', None, None, None, None, None, None, None, None)
('3781', '4453840', '1050302186', None, None, None, None, None, None)
('3781', None, None, None, None, None, None, None, None)
('3781', '4453840', '1050302153', None, None, None, None, None, None)
('3781', None, None, None, None, None, None, None, None)
('3781', '4453840', '1050302152', None, None, None, None, None, None)
('3781', None, None, None, None, None, None, None, None)
('3781', '4453840', '1050302146', None, None, None, None, None, None)
('3781', None, None, None, None, None, None, None, None)
('3781', '4453840', '1050302113', None, None, None, None, None, None)
('3781', None, None, None, None, None, None, None, None)
('3781', '4453840', '1050302110', None, None, None, None, None, None)
('3781', None, None, None, None, None, None, None, None)
('3781', '4453840', '1050302106', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 8
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('3781', None, '4453840', 'None', None, None, None, None, None), ('3781', '4453840', '1050302186', None, None, None, None, None, None), ('3781', '4453840', '1050302153', None, None, None, None, None, None), ('3781', '4453840', '1050302152', None, None, None, None, None, None), ('3781', '4453840', '1050302146', None, None, None, None, None, None), ('3781', '4453840', '1050302113', None, None, None, None, None, None), ('3781', '4453840', '1050302110', None, None, None, None, None, None), ('3781', '4453840', '1050302106', None, None, None, None, None, None)]
time used for this insertion : 0.037673234939575195
 save_final 

save missing photos in datou_result :
After save, about to update current !
Result test cod : {4453840: ([[0, 1, 2, 3], [4, 5, 6]], {'Rungis_jrm': [(0, 1), (1, 2)]}, {4453926: {'list_of_photos': [1050302106, 1050302146, 1050302110, 1050302152], 'hashtag': 'jrm'}, 4652336: {'list_of_photos': [1050302113, 1050302153, 1050302186], 'hashtag': 'jrm'}}, {2107757407: 7}, {'amount_uploaded_and_tagged': {'17082021': {'nb_upload': 7, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2)]}})}|

############################### TEST cod_download  ################################
warning , we can't find thcl infos in json_data
warning , we can't find pdt infos in json_data
[]
["[Errno 13] Permission denied: '/data/data_root'\n", "[Errno 13] Permission denied: '/data/data_root'\n"]

############################### TEST sendgrid  ################################
test sendgrid
senders@fotonower.com
no problem of authentification, for test if the email can be received, try with a real receiver
fin du test de sendgrid

############################### TEST rym_consolidate  ################################
test_rym_consolidate

    DELETE FROM MTRUser.portfolio_carac_ratio WHERE portfolio_id = 4709558 AND hashtag_type = 4016
    
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=3818

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=3818
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 3818
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=3818
# 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 !
 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 9321 copy_chis is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 9357 consolidate_hashtags_from_manual_portfolio is not consistent : 2 used against 1 in the step definition !
WARNING : number of inputs for step 9318 rle_unique_nms_with_priority is not consistent : 3 used against 1 in the step definition !
WARNING : number of outputs for step 9318 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition !
WARNING : number of outputs for step 9410 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition !
Step 9319 final have less outputs used (1) than in the step definition (2) : some outputs may be not used !
Step 9328 blur_detection have less inputs used (0) than in the step definition (1) : maybe we manage optionnal inputs !
Step 9327 brightness have less inputs used (0) than in the step definition (1) : maybe we manage optionnal inputs !
Step 9326 send_mail_cod have less inputs used (4) 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 !
We ignore checkConsistencyTypeOutputInput for datou_step final !
WARNING : output 0 of step 9321 have datatype=11 whereas input 0 of step 9318 have datatype=2
We ignore checkConsistencyTypeOutputInput for datou_step final !
WARNING : type of output 1 of step 9357 doesn't seem to be define in the database(
WARNING : type of input 1 of step 9318 doesn't seem to be define in the database(
WARNING : type of output 1 of step 9357 doesn't seem to be define in the database(
WARNING : type of input 3 of step 9319 doesn't seem to be define in the database(
We ignore checkConsistencyTypeOutputInput for datou_step final !
WARNING : type of input 1 of step 9410 doesn't seem to be define in the database(
WARNING : output 1 of step 9318 have datatype=7 whereas input 1 of step 9410 have datatype=None
WARNING : type of output 1 of step 9410 doesn't seem to be define in the database(
WARNING : type of input 4 of step 9319 doesn't seem to be define in the database(
We ignore checkConsistencyTypeOutputInput for datou_step final !
WARNING : output 0 of step 9410 have datatype=10 whereas input 3 of step 9326 have datatype=6
WARNING : type of output 1 of step 9321 doesn't seem to be define in the database(
WARNING : type of input 1 of step 9357 doesn't seem to be define in the database(
DataTypes for each output/input checked !
no param json to modify
List Step Type Loaded in datou : copy_chis, consolidate_hashtags_from_manual_portfolio, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final, blur_detection, brightness, send_mail_cod
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (4709558) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (4709558)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 1057289546, 'portfolio_id': 4709558}, {'photo_id': 1057289467, 'portfolio_id': 4709558}]
 map_portfolio_id_photo_id: {4709558: [1057289546, 1057289467]}
##### Call download_photos : nb_thread : 5
begin to download photo : 1057289546
begin to download photo : 1057289467
download finish for photo 1057289546
download finish for photo 1057289467
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 2 ; length of list_pids : 2 ; length of list_args : 2
##### After load_data_input 
time to download the photos : 0.25456714630126953
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  8
step1:copy_chis
Mon Sep 29 11:48:30 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139310_2886529_1057289546_da9d43be274d4fe0bc4d06d071dbd3bd.jpg': 1057289546, 'temp/1759139310_2886529_1057289467_5eaa0f3f506e9775c4ed0da9d66dfffc.jpg': 1057289467}
map_photo_id_path_extension : {1057289546: {'path': 'temp/1759139310_2886529_1057289546_da9d43be274d4fe0bc4d06d071dbd3bd.jpg', 'extension': 'jpg'}, 1057289467: {'path': 'temp/1759139310_2886529_1057289467_5eaa0f3f506e9775c4ed0da9d66dfffc.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
Begin step datou_step_copy_crop
processing picture : temp/1759139310_2886529_1057289546_da9d43be274d4fe0bc4d06d071dbd3bd.jpg
processing picture : temp/1759139310_2886529_1057289467_5eaa0f3f506e9775c4ed0da9d66dfffc.jpg
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 1057289546,1057289467) and `type` in (3326)  and score>0.7and hashtag_id in (2107755846,492622729) 
Loaded 0 chid ids of type : 0
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in ()
 map_nb_chis : {}

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 0.02265644073486328
time spend to save output : 3.4332275390625e-05
total time spend for step 1 : 0.022690773010253906

step2:consolidate_hashtags_from_manual_portfolio
Mon Sep 29 11:48:30 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior 
Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed
We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input !
VR 22-3-18 : For now we do not clean correctly the datou structure 

input_args_next_step, len :0, first value : []
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139310_2886529_1057289546_da9d43be274d4fe0bc4d06d071dbd3bd.jpg': 1057289546, 'temp/1759139310_2886529_1057289467_5eaa0f3f506e9775c4ed0da9d66dfffc.jpg': 1057289467}
map_photo_id_path_extension : {1057289546: {'path': 'temp/1759139310_2886529_1057289546_da9d43be274d4fe0bc4d06d071dbd3bd.jpg', 'extension': 'jpg'}, 1057289467: {'path': 'temp/1759139310_2886529_1057289467_5eaa0f3f506e9775c4ed0da9d66dfffc.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
beginning of datou step consolidate_hashtags_from_manual_portfolio
Iterating over portfolio : 4709558
on est dans le IF portfolio mere 26T
 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`=4709558 AND  mptpi.`type`=4016 AND  mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','etiquette','bouchon','pehd','barquette_avec_film','metal','pet_fonce','aluminium','carton','film_plastique','papier','autre')) 
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`=4709558 AND  mptpi.`type`=4016 AND  mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','etiquette','bouchon','pehd','barquette_avec_film','metal','pet_fonce','aluminium','carton','film_plastique','papier','autre')) 
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`=4683188 AND  mptpi.`type`=4016 AND  mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','etiquette','bouchon','pehd','barquette_avec_film','metal','pet_fonce','aluminium','carton','film_plastique','papier','autre')) 
To do
TODO : # On doit donc construire les chi a partir des informations dans les photos filles
query : 
            SELECT ph.photo_id
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673496 
            AND mpp.hide_status=0 ORDER BY ph.size desc
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
        select 4673496, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673496;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
        on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673496 and b.mtr_portfolio_id = 4673496;
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
select 4673496, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673496;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673496 and b.mtr_portfolio_id = 4673496;
TODO : # On doit donc construire les chi a partir des informations dans les photos filles
query : 
            SELECT ph.photo_id
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673497 
            AND mpp.hide_status=0 ORDER BY ph.size desc
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
        select 4673497, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673497;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
        on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673497 and b.mtr_portfolio_id = 4673497;
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
select 4673497, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673497;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673497 and b.mtr_portfolio_id = 4673497;
TODO : # On doit donc construire les chi a partir des informations dans les photos filles
query : 
            SELECT ph.photo_id
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673498 
            AND mpp.hide_status=0 ORDER BY ph.size desc
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
        select 4673498, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673498;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
        on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673498 and b.mtr_portfolio_id = 4673498;
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
select 4673498, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673498;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673498 and b.mtr_portfolio_id = 4673498;
TODO : # On doit donc construire les chi a partir des informations dans les photos filles
query : 
            SELECT ph.photo_id
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673500 
            AND mpp.hide_status=0 ORDER BY ph.size desc
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
        select 4673500, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673500;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
        on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673500 and b.mtr_portfolio_id = 4673500;
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
select 4673500, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673500;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673500 and b.mtr_portfolio_id = 4673500;
TODO : # On doit donc construire les chi a partir des informations dans les photos filles
query : 
            SELECT ph.photo_id
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673501 
            AND mpp.hide_status=0 ORDER BY ph.size desc
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
        select 4673501, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673501;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
        on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673501 and b.mtr_portfolio_id = 4673501;
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
select 4673501, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673501;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673501 and b.mtr_portfolio_id = 4673501;
TODO : # On doit donc construire les chi a partir des informations dans les photos filles
query : 
            SELECT ph.photo_id
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673502 
            AND mpp.hide_status=0 ORDER BY ph.size desc
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
        select 4673502, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673502;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
        on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673502 and b.mtr_portfolio_id = 4673502;
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
select 4673502, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673502;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673502 and b.mtr_portfolio_id = 4673502;
TODO : # On doit donc construire les chi a partir des informations dans les photos filles
query : 
            SELECT ph.photo_id
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673503 
            AND mpp.hide_status=0 ORDER BY ph.size desc
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
        select 4673503, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673503;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
        on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673503 and b.mtr_portfolio_id = 4673503;
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
select 4673503, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673503;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673503 and b.mtr_portfolio_id = 4673503;
TODO : # On doit donc construire les chi a partir des informations dans les photos filles
query : 
            SELECT ph.photo_id
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673504 
            AND mpp.hide_status=0 ORDER BY ph.size desc
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
        select 4673504, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673504;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
        on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673504 and b.mtr_portfolio_id = 4673504;
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
select 4673504, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673504;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673504 and b.mtr_portfolio_id = 4673504;
TODO : # On doit donc construire les chi a partir des informations dans les photos filles
query : 
            SELECT ph.photo_id
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673505 
            AND mpp.hide_status=0 ORDER BY ph.size desc
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
        select 4673505, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673505;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
        on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673505 and b.mtr_portfolio_id = 4673505;
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
select 4673505, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673505;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673505 and b.mtr_portfolio_id = 4673505;
TODO : # On doit donc construire les chi a partir des informations dans les photos filles
query : 
            SELECT ph.photo_id
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673506 
            AND mpp.hide_status=0 ORDER BY ph.size desc
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
        select 4673506, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673506;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
        on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673506 and b.mtr_portfolio_id = 4673506;
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
select 4673506, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673506;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673506 and b.mtr_portfolio_id = 4673506;
TODO : # On doit donc construire les chi a partir des informations dans les photos filles
query : 
            SELECT ph.photo_id
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673507 
            AND mpp.hide_status=0 ORDER BY ph.size desc
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
        select 4673507, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673507;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
        on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673507 and b.mtr_portfolio_id = 4673507;
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
select 4673507, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673507;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673507 and b.mtr_portfolio_id = 4673507;
TODO : # On doit donc construire les chi a partir des informations dans les photos filles
query : 
            SELECT ph.photo_id
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673508 
            AND mpp.hide_status=0 ORDER BY ph.size desc
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
        select 4673508, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673508;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
        on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673508 and b.mtr_portfolio_id = 4673508;
insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id)
select 4673508, mtr_photo_id, 0 from MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 4673508;
update MTRUser.mtr_portfolio_photos as a inner join MTRUser.mtr_portfolio_photos as b
on a.mtr_photo_id = b.mtr_photo_id set  a.hide_status = b.hide_status where a.mtr_portfolio_id =4673508 and b.mtr_portfolio_id = 4673508;
UPDATE MTRPhoto.mtr_port_to_port_ids set last_updated_at_asc=NOW(), updated_at=NOW() WHERE id in (572316,572317,572315,572314,572313,572312,572306,572311,572310,572309,572308,572307);
UPDATE MTRPhoto.mtr_port_to_port_ids set last_updated_at_asc=NOW(), updated_at=NOW() WHERE id in (572316,572317,572315,572314,572313,572312,572306,572311,572310,572309,572308,572307);
UPDATE MTRPhoto.mtr_port_to_port_ids set last_updated_at_asc=NOW(), updated_at=NOW() WHERE id in (561088,561089,561087,561086,561082,561081,561075,561084,561079,561078,561077,561076);
To test !
Use context local managing function !

