cv2最强仿射变换（支持n点对齐，可进行人脸对齐） - 好文

人脸识别过程中，人脸对齐往往是最重要的一步，对齐的结果往往影响之后提取人脸特征的准确率，opencv内置的仿射变换仅仅需要三个点，而需对齐的人脸关键点一般是5个、68个、128个，本文提供一种n点对齐的放射变换点，以5个关键点的对齐为例：
归一化的五点坐标为：
[(0.31556875000000000, 0.4615741071428571), (0.68262291666666670,
0.4615741071428571), (0.50026249999999990, 0.6405053571428571),
(0.34947187500000004, 0.8246919642857142), (0.65343645833333330,
0.8246919642857142)]
假如要裁剪的face大小为(112, 96)，即：(height, width)，则face的最终location点为：(xwidth, yheight)
[(30.2946, 51.6963), (65.5318, 51.6963), (48.0252, 71.7366), (33.5493,
92.3655), (62.7299, 92.3655)]
代码参考来源：https://matthewearl.github.io/2015/07/28/switching-eds-with-python/
<https://matthewearl.github.io/2015/07/28/switching-eds-with-python/>
#coding=utf-8 import os,cv2,numpy import logging logging.basicConfig(
level=logging.DEBUG, format='%(asctime)s %(levelname)s: %(message)s',
datefmt='%Y-%m-%d %H:%M:%S' ) logger = logging.getLogger(__name__) imgSize =
[112, 96]; coord5point = [[30.2946, 51.6963], [65.5318, 51.6963], [48.0252,
71.7366], [33.5493, 92.3655], [62.7299, 92.3655]] face_landmarks = [[259, 137],
[319, 150], [284, 177], [253, 206], [297, 216]] def
transformation_from_points(points1, points2): points1 =
points1.astype(numpy.float64) points2 = points2.astype(numpy.float64) c1 =
numpy.mean(points1, axis=0) c2 = numpy.mean(points2, axis=0) points1 -= c1
points2 -= c2 s1 = numpy.std(points1) s2 = numpy.std(points2) points1 /= s1
points2 /= s2 U, S, Vt = numpy.linalg.svd(points1.T * points2) R = (U * Vt).T
return numpy.vstack([numpy.hstack(((s2 / s1) * R,c2.T - (s2 / s1) * R *
c1.T)),numpy.matrix([0., 0., 1.])]) def warp_im(img_im,
orgi_landmarks,tar_landmarks): pts1 = numpy.float64(numpy.matrix([[point[0],
point[1]] for point in orgi_landmarks])) pts2 =
numpy.float64(numpy.matrix([[point[0], point[1]] for point in tar_landmarks]))
M = transformation_from_points(pts1, pts2) dst = cv2.warpAffine(img_im, M[:2],
(img_im.shape[1], img_im.shape[0])) return dst def main(): pic_path =
r'D:\20171117190537959.jpg' img_im = cv2.imread(pic_path)
cv2.imshow('affine_img_im', img_im) dst = warp_im(img_im, face_landmarks,
coord5point) cv2.imshow('affine', dst) crop_im = dst[0:imgSize[0],
0:imgSize[1]] cv2.imshow('affine_crop_im', crop_im) if __name__=='__main__':
main() cv2.waitKey() pass

热门工具换一换