Face Alignment

输入：face landmark
输出：大小大致相同的face，face的眼睛在同一水平线
code：

# import the necessary packages
from imutils.face_utils import FaceAligner
from imutils.face_utils import rect_to_bb
# 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())
OriginalImg_dir = 'E:/zyl.jpg'

# initialize dlib's face detector (HOG-based) and then create
# the facial landmark predictor and the face aligner
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
fa = FaceAligner(predictor, desiredFaceWidth=256)

# load the input image, resize it, and convert it to grayscale
image = cv2.imread(OriginalImg_dir)
image = imutils.resize(image, width=800)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# show the original input image and detect faces in the grayscale
# image
cv2.imshow("Input", image)
rects = detector(gray, 2)


# loop over the face detections
for rect in rects:
    # extract the ROI of the *original* face, then align the face
    # using facial landmarks
    (x, y, w, h) = rect_to_bb(rect)
    faceOrig = imutils.resize(image[y:y + h, x:x + w], width=256)
    faceAligned = fa.align(image, gray, rect)

    # display the output images
    cv2.imshow("Original", faceOrig)
    cv2.imshow("Aligned", faceAligned)
    cv2.waitKey(0)

reference

note：其实还没懂怎么对齐的，理解后删掉note！！