https://github.com/mktechai-0786/virtualmousex

Virtual Mouse by Hand Gestures
https://github.com/mktechai-0786/virtualmousex

machine-learning mediapipe-hands opencv-python

Last synced: 16 days ago
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Virtual Mouse by Hand Gestures

• Host: GitHub
• URL: https://github.com/mktechai-0786/virtualmousex
• Owner: MKTechAI-0786
• Created: 2024-12-16T17:20:37.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-12-16T17:35:48.000Z (about 1 month ago)
• Last Synced: 2024-12-16T18:27:05.385Z (about 1 month ago)
• Topics: machine-learning, mediapipe-hands, opencv-python
• Language: Python
• Homepage:
• Size: 5.86 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# VirtualMouseX

This project leverages Mediapipe, OpenCV, and PyAutoGUI to create a virtual mouse controlled by hand gestures and add various features.The application enables intuitive and hands-free computer control.

#Features

  Hand Gesture Control:

    Move the mouse cursor using hand gestures.

    Perform left-click, right-click, double-click, and scrolling using specific gestures.

    Take screenshots by extending all fingers.

    

  Gesture-Based Commands:

    Scroll up/down using thumb and ring or pinky fingers.

    App switching using swipes.

    

  Dynamic Recognition:

    All gestures are detected in real-time using Mediapipe’s hand tracking module.

#Software Requirements

  Python 3.7 or later

  Required Python libraries:

    opencv-python

    mediapipe

    pyautogui

    pynput

  You can install the dependencies by running the following command:

    pip install opencv-python mediapipe pyautogui pynput

#Hardware Requirements

  Webcam or any external camera for real-time hand tracking.

  A system capable of running Python 3 smoothly.

#Procedure to Run the Project

  1. Clone this repository:

    git clone https://github.com/your-username/facial-emotion-recognition.git

    cd facial-emotion-recognition

  2. Install the required dependencies:

    pip install -r requirements.txt

  3. Run the script:

    python gesture_mouse_control.py

Ensure that the webcam is active and your hand gestures are visible to the camera. The application will process the hand gestures in real time and perform corresponding actions.

#How It Works

  1. Hand Tracking:

    Uses Mediapipe’s hand detection and tracking module to capture real-time hand landmarks.

    Calculates the position and distance between landmarks to detect gestures.

  2. Gesture Recognition:

    Predefined gestures are mapped to mouse and system actions like scrolling, clicking, and volume control.

  3. System Interaction:

    PyAutoGUI is used to simulate mouse and keyboard actions based on detected gestures.

#Conclusion

This project demonstrates the potential of combining computer vision and system automation for hands-free interaction. It offers a convenient alternative to traditional input devices and can be further extended for accessibility applications and advanced gesture control in smart environments.

#Acknowledgements

Mediapipe for the robust hand tracking framework.

OpenCV for video processing.

PyAutoGUI for system control integration.