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🎯 RealVision Object Understanding AI

Python 3.8+ PyQt6 OpenCV License: MIT

A professional real-time object detection and understanding system powered by state-of-the-art AI models. Features advanced computer vision capabilities, comprehensive analytics, and an intuitive GUI interface.

✨ Key Features

  • 🎥 Real-time Detection: Live camera feed and video file processing
  • 🤖 Multiple AI Models: YOLOv8, MobileNet-SSD, ONNX, and EfficientDet support
  • 📊 Advanced Analytics: Performance monitoring and detailed reporting
  • 🖥️ Professional GUI: Modern PyQt6 interface with real-time controls
  • 📈 Data Science Integration: Comprehensive performance analysis
  • 🎯 Object Tracking: Advanced multi-object tracking capabilities
  • 📷 Export Features: Screenshots, reports, and analytics data export

🚀 Quick Start

Installation

  1. Clone the repository:

    git clone https://github.com/yourusername/RealVision-ObjectUnderstandingAI.git
cd RealVision-ObjectUnderstandingAI

  2. Install dependencies:

    pip install -r requirements.txt

  3. Run the application:

    # Launch GUI version
python main.py --gui

# Or use the console version
python main.py

Usage Examples

# Launch GUI application
python main.py --gui

# Run with specific camera
python main.py --camera 1

# Process video file
python main.py --video path/to/video.mp4

# Use specific AI model
python main.py --model yolo

# Run analytics demo
python main.py --demo

🏗️ Project Structure

RealVision-ObjectUnderstandingAI/
├── main.py                 # Main application entry point
├── gui.py                  # PyQt6 GUI interface
├── requirements.txt        # Project dependencies
├── src/                    # Core source code
│   ├── main.py            # Main application logic
│   ├── performance_analyzer.py  # Analytics engine
│   ├── demo_analytics.py  # Demo and sample data
│   └── run.py             # Console runner
├── models/                 # AI model files
├── data/                   # Performance data storage
├── output/                 # Generated reports and exports
├── screenshots/            # Captured screenshots
└── docs/                   # Documentation

🎮 Controls

GUI Interface

  • Open Camera: Start live camera feed
  • Upload Video: Load and process video files
  • Stop: Stop current processing
  • Show Analytics: View performance dashboards
  • Generate Report: Create comprehensive analytics reports

Keyboard Controls (Console Mode)

  • Q - Quit application
  • SPACE - Pause/Resume
  • S - Take screenshot
  • M - Switch between AI models
  • C - Toggle confidence scores
  • T - Toggle tracking IDs
  • +/- - Adjust confidence threshold
  • A - Generate analytics report
  • D - Toggle data logging

🤖 Supported AI Models

Model Description Speed Accuracy
YOLOv8 Latest YOLO architecture ⚡⚡⚡ 🎯🎯🎯🎯
MobileNet-SSD Lightweight detection ⚡⚡⚡⚡ 🎯🎯🎯
ONNX Cross-platform inference ⚡⚡⚡ 🎯🎯🎯🎯
EfficientDet Google's efficient detection ⚡⚡ 🎯🎯🎯🎯🎯

📊 Analytics Features

  • Real-time Performance Monitoring: FPS, processing time, detection counts
  • Model Comparison: Side-by-side performance analysis
  • Statistical Analysis: Comprehensive performance metrics
  • Interactive Dashboards: Visual performance reports
  • Data Export: JSON, CSV, and plot exports
  • Time Series Analysis: Performance trends over time

🛠️ Advanced Configuration

Environment Variables

# Model preferences
export REALVISION_MODEL=yolo
export REALVISION_CONFIDENCE=0.6

# Performance settings
export REALVISION_MAX_FPS=30
export REALVISION_BUFFER_SIZE=3

Custom Model Integration

from src.main import ObjectUnderstandingApp

# Custom model configuration
app = ObjectUnderstandingApp(preferred_model='yolo')
app.confidence_threshold = 0.7
app.run()

🔧 Development

Running Tests

# Run all tests
python -m pytest tests/

# Test specific components
python tests/test_imports.py
python tests/test_data_science.py

Code Quality

# Format code
black src/ gui.py main.py

# Type checking
mypy src/

📈 Performance Benchmarks

Model FPS (1080p) Memory Usage CPU Usage
YOLOv8 30-45 800MB 65%
MobileNet-SSD 45-60 400MB 45%
ONNX 35-50 600MB 55%

🤝 Contributing

  1. Fork the repository
  2. Create a 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.

🙏 Acknowledgments

📞 Support

For support, please open an issue in the GitHub repository or contact the development team.

Author: Mehmet Kahya
Date: July 2025
Version: 1.0.0