8.2 寻找物体的凸包
8.2.1 概念
1.给定二维平面上的点集，将最外层点连接起来构成的凸多边形。
2.理解物体形状或轮廓的一种比较有用的方法是计算一个物体的凸包，然后计算其凸缺陷（convexity defects）例如，图中A-H区域是凸包的各个”缺陷”：
3.函数：convexHull()函数
4.函数原型：
void convexHull(InputArray points,OutputArray hull,bool clockwise=false, bool returnPoints=true)
5.参数说明：
（1）输入的二维点集，Mat类型或std::vector
（2）输出参数，找到的凸包，返回的hull是points中点的索引
（3）操作方向标识符，为真时输出凸包为顺时针方向，否则逆时针
（4）操作标识符，默认true，标志为真时函数返回各凸包的各个点，否则返回凸包各点的指数，输出数组是std:vector时此标志忽略
8.2.2 示例程序
1. 凸包检测基础
#include<opencv2/opencv.hpp>
#include<opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>
using namespace cv;
using namespace std;
int main()
{
//初始化变量和随机值
Mat image(600, 600, CV_8UC3);
RNG& rng = theRNG();
//循环，按下ESC退出程序，否则有键按下便一直更新
while (1)
{
//参数初始化
char key;
int count = (unsigned)rng % 100 + 3;//随机生成点的数量
vector<Point>points;//点值
//随机生成点坐标
for (int i = 0; i < count; i++)
{
Point point;
point.x = rng.uniform(image.cols / 4, image.cols * 3 / 4);
point.y = rng.uniform(image.rows / 4, image.rows * 3 / 4);
points.push_back(point);
}
//检测凸包
vector<int>hull;
convexHull(Mat(points), hull, true);//返回的hull是points中点的索引
//绘制出随机颜色的点
image = Scalar::all(0);
for (int i = 0; i < count; i++)
{
circle(image, points[i], 3, Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255)), FILLED, LINE_AA);
}
//准备参数
int hullcount = (int)hull.size();//凸包边数
Point point0 = points[hull[hullcount - 1]];//连接凸包边的坐标点
//绘制凸包的边
for (int i = 0; i < hullcount; i++)
{
Point point = points[hull[i]];
line(image, point0, point, Scalar(255,255,255), 2, LINE_AA);
point0 = point;
}
//显示效果图
imshow("凸包检测示例", image);
//按下ESC，程序退出
key = (char)waitKey();
if (key == 27) break;
}
return 0;
}
运行效果：
2.综合示例
/*
效果：
滑动条控制阈值g_nThresh，以改变g_thresholdImage_output,findContours以此参数为输入，查找不同轮廓图，从而得到不同凸包检测效果图
*/
#include<opencv2/opencv.hpp>
#include<opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include<iostream>
using namespace cv;
using namespace std;
#define WINDOW_NAME1 "【原始图】"
#define WINDOW_NAME2 "【效果图】"
//全局变量
Mat g_srcImage, g_grayImage, g_thresholdImage_output, g_dstImage;
int g_nThresh = 80;
int g_nThresh_max = 255;
RNG g_rng(12345);
Mat srcImage_copy = g_srcImage.clone();
vector<vector<Point>>g_vContours;
vector<Vec4i>g_vHierarchy;
//全局函数
static void ShowHelpText();
static void on_ThreshChange(int, void*);
int main()
{
//改变console字体颜色
system("color 1F");
ShowHelpText();
//载入原图
g_srcImage = imread("girl.jpg", 1);
if (!g_srcImage.data)
{
printf("图片载入失败~！\n");
return false;
}
//创建窗口
namedWindow(WINDOW_NAME1, WINDOW_AUTOSIZE);
namedWindow(WINDOW_NAME2, WINDOW_AUTOSIZE);
imshow(WINDOW_NAME1, g_srcImage);
//转成灰度图并模糊化降噪
cvtColor(g_srcImage, g_grayImage, CV_BGR2GRAY);
blur(g_grayImage, g_grayImage, Size(3, 3));
//创建滚动条并初始化
createTrackbar("阈值", WINDOW_NAME2, &g_nThresh, g_nThresh_max, on_ThreshChange);
on_ThreshChange(0, 0);
waitKey(0);
return 0;
}
static void on_ThreshChange(int, void*)
{
//对图像进行二值化，控制阈值
threshold(g_grayImage, g_thresholdImage_output, g_nThresh, 255, THRESH_BINARY);
//查找轮廓
findContours(g_thresholdImage_output, g_vContours, g_vHierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0));
//遍历每个轮廓，寻找其凸包
vector<vector<Point>>hull(g_vContours.size());
for (int i = 0; i < g_vContours.size(); i++)
{
convexHull(Mat(g_vContours[i]), hull[i], false);
}
//绘制轮廓及其凸包
Mat g_dstImage = Mat::zeros(g_thresholdImage_output.size(), CV_8UC3);
//或 for (int index = 0; index >= 0; index = g_vHierarchy[index][0])
for (int index = 0; index < g_vContours.size(); index++)
{
//或 Scalar color(g_rng.uniform(0, 255), g_rng.uniform(0, 255), g_rng.uniform(0, 255));//任意值
Scalar color(rand() & 255, rand() & 255, rand() & 255);
drawContours(g_dstImage, g_vContours, index, color, 1, 8, vector<Vec4i>(), 0, Point());
drawContours(g_dstImage, hull, index, color, 1, 8, vector<Vec4i>(), 0, Point());
}
//显示效果图
imshow(WINDOW_NAME2, g_dstImage);
}
static void ShowHelpText()
{
printf("\n\n\t欢迎来到【在图形中寻找轮廓及其凸包】示例程序!\n\n");
printf("\n\n\t操作说明：\n\n");
printf("\t\t键盘任意键-退出程序\n\n");
printf("\t\t滑动滚动条-改变阈值\n");
}
运行效果：
