distraction detection

README.md

@@ -1,100 +1,13 @@
-# Head pose estimation
-
-Realtime human head pose estimation with ONNX Runtime and OpenCV.
-
-![demo](doc/demo.gif)
-![demo](doc/demo1.gif)
-
-## How it works
-
-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.
-
-## Getting Started
-
-These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
-
-### Prerequisites
-
-The code was tested on Ubuntu 22.04 with following frameworks:
-- ONNX Runtime: 1.17.1
-- OpenCV: 4.5.4
-
-### Installing
-
-Clone the repo:
-```bash
-git clone https://github.com/yinguobing/head-pose-estimation.git
-```
-
-Install dependencies with pip:
-```bash
-pip install -r requirements.txt
-```
-
-Pre-trained models provided in the `assets` directory. Download them with Git LFS:
-```bash
-git lfs pull
-```
-
-Or, download manually from the [release page](https://github.com/yinguobing/head-pose-estimation/releases).
-
-## Running
-
-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.
-
-### Video file
-
-For any video format that OpenCV supports (`mp4`, `avi` etc.):
-
-```bash
-python3 main.py --video /path/to/video.mp4
-```
-
-### Webcam
-
-The webcam index should be provided:
-
-```bash
-python3 main.py --cam 0
-``` 
-
-## Retrain the model
-
-Tutorials: https://yinguobing.com/deeplearning/
-
-Training code: https://github.com/yinguobing/cnn-facial-landmark
-
-Note: PyTorch version coming soon!
-
-## License
-This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. 
-
-Meanwhile: 
-
-- The face detector is [SCRFD](https://github.com/deepinsight/insightface/tree/master/detection/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.
-
-## Authors
-Yin Guobing (尹国冰) - [yinguobing](https://yinguobing.com)
-
-![](doc/wechat_logo.png)
-
-## Acknowledgments
-
-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](https://github.com/TadasBaltrusaitis/OpenFace/blob/master/lib/local/LandmarkDetector/model/pdms/In-the-wild_aligned_PDM_68.txt).
-
-The build in face detector is [SCRFD](https://github.com/deepinsight/insightface/tree/master/detection/scrfd) from InsightFace. 
+---
+title: Distraction
+emoji: 🌖
+colorFrom: red
+colorTo: gray
+sdk: streamlit
+sdk_version: 1.40.1
+app_file: app.py
+pinned: false
+short_description: This is meant to detect whether someone is distracted or not
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference