org.rhwlab.variationalbayesian.TifMaskSuperVoxelDataSource.java Source code

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Here is the source code for org.rhwlab.variationalbayesian.TifMaskSuperVoxelDataSource.java

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package org.rhwlab.variationalbayesian;

import ij.ImagePlus;
import ij.io.Opener;
import java.util.ArrayList;
import java.util.List;
import net.imglib2.Cursor;
import net.imglib2.img.ImagePlusAdapter;
import net.imglib2.img.Img;
import net.imglib2.type.numeric.integer.UnsignedByteType;
import org.apache.commons.math3.linear.ArrayRealVector;
import org.apache.commons.math3.linear.RealVector;
import org.apache.commons.math3.ml.clustering.CentroidCluster;
import org.apache.commons.math3.ml.clustering.Clusterable;
import org.apache.commons.math3.ml.clustering.DoublePoint;
import org.apache.commons.math3.ml.clustering.KMeansPlusPlusClusterer;

/**
 *
 * @author gevirl
 */
public class TifMaskSuperVoxelDataSource implements SuperVoxelDataSource {
    public TifMaskSuperVoxelDataSource(String file) {
        ArrayList<DoublePoint> allVoxels = new ArrayList<>();
        final ImagePlus imp = new Opener().openImage(file);
        final Img image = ImagePlusAdapter.wrap(imp);
        Cursor cursor = image.localizingCursor();
        int[] pos = new int[3];
        while (cursor.hasNext()) {
            UnsignedByteType obj = (UnsignedByteType) cursor.next();
            int i = obj.getInteger();
            if (obj.getInteger() != 1) {
                cursor.localize(pos);
                DoublePoint point = new DoublePoint(pos);
                allVoxels.add(point);
            }
        }
        this.T = allVoxels.size();
        int K = allVoxels.size() / 1000;
        superVoxels = new SuperVoxel[K];
        KMeansPlusPlusClusterer clusterer = new KMeansPlusPlusClusterer(K);
        List<CentroidCluster<Clusterable>> clusters = clusterer.cluster(allVoxels);
        int k = 0;
        for (CentroidCluster<Clusterable> cluster : clusters) {
            List<Clusterable> points = cluster.getPoints();
            RealVector[] voxels = new RealVector[points.size()];
            for (int i = 0; i < points.size(); ++i) {
                voxels[i] = new ArrayRealVector(points.get(i).getPoint());
            }
            RealVector center = new ArrayRealVector(cluster.getCenter().getPoint());
            superVoxels[k] = new SuperVoxel(voxels, center);
            ++k;
        }
        int ausgdf = 0;
    }

    @Override
    public int getN() {
        return superVoxels.length;
    }

    @Override
    public int getD() {
        return 3;
    }

    @Override
    public SuperVoxel get(int i) {
        return superVoxels[i];
    }

    SuperVoxel[] superVoxels;
    int T;

    static public void main(String[] args) throws Exception {
        TifMaskSuperVoxelDataSource source = new TifMaskSuperVoxelDataSource(
                "/nfs/waterston/pete/Segmentation/Cherryimg75_SimpleSegmentation.tiff");
        SuperVoxelGaussianMixture gm = new SuperVoxelGaussianMixture();
        gm.setSource(source);
        gm.init(50);
        gm.run();
    }

    @Override
    public int getT() {
        return T;
    }

}