Drowsiness Detection

This project implements a drowsiness detection system using Python, OpenCV, and Dlib. The system detects the drowsiness of a driver by monitoring their eye aspect ratio (EAR) and displays a warning message on the screen when drowsiness is detected.

Features

• Real-time drowsiness detection using a webcam.
• Uses Dlib's pre-trained facial landmark predictor.
• Calculates the eye aspect ratio to detect drowsiness.
• Displays warning messages on the screen when drowsiness is detected.

Code Explanation

For a detailed explanation of the code, see Code Explanation

Requirements

• Python 3.x
• OpenCV
• Dlib
• Scipy
• Time

Installation

1. Clone this repository to your local machine:

   https://github.com/prakash-kannaiah/Drowsiness_Detection.git
   cd drowsiness-detection

2. Create and activate a new Python virtual environment:

   python -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install the required packages:

   pip install -r requirements.txt

4. Download the facial landmark predictor model and place it in the shape directory:

   • shape_predictor_68_face_landmarks.dat

5. Extract the .bz2 file:

   On Windows:

   • Using 7-Zip:

     1. Right-click the .bz2 file in File Explorer.
     2. Select 7-Zip > Extract to "shape_predictor_68_face_landmarks.dat\".

   • Using WinRAR:

     1. Right-click the .bz2 file in File Explorer.
     2. Select Extract Here or Extract to "shape_predictor_68_face_landmarks.dat\".

   Alternatively, you can use the following command in a command prompt if you have bzip2 installed:

   bzip2 -d shape/shape_predictor_68_face_landmarks.dat.bz2

   This will extract the file directly into the shape folder as shape_predictor_68_face_landmarks.dat.

   If you don’t have bzip2 installed, you can download it from the GnuWin32 project or use the GUI tools mentioned above.

Project Structure

drowsiness-detection/

• shape/shape_predictor_68_face_landmarks.dat
• drowsiness_detection.py
• requirements.txt

Usage

Run the drowsiness detection script:

python drowsiness_detection.py

Contributing

We welcome contributions to improve this project! Here are some guidelines to get started:

1. Fork the Repository: Click on the "Fork" button at the top right of this repository page to create a copy of this repository under your GitHub account.

2. Clone the Repository: Use git clone to clone the forked repository to your local machine.

   git clone https://github.com/your-username/drowsiness-detection.git
   cd drowsiness-detection

3. Create a New Branch: Create a new branch for your feature or bugfix.

   git checkout -b my-new-feature

4. Make Changes: Make your changes to the codebase, ensuring you follow the existing code style and add appropriate tests if needed.

5. Commit Your Changes: Commit your changes with a meaningful commit message.

   git commit -m "Add a meaningful commit message here"

6. Push to Your Fork: Push your changes to your forked repository.

   git push origin my-new-feature

7. Open a Pull Request: Go to the original repository on GitHub and open a pull request with a clear description of your changes.

Thank you for contributing!

License

This project is licensed under a custom license. You are free to use, copy, modify, and distribute the code and associated documentation, provided that you include the original copyright notice and adhere to the following condition:

• Non-Commercial Use: This project may not be sold or otherwise used for commercial purposes. Redistribution or use of this project for commercial purposes, including but not limited to selling or licensing, is not permitted.

For more details, please refer to the LICENSE file in this repository.