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!