Drowsiness2

import numpy as np
import dlib
import cv2
from math import hypot


cap = cv2.VideoCapture(0)

detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(r"C:\Users\KIIT\OneDrive - kiit.ac.in\Documents\TTL-drowsiness-project\shape_predictor_68_face_landmarks.dat")


def mid(p1 ,p2):
    return int((p1.x + p2.x)/2), int((p1.y + p2.y)/2)


def eye_aspect_ratio(eye_landmark, face_roi_landmark):
    left_point = (face_roi_landmark.part(eye_landmark[0]).x, face_roi_landmark.part(eye_landmark[0]).y)
    right_point = (face_roi_landmark.part(eye_landmark[3]).x, face_roi_landmark.part(eye_landmark[3]).y)
    center_top = mid(face_roi_landmark.part(eye_landmark[1]), face_roi_landmark.part(eye_landmark[2]))
    center_bottom = mid(face_roi_landmark.part(eye_landmark[5]), face_roi_landmark.part(eye_landmark[4]))

    hor_line_length = hypot((left_point[0] - right_point[0]), (left_point[1] - right_point[1]))
    ver_line_length = hypot((center_top[0] - center_bottom[0]), (center_top[1] - center_bottom[1]))

    ratio = hor_line_length / ver_line_length
    return ratio


def mouth_aspect_ratio(lips_landmark, face_roi_landmark):
    left_point = (face_roi_landmark.part(lips_landmark[0]).x, face_roi_landmark.part(lips_landmark[0]).y)
    right_point = (face_roi_landmark.part(lips_landmark[2]).x, face_roi_landmark.part(lips_landmark[2]).y)
    center_top = (face_roi_landmark.part(lips_landmark[1]).x, face_roi_landmark.part(lips_landmark[1]).y)
    center_bottom = (face_roi_landmark.part(lips_landmark[3]).x, face_roi_landmark.part(lips_landmark[3]).y)

    hor_line_length = hypot((left_point[0] - right_point[0]), (left_point[1] - right_point[1]))
    ver_line_length = hypot((center_top[0] - center_bottom[0]), (center_top[1] - center_bottom[1]))

    ratio = ver_line_length / hor_line_length
    return ratio


count = 0
font = cv2.FONT_HERSHEY_TRIPLEX

while True:
    _, img = cap.read()
    img = cv2.flip(img, 1)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    faces = detector(gray)
    for face_roi in faces:

        landmark_list = predictor(gray, face_roi)

        left_eye_ratio = eye_aspect_ratio([36, 37, 38, 39, 40, 41], landmark_list)
        right_eye_ratio = eye_aspect_ratio([42, 43, 44, 45, 46, 47], landmark_list)
        avg_eye_ratio = (left_eye_ratio + right_eye_ratio) / 2

        mouth_ratio = mouth_aspect_ratio([48, 49, 50, 51, 52, 53, 54, 55, 56, 57], landmark_list)

        if avg_eye_ratio < EYE_ASPECT_RATIO_THRESHOLD or mouth_ratio > MOUTH_ASPECT_RATIO_THRESHOLD:
            if not yawning_alert:
                yawning_alert = True
                last_alert_time = time.time()
                print("Yawning Alert!")
                # implement any necessary action here, such as sounding an alarm
        else:
            yawning_alert = False

        cv2.putText(img, f"EYE RATIO: {avg_eye_ratio:.2f}", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
        cv2.putText(img, f"MOUTH RATIO: {mouth_ratio:.2f}", (50, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)

    cv2.imshow('Yawning Detection', img)
    key = cv2.waitKey(1) & 0xFF
    if key == ord('q'):
        break