python+opencv|python dlib实现小程序（4）实现换脸 dlib|opencv|python

换脸步骤：

文章图片

# coding=utf-8 import cv2 import dlib import os import numpy as np import globclass NoFace(Exception): pass class Facechanger(object):def __init__(self,which_predictor = '68',windowname = ' exchange face'): self.current_path = os.getcwd() self.predictor_68_path = self.current_path + "\shape_predictor_68_face_landmarks.dat" self.predictor_5_points_path = self.current_path + "\shape_predictor_5_face_landmarks.dat" self.detector = dlib.get_frontal_face_detector() self.predictor= dlib.shape_predictor(self.predictor_68_path)self.SCALE_FACTOR = 1 self.FEATHER_AMOUNT = 11 self.COLOUR_CORRECT_BLUR_FRAC = 0.6self.FACE_POINTS = list(range(17, 68)) self.MOUTH_POINTS = list(range(48, 61)) self.RIGHT_BROW_POINTS = list(range(17, 22)) self.LEFT_BROW_POINTS = list(range(22, 27)) self.RIGHT_EYE_POINTS = list(range(36, 42)) self.LEFT_EYE_POINTS = list(range(42, 48)) self.NOSE_POINTS = list(range(27, 35)) self.JAW_POINTS = list(range(0, 17)) self.ALIGN_POINTS = (self.LEFT_BROW_POINTS + self.RIGHT_EYE_POINTS + self.LEFT_EYE_POINTS + self.RIGHT_BROW_POINTS + self.NOSE_POINTS + self.MOUTH_POINTS) self.OVERLAY_POINTS = [self.LEFT_BROW_POINTS + self.RIGHT_EYE_POINTS + self.LEFT_EYE_POINTS + self.RIGHT_BROW_POINTS + self.NOSE_POINTS + self.MOUTH_POINTS]cv2.namedWindow(windowname,cv2.WINDOW_AUTOSIZE)self.windowname = windownameself.cap = cv2.VideoCapture(0)def det_landmark(self): #dets = self.detector(self.frame,1) face1 = self.dets[0] face2 = self.dets[1] return face1, face2def matrix(self,image,face): return np.matrix([[p.x, p.y] for p in self.predictor(image, face).parts()])def transformation_from_points(self,points1,points2): points1 = points1.astype(np.float64) points2 = points2.astype(np.float64)c1 = np.mean(points1, axis=0) c2 = np.mean(points2, axis=0) points1 -= c1 points2 -= c2s1 = np.std(points1) s2 = np.std(points2) points1 /= s1 points2 /= s2U, S, Vt = np.linalg.svd(points1.T * points2) R = (U * Vt).Treturn np.vstack([np.hstack(((s2 / s1) * R, c2.T - (s2 / s1) * R * c1.T)), np.matrix([0., 0., 1.])])def warp_image(self, image, M, dshape): output_image = np.zeros(dshape, dtype=image.dtype) cv2.warpAffine(image, M[:2], (dshape[1], dshape[0]), dst=output_image, flags=cv2.WARP_INVERSE_MAP, borderMode=cv2.BORDER_TRANSPARENT) return output_imagedef correct_colours(self, im1, im2, landmarks1):#颜色调整 blur_amount = self.COLOUR_CORRECT_BLUR_FRAC * np.linalg.norm( np.mean(landmarks1[self.LEFT_EYE_POINTS], axis=0) - np.mean(landmarks1[self.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)im2_blur += (128 * (im2_blur <= 1.0)).astype(im2_blur.dtype)return (im2.astype(np.float64) * im1_blur.astype(np.float64) / im2_blur.astype(np.float64))def draw_convex_hull(self, img, points, color): points = cv2.convexHull(points) cv2.fillConvexPoly(img, points, color)def get_face_mask(self, img, landmarks):#获取掩膜 img = np.zeros(img.shape[:2], dtype=np.float64) for group in self.OVERLAY_POINTS: