Real-Time Drowsiness Detection using OpenCV Python With Source Code

The Real-Time Drowsiness Detection OpenCV Python was developed using Python OpenCV, this Drowsiness Detection is a safety technology that can prevent accidents that are caused by drivers who fall asleep while driving.

In a Drowsiness Detection OpenCV Python project, we will be using OpenCV to gather the images from the webcam and feed them into a Deep Learning model which will classify whether the person’s eyes are ‘Open’ or ‘Closed’.

In this Python OpenCV Project also includes a downloadable Python Project With Source Code for free, just find the downloadable source code below and click to start downloading.

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To start executing Real-Time Drowsiness Detection OpenCV Python With Source Code, make sure that you have installed Python 3.9 and PyCharm on your computer.

How to run the Real-Time Drowsiness Detection using OpenCV Python? A step-by-step Guide with Source Code

Time needed: 5 minutes

These are the steps on how to run Real-Time Drowsiness Detection OpenCV Python With Source Code

Step 1: Download the given source code below.
First, download the given source code below and unzip the source code.
Step 2: Import the project to your PyCharm IDE.
Next, import the source code you’ve downloaded to your PyCharm IDE.
Step 3: Run the project.
Lastly, run the project with the command “py main.py”

Installed Libraries

from scipy.spatial import distance
from imutils import face_utils
import imutils
import dlib
import cv2

Complete Source Code

from scipy.spatial import distance
from imutils import face_utils
import imutils
import dlib
import cv2

def eye_aspect_ratio(eye):
	A = distance.euclidean(eye[1], eye[5])
	B = distance.euclidean(eye[2], eye[4])
	C = distance.euclidean(eye[0], eye[3])
	ear = (A + B) / (2.0 * C)
	return ear
	
thresh = 0.25
frame_check = 20
detect = dlib.get_frontal_face_detector()
predict = dlib.shape_predictor(".\shape_predictor_68_face_landmarks.dat")# Dat file is the crux of the code

(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_68_IDXS["left_eye"]
(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_68_IDXS["right_eye"]
cap=cv2.VideoCapture(0)
flag=0
while True:
	ret, frame=cap.read()
	frame = imutils.resize(frame, width=450)
	gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
	subjects = detect(gray, 0)
	for subject in subjects:
		shape = predict(gray, subject)
		shape = face_utils.shape_to_np(shape)#converting to NumPy Array
		leftEye = shape[lStart:lEnd]
		rightEye = shape[rStart:rEnd]
		leftEAR = eye_aspect_ratio(leftEye)
		rightEAR = eye_aspect_ratio(rightEye)
		ear = (leftEAR + rightEAR) / 2.0
		leftEyeHull = cv2.convexHull(leftEye)
		rightEyeHull = cv2.convexHull(rightEye)
		cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)
		cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)
		if ear < thresh:
			flag += 1
			print (flag)
			if flag >= frame_check:
				cv2.putText(frame, "****************ALERT!****************", (10, 30),
					cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
				cv2.putText(frame, "****************ALERT!****************", (10,325),
					cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
				#print ("Drowsy")
		else:
			flag = 0
	cv2.imshow("Frame", frame)
	key = cv2.waitKey(1) & 0xFF
	if key == ord("q"):
		break
cv2.destroyAllWindows()
cap.stop()

Output:

Download the Source Code below

DOWNLOAD

Summary

In this Python project, we have built a drowsy driver alert system that you can implement in numerous ways.

We used OpenCV to detect faces and eyes using a haar cascade classifier and then we used a CNN model to predict the status.

Inquiries

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Real-Time Drowsiness Detection using OpenCV Python