Study of synthesis images noise detection using 26 attributes

Jérôme BUISINE 8c52a1a2ef Merge branch 'release/v0.1.5' 11 months ago
analysis cb6026f2c7 Add of 26 features metric 1 year ago
data_processing cf7e7e11b5 Update of generate data scripts 1 year ago
display 9440fdcedb Update of display simulation curves script 1 year ago
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optimization @ 6dcd32014a 46db33ffad Update use of optimization module from crossover 11 months ago
others eac3bc12d9 Update of project documentation 1 year ago
prediction eac3bc12d9 Update of project documentation 1 year ago
run eac3bc12d9 Update of project documentation 1 year ago
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.gitignore 1f0a9d0e99 Add of optimization process to find best filters 11 months ago
.gitmodules b693a667bb Add of optimization modules 11 months ago
LICENSE dc0463b6b5 Project initialization 1 year ago
README.md 9440fdcedb Update of display simulation curves script 1 year ago
custom_config.py 1f0a9d0e99 Add of optimization process to find best filters 11 months ago
data_attributes.py eac3bc12d9 Update of project documentation 1 year ago
find_best_attributes.py 46db33ffad Update use of optimization module from crossover 11 months ago
models.py 1f0a9d0e99 Add of optimization process to find best filters 11 months ago
requirements.txt 4093aaaf5c Dependencies and documentation updates 1 year ago
train_model.py 4093aaaf5c Dependencies and documentation updates 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

Generate all needed data for each features (which requires the whole dataset. In order to get it, you need to contact us).

python generate/generate_all_data.py --feature all

Project structure

Link to your dataset

You have to create a symbolic link to your own database which respects this structure:

  • dataset/
    • Scene1/
    • zone00/
    • ...
    • zone15/
      • seuilExpe (file which contains threshold samples of zone image perceived by human)
    • Scene1_00050.png
    • Scene1_00070.png
    • ...
    • Scene1_01180.png
    • Scene1_01200.png
    • Scene2/
    • ...
    • ...

Create your symbolic link:

ln -s /path/to/your/data dataset

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
  • simulation/*: contains all bash scripts used for run simulation from models
  • display/*: contains all python scripts used for display Scene information (such as Singular values...)
  • run/*: bash scripts to run few step at once :
    • generate custom dataset
    • train model
    • keep model performance
    • run simulation (if necessary)
  • others/*: folders which contains others scripts such as script for getting performance of model on specific scene and write it into Mardown file.
  • 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.
  • saved_models/*: all scikit learn or keras models saved.
  • models_info/*: all markdown files generated to get quick information about model performance and prediction obtained after running run/runAll_*.sh script.
  • results/: This folder contains model_comparisons.csv file used for store models performance.

How to use ?

Remark: Note here that all python script have --help command.

python generate_data_model.py --help

Parameters explained:

  • feature: feature choice wished
  • output: filename of data (which will be split into two parts, .train and .test relative to your choices). Need to be into data folder.
  • interval: the interval of data you want to use from SVD vector.
  • kind: kind of data ['svd', 'svdn', 'svdne']; not normalize, normalize vector only and normalize together.
  • scenes: scenes choice for training dataset.
  • zones: zones to take for training dataset.
  • step: specify if all pictures are used or not using step process.
  • percent: percent of data amount of zone to take (choose randomly) of zone
  • custom: specify if you want your data normalized using interval and not the whole singular values vector. If it is, the value of this parameter is the output filename which will store the min and max value found. This file will be usefull later to make prediction with model (optional parameter).

Train model

This is an example of how to train a model

python train_model.py --data 'data/xxxx' --output 'model_file_to_save' --choice 'model_choice'

Expected values for the choice parameter are ['svm_model', 'ensemble_model', 'ensemble_model_v2'].

Predict image using model

Now we have a model trained, we can use it with an image as input:

python prediction/predict_noisy_image_svd.py --image path/to/image.png --interval "x,x" --model saved_models/xxxxxx.joblib --feature 'lab' --mode 'svdn' --custom 'min_max_filename'
  • feature: feature choice need to be one of the listed above.
  • custom: specify filename with custom min and max from your data interval. This file was generated using custom parameter of one of the generate_data_model*.py script (optional parameter).

The model will return only 0 or 1:

  • 1 means noisy image is detected.
  • 0 means image seem to be not noisy.

All SVD features developed need:

  • Name added into feature_choices_labels global array variable of custom_config.py file.
  • A specification of how you compute the feature into get_image_features method of data_attributes.py file.

Predict scene using model

Now we have a model trained, we can use it with an image as input:

python prediction_scene.py --data path/to/xxxx.csv --model saved_model/xxxx.joblib --output xxxxx --scene xxxx

Remark: scene parameter expected need to be the correct name of the Scene.

Visualize data

All scripts with names display/display_*.py are used to display data information or results.

Just use --help option to get more information.

Simulate model on scene

All scripts named prediction/predict_seuil_expe*.py are used to simulate model prediction during rendering process. Do not forget the custom parameter filename if necessary.

Once you have simulation done. Checkout your threshold_map/%MODEL_NAME%/simulation_curves_zones_*/ folder and use it with help of display_simulation_curves.py script.

License

The MIT license