OAKI is your personal trainer that tries to do the training in home more fun and with metrics to find if you are doing a good exercise. It combines mediaPipe algorithms, depth from the OAKD-lite, functions to find angles and distances between points, some multiprocess to run everything in parallel, and an assistant voice to have a fantastic experience in training.
Install libraries necessary for the project
pip install -r requirements.txt
Run this command to start the app.
python app.py
As our application grows we would refactor our app.py file into multiple folders and files.
.
βββ app.py
βββ oakiAgent.py
βββ .gitignore
βββ requirements.txt
βββ README.md
βββ voice
| βββ 0.mp4
| βββ 1.mp4
| βββ 2.mp4
| βββ 3.mp4
| βββ 4.mp4
| βββ 5.mp4
| βββ 6.mp4
| βββ 7.mp4
| βββ 8.mp4
| βββ 9.mp4
| βββ 10.mp4
| βββ 11.mp4
| βββ 12.mp4
βββ methods
| βββ body_part_angle.py
| βββ types_of_exercise.py
| βββ utils.py
βββ img
Mention the tools you used to create your project
- OpenCV - OpenCV provides a real-time optimized Computer Vision library, tools, and hardware
- MediaPipe - MediaPipe offers cross-platform, customizable ML solutions for live and streaming media.
- DepthAI - DepthAI is the embedded spatial AI platform built around Myriad X
- NVIDIA GTX960M - Run in RealTime
- OAKD-Lite - Depth Camera
We use SemVer for versioning. For all available versions, see whattags in this repository.
- Adonai Vera - Member DS4a - AdonaiVera
You can also look at the list of all contributors who have participated in this project.
This project is under the License, see the file LICENSE.md more details.
- Tell others about this project π’
- Give thanks publicly π€.