After datou_step_exec type output : <class 'str'>

time spend for datou_step_exec : 1.8243787288665771
time spend to save output : 7.796287536621094e-05
total time spend for step 2 : 1.8244566917419434

step3:rle_unique_nms_with_priority
Mon Sep 29 11:48:32 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
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 : ['temp/1759139310_2886529_1057289546_da9d43be274d4fe0bc4d06d071dbd3bd.jpg', 'temp/1759139310_2886529_1057289467_5eaa0f3f506e9775c4ed0da9d66dfffc.jpg']
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 

input_args_next_step, len :2, first value : ['temp/1759139310_2886529_1057289546_da9d43be274d4fe0bc4d06d071dbd3bd.jpg']
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139310_2886529_1057289546_da9d43be274d4fe0bc4d06d071dbd3bd.jpg': 1057289546, 'temp/1759139310_2886529_1057289467_5eaa0f3f506e9775c4ed0da9d66dfffc.jpg': 1057289467}
map_photo_id_path_extension : {1057289546: {'path': 'temp/1759139310_2886529_1057289546_da9d43be274d4fe0bc4d06d071dbd3bd.jpg', 'extension': 'jpg'}, 1057289467: {'path': 'temp/1759139310_2886529_1057289467_5eaa0f3f506e9775c4ed0da9d66dfffc.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
Begin step rle-unique-nms
on traite la photo : temp/1759139310_2886529_1057289546_da9d43be274d4fe0bc4d06d071dbd3bd.jpg
on traite la photo : temp/1759139310_2886529_1057289467_5eaa0f3f506e9775c4ed0da9d66dfffc.jpg
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 1057289546,1057289467) and `type` in (4016) and hashtag_id in (492622729,494826614,492668766,2107756122,492774966,493546845,2107755900,492628673,2107756859,628944319,492645504,492636447,2107755846,2107755527,493012381) 
Loaded 65 chid ids of type : 4016
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (2211485914,2211485915,2211485916,2211485917,2211485918,2211485919,2211485920,2211485921,2211485922,2211485923,2211485924,2211485925,2211485926,2211485927,2211485928,2211485929,2241206167,2241206166,2211485930,2211485931,2211485932,2211485933,2211485934,2211485935,2211485936,2211485937,2211485938,2211485939,2211485940,2211485941,2211485942,2211485943,2211485944,2211486029,2211486030,2211486031,2211486032,2211486033,2211486034,2211486035,2211486036,2211486037,2211486038,2241206168,2241206169,2211486039,2211486040,2211486041,2211486042,2211486043,2211486044,2211486045,2211486046,2211486047,2211486048,2211486049,2211486050,2211486051,2211486052,2211486053,2211486054,2211486055,2211486056,2211486057,2211486058)
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (2211485914,2211485915,2211485916,2211485917,2211485918,2211485919,2211485920,2211485921,2211485922,2211485923,2211485924,2211485925,2211485926,2211485927,2211485928,2211485929,2241206167,2241206166,2211485930,2211485931,2211485932,2211485933,2211485934,2211485935,2211485936,2211485937,2211485938,2211485939,2211485940,2211485941,2211485942,2211485943,2211485944,2211486029,2211486030,2211486031,2211486032,2211486033,2211486034,2211486035,2211486036,2211486037,2211486038,2241206168,2241206169,2211486039,2211486040,2211486041,2211486042,2211486043,2211486044,2211486045,2211486046,2211486047,2211486048,2211486049,2211486050,2211486051,2211486052,2211486053,2211486054,2211486055,2211486056,2211486057,2211486058)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (2211485914,2211485915,2211485916,2211485917,2211485918,2211485919,2211485920,2211485921,2211485922,2211485923,2211485924,2211485925,2211485926,2211485927,2211485928,2211485929,2241206167,2241206166,2211485930,2211485931,2211485932,2211485933,2211485934,2211485935,2211485936,2211485937,2211485938,2211485939,2211485940,2211485941,2211485942,2211485943,2211485944,2211486029,2211486030,2211486031,2211486032,2211486033,2211486034,2211486035,2211486036,2211486037,2211486038,2241206168,2241206169,2211486039,2211486040,2211486041,2211486042,2211486043,2211486044,2211486045,2211486046,2211486047,2211486048,2211486049,2211486050,2211486051,2211486052,2211486053,2211486054,2211486055,2211486056,2211486057,2211486058)
nb_obj : 0
nb_hashtags :  3
time to prepare the origin masks  : 9.68644642829895
create new chi : 2.1219253540039062e-05
 proportion hashtag : pet_clair 0.5358597032000172
 proportion hashtag : environment 0.16202715754672498
 proportion hashtag : etiquette 0.004007054290980795
time to delete rle : 0.4338653087615967
save time : 4.76837158203125e-06
nb_obj : 0
nb_hashtags :  3
time to prepare the origin masks  : 8.609299421310425
create new chi : 3.0517578125e-05
 proportion hashtag : environment 0.29592690382801784
 proportion hashtag : pet_clair 0.39099757399905694
 proportion hashtag : etiquette 0.0016148780435528121
time to delete rle : 0.1654059886932373
save time : 2.288818359375e-05
INSERT IGNORE INTO MTRPhoto.photo_carac_ratio (`photo_id`, `hashtag_type`, `hashtag_id`, `ratio`)
                            VALUES (%s, %s, %s, %s) on duplicate key update `ratio` = VALUES(`ratio`)
 map_output_result : {1057289546: (0.0, 'Should be the crop_list due to order', 0.0), 1057289467: (0.0, 'Should be the crop_list due to order', 0.0)}
End step rle-unique-nms

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 19.482974767684937
time spend to save output : 0.00014519691467285156
total time spend for step 3 : 19.48311996459961

step4:ventilate_hashtags_in_portfolio
Mon Sep 29 11:48:52 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 

