https://github.com/johnwmillr/facer

Simple (🤞) face averaging (🙂) in Python (🐍)
https://github.com/johnwmillr/facer

dlib face-average face-averaging face-detection facer image-processing opencv

Last synced: 5 days ago
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Simple (🤞) face averaging (🙂) in Python (🐍)

• Host: GitHub
• URL: https://github.com/johnwmillr/facer
• Owner: johnwmillr
• License: mit
• Created: 2019-07-29T19:33:04.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2024-12-24T02:36:04.000Z (about 1 month ago)
• Last Synced: 2025-01-21T00:11:13.828Z (12 days ago)
• Topics: dlib, face-average, face-averaging, face-detection, facer, image-processing, opencv
• Language: Python
• Homepage:
• Size: 1.59 MB
• Stars: 80
• Watchers: 5
• Forks: 11
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

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README

        
# Facer

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

Facer draws heavily on [this tutorial](https://www.learnopencv.com/average-face-opencv-c-python-tutorial/) from [Satya Mallick](https://github.com/spmallick). I had to update the code pretty heavily to get the project to work, so I thought I'd share my modifications.

## Example

The image below is an example of Facer's output for one of my posts on [`r/dataisbeautiful`](https://www.reddit.com/r/dataisbeautiful/comments/crxrud/the_average_faces_of_rap_rock_and_country/).

[![Average faces of rap, rock, and country music](https://www.johnwmillr.com/assets/images/FaceAverages/Faces_RapRockCountry.png)](https://www.reddit.com/r/dataisbeautiful/comments/crxrud/the_average_faces_of_rap_rock_and_country/)

## 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~~ should be smooth sailing. I've had the best luck with the OpenCV using the `opencv-python` package from PyPI.

Install `facer` using `pip`:

```bash

pip install -U average-facer

```

### Pre-trained detection model

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

```shell

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

```

Unzip the compressed file after it finishes downloading and move it into a `./model` directory.

If you store the downloaded file somewhere other than `./model` (or if you use an entirely different model name), you can set a custom model path as an environment variable:

```bash

export FACER_PREDICTOR_PATH="./custom/path/to/your/model.dat"

```

## Usage

```python

from facer import facer

import matplotlib.pyplot as plt

# Load face images

path_to_images = "./face_images"  # Put your images here

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:

```python

from facer import facer

path_to_images = "./face_images"

gif, average_face = facer.create_animated_gif(path_to_images)

```