self.draw_convex_hull(img, landmarks[group], color=1)img = np.array([img, img, img]).transpose((1, 2, 0)) #变成图片的原来格式（最后一维是三通道的）img = (cv2.GaussianBlur(img, (self.FEATHER_AMOUNT, self.FEATHER_AMOUNT), 0) > 0) * 1.0 img = cv2.GaussianBlur(img, (self.FEATHER_AMOUNT, self.FEATHER_AMOUNT), 0)return imgdef run(self): while True: ret,self.frame1 = self.cap.read()if ret is True: b, g, r = cv2.split(self.frame1)# 分离色道 opencv读入的色道是B,G,R self.frame = cv2.merge([r, g, b])# 合成R,G,B self.dets = self.detector(self.frame, 1) if len(self.dets) ==1: cv2.imshow(self.windowname, self.frame1) k = cv2.waitKey(5) if k & 0XFF == ord('q'): breakelif len(self.dets) ==2:self.face1,self.face2 = self.det_landmark() self.point1 = self.matrix(self.frame1,self.face1) self.point2 = self.matrix(self.frame1,self.face2) self.mask1 = self.get_face_mask(self.frame1,self.point1) self.mask2 = self.get_face_mask(self.frame1,self.point2)self.tran1 = self.transformation_from_points(self.point1[self.ALIGN_POINTS],self.point2[self.ALIGN_POINTS]) self.tran2 = self.transformation_from_points(self.point2[self.ALIGN_POINTS],self.point1[self.ALIGN_POINTS]) self.warped_mask1 = self.warp_image(self.mask2,self.tran1,self.frame1.shape) self.warped_mask2 = self.warp_image(self.mask1, self.tran2, self.frame1.shape) self.conbined_mask1 = np.max([self.mask1,self.warped_mask1],axis=0) self.conbined_mask2 = np.max([self.mask2,self.warped_mask2],axis=0) self.warped_img2 = self.warp_image(self.frame1,self.tran1,self.frame1.shape) self.warped_img1 = self.warp_image(self.frame1, self.tran2, self.frame1.shape) self.warped_corrected_img2 = self.correct_colours(self.frame1,self.warped_img2,self.point1) self.warped_corrected_img2_temp = np.zeros(self.warped_corrected_img2.shape,dtype=self.warped_corrected_img2.dtype) self.warped_corrected_img1 = self.correct_colours(self.frame1, self.warped_img1, self.point2) self.warped_corrected_img1_temp = np.zeros(self.warped_corrected_img1.shape, dtype=self.warped_corrected_img1.dtype) #cv2.normalize(self.warped_corrected_img2, self.warped_corrected_img2_temp, 0, 1, cv2.NORM_MINMAX) #cv2.normalize(self.warped_corrected_img1, self.warped_corrected_img1_temp, 0, 1, cv2.NORM_MINMAX) self.output = self.frame1*(1.0-self.conbined_mask1) + self.warped_corrected_img2*self.conbined_mask1 self.output = self.output * ( 1.0 - self.conbined_mask2) + self.warped_corrected_img1 * self.conbined_mask2 cv2.normalize(self.output,self.output,0,1,cv2.NORM_MINMAX) cv2.imshow(self.windowname,self.output.astype(self.output.dtype)) k = cv2.waitKey(5) if k & 0XFF == ord('q'): break self.cap.release() cv2.destroyAllWindows()if __name__ == '__main__': corrrent = Facechanger(which_predictor = '68',windowname = ' exchange face') corrrent.run()

大部分代码参考原PO：https://blog.csdn.net/hongbin_xu/article/details/78878745
transformation_from_points(self,points1,points2):普什分析，仿射变换得到矩阵可以参考：
https://en.wikipedia.org/wiki/Orthogonal_Procrustes_problem
https://en.wikipedia.org/wiki/Transformation_matrix#Affine_transformations