Examples with ImageProcessor ij.process.ImageProcessor used on opensource projects

Example 1 with ImageProcessor

use of ij.process.ImageProcessor in project GDSC-SMLM by aherbert.

the class PulseActivationAnalysis method runSimulation.

private void runSimulation() {
    TITLE += " Simulation";
    if (!showSimulationDialog())
        return;
    long start = System.currentTimeMillis();
    RandomDataGenerator rdg = getRandomDataGenerator();
    // Draw the molecule positions
    Utils.showStatus("Simulating molecules ...");
    float[][][] molecules = new float[3][][];
    MemoryPeakResults[] results = new MemoryPeakResults[3];
    Rectangle bounds = new Rectangle(0, 0, sim_size, sim_size);
    for (int c = 0; c < 3; c++) {
        molecules[c] = simulateMolecules(rdg, c);
        // Create a dataset to store the activations
        MemoryPeakResults r = new MemoryPeakResults();
        r.setCalibration(new Calibration(sim_nmPerPixel, 1, 100));
        r.setBounds(bounds);
        r.setName(TITLE + " C" + (c + 1));
        results[c] = r;
    }
    // Simulate activation
    Utils.showStatus("Simulating activations ...");
    for (int c = 0; c < 3; c++) simulateActivations(rdg, molecules, c, results);
    // Combine
    Utils.showStatus("Producing simulation output ...");
    MemoryPeakResults r = new MemoryPeakResults();
    r.setCalibration(new Calibration(sim_nmPerPixel, 1, 100));
    r.setBounds(new Rectangle(0, 0, sim_size, sim_size));
    r.setName(TITLE);
    ImageProcessor[] images = new ImageProcessor[3];
    for (int c = 0; c < 3; c++) {
        ArrayList<PeakResult> list = (ArrayList<PeakResult>) results[c].getResults();
        r.addAllf(list);
        // Draw the unmixed activations
        IJImagePeakResults image = ImagePeakResultsFactory.createPeakResultsImage(ResultsImage.LOCALISATIONS, true, true, TITLE, bounds, sim_nmPerPixel, 1, 1024.0 / sim_size, 0, ResultsMode.ADD);
        image.setLiveImage(false);
        image.setDisplayImage(false);
        image.begin();
        image.addAll(list);
        image.end();
        images[c] = image.getImagePlus().getProcessor();
    }
    displayComposite(images, TITLE);
    // Add to memory. Set the composite dataset first.
    MemoryPeakResults.addResults(r);
    for (int c = 0; c < 3; c++) MemoryPeakResults.addResults(results[c]);
    // TODO:
    // Show an image of what it looks like with no unmixing, i.e. colours allocated 
    // from the frame
    Utils.showStatus("Simulation complete: " + Utils.timeToString(System.currentTimeMillis() - start));
}

Example 2 with ImageProcessor

use of ij.process.ImageProcessor in project GDSC-SMLM by aherbert.

the class DriftCalculator method calculateDriftUsingFrames.

/**
	 * Calculate the drift by aligning N consecutive frames with the overall image. Update the current drift parameters.
	 * 
	 * @param blocks
	 * @param blockT
	 * @param bounds
	 * @param scale
	 * @param dx
	 * @param dy
	 * @param smoothing
	 * @param iterations
	 * @return
	 */
private double calculateDriftUsingFrames(ArrayList<ArrayList<Localisation>> blocks, int[] blockT, Rectangle bounds, float scale, double[] dx, double[] dy, double[] originalDriftTimePoints, double smoothing, int iterations) {
    // Construct images using the current drift
    tracker.status("Constructing images");
    // Built an image for each block of results.
    final ImageProcessor[] images = new ImageProcessor[blocks.size()];
    List<Future<?>> futures = new LinkedList<Future<?>>();
    progressCounter = 0;
    totalCounter = images.length * 2;
    for (int i = 0; i < images.length; i++) {
        futures.add(threadPool.submit(new ImageBuilder(blocks.get(i), images, i, bounds, scale, dx, dy)));
    }
    Utils.waitForCompletion(futures);
    for (int i = 0; i < blocks.size(); i++) {
        tracker.progress(i, blocks.size());
        IJImagePeakResults blockImage = newImage(bounds, scale);
        for (Localisation r : blocks.get(i)) {
            blockImage.add(r.t, (float) (r.x + dx[r.t]), (float) (r.y + dy[r.t]), r.s);
        }
        images[i] = getImage(blockImage);
    }
    // Build an image with all results.
    FloatProcessor allIp = new FloatProcessor(images[0].getWidth(), images[0].getHeight());
    for (ImageProcessor ip : images) allIp.copyBits(ip, 0, 0, Blitter.ADD);
    return calculateDrift(blockT, scale, dx, dy, originalDriftTimePoints, smoothing, iterations, images, allIp, true);
}

Example 3 with ImageProcessor

use of ij.process.ImageProcessor in project GDSC-SMLM by aherbert.

the class BackgroundEstimator method drawPlot.

