What next?

Requirements:

Dlib library installed  Dlib face landmark ‘.dat’ file. Optional: XML file for harcasscade classifier (if you want to use the harcasscade classifier link:-https://github.com/opencv/opencv/tree/master/data/haarcascades)  OpenCV package should be installed in your environment...

STEPS:

Initialize the Video Rendering Object using VideoCapture Method in OpenCV Create a grayscale image Instantiate Model objects both for face and landmark detection Detect Faces and then pass the Face as input to the Landmark detection model Calculate the upper and lower lip distance ( or whatever metric you want to use for yawn detection) Create an If the condition for the lip distance Show the frame/image...

Implementation:

Python3 import numpy as np import cv2 import dlib import time  from scipy.spatial import distance as dist from imutils import face_utils          def cal_yawn(shape):      top_lip = shape[50:53]     top_lip = np.concatenate((top_lip, shape[61:64]))        low_lip = shape[56:59]     low_lip = np.concatenate((low_lip, shape[65:68]))        top_mean = np.mean(top_lip, axis=0)     low_mean = np.mean(low_lip, axis=0)        distance = dist.euclidean(top_mean,low_mean)     return distance    cam = cv2.VideoCapture('http://192.168.1.50:4747/video')       #-------Models---------# face_model = dlib.get_frontal_face_detector() landmark_model = dlib.shape_predictor('Model\shape_predictor_68_face_landmarks.dat')    #--------Variables-------# yawn_thresh = 35 ptime = 0 while True :      suc,frame = cam.read()        if not suc :          break           #---------FPS------------#         ctime = time.time()      fps= int(1/(ctime-ptime))     ptime = ctime     cv2.putText(frame,f'FPS:{fps}',(frame.shape[1]-120,frame.shape[0]-20),cv2.FONT_HERSHEY_PLAIN,2,(0,200,0),3)        #------Detecting face------#     img_gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)     faces = face_model(img_gray)     for face in faces:         # #------Uncomment the following lines if you also want to detect the face ----------#         # x1 = face.left()         # y1 = face.top()         # x2 = face.right()         # y2 = face.bottom()         # # print(face.top())         # cv2.rectangle(frame,(x1,y1),(x2,y2),(200,0,00),2)               #----------Detect Landmarks-----------#         shapes = landmark_model(img_gray,face)         shape = face_utils.shape_to_np(shapes)            #-------Detecting/Marking the lower and upper lip--------#         lip = shape[48:60]         cv2.drawContours(frame,[lip],-1,(0, 165, 255),thickness=3)            #-------Calculating the lip distance-----#         lip_dist = cal_yawn(shape)         # print(lip_dist)         if lip_dist > yawn_thresh :              cv2.putText(frame, f'User Yawning!',(frame.shape[1]//2 - 170 ,frame.shape[0]//2),cv2.FONT_HERSHEY_SIMPLEX,2,(0,0,200),2)             cv2.imshow('Webcam' , frame)     if cv2.waitKey(1) & 0xFF == ord('q') :          break    cam.release() cv2.destroyAllWindows()...

Output:

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What next?

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