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Study of synthesis images noise detection using 26 attributes

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Jérôme BUISINE 65849b6228 reduce of number of params il y a 3 ans
OpenML_datasets f94ba1ea70 update openML datasets used il y a 4 ans
analysis cb6026f2c7 Add of 26 features metric il y a 5 ans
features_selection 48c4349333 sorted open ml problems in order to well restart il y a 4 ans
generate c409191b1c update prediction script for new dataset structure il y a 4 ans
modules @ cebf2adbf1 0a05939b74 Update of optimization process using backups il y a 5 ans
optimization c3a4e96cac avoid train every modulo 0 il y a 3 ans
prediction c409191b1c update prediction script for new dataset structure il y a 4 ans
rnn @ 8c2fc8888c 427d117327 Update use of surrogate il y a 3 ans
utils 2fc4db3bfb use of wsao module for accelerate ILS (using surrogate) il y a 4 ans
wsao @ a92ca5a285 c41293b6fa computation of mae and save it il y a 4 ans
.gitignore 0a05939b74 Update of optimization process using backups il y a 5 ans
.gitmodules 41659bc1c2 add of training using rnn models il y a 4 ans
LICENSE dc0463b6b5 Project initialization il y a 5 ans
README.md 30d257e0f7 update of the whole project to enable use of new dataset il y a 4 ans
check_random_forest_perfomance.py 8dc3803465 update number of features as input il y a 3 ans
custom_config.py e4f5839e36 Use surrogate from scract as proposed framework il y a 4 ans
data_attributes.py a4119a186e update kolmogorov attributes il y a 4 ans
find_best_attributes.py 6032efa1b1 Use of population for rendering surrogate il y a 3 ans
find_best_attributes_from.py a2fb893050 svm can now be used for selector il y a 4 ans
find_best_attributes_surrogate.py 4fc3142e09 use of SVM as desired model il y a 3 ans
find_best_attributes_surrogate_dl.py 6032efa1b1 Use of population for rendering surrogate il y a 3 ans
find_best_attributes_surrogate_openML.py 6032efa1b1 Use of population for rendering surrogate il y a 3 ans
find_best_attributes_surrogate_openML_multi.py 6032efa1b1 Use of population for rendering surrogate il y a 3 ans
find_best_attributes_surrogate_openML_multi_specific.py 6032efa1b1 Use of population for rendering surrogate il y a 3 ans
find_best_filters.py 6032efa1b1 Use of population for rendering surrogate il y a 3 ans
models.py 65849b6228 reduce of number of params il y a 3 ans
requirements.txt c52c6fae6c now use of macop Python package for optimization process il y a 4 ans
run_openML_surrogate.py 48c4349333 sorted open ml problems in order to well restart il y a 4 ans
run_openML_surrogate_multi.py c41293b6fa computation of mae and save it il y a 4 ans
run_openML_surrogate_multi_specific.py 33cf98b131 enable run of commands il y a 4 ans
run_surrogate_rendering.sh b111f51621 Reduce pop size il y a 3 ans
train_model.py c409191b1c update prediction script for new dataset structure il y a 4 ans
train_model_attributes.py b73b27ab44 add balanced data into SVC il y a 4 ans
train_model_filters.py b73b27ab44 add balanced data into SVC il y a 4 ans

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