skeh/head-pose-estimation
1
0
Fork 0
Code Issues Pull requests Projects Releases Wiki Activity

update readme

Browse source 
TensorFlow 2 is the default version now
This commit is contained in:
yinguobing 2020-12-17 17:21:01 +08:00
parent 798fab93ff
commit ddb377b682
1 changed files with 21 additions and 18 deletions

39
README.md
View file

@ -1,23 +1,25 @@
# Head pose estimation
This repo shows how to estimate human head pose from videos using TensorFlow and OpenCV.
Real time human head pose estimation using TensorFlow and OpenCV.
![demo](doc/demo.gif)
![demo](doc/demo1.gif)
## Getting Started
The following packages are required:
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
- TensorFlow 2.2
- OpenCV 4.3
- Python 3.6
### Prerequisites
The code is tested in Ubuntu 18.04.
![TensorFlow](https://img.shields.io/badge/TensorFlow-v2.4-brightgreen)
![OpenCV](https://img.shields.io/badge/OpenCV-v4.5-brightgreen)
![Numpy](https://img.shields.io/badge/Numpy-v1.19-brightgreen)
## Installing
The code was tested on Ubuntu 20.04.
This repository comes with a pre-trained model for facial landmark detection. Just git clone then you are good to go.
### Installing
This repository already provided a pre-trained model for facial landmarks detection. Just git clone then you are good to go.
```bash
# From your favorite development directory:
@ -26,19 +28,19 @@ git clone https://github.com/yinguobing/head-pose-estimation.git
## Running
A video file or a webcam index should be assigned through arguments. If no source provided, the default webcam will be used.
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 video file
### With video file
For any video format that OpenCV supported (`mp4`, `avi` etc.):
For any video format that OpenCV supports (`mp4`, `avi` etc.):
```bash
python3 estimate_head_pose.py --video /path/to/video.mp4
```
### For webcam
### With webcam
The webcam index should be assigned:
The webcam index should be provided:
```bash
python3 estimate_head_pose.py --cam 0
@ -48,17 +50,18 @@ python3 estimate_head_pose.py --cam 0
There are three major steps:
1. Face detection. A face detector is adopted to provide a face box containing a human face. Then the face box is expanded and transformed to a square to suit the needs of later 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 custom trained facial landmark detector based on TensorFlow is responsible for output 68 facial landmarks.
2. Facial landmark detection. A pre-trained deep learning model take the face image as input and output 68 facial landmarks.
3. Pose estimation. Once we got the 68 facial landmarks, a mutual PnP algorithms is adopted to calculate the pose.
3. Pose estimation. After getting 68 facial landmarks, the pose could be calculated by a mutual PnP algorithm.
The marks is detected frame by frame, which result in small variance between adjacent frames. This makes the pose unstable. A Kalman filter is used to solve this problem, you can draw the original pose to observe the difference.
The marks are detected frame by frame that makes the pose unstable. A Kalman filter is used to solve this problem, you can draw the original pose to observe the difference.
## Retrain the model
To reproduce the facial landmark detection model, you can refer to this [series](https://yinguobing.com/deeplearning/) of posts(in Chinese only). And the training code is also open sourced: https://github.com/yinguobing/cnn-facial-landmark
To retrain the facial landmark detection model please refer to this series of [posts]((https://yinguobing.com/deeplearning/) (in Chinese only). The training code is also open sourced:
https://github.com/yinguobing/cnn-facial-landmark
## License