学习换脸：Switching Eds: Face swapping with Python, dlib, and OpenCV

学习GitHub上比较火换脸博客，原英文版：https://matthewearl.github.io/2015/07/28/switching-eds-with-python/

系统win10，x64

安装python 2.7
opencv3.0下载，安装，配置环境变量（所需python版本为2.7）
下载numpy，版本numpy-1.10.2-win32-superpack-python2.7，必须与python版本一致，不然即使找到了cv模块也不能够运行。
opencv文件夹中，build->python->2.7 复制2.7下面的所有文件到C:\Python27\Lib\site-packages 中

测试是否配置成功：

import cv2
image = cv2.imread("0.png")
cv2.imshow("Image",image)
cv2.waitKey(0)

开始学习换脸：

下载boost，编译boost：解压，执行bootstrap.bat（使用vs2015编译），会在boost根目录生成 b2.exe 、bjam.exe 、project-config.jam 、bootstrap.log四个文件，其中，b2.exe 、bjam.exe 这两个exe作用是一样的，bjam.exe 是老版本，b2是bjam的升级版本。运行bjam.exe，编译c++版本的boost库，配置环境变量BOOST_ROOT=C:\boost_1_60_0；BOOST_LIBRARYDIR=C:\boost_1_60_0\stage\lib。再编译python动态链接库，b2.exe --with-python --build-type=complete。
下载dlib从http://dlib.net/，Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.编译python API，命令python setup.py install

使用dlib抽取脸部标志点：Dlib实现了paper ”one millisecond face alignment with an ensemble of regression trees" by Vahid Kazemi and Josephine Sullivan. 虽然算法本身很复杂，但是它的python接口的使用很简单：

 1 import cv2
 2 import dlib
 3 import numpy
 4 import sys
 5 
 6 PREDICTOR_PATH = "shape_predictor_68_face_landmarks.dat"
 7 SCALE_FACTOR = 1
 8 FEATURE_AMOUNT = 11
 9 
10 FACE_POINTS = list(range(17, 68))
11 MOUTH_POINTS = list(range(48, 68))
12 RIGHT_BROW_POINTS = list(range(17, 22))
13 LEFT_BROW_POINTS = list(range(22, 27))
14 RIGHT_EYE_POINTS = list(range(36, 42))
15 LEFT_EYE_POINTS = list(range(42, 48))
16 NOSE_POINTS = list(range(27, 35))
17 JAW_POINTS = list(range(0, 17))
18 
19 # Points used to line up the images
20 ALIGN_POINTS = (LEFT_BROW_POINTS + RIGHT_EYE_POINTS + LEFT_EYE_POINTS +
21                 RIGHT_BROW_POINTS + NOSE_POINTS + MOUTH_POINTS)
22 
23 # Points from the second image to overlay on the first. The convex hull of
24 # each element will be overlaid
25 OVERLAY_POINTS = [
26     LEFT_EYE_POINTS + RIGHT_EYE_POINTS + LEFT_BROW_POINTS
27                   + RIGHT_BROW_POINTS,
28     NOSE_POINTS + MOUSE_POINTS,
29     ]
30 
31 # Amount of blur to use during color correction, as a fraction of the
32 # pupillary distance
33 COLOUR_CORRECT_BLUR_FRAC = 0.6
34 
35 detector = dlib.get_frontal_face_detector()
36 predictor = dlib.shape_predictor(PREDICTOR_PATH)
37 
38 class TooManyFaces(Exception):
39     pass
40 
41 class NoFaces(Exception):
42     pass
43 
44 ## input: an image in the form of a numpy array
45 ## return: a 68 * 2 element matrix, each row corresponding with
46 ## the x, y coordintes of a pariticular feature point in the input image
47 def get_landmarks(im):
48     rects = detector(im, 1)
49 
50     if len(rects) > 1:
51         raise TooManyFaces
52     if len(rects) == 0:
53         raise NoFaces
54 
55     # the feature extractor (predictor) requires a rough bounding box as input
56     # to the algorithm. This is provided by a traditional face detector (
57     # detector) which returns a list of rectangles, each of which corresponding
58     # a face in the image
59     return numpy.matrix([[p.x p.y] for p in predictor(im, rects[0]).parts()])

