🏸 Badminton AI LangGraph Analysis

AI-powered badminton performance analysis using computer vision, LangGraph orchestration, and Gemini AI

🎥 Click above to watch: Revolutionizing Badminton Training with AI: A Deep Dive into LangGraph-Powered Analysis

🚀 What It Does

Transform your badminton videos into comprehensive performance insights with cutting-edge AI technology:

🎯 Core Features

• 🧠 Real-time pose detection - Track 33 body keypoints with MediaPipe precision
• ⚡ LangGraph orchestration - Seamless linear pipeline processing
• 📊 AI-generated reports - Customized insights for coaches, players & parents
• 🌍 Multi-language support - Available in English, Hindi, Tamil, Telugu, Kannada
• 🎥 Video annotations - Professional pose tracking visualization
• 📈 Performance metrics - Advanced analysis including elbow angles and wrist distances
• 📄 PDF report generation - Professional formatting with multilingual support

🏗️ System Architecture

Linear pipeline architecture powered by LangGraph orchestration

The system follows a sophisticated pipeline:

1. Video Input → MediaPipe pose detection
2. Data Processing → Performance metrics calculation
3. AI Analysis → Gemini-powered insights generation
4. Report Generation → Multi-format output (PDF, TXT, Annotated Video)

🛠️ Prerequisites

Before getting started, ensure you have:

Requirement	Version	Purpose
Python	3.8+	Core runtime
OpenCV	Latest	Video processing
MediaPipe	Latest	Pose detection
Gemini API Key	-	AI analysis
CUDA GPU	Optional	Faster processing

⚡ Quick Start

🔧 Installation

# 1. Clone the repository
git clone https://github.com/Shree2604/Badminton-AI-LangGraph-Analysis.git
cd Badminton-AI-LangGraph-Analysis

# 2. Create virtual environment
python -m venv venv

# 3. Activate virtual environment
# Linux/Mac:
source venv/bin/activate
# Windows:
.\venv\Scripts\activate

# 4. Install dependencies
pip install -r requirements.txt

🔑 Environment Setup

Create a .env file in the project root:

GEMINI_API_KEY=your_gemini_api_key_here

🚀 Run Analysis

# Basic usage
python main.py --video_path your_match.mp4

# Advanced usage with all options
python main.py \
  --video_path your_match.mp4 \
  --roles coach,student,parent \
  --language en \
  --output_dir ./analysis_results

🌐 Web Application

Deploy the web interface for easy access:

# Navigate to web app
cd web_app

# Install web dependencies
pip install -r requirements.txt

# Launch Flask application
python app.py

🌟 Access at: http://localhost:5000

🎯 Feature Overview

🎥 Video Processing	📊 Analytics	📝 Reporting
MediaPipe pose detection	Performance metrics	Role-based insights
Frame extraction	Elbow angle analysis	Multi-language support
RGB conversion	Wrist distance tracking	PDF generation
Pose visualization	Movement patterns	Professional formatting

🔍 Detailed Capabilities

🎯 Pose Analysis	📈 Performance Metrics	🗣️ Multi-Modal Analysis
33-point body keypoint tracking	Joint angle calculations	Video pose detection
Real-time pose estimation	Distance measurements	Audio transcription (Google Web Speech API)
Movement pattern recognition	Movement velocity analysis	Combined insights generation
Biomechanical analysis	Form assessment	Speech-to-text processing

🤝 Contributing

We welcome contributions! Here's how to get involved:

🔄 Development Workflow

1. Fork the repository
2. Create feature branch: git checkout -b feature/amazing-feature
3. Commit changes: git commit -m 'Add amazing feature'
4. Push to branch: git push origin feature/amazing-feature
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📧 Support & Contact

Need help or have questions?

Built with ❤️ by ShreeRaj Mummidivarapu

Elevating badminton training through artificial intelligence