# USAGE
# python detect_face_parts.py --shape-predictor shape_predictor_68_face_landmarks.dat --image images/example_01.jpg 

# import the necessary packages
from imutils import face_utils
import numpy as np
import argparse
import imutils
import dlib
import cv2

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--shape-predictor", required=True,
	help="path to facial landmark predictor")
ap.add_argument("-i", "--image", required=True,
	help="path to input image")
args = vars(ap.parse_args())

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

# load the input image, resize it, and convert it to grayscale
image = cv2.imread(args["image"])
image = imutils.resize(image, width=500)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)



# detect faces in the grayscale image
rects = detector(gray, 1)
LeftEyeCoorX = []  # left eye X coordinates array
LeftEyeCoorY = []  # left eye Y coordinates array
RightEyeCoorX = []  # right eye X coordinates array
RightEyeCoorY = []  # right eye Y coordinates array
# loop over the face detections
for (i, rect) in enumerate(rects):
	# determine the facial landmarks for the face region, then
	# convert the landmark (x, y)-coordinates to a NumPy array
	shape = predictor(gray, rect)
	shape = face_utils.shape_to_np(shape)

	# loop over the face parts individually
	for (name, (i, j)) in face_utils.FACIAL_LANDMARKS_IDXS.items():
		# clone the original image so we can draw on it, then
		# display the name of the face part on the image
		clone = image.copy()
		cv2.putText(clone, name, (10, 30), cv2.FONT_HERSHEY_SIMPLEX,
			0.7, (0, 0, 255), 2)

		# loop over the subset of facial landmarks, drawing the
		# specific face part
		for (x, y) in shape[i:j]:
			cv2.circle(clone, (x, y), 1, (0, 0, 255), -1)
			if name == "right_eye":
				RightEyeCoorX.append(x)
				RightEyeCoorY.append(y)
			if name == "left_eye":
				LeftEyeCoorX.append(x)
				LeftEyeCoorY.append(y)
		# extract the ROI of the face region as a separate image
		(x, y, w, h) = cv2.boundingRect(np.array([shape[i:j]]))
		roi = image[y:y + h, x:x + w]
		roi = imutils.resize(roi, width=250, inter=cv2.INTER_CUBIC)

		# show the particular face part
		cv2.imshow("ROI", roi)
		cv2.imshow("Image", clone)
		cv2.waitKey(0)

	# visualize all facial landmarks with a transparent overlay
	output = face_utils.visualize_facial_landmarks(image, shape)
	cv2.imshow("Image", output)
	cv2.waitKey(0)

# extract dimensions of right eye
RightEyeHorizontalDistance = np.abs((np.max(RightEyeCoorX) - np.min(RightEyeCoorX)))
RightEyeVerticalDistance = np.abs((np.max(RightEyeCoorY) - np.min(RightEyeCoorY)))

# extract dimensions of left eye
LeftEyeHorizontalDistance = np.abs((np.max(LeftEyeCoorX) - np.min(LeftEyeCoorX)))
LeftEyeVerticalDistance = np.abs((np.max(LeftEyeCoorY) - np.min(LeftEyeCoorY)))

# calculate distance between eyes
DistanceBetweenEyes = np.abs((np.max(LeftEyeCoorX) - np.min(RightEyeCoorX)))