/**
	 * Build a plot of the noise estimate from the current frame.
	 * Limit the preview to 100 frames.
	 */
private void drawPlot() {
    IJ.showStatus("Estimating background ...");
    int start = imp.getCurrentSlice();
    int end = FastMath.min(imp.getStackSize(), start + 100);
    int size = end - start + 1;
    double[] xValues = new double[size];
    double[] noise1 = new double[size];
    double[] noise2 = new double[size];
    double[] background = new double[size];
    double[] threshold = new double[size];
    double[] percentile = new double[size];
    ImageStack stack = imp.getImageStack();
    Rectangle bounds = imp.getProcessor().getRoi();
    float[] buffer = null;
    for (int slice = start, i = 0; slice <= end; slice++, i++) {
        IJ.showProgress(i, size);
        final ImageProcessor ip = stack.getProcessor(slice);
        buffer = ImageConverter.getData(ip.getPixels(), ip.getWidth(), ip.getHeight(), bounds, buffer);
        final DataEstimator de = new DataEstimator(buffer, bounds.width, bounds.height);
        de.setFraction(fraction);
        de.setHistogramSize(histogramSize);
        de.setThresholdMethod(thresholdMethod);
        xValues[i] = slice;
        try {
            noise1[i] = de.getNoise();
            noise2[i] = de.getNoise(noiseMethod);
            background[i] = de.getBackground();
            threshold[i] = de.getThreshold();
            percentile[i] = de.getPercentile(BackgroundEstimator.percentile);
        } catch (Exception e) {
            e.printStackTrace();
            throw new RuntimeException(e);
        }
    }
    IJ.showProgress(1);
    IJ.showStatus("Plotting background ...");
    WindowOrganiser wo = new WindowOrganiser();
    plot(wo, xValues, noise1, noise2, null, "Noise", "Background Noise", "Global Noise", null);
    plot(wo, xValues, background, threshold, percentile, "Background", "Background", "Threshold", "Percentile");
    wo.tile();
    IJ.showStatus("");
}

Example 4 with ImageProcessor

use of ij.process.ImageProcessor in project GDSC-SMLM by aherbert.

the class PSFEstimator method calculateStatistics.

private boolean calculateStatistics(PeakFit fitter, double[] params, double[] params_dev) {
    debug("  Fitting PSF");
    swapStatistics();
    // Create the fit engine using the PeakFit plugin
    FitConfiguration fitConfig = config.getFitConfiguration();
    fitConfig.setInitialAngle((float) params[0]);
    fitConfig.setInitialPeakStdDev0((float) params[1]);
    fitConfig.setInitialPeakStdDev1((float) params[2]);
    ImageStack stack = imp.getImageStack();
    Rectangle roi = stack.getProcessor(1).getRoi();
    ImageSource source = new IJImageSource(imp);
    // Allow interlaced data by wrapping the image source
    if (interlacedData) {
        source = new InterlacedImageSource(source, dataStart, dataBlock, dataSkip);
    }
    // Allow frame aggregation by wrapping the image source
    if (integrateFrames > 1) {
        source = new AggregatedImageSource(source, integrateFrames);
    }
    fitter.initialiseImage(source, roi, true);
    fitter.addPeakResults(this);
    fitter.initialiseFitting();
    FitEngine engine = fitter.createFitEngine();
    // Use random slices
    int[] slices = new int[stack.getSize()];
    for (int i = 0; i < slices.length; i++) slices[i] = i + 1;
    Random rand = new Random();
    rand.shuffle(slices);
    IJ.showStatus("Fitting ...");
    // Use multi-threaded code for speed
    int i;
    for (i = 0; i < slices.length; i++) {
        int slice = slices[i];
        //debug("  Processing slice = %d\n", slice);
        IJ.showProgress(size(), settings.numberOfPeaks);
        ImageProcessor ip = stack.getProcessor(slice);
        // stack processor does not set the bounds required by ImageConverter
        ip.setRoi(roi);
        FitJob job = new FitJob(slice, ImageConverter.getData(ip), roi);
        engine.run(job);
        if (sampleSizeReached() || Utils.isInterrupted()) {
            break;
        }
    }
    if (Utils.isInterrupted()) {
        IJ.showProgress(1);
        engine.end(true);
        return false;
    }
    // Wait until we have enough results
    while (!sampleSizeReached() && !engine.isQueueEmpty()) {
        IJ.showProgress(size(), settings.numberOfPeaks);
        try {
            Thread.sleep(50);
        } catch (InterruptedException e) {
            break;
        }
    }
    // End now if we have enough samples
    engine.end(sampleSizeReached());
    IJ.showStatus("");
    IJ.showProgress(1);
    // This count will be an over-estimate given that the provider is ahead of the consumer
    // in this multi-threaded system
    debug("  Processed %d/%d slices (%d peaks)", i, slices.length, size());
    setParams(ANGLE, params, params_dev, sampleNew[ANGLE]);
    setParams(X, params, params_dev, sampleNew[X]);
    setParams(Y, params, params_dev, sampleNew[Y]);
    if (settings.showHistograms) {
        int[] idList = new int[NAMES.length];
        int count = 0;
        boolean requireRetile = false;
        for (int ii = 0; ii < 3; ii++) {
            if (sampleNew[ii].getN() == 0)
                continue;
            StoredDataStatistics stats = new StoredDataStatistics(sampleNew[ii].getValues());
            idList[count++] = Utils.showHistogram(TITLE, stats, NAMES[ii], 0, 0, settings.histogramBins, "Mean = " + Utils.rounded(stats.getMean()) + ". Median = " + Utils.rounded(sampleNew[ii].getPercentile(50)));
            requireRetile = requireRetile || Utils.isNewWindow();
        }
        if (requireRetile && count > 0) {
            new WindowOrganiser().tileWindows(Arrays.copyOf(idList, count));
        }
    }
    if (size() < 2) {
        log("ERROR: Insufficient number of fitted peaks, terminating ...");
        return false;
    }
    return true;
}

