Noise detection using 26 attributes

Description

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

Filters list:

• average
• wiener
• median
• gaussian
• wavelet

Requirements

pip install -r requirements.txt

Project structure

Link to your dataset

You need database which respects this structure:

• dataset/
  • Scene1/
    • Scene1_00050.png
    • Scene1_00070.png
    • ...
    • Scene1_01180.png
    • Scene1_01200.png
  • Scene2/
    • ...
  • ...

Code architecture description

• 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.

Generated data directories:

• 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.

License

The MIT license