LiveDance 💃

Computer vision-powered dance training application with real-time pose feedback

🎯 Project Goal

LiveDance uses computer vision to track a user's body pose from a live camera feed. The system identifies joint positioning and compares it to a reference dance video in order to provide the user with feedback for improvement.

🏗 Architecture

This project uses a clean MVC (Model-View-Controller) architecture:

• Frontend (React): View + Controller layers for UI and logic
• Backend (Python): Model layer with MediaPipe for pose estimation
• Communication: Simple REST API (localhost only, no database)

React Frontend  ←→  Python Backend

📖 Quick Start

# Terminal 1 - Start Python backend
cd backend
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python app.py

# Terminal 2 - Start React frontend
cd frontend
npm install
npm start

📂 Project Structure

LiveDance/
├── frontend/              # React frontend (MVC structure)
│   └── src/
│       ├── LiveDance.js                       # Main component
│       ├── controllers/PoseDetectorController.js
│       ├── views/PoseDetectorView.js
│       └── services/PoseEstimationService.js
│
├── backend/               # Python Flask server
│   ├── app.py            # MediaPipe pose estimation
│   └── requirements.txt
│
└── ARCHITECTURE.md       # Technical documentation

✨ Current Features

• ✅ Real-time body pose tracking (17 keypoints)
• ✅ Hand landmark detection (21 points per hand)
• ✅ Live skeleton overlay visualization
• ✅ Landmark data export (JSON)
• ✅ Clean MVC architecture
• ✅ Python-powered pose estimation

🚧 Coming Soon

• Reference video upload
• Pose comparison algorithm
• Real-time feedback system
• Performance scoring

🛠 Technology Stack

Frontend:

• React 19
• HTML5 Canvas

Backend:

• Python 3.8+
• Flask
• MediaPipe
• OpenCV
• NumPy

📝 License

MIT License - See LICENSE file for details