Example 5 with ImageProcessor

use of ij.process.ImageProcessor in project GDSC-SMLM by aherbert.

the class Noise method drawPlot.

/**
	 * Build a plot of the noise estimate from the current frame.
	 * Limit the preview to 100 frames.
	 */
private void drawPlot() {
    NoiseEstimator.Method method1 = NoiseEstimator.Method.values()[algorithm];
    NoiseEstimator.Method method2 = NoiseEstimator.Method.values()[algorithm2];
    IJ.showStatus("Estimating noise ...");
    boolean twoMethods = method1 != method2;
    boolean preserveResiduals = method1.name().contains("Residuals") && method2.name().contains("Residuals") && twoMethods;
    int start = imp.getCurrentSlice();
    int end = FastMath.min(imp.getStackSize(), start + 100);
    int size = end - start + 1;
    double[] xValues = new double[size];
    double[] yValues1 = new double[size];
    double[] yValues2 = (twoMethods) ? new double[size] : null;
    ImageStack stack = imp.getImageStack();
    Rectangle bounds = imp.getProcessor().getRoi();
    float[] buffer = null;
    for (int slice = start, i = 0; slice <= end; slice++, i++) {
        IJ.showProgress(i, size);
        final ImageProcessor ip = stack.getProcessor(slice);
        buffer = ImageConverter.getData(ip.getPixels(), ip.getWidth(), ip.getHeight(), bounds, buffer);
        final NoiseEstimator ne = new NoiseEstimator(buffer, bounds.width, bounds.height);
        ne.preserveResiduals = preserveResiduals;
        ne.setRange(lowestPixelsRange);
        xValues[i] = slice;
        yValues1[i] = ne.getNoise(method1);
        if (twoMethods)
            yValues2[i] = ne.getNoise(method2);
    }
    IJ.showProgress(1);
    IJ.showStatus("Plotting noise ...");
    // Get limits
    double[] a = Tools.getMinMax(xValues);
    double[] b1 = Tools.getMinMax(yValues1);
    if (twoMethods) {
        double[] b2 = Tools.getMinMax(yValues2);
        b1[0] = FastMath.min(b1[0], b2[0]);
        b1[1] = FastMath.max(b1[1], b2[1]);
    }
    String title = imp.getTitle() + " Noise";
    Plot2 plot = new Plot2(title, "Slice", "Noise", xValues, yValues1);
    double range = b1[1] - b1[0];
    if (range == 0)
        range = 1;
    plot.setLimits(a[0], a[1], b1[0] - 0.05 * range, b1[1] + 0.05 * range);
    plot.setColor(Color.blue);
    plot.draw();
    String label = String.format("Blue = %s", Utils.rounded(new Statistics(yValues1).getMean()));
    if (twoMethods) {
        plot.setColor(Color.red);
        plot.addPoints(xValues, yValues2, Plot2.LINE);
        label += String.format(", Red = %s", Utils.rounded(new Statistics(yValues2).getMean()));
    }
    plot.addLabel(0, 0, label);
    Utils.display(title, plot);
    IJ.showStatus("");
}