A real-time system for detecting driver drowsiness using computer vision and facial landmarks detection. This system monitors a driver's eyes and alerts them when signs of drowsiness are detected.

• Real-time face detection using Haar Cascades and dlib
• Eye state monitoring using Eye Aspect Ratio (EAR)
• Facial landmarks detection for precise eye tracking
• Audio alerts when drowsiness is detected
• Multiple detection methods (Haar Cascade and dlib) for comparison
• Face direction monitoring for distraction detection
• Yawn detection for additional drowsiness indicators

driver-drowsiness-detection/ ├── data/ │ ├── alert_sounds/ # Alert sound files │ │ ├── alert-sound.mp3 │ │ ├── focus.mp3 │ │ └── take_a_break.mp3 │ ├── database/ # Shape predictor data file │ │ └── shape_predictor_68_face_landmarks.dat │ ├── images/ # Output directory for eye images │ └── xml/ # Haar cascade classifier files │ ├── haarcascade_eye.xml │ ├── haarcascade_frontalface_alt.xml │ ├── haarcascade_frontalface_alt2.xml │ ├── haarcascade_frontalface_alt_tree.xml │ └── haarcascade_frontalface_default.xml └── src/ # Source code ├── final_integration.py # Main implementation with all features ├── main.py # Basic implementation using Haar cascades ├── main_dlib.py # Implementation using dlib └── train.py # Training utility for eye state detection 

• Python 3.x
• OpenCV (cv2)
• dlib
• numpy
• imutils
• matplotlib
• python-vlc

1. Clone the repository:

git clone https://github.com/LohiyaH/DrowsinessGuard.git cd driver-drowsiness-detection

2. Install the required dependencies:

pip install opencv-python dlib numpy imutils matplotlib python-vlc

3. Download the shape predictor file:

• Download shape_predictor_68_face_landmarks.dat from dlib's official website
• Place it in the data/database/ directory

The project provides three different implementations:

1. Basic Implementation (main.py)

python src/main.py

• Uses Haar cascades for face and eye detection
• Saves detected eye images to data/images/

2. Dlib Implementation (main_dlib.py)

python src/main_dlib.py

• Uses dlib for facial landmark detection
• More accurate than Haar cascade method
• Includes basic drowsiness detection

3. Full Featured Implementation (final_integration.py)

python src/final_integration.py

• Combines all features including:
  • Face direction monitoring
  • Yawn detection
  • Advanced drowsiness detection
  • Multiple alert thresholds
  • Audio alerts

1. Face Detection: The system first detects the driver's face using either Haar cascades or dlib's face detector.

2. Facial Landmarks: Using dlib's facial landmark predictor, 68 points on the face are detected, including 6 points for each eye.

3. Eye Aspect Ratio (EAR): The system calculates the eye aspect ratio to determine if the eyes are closed:

   EAR = (||p2-p6|| + ||p3-p5||) / (2||p1-p4||) 

   where p1, ..., p6 are the 2D landmark points around the eye.

4. Drowsiness Detection: If the EAR falls below a threshold for a certain number of consecutive frames, the system triggers an alert.

5. Additional Features:

   • Face direction monitoring to detect distraction
   • Yawn detection using mouth aspect ratio
   • Multiple alert levels based on drowsiness severity

Contributions are welcome! Please feel free to submit a Pull Request.

This project is under MIT License.

• The shape predictor model is provided by dlib
• Haar cascade classifiers are from OpenCV's repository