Study of synthesis images noise detection using 26 attributes

Jérôme BUISINE c52c6fae6c now use of macop Python package for optimization process		3 years ago
analysis	cb6026f2c7 Add of 26 features metric	4 years ago
generate	c409191b1c update prediction script for new dataset structure	3 years ago
modules @ cebf2adbf1	0a05939b74 Update of optimization process using backups	4 years ago
prediction	c409191b1c update prediction script for new dataset structure	3 years ago
.gitignore	0a05939b74 Update of optimization process using backups	4 years ago
.gitmodules	c52c6fae6c now use of macop Python package for optimization process	3 years ago
LICENSE	dc0463b6b5 Project initialization	4 years ago
README.md	30d257e0f7 update of the whole project to enable use of new dataset	3 years ago
custom_config.py	e567b11e90 add svm gpu usage	3 years ago
data_attributes.py	a4119a186e update kolmogorov attributes	3 years ago
find_best_attributes.py	c52c6fae6c now use of macop Python package for optimization process	3 years ago
find_best_filters.py	c52c6fae6c now use of macop Python package for optimization process	3 years ago
models.py	e26e4afc69 enable thundersvm choice	3 years ago
requirements.txt	c52c6fae6c now use of macop Python package for optimization process	3 years ago
train_model.py	c409191b1c update prediction script for new dataset structure	3 years ago
train_model_attributes.py	b73b27ab44 add balanced data into SVC	3 years ago
train_model_filters.py	b73b27ab44 add balanced data into SVC	3 years ago

Noise detection using 26 attributes

Description

Noise detection on synthesis images with 26 attributes obtained using few filters.

Filters list:

pip install -r requirements.txt

You need database which respects this structure:

modules/*: contains all modules usefull for the whole project (such as configuration variables)
analysis/*: contains all jupyter notebook used for analysis during thesis
generate/*: contains python scripts for generate data from scenes (described later)
data_processing/*: all python scripts for generate custom dataset for models
prediction/*: all python scripts for predict new threshold from computed models
data_attributes.py: files which contains all extracted features implementation from an image.
custom_config.py: override the main configuration project of modules/config/global_config.py
train_model.py: script which is used to run specific model available.

data/*: folder which will contain all generated .train & .test files in order to train model.
data/saved_models/*: all scikit learn or keras models saved.
data/models_info/*: all markdown files generated to get quick information about model performance and prediction obtained after running run/runAll_*.sh script.
data/results/: This folder contains model_comparisons.csv file used for store models performance.