face_align.py

import numpy as np
import cv2

def align(image, leftEyeCenter, rightEyeCenter,
    desiredLeftEye=(0.35, 0.35), desiredFaceWidth=128, desiredFaceHeight=128):

    # 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 - 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 - desiredLeftEye[0])
    desiredDist *= 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 = desiredFaceWidth * 0.5
    tY = desiredFaceHeight * desiredLeftEye[1]
    M[0, 2] += (tX - eyesCenter[0])
    M[1, 2] += (tY - eyesCenter[1])
    
    # apply the affine transformation
    (w, h) = (desiredFaceWidth, desiredFaceHeight)
    output = cv2.warpAffine(image, M, (w, h),
    	flags=cv2.INTER_CUBIC)
    
    # return the aligned face
    return output, M