# run code
# python detect_blinks.py --shape-predictor shape_predictor_68_face_landmarks.dat
# import the necessary packages
import face_recognition
from scipy.spatial import distance as dist
from imutils.video import FileVideoStream
from imutils.video import VideoStream
from imutils import face_utils
from collections import OrderedDict
import numpy as np
import argparse
import imutils
import time
import dlib
import cv2

# landmarks to specific face regions
FACIAL_LANDMARKS_IDXS = OrderedDict([
	("mouth", (48, 68)),
	("right_eyebrow", (17, 22)),
	("left_eyebrow", (22, 27)),
	("right_eye", (36, 42)),
	("left_eye", (42, 48)),
	("nose", (27, 36)),
	("jaw", (0, 17))
])

def rect_to_bb(rect):
	# take a bounding predicted by dlib and convert it
	# to the format (x, y, w, h) as we would normally do
	# with OpenCV
	x = rect.left()
	y = rect.top()
	w = rect.right() - x
	h = rect.bottom() - y

	# return a tuple of (x, y, w, h)
	return (x, y, w, h)

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

def shape_to_np(shape, dtype="int"):
	# initialize the list of (x, y)-coordinates
	coords = np.zeros((68, 2), dtype=dtype)

	# loop over the 68 facial landmarks and convert them
	# to a 2-tuple of (x, y)-coordinates
	for i in range(0, 68):
		coords[i] = (shape.part(i).x, shape.part(i).y)

	# return the list of (x, y)-coordinates
	return coords
 
# 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"])	

# grab the indexes of the facial landmarks for the left and
# right eye, respectively
(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)
picture_of_me = face_recognition.load_image_file("me.jpg")
my_face_encoding = face_recognition.face_encodings(picture_of_me)[0]

known_face_encodings = [
    my_face_encoding
]
known_face_names = [
    "Triet",
]


# 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()
	# _, frame = cap.read()

	frame = imutils.resize(frame, width=450)
	gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
	rgb_frame = frame[:, :, ::-1]

	# Find all the faces and face enqcodings in the frame of video
	face_locations = face_recognition.face_locations(rgb_frame)
	face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

	# detect faces in the grayscale frame
	rects = detector(gray, 0)
	for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
		# See if the face is a match for the known face(s)
		matches = face_recognition.compare_faces(known_face_encodings, face_encoding)

		name = "Unknown"

        # If a match was found in known_face_encodings, just use the first one.
        # if True in matches:
        #     first_match_index = matches.index(True)
        #     name = known_face_names[first_match_index]

        # Or instead, use the known face with the smallest distance to the new face
		face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
		print("facedistance", face_distances)
		if np.linalg.norm(face_distances) < 0.35:
			best_match_index = np.argmin(face_distances)
			if matches[best_match_index]:
				name = known_face_names[best_match_index]

        # Draw a box around the face
		cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

        # Draw a label with a name below the face
		cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
		font = cv2.FONT_HERSHEY_DUPLEX
		cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
	# loop over the face detections
	for (i, rect) in enumerate(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

		# compute the convex hull for the left and right eye, then
		# 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)
		# convert dlib's rectangle to a OpenCV-style bounding box
		# [i.e., (x, y, w, h)], then draw the face bounding box
		(x, y, w, h) = face_utils.rect_to_bb(rect)
		cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

		# show the face number
		cv2.putText(frame, "Face #{}".format(i + 1), (x - 10, y - 10),
			cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

		# loop over the (x, y)-coordinates for the facial landmarks
		# and draw them on the image
		for (x, y) in shape:
			cv2.circle(frame, (x, y), 1, (0, 0, 255), -1)
		# check to see if the eye aspect ratio is below the blink
		# threshold, and if so, increment the blink frame counter
		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
cv2.destroyAllWindows()
vs.stop()