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("models/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

import cv2  # for video rendering

import dlib  # for face and landmark detection

import imutils
 
# for calculating dist b/w the eye landmarks

from scipy.spatial import distance as dist
 
# to get the landmark ids of the left
# and right eyes ----you can do this 
# manually too

from imutils import face_utils
 

cam = cv2.VideoCapture('assets/Video.mp4')
 
 
# Initializing the Models for Landmark and
# face Detection

detector = dlib.get_frontal_face_detector()

landmark_predict = dlib.shape_predictor(

    'Model/shape_predictor_68_face_landmarks.dat')
 

while 1:
 

    # If the video is finished then reset it 

    # to the start

    if cam.get(cv2.CAP_PROP_POS_FRAMES) == cam.get(

      cv2.CAP_PROP_FRAME_COUNT):

        cam.set(cv2.CAP_PROP_POS_FRAMES, 0)
 

    else:

        _, frame = cam.read()

        frame = imutils.resize(frame, width=640)
 

        # converting frame to gray scale to pass

        # to detector

        img_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

         

        # detecting the faces---#

        faces = detector(img_gray)

        for face in faces:

            cv2.rectangle(frame, face[0], face[1],

                          (200, 0, 0), 1)
 

        cv2.imshow("Video", frame)

        if cv2.waitKey(5) & 0xFF == ord('q'):

            break
 
cam.release()
cv2.destroyAllWindows()