为了使用predictor，需要利用一个提前训练好的model：shape_predictor_68_face_landmarks.dat，从http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2下载

4. 用Procrustes Analysis进行脸部对准：目前我们已经有两个人脸的landmark矩阵，矩阵的每一行代表一个脸部特征的坐标。现在我们要做的是找出如何通过旋转、平移、和尺度操作使得第一张脸的特征点与第二张脸的尽可能的匹配。找到这个合适的匹配变换之后，我们就可以将第二张脸用同样的变换覆盖第一张脸。

从数学上考虑，我们寻找平移参数T，尺度参数s，和旋转变换矩阵R使得如下目标函数

$\sum_{i = 1}^{68} | | s R p_{i}^{T} + T - q_{i}^{T} | |^{2}$

最小化，其中R是2*2的正交矩阵，s是标量，T是2*1的向量，pi和qi是landmark矩阵的行（对应的脸部特征坐标）。

这个问题可以被Ordinary Procrustes Analysis求解。

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 substracting 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.])])

求解步骤：

1) 将输入矩阵转化为浮点型，这一操作被后面步骤需要；

2) 每个点集减去中心点（去中心操作）；

3) 每个点集除以标准差，解决尺度问题；

4) 使用SVD (Singular Value Decomposition) 计算旋转矩阵，解Orthogonal Procrustes Problem;

5) 返回完整的仿射变换矩阵，维度3* 3.

获得的仿射变换可以应用到第二幅图像，与第一张图像匹配：

1 def warp_im(im, M, dshape):
2     output_im = numpy.zeros(dshape, dtype=im.dtype)
3     cv2.warpAffine(im,
4                    M[:2],
5                    (dshape[1], dshape[0]),
6                    dst=output_im,
7                    borderMode=cv2.BORDER_TRANSPARENT,
8                    flags=cv2.WARP_INVERSE_MAP)
9     return output_im

5. 计算mask，并进行色彩校正：利用眼部和眉毛区域特征点计算二维凸包，鼻子和嘴部特征点再计算二维凸包，获得一个五官的mask，代码和结果如下：

 1 def draw_convex_hull(im, points, color):
 2     points = cv2.convexHull(points)
 3     cv2.fillConvexPoly(im, points, color=color)
 4 
 5 def get_face_mask(im, landmarks):
 6     im = numpy.zeros(im.shape[:2], dtype=numpy.float64)
 7 
 8     for group in OVERLAY_POINTS:
 9         draw_convex_hull(im,
10                          landmarks[group],
11                          color=1)
12 
13     im = numpy.array([im, im, im]).transpose((1, 2, 0))
14 
15     im = (cv2.GaussianBlur(im, (FEATURE_AMOUNT, FEATURE_AMOUNT), 0) > 0) * 1.0
16     im = cv2.GaussianBlur(im, (FEATURE_AMOUNT, FEATURE_AMOUNT), 0)
17 
18     return im

如果我们直接将脸部mask区域覆盖，我们会发现脸部颜色不一致的问题：

进行色彩矫正，改变第二张脸的颜色使其可以与第一张脸匹配。做法是将第二张脸的颜色除以第二张脸的高斯模糊值，再乘以第一张脸的高斯模糊值，点操作。参考https://en.wikipedia.org/wiki/Color_balance#Scaling_monitor_R.2C_G.2C_and_B，并没有将整幅图像乘以常数因子，而是将每个像素乘以它自己的尺度因子。

通过这个操作，可以一定程度上弥补两幅图像之间的亮度不同问题。代码如下：

def correct_colors(im1, im2, landmarks1):
    blur_amount = COLOUR_CORRECT_BLUR_FRAC * numpy.linalg.norm(
        numpy.mean(landmarks1[LEFT_EYE_POINTS], axis=0) -
        numpy.mean(landmarks2[RIGHT_EYE_POINTS], axis=0))
    blur_amount = int(blur_amount)
    if blur_amount % 2 == 0:
        blur_amount += 1

    print blur_amount

    im1_blur = cv2.GaussianBlur(im1, (blur_amount, blur_amount), 0)
    im2_blur = cv2.GaussianBlur(im2, (blur_amount, blur_amount), 0)

    cv2.imshow("Image", im1_blur) # warp_im(im2, M, im1.shape)
    cv2.waitKey(0)
    cv2.imshow("Image", im2_blur) # warp_im(im2, M, im1.shape)
    cv2.waitKey(0)

