Metadata-Version: 2.1
Name: ptgaze
Version: 0.0.3
Summary: Gaze estimation using MPIIGaze and MPIIFaceGaze
Home-page: https://github.com/hysts/pytorch_mpiigaze_demo
Author: hysts
License: UNKNOWN
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.
        
        ![MPIIGaze video result](figures/mpiigaze_video.gif)
        ![MPIIFaceGaze video result](figures/mpiifacegaze_video.gif)
        
        (The original video is from [this public domain](https://www.pexels.com/video/woman-in-a-group-having-a-drink-while-listening-3201742/).)
        
        ![MPIIGaze image result](figures/mpiigaze_image.jpg)
        
        (The original image is from [this public domain](https://www.pexels.com/photo/photography-of-a-beautiful-woman-smiling-1024311/).)
        
        To train a model, use [this repository](https://github.com/hysts/pytorch_mpiigaze).
        
        ## Quick start
        
        ### Installation
        
        ```bash
        pip install ptgaze
        ```
        
        
        ### Run demo
        
        ```bash
        ptgaze --mode eye
        ```
        
        
        ### Usage
        
        
        ```
        usage: ptgaze [-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/ptgaze/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`: landmarks
        * `h`: head pose
        * `t`: projected points of 3D face model
        * `b`: 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](https://arxiv.org/abs/1504.02863), [Project Page](https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/gaze-based-human-computer-interaction/appearance-based-gaze-estimation-in-the-wild/)
        * 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](https://arxiv.org/abs/1611.08860), [Project Page](https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/research/gaze-based-human-computer-interaction/its-written-all-over-your-face-full-face-appearance-based-gaze-estimation/)
        * 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](https://arxiv.org/abs/1711.09017)
        
        
        
        
Platform: UNKNOWN
Requires-Python: >=3.7
Description-Content-Type: text/markdown
