This project combines computer vision with hand tracking and gesture recognition to control system volume using hand movements. The project utilizes Python libraries such as mediapipe, opencv, and pycaw to detect and process hand gestures, calculate the distance between fingers, and map it to system volume levels.
- Hand Tracking: Detect and track hand landmarks in real-time using the Mediapipe library.
- Gesture Recognition: Measure the distance between the thumb and index finger to adjust volume.
- Volume Control: Leverage the
pycawlibrary to directly manipulate the system volume. - Real-Time Performance: Achieve smooth real-time performance using optimized hand tracking and frame processing.
HandTrackingModule.ipynb: A Jupyter Notebook implementing the hand tracking module with Mediapipe. This serves as a reusable component for detecting and processing hand gestures.VolumeHandcontrol.ipynb: A Jupyter Notebook that demonstrates the full functionality of gesture-based volume control using the hand tracking module.
Ensure the following tools are installed:
- Python 3.8 or later
pip(Python package manager)
Activate your virtual environment (if applicable) and install the required libraries:
pip install opencv-python mediapipe numpy comtypes pycaw