This project enables real-time hand motion recognition based on color detection using OpenCV in Python. By detecting color changes in a live video feed, the script tracks hand movements and draws corresponding trajectories on the screen.
Hand motion recognition is a fundamental aspect of human-computer interaction, with applications ranging from virtual reality gaming to sign language translation. This project utilizes the power of OpenCV, a popular computer vision library, to detect and track hand movements based on predefined color thresholds.
- Real-time Tracking: The script captures live video feed and tracks hand movements in real-time.
- Color Detection: Hand motions are recognized based on changes in predefined color thresholds.
- Trajectory Drawing: Detected hand movements are visualized by drawing trajectories on the screen.
- Color Calibration: Use the trackbars provided in the GUI window to adjust upper and lower HSV (Hue, Saturation, Value) thresholds for color detection.
- Hand Motion Recognition: As you move your hand within the camera's field of view, the script detects color changes and tracks your hand's movements.
- Visualization: Visualize your hand movements on the screen as the script draws trajectories corresponding to detected color changes.
- Color Detection: Adjust the upper and lower HSV thresholds using the trackbars to optimize color detection based on environmental conditions.
- Frame Processing: Fine-tune frame processing parameters such as kernel size for morphological operations to enhance detection accuracy.
Contributions to this project are welcome! Whether it's bug fixes, enhancements, or new features, feel free to open issues or submit pull requests to improve the functionality of this hand motion recognition script.
This project is licensed under the MIT License. Refer to the LICENSE file for detailed terms and conditions.