Thesis-NoiseDetection-CNN/README.md

Noise detection project

Requirements

git clone --recursive https://github.com/prise-3d/Thesis-NoiseDetection-CNN.git XXXXX

pip install -r requirements.txt

How to use

Generate reconstructed data from specific method of reconstruction (run only once time or clean data folder before):

python generate/generate_reconstructed_data.py -h

Generate custom dataset from one reconstructed method or multiples (implemented later)

python generate/generate_dataset.py -h

Reconstruction parameter (--params)

List of expected parameter by reconstruction method:

• svd_reconstruction: Singular Values Decomposition
  • Param definition: interval data used for reconstruction (begin, end)
  • Example: "100, 200"
• ipca_reconstruction: Iterative Principal Component Analysis
  • Param definition: number of components used for compression and batch size
  • Example: "30, 35"
• fast_ica_reconstruction: Fast Iterative Component Analysis
  • Param definition: number of components used for compression
  • Example: "50"
• static Use static file to manage (such as z-buffer, normals card...)
  • Param definition: Name of image of scene need to be in {sceneName}/static/xxxx.png
  • Example: "img.png"

Example:

python generate/generate_dataset.py --output data/output_data_filename --features "svd_reconstruction, ipca_reconstruction, fast_ica_reconstruction" --renderer "maxwell" --scenes "A, D, G, H" --params "100, 200 :: 50, 10 :: 50" --nb_zones 10 --random 1

Then, train model using your custom dataset:

python train_model --data data/custom_dataset --output output_model_name

Modules

This project contains modules:

• modules/utils/config.py: Store all configuration information about the project and dataset information
• modules/utils/data.py: Usefull methods used for dataset
• modules/models/metrics.py: Usefull methods for performance comparisons
• modules/models/models.py: Generation of CNN model
• modules/classes/Transformation.py: Transformation class for more easily manage computation

All these modules will be enhanced during development of the project

License

MIT