K-Means聚类用于在Android中使用Opencv进行基于颜色的分割

Question

我想实现基于颜色强度分割图像的K-means聚类，实际上我不知道在应用Core.kmeans函数后如何获得分割图像和ROI。我按照here中的问题中的步骤进行了操作，但是没有回答如何从这一点开始。任何帮助将不胜感激。

提前致谢。

我使用的代码基于不同的来源，实际上它不起作用

 //convert to lab with three channels
            Mat imgLab = new Mat();
            Mat imgMat = new Mat();
            Imgproc.cvtColor(imgMat, imgLab, Imgproc.COLOR_RGB2Lab, 3);

            // separate channels
            List<Mat> lab_planes = new ArrayList<Mat>(3);
            Core.split(imgMat, lab_planes);
            Mat channel = lab_planes.get(2);
            channel = Mat.zeros(imgLab.rows(), imgLab.cols(), CvType.CV_8UC1);

            // use only AB channels in Lab color space
            lab_planes.set(2, channel);
            Core.merge(lab_planes,imgLab);

            Mat samples = imgLab.reshape(1, imgLab.cols() * imgLab.rows());
            Mat samples32f = new Mat();
            samples.convertTo(samples32f, CvType.CV_32F, 1.0 / 255.0);
            Mat labels = new Mat();
            TermCriteria criteria = new TermCriteria(TermCriteria.EPS + TermCriteria.MAX_ITER, 100, 1);
            Mat centers = new Mat();
            //Mat clusteredLab = new Mat();

            int nColors = 3; //number of clusters (k)
            int attempt = 3; //number of attempts

            // repeat the clustering 3 times to avoid local minima
            Core.kmeans(samples32f, nColors, labels, criteria, attempt, Core.KMEANS_PP_CENTERS, centers);

            centers.convertTo(centers, CvType.CV_8UC1);
            centers.reshape(3);

            //the rest of code for RGB image not Lab

            List<Mat> clusters = new ArrayList<Mat>();
            for (int i = 0; i < centers.rows(); i++) {
                clusters.add(Mat.zeros(samples.size(), samples.type()));
            }

            Map<Integer, Integer> counts = new HashMap<Integer, Integer>();
            for (int i = 0; i < centers.rows(); i++) counts.put(i, 0);

            int rows = 0;
            for (int y = 0; y < samples.rows(); y++) {
                for (int x = 0; x < samples.cols(); x++) {
                    int label = (int) labels.get(rows, 0)[0];
                    int r = (int) centers.get(label, 2)[0];
                    int g = (int) centers.get(label, 1)[0];
                    int b = (int) centers.get(label, 0)[0];
                    counts.put(label, counts.get(label) + 1);
                    clusters.get(label).put(y, x, b, g, r);
                    rows++;
                }
            }
            return clusters;

        }