Realtime human head pose estimation with ONNXRuntime and OpenCV.

There are three major steps:

1. Face detection. A face detector is introduced to provide a face bounding box containing a human face. Then the face box is expanded and transformed to a square to suit the needs of later steps.
2. Facial landmark detection. A pre-trained deep learning model take the face image as input and output 68 facial landmarks.
3. Pose estimation. After getting 68 facial landmarks, the pose could be calculated by a mutual PnP algorithm.

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

The code was tested on Ubuntu 22.04 with following frameworks:

• ONNXRuntime: 1.14.1
• OpenCV: 4.5.4

Clone the repo:

git clone https://github.com/yinguobing/head-pose-estimation.git

Install dependencies with pip:

pip install -r requirements.txt

Note there are pre-trained models provided in the assets directory.

A video file or a webcam index should be assigned through arguments. If no source provided, the built in webcam will be used by default.

For any video format that OpenCV supports (mp4, avi etc.):

python3 main.py --video /path/to/video.mp4

The webcam index should be provided:

python3 main.py --cam 0

Tutorials: https://yinguobing.com/deeplearning/

Training code: https://github.com/yinguobing/cnn-facial-landmark

Note: PyTorch version coming soon!

This project is licensed under the MIT License - see the LICENSE file for details.

Meanwhile:

• The face detector is SCRFD from InsightFace.
• The pre-trained model file was trained with various public datasets which have their own licenses.

Please refer to them for details.

Yin Guobing (尹国冰) - yinguobing

All datasets used in the training process:

• 300-W: https://ibug.doc.ic.ac.uk/resources/300-W/
• 300-VW: https://ibug.doc.ic.ac.uk/resources/300-VW/
• LFPW: https://neerajkumar.org/databases/lfpw/
• HELEN: http://www.ifp.illinois.edu/~vuongle2/helen/
• AFW: https://www.ics.uci.edu/~xzhu/face/
• IBUG: https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/

The 3D face model is from OpenFace, you can find the original file here.

The build in face detector is SCRFD from InsightFace.