Hand Gesture Detection with MediaPipe and TensorFlow/Keras

This project utilizes MediaPipe and TensorFlow/Keras to detect hand gestures in real-time using a webcam feed. It recognizes various hand gestures and displays the corresponding class names on the video stream.

Table of Contents

• Introduction
• Installation
• Usage
• Contributing

Introduction

Hand gesture detection has numerous applications, from sign language recognition to human-computer interaction. This project demonstrates how to use MediaPipe for hand landmark detection and TensorFlow/Keras for gesture recognition, providing a simple yet effective solution for real-time gesture detection.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/hand-gesture-detection.git

2. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Download the pre-trained gesture recognizer model (mp_hand_gesture) and class names (gesture.names) files.

2. Place the downloaded model and class names files in the project directory.

3. Run the main script:

   python hand_gesture_detection.py

4. Position your hand in front of the webcam and perform various gestures.

5. The recognized gesture will be displayed on the video stream in real-time.

6. Press 'q' to quit the application.

Contributing

Contributions are welcome! If you want to contribute to this project, please follow these steps:

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