实验源代码
import cv2 import dlib import numpy import sys PREDICTOR_PATH = "./shape_predictor_68_face_landmarks.dat" SCALE_FACTOR = 1 FEATHER_AMOUNT = 11 # 代表各个区域的关键点标号 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. 17-61 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. 17-61 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 # 获取关键点坐标位置，只获取一张人脸 # input：代表一张图片的numpy array # output：68*2的关键点坐标位置matrix 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 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 # 注解关键点 def annotate_landmarks(im, landmarks): # 数组切片是原始数组的视图，这意味着数据不会被复制，视图上的任何修改都会被直接反映到源数组上. # 若想要得到的是ndarray切片的一份副本而非视图，就需要显式的进行复制操作函数copy()。 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=0.2, color=(0, 0, 255)) cv2.circle(im, pos, 1, color=(0, 255, 255)) cv2.imwrite("landmak.jpg", im) return im def draw_convex_hull(im, points, color): points = cv2.convexHull(points) # 检测凸包函数 cv2.fillConvexPoly(im, points, color=color) # 绘制好多边形后并填充 点的顺序不同绘制出来的凸包也不同 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) # 11. 下面这行代码用来替代上面两行代码 draw_convex_hull(im, landmarks, color=1) im = numpy.array([im, im, im]).transpose((1, 2, 0)) # 得到一个类似于3通道的图片 # 22. 高斯滤波，注释掉效果更好 # im = (cv2.GaussianBlur(im, (FEATHER_AMOUNT, FEATHER_AMOUNT), 0) > 0) * 1.0 # im = cv2.GaussianBlur(im, (FEATHER_AMOUNT, FEATHER_AMOUNT), 0) return im # 用普氏分析(Procrustes analysis)调整脸部 def transformation_from_points(points1, points2): """ Return an affine transformation [s * R | T] such that:返回一个仿射变换矩阵 sum ||s*R*p1,i + T - p2,i||^2 is minimized. """ # 通过减去中心id，通过标准偏差进行缩放，然后使用SVD来计算旋转，从而解决了普是问题 # 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 # 计算标准差 s1 = numpy.std(points1) s2 = numpy.std(points2) points1 /= s1 points2 /= s2 # 通过奇异值分解求得旋转矩阵R 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 # 维度:2*2 # 仿射变换矩阵3*3 # numpy.hstack用来在第1个维度上拼接tup numpy.vstack在第0个维度上拼接tup return numpy.vstack([numpy.hstack(((s2 / s1) * R, c2.T - (s2 / s1) * R * c1.T)), numpy.matrix([0., 0., 1.])]) def warp_im(im, M, dshape): output_im = numpy.zeros(dshape, dtype=im.dtype) # cv2.warpAffine(src, M, dsize[, dst[, flags[, borderMode[, borderValue ]]]])-->dst cv2.warpAffine(im, M[:2], (dshape[1], dshape[0]), dst=output_im, borderMode=cv2.BORDER_TRANSPARENT, flags=cv2.WARP_INVERSE_MAP) return output_im # 颜色校正 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("1.jpg") im2, landmarks2 = read_im_and_landmarks("2.jpg") # 44. 参数landmarks1[ALIGN_POINTS]-->landmarks1 M = transformation_from_points(landmarks1, landmarks2) # [ALIGN_POINTS] # get_face_mask()的定义是为一张图像和一个标记矩阵生成一个掩膜 mask = get_face_mask(im2, landmarks2) warped_mask = warp_im(mask, M, im1.shape) # 33. 用min函数取掩膜区域效果更好 combined_mask = numpy.min([get_face_mask(im1, landmarks1), warped_mask], axis=0) # 将图像2的掩膜转换到图像1的坐标空间 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)
