opencv的Kmeans聚類算法應用

1、輸入原始圖片

 2、代碼實現：

#include<opencv2\opencv.hpp>
#include<iostream>
using namespace std;
using namespace cv;
int main() {
    Mat src = imread("C:/Users/lzg/Desktop/opencv_test/Project1/1.png");
    if (src.empty()) {
        cout << "meiyoutu" << endl;
    }
    namedWindow("input", CV_WINDOW_AUTOSIZE);
    imshow("input", src);
    Scalar colorTab[] = {
        Scalar(0,0,255),
        Scalar(0,255,0),
        Scalar(255,0,0),
        Scalar(0,255,255),
        Scalar(255,0,255)
    };
    int width = src.cols;
    int height = src.rows;
    int dims = src.channels();
    int sampleCount = width*height;
    int clusterCount = 4;
    Mat points(sampleCount, dims, CV_32F, Scalar(10));
    Mat labels;
    Mat centers(clusterCount, 1, points.type());
    int index = 0;
    for (int row = 0; row < height; row++) {    //RGB數據轉換到樣本數據
        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]);
        }
    }
    //運行Kmeans
    TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 0.1); //迭代停止條件
    kmeans(points, clusterCount, labels, criteria, 3, KMEANS_PP_CENTERS, centers);
    //顯示圖像分割結果，要把樣本數據點轉換回去
    Mat result = Mat::zeros(src.size(), src.type());
    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];
        }
    }
    imshow("KMeans image segmentation demo", result);
    waitKey(0);
    return 0;

效果圖：