Real-Time Drowsiness Detection OpenCV Python With Source Code

Real-Time Drowsiness Detection OpenCV Python With Source Code

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Real-Time Drowsiness Detection 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 fell asleep while driving.

A Drowsiness Detection OpenCV Python project, we will be using OpenCV for gathering the images from 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.

By the way if you are new to python programming and you don’t know what would be the the Python IDE to use, I have here a list of Best Python IDE for Windows, Linux, Mac OS that will suit for you. I also have here How to Download and Install Latest Version of Python on Windows.

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

Real-Time Drowsiness Detection OpenCV Python With Source Code : Steps on how to run the project

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.
    drowsiness detection download source code

  • Step 2: Import the project to your PyCharm IDE.

    Next, import the source code you’ve download to your PyCharm IDE.
    drowsiness detection open project

  • Step 3: Run the project.

    last, run the project with the command “py main.py”
    drowsiness detection run project

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 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.

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Inquiries

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