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def 

import cv2
import dlib
import numpy
import syssys.argv = ["faceswap.py","head2.jpg","face3.jpg"]
PREDICTOR_PATH = "shape_predictor_68_face_landmarks.dat"
SCALE_FACTOR = 1
FEATHER_AMOUNT = 11FACE_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))# 用于排列图像的点
ALIGN_POINTS = (LEFT_BROW_POINTS + RIGHT_EYE_POINTS + LEFT_EYE_POINTS +RIGHT_BROW_POINTS + NOSE_POINTS + MOUTH_POINTS)OVERLAY_POINTS = [LEFT_EYE_POINTS + RIGHT_EYE_POINTS + LEFT_BROW_POINTS + RIGHT_BROW_POINTS,NOSE_POINTS + MOUTH_POINTS,
]# 颜色校正期间使用的模糊量, as a fraction of the
# pupillary distance.
COLOUR_CORRECT_BLUR_FRAC = 0.6detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(PREDICTOR_PATH)class TooManyFaces(Exception):passclass NoFaces(Exception):passdef get_landmarks(im):"""将一个图像转化成numpy数组，并返回一个68×2元素矩阵，输入图像的每个特征点对应每行的一个x，y坐标为一张图像和一个标记矩阵生成一个遮罩，它画出了两个白色的凸多边形：一个是眼睛周围的区域，一个是鼻子和嘴部周围的区域。之后它由11个像素向遮罩的边缘外部羽化扩展，可以帮助隐藏任何不连续的区域。这样一个遮罩同时为这两个图像生成，使用与步骤2中相同的转换，可以使图像2的遮罩转化为图像1的坐标空间。
之后，通过一个element-wise最大值，这两个遮罩结合成一个。结合这两个遮罩是为了确保图像1被掩盖，而显现出图像2的特性。:param im::return:"""rects = detector(im, 1)if len(rects) > 1:raise TooManyFacesif len(rects) == 0:raise NoFacesreturn 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 imdef 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) * 1.0im = cv2.GaussianBlur(im, (FEATHER_AMOUNT, FEATHER_AMOUNT), 0)return imdef transformation_from_points(points1, points2):"""Return an affine transformation [s * R | T] such that:sum ||s*R*p1,i + T - p2,i||^2is minimized.
1.将输入矩阵转换为浮点数。这是后续操作的基础。2.每一个点集减去它的矩心。一旦为点集找到了一个最佳的缩放和旋转方法，这两个矩心 c1 和 c2 就可以用来找到完整的解决方案。3.同样，每一个点集除以它的标准偏差。这会消除组件缩放偏差的问题。4.使用奇异值分解计算旋转部分。可以在维基百科上看到关于解决正交 Procrustes 问题的细节。5.利用仿射变换矩阵返回完整的转化。"""# 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_problempoints1 = points1.astype(numpy.float64)points2 = points2.astype(numpy.float64)c1 = numpy.mean(points1, axis=0)c2 = numpy.mean(points2, axis=0)points1 -= c1points2 -= c2s1 = numpy.std(points1)s2 = numpy.std(points2)points1 /= s1points2 /= s2U, 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).Treturn 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, sdef 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_imdef 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 += 1im1_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  #应用遮罩cv2.imwrite('output2.jpg', output_im)