changeface.py
import cv2 import dlib import numpy import sysPREDICTOR_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 = 0.6detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor(PREDICTOR_PATH)class TooManyFaces(Exception):passclass 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 TooManyFacesif len(rects) == 0:raise NoFacesreturn 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 imdef 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||^2is 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 -= c1points2 -= c2# 计算标准差s1 = numpy.std(points1)s2 = numpy.std(points2)points1 /= s1points2 /= s2# 通过奇异值分解求得旋转矩阵RU, 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tupreturn 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 ]]]])-->dstcv2.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 += 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("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)
