This project implements a real-time hand gesture–based brightness control system using OpenCV and MediaPipe. The application detects a user’s hand through a webcam feed and dynamically adjusts the screen brightness based on the distance between the thumb tip and index fingertip.
By mapping finger distance to a brightness range of 0–100, the system enables touchless, intuitive control of screen brightness. This project demonstrates the practical use of computer vision and human–computer interaction techniques.
- Real-time hand tracking using MediaPipe
- Detection of thumb tip and index fingertip landmarks
- Distance-based gesture measurement
- Dynamic screen brightness control
- Smooth and continuous brightness mapping
- Cross-platform support (Windows and Linux)
- Capture live video stream using OpenCV
- Detect and track hand landmarks using MediaPipe
- Extract thumb and index fingertip coordinates
- Compute Euclidean distance between fingertips
- Map distance to brightness percentage
- Update system brightness in real time
- Python
- OpenCV
- MediaPipe
- NumPy
- Screen-Brightness-Control
Install the required libraries before running the project:
pip install mediapipe
pip install opencv-python
pip install screen-brightness-control
pip install numpy- Touchless human–computer interaction
- Assistive technology systems
- Smart environment control
- Computer vision–based automation
- Gesture-controlled interfaces
This project demonstrates how computer vision and hand landmark detection can be used to build a real-time, gesture-controlled brightness adjustment system. It highlights the integration of OpenCV and MediaPipe for intuitive human–machine interaction and practical desktop automation.
- Add gesture smoothing and calibration
- Extend control to volume, scrolling, or media playback
- Build a graphical user interface
- Integrate voice commands
- Package as a desktop application