kmeans

函数原型

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

参数说明

• Parameters

  data	待聚类的数据集，数据集的每一个样本是一个N维的点，点坐标都是float型的，例如：有m个样本，每个样本有n个维度，那data的格式就为cv::Mat dataSet(m,n,CV_32F)
  K	聚类数，即要把数据集聚成k类.
  bestLabels	存储data中每一个样本的标签，数据类型为int型
  criteria	opencv中迭代算法的终止条件，例如迭代的次数限制，或者迭代的精度达到要求时，算法迭代终止
  attempts	使用不同的初始聚类中心执行算法的次数
  flags	cv::KmeansFlags见下表，选择聚类中心的初始化方式
  centers	Output matrix of the cluster centers, one row per each cluster center.

• cv::KmeansFlags

示例

读取一张图片，把图片中每一个像素点的RGB值作为特征进行聚类（颜色量化），聚类数目根据需要进行调整。

#include "opencv.hpp"


int kmeansDemo(cv::Mat &srcImage, cv::Mat &dst, int clusterCount)
{
    if (srcImage.empty())
        return -1;
    if (clusterCount <= 0)
        return -1;

    //cv::GaussianBlur(srcImage, srcImage, cv::Size(0, 0), 2);
    int width = srcImage.cols;
    int height = srcImage.rows;

    //init
    int sampleCount = width * height;
    cv::Mat labels;//Input/output integer array that stores the cluster indices for every sample
    cv::Mat centers;//Output matrix of the cluster centers, one row per each cluster center.

    // convert image to kmeans data
    cv::Mat sampleData = srcImage.reshape(3, sampleCount);//every pixel is a sample
    cv::Mat data;
    sampleData.convertTo(data, CV_32F);

    //K-Means
    cv::TermCriteria criteria = cv::TermCriteria(cv::TermCriteria::EPS + cv::TermCriteria::COUNT, 5, 0.1);
    cv::kmeans(data, clusterCount, labels, criteria, clusterCount, cv::KMEANS_PP_CENTERS, centers);

    //create a color map
    std::vector<cv::Scalar> colorMaps;
    uchar b, g, r;;
    //clusterCount is equal to centers.rows
    for (int i = 0; i < centers.rows; i++)
    {
        b = (uchar)centers.at<float>(i, 0);
        g = (uchar)centers.at<float>(i, 1);
        r = (uchar)centers.at<float>(i, 2);
        colorMaps.push_back(cv::Scalar(b, g, r));
    }
    // Show  result
    int index = 0;
    dst = cv::Mat::zeros(srcImage.size(), srcImage.type());
    uchar *ptr=NULL;
    int *label = NULL;
    for (int row = 0; row < height; row++) {
        ptr = dst.ptr<uchar>(row);
        for (int col = 0; col < width; col++) {
            index = row * width + col;
            label = labels.ptr<int>(index);
            *(ptr + col * 3) = colorMaps[*label][0];
            *(ptr + col * 3 + 1) = colorMaps[*label][1];
            *(ptr + col * 3 + 2) = colorMaps[*label][2];
        }
    }
        
    return 0;
}

int main()
{
    int clusterCount = 8;//the number of clusters
    std::string path = "K:\\deepImage\\fruit.jpg";
    cv::Mat srcImage = cv::imread(path);
    cv::imshow("srcImage", srcImage);
    cv::Mat dst;
    
    kmeansDemo(srcImage,dst,clusterCount);

    std::string txt = "clusters:" + std::to_string(clusterCount);
    cv::putText(dst, txt, cv::Point(5, 35), 0, 1, cv::Scalar(0, 255, 250), 2);
    cv::imshow("result", dst);
    cv::waitKey(0);
    return 0;
}

• 效果

  
  颜色聚类数为8的效果
  颜色聚类数为6
  颜色聚类数为16