😴 Drowsiness Detection System using OpenCV and Dlib

This is a Python-based real-time Drowsiness Detection System that uses a webcam to detect signs of drowsiness by monitoring the Eye Aspect Ratio (EAR) through facial landmarks.

If the user's eyes remain closed for a certain number of consecutive frames, the system triggers:

• 🔔 Beep alarm
• 🗣️ Voice alert: "Wake up! You're feeling drowsy."

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📌 Features

• Real-time facial landmark detection
• Eye Aspect Ratio (EAR) calculation
• Drowsiness alert with beeping sound
• Text-to-speech voice warning
• Live webcam visualization with eye landmarks

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🛠️ Technologies Used

• Python 3.11
• OpenCV
• Dlib (with shape_predictor_68_face_landmarks.dat)
• NumPy
• SciPy
• Pyttsx3 (text-to-speech)
• Winsound (for beep alert on Windows)

📸 How It Works

• The webcam captures live video.
• Dlib’s 68 facial landmark model locates eyes.
• The Eye Aspect Ratio (EAR) is computed for each frame.
• If eyes remain closed beyond a threshold duration, the system:
  • Triggers a beep alert
  • Speaks a voice warning: "Wake up! You're feeling drowsy."

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🚗 Real-World Use Case : Driver Drowsiness Detection

🛣️ Problem:

Drowsy driving is a major cause of road accidents due to slow reaction time and loss of alertness.

💡 Solution:

This system works like a safety assistant, continuously monitoring the driver's eyes and alerting them before microsleep causes harm.

📦 Applications:

• Automotive vehicles (dashboard camera-based alert system)
• Truck/fleet monitoring systems
• Railway or aviation crew fatigue detection
• Industrial workers' safety monitoring

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