OpenCV学习笔记——寻找连通域的几何中心（不规则图形也适用），及筛选最大连通域中心

参考https://blog.csdn.net/qq_34914551/article/details/78916084

Opencv寻找连通域的几何中心

其中连通域的轮廓选取用到了OTSU算法

#include "stdafx.h"
#include 
#include 
using namespace cv; 
using namespace std;
//otsu算法实现函数
int Otsu(Mat &image)
{
    int width = image.cols;
    int height = image.rows;
    int x = 0, y = 0;
    int pixelCount[256];
    float pixelPro[256];
    int i, j, pixelSum = width * height, threshold = 0;

    uchar* data = (uchar*)image.data;

    //初始化  
    for (i = 0; i < 256; i++)
    {
        pixelCount[i] = 0;
        pixelPro[i] = 0;
    }

    //统计灰度级中每个像素在整幅图像中的个数  
    for (i = y; i < height; i++)
    {
        for (j = x; j deltaMax)
        {
            deltaMax = deltaTmp;
            threshold = i;
        }
    }
    //返回最佳阈值;  
    return threshold;
}

int main()
{
	Mat matSrc = imread("1.png", 0);
	GaussianBlur(matSrc, matSrc, Size(5, 5), 0);
	vector > contours;//contours的类型，双重的vector
	vector hierarchy;//Vec4i是指每一个vector元素中有四个int型数据。
	//阈值
	threshold(matSrc, matSrc, 100, 255, THRESH_BINARY);
	imshow("threshold", matSrc);
	//寻找轮廓，这里注意，findContours的输入参数要求是二值图像，二值图像的来源大致有两种，第一种用threshold，第二种用canny
	findContours(matSrc.clone(), contours, hierarchy,CV_RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0, 0));
	/// 计算矩
	vector mu(contours.size());
	for (int i = 0; i < contours.size(); i++)
	{
		mu[i] = moments(contours[i], false);
	}
	///  计算中心矩:
	vector mc(contours.size());
	for (int i = 0; i < contours.size(); i++)
	{
		mc[i] = Point2f(mu[i].m10 / mu[i].m00, mu[i].m01 / mu[i].m00);
	}
	/// 绘制轮廓
	Mat drawing = Mat::zeros(matSrc.size(), CV_8UC1);
	for (int i = 0; i < contours.size(); i++)
	{
		Scalar color = Scalar(255);
		drawContours(drawing, contours, i, color, 2, 8, hierarchy, 0, Point());
		circle(drawing, mc[i], 4, Scalar(128), -1, 8, 0);	//中心用灰点表示
	}
	imshow("outImage",drawing);
	waitKey();
	return 0;
 
}

筛选最大连通域中心

#include "stdafx.h"
#include 
#include 
using namespace cv; 
using namespace std;
//otsu算法实现函数
int Otsu(Mat &image)
{
    int width = image.cols;
    int height = image.rows;
    int x = 0, y = 0;
    int pixelCount[256];
    float pixelPro[256];
    int i, j, pixelSum = width * height, threshold = 0;

    uchar* data = (uchar*)image.data;

    //初始化  
    for (i = 0; i < 256; i++)
    {
        pixelCount[i] = 0;
        pixelPro[i] = 0;
    }

    //统计灰度级中每个像素在整幅图像中的个数  
    for (i = y; i < height; i++)
    {
        for (j = x; j deltaMax)
        {
            deltaMax = deltaTmp;
            threshold = i;
        }
    }
    //返回最佳阈值;  
    return threshold;
}

int main()
{
	Mat matSrc = imread("1.png", 0);
	GaussianBlur(matSrc, matSrc, Size(5, 5), 0);
	vector > contours;//contours的类型，双重的vector
	vector hierarchy;//Vec4i是指每一个vector元素中有四个int型数据。
	//阈值
	threshold(matSrc, matSrc, 100, 255, THRESH_BINARY);
	imshow("threshold", matSrc);
	//寻找轮廓，这里注意，findContours的输入参数要求是二值图像，二值图像的来源大致有两种，第一种用threshold，第二种用canny
	findContours(matSrc.clone(), contours, hierarchy,CV_RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0, 0));
	//计算最大连通域中心点
	int index;
	double area, maxArea(0);
	for (int i = 0; i < contours.size(); i++)
	{
		area = contourArea(Mat(contours[i]));
		if (area > maxArea)
		{
			maxArea = area;
			index = i;
		}
	}
	// 计算矩
	vector mu(contours.size());
	mu[index] = moments(contours[index], false);
	//  计算中心矩:
	vector mc(contours.size());
	mc[index] = Point2f(mu[index].m10 / mu[index].m00, mu[index].m01 / mu[index].m00);
	// 绘制轮廓
	Mat drawing = Mat::zeros(matSrc.size(), CV_8UC1);
	Scalar color = Scalar(255);
	drawContours(drawing, contours, index, color, 2, 8, hierarchy, 0, Point());
	circle(drawing, mc[index], 4, Scalar(128), -1, 8, 0);	//中心用灰点表示
	imshow("outImage",drawing);
	waitKey();
	return 0;
 
}