【随笔】轻量级换脸算法的实现
这个算法的实现是国外一个叫Matt的大牛程序员。他的博客文章在这:http://matthewearl.github.io/2015/07/28/switching-eds-with-python/
总的来说,其核心思维其实不是用神经网络的方法,
使用Dlib进行人脸检测和68个关键点定位:
这里使用的是ERT方法,源自论文<< One Millisecond Face Alignment with an Ensemble of RegressionTrees>>,算是一种比较老的方法了。但是效果很好。进行旋转,缩放,平移等变换,即aligement操作,使得第二个图和原始图相互吻合:
这里使用的是普式变换(Ordinary Procrustes Analysis),最终结果是要使得变换过程满足下面式子最小化,简单的说,就是第一个图的68个点经过仿射变换(warp_affine)后,和第二个图的68个点的距离最小,优化目标是:
解释一下这个优化目标: 是一个正交的 矩阵, 是标量, 是2矢量, 和 是上面计算的人脸定位矩阵的68个点坐标。
使用奇异值SVD分解( Singular Value Decomposition),从而求出R:
U, S, Vt = numpy.linalg.svd(points1.T * points2)
R = (U * Vt).T
通过如下步骤实现(https://github.com/matthewearl/faceswap):
- 将输入矩阵转换为浮点数。这对于后续操作是必需的。
- 减去每个点集的质心。一旦找到点集的最佳的缩放和旋转方式,质心 和 可以用来求解最终结果。
- 同样,将每个点按其标准偏差进行划分。这消除了问题的缩放变换的麻烦。
- 利用奇异值分解计算旋转部分。请参阅维基百科关于正交普鲁克斯特问题的文章。
- 返回完整的变换作为仿射变换矩阵。
程序所需的库包括dlib(需要上Github下载源码来安装):
$ python setup.py install
会编译比较久。。。
然后就是使用:
$ ./faceswap.py head_image.jpg face_image.jpg
程序源代码:
import cv2
import dlib
import numpy
import sys
PREDICTOR_PATH = "/home/matt/dlib-18.16/shape_predictor_68_face_landmarks.dat"
SCALE_FACTOR = 1
FEATHER_AMOUNT = 11
FACE_POINTS = list(range(17, 68))
MOUTH_POINTS = list(range(48, 61))
RIGHT_BROW_POINTS = list(range(17, 22))
LEFT_BROW_POINTS = list(range(22, 27))
RIGHT_EYE_POINTS = list(range(36, 42))
LEFT_EYE_POINTS = list(range(42, 48))
NOSE_POINTS = list(range(27, 35))
JAW_POINTS = list(range(0, 17))
# Points used to line up the images.
ALIGN_POINTS = (LEFT_BROW_POINTS + RIGHT_EYE_POINTS + LEFT_EYE_POINTS +
RIGHT_BROW_POINTS + NOSE_POINTS + MOUTH_POINTS)
# Points from the second image to overlay on the first. The convex hull of each
# element will be overlaid.
OVERLAY_POINTS = [
LEFT_EYE_POINTS + RIGHT_EYE_POINTS + LEFT_BROW_POINTS + RIGHT_BROW_POINTS,
NOSE_POINTS + MOUTH_POINTS,
]