    # Avoid divide-by-zero errors:
    im2_blur += (128 * (im2_blur <= 1.0)).astype(im2_blur.dtype)

    cv2.imshow("Image", im2_blur) # warp_im(im2, M, im1.shape)
    cv2.waitKey(0)
    cv2.destroyWindow("Image")
    return (im2.astype(numpy.float64) * im1_blur.astype(numpy.float64) /
            im2_blur.astype(numpy.float64))

这种做法可以在粗略地解决色彩不一致问题，效果与高斯kernel的大小密切相关：kernel太小，第一张脸中本应该被覆盖的脸部特征会出现在最后的融合图中；kernel太大，第二张脸外部的像素会被引入融合图像，产生污点。下图的kernel size等于0.05*瞳间距。

6. 融合：将经过色彩矫正的第二张脸的mask区域与第一张脸融合：

output_im = im1 * (1.0 - combined_mask) + warped_corrected_im2 * combined_mask

至此换脸全部完成，全部代码如下：

import cv2
import dlib
import numpy
import sys

PREDICTOR_PATH = "shape_predictor_68_face_landmarks.dat"
SCALE_FACTOR = 1
FEATURE_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 color correction, as a fraction of the
# pupillary distance
COLOUR_CORRECT_BLUR_FRAC = 0.05

detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(PREDICTOR_PATH)

class TooManyFaces(Exception):
    pass

class NoFaces(Exception):
    pass

## input: an image in the form of a numpy array
## return: a 68 * 2 element matrix, each row corresponding with
## the x, y coordintes of a pariticular feature point in the input image
def get_landmarks(im):
    rects = detector(im, 1)

    if len(rects) > 1:
        raise TooManyFaces
    if len(rects) == 0:
        raise NoFaces

    # the feature extractor (predictor) requires a rough bounding box as input
    # to the algorithm. This is provided by a traditional face detector (
    # detector) which returns a list of rectangles, each of which corresponding
    # a face in the image
    return numpy.matrix([[p.x, p.y] for p in predictor(im, rects[0]).parts()])

def annote_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 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 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 substracting 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 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, (FEATURE_AMOUNT, FEATURE_AMOUNT), 0) > 0) * 1.0
    im = cv2.GaussianBlur(im, (FEATURE_AMOUNT, FEATURE_AMOUNT), 0)

    return im

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_colors(im1, im2, landmarks1):
    blur_amount = COLOUR_CORRECT_BLUR_FRAC * numpy.linalg.norm(
        numpy.mean(landmarks1[LEFT_EYE_POINTS], axis=0) -
        numpy.mean(landmarks2[RIGHT_EYE_POINTS], axis=0))
    blur_amount = int(blur_amount)
    if blur_amount % 2 == 0:
        blur_amount += 1

    print blur_amount

    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("0.jpg")
im2, landmarks2 = read_im_and_landmarks("1.jpg")

# draw landmarks
##for i in landmarks2:
##    im2[i[0,1], i[0,0]] = [0,0,0]

##cv2.imshow("Image0", annote_landmarks(im1, landmarks1))
##cv2.waitKey(0)
##cv2.destroyWindow("Image0")
##cv2.imshow("Image1", annote_landmarks(im2, landmarks2))
##cv2.waitKey(0)

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_colors(im1, warped_im2, landmarks1)

output_im = im1 * (1.0 - combined_mask) + warped_corrected_im2 * combined_mask

cv2.imshow("Image1", output_im.astype(output_im.dtype)) # warp_im(im2, M, im1.shape)
cv2.waitKey(0)
cv2.destroyWindow("Image1")

cv2.imwrite("output.jpg", output_im)

posted @ 2016-04-11 16:35 dongbeidami Views(7091) Comments(5) Edit 收藏举报

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学习换脸：Switching Eds: Face swapping with Python, dlib, and OpenCV

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