Java ResultsTable.getHeadings方法代码示例

import ij.measure.ResultsTable; //导入方法依赖的package包/类
/**
 * Turn an array of ImageJ ROIs into a list of QuPath PathObjects, optionally adding measurements as well.
 * 
 * @param imp
 * @param rois
 * @param server
 * @param downsample
 * @param asDetection
 * @param includeMeasurements
 * @return
 */
public static List<PathObject> createPathObjectsFromROIs(final ImagePlus imp, final Roi[] rois, final ImageServer<?> server, final double downsample, final boolean asDetection, final boolean includeMeasurements, final int c, final int z, final int t) {
	List<PathObject> pathObjects = new ArrayList<>();
	ResultsTable rt = new ResultsTable();
	Analyzer analyzer = new Analyzer(imp, Analyzer.getMeasurements(), rt);
	String[] headings = null;
	for (Roi roi : rois) {
		PathObject pathObject = IJTools.convertToPathObject(imp, server, roi, downsample, asDetection, c, z, t);
		if (pathObject == null)
			IJ.log("Sorry, I could not convert " + roi + " to a value QuPath object");
		else {
			// Make measurements
			if (includeMeasurements) {
				ImageProcessor ip = imp.getProcessor();
				ip.setRoi(roi);
				ImageStatistics stats = ImageStatistics.getStatistics(ip, Analyzer.getMeasurements(), imp.getCalibration());
				analyzer.saveResults(stats, roi);
				// Get measurements from table and append
				if (headings == null)
					headings = rt.getHeadings();
				int row = rt.getCounter()-1;
				MeasurementList ml = pathObject.getMeasurementList();
				for (String h : headings) {
					if ("Label".equals(h))
						continue;
					ml.putMeasurement(h, rt.getValue(h, row));
				}
				ml.closeList();
			}
			pathObjects.add(pathObject);
		}
	}
	return pathObjects;
}
 

import ij.measure.ResultsTable; //导入方法依赖的package包/类
@Override
public void run(String arg) {
    if ("about".equalsIgnoreCase(arg)) {
        IJ.showMessage("About " + TITLE, ABOUT);
        return;
    }

    final Pair<List<ResultsTable>, List<String>> resultTables = listTextWindows();
    if (resultTables.getFirst().size() < 1) {
        IJ.error("Expecting at least one open Result Table window.");
        return;
    }

    final Pair<List<ImagePlus>, List<String>> images = listSupportedImages();
    if (images.getFirst().size() < 1) {
        IJ.error("Expecting at least one open image (that is not indexed color).");
        return;
    }

    // Ask user for image, results table, and other options
    if (!showOptionsDialog(resultTables.getSecond(), images.getSecond())) {
        return;
    }

    if (CONFIG.interpretStackAs3D) {
        IJ.error(TITLE, "Interpreting stacks as 3D images not yet supported.");
        return;
    }

    final ResultsTable rt = resultTables.getFirst().get(CONFIG.tableIndex);
    final ImagePlus imp = images.getFirst().get(CONFIG.imageIndex);

    //
    // Verify that table headings match image bands
    //
    final ImagePlus stack = KMeansClusteringPlugin.convertToFloatStack(imp);

    final int stackSize = stack.getStackSize();
    final String[] bandLabels = stack.getStack().getSliceLabels();
    final String[] expectedHeadings = new String[stackSize + 1];
    expectedHeadings[0] = "Cluster";
    System.arraycopy(bandLabels, 0, expectedHeadings, 1, stackSize);
    final String[] tableHeadings = rt.getHeadings();
    if (tableHeadings.length < expectedHeadings.length) {
        IJ.error(TITLE, "Not enough headings, expecting: " + Arrays.toString(expectedHeadings));
        return;
    }
    for (int i = 0; i < expectedHeadings.length; i++) {
        if (!expectedHeadings[i].equals(tableHeadings[i])) {
            IJ.error(TITLE, "Expecting heading " + (i + 1) + " to be " + expectedHeadings[i] + ", but got: " + tableHeadings[i] + ".");
            return;
        }
    }

    // Read cluster centers from the table
    final int nbClusters = rt.getCounter();
    final float[][] clusterCenters = new float[nbClusters][expectedHeadings.length - 1];
    for (int clusterIndex = 0; clusterIndex < nbClusters; clusterIndex++) {
        for (int bandIndex = 1; bandIndex < expectedHeadings.length; bandIndex++)
            clusterCenters[clusterIndex][bandIndex - 1] = (float) rt.getValueAsDouble(bandIndex, clusterIndex);
    }

    // Apply clustering to input image
    final VectorProcessor vp = new VectorProcessor(stack);
    final ByteProcessor bp = KMeans2D.encodeSegmentedImage(vp, clusterCenters);
    // Apply default color map
    if (KMeansClusteringPlugin.APPLY_LUT) {
        bp.setColorModel(KMeansClusteringPlugin.defaultColorModel());
    }
    if (KMeansClusteringPlugin.AUTO_BRIGHTNESS) {
        bp.setMinAndMax(0, nbClusters);
    }
    new ImagePlus("Clusters", bp).show();

    // Apply clustering
    if (CONFIG.showCentroidImage) {
        final ImageStack clustered = KMeansUtils.encodeCentroidValueImage(clusterCenters, new VectorProcessor(stack));
        final ImagePlus cvImp = KMeansUtils.createCentroidImage(imp.getType(), clustered);
        cvImp.show();
    }
}