Pose Classification Project

Introduction

This project uses MoveNet for real-time pose estimation and a custom neural network model for pose classification, specifically for squat classification. It detects and counts squat reps performed by the user. The application is built with Streamlit and OpenCV for easy use and visualization.

Installation and Usage

To set up the project, follow these steps:

1. Clone the repository to your local machine with git clone https://github.com/MohamedMBashir/MoveNet-Pose-Classifier.git.
2. Move the the projet directory and Install the required dependencies using pip install -r requirements.txt.
3. Run streamlit run streamlit_app.py.

Training Your Own Model

If you're interested in training your own classification model, you can refer to this tutorial for guidance on how to get started:

• Pose Classification Tutorial

This tutorial provides step-by-step instructions on how to train a pose classification model using TensorFlow Lite.

License

MIT