Hand Tracking Module

A robust and efficient hand tracking solution built with MediaPipe and OpenCV, designed to detect and track hand landmarks in real-time. This module serves as a foundation for building more complex hand gesture-based applications.

Features

• Real-time hand detection and tracking
• 21 hand landmark detection points
• Finger position identification
• Modular design for easy integration
• Optimized performance using MediaPipe's ML pipeline

Technical Stack

• Python - Core programming language
• OpenCV - Computer vision and image processing
• MediaPipe - Machine learning pipeline and hand tracking solutions
• NumPy - Numerical computing and array operations

Prerequisites

• Python 3.9 or higher
• Webcam or video input device

Installation

1. Clone the repository:

git clone https://github.com/navid001/Hand-Tracking-Module.git
cd Hand-Tracking-Module

2. Create and activate a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install required packages:

pip install -r requirements.txt

Usage

Basic Implementation

import cv2
from hand_tracking_module import HandTracker

# Initialize the hand tracker
tracker = HandTracker()

# Start video capture
cap = cv2.VideoCapture(0)

while True:
    success, image = cap.read()
    if not success:
        break
        
    # Process the image and get hand landmarks
    image = tracker.find_hands(image)
    landmark_list = tracker.find_position(image)
    
    cv2.imshow("Hand Tracking", image)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

Landmark Reference

The module detects 21 hand landmarks, numbered from 0 to 20:

• 0-4: Thumb
• 5-8: Index finger
• 9-12: Middle finger
• 13-16: Ring finger
• 17-20: Pinky

Configuration

You can customize the tracking parameters when initializing the HandTracker:

tracker = HandTracker(
    static_image_mode=False,
    max_num_hands=2,
    min_detection_confidence=0.5,
    min_tracking_confidence=0.5
)

Performance Considerations

• The module is optimized for real-time processing
• Recommended minimum specifications:
  • CPU: Dual-core processor, 2.0 GHz or higher
  • RAM: 4GB or higher
  • Camera: 30 FPS minimum

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• MediaPipe team for their excellent hand tracking solutions
• OpenCV community for their comprehensive computer vision tools

Contact

Your Name - @navid001 Project Link: https://github.com/navid001/Hand-Tracking-Module