Study of synthesis images noise detection using 26 attributes

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find_best_attributes_surrogate_openML_multi_specific.py 6032efa1b1 Use of population for rendering surrogate 2 months ago
find_best_filters.py 6032efa1b1 Use of population for rendering surrogate 2 months ago
models.py 2fc4db3bfb use of wsao module for accelerate ILS (using surrogate) 6 months ago
requirements.txt c52c6fae6c now use of macop Python package for optimization process 7 months ago
run_openML_surrogate.py 48c4349333 sorted open ml problems in order to well restart 4 months ago
run_openML_surrogate_multi.py c41293b6fa computation of mae and save it 4 months ago
run_openML_surrogate_multi_specific.py 33cf98b131 enable run of commands 3 months ago
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train_model.py c409191b1c update prediction script for new dataset structure 9 months ago
train_model_attributes.py b73b27ab44 add balanced data into SVC 8 months ago
train_model_filters.py b73b27ab44 add balanced data into SVC 8 months ago

README.md

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