input_args_next_step, len :0, first value : []
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139310_2886529_1057289546_da9d43be274d4fe0bc4d06d071dbd3bd.jpg': 1057289546, 'temp/1759139310_2886529_1057289467_5eaa0f3f506e9775c4ed0da9d66dfffc.jpg': 1057289467}
map_photo_id_path_extension : {1057289546: {'path': 'temp/1759139310_2886529_1057289546_da9d43be274d4fe0bc4d06d071dbd3bd.jpg', 'extension': 'jpg'}, 1057289467: {'path': 'temp/1759139310_2886529_1057289467_5eaa0f3f506e9775c4ed0da9d66dfffc.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
beginning of datou step ventilate_hashtags_in_portfolio : To implement !
Iterating over portfolio : 4709558
get user id for portfolio 4709558
get sub ptf for main ptf 4709558, type 4016 and hashtags ['environnement', 'mal_croppe', 'pet_clair', 'etiquette', 'bouchon', 'pehd', 'barquette_avec_film', 'metal', 'pet_fonce', 'aluminium', 'carton', 'film_plastique', 'papier', 'autre']
 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`=4709558 AND  mptpi.`type`=4016 AND  mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('environnement','mal_croppe','pet_clair','etiquette','bouchon','pehd','barquette_avec_film','metal','pet_fonce','aluminium','carton','film_plastique','papier','autre'))  AND  mptpi.`min_score`=0.7
 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`=4709558 AND  mptpi.`type`=4016 AND  mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('environnement','mal_croppe','pet_clair','etiquette','bouchon','pehd','barquette_avec_film','metal','pet_fonce','aluminium','carton','film_plastique','papier','autre'))  AND  mptpi.`min_score`=0.7
To do
returned map {'mal_croppe': {'main_port_id': 4709558, 'sub_port_id': 4673494, 'hashtag': 'mal_croppe', 'pht': 4016, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 572305, 'last_updated_at': datetime.datetime(2025, 9, 26, 9, 11, 12), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 11, 12), 'last_updated_at_asc': datetime.datetime(2021, 10, 28, 16, 30, 19)}, 'papier': {'main_port_id': 4709558, 'sub_port_id': 4673496, 'hashtag': 'papier', 'pht': 4016, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 572316, 'last_updated_at': datetime.datetime(2025, 9, 29, 11, 48, 32), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 11, 12), 'last_updated_at_asc': datetime.datetime(2025, 9, 29, 11, 48, 32)}, 'autre': {'main_port_id': 4709558, 'sub_port_id': 4673497, 'hashtag': 'autre', 'pht': 4016, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 572317, 'last_updated_at': datetime.datetime(2025, 9, 29, 11, 48, 32), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 11, 12), 'last_updated_at_asc': datetime.datetime(2025, 9, 29, 11, 48, 32)}, 'film_plastique': {'main_port_id': 4709558, 'sub_port_id': 4673498, 'hashtag': 'film_plastique', 'pht': 4016, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 572315, 'last_updated_at': datetime.datetime(2025, 9, 29, 11, 48, 32), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 11, 12), 'last_updated_at_asc': datetime.datetime(2025, 9, 29, 11, 48, 32)}, 'carton': {'main_port_id': 4709558, 'sub_port_id': 4673500, 'hashtag': 'carton', 'pht': 4016, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 572314, 'last_updated_at': datetime.datetime(2025, 9, 29, 11, 48, 32), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 11, 12), 'last_updated_at_asc': datetime.datetime(2025, 9, 29, 11, 48, 32)}, 'aluminium': {'main_port_id': 4709558, 'sub_port_id': 4673501, 'hashtag': 'aluminium', 'pht': 4016, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 572313, 'last_updated_at': datetime.datetime(2025, 9, 29, 11, 48, 32), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 11, 12), 'last_updated_at_asc': datetime.datetime(2025, 9, 29, 11, 48, 32)}, 'pet_fonce': {'main_port_id': 4709558, 'sub_port_id': 4673502, 'hashtag': 'pet_fonce', 'pht': 4016, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 572312, 'last_updated_at': datetime.datetime(2025, 9, 29, 11, 48, 32), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 11, 12), 'last_updated_at_asc': datetime.datetime(2025, 9, 29, 11, 48, 32)}, 'pet_clair': {'main_port_id': 4709558, 'sub_port_id': 4673503, 'hashtag': 'pet_clair', 'pht': 4016, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 572306, 'last_updated_at': datetime.datetime(2025, 9, 29, 11, 48, 32), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 11, 12), 'last_updated_at_asc': datetime.datetime(2025, 9, 29, 11, 48, 32)}, 'metal': {'main_port_id': 4709558, 'sub_port_id': 4673504, 'hashtag': 'metal', 'pht': 4016, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 572311, 'last_updated_at': datetime.datetime(2025, 9, 29, 11, 48, 32), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 11, 12), 'last_updated_at_asc': datetime.datetime(2025, 9, 29, 11, 48, 32)}, 'barquette_avec_film': {'main_port_id': 4709558, 'sub_port_id': 4673505, 'hashtag': 'barquette_avec_film', 'pht': 4016, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 572310, 'last_updated_at': datetime.datetime(2025, 9, 29, 11, 48, 32), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 11, 12), 'last_updated_at_asc': datetime.datetime(2025, 9, 29, 11, 48, 32)}, 'pehd': {'main_port_id': 4709558, 'sub_port_id': 4673506, 'hashtag': 'pehd', 'pht': 4016, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 572309, 'last_updated_at': datetime.datetime(2025, 9, 29, 11, 48, 32), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 11, 12), 'last_updated_at_asc': datetime.datetime(2025, 9, 29, 11, 48, 32)}, 'bouchon': {'main_port_id': 4709558, 'sub_port_id': 4673507, 'hashtag': 'bouchon', 'pht': 4016, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 572308, 'last_updated_at': datetime.datetime(2025, 9, 29, 11, 48, 32), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 11, 12), 'last_updated_at_asc': datetime.datetime(2025, 9, 29, 11, 48, 32)}, 'etiquette': {'main_port_id': 4709558, 'sub_port_id': 4673508, 'hashtag': 'etiquette', 'pht': 4016, 'min_score': 0.7, 'mtr_user_id': 0, 'id': 572307, 'last_updated_at': datetime.datetime(2025, 9, 29, 11, 48, 32), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 11, 12), 'last_updated_at_asc': datetime.datetime(2025, 9, 29, 11, 48, 32)}, 'environnement': {'main_port_id': 4709558, 'sub_port_id': 4709559, 'hashtag': 'environnement', 'pht': 4016, 'min_score': 0.7, 'mtr_user_id': 970, 'id': 572318, 'last_updated_at': datetime.datetime(2025, 9, 26, 9, 11, 12), 'last_updated_at_desc': datetime.datetime(2025, 9, 26, 9, 11, 12), 'last_updated_at_asc': datetime.datetime(2021, 10, 28, 16, 34, 8)}}
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 4673494 , sub_photo_id, 970,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=4709558
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=4016
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='mal_croppe'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 4673496 , sub_photo_id, 970,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=4709558
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=4016
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='papier'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 4673497 , sub_photo_id, 970,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=4709558
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=4016
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='autre'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 4673498 , sub_photo_id, 970,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=4709558
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=4016
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='film_plastique'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 4673500 , sub_photo_id, 970,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=4709558
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=4016
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='carton'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 4673501 , sub_photo_id, 970,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=4709558
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=4016
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='aluminium'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 4673502 , sub_photo_id, 970,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=4709558
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=4016
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='pet_fonce'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 4673503 , sub_photo_id, 970,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=4709558
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=4016
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='pet_clair'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 4673504 , sub_photo_id, 970,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=4709558
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=4016
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='metal'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 4673505 , sub_photo_id, 970,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=4709558
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=4016
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='barquette_avec_film'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 4673506 , sub_photo_id, 970,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=4709558
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=4016
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='pehd'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 4673507 , sub_photo_id, 970,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=4709558
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=4016
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='bouchon'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 4673508 , sub_photo_id, 970,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=4709558
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=4016
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='etiquette'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
 insert ignore into MTRUser.mtr_portfolio_photos (mtr_portfolio_id, mtr_photo_id, mtr_user_id,created_at)
SELECT 4709559 , sub_photo_id, 970,now() 
FROM (SELECT csp.sub_photo_id, csp.crop_hashtag_id, csp.created_at
FROM MTRPhoto.crop_sub_photo_ids csp,
MTRPhoto.crop_hashtag_ids chi,
MTRBack.hashtags h,
MTRUser.mtr_portfolio_photos mpp
WHERE mpp.mtr_portfolio_id=4709558
AND mpp.mtr_photo_id=chi.photo_id
AND chi.id=csp.crop_hashtag_id
AND chi.score>0.7
AND chi.type=4016
AND chi.hashtag_id=h.hashtag_id
AND h.hashtag='environnement'
GROUP BY csp.crop_hashtag_id, csp.created_at
order by csp.created_at desc) as a group by crop_hashtag_id; 
To do !
Use context local managing function !

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 0.8322165012359619
time spend to save output : 8.058547973632812e-05
total time spend for step 4 : 0.8322970867156982

step5:final
Mon Sep 29 11:48:53 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 : {1057289546: (0.0, 'Should be the crop_list due to order', 0.0), 1057289467: (0.0, 'Should be the crop_list due to order', 0.0)}
 input_args_next_step : {1057289546: ()}
 output_args : {1057289546: (0.0, 'Should be the crop_list due to order', 0.0), 1057289467: (0.0, 'Should be the crop_list due to order', 0.0)}
 args : 1057289546
 depend.output_id : 0
 input_args_next_step : {1057289546: (0.0,), 1057289467: ()}
 output_args : {1057289546: (0.0, 'Should be the crop_list due to order', 0.0), 1057289467: (0.0, 'Should be the crop_list due to order', 0.0)}
 args : 1057289467
 depend.output_id : 0
VR 22-3-18 : For now we do not clean correctly the datou structure 

input_args_next_step, len :2, first value : (0.0,)
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139310_2886529_1057289546_da9d43be274d4fe0bc4d06d071dbd3bd.jpg': 1057289546, 'temp/1759139310_2886529_1057289467_5eaa0f3f506e9775c4ed0da9d66dfffc.jpg': 1057289467}
map_photo_id_path_extension : {1057289546: {'path': 'temp/1759139310_2886529_1057289546_da9d43be274d4fe0bc4d06d071dbd3bd.jpg', 'extension': 'jpg'}, 1057289467: {'path': 'temp/1759139310_2886529_1057289467_5eaa0f3f506e9775c4ed0da9d66dfffc.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou step final !
 query to retrieve results for portfolio 4709558 : SELECT photo_id, hashtag_id, ratio from MTRPhoto.photo_carac_ratio where hashtag_type = 4016  and photo_id in (1057289467,1057289546);
insert ignore into MTRUser.portfolio_carac_ratio (portfolio_id, hashtag_type, hashtag_id, value) values (%s,%s,%s,%s) on DUPLICATE KEY UPDATE value=VALUES(value)

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 0.07688236236572266
time spend to save output : 3.695487976074219e-05
total time spend for step 5 : 0.0769193172454834

step6:blur_detection
Mon Sep 29 11:48:53 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 

input_args_next_step, len :0, first value : []
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139310_2886529_1057289546_da9d43be274d4fe0bc4d06d071dbd3bd.jpg': 1057289546, 'temp/1759139310_2886529_1057289467_5eaa0f3f506e9775c4ed0da9d66dfffc.jpg': 1057289467}
map_photo_id_path_extension : {1057289546: {'path': 'temp/1759139310_2886529_1057289546_da9d43be274d4fe0bc4d06d071dbd3bd.jpg', 'extension': 'jpg'}, 1057289467: {'path': 'temp/1759139310_2886529_1057289467_5eaa0f3f506e9775c4ed0da9d66dfffc.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
inside step blur_detection
toutes les photos sont déjà traitées, on saute les calculs
score_blur_detection : {1057289546: [(1057289546, -4.333383571220791, 492609224)], 1057289467: [(1057289467, -4.424440243329978, 492609224)]}

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 0.01930069923400879
time spend to save output : 2.574920654296875e-05
total time spend for step 6 : 0.019326448440551758

step7:brightness
Mon Sep 29 11:48:53 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 

input_args_next_step, len :0, first value : []
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139310_2886529_1057289546_da9d43be274d4fe0bc4d06d071dbd3bd.jpg': 1057289546, 'temp/1759139310_2886529_1057289467_5eaa0f3f506e9775c4ed0da9d66dfffc.jpg': 1057289467}
map_photo_id_path_extension : {1057289546: {'path': 'temp/1759139310_2886529_1057289546_da9d43be274d4fe0bc4d06d071dbd3bd.jpg', 'extension': 'jpg'}, 1057289467: {'path': 'temp/1759139310_2886529_1057289467_5eaa0f3f506e9775c4ed0da9d66dfffc.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
inside step calcul brightness
toutes les photos sont déjà traitées, on saute les calculs
brightness_score : {1057289467: [(1057289467, -0.4062218880770088, 496442774)], 1057289546: [(1057289546, -0.5998675991292823, 501862349)]}

