Structure

The __init__.py file comprises of the main method calls while the different classes are stored in the fcn/ directory. Under this directory, we find: img_prep.py which will prepare the image by scaling and transforming it to grayscale, face_detect.py which runs the haar cascade detecting the face on the prepared image, annotate.py which places the landmarks on the detected faces of the image, analyze.py calls the stats.R script which runs the statistical analyses for the study.

The output images are stored as they are processed in their respective directories: img_raw/ for the raw inputed images, img_prep/ for the prepared images, img_proc/ for the processed images (faces detected and landmarks placed).

The data/ directory contains the cascade classifier and shape predictor. Under faces/ are stored the coordinates of the rectangles from the detected faces in each image. The ldmks/ directory contains the matrices of the landmarks for each groups to be analyzed using the R script.

The gross structure of the package is outlined below:

pfla
├── contributing.md
├── docs
│   ├── build
│   ├── make.bat
│   ├── Makefile
│   └── source
│       ├── analyze.rst
│       ├── annotate.rst
│       ├── conf.py
│       ├── face_detect.rst
│       ├── img_prep.rst
│       ├── index.rst
│       ├── install.rst
│       ├── modules.rst
│       ├── overview.rst
│       └── structure.rst
├── LICENSE.txt
├── MANIFEST.in
├── paper
│   ├── histo_02.png
│   ├── paper.bib
│   ├── paper.md
│   └── pfla.png
├── pfla
│   ├── annotate.py
│   ├── cli.py
│   ├── face_detect.py
│   ├── img_prep.py
│   ├── __init__.py
│   ├── linear.py
│   ├── logger.py
│   ├── metrics.py
│   └── tests
│       ├── data
│       │   ├── __init__.py
│       │   ├── m01.jpg
│       │   ├── m02.jpg
│       │   ├── m03.jpg
│       │   ├── m04.jpg
│       │   └── m05.jpg
│       ├── __init__.py
├── PROGRESS.md
├── README.md
├── requirements-pytorch.txt
├── requirements.txt
└── setup.py