Drowsiness Detection System

📌 Project Overview

This project is a real-time drowsiness detection system using OpenCV, Dlib, and Streamlit. It helps detect drowsiness in drivers or users by analyzing eye closure duration using the PERCLOS method.

⚙️ Features

• Real-time eye detection using Dlib's face landmark predictor
• PERCLOS Calculation for drowsiness detection
• Audio Alerts to warn the user when drowsiness is detected
• Live Streaming UI built with Streamlit for easy interaction

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🛠 Installation Guide

1️⃣ Clone the Repository

git clone https://github.com/ayushh0406/Drowsiness_-Detection.git
cd Drowsiness_-Detection

2️⃣ Install Dependencies

Ensure you have Python installed (recommended: Python 3.8+). Then, install dependencies:

pip install -r requirements.txt

3️⃣ Download Shape Predictor Model

Download the shape_predictor_68_face_landmarks.dat file from: 🔗 Dlib Model Download

Extract the .bz2 file and place shape_predictor_68_face_landmarks.dat inside the project folder.

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🚀 How to Run the Project

▶️ Running with OpenCV (Terminal)

python app.py

▶️ Running with Streamlit (Web UI)

streamlit run app.py

This will launch a web-based UI where you can see the live video feed and drowsiness alerts.

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🌍 Deployment on Streamlit Cloud

To deploy the app online: 1️⃣ Go to Streamlit Cloud 2️⃣ Click "New App" 3️⃣ Select your GitHub repo 4️⃣ Choose app.py as the entry file 5️⃣ Click "Deploy"

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🤝 Contributing

Feel free to fork this repo, make improvements, and submit a pull request! 😊

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📩 Contact

For queries, reach out to [Your Email or GitHub Profile].

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⭐ If you like this project, please give it a star on GitHub! ⭐