Implementation of the GaussianFace algorithm for TU Delft IN4393 Computer Vision 2016/2017
The repository does not contain the dataset of images. The data can be places in colorferet/output/ and is expected to be normalized images from the gray FERET dataset of size 150x130pixels.
The code used in the final system can be found in src/gaussianface.py and demos/
To run the system install and create a virtual environment with Python 2.7. Please note that creating the F matrix for a lot of images as required in training and testing takes a long time.
To use precalculated F and W matrices (k=3). Unzip savedMatrix/savedMatrix.zip so that the npy files are in ./savedMatrix/
pip install virtualenv
virtualenv projectActivate and configure virtualenv
source project/bin/activate (Linux)
pip install -r requirements.txtTo run training and testing: (Requires FERET normalized images and takes a long time or the precalculated F matrices) The normalized feret images should be placed in /colorferet/output in pgm format (examplename: 00001fa010_930831.pgm). Train the LDA transformation matrix
python -m demos.trainWLDA
Test on the testdata
python -m demos.testAlgorithm
Test the LBP faces
python -m demos.lbpfaces <imgpath>