OpenCV3之——寻找和绘制物体的凸包convexHull()

凸包就是将物体最外层的点连接起来构成的凸多边形。理解物体形状或轮廓的一种比较有用的方法便是计算一个物体的凸包，然后计算其凸缺陷（convexity defects）。很多复杂物体的特性能很好的被这种缺陷表现出来。

基础示例：

#include <opencv2/opencv.hpp>
#include <iostream>
using namespace std;
using namespace cv;
int main(int argc,char** agrv) {
	//初始化变量和随机值
	Mat image(600, 600, CV_8UC3);
	RNG& rng = theRNG();
	//RNG rng;

	//循环，按下ESC，Q，q键程序退出，否则有按键按下便一直更新
	while (1) {
		//参数初始化
		char key;//键值
		int count = (unsigned)rng % 100 + 3;//随机生成点的数量,对100求余+3至少有三个点，确立一个平面
		vector<Point> points;//点值，点数组

		//随机生成点的坐标
		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);

		//绘出随机颜色的点
		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，最后一个凸包点

		//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;
		}

		imshow("凸包检测示例", image);

		//按下ESC，Q，q，程序退出
		key = (char)waitKey();
		if (key == 27 || key == 'q' || key == 'Q')
			break;
	}
	return 0;
}

效果图：

综合示例：

#include <opencv2/opencv.hpp>
#include <iostream>
using namespace std;
using namespace cv;

#define WINDOW_NAME1 "原始窗口"
#define WINDOW_NAME2 "效果图窗口"

//全局变量声明部分
Mat g_srcImage, g_grayImage;
int g_nThresh = 50;
int g_maxThresh = 255;
RNG g_rng(12345);
Mat g_thresholdImage_output;
vector<vector<Point>> g_vContours;
vector<Vec4i> g_vHierarchy;

//全局函数声明部分
void on_ThreshChange(int, void*);

int main() {
	g_srcImage = imread("1.jpg", 1);
	cvtColor(g_srcImage, g_grayImage, COLOR_BGR2GRAY);
	blur(g_grayImage, g_grayImage, Size(3, 3));

	//创建原图窗口并显示
	namedWindow(WINDOW_NAME1, WINDOW_AUTOSIZE);
	imshow(WINDOW_NAME1, g_srcImage);

	//创建滚动条
	createTrackbar("阈值：", WINDOW_NAME1, &g_nThresh, g_maxThresh, on_ThreshChange);
	on_ThreshChange(0, 0);

	waitKey(0);
	return 0;
}
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);
	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());//轮廓/凸包尺寸的数组
	for (unsigned int i = 0; i < g_vContours.size(); i++) {
		convexHull(Mat(g_vContours[i]), hull[i], false);//给数组赋值
	}

	//绘出轮廓及其凸包
	Mat drawing = Mat::zeros(g_thresholdImage_output.size(), CV_8UC3);
	for (unsigned int i = 0; i < g_vContours.size(); i++) {
		Scalar color = Scalar(g_rng.uniform(0, 255), g_rng.uniform(0, 255), g_rng.uniform(0, 255));
		drawContours(drawing, g_vContours, i, color, 1, 8, vector<Vec4i>(), 0, Point());//绘出轮廓
		drawContours(drawing, hull, i, color, 1, 8, vector<Vec4i>(), 0, Point());//绘出轮廓的凸包
	}
	imshow(WINDOW_NAME2, drawing);

}

运行效果：

OpenCV3之——寻找和绘制物体的凸包convexHull()

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