🚀 Drowsiness Detection System

📌 Overview

This project is a real-time drowsiness detection system that monitors eye activity using a webcam and alerts the user when signs of fatigue are detected. It enhances safety in critical situations such as driving or operating machinery.

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⚡ Features

✅ Real-time face and eye detection
✅ Eye blink and drowsiness analysis using facial landmarks
✅ Visual feedback: "Active," "Drowsy," or "Sleeping" state
✅ Buzzer alert if the user is detected as "Sleeping"

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🛠 Technologies Used

• Python: Core logic and detection algorithms
• OpenCV: Video capture and image processing
• Dlib: Face and facial landmark detection
• NumPy: Numerical computations
• Pillow: GUI video feed display
• Tkinter: Graphical user interface
• Playsound: Alert sounds

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🔍 How It Works

📸 Face Detection: Detects the user's face using Dlib’s pre-trained model.
👀 Eye Aspect Ratio (EAR): Measures eye openness using key facial landmarks.
⚡ State Classification:

• Active: Eyes are open and blinking normally.
• Drowsy: Eyes are slightly closed or blinking slowly.
• Sleeping: Eyes remain closed for over 7 seconds. 🔊 Audio Alert: A buzzer sound is triggered if the user remains "Sleeping".

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⚙️ Setup Instructions

1️⃣ Install Dependencies:

pip install opencv-python numpy dlib imutils playsound pillow

2️⃣ Download Pre-Trained Model:

• Download shape_predictor_68_face_landmarks.dat from Dlib’s GitHub and place it in the project directory.

3️⃣ Run the Project:

python drowsiness_detection.py

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📌 Usage Guide

1️⃣ Open the application.
2️⃣ The webcam feed will display in the GUI.
3️⃣ The system monitors eye activity and updates the state: "Active," "Drowsy," or "Sleeping".
4️⃣ If "Sleeping" is detected for over 7 seconds, an alert will sound.

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📂 Project Structure

Drowsiness Detection/
│
├── drowsiness_detection.py         # Main script
├── buzzer.mp3                      # Audio alert file
├── shape_predictor_68_face_landmarks.dat  # Pre-trained model
└── README.md                       # Project documentation

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🚗 Applications

🔹 Driver Safety: Prevent drowsy driving accidents.
🔹 Workplace Monitoring: Improve worker alertness in critical environments.
🔹 Personal Alertness: Help individuals stay focused during long tasks.

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🔮 Future Enhancements

🔹 Yawning detection for better accuracy.
🔹 Deep learning models for advanced drowsiness prediction.
🔹 Mobile-friendly version of the application.
🔹 Multilingual support for international users.

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📜 License

This project is available for personal or educational use. Feel free to customize or extend it! 🚀

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🏆 Connect with Me!

🔥 Stay Alert, Stay Safe! 🚀