OpenCV利用K-means实现根据颜色进行图像分割

#include<OpenCV2/opencv.hpp>
#include<iOStream>


int main(int arGC, char** argv) {

    cv::Mat img(500, 500, CV_8UC3);
    cv::RNG rng(12345);
    cv::Scalar colorTab[] = {
        cv::Scalar(0, 0, 255),
        cv::Scalar(0, 255, 0),
        cv::Scalar(255, 0, 0),
        cv::Scalar(0, 255, 255),
        cv::Scalar(255, 0, 255)
    };
    int numCluster = rng.unifORM(2, 5);
    printf("种类数量 : %d\n", numCluster); 
    //4
    int sampleCount = rng.uniform(2, 1000);//随机样本
    printf("样本数量 : %d\n", sampleCount);
    //591
    cv::Mat points(sampleCount, 1, CV_32FC2);
    cv::Mat labels;
    cv::Mat centers;

    for (int k = 0; k < numCluster; k++) {
        cv::Point center;
        center.x = rng.uniform(0, img.cols);//随机坐标
        center.y = rng.uniform(0, img.rows);
        cv::Mat pointChunk = points.rowRange(k * sampleCount / numCluster, k == numCluster - 1 ? sampleCount : (k + 1) * sampleCount / numCluster);
        //每一类占1/numCluster 行
        //rng.fill(pointChunk, cv::RNG::NORMAL, cv::Scalar(center.x, center.y), cv::Scalar(img.cols * 0.05, img.rows * 0.05));//用随机数填充矩阵
        rng.fill(pointChunk, cv::RNG::UNIFORM, 0, 255);//用随机数填充矩阵
        
    }

    randShuffle(points, 1, &rng);//算法打乱元素排列顺序
    kmeans(points, numCluster, labels, cv::TermCriteria(cv::TermCriteria::EPS + cv::TermCriteria::COUNT, 10, 0.1), 3, cv::KMEANS_PP_CENTERS, centers);
    


    // 用不同颜色显示分类
    //初始化图片颜色。
    img = cv::Scalar::all(255);
    for (int i = 0; i < sampleCount; i++) {
        int index = labels.at<int>(i);
        //获取ponint点
        cv::Point p = points.at<cv::Point2f>(i);
        //填充
        circle(img, p, 2, colorTab[index], -1, 8);
    }

    // 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);
        printf("c.x= %d, c.y=%d", x, y);
        circle(img, cv::Point(x, y), 40, colorTab[i], 1, cv::LINE_AA);
    }

    imshow("KMeans-Data-Demo", img);



        
    cv::waiTKEy(0);
    return 0;
}

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

int main(int argc, char** argv) {

    
    cv::Scalar colorTab[] = {
        cv::Scalar(0, 0, 255),
        cv::Scalar(0, 255, 0),
        cv::Scalar(255, 0, 0),
        cv::Scalar(0, 255, 255),
        
        };
    
    cv::Mat src = cv::imread("D:/bb/tu1/2.png");
    if (!src.data)
    {
        printf("图像读取失败...\n");
        return -1;
    }
    cv::imshow("src", src);

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

    int sampleCount = width * height;  //总像素
    int clusterCount = 4;  //分类数量
    cv::Mat points(sampleCount, dims,CV_32F,cv::Scalar(10));
    //一个像素为一行
    cv::Mat labels;
    cv::Mat centers(clusterCount,1, points.type());
    //保存中心坐标

    //把RGB数据转换成样本数据
    int index = 0;//像素序号
    for (int row = 0; row < height;row++) {
        for (int col = 0; col < width;col++) {
            index = row * width + col;
            cv::Vec3b bgr = src.at<cv::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]);

        }

    }

      
    cv::TermCriteria criteria = cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS,10,0.1);
    //kmeans的终止的条件
    
    kmeans(points, clusterCount, labels, criteria, 3, cv::KMEANS_PP_CENTERS, centers);//运行kmeans 

    //显示图像分割结果
    cv::Mat result = cv::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); //获取类序号
            result.at<cv::Vec3b>(row, col)[0] = colorTab[label][0];
            result.at<cv::Vec3b>(row, col)[1] = colorTab[label][1];
            result.at<cv::Vec3b>(row, col)[2] = colorTab[label][2];
            
        }
    }

    cv::imshow("result", result);





    cv::waitKey(0);
    return 0;
}