singleImage.py

import cv2
from imutils import face_utils
import imutils
import dlib
import numpy as np
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')

# load the input image, resize it, and convert it to grayscale
image = cv2.imread("test.jpg")
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)
	# 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), 3, (0, 0, 255), -1)
# show the output image with the face detections + facial landmarks
cv2.imshow("Output", image)
cv2.waitKey(0)