ARNet: Augmented Reality Network Troubleshooting Tool

ARNet is an innovative tool that combines Augmented Reality with network diagnostics to help technicians troubleshoot network devices more efficiently. It overlays real-time network information onto physical devices using QR code identification.

Features

• Real-time device identification using QR codes
• Live network diagnostics overlay
• Support for multiple network protocols (SNMP, SSH)
• Configurable AR visualization
• Automatic device status monitoring
• Frame capture and logging capabilities

Requirements

• Python 3.8 or higher
• OpenCV
• NumPy
• pyzbar
• pysnmp
• paramiko
• netmiko
• Flask
• Pillow

Installation

1. Clone the repository:

git clone https://github.com/yourusername/ARNet.git
cd ARNet

2. Create a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

Configuration

The application can be configured through the config.json file. Key settings include:

• Camera settings (resolution, FPS)
• Scanning intervals
• Network timeouts
• AR overlay appearance
• QR detection parameters

Usage

1. Start the application:

python ARNet/main.py

2. Controls:

• 'q' - Quit application
• 'h' - Show help
• 'r' - Refresh device scan
• 's' - Save current frame
• 'c' - Clear current device

3. Point the camera at a network device with a QR code to begin scanning.

QR Code Format

QR codes should contain JSON-formatted device information:

{
    "device_id": "TL-WR940N",
    "ip": "192.168.0.1",
    "model": "TL-WR940N V6",
    "type": "Router"
}

Logging

Logs are stored in arnet.log with detailed information about:

• Device detection events
• Network diagnostic results
• Error conditions
• System status

Project Structure

ARNet/
├── ar_overlay/      # AR visualization components
├── network_tools/   # Network communication and diagnostics
├── qr_generation/   # QR code creation functionality
├── qr_detection/    # QR code scanning and recognition
├── data/           # Data storage
└── main.py         # Application entry point

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

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

Acknowledgments

• OpenCV team for computer vision capabilities
• ZBar project for QR code detection
• Network protocol library maintainers