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Gaze estimation using MPIIGaze and MPIIFaceGaze
| Date | Stars |
|---|---|
| 2026-07-24 | 371 |
| 2026-07-25 | 370 |
| 2026-07-28 | 370 |
| 2026-07-30 | 370 |
| 2026-08-06 | 370 |
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# A demo program of gaze estimation models (MPIIGaze, MPIIFaceGaze, ETH-XGaze)
[](https://pypi.org/project/ptgaze/)
[](https://pepy.tech/project/ptgaze)
[](https://colab.research.google.com/github/hysts/pytorch_mpiigaze_demo/blob/main/demo.ipynb)
[](https://opensource.org/licenses/MIT)
[](https://github.com/hysts/pytorch_mpiigaze_demo)
This program runs gaze estimation on images and videos. By default, it uses the video from a webcam.
To train models on MPIIGaze and MPIIFaceGaze, use [this repository](https://github.com/hysts/pytorch_mpiigaze). You can also use [this repository](https://github.com/hysts/pl_gaze_estimation) to train a model on the ETH-XGaze dataset.
## Quick start
This program has only been tested on Ubuntu.
### Installation
```bash
pip install ptgaze
```
The default mediapipe face detector downloads its model file (`face_landmarker.task`) to `~/.ptgaze/mediapipe/` on first run. On Linux, mediapipe requires the GLES system libraries (`sudo apt install libgles2` on Ubuntu).
The dlib-based face detectors (`--face-detector dlib` and `--face-detector face_alignment_dlib`) require the optional dlib dependency. dlib ships no prebuilt wheels, so installing it builds from source and needs CMake and a C++ compiler:
```bash
pip install ptgaze[dlib]
```
### Run demo
```bash
ptgaze --mode eth-xgaze
```
### Usage
```
usage: ptgaze [-h] [--config CONFIG]
[--mode {mpiigaze,mpiifacegaze,eth-xgaze}]
[--face-detector {dlib,face_alignment_dlib,face_alignment_sfd,mediapipe}]
[--device {cpu,cuda}] [--image IMAGE] [--video VIDEO]
[--camera CAMERA] [--output-dir OUTPUT_DIR] [--ext {avi,mp4}]
[--no-screen] [--debug]
options:
-h, --help show this help message and exit
--config CONFIG Config file. When using a config file, all the other
commandline arguments are ignored. See
https://github.com/hysts/pytorch_mpiigaze_demo/blob/main/ptgaze/data/configs/eth-xgaze.yaml
--mode {mpiigaze,mpiifacegaze,eth-xgaze}
With 'mpiigaze', MPIIGaze model will be used. With
'mpiifacegaze', MPIIFaceGaze model will be used. With
'eth-xgaze', ETH-XGaze model will be used.
--face-detector {dlib,face_alignment_dlib,face_alignment_sfd,mediapipe}
The method used to detect faces and find face
landmarks (default: 'mediapipe')
--device {cpu,cuda} Device used for model inference.
--image IMAGE Path to an input image file.
--video VIDEO Path to an input video file.
--camera CAMERA Camera calibration file. See
https://github.com/hysts/pytorch_mpiigaze_demo/blob/main/ptgaze/data/calibExcerpt of 9,675 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:40a65d4861420222, topic:pytorch
matched fp:40a65d4861420222, topic:computer-vision