Hand Gesture Recognition is a real-time system that detects and recognizes hand gestures using MediaPipe and OpenCV. This project demonstrates the potential of computer vision for dynamic, interactive applications such as finger counting and gesture-based control.
- 🔍 Real-Time Hand Tracking: Accurately detects and tracks hand landmarks in video streams.
- ✋ Gesture Recognition: Counts the number of fingers raised dynamically.
- 🌐 Cross-Platform Compatibility: Works on any system supporting Python, OpenCV, and MediaPipe.
- Python 3.7+
- OpenCV
- MediaPipe
git clone https://github.com/Arfazrll/Hand-Gesture-Recognition.git
cd your-repo-namepython -m venv HandTracking-env
HandTracking-env\Scripts\Activatepip install opencv-python mediapipepython HandsTrackingAI.py- Allow camera access to start hand tracking.
- Observe the live feed with hand landmarks highlighted and the number of raised fingers printed in the console.
- Press
qto close the application.
The system leverages the MediaPipe Hands solution for robust hand detection:
mp_hands = mp.solutions.hands
hands = mp_hands.Hands(
static_image_mode=False,
max_num_hands=2,
min_detection_confidence=0.5,
min_tracking_confidence=0.5
)- Add recognition for specific gestures (e.g., thumbs up, peace sign).
- Support multiple hand tracking simultaneously.
- Optimize for mobile deployment using TensorFlow Lite.
- 🐍 Python: Core programming language.
- 📦 MediaPipe: For hand landmark detection.
- 📊 OpenCV: For video processing and visualization.
Feel free to fork this repository, submit pull requests, or open issues for suggestions and improvements. Thank You :)