README.md 2.2 KB
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_reconstructed_data.py -h
Generate custom dataset from one reconstructed method or multiples (implemented later)
python 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_dataset.py --output data/output_data_filename --metrics "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