After datou_step_exec type output : <class 'dict'>
imagette46734941759139333/
imagette46734961759139333/
imagette46734971759139333/
imagette46734981759139333/
imagette46735001759139333/
imagette46735011759139333/
imagette46735021759139333/

time spend for datou_step_exec : 0.018121957778930664
time spend to save output : 5.793571472167969e-05
total time spend for step 7 : 0.018179893493652344

step8:send_mail_cod
Mon Sep 29 11:48:53 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 : {1057289546: [(1057289546, -4.333383571220791, 492609224)], 1057289467: [(1057289467, -4.424440243329978, 492609224)]}
 input_args_next_step : {1057289546: ()}
 output_args : {1057289546: [(1057289546, -4.333383571220791, 492609224)], 1057289467: [(1057289467, -4.424440243329978, 492609224)]}
 args : 1057289546
 depend.output_id : 0
 input_args_next_step : {1057289546: ((1057289546, -4.333383571220791, 492609224),), 1057289467: ()}
 output_args : {1057289546: [(1057289546, -4.333383571220791, 492609224)], 1057289467: [(1057289467, -4.424440243329978, 492609224)]}
 args : 1057289467
 depend.output_id : 0
complete output_args for input 1 : {1057289467: [(1057289467, -0.4062218880770088, 496442774)], 1057289546: [(1057289546, -0.5998675991292823, 501862349)]}
 input_args_next_step : {1057289546: ((1057289546, -4.333383571220791, 492609224),), 1057289467: ((1057289467, -4.424440243329978, 492609224),)}
 output_args : {1057289467: [(1057289467, -0.4062218880770088, 496442774)], 1057289546: [(1057289546, -0.5998675991292823, 501862349)]}
 args : 1057289467
 depend.output_id : 0
 input_args_next_step : {1057289546: ((1057289546, -4.333383571220791, 492609224),), 1057289467: ((1057289467, -4.424440243329978, 492609224), (1057289467, -0.4062218880770088, 496442774))}
 output_args : {1057289467: [(1057289467, -0.4062218880770088, 496442774)], 1057289546: [(1057289546, -0.5998675991292823, 501862349)]}
 args : 1057289546
 depend.output_id : 0
complete output_args for input 2 : {1057289546: ('0.009511382621534484',), 1057289467: ('0.009511382621534484',)}
 input_args_next_step : {1057289546: ((1057289546, -4.333383571220791, 492609224), (1057289546, -0.5998675991292823, 501862349)), 1057289467: ((1057289467, -4.424440243329978, 492609224), (1057289467, -0.4062218880770088, 496442774))}
 output_args : {1057289546: ('0.009511382621534484',), 1057289467: ('0.009511382621534484',)}
 args : 1057289546
 depend.output_id : 0
 input_args_next_step : {1057289546: ((1057289546, -4.333383571220791, 492609224), (1057289546, -0.5998675991292823, 501862349), '0.009511382621534484'), 1057289467: ((1057289467, -4.424440243329978, 492609224), (1057289467, -0.4062218880770088, 496442774))}
 output_args : {1057289546: ('0.009511382621534484',), 1057289467: ('0.009511382621534484',)}
 args : 1057289467
 depend.output_id : 0
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 : {4709558: [{'mal_croppe': 4673494, 'papier': 4673496, 'autre': 4673497, 'film_plastique': 4673498, 'carton': 4673500, 'aluminium': 4673501, 'pet_fonce': 4673502, 'pet_clair': 4673503, 'metal': 4673504, 'barquette_avec_film': 4673505, 'pehd': 4673506, 'bouchon': 4673507, 'etiquette': 4673508, 'environnement': 4709559}]}
 input_args_next_step : {1057289546: ((1057289546, -4.333383571220791, 492609224), (1057289546, -0.5998675991292823, 501862349), '0.009511382621534484'), 1057289467: ((1057289467, -4.424440243329978, 492609224), (1057289467, -0.4062218880770088, 496442774), '0.009511382621534484'), 4709558: ()}
 output_args : {4709558: [{'mal_croppe': 4673494, 'papier': 4673496, 'autre': 4673497, 'film_plastique': 4673498, 'carton': 4673500, 'aluminium': 4673501, 'pet_fonce': 4673502, 'pet_clair': 4673503, 'metal': 4673504, 'barquette_avec_film': 4673505, 'pehd': 4673506, 'bouchon': 4673507, 'etiquette': 4673508, 'environnement': 4709559}]}
 args : 4709558
 depend.output_id : 0
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 

input_args_next_step, len :3, first value : ((1057289546, -4.333383571220791, 492609224), (1057289546, -0.5998675991292823, 501862349), '0.009511382621534484')
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139310_2886529_1057289546_da9d43be274d4fe0bc4d06d071dbd3bd.jpg': 1057289546, 'temp/1759139310_2886529_1057289467_5eaa0f3f506e9775c4ed0da9d66dfffc.jpg': 1057289467}
map_photo_id_path_extension : {1057289546: {'path': 'temp/1759139310_2886529_1057289546_da9d43be274d4fe0bc4d06d071dbd3bd.jpg', 'extension': 'jpg'}, 1057289467: {'path': 'temp/1759139310_2886529_1057289467_5eaa0f3f506e9775c4ed0da9d66dfffc.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
dans la step send mail cod
work_area:  /home/admin/temp
in order to get the selector url, please entre the license of selector
results_COD_P4709558_29-09-2025_11_48_53.pdf
4673494

            SELECT ph.photo_id,ph.url,ph.username,ph.text
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673494  AND mpp.hide_status=0  ORDER BY mpp.order  LIMIT 0, 20
 map_photo_id_text : {}
imagette46734941759139333
4673496

            SELECT ph.photo_id,ph.url,ph.username,ph.text
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673496  AND mpp.hide_status=0  ORDER BY mpp.order  LIMIT 0, 20
 map_photo_id_text : {}
imagette46734961759139333
4673497

            SELECT ph.photo_id,ph.url,ph.username,ph.text
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673497  AND mpp.hide_status=0  ORDER BY mpp.order  LIMIT 0, 20
 map_photo_id_text : {}
imagette46734971759139333
4673498

            SELECT ph.photo_id,ph.url,ph.username,ph.text
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673498  AND mpp.hide_status=0  ORDER BY mpp.order  LIMIT 0, 20
 map_photo_id_text : {}
imagette46734981759139333
4673500

            SELECT ph.photo_id,ph.url,ph.username,ph.text
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673500  AND mpp.hide_status=0  ORDER BY mpp.order  LIMIT 0, 20
 map_photo_id_text : {}
imagette46735001759139333
4673501

            SELECT ph.photo_id,ph.url,ph.username,ph.text
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673501  AND mpp.hide_status=0  ORDER BY mpp.order  LIMIT 0, 20
 map_photo_id_text : {}
imagette46735011759139333
4673502

            SELECT ph.photo_id,ph.url,ph.username,ph.text
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673502  AND mpp.hide_status=0  ORDER BY mpp.order  LIMIT 0, 20
 map_photo_id_text : {}
imagette46735021759139333
4673503
imagette46735031759139333/
imagette46735041759139333/
imagette46735051759139333/
imagette46735061759139333/
imagette46735071759139333/
imagette46735081759139333/

            SELECT ph.photo_id,ph.url,ph.username,ph.text
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673503  AND mpp.hide_status=0  ORDER BY mpp.order  LIMIT 0, 20
 map_photo_id_text : {}
imagette46735031759139333
4673504

            SELECT ph.photo_id,ph.url,ph.username,ph.text
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673504  AND mpp.hide_status=0  ORDER BY mpp.order  LIMIT 0, 20
 map_photo_id_text : {}
imagette46735041759139333
4673505

            SELECT ph.photo_id,ph.url,ph.username,ph.text
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673505  AND mpp.hide_status=0  ORDER BY mpp.order  LIMIT 0, 20
 map_photo_id_text : {}
imagette46735051759139333
4673506

            SELECT ph.photo_id,ph.url,ph.username,ph.text
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673506  AND mpp.hide_status=0  ORDER BY mpp.order  LIMIT 0, 20
 map_photo_id_text : {}
imagette46735061759139333
4673507

            SELECT ph.photo_id,ph.url,ph.username,ph.text
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673507  AND mpp.hide_status=0  ORDER BY mpp.order  LIMIT 0, 20
 map_photo_id_text : {}
imagette46735071759139333
4673508

            SELECT ph.photo_id,ph.url,ph.username,ph.text
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4673508  AND mpp.hide_status=0  ORDER BY mpp.order  LIMIT 0, 20
 map_photo_id_text : {}
imagette46735081759139333
velour_link :  


ce batch n'est pas dans un dashboard, on n'envoi pas de mail. si vous voulez quand même des mail , veuillez configurer no_mail = 2


args[1057289546] : ((1057289546, -4.333383571220791, 492609224), (1057289546, -0.5998675991292823, 501862349), '0.009511382621534484')
We are sending mail with results at marine@fotonower.com
args[1057289467] : ((1057289467, -4.424440243329978, 492609224), (1057289467, -0.4062218880770088, 496442774), '0.009511382621534484')
We are sending mail with results at marine@fotonower.com
refus_total : 0.009511382621534484

            SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4709558  AND mpp.hide_status=0  ORDER BY mpp.order  LIMIT 0, 1000
error for generate pdf :  'NoneType' object has no attribute 'startswith'

After datou_step_exec type output : <class 'str'>

time spend for datou_step_exec : 0.578066349029541
time spend to save output : 3.5762786865234375e-05
total time spend for step 8 : 0.5781021118164062

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 0
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 8
 output : 

    SELECT value FROM MTRUser.portfolio_carac_ratio WHERE portfolio_id = 4709558 AND hashtag_type = 4016 ORDER BY hashtag_id;
    
fin du test de rym_consolidate

############################### TEST generate_new_image_add_crop  ################################
test_generate_new_image_add_crop

    DELETE FROM MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id = 4789106
    

    DELETE FROM MTRPhoto.crop_hashtag_ids WHERE type = 4086
    
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=3870

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=3870
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 3870
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=3870
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : generate_new_image
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (4789595) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (4789595)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 1057314774, 'portfolio_id': 4789595}, {'photo_id': 1057314768, 'portfolio_id': 4789595}, {'photo_id': 1057314766, 'portfolio_id': 4789595}]
 map_portfolio_id_photo_id: {4789595: [1057314774, 1057314768, 1057314766]}
##### Call download_photos : nb_thread : 5
begin to download photo : 1057314774
begin to download photo : 1057314768
begin to download photo : 1057314766
download finish for photo 1057314766
download finish for photo 1057314774
download finish for photo 1057314768
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3
##### After load_data_input 
time to download the photos : 0.24293804168701172
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  1
step1:generate_new_image
Mon Sep 29 11:48:54 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139333_2886529_1057314766_850e771a4800ad0a89e4fec977bcfedd_29707587.jpg': 1057314766, 'temp/1759139333_2886529_1057314774_6bab7ce0e063fda7d35a74c0adf7f25b_17612544.jpg': 1057314774, 'temp/1759139333_2886529_1057314768_3b6c8b2738f519f078355064487ef55f_72134828.jpg': 1057314768}
map_photo_id_path_extension : {1057314766: {'path': 'temp/1759139333_2886529_1057314766_850e771a4800ad0a89e4fec977bcfedd_29707587.jpg', 'extension': 'jpg'}, 1057314774: {'path': 'temp/1759139333_2886529_1057314774_6bab7ce0e063fda7d35a74c0adf7f25b_17612544.jpg', 'extension': 'jpg'}, 1057314768: {'path': 'temp/1759139333_2886529_1057314768_3b6c8b2738f519f078355064487ef55f_72134828.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
query : 
            SELECT ph.photo_id
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4789595 ORDER BY ph.size desc
select id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 1057314774,1057314766,1057314768) and `type` in (4021) 

