opencv图像切割1-KMeans方法

kMeans随机数据分类：

#include<opencv2\opencv.hpp>
#include<iostream>
using namespace cv;
using namespace std;
int main1()
{
	Mat img(500, 500, CV_8UC3);
	RNG rng(12345);
	Scalar colorTab[] = {
		Scalar(0,0,255),
		Scalar(0,255,0),
		Scalar(255,0,0),
		Scalar(0,255,255),
		Scalar(255,0,255)
	};

	int numCluster = rng.uniform(2, 5);  //分类个数
	cout << "分类个数：" << numCluster << endl;

	int sampleCount = rng.uniform(2, 1000);   //需要分类的点数
	Mat points(sampleCount, 1, CV_32FC2);  //每一列两个数
	Mat labels;  //存储每一个数据点的聚类编号
	Mat centers;  //存储每一个聚类的中心位置

	//生成随机数
	for (int k = 0; k < numCluster; k++)
	{
		Point center;
		center.x = rng.uniform(0, img.cols);
		center.y = rng.uniform(0, img.rows);
		//随机数据块
		Mat pointChunk = points.rowRange(k*sampleCount / numCluster, k == numCluster - 1 ? sampleCount: (k + 1)*sampleCount / numCluster);
		rng.fill(pointChunk, RNG::NORMAL, Scalar(center.x, center.y), Scalar(img.cols*0.05, img.rows*0.05));
	}
	randShuffle(points, 1, &rng); //将随机数据块打乱
	//使用kmeans
	kmeans(points, numCluster, labels, TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 0.1), 3, KMEANS_PP_CENTERS, centers);
	
	//用不同颜色显示分类
	img = Scalar::all(255);
	for (int i = 0; i < sampleCount; i++)
	{
		int index = labels.at<int>(i);
		Point p = points.at<Point2f>(i);
		circle(img, p, 2, colorTab[index], -1, 8);   //-1表示填充
	}

	//每个聚类的中心来绘制圆
	for (int i = 0; i < centers.rows; i++)
	{
		int x = centers.at<float>(i, 0);
		int y = centers.at<float>(i, 1);
		cout << "x:" << x << "y:" << y << endl;
		circle(img, Point(x, y), 40, colorTab[i], 1, LINE_AA);
	}
	imshow("KMean-Demo", img);
	waitKey(0);
	return 0;  //返回值为0表示成功执行此函数
}

运行结果：

#include<opencv2\opencv.hpp>
#include<iostream>
using namespace std;
using namespace cv;
using namespace cv::ml;

int main2(int argc, char **argv)
{
	Mat src = imread("E:\\vs2015\\opencvstudy\\2kmeans.jpg", 1);
	if (src.empty())
	{
		cout << "could not load the image!" << endl;
		return -1;  //返回-1代表函数执行失败
	}
	imshow("input", src);

	int width = src.cols;
	int height = src.rows;
	int dims = src.channels();

	////初始化定义
	int sampleCount = width*height;
	int clusterCount = 4;
	Mat points(sampleCount, dims, CV_32F, Scalar(10));
	Mat labels;
	Mat centers(clusterCount,1,points.type());

	////RGB数据转换到样本数据
	int index = 0;
	for (int row = 0; row < height; row++)
	{
		for (int col = 0; col < width; col++)
		{
			index = row*width + col;
			Vec3b bgr = src.at<Vec3b>(row, col);
			points.at<float>(index, 0) = static_cast<int>(bgr[0]);
			points.at<float>(index, 1) = static_cast<int>(bgr[1]);
			points.at<float>(index, 2) = static_cast<int>(bgr[2]);

		}
	}

	////运行kMeans
	TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 0.1);
	kmeans(points, sampleCount, labels, criteria, 3, KMEANS_PP_CENTERS, centers);


	////显示图像分割结果
	Mat result = Mat::zeros(src.size(), src.type());
	Scalar colorTab[] = {
		Scalar(0,0,255),
		Scalar(0,255,0),
		Scalar(255,0,0),
		Scalar(0,255,255),
		Scalar(255,0,255)
	};
	for (int row = 0; row < height; row++)
	{
		for (int col = 0; col < width; col++)
		{
			index = row*width + col;
			int label = labels.at<int>(index,0);
			result.at<Vec3b>(row, col)[0] = colorTab[label][0];
			result.at<Vec3b>(row, col)[1] = colorTab[label][1];
			result.at<Vec3b>(row, col)[2] = colorTab[label][2];

		}
	}
	for (int i = 0; i < centers.rows; i++)
	{
		int x = centers.at<float>(i, 0);
		int y = centers.at<float>(i, 1);
		cout << "第" << i << "个：" << "c.x" << x << "c.y" << y << endl;
	}
	imshow("KMeans_Result", result);

	waitKey(0);
	return 0;
}

https://www.cnblogs.com/mikewolf2002/p/3372846.html