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<>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
criteriaopencv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
flagscv::KmeansFlags见下表，选择聚类中心的初始化方式
centersOutput matrix of the cluster centers, one row per each cluster center.
*
cv::KmeansFlags
KMEANS_RANDOM_CENTERS Python: cv.KMEANS_RANDOM_CENTERSSelect random initial
centers in each attempt.
KMEANS_PP_CENTERS Python: cv.KMEANS_PP_CENTERSUse kmeans++ center
initialization by Arthur and Vassilvitskii [Arthur2007].
KMEANS_USE_INITIAL_LABELS Python: cv.KMEANS_USE_INITIAL_LABELSDuring the first
(and possibly the only) attempt, use the user-supplied labels instead of
computing them from the initial centers. For the second and further attempts,
use the random or semi-random centers. Use one of KMEANS_*_CENTERS flag to
specify the exact method.
<>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