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (4789595) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 1000000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (4789595)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 1000000
 list_result: [{'photo_id': 1057314774, 'portfolio_id': 4789595}, {'photo_id': 1057314768, 'portfolio_id': 4789595}, {'photo_id': 1057314766, 'portfolio_id': 4789595}]
 map_portfolio_id_photo_id: {4789595: [1057314774, 1057314768, 1057314766]}
we need 3 photos
there is already 3 photos exist in our local_cache
we have to download 0 photos
we have successful downloaded 0 photos
there are 0 photos missing
finally, we can get 3 photo needed from our local_cache
list of photo_id_missing : 
[]
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where id in (2208326711,2208326712)
Loaded 2 chid ids of type : 4021
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (2208326711,2208326712)
++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (2208326711,2208326712)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (2208326711,2208326712)

        SELECT photo_id, url
        FROM MTRBack.photos_view ph
        WHERE photo_id IN (1057314766) 
 Found this number of photos: 1
we need 1 photos
there is already 1 photos exist in our local_cache
we have to download 0 photos
we have successful downloaded 0 photos
there are 0 photos missing
finally, we can get 1 photo needed from our local_cache
list of photo_id_missing : 
[]
begin to treate photo :1057314774
add chi : 2208326711 , rotate : 57
(885, 885)
(155, 641, 2, 336)
(334, 486, 3)
(334, 486)
(885, 885, 3)

time for calcul the mask position with numpy : 0.001918792724609375
nb_pixel_total : 65388
time to create 1 rle with old method : 0.07542276382446289
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 1057314774) and `type` in (4021) 
Loaded 0 chid ids of type : 0
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in ()
time for calcul the mask position with numpy : 0.0016548633575439453
nb_pixel_total : 65388
time to create 1 rle with old method : 0.0703878402709961
begin to treate photo :1057314768
add chi : 2208326712 , rotate : 102
(759, 759)
(72, 619, 128, 464)
(336, 547, 3)
(336, 547)
(759, 759, 3)

time for calcul the mask position with numpy : 0.0015523433685302734
nb_pixel_total : 98278
time to create 1 rle with old method : 0.1063222885131836
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 1057314768) and `type` in (4021) 
Loaded 2 chid ids of type : 4021
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (2208326718,2208326717)
++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (2208326718,2208326717)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (2208326718,2208326717)
time for calcul the mask position with numpy : 0.0016028881072998047
nb_pixel_total : 49788
time to create 1 rle with old method : 0.052776336669921875
time for calcul the mask position with numpy : 0.0019278526306152344
nb_pixel_total : 98278
time to create 1 rle with old method : 0.14400339126586914
time for calcul the mask position with numpy : 0.002036571502685547
nb_pixel_total : 8755
time to create 1 rle with old method : 0.009363412857055664
begin to treate photo :1057314766
add chi : 2208326712 , rotate : 4
(683, 683)
(118, 401, 103, 658)
(555, 283, 3)
(555, 283)
(683, 683, 3)

time for calcul the mask position with numpy : 0.001424551010131836
nb_pixel_total : 98279
time to create 1 rle with old method : 0.10567069053649902
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 1057314766) and `type` in (4021) 
Loaded 3 chid ids of type : 4021
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (2208326711,2208326712,2208326713)
+++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (2208326711,2208326712,2208326713)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (2208326711,2208326712,2208326713)
time for calcul the mask position with numpy : 0.0014657974243164062
nb_pixel_total : 37368
time to create 1 rle with old method : 0.04125523567199707
time for calcul the mask position with numpy : 0.0016176700592041016
nb_pixel_total : 98279
time to create 1 rle with old method : 0.10475587844848633
time for calcul the mask position with numpy : 0.034795522689819336
nb_pixel_total : 44876
time to create 1 rle with old method : 0.05287003517150879
time for calcul the mask position with numpy : 0.0016434192657470703
nb_pixel_total : 6807
time to create 1 rle with old method : 0.007706880569458008
init cache_photo without model_param  
we have 1 photo to upload
uploaded to storage server : ovh
folder_temporaire : temp/1759139337_2886529

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139337), 0.0, 0.0, 14, '', 0, 0, '1057314774_aug_1.jpg', 0,
640, 640, 0, 1759139337,'0',0)

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !
we have uploaded 1 photos in the portfolio 4789106
insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s)
batch 1
Loaded 1 chid ids of type : 4086
Number RLEs to save : 352
INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) 
 first line : ('3980044343', '502', '49', '1')
...
 last line : ('3980044343', '188', '382', '7')
INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) 
 TO DO : save crop sub photo not yet done ! 
we have 1 photo to upload
uploaded to storage server : ovh
folder_temporaire : temp/1759139337_2886529

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139338), 0.0, 0.0, 14, '', 0, 0, '1057314768_aug_2.jpg', 0,
640, 640, 0, 1759139338,'0',0)

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !
we have uploaded 1 photos in the portfolio 4789106
insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s)
batch 1
Loaded 3 chid ids of type : 4086
Number RLEs to save : 747
INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) 
 first line : ('3980044344', '380', '0', '156')
...
 last line : ('3980044346', '13', '639', '352')
INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) 
 TO DO : save crop sub photo not yet done ! 
we have 1 photo to upload
uploaded to storage server : ovh
folder_temporaire : temp/1759139338_2886529

        INSERT INTO MTRBack.photos
(`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`,
`size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`)
VALUES (FROM_UNIXTIME(1759139339), 0.0, 0.0, 14, '', 0, 0, '1057314766_aug_3.jpg', 0,
640, 640, 0, 1759139339,'0',0)

 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first
Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg
 This is a hack !
we have uploaded 1 photos in the portfolio 4789106
insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s)
batch 1
Loaded 4 chid ids of type : 4086
Number RLEs to save : 1724
INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) 
 first line : ('3980044347', '0', '4', '1')
...
 last line : ('3980044350', '355', '298', '2')
INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) 
 TO DO : save crop sub photo not yet done ! 

After datou_step_exec type output : <class 'NoneType'>

time spend for datou_step_exec : 5.370847940444946
time spend to save output : 2.8848648071289062e-05
total time spend for step 1 : 5.370876789093018

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 0
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : None

    SELECT * FROM MTRPhoto.crop_hashtag_ids where photo_id in (SELECT mtr_photo_id FROM MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id = 4789106) and type = 4086
    
nombre de crop attendu : 8, nombre de crop obtenu :  0
fin du test de generate_new_image
ERROR generate_new_image_add_crop FAILED

############################### TEST velours_tree  ################################
test velours_tree
- Retrieving photos to tag...
query : 
            SELECT ph.photo_id
            FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp
            WHERE ph.photo_id=mpp.mtr_photo_id
            AND mpp.mtr_portfolio_id=4837217 ORDER BY ph.size desc
- Loading descriptors...
Size : 512
 len(descriptors) : 5
Compute structured hierarchical clustering...
 ward : AgglomerativeClustering(n_clusters=5)
 ward.labels_ : [4 3 2 1 0]
Elapsed time:  0.015027761459350586
 graph_id used : 1145
- Beta version, working pretty good on 11-5-16 !
fin du test de velours_tree

############################### TEST step ACP  ################################
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4208

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=4208
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 4208
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=4208
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : acp
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (5709050) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (5709050)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 1069306967, 'portfolio_id': 5709050}, {'photo_id': 1069306964, 'portfolio_id': 5709050}, {'photo_id': 1069306954, 'portfolio_id': 5709050}, {'photo_id': 1069306844, 'portfolio_id': 5709050}, {'photo_id': 1069306843, 'portfolio_id': 5709050}, {'photo_id': 1069306841, 'portfolio_id': 5709050}, {'photo_id': 1069306815, 'portfolio_id': 5709050}, {'photo_id': 1069306805, 'portfolio_id': 5709050}, {'photo_id': 1069306710, 'portfolio_id': 5709050}, {'photo_id': 1069306708, 'portfolio_id': 5709050}]
 map_portfolio_id_photo_id: {5709050: [1069306967, 1069306964, 1069306954, 1069306844, 1069306843, 1069306841, 1069306815, 1069306805, 1069306710, 1069306708]}
##### Call download_photos : nb_thread : 5
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
##### After load_data_input 
time to download the photos : 0.05334925651550293
#### fin chargement data
Blocking on flush ?
No conitnuing 
About to test input to load 
Calling datou_exec

