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# import the necessary packages
from .helpers import FACIAL_LANDMARKS_68_IDXS
from .helpers import FACIAL_LANDMARKS_5_IDXS
from .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)
#simple hack ;)
if (len(shape)==68):
# extract the left and right eye (x, y)-coordinates
(lStart, lEnd) = FACIAL_LANDMARKS_68_IDXS["left_eye"]
(rStart, rEnd) = FACIAL_LANDMARKS_68_IDXS["right_eye"]
else:
(lStart, lEnd) = FACIAL_LANDMARKS_5_IDXS["left_eye"]
(rStart, rEnd) = FACIAL_LANDMARKS_5_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 = ((leftEyeCenter[0] + rightEyeCenter[0]) // 2,
(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
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