# Amount of blur to use during colour correction, as a fraction of the
# pupillary distance.
COLOUR_CORRECT_BLUR_FRAC = 0.6
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(PREDICTOR_PATH)
class TooManyFaces(Exception):
pass
class NoFaces(Exception):
pass
def get_landmarks(im):
rects = detector(im, 1)
if len(rects) > 1:
raise TooManyFaces
if len(rects) == 0:
raise NoFaces
return numpy.matrix([[p.x, p.y] for p in predictor(im, rects[0]).parts()])
def annotate_landmarks(im, landmarks):
im = im.copy()
for idx, point in enumerate(landmarks):
pos = (point[0, 0], point[0, 1])
cv2.putText(im, str(idx), pos,
fontFace=cv2.FONT_HERSHEY_SCRIPT_SIMPLEX,
fontScale=0.4,
color=(0, 0, 255))
cv2.circle(im, pos, 3, color=(0, 255, 255))
return im
def draw_convex_hull(im, points, color):
points = cv2.convexHull(points)
cv2.fillConvexPoly(im, points, color=color)
def get_face_mask(im, landmarks):
im = numpy.zeros(im.shape[:2], dtype=numpy.float64)
for group in OVERLAY_POINTS:
draw_convex_hull(im,
landmarks[group],
color=1)
im = numpy.array([im, im, im]).transpose((1, 2, 0))
im = (cv2.GaussianBlur(im, (FEATHER_AMOUNT, FEATHER_AMOUNT), 0) > 0) * 1.0
im = cv2.GaussianBlur(im, (FEATHER_AMOUNT, FEATHER_AMOUNT), 0)
return im
def transformation_from_points(points1, points2):
"""
Return an affine transformation [s * R | T] such that:
sum ||s*R*p1,i + T - p2,i||^2
is minimized.
"""
# Solve the procrustes problem by subtracting centroids, scaling by the
# standard deviation, and then using the SVD to calculate the rotation. See
# the following for more details:
# https://en.wikipedia.org/wiki/Orthogonal_Procrustes_problem
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)
# The R we seek is in fact the transpose of the one given by U * Vt. This
# is because the above formulation assumes the matrix goes on the right
# (with row vectors) where as our solution requires the matrix to be on the
# left (with column vectors).
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 read_im_and_landmarks(fname):
im = cv2.imread(fname, cv2.IMREAD_COLOR)
im = cv2.resize(im, (im.shape[1] * SCALE_FACTOR,
im.shape[0] * SCALE_FACTOR))
s = get_landmarks(im)
return im, s
def warp_im(im, M, dshape):
output_im = numpy.zeros(dshape, dtype=im.dtype)
cv2.warpAffine(im,
M[:2],
(dshape[1], dshape[0]),
dst=output_im,
borderMode=cv2.BORDER_TRANSPARENT,
flags=cv2.WARP_INVERSE_MAP)
return output_im
def correct_colours(im1, im2, landmarks1):
blur_amount = COLOUR_CORRECT_BLUR_FRAC * numpy.linalg.norm(
numpy.mean(landmarks1[LEFT_EYE_POINTS], axis=0) -
numpy.mean(landmarks1[RIGHT_EYE_POINTS], axis=0))
blur_amount = int(blur_amount)
if blur_amount % 2 == 0:
blur_amount += 1
im1_blur = cv2.GaussianBlur(im1, (blur_amount, blur_amount), 0)
im2_blur = cv2.GaussianBlur(im2, (blur_amount, blur_amount), 0)
# Avoid divide-by-zero errors.
im2_blur += (128 * (im2_blur <= 1.0)).astype(im2_blur.dtype)
return (im2.astype(numpy.float64) * im1_blur.astype(numpy.float64) /
im2_blur.astype(numpy.float64))
im1, landmarks1 = read_im_and_landmarks(sys.argv[1])
im2, landmarks2 = read_im_and_landmarks(sys.argv[2])
M = transformation_from_points(landmarks1[ALIGN_POINTS],
landmarks2[ALIGN_POINTS])
mask = get_face_mask(im2, landmarks2)
warped_mask = warp_im(mask, M, im1.shape)
combined_mask = numpy.max([get_face_mask(im1, landmarks1), warped_mask],
axis=0)
warped_im2 = warp_im(im2, M, im1.shape)
warped_corrected_im2 = correct_colours(im1, warped_im2, landmarks1)
output_im = im1 * (1.0 - combined_mask) + warped_corrected_im2 * combined_mask
cv2.imwrite('output.jpg', output_im)