Inside datou_exec : verbose : True
number of steps :  1
step1:acp
Mon Sep 29 11:49:01 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {}
map_photo_id_path_extension : {}
map_subphoto_mainphoto : {}
Cette step permet de calculer une ACP.
ATTENTION : le size etait trop grand : 20, on a changé sa valeur à : 9
SELECT id FROM MTRPhoto.photo_desc_type_params where name = 'ACP_from_type_5619_size_9';
find save_photo_desc_type : 5705
On sauvegarde les nouveaux descripteurs dans le photo desc type : 5705
 SELECT * FROM MTRDatou.photo_desc_type_params  WHERE id in (5705)
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5705, 'ACP_from_type_5619_size_9', 9, 9, 'ACP_from_type_5619_size_9', None, 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2022, 6, 16, 11, 6, 15), None)
(10, 9)
Got the blobs of the net
 to insert :  
[255, 0, 0, 0, 0, 8, 20, 3, 5]
code_as_byte_string:b'c3bf000000'|
Got the blobs of the net
 to insert :  
[0, 0, 0, 0, 0, 0, 0, 34, 179]
code_as_byte_string:b'0000000000'|
Got the blobs of the net
 to insert :  
[0, 6, 0, 0, 0, 0, 0, 138, 0]
code_as_byte_string:b'0006000000'|
Got the blobs of the net
 to insert :  
[0, 108, 0, 0, 0, 196, 62, 0, 0]
code_as_byte_string:b'006c000000'|
Got the blobs of the net
 to insert :  
[0, 52, 0, 237, 64, 61, 115, 122, 0]
code_as_byte_string:b'003400c3ad'|
Got the blobs of the net
 to insert :  
[0, 0, 45, 0, 249, 0, 0, 0, 0]
code_as_byte_string:b'00002d00c3'|
Got the blobs of the net
 to insert :  
[0, 0, 0, 134, 0, 84, 0, 0, 0]
code_as_byte_string:b'000000c286'|
Got the blobs of the net
 to insert :  
[20, 131, 255, 31, 0, 0, 0, 0, 0]
code_as_byte_string:b'14c283c3bf'|
Got the blobs of the net
 to insert :  
[6, 255, 0, 88, 26, 0, 0, 0, 31]
code_as_byte_string:b'06c3bf0058'|
Got the blobs of the net
 to insert :  
[0, 0, 0, 0, 0, 0, 171, 0, 0]
code_as_byte_string:b'0000000000'|
time to traite the descriptors :  0.00041604042053222656
Testing : ['1069306708', '1069306710', '1069306805', '1069306815', '1069306841', '1069306843', '1069306844', '1069306954', '1069306964', '1069306967']
 In select_photos_meta_from_ids: 
        SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text
        FROM MTRBack.photos_view
        WHERE photo_id IN (1069306708,1069306710,1069306805,1069306815,1069306841,1069306843,1069306844,1069306954,1069306964,1069306967) 
result : {1069306708: {'photo_id': 1069306708, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/1/4/7eba568f0aa961284ede154354a8b509.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_04012022_05_46_33_10134.jpg'}, 1069306710: {'photo_id': 1069306710, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/1/4/fe3d201fbfd00dfcd98aa3625d2cfe6a.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_04012022_05_46_43_10066.jpg'}, 1069306805: {'photo_id': 1069306805, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/1/4/42a49f97933b37a87ca53e38379f29b0.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_04012022_05_46_53_9809.jpg'}, 1069306815: {'photo_id': 1069306815, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/1/4/2f5091223d1b3946ee6eb8576faac354.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_04012022_05_47_03_10054.jpg'}, 1069306841: {'photo_id': 1069306841, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/1/4/547760f05364a61b63ad9db90d0b0dec.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_04012022_05_47_13_10036.jpg'}, 1069306843: {'photo_id': 1069306843, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/1/4/5d2b7a6367578e8ac1382d2a1a0bba36.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_04012022_05_47_23_10032.jpg'}, 1069306844: {'photo_id': 1069306844, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/1/4/5b215fd7500b354740ce88c56edeaf99.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_04012022_05_47_33_10008.jpg'}, 1069306954: {'photo_id': 1069306954, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/1/4/2019d5d809e04d2e01c269b108974cd3.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_04012022_05_48_03_9803.jpg'}, 1069306964: {'photo_id': 1069306964, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/1/4/d58c1cb31781763bc3bb045dfac967c3.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_04012022_05_48_13_10051.jpg'}, 1069306967: {'photo_id': 1069306967, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/1/4/642d8a900dd65f94ea75eb2a9d95b7dd.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_04012022_05_48_23_10019.jpg'}}
list_photo_exists : [1069306708, 1069306710, 1069306805, 1069306815, 1069306841, 1069306843, 1069306844, 1069306954, 1069306964, 1069306967]
storage_type for insertDescriptorsMulti : 3
Missing photo l117 : 1069306708
Missing photo l117 : 1069306710
Missing photo l117 : 1069306805
Missing photo l117 : 1069306815
Missing photo l117 : 1069306841
Missing photo l117 : 1069306843
Missing photo l117 : 1069306844
Missing photo l117 : 1069306954
Missing photo l117 : 1069306964
Missing photo l117 : 1069306967
To insert : 1069306708
To insert : 1069306710
To insert : 1069306805
To insert : 1069306815
To insert : 1069306841
To insert : 1069306843
To insert : 1069306844
To insert : 1069306954
To insert : 1069306964
To insert : 1069306967
time to insert the descriptors : 2.935619592666626
res : {'1069306708': b'\xc3\xbf\x00\x00\x00\x00\x08\x14\x03\x05', '1069306710': b'\x00\x00\x00\x00\x00\x00\x00"\xc2\xb3', '1069306805': b'\x00\x06\x00\x00\x00\x00\x00\xc2\x8a\x00', '1069306815': b'\x00l\x00\x00\x00\xc3\x84>\x00\x00', '1069306841': b'\x004\x00\xc3\xad@=sz\x00', '1069306843': b'\x00\x00-\x00\xc3\xb9\x00\x00\x00\x00', '1069306844': b'\x00\x00\x00\xc2\x86\x00T\x00\x00\x00', '1069306954': b'\x14\xc2\x83\xc3\xbf\x1f\x00\x00\x00\x00\x00', '1069306964': b'\x06\xc3\xbf\x00X\x1a\x00\x00\x00\x1f', '1069306967': b'\x00\x00\x00\x00\x00\x00\xc2\xab\x00\x00'}

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 4.385204076766968
time spend to save output : 8.606910705566406e-05
total time spend for step 1 : 4.385290145874023

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
 saveOutput not yet implemented for datou_step.type : acp we use saveGeneral 
[1069306967, 1069306964, 1069306954, 1069306844, 1069306843, 1069306841, 1069306815, 1069306805, 1069306710, 1069306708]
 map_info['map_portfolio_photo'] : {5709050: [1069306967, 1069306964, 1069306954, 1069306844, 1069306843, 1069306841, 1069306815, 1069306805, 1069306710, 1069306708]}
 final :  True mtd_id 4208 list_pids : [1069306967, 1069306964, 1069306954, 1069306844, 1069306843, 1069306841, 1069306815, 1069306805, 1069306710, 1069306708]
Looping around the photos to save general results
len do output : 10
/1069306708Didn't retrieve data
.
/1069306710Didn't retrieve data
.
/1069306805Didn't retrieve data
.
/1069306815Didn't retrieve data
.
/1069306841Didn't retrieve data
.
/1069306843Didn't retrieve data
.
/1069306844Didn't retrieve data
.
/1069306954Didn't retrieve data
.
/1069306964Didn't retrieve data
.
/1069306967Didn't retrieve data
.
before output type
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f15343f9610>
Managing all output in save final without adding information in the mtr_datou_result 
('4208', None, None, None, None, None, None, None, None)
('4208', '5709050', '1069306967', None, None, None, None, None, None)
('4208', None, None, None, None, None, None, None, None)
('4208', '5709050', '1069306964', None, None, None, None, None, None)
('4208', None, None, None, None, None, None, None, None)
('4208', '5709050', '1069306954', None, None, None, None, None, None)
('4208', None, None, None, None, None, None, None, None)
('4208', '5709050', '1069306844', None, None, None, None, None, None)
('4208', None, None, None, None, None, None, None, None)
('4208', '5709050', '1069306843', None, None, None, None, None, None)
('4208', None, None, None, None, None, None, None, None)
('4208', '5709050', '1069306841', None, None, None, None, None, None)
('4208', None, None, None, None, None, None, None, None)
('4208', '5709050', '1069306815', None, None, None, None, None, None)
('4208', None, None, None, None, None, None, None, None)
('4208', '5709050', '1069306805', None, None, None, None, None, None)
('4208', None, None, None, None, None, None, None, None)
('4208', '5709050', '1069306710', None, None, None, None, None, None)
('4208', None, None, None, None, None, None, None, None)
('4208', '5709050', '1069306708', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 20
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('4208', '5709050', '1069306708', 'None', None, None, None, None, None), ('4208', '5709050', '1069306710', 'None', None, None, None, None, None), ('4208', '5709050', '1069306805', 'None', None, None, None, None, None), ('4208', '5709050', '1069306815', 'None', None, None, None, None, None), ('4208', '5709050', '1069306841', 'None', None, None, None, None, None), ('4208', '5709050', '1069306843', 'None', None, None, None, None, None), ('4208', '5709050', '1069306844', 'None', None, None, None, None, None), ('4208', '5709050', '1069306954', 'None', None, None, None, None, None), ('4208', '5709050', '1069306964', 'None', None, None, None, None, None), ('4208', '5709050', '1069306967', 'None', None, None, None, None, None)]
time used for this insertion : 0.046746015548706055
 save_final 

save missing photos in datou_result :
After save, about to update current !
ret : {'1069306708': b'\xc3\xbf\x00\x00\x00\x00\x08\x14\x03\x05', '1069306710': b'\x00\x00\x00\x00\x00\x00\x00"\xc2\xb3', '1069306805': b'\x00\x06\x00\x00\x00\x00\x00\xc2\x8a\x00', '1069306815': b'\x00l\x00\x00\x00\xc3\x84>\x00\x00', '1069306841': b'\x004\x00\xc3\xad@=sz\x00', '1069306843': b'\x00\x00-\x00\xc3\xb9\x00\x00\x00\x00', '1069306844': b'\x00\x00\x00\xc2\x86\x00T\x00\x00\x00', '1069306954': b'\x14\xc2\x83\xc3\xbf\x1f\x00\x00\x00\x00\x00', '1069306964': b'\x06\xc3\xbf\x00X\x1a\x00\x00\x00\x1f', '1069306967': b'\x00\x00\x00\x00\x00\x00\xc2\xab\x00\x00'}
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4209

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=4209
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 4209
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=4209
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : acp
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (5709050) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (5709050)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 1069306967, 'portfolio_id': 5709050}, {'photo_id': 1069306964, 'portfolio_id': 5709050}, {'photo_id': 1069306954, 'portfolio_id': 5709050}, {'photo_id': 1069306844, 'portfolio_id': 5709050}, {'photo_id': 1069306843, 'portfolio_id': 5709050}, {'photo_id': 1069306841, 'portfolio_id': 5709050}, {'photo_id': 1069306815, 'portfolio_id': 5709050}, {'photo_id': 1069306805, 'portfolio_id': 5709050}, {'photo_id': 1069306710, 'portfolio_id': 5709050}, {'photo_id': 1069306708, 'portfolio_id': 5709050}]
 map_portfolio_id_photo_id: {5709050: [1069306967, 1069306964, 1069306954, 1069306844, 1069306843, 1069306841, 1069306815, 1069306805, 1069306710, 1069306708]}
##### Call download_photos : nb_thread : 5
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
##### After load_data_input 
time to download the photos : 0.05245065689086914
#### fin chargement data
Blocking on flush ?
No conitnuing 
About to test input to load 
Calling datou_exec

Inside datou_exec : verbose : True
number of steps :  1
step1:acp
Mon Sep 29 11:49:05 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
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {}
map_photo_id_path_extension : {}
map_subphoto_mainphoto : {}
Cette step permet de calculer une ACP.
ATTENTION : le size etait trop grand : 20, on a changé sa valeur à : 9
Sauvegarde du modèle et envoi sur OVH
Attention : /data/models_weight/ACP_from_port_5709050_type_5619_size_9 existe, son contenu risque d'être écrasé
model_param file didn't exist 
model didn't exist , save the new model in s3
local folder to save in s3 :  /data/models_weight/ACP_from_port_5709050_type_5619_size_9
update :  1759139348.6441097
done !  1759139348.9158645
{'files': [{'name': 'pca_model.pkl', 'size': 103314, 'last_modified': '2025-09-29T09:49:08.681610', 'hash': 'd7e2c6aa9a1ef592ffdfc4abe9c66263'}], 'directories': []}
Création d'un nouveau thème de classification
Le thème de classification 'ACP_from_port_5709050_type_5619_size_9' existe déjà, merci de relancer avec un nouveau nom dans les params-json.

