🎮 Valorant Gameplay Analyzer

A computer vision-based tool for analyzing Valorant gameplay videos, focusing on kill feed detection and weapon tracking using YOLO models.

✨ Core Features

🎯 Kill Feed Analysis

• 🔍 Detects and parses kill events from gameplay footage
• 👥 Tracks killer, victim, weapon used, and special events (headshots, wallbangs)
• 📊 Generates detailed scoreboard statistics
• 🎨 Supports team color detection and character identification

🔫 Weapon Tracking

• 📈 Tracks weapon usage throughout the game
• 🎯 Identifies 19 different weapons including:
  • 🏹 Primary weapons (Vandal, Phantom, Operator, etc.)
  • 🔫 Secondary weapons (Classic, Shorty, Sheriff, etc.)
  • ⚔️ Melee weapons
• 📊 Generates weapon usage statistics and patterns

🛠️ Technical Implementation

🔧 Backend (Python)

• Computer Vision: OpenCV for video processing
• Object Detection: YOLO models for:
  • Kill feed detection
  • Weapon identification
  • Character recognition
• Data Processing:
  • Kill event parsing
  • Weapon tracking
  • Scoreboard generation
• API: Flask server for video upload and analysis

🎨 Frontend (Next.js)

• Video upload interface
• Results visualization
• Real-time processing status

📁 Project Structure

├── server/
│   ├── models/            # YOLO model files
│   ├── killlfeed.py      # Kill feed detection and parsing
│   ├── weapon_tracker.py # Weapon usage tracking
│   ├── color_detection.py # Team color detection
│   ├── constants.py      # Game constants and mappings
│   └── app.py           # Flask API server
│
└── client/
    ├── app/             # Next.js application
    ├── lib/             # Utility functions
    └── public/          # Static assets

⚙️ Setup

1. Install Dependencies

   # Backend
   cd server
   pip install -r requirements.txt
   
   # Frontend
   cd client
   npm install

2. Configure Models

   • Place YOLO model files in server/models/
   • Ensure correct model paths in config.py

3. Start Servers

   # Backend
   python app.py
   
   # Frontend
   npm run dev

📝 Usage

1. Upload a Valorant gameplay video through the web interface
2. The system will:
   • Process the video frame by frame
   • Detect and parse kill events
   • Track weapon usage
   • Generate statistics
3. View the analysis results including:
   • Kill feed events
   • Weapon usage patterns
   • Player statistics

📋 Requirements

• Python 3.8+
• Node.js 18+
• FFmpeg
• CUDA-capable GPU (recommended for faster processing)
• YOLO model files for:
  • Kill feed detection
  • Weapon identification
  • Character recognition

💻 Development

The project uses:

• YOLOv8 for object detection
• OpenCV for video processing
• Flask for API server
• Next.js for frontend
• TypeScript for type safety