1，介绍

开始之前，向大家提前说声抱歉，上一篇文章末尾提到了，在这篇文章将给大家介绍关于用 OpenCV 实现人脸融合技术，由于人脸融合技术所需的知识储备有点多，不只是之前介绍的的特征点提取，还有本文所提到的三角剖分，因此文章会向后面推迟一点，但请大家放心，人脸融合技术一定会在随后的几篇文章安排上日程。

看到标题里的两个词 Delaunay 三角剖分 和 Voronoi，估计第一次见到的小伙伴可能一脸懵(说的就是我自己)，为了更直观地认识这两个概念，请看下图：

左图：68个人脸特征点 中图：Delaunay 三角剖分，右图 Voronoi 图表

左图是上篇文章提到的 68个人脸特征点标记，中图是基于左图的基础上对 68个点进行 点与点之间形成 Delaunay 三角剖分(德劳内)，左图是基于中间图绘制的的 Voronoi Diagram (沃罗诺伊图)

2，Delaunay 三角剖分

Delaunay 三角剖分算法命名那个来源于俄国数学家 Boris Delaunay，该方法目的是最大化三角剖分中三角形中最小角，目的是避免“极瘦“的三角形的出现

Snipaste_2020-06-04_15-23-46.png

上方左图与右图的变换站示的就是 Delaunay 怎样最大化最小角，左右两图是对于四个顶点的两种不同的剖分方式；但左图中 顶点 A、C 不在三角形 BCD、ABD 的外接圆内，使得 角 C 非常大

右图对剖分形式有两个方的 改动：1，B、D 坐标右移；2，剖分线由 BD 变为 AC ；最后使得剖分后的三角形不那么”瘦“

3，Voronoi Diagram

Voronoi 命名同样也是来源于一个 俄国数学家 Georgy Voronoy，有趣的是 Georgy Voronoy 是 Boris Delaunay 的博士导师

Voronoi 图是基于 Delaunay 三角剖分创建，取 Delaunay 剖分的所有顶点，用线段连接相邻三角形的外接圆心，构成一个区域，相邻不同区域用不同颜色覆盖；Voronoi 图目前常用于凸边形区域分割领域

从下面20个顶点组成的 Voronoi 图种可以了解到，图中相邻点与点之间的距离是等长的

20个顶点构成的 Voronoi

4，OpenCV 代码实现

1，首先需要获取人脸 68 个特征点坐标，并写入 txt 文件，方便后面使用,这里会用到的代码

import dlib

import cv2

predictor_path = "E:/data_ceshi/shape_predictor_68_face_landmarks.dat"

png_path = "E:/data_ceshi/timg.jpg"

txt_path = "E:/data_ceshi/points.txt"

f = open(txt_path,'w+')

detector = dlib.get_frontal_face_detector()

#相撞

predicator = dlib.shape_predictor(predictor_path)

win = dlib.image_window()

img1 = cv2.imread(png_path)

dets = detector(img1,1)

print("Number of faces detected : {}".format(len(dets)))

for k,d in enumerate(dets):

print("Detection {} left:{} Top: {} Right {} Bottom {}".format(

k,d.left(),d.top(),d.right(),d.bottom()

))

lanmarks = [[p.x,p.y] for p in predicator(img1,d).parts()]

for idx,point in enumerate(lanmarks):

f.write(str(point[0]))

f.write("\t")

f.write(str(point[1]))

f.write('\n')

写入后，txt 中格式如下

image

2，利用图像大小创建一个矩形范围( 因为脸部特征点都是图中)，创建一个 Subdiv2D 实例(后面两个图的绘制都会用到这个类)，把点都插入创建的类中：

#Create an instance of Subdiv2d

subdiv = cv2.Subdiv2D(rect)

#Create an array of points

points = []

#Read in the points from a text file

with open("E:/data_ceshi/points.txt") as file:

for line in file:

x,y = line.split()

points.append((int(x),int(y)))

#Insert points into subdiv

for p in points:

subdiv.insert(p)

3，在原图上绘制 Delaunay 三角剖分并预览，这里我加入了动画效果 — 逐线段绘制(用了 for 循环)

#Draw delaunay triangles

def draw_delaunay(img,subdiv,delaunay_color):

trangleList = subdiv.getTriangleList()

size = img.shape

r = (0,0,size[1],size[0])

for t in trangleList:

pt1 = (t[0],t[1])

pt2 = (t[2],t[3])

pt3 = (t[4],t[5])

if (rect_contains(r,pt1) and rect_contains(r,pt2) and rect_contains(r,pt3)):

cv2.line(img,pt1,pt2,delaunay_color,1)

cv2.line(img,pt2,pt3,delaunay_color,1)

cv2.line(img,pt3,pt1,delaunay_color,1)

#Insert points into subdiv

for p in points:

subdiv.insert(p)

#Show animate

if animate:

img_copy = img_orig.copy()

