利用 OpenCV |
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利用 OpenCV
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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
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 DiagramVoronoi 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
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
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：
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)
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] 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： https://www.learnopencv.com/ |
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