Facer

Face detection, alignment, and averaging using OpenCV and dlib.

Facer draws heavily on this tutorial from Satya Mallick. I had to update the code pretty heavily to get the project to work, so I thought I'd share my modifications.

Installation

You have my 100% money-back guarantee that the most difficult part of using this package is installing its requirements. Once you've got OpenCV installed, the rest will be smooth sailing.

OpenCV

On Mac, use homebrew to install OpenCV. On Windows, I have no clue. Sorry.

brew install opencv

Using brew to install OpenCV did actually work for me, but it also broke my previous Python installation and all my virtual environments. So uhh, good luck with that.

Python packages

After installing OpenCV, use pip to install dlib, matplotlib, and numpy from the requirements.txt file.

pip install -r requirements.txt

Pre-trained detection model

The face landmark detection relies on a pre-trained model that must be downloaded separately from the dlib package itself.

wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2

Unzip the compressed file after it finishes downloading and move it into the ./Facer/dlib directory.

Usage

from facer import facer

# Load face images
path_to_images = "./face_images"
images = facer.load_images(path_to_images)

# Detect landmarks for each face
landmarks, faces = facer.detect_face_landmarks(images)

# Use  the detected landmarks to create an average face
average_face = facer.create_average_face(faces, landmarks, save_image=True)

# View the composite image
plt.imshow(average_face)
plt.show()

Facer also supports creating animated GIFs of the averaging process:

from facer import facer

path_to_images = "./face_images"
gif, average_face = facer.create_animated_gif(path_to_images)