A real-time computer vision project built using Python, OpenCV, and MediaPipe that tracks hand movements and controls system volume through hand gestures.
- Real-time hand tracking using webcam
- Detection of hand landmarks and finger positions
- Gesture-based system volume control
- Smooth hand movement recognition
- FPS display for performance monitoring
- Interactive visual feedback with tracking points and connections
- Python
- OpenCV
- MediaPipe
- NumPy
- Pycaw
- CV2
- Math Libraries
The project uses MediaPipe Hands to detect and track hand landmarks in real time. By calculating the distance between the thumb and index finger, the program dynamically adjusts the system volume.
- Fingers close together → Lower volume
- Fingers far apart → Higher volume
The hand landmarks are continuously processed using OpenCV for live visualization and interaction.
├── HandTracking.py
├── volumehandtracking.py
├── HandTrackingModule.py
└── README.mdClone the repository:
git clone https://github.com/your-username/your-repository-name.gitInstall dependencies:
pip install opencv-python mediapipe numpy pycaw comtypesRun the project:
python volumehandtracking.py- Gesture-based media controls
- Virtual mouse using hand tracking
- Brightness control through gestures
- Multi-hand interaction support
- AI-based gesture recognition
Real-time webcam visualization with:
- Hand landmark detection
- Finger tracking
- Gesture recognition
- Dynamic volume adjustment
This project helped in understanding:
- Computer Vision fundamentals
- Real-time image processing
- Gesture recognition systems
- Human-computer interaction
- MediaPipe hand landmark detection
This project is licensed under the MIT License. You are free to use, modify, and distribute this project with proper attribution.
⭐ If you like this project, consider giving it a star on GitHub!