基于Landmark的人脸对齐计算变换后关键点对应坐标

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最新推荐文章于 2025-02-21 21:04:30 发布

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简介：以5个关键点为例，进行人脸对齐和裁剪，并使用仿射矩阵将原坐标(x, y)变换为新坐标(x’, y’)，计算出原图像经过变换后的新图像上的68个关键点信息。

参考代码：

#coding=utf-8
import os,cv2,numpy
import numpy as np
import logging
import copy
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 = [[325.014, 151.109],
[400.579, 168.538],
[365.502, 204.401],
[315.705, 240.369],
[369.122, 251.158]]

face_landmarks68 = [[282, 156], [278, 176], [277, 198], [276, 219],
[279, 241], [287, 262], [299, 280], [315, 293],
[333, 300], [352, 300], [372, 293], [391, 282],
[406, 270], [418, 253], [426, 236], [434, 217],
[439, 197], [301, 138], [313, 128], [328, 127],
[343, 130], [356, 138], [384, 144], [400, 142],
[417, 145], [431, 156], [437, 171], [365, 160],
[362, 174], [359, 188], [357, 202], [337, 211],
[344, 215], [352, 219], [360, 219], [368, 218],
[312, 152], [322, 147], [332, 149], [340, 159],
[330, 159], [320, 157], [385, 169], [396, 164],
[407, 167], [414, 176], [405, 177], [394, 174],
[317, 242], [329, 237], [341, 235], [347, 238],
[356, 237], [364, 243], [371, 253], [361, 258],
[351, 259], [343, 259], [335, 257], [326, 251],
[322, 242], [339, 243], [346, 245], [354, 245],
[366, 251], [353, 247], [345, 247], [338, 245]]

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]))
print(M[:2])
return dst,M[:2]

def draw_landmark(img_im,land):

img = copy.deepcopy(img_im)
n = np.array(land)
for i in range(len(land)):
    cv2.circle(img,(int(n[i][0]),int(n[i][1])),2,(0,0,255),-1)
cv2.imshow('aaa',img)
#cv2.imwrite('land5.jpg',img)
#cv2.waitKey(0)

def draw_landmark_warpAffine(img,land,M):

new_n = []
for i in range(len(land)):
    pts = []    
    pts.append(np.squeeze(np.array(M[0]))[0]*land[i][0]+np.squeeze(np.array(M[0]))[1]*land[i][1]+np.squeeze(np.array(M[0]))[2])
    pts.append(np.squeeze(np.array(M[1]))[0]*land[i][0]+np.squeeze(np.array(M[1]))[1]*land[i][1]+np.squeeze(np.array(M[1]))[2])
    new_n.append(pts)
n = np.array(new_n)    
for i in range(len(land)):
    cv2.circle(img,(int(n[i][0]),int(n[i][1])),2,(0,0,255),-1)
cv2.imshow('bbb',img)
#cv2.imwrite('land68.jpg',img)
#cv2.waitKey(0)

def main():
pic_path = r’XZQ.jpg’
img_im = cv2.imread(pic_path)
draw_landmark(img_im,face_landmarks)
#cv2.imshow(‘affine_img_im’, img_im)
dst,M = warp_im(img_im, face_landmarks, coord5point)
cv2.imshow(‘affine’, dst)
#cv2.imwrite(‘affine.jpg’,dst)
draw_landmark_warpAffine(dst,face_landmarks68,M)
crop_im = dst[0:imgSize[0], 0:imgSize[1]]
cv2.imshow(‘affine_crop_im’, crop_im)
#cv2.imwrite(‘affine_crop_im.jpg’,crop_im)

if name==‘main’:
main()
cv2.waitKey()
pass