# import the necessary packages
from scipy.spatial import distance as dist
from imutils.video import FileVideoStream
from imutils.video import VideoStream
from imutils import face_utils
import numpy as np
import argparse
import imutils
import time
import dlib
import cv2


def eye_aspect_ratio(eye):
    # compute the euclidean distances between the two sets of
    # vertical eye landmarks (x, y)-coordinates
    A = dist.euclidean(eye[1], eye[5])
    B = dist.euclidean(eye[2], eye[4])
    # compute the euclidean distance between the horizontal
    # eye landmark (x, y)-coordinates
    C = dist.euclidean(eye[0], eye[3])
    # compute the eye aspect ratio
    ear = (A + B) / (2.0 * C)
    # return the eye aspect ratio
    return ear


# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--shape-predictor", required=True,
                help="path to facial landmark predictor")
ap.add_argument("-v", "--video", type=str, default="",
                help="path to input video file")
args = vars(ap.parse_args())


# define two constants, one for the eye aspect ratio to indicate
# blink and then a second constant for the number of consecutive
# frames the eye must be below the threshold
EYE_AR_THRESH = 0.3
EYE_AR_CONSEC_FRAMES = 3
# initialize the frame counters and the total number of blinks
COUNTER = 0
TOTAL = 0


# initialize dlib's face detector (HOG-based) and then create
# the facial landmark predictor
print("[INFO] loading facial landmark predictor...")
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(args["shape_predictor"])

(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]

# start the video stream thread
print("[INFO] starting video stream thread...")
vs = FileVideoStream(args["video"]).start()
fileStream = True
# vs = VideoStream(src=0).start()
# vs = VideoStream(usePiCamera=True).start()
# fileStream = False
time.sleep(1.0)



# loop over frames from the video stream
while True:
	# if this is a file video stream, then we need to check if
	# there any more frames left in the buffer to process
	if fileStream and not vs.more():
		break
	# grab the frame from the threaded video file stream, resize
	# it, and convert it to grayscale
	# channels)
	frame = vs.read()
	try:
		frame = imutils.resize(frame, width=450)
		gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
		# detect faces in the grayscale frame
		rects = detector(gray, 0)



		for rect in rects:
			# determine the facial landmarks for the face region, then
			# convert the facial landmark (x, y)-coordinates to a NumPy
			# array
			shape = predictor(gray, rect)
			shape = face_utils.shape_to_np(shape)
			# extract the left and right eye coordinates, then use the
			# coordinates to compute the eye aspect ratio for both eyes
			leftEye = shape[lStart:lEnd]
			rightEye = shape[rStart:rEnd]
			leftEAR = eye_aspect_ratio(leftEye)
			rightEAR = eye_aspect_ratio(rightEye)
			# average the eye aspect ratio together for both eyes
			ear = (leftEAR + rightEAR) / 2.0

	# visualize each of the eyes
			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 < EYE_AR_THRESH:
				COUNTER += 1
			# otherwise, the eye aspect ratio is not below the blink
			# threshold
			else:
				# if the eyes were closed for a sufficient number of
				# then increment the total number of blinks
				if COUNTER >= EYE_AR_CONSEC_FRAMES:
					TOTAL += 1
				# reset the eye frame counter
				COUNTER = 0

				# draw the total number of blinks on the frame along with
				# the computed eye aspect ratio for the frame
				cv2.putText(frame, "Blinks: {}".format(TOTAL), (10, 30),
							cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
				cv2.putText(frame, "EAR: {:.2f}".format(ear), (300, 30),
							cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)

	# show the frame
		cv2.imshow("Frame", frame)
		key = cv2.waitKey(1) & 0xFF

		# if the `q` key was pressed, break from the loop
		if key == ord("q"):
			break
		# do a bit of cleanup
	
	except:
		print(f"BLINKS COUNT: {TOTAL}")
cv2.destroyAllWindows()
vs.stop()