Wiring Diagram:

LED (+) → Arduino Pin → LED (-) → 220Ω Resistor → GND

💻 Running the Project

Step 1: Update COM Port

Edit open_cv_python_arduino_controll_led_/controller.py and update the COM port:

comport = 'COM18'  # Windows
# comport = '/dev/ttyUSB0'  # Linux
# comport = '/dev/tty.usbserial'  # macOS

Find your COM port:

python -c "import serial.tools.list_ports; print([p.device for p in serial.tools.list_ports.comports()])"

Step 2: Run the Script

Option A - Step by Step:

cd open_cv_python_arduino_controll_led_
python new.py

Option B - Quick Start (Windows):

Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass -Force; .\venv_310\Scripts\Activate.ps1; cd open_cv_python_arduino_controll_led_; python new.py

Step 3: Use Gestures to Control LEDs

Show your hand to the webcam and make different gestures to control the LEDs!

Press 'k' key to exit the program.

🤲 Gesture Controls

📁 Project Structure

Real-Time-Hand-Gesture-Recognition-for-LED-Control-Using-OpenCV-and-CVZone/
│
├── arduino_code/
│   └── arduino_code.ino          # Arduino sketch for LED control
│
├── open_cv_python_arduino_controll_led_/
│   ├── new.py                     # Main Python script (hand detection)
│   ├── controller.py              # Arduino communication logic
│   └── SETUP_AND_RUN_NOTES.md    # Detailed setup instructions
│
├── record.mp4                     # Demo video
├── README.md                      # This file
├── LICENSE                        # MIT License
├── . gitignore                     # Git ignore rules
└── requirements.txt               # Python dependencies

🔧 Customization

Adjust Detection Sensitivity

Edit new.py and modify detection confidence:

detector = HandDetector(detectionCon=0.8, maxHands=1)

• Lower value (0.5): More sensitive, may detect false positives
• Higher value (0.9): Less sensitive, requires clearer hand visibility

Add Custom Gestures

In controller.py, add new gesture patterns:

def led(fingerUp):
    if fingerUp == [1,0,0,0,1]:  # Thumb + Pinky = "Rock on" gesture
        led_1.write(1)
        led_5.write(1)
        # Turn on other LEDs as needed

Change LED Pins

Update both arduino_code. ino and controller.py if using different pins.

🎯 How It Works

1. Webcam Capture: OpenCV captures live video from your webcam
2. Hand Detection: CVZone's HandDetector identifies hand landmarks (21 points)
3. Finger Tracking: Algorithm analyzes finger positions to determine which are up/down
4. Serial Communication: Python sends finger state data to Arduino via PyFirmata
5. LED Control: Arduino receives commands and controls corresponding LEDs

Technology Stack:

• OpenCV: Video capture and image processing
• CVZone: Simplified hand tracking using MediaPipe
• PyFirmata: Python-Arduino communication protocol
• Arduino: Hardware control and LED management

🐛 Troubleshooting

🔮 Future Enhancements

• Support for multiple hands simultaneously
• RGB LED control with color-changing gestures
• Gesture-based brightness control (PWM)
• Mobile app integration via Bluetooth/WiFi
• Voice + gesture hybrid control system
• Machine learning for custom gesture training
• Web dashboard for remote monitoring

🤝 Contributing

Contributions are welcome! Here's how you can help:

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

📝 License

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

👨‍💻 Author

Saviru Ferdinando

• GitHub: @Saviru1
• Project Link: Real-Time Hand Gesture Recognition for LED Control

🙏 Acknowledgments

• MediaPipe by Google for hand landmark detection
• CVZone for simplified computer vision workflows
• Arduino community for excellent hardware support
• OpenCV for powerful image processing capabilities

📸 Screenshots & Demo

circuit_diagram.png

Watch the demo video: record.mp4

💡 Tips for Best Results

1. Lighting: Use bright, even lighting for better detection
2. Background: Plain, contrasting background improves accuracy
3. Distance: Keep hand 1-2 feet from camera
4. Steady Hand: Hold gestures steady for 0.5-1 second
5. Camera Position: Position camera at eye level

Made with ❤️ using Python, OpenCV, and Arduino

⭐ Star this repo if you found it helpful!