After datou_step_exec type output : <class 'int'>

time spend for datou_step_exec : 3.351212978363037
time spend to save output : 3.528594970703125e-05
total time spend for step 1 : 3.351248264312744

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
 saveOutput not yet implemented for datou_step.type : acp we use saveGeneral 
[1069306967, 1069306964, 1069306954, 1069306844, 1069306843, 1069306841, 1069306815, 1069306805, 1069306710, 1069306708]
 map_info['map_portfolio_photo'] : {5709050: [1069306967, 1069306964, 1069306954, 1069306844, 1069306843, 1069306841, 1069306815, 1069306805, 1069306710, 1069306708]}
 final :  True mtd_id 4209 list_pids : [1069306967, 1069306964, 1069306954, 1069306844, 1069306843, 1069306841, 1069306815, 1069306805, 1069306710, 1069306708]
Looping around the photos to save general results
object of type 'int' has no len()


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 10
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('4209', '5709050', '1069306967', '0', '0', '-1.0', '501120777', '1.0', None), ('4209', '5709050', '1069306964', '0', '0', '-1.0', '501120777', '1.0', None), ('4209', '5709050', '1069306954', '0', '0', '-1.0', '501120777', '1.0', None), ('4209', '5709050', '1069306844', '0', '0', '-1.0', '501120777', '1.0', None), ('4209', '5709050', '1069306843', '0', '0', '-1.0', '501120777', '1.0', None), ('4209', '5709050', '1069306841', '0', '0', '-1.0', '501120777', '1.0', None), ('4209', '5709050', '1069306815', '0', '0', '-1.0', '501120777', '1.0', None), ('4209', '5709050', '1069306805', '0', '0', '-1.0', '501120777', '1.0', None), ('4209', '5709050', '1069306710', '0', '0', '-1.0', '501120777', '1.0', None), ('4209', '5709050', '1069306708', '0', '0', '-1.0', '501120777', '1.0', None)]
time used for this insertion : 0.04049038887023926
 save_final 

save missing photos in datou_result :
After save, about to update current !
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4212

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=4212
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 4212
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=4212
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : acp
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (5709050) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (5709050)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 1069306967, 'portfolio_id': 5709050}, {'photo_id': 1069306964, 'portfolio_id': 5709050}, {'photo_id': 1069306954, 'portfolio_id': 5709050}, {'photo_id': 1069306844, 'portfolio_id': 5709050}, {'photo_id': 1069306843, 'portfolio_id': 5709050}, {'photo_id': 1069306841, 'portfolio_id': 5709050}, {'photo_id': 1069306815, 'portfolio_id': 5709050}, {'photo_id': 1069306805, 'portfolio_id': 5709050}, {'photo_id': 1069306710, 'portfolio_id': 5709050}, {'photo_id': 1069306708, 'portfolio_id': 5709050}]
 map_portfolio_id_photo_id: {5709050: [1069306967, 1069306964, 1069306954, 1069306844, 1069306843, 1069306841, 1069306815, 1069306805, 1069306710, 1069306708]}
##### Call download_photos : nb_thread : 5
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
##### After load_data_input 
time to download the photos : 0.05250668525695801
#### fin chargement data
Blocking on flush ?
No conitnuing 
About to test input to load 
Calling datou_exec

Inside datou_exec : verbose : True
number of steps :  1
step1:acp
Mon Sep 29 11:49:09 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {}
map_photo_id_path_extension : {}
map_subphoto_mainphoto : {}
Cette step permet de calculer une ACP.
To do loadFromThcl(), then load ParamDescType : thcl3412
 get_desc_type_from_thcl : type of cat <class 'mtr.database_queries.classification_admin_tools.ClassificationMetaTools'>

            SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists,
            photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at,
            type_classification
            FROM MTRDatou.classification_theme  WHERE  `id` IN (3412) 
 thcls : [{'id': 3412, 'mtr_user_id': 31, 'name': 'ACP_from_port_5709050_type_5619_size_9', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '', 'svm_portfolios_learning': '0', 'photo_hashtag_type': 4398, 'photo_desc_type': 5706, 'type_classification': 'ACP', 'hashtag_id_list': '0'}]
 thcl {'id': 3412, 'mtr_user_id': 31, 'name': 'ACP_from_port_5709050_type_5619_size_9', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '', 'svm_portfolios_learning': '0', 'photo_hashtag_type': 4398, 'photo_desc_type': 5706, 'type_classification': 'ACP', 'hashtag_id_list': '0'}
 Update svm_hashtag_type_desc : 5706
 SELECT * FROM MTRDatou.photo_desc_type_params  WHERE id in (5706)
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5706, 'ACP_from_port_5709050_type_5619_size_9', 9, 9, 'ACP_from_port_5709050_type_5619_size_9', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2022, 6, 16, 11, 8, 14), datetime.datetime(2022, 6, 16, 11, 8, 14))
model_param file didn't exist 
model_name : ACP_from_port_5709050_type_5619_size_9
model_type : acp
list file need :
['pca_model.pkl']
file exist in s3 :
['pca_model.pkl']
file manque in s3 : 
[]
local folder  : /data/models_weight/ACP_from_port_5709050_type_5619_size_9


/data/models_weight/ACP_from_port_5709050_type_5619_size_9/pca_model.pkl
size_local : 103314 size in s3 : 103314
create time local : 2025-09-29 11:49:07 create time in s3 : 2025-09-29 09:49:08
pca_model.pkl already exist and didn't need to update
model_name : ACP_from_port_5709050_type_5619_size_9
On sauvegarde les nouveaux descripteurs dans le photo desc type : 5706
(10, 9)
Got the blobs of the net
 to insert :  
[255, 0, 0, 0, 0, 8, 20, 3, 5]
code_as_byte_string:b'c3bf000000'|
Got the blobs of the net
 to insert :  
[0, 0, 0, 0, 0, 0, 0, 34, 179]
code_as_byte_string:b'0000000000'|
Got the blobs of the net
 to insert :  
[0, 6, 0, 0, 0, 0, 0, 138, 0]
code_as_byte_string:b'0006000000'|
Got the blobs of the net
 to insert :  
[0, 108, 0, 0, 0, 196, 62, 0, 0]
code_as_byte_string:b'006c000000'|
Got the blobs of the net
 to insert :  
[0, 52, 0, 237, 64, 61, 115, 122, 0]
code_as_byte_string:b'003400c3ad'|
Got the blobs of the net
 to insert :  
[0, 0, 45, 0, 249, 0, 0, 0, 0]
code_as_byte_string:b'00002d00c3'|
Got the blobs of the net
 to insert :  
[0, 0, 0, 134, 0, 84, 0, 0, 0]
code_as_byte_string:b'000000c286'|
Got the blobs of the net
 to insert :  
[20, 131, 255, 31, 0, 0, 0, 0, 0]
code_as_byte_string:b'14c283c3bf'|
Got the blobs of the net
 to insert :  
[6, 255, 0, 88, 26, 0, 0, 0, 31]
code_as_byte_string:b'06c3bf0058'|
Got the blobs of the net
 to insert :  
[0, 0, 0, 0, 0, 0, 171, 0, 0]
code_as_byte_string:b'0000000000'|
time to traite the descriptors :  0.0003371238708496094
Testing : ['1069306708', '1069306710', '1069306805', '1069306815', '1069306841', '1069306843', '1069306844', '1069306954', '1069306964', '1069306967']
 In select_photos_meta_from_ids: 
        SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text
        FROM MTRBack.photos_view
        WHERE photo_id IN (1069306708,1069306710,1069306805,1069306815,1069306841,1069306843,1069306844,1069306954,1069306964,1069306967) 
result : {1069306708: {'photo_id': 1069306708, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/1/4/7eba568f0aa961284ede154354a8b509.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_04012022_05_46_33_10134.jpg'}, 1069306710: {'photo_id': 1069306710, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/1/4/fe3d201fbfd00dfcd98aa3625d2cfe6a.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_04012022_05_46_43_10066.jpg'}, 1069306805: {'photo_id': 1069306805, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/1/4/42a49f97933b37a87ca53e38379f29b0.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_04012022_05_46_53_9809.jpg'}, 1069306815: {'photo_id': 1069306815, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/1/4/2f5091223d1b3946ee6eb8576faac354.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_04012022_05_47_03_10054.jpg'}, 1069306841: {'photo_id': 1069306841, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/1/4/547760f05364a61b63ad9db90d0b0dec.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_04012022_05_47_13_10036.jpg'}, 1069306843: {'photo_id': 1069306843, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/1/4/5d2b7a6367578e8ac1382d2a1a0bba36.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_04012022_05_47_23_10032.jpg'}, 1069306844: {'photo_id': 1069306844, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/1/4/5b215fd7500b354740ce88c56edeaf99.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_04012022_05_47_33_10008.jpg'}, 1069306954: {'photo_id': 1069306954, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/1/4/2019d5d809e04d2e01c269b108974cd3.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_04012022_05_48_03_9803.jpg'}, 1069306964: {'photo_id': 1069306964, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/1/4/d58c1cb31781763bc3bb045dfac967c3.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_04012022_05_48_13_10051.jpg'}, 1069306967: {'photo_id': 1069306967, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/1/4/642d8a900dd65f94ea75eb2a9d95b7dd.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_04012022_05_48_23_10019.jpg'}}
list_photo_exists : [1069306708, 1069306710, 1069306805, 1069306815, 1069306841, 1069306843, 1069306844, 1069306954, 1069306964, 1069306967]
storage_type for insertDescriptorsMulti : 3
Missing photo l117 : 1069306708
Missing photo l117 : 1069306710
Missing photo l117 : 1069306805
Missing photo l117 : 1069306815
Missing photo l117 : 1069306841
Missing photo l117 : 1069306843
Missing photo l117 : 1069306844
Missing photo l117 : 1069306954
Missing photo l117 : 1069306964
Missing photo l117 : 1069306967
To insert : 1069306708
To insert : 1069306710
To insert : 1069306805
To insert : 1069306815
To insert : 1069306841
To insert : 1069306843
To insert : 1069306844
To insert : 1069306954
To insert : 1069306964
To insert : 1069306967
time to insert the descriptors : 2.4628238677978516
res : {'1069306708': b'\xc3\xbf\x00\x00\x00\x00\x08\x14\x03\x05', '1069306710': b'\x00\x00\x00\x00\x00\x00\x00"\xc2\xb3', '1069306805': b'\x00\x06\x00\x00\x00\x00\x00\xc2\x8a\x00', '1069306815': b'\x00l\x00\x00\x00\xc3\x84>\x00\x00', '1069306841': b'\x004\x00\xc3\xad@=sz\x00', '1069306843': b'\x00\x00-\x00\xc3\xb9\x00\x00\x00\x00', '1069306844': b'\x00\x00\x00\xc2\x86\x00T\x00\x00\x00', '1069306954': b'\x14\xc2\x83\xc3\xbf\x1f\x00\x00\x00\x00\x00', '1069306964': b'\x06\xc3\xbf\x00X\x1a\x00\x00\x00\x1f', '1069306967': b'\x00\x00\x00\x00\x00\x00\xc2\xab\x00\x00'}

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 4.688571214675903
time spend to save output : 7.2479248046875e-05
total time spend for step 1 : 4.68864369392395

