人臉檢測實戰終極之OpenCV+Python實現人臉對齊

# import the necessary packages
from imutils.face_utils.helpers import FACIAL_LANDMARKS_68_IDXS
from imutils.face_utils.helpers import FACIAL_LANDMARKS_5_IDXS
from imutils.face_utils.helpers import shape_to_np
import numpy as np
import cv2
class FaceAligner:
    def __init__(self, predictor, desiredLeftEye=(0.35, 0.35),
        desiredFaceWidth=256, desiredFaceHeight=None):
        # store the facial landmark predictor, desired output left
        # eye position, and desired output face width + height
        self.predictor = predictor
        self.desiredLeftEye = desiredLeftEye
        self.desiredFaceWidth = desiredFaceWidth
        self.desiredFaceHeight = desiredFaceHeight
        # if the desired face height is None, set it to be the
        # desired face width (normal behavior)
        if self.desiredFaceHeight is None:
            self.desiredFaceHeight = self.desiredFaceWidth

 def align(self, image, gray, rect):
        # convert the landmark (x, y)-coordinates to a NumPy array
        shape = self.predictor(gray, rect)
        shape = shape_to_np(shape)
        # extract the left and right eye (x, y)-coordinates
        (lStart, lEnd) = FACIAL_LANDMARKS_IDXS["left_eye"]
        (rStart, rEnd) = FACIAL_LANDMARKS_IDXS["right_eye"]
        leftEyePts = shape[lStart:lEnd]
        rightEyePts = shape[rStart:rEnd]

		# compute the center of mass for each eye
        leftEyeCenter = leftEyePts.mean(axis=0).astype("int")
        rightEyeCenter = rightEyePts.mean(axis=0).astype("int")
        # compute the angle between the eye centroids
        dY = rightEyeCenter[1] - leftEyeCenter[1]
        dX = rightEyeCenter[0] - leftEyeCenter[0]
        angle = np.degrees(np.arctan2(dY, dX)) - 180

 		# compute the desired right eye x-coordinate based on the
        # desired x-coordinate of the left eye
        desiredRightEyeX = 1.0 - self.desiredLeftEye[0]
        # determine the scale of the new resulting image by taking
        # the ratio of the distance between eyes in the *current*
        # image to the ratio of distance between eyes in the
        # *desired* image
        dist = np.sqrt((dX ** 2) + (dY ** 2))
        desiredDist = (desiredRightEyeX - self.desiredLeftEye[0])
        desiredDist *= self.desiredFaceWidth
        scale = desiredDist / dist

		# compute center (x, y)-coordinates (i.e., the median point)
        # between the two eyes in the input image
        eyesCenter = (int((leftEyeCenter[0] + rightEyeCenter[0]) // 2),
                      int((leftEyeCenter[1] + rightEyeCenter[1]) // 2))
        # grab the rotation matrix for rotating and scaling the face
        M = cv2.getRotationMatrix2D(eyesCenter, angle, scale)
        # update the translation component of the matrix
        tX = self.desiredFaceWidth * 0.5
        tY = self.desiredFaceHeight * self.desiredLeftEye[1]
        M[0, 2] += (tX - eyesCenter[0])
        M[1, 2] += (tY - eyesCenter[1])

		# apply the affine transformation
        (w, h) = (self.desiredFaceWidth, self.desiredFaceHeight)
        output = cv2.warpAffine(image, M, (w, h),
            flags=cv2.INTER_CUBIC)
        # return the aligned face
        return output

# 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())

pip install --upgrade imutils
pip install --upgrade dlib

# 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(args["shape_predictor"])
fa = FaceAligner(predictor, desiredFaceWidth=256)

# load the input image, resize it, and convert it to grayscale
image = cv2.imread(args["image"])
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)

python align_faces.py --shape-predictor shape_predictor_68_face_landmarks.dat --image 11.jpg