基于OpenCV的图像分割

用户9831583

发布于 2022-06-16 16:36:46

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代码可运行

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代码可运行

输入图像

    const int N = 3; //聚类个数
  //  const int N1 = (int)sqrt((double)N);
    //每一类用一种颜色
    // const Scalar colors[] =
    // {
    //     Scalar(0,0,255), Scalar(0,255,0),
    //     Scalar(0,255,255),Scalar(255,255,0)
    // };

    Vec3b colorTab[] =
    {
        Vec3b(0, 0, 255),
        Vec3b(0, 255, 0),
        Vec3b(255, 100, 100),
        Vec3b(255, 0, 255),
        Vec3b(0, 255, 255)
    };


    // int i, j;
    // int nsamples = 100;
    // Mat samples( nsamples, 2, CV_32FC1 );
    // Mat labels;
    // Mat img = Mat::zeros( Size( 500, 500 ), CV_8UC3 );
    // Mat sample( 1, 2, CV_32FC1 );

    // samples = samples.reshape(2, 0);
    // for( i = 0; i < N; i++ )
    // {
    //     // form the training samples
    //     Mat samples_part = samples.rowRange(i*nsamples/N, (i+1)*nsamples/N );

    //     Scalar mean(((i%N1)+1)*img.rows/(N1+1),
    //                 ((i/N1)+1)*img.rows/(N1+1));
    //     Scalar sigma(30,30);
    //     randn( samples_part, mean, sigma );
    // }
    // samples = samples.reshape(1, 0);

    Mat sample, labels;
    Mat img = imread("mmmm.jpg");
    for (int i = 0; i < img.rows; i++)
    for (int j = 0; j < img.cols; j++)
    {
        Vec3b point = img.at<Vec3b>(i, j);
        Mat tmp = (Mat_<float>(1, 3) << point[0], point[1], point[2]);
        sample.push_back(tmp);
    }

生成训练器

    Ptr<EM> em_model = EM::create();
    em_model->setClustersNumber(N);
    em_model->setCovarianceMatrixType(EM::COV_MAT_SPHERICAL);
    em_model->setTermCriteria(TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 300, 0.1));
    em_model->trainEM( sample, noArray(), labels, noArray() );

输出图像

    // classify every image pixel
    int n = 0;
    for( int i = 0; i < img.rows; i++ )
    {
        for(int j = 0; j < img.cols; j++ )
        {
            // sample.at<float>(0) = (float)j;
            // sample.at<float>(1) = (float)i;
            // int response = cvRound(em_model->predict2( sample, noArray() )[1]);
            // Scalar c = colors[response];

            // circle( img, Point(j, i), 1, c*0.75, FILLED );

            int clusterIdx = labels.at<int>(n);
            img.at<Vec3b>(i, j) = colorTab[clusterIdx];
            n++;
        }
    }

    //draw the clustered samples
    // for( i = 0; i < nsamples; i++ )
    // {
    //     Point pt(cvRound(samples.at<float>(i, 0)), cvRound(samples.at<float>(i, 1)));
    //     circle( img, pt, 1, colors[labels.at<int>(i)], FILLED );
    // }

    imshow( "EM-clustering result", img );
    waitKey(0);