Drowsiness Detection System for yawning as well as sleepy eyes 😴 🚫 🚗

A real-time drowsiness detection system using computer vision, OpenCV, and other relevant libraries. This system detects when a person is drowsy and alerts them to prevent accidents.

Features

Real-time drowsiness detection
Alerts the user with a sound when drowsiness is detected
Utilizes facial landmarks to detect eye closure and yawning

How It Works

Facial Landmark Detection: Uses dlib’s pre-trained shape predictor model to detect facial landmarks. The landmarks around the eyes are used to compute the Eye Aspect Ratio (EAR).
Eye Aspect Ratio (EAR): The EAR is calculated based on the distances between the vertical eye landmarks and the horizontal eye landmarks. When the EAR falls below a certain threshold, it indicates that the eyes are closed.
Yawning Detection: Measures the distance between upper and lower lips to detect yawning, another indicator of drowsiness.
Alert Mechanism: When drowsiness or yawning is detected for a certain duration, the system triggers an alert sound to wake up the user.

Working images

Requirements

Python 3.x
OpenCV
dlib
imutils
scipy
numpy

Installation

Clone the repository:

git clone git@github.com:sanskaryo/Drowsiness_detection_using_dlib_and_cv.git
cd drowsiness-detection-system

python drowsiness_detection.py --webcam 0

Download the Pre-trained Shape Predictor:

Download the shape predictor and extract the .dat file.
Place the shape_predictor_68_face_landmarks.dat file in the project directory.

This version includes structured sections, improved readability, and direct instructions for installation and usage. Adjust paths and details as necessary for your specific setup.