saurongaze - accurate gaze and head pose information
Project description
A demo program of MPIIGaze and MPIIFaceGaze
With this program, you can runs gaze estimation on images and videos. By default, the video from a webcam is used.
(The original video is from this public domain.)
(The original image is from this public domain.)
To train a model, use this repository.
Quick start
Installation
pip install saurongaze
Run demo
saurongaze --mode eye
Usage
usage: saurongaze [-h] [--config CONFIG] [--mode {eye,face}]
[--face-detector {dlib,face_alignment_dlib,face_alignement_sfd}]
[--device {cpu,cuda}] [--image IMAGE] [--video VIDEO]
[--camera CAMERA] [--output-dir OUTPUT_DIR] [--ext {avi,mp4}]
[--no-screen] [--debug]
optional arguments:
-h, --help show this help message and exit
--config CONFIG Config file for YACS. When using a config file, all
the other commandline arguments are ignored. See https
://github.com/hysts/pytorch_mpiigaze_demo/configs/demo
_mpiigaze.yaml
--mode {eye,face} With 'eye', MPIIGaze model will be used. With 'face',
MPIIFaceGaze model will be used. (default: 'eye')
--face-detector {dlib,face_alignment_dlib,face_alignement_sfd}
The method used to detect faces and find face
landmarks (default: 'dlib')
--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/saurongaze/data/calib/sample_params.
yaml
--output-dir OUTPUT_DIR, -o OUTPUT_DIR
If specified, the overlaid video will be saved to this
directory.
--ext {avi,mp4}, -e {avi,mp4}
Output video file extension.
--no-screen If specified, the video is not displayed on screen,
and saved to the output directory.
--debug
While processing an image or video, press the following keys on the window to show or hide intermediate results:
l
: landmarksh
: head poset
: projected points of 3D face modelb
: face bounding box
References
- Zhang, Xucong, Yusuke Sugano, Mario Fritz, and Andreas Bulling. "Appearance-based Gaze Estimation in the Wild." Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. arXiv:1504.02863, Project Page
- Zhang, Xucong, Yusuke Sugano, Mario Fritz, and Andreas Bulling. "It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation." Proc. of the IEEE Conference on Computer Vision and Pattern Recognition Workshops(CVPRW), 2017. arXiv:1611.08860, Project Page
- Zhang, Xucong, Yusuke Sugano, Mario Fritz, and Andreas Bulling. "MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation." IEEE transactions on pattern analysis and machine intelligence 41 (2017). arXiv:1711.09017
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