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
 saveOutput not yet implemented for datou_step.type : acp we use saveGeneral 
[1069306967, 1069306964, 1069306954, 1069306844, 1069306843, 1069306841, 1069306815, 1069306805, 1069306710, 1069306708]
 map_info['map_portfolio_photo'] : {5709050: [1069306967, 1069306964, 1069306954, 1069306844, 1069306843, 1069306841, 1069306815, 1069306805, 1069306710, 1069306708]}
 final :  True mtd_id 4212 list_pids : [1069306967, 1069306964, 1069306954, 1069306844, 1069306843, 1069306841, 1069306815, 1069306805, 1069306710, 1069306708]
Looping around the photos to save general results
len do output : 10
/1069306708Didn't retrieve data
.
/1069306710Didn't retrieve data
.
/1069306805Didn't retrieve data
.
/1069306815Didn't retrieve data
.
/1069306841Didn't retrieve data
.
/1069306843Didn't retrieve data
.
/1069306844Didn't retrieve data
.
/1069306954Didn't retrieve data
.
/1069306964Didn't retrieve data
.
/1069306967Didn't retrieve data
.
before output type
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f1516d2e1c0>
Managing all output in save final without adding information in the mtr_datou_result 
('4212', None, None, None, None, None, None, None, None)
('4212', '5709050', '1069306967', None, None, None, None, None, None)
('4212', None, None, None, None, None, None, None, None)
('4212', '5709050', '1069306964', None, None, None, None, None, None)
('4212', None, None, None, None, None, None, None, None)
('4212', '5709050', '1069306954', None, None, None, None, None, None)
('4212', None, None, None, None, None, None, None, None)
('4212', '5709050', '1069306844', None, None, None, None, None, None)
('4212', None, None, None, None, None, None, None, None)
('4212', '5709050', '1069306843', None, None, None, None, None, None)
('4212', None, None, None, None, None, None, None, None)
('4212', '5709050', '1069306841', None, None, None, None, None, None)
('4212', None, None, None, None, None, None, None, None)
('4212', '5709050', '1069306815', None, None, None, None, None, None)
('4212', None, None, None, None, None, None, None, None)
('4212', '5709050', '1069306805', None, None, None, None, None, None)
('4212', None, None, None, None, None, None, None, None)
('4212', '5709050', '1069306710', None, None, None, None, None, None)
('4212', None, None, None, None, None, None, None, None)
('4212', '5709050', '1069306708', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 20
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('4212', '5709050', '1069306708', 'None', None, None, None, None, None), ('4212', '5709050', '1069306710', 'None', None, None, None, None, None), ('4212', '5709050', '1069306805', 'None', None, None, None, None, None), ('4212', '5709050', '1069306815', 'None', None, None, None, None, None), ('4212', '5709050', '1069306841', 'None', None, None, None, None, None), ('4212', '5709050', '1069306843', 'None', None, None, None, None, None), ('4212', '5709050', '1069306844', 'None', None, None, None, None, None), ('4212', '5709050', '1069306954', 'None', None, None, None, None, None), ('4212', '5709050', '1069306964', 'None', None, None, None, None, None), ('4212', '5709050', '1069306967', 'None', None, None, None, None, None)]
time used for this insertion : 0.038102149963378906
 save_final 

save missing photos in datou_result :
After save, about to update current !
ret : {'1069306708': b'\xc3\xbf\x00\x00\x00\x00\x08\x14\x03\x05', '1069306710': b'\x00\x00\x00\x00\x00\x00\x00"\xc2\xb3', '1069306805': b'\x00\x06\x00\x00\x00\x00\x00\xc2\x8a\x00', '1069306815': b'\x00l\x00\x00\x00\xc3\x84>\x00\x00', '1069306841': b'\x004\x00\xc3\xad@=sz\x00', '1069306843': b'\x00\x00-\x00\xc3\xb9\x00\x00\x00\x00', '1069306844': b'\x00\x00\x00\xc2\x86\x00T\x00\x00\x00', '1069306954': b'\x14\xc2\x83\xc3\xbf\x1f\x00\x00\x00\x00\x00', '1069306964': b'\x06\xc3\xbf\x00X\x1a\x00\x00\x00\x1f', '1069306967': b'\x00\x00\x00\x00\x00\x00\xc2\xab\x00\x00'}
fin du test de la step acp

############################### TEST blur_crop  ################################
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=3990

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=3990
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 3990
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=3990
# 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)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : crop_condition
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

            SELECT ph.photo_id, ph.url
            FROM MTRBack.photos_view ph  WHERE  ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (6135916) and hide_status = 0 ) 
            ORDER BY ph.photo_id DESC  LIMIT 0, 10000
We have 1 , 
{}

        SELECT mtr_photo_id, mtr_portfolio_id
        FROM MTRUser.mtr_portfolio_photos
        WHERE mtr_portfolio_id in (6135916)
        AND hide_status = 0
        ORDER by mtr_photo_id desc LIMIT 0, 10000
 list_result: [{'photo_id': 1105701516, 'portfolio_id': 6135916}, {'photo_id': 1105701500, 'portfolio_id': 6135916}]
 map_portfolio_id_photo_id: {6135916: [1105701516, 1105701500]}
##### Call download_photos : nb_thread : 5
begin to download photo : 1105701516
begin to download photo : 1105701500
download finish for photo 1105701500
download finish for photo 1105701516
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 2 ; length of list_pids : 2 ; length of list_args : 2
##### After load_data_input 
time to download the photos : 0.21669435501098633
#### fin chargement data
Blocking on flush ?
No conitnuing 
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 : True
number of steps :  1
step1:crop_condition
Mon Sep 29 11:49:14 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1759139353_2886529_1105701500_b57a1caec2d74ede6814095fdd28cb27.jpg': 1105701500, 'temp/1759139353_2886529_1105701516_047b0ce16fe5e308d8512c83125c4058.jpg': 1105701516}
map_photo_id_path_extension : {1105701500: {'path': 'temp/1759139353_2886529_1105701500_b57a1caec2d74ede6814095fdd28cb27.jpg', 'extension': 'jpg'}, 1105701516: {'path': 'temp/1759139353_2886529_1105701516_047b0ce16fe5e308d8512c83125c4058.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}
param_json : {'filter': {'Papier_Magazine': {}, 'carton_brun': {}, 'carton_gris': {}, 'cartonnette': {}, 'kraft': {}, 'autre_refus': {}, 'metal': {}, 'plastique': {}, 'teint_dans_la_masse': {}, 'environnement': {}, 'contaminant': {}}, 'upload_type': 'python', 'photo_hashtag_type': 4356, 'token': 'bdba771f7b99dd431d09e7a0ad6ec08f', 'portfolio_name': 'crop_qualipapia_rle', 'crop_type': 'blur', 'min_score': 0.5, 'feed_id_new_photos': 6163545}
Loading chi in step crop with photo_hashtag_type : 4356
Loading chi in step crop for list_pids : 2 !
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 1105701500,1105701516) and `type` in (4356) 
Loaded 3 chid ids of type : 4356
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (2436373819,2436374092,2436374262)
++WARNING : duplicated polygon, we should remove this data for chi_id : 2436373819. Ignored now
+++WARNING : duplicated polygon, we should remove this data for chi_id : 2436374262. Ignored now
SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (2436373819,2436374092,2436374262)
SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (2436373819,2436374092,2436374262)
select photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type, id from MTRPhoto.photo_sub_photos  where photo_id in ( 1105701500,1105701516) 
select sub_photo_id, crop_hashtag_id from MTRPhoto.crop_sub_photo_ids where sub_photo_id in (1105703688,1105703689,1105703686) 

begin to crop the class : Papier_Magazine
param for this class : {}
filtre for class : Papier_Magazine 
hashtag_id of this class : 2107752386
select * from
(select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge,
IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage
from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on
chi.id =css.crop_hashtag_id
where type = 4356 and photo_id in (1105701500,1105701516) and hashtag_id = 2107752386) as a 
where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 
and coverage >= 0 and surface_rectangle >= 0;
        

begin to crop the class : carton_brun
param for this class : {}
filtre for class : carton_brun 
hashtag_id of this class : 2107753024
select * from
(select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge,
IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage
from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on
chi.id =css.crop_hashtag_id
where type = 4356 and photo_id in (1105701500,1105701516) and hashtag_id = 2107753024) as a 
where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 
and coverage >= 0 and surface_rectangle >= 0;
        

begin to crop the class : carton_gris
param for this class : {}
filtre for class : carton_gris 
hashtag_id of this class : 2107753020
select * from
(select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge,
IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage
from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on
chi.id =css.crop_hashtag_id
where type = 4356 and photo_id in (1105701500,1105701516) and hashtag_id = 2107753020) as a 
where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 
and coverage >= 0 and surface_rectangle >= 0;
        

begin to crop the class : cartonnette
param for this class : {}
filtre for class : cartonnette 
hashtag_id of this class : 702398920
select * from
(select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge,
IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage
from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on
chi.id =css.crop_hashtag_id
where type = 4356 and photo_id in (1105701500,1105701516) and hashtag_id = 702398920) as a 
where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 
and coverage >= 0 and surface_rectangle >= 0;
        

begin to crop the class : kraft
param for this class : {}
filtre for class : kraft 
hashtag_id of this class : 493202403
select * from
(select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge,
IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage
from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on
chi.id =css.crop_hashtag_id
where type = 4356 and photo_id in (1105701500,1105701516) and hashtag_id = 493202403) as a 
where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 
and coverage >= 0 and surface_rectangle >= 0;
        

begin to crop the class : autre_refus
param for this class : {}
filtre for class : autre_refus 
hashtag_id of this class : 2107752406
select * from
(select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge,
IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage
from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on
chi.id =css.crop_hashtag_id
where type = 4356 and photo_id in (1105701500,1105701516) and hashtag_id = 2107752406) as a 
where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 
and coverage >= 0 and surface_rectangle >= 0;
        

begin to crop the class : metal
param for this class : {}
filtre for class : metal 
hashtag_id of this class : 492628673
select * from
(select chi.id, chi.score,(chi.x1-chi.x0)*(chi.y1-chi.y0) as surface_rectangle, (chi.x1-chi.x0)/(chi.y1-chi.y0) as proportion_allonge,
IFNULL(css.sum_segments, 0) as surface_crop, IFNULL(css.sum_segments, 0) / ((chi.x1-chi.x0)*(chi.y1-chi.y0)) as coverage
from MTRPhoto.crop_hashtag_ids chi left join MTRPhoto.crop_sum_segments css on
chi.id =css.crop_hashtag_id
where type = 4356 and photo_id in (1105701500,1105701516) and hashtag_id = 492628673) as a 
where proportion_allonge >= 1/1000000 and proportion_allonge <= 1000000 
and coverage >= 0 and surface_rectangle >= 0;
        

begin to crop the class : plastique
param for this class : {}
filtre for class : plastique 
hashtag_id of this class : 492725882
Command terminated by signal 15
162.80user 89.49system 8:25.33elapsed 49%CPU (0avgtext+0avgdata 6013080maxresident)k
7386280inputs+881672outputs (14614major+10547160minor)pagefaults 0swaps