Raise Hand Detector

This is a simple application that uses the webcam to detect if you raise your hand, built with OpenCV, MediaPipe, and Streamlit. It displays a message whenever hand is raised.

Features

• Detects if your hand is raised in front of the webcam.
• Uses MediaPipe for hand landmark detection.
• Streamlit for a simple web interface.
• Real-time video streaming with Streamlit WebRTC.

Requirements

• Python 3.7+
• OpenCV
• MediaPipe
• Streamlit
• Streamlit WebRTC
• NumPy

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/raise-hand-detection.git
   cd raise-hand-detector

2. Install the required dependencies:

   pip install -r requirements.txt

Usage

To run the hand raise detector, execute:

streamlit run main.py

This will start a Streamlit server, and you can access the application in your web browser.

File Structure

• main.py: The main script that runs the hand raise detector.
• requirements.txt: Lists all dependencies required to run the project.
• README.md: Project documentation.

How It Works

• The script uses MediaPipe to track hand landmarks.
• It checks if all fingers are straight up (by comparing the Y-coordinates of each fingertip with the preceding joint).
• If all fingers are raised, it displays a message "I raised my hand!" on the video feed.

Images Displayed

• Hand Raised: When the application detects a raised hand, it displays a message saying "I raised my hand!" on the video feed. The image shown below (images/raised.png) provides a visual representation of what it looks like when a hand is successfully detected as raised:

  

• Hand Not Raised: If no hand is raised, or the conditions for detecting a raised hand are not met, the application will not display the message. The default state of the application is depicted in the image below (images/not.png):

  

Contributing

Feel free to open issues or submit pull requests to improve the functionality or extend the project.

License

This project is open-source and available under the MIT License.

Acknowledgments

• MediaPipe for the hand detection model.
• Streamlit for making web applications easy to build.

Explanation:

• Project Overview: Provides a simple description of what the project does.
• Features: Describes the main capabilities.
• Requirements and Installation: Guides users through installing necessary packages and running the app.
• Usage Instructions: Shows how to start the application.
• How It Works: Explains the logic of hand detection.
• Contribution and License: Information for those who want to contribute or know the legal permissions.