- First import the libaray that we need
import cv2
import numpy as np
import dlib
from playsound import playsound
from imutils import face_utils
from scipy.spatial import distance as dist
- Connect the webcam to the program
cap = cv2.VideoCapture(0)
while True:
_, frame = cap.read()
cv2.imshow('Blink detection', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
- Make the program to target or detect the face
detector = dlib.get_frontal_face_detector()
- Insert the model that we need
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
- Change the BGR to the grayscale
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
- Detect the face in the grayscale image
faces = detector(gray)
- Convert the coordinates of facial landmark to a numpy
point = predictor(gray, face)
points = face_utils.shape_to_np(point)
- Calculate the coordinates/indexes for the up and down lip
def calculate_lip(lips):
dist1 = dist.euclidean(lips[2], lips[6])
dist2 = dist.euclidean(lips[0], lips[4])
- Calculate the ratio of the lip using the coordinates/indexes that have been obtained
EAR = (A + B) / (2.0 * C)`
- Determine the lip aspect ratio for yawn and the number of frames the person has yawned
counter = 0
lip_LAR = 0.4
lip_per_frame = 30
- Insert the indexes of the lip
lips = [60,61,62,63,64,65,66,67]
- Change the indexes into the numpy array
lip_point = points[lips]
- Calculate the lip aspect ratio
LAR = calculate_lip(lip_point)
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