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Hand Gesture Recognition System

Overview

This project implements a hand gesture recognition system using a webcam, leveraging MediaPipe for detecting hand landmarks and a pre-trained TensorFlow model for classifying gestures. The gestures are displayed in real-time on the webcam feed.

Features

  • Real-time hand gesture recognition using a webcam.
  • Gesture classification based on a pre-trained model.
  • Supported gestures include:
    • Okay
    • Peace
    • Thumbs up
    • Thumbs down
    • Call me
    • Stop
    • Rock
    • Live long
    • Fist
    • Smile

Requirements

  • Python 3.x
  • OpenCV
  • NumPy
  • MediaPipe
  • TensorFlow

Setup and Installation

  1. Clone this repository:

    git clone https://github.com/Samuel-0316/hand-gesture-recognition.git
cd hand-gesture-recognition

  2. Install the required packages:

    pip install opencv-python numpy mediapipe tensorflow

  3. Download or create a pre-trained model and save it as mp_hand_gesture.h5

  4. Create a text file named gesture.names containing the class names of the gestures.

Usage

-> Run the script to start the gesture recognition:

python your_script_name.py

Make sure your webcam is connected. The recognized gesture will be displayed on the video feed. Press q to exit the application.

Contributing

Feel free to submit issues, fork the repository, or create pull requests. Contributions are welcome!