facial_landmarks.py

# 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=False,
	help="path to facial landmark predictor", default="shape_predictor_68_face_landmarks.dat")
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)

# 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)
	# 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(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
	# show the face number
	cv2.putText(image, "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(image, (x, y), 1, (0, 0, 255), -1)
	# show the output image with the face detections + facial landmarks
	cv2.imshow("Output", image)
	cv2.imwrite("result/face_pic.jpg", image)

	for (name, (i, j)) in face_utils.FACIAL_LANDMARKS_IDXS.items():
		# extract the ROI of the face region as a separate image
		(x1, y1, w1, h1) = cv2.boundingRect(np.array([shape[i:j]]))
		roi = image[y1:y1 + h1, x1:x1 + w1]
		roi = imutils.resize(roi, width=250, inter=cv2.INTER_CUBIC)
        # show the particular face part
		cv2.imshow(name, roi)
		cv2.imwrite("result/face_"+name+"_pic.jpg", roi)
			
            
            
cv2.waitKey(0)