Study of synthesis images noise detection using 26 attributes

Jérôme BUISINE c945da58a7 Merge branch 'release/v0.4.0' 3 months ago
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LICENSE dc0463b6b5 Project initialization 2 years ago
README.md 30d257e0f7 update of the whole project to enable use of new dataset 1 year ago
check_random_forest_perfomance.py 8dc3803465 update number of features as input 5 months ago
custom_config.py e4f5839e36 Use surrogate from scract as proposed framework 9 months ago
data_attributes.py a4119a186e update kolmogorov attributes 11 months ago
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find_best_attributes_surrogate_openML.py 6032efa1b1 Use of population for rendering surrogate 6 months ago
find_best_attributes_surrogate_openML_multi.py 6032efa1b1 Use of population for rendering surrogate 6 months ago
find_best_attributes_surrogate_openML_multi_specific.py 6032efa1b1 Use of population for rendering surrogate 6 months ago
find_best_filters.py 6032efa1b1 Use of population for rendering surrogate 6 months ago
models.py 2fc4db3bfb use of wsao module for accelerate ILS (using surrogate) 10 months ago
requirements.txt c52c6fae6c now use of macop Python package for optimization process 11 months ago
run_openML_surrogate.py 48c4349333 sorted open ml problems in order to well restart 8 months ago
run_openML_surrogate_multi.py c41293b6fa computation of mae and save it 7 months ago
run_openML_surrogate_multi_specific.py 33cf98b131 enable run of commands 7 months ago
run_surrogate_rendering.sh 04a092bd8d add run number in script 4 months ago
train_model.py c409191b1c update prediction script for new dataset structure 1 year ago
train_model_attributes.py b73b27ab44 add balanced data into SVC 1 year ago
train_model_filters.py b73b27ab44 add balanced data into SVC 1 year 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