K-means算法在图像分割中的应用实例

#include <opencv2/opencv.hpp>
#include <iostream>

using namespace cv;
using namespace std;

void Kmeans(Mat& img,Mat& r)
{
//定义图像分割颜色
    Scalar color[]=
    {
        Scalar(0,0,255),
        Scalar(0,255,0),
        Scalar(255,0,0),
        Scalar(0,255,255),
        Scalar(255,0,255)
    };
    
    int w=img.cols;
    int h=img.rows;
    int d=img.channels();

    int count=w*h;
    int cluster_count=2;
    Mat Points(count,d,CV_32F,Scalar(10));

    Mat labels;//输出
    Mat centers(cluster_count,1,Points.type());//存储聚类后的中心点

    int index=0;
    for(int i=0;i<h;i++)
    {
        for(int j=0;j<w;j++)
        {
            index=i*w+j;//每个像素点的索引值，一行一行的遍历
            Vec3b bgr=img.at<Vec3b>(i,j);
            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]);       
        }
    }
    //k均值
    TermCriteria criteria=
    TermCriteria(TermCriteria::EPS+TermCriteria::COUNT,10,0.1);
    
    kmeans(Points,cluster_count,labels,criteria,3,KMEANS_PP_CENTERS,centers);
  
        for(int i=0;i<h;i++)
    {
        for(int j=0;j<w;j++)
        {
            index=i*w+j;//每个像素点的索引值，一行一行的遍历
            int label=labels.at<int>(index,0);

            r.at<Vec3b>(i,j)[0]=color[label][0];
            r.at<Vec3b>(i,j)[1]=color[label][1];
            r.at<Vec3b>(i,j)[2]=color[label][2];       
        }
    }
   /最后聚类中心点的坐标
   for(int i=0;i<centers.rows;i++)
   {
       int x=centers.at<float>(i,0);
       int y=centers.at<float>(i,1);

       cout<<"centers: "<<i<<" "<<x<<" "<<y<<" "<<endl;
   }
}
int main(int argc,char* argv[])
{
    Mat img=imread(argv[1],1);
    if(img.empty())
    {
        cout<<"not img"<<endl;
        return -1;
    }

    imshow("img",img);
    waitKey(0);
      
    Mat r=Mat::zeros(img.size(),img.type());
     Kmeans( img, r);
    imshow("result",r);
    waitKey(0);
    return 0;
}

TermCriteria::TermCriteria
(
  int type, 
  int maxCount, 
  double epsilon
)

 double kmeans
(
  InputArray data, 
  int K, 
  InputOutputArray bestLabels, 
  TermCriteria criteria, 
  int attempts, 
  int flags, 
  OutputArray centers=noArray() 
)

- Mat points(count, 2, CV_32F);
      - Mat points(count, 1, CV_32FC2);
      - Mat points(1, count, CV_32FC2);
      - std::vector<cv::Point2f> points(sampleCount);