#Draw delaunay triangles

draw_delaunay(img_copy,subdiv,(255,255,255))

cv2.imshow(win_delaunary,img_copy)

cv2.waitKey(100)

预览效果如下：

imag11252323.gif

4，最后绘制 Voronoi Diagram

def draw_voronoi(img,subdiv):

(facets,centers) = subdiv.getVoronoiFacetList([])

for i in range(0,len(facets)):

ifacet_arr = []

for f in facets[i]:

ifacet_arr.append(f)

ifacet = np.array(ifacet_arr,np.int)

color = (random.randint(0,255),random.randint(0,255),random.randint(0,255))

cv2.fillConvexPoly(img,ifacet,color)

ifacets = np.array([ifacet])

cv2.polylines(img,ifacets,True,(0,0,0),1)

cv2.circle(img,(centers[i][0],centers[i][1]),3,(0,0,0))

for p in points:

draw_point(img,p,(0,0,255))

#Allocate space for Voroni Diagram

img_voronoi = np.zeros(img.shape,dtype = img.dtype)

#Draw Voonoi diagram

draw_voronoi(img_voronoi,subdiv)

Snipaste_2020-06-04_14-43-10.png

4，小总结

Delaunay 三角剖分对于第一次接触的小伙伴来说可能还未完全理解，但这一剖分技术对于做人脸识别、融合、换脸是不可或缺的，本篇文章只是仅通过 OpenCV 的 Subdiv2D 函数下实现此功能，真正的识别技术要比这个复杂地多。

对于感兴趣的小伙伴们，我的建议还是跟着提供的代码敲一遍，完整代码贴在下面：

import cv2

import numpy as np

import random

#Check if a point is insied a rectangle

def rect_contains(rect,point):

if point[0]

return False

elif point[1]

return False

elif point[0]>rect[2]:

return False

elif point[1] >rect[3]:

return False

return True

# Draw a point

def draw_point(img,p,color):

cv2.circle(img,p,2,color)

#Draw delaunay triangles

def draw_delaunay(img,subdiv,delaunay_color):

trangleList = subdiv.getTriangleList()

size = img.shape

r = (0,0,size[1],size[0])

for t in trangleList:

pt1 = (t[0],t[1])

pt2 = (t[2],t[3])

pt3 = (t[4],t[5])

if (rect_contains(r,pt1) and rect_contains(r,pt2) and rect_contains(r,pt3)):

cv2.line(img,pt1,pt2,delaunay_color,1)

cv2.line(img,pt2,pt3,delaunay_color,1)

cv2.line(img,pt3,pt1,delaunay_color,1)

# Draw voronoi diagram

def draw_voronoi(img,subdiv):

(facets,centers) = subdiv.getVoronoiFacetList([])

for i in range(0,len(facets)):

ifacet_arr = []

for f in facets[i]:

ifacet_arr.append(f)

ifacet = np.array(ifacet_arr,np.int)

color = (random.randint(0,255),random.randint(0,255),random.randint(0,255))

cv2.fillConvexPoly(img,ifacet,color)

ifacets = np.array([ifacet])

cv2.polylines(img,ifacets,True,(0,0,0),1)

cv2.circle(img,(centers[i][0],centers[i][1]),3,(0,0,0))

if __name__ == '__main__':

#Define window names;

win_delaunary = "Delaunay Triangulation"

win_voronoi = "Voronoi Diagram"

#Turn on animations while drawing triangles

animate = True

#Define colors for drawing

delaunary_color = (255,255,255)

points_color = (0,0,255)

#Read in the image

img_path = "E:/data_ceshi/timg.jpg"

img = cv2.imread(img_path)

#Keep a copy around

img_orig = img.copy()

#Rectangle to be used with Subdiv2D

size = img.shape

rect = (0,0,size[1],size[0])

#Create an instance of Subdiv2d

subdiv = cv2.Subdiv2D(rect)

#Create an array of points

points = []

#Read in the points from a text file

with open("E:/data_ceshi/points.txt") as file:

for line in file:

x,y = line.split()

points.append((int(x),int(y)))

#Insert points into subdiv

for p in points:

subdiv.insert(p)

#Show animate

if animate:

img_copy = img_orig.copy()

#Draw delaunay triangles

draw_delaunay(img_copy,subdiv,(255,255,255))

cv2.imshow(win_delaunary,img_copy)

cv2.waitKey(100)

#Draw delaunary triangles

draw_delaunay(img,subdiv,(255,255,255))

#Draw points

for p in points:

draw_point(img,p,(0,0,255))

#Allocate space for Voroni Diagram

img_voronoi = np.zeros(img.shape,dtype = img.dtype)

#Draw Voonoi diagram

draw_voronoi(img_voronoi,subdiv)

#Show results

cv2.imshow(win_delaunary,img)

cv2.imshow(win_voronoi,img_voronoi)

cv2.waitKey(0)

参考链接：