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

Example 96 with FloatProcessor

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

the class SpotInspector method run.

/*
	 * (non-Javadoc)
	 * 
	 * @see ij.plugin.PlugIn#run(java.lang.String)
	 */
public void run(String arg) {
    SMLMUsageTracker.recordPlugin(this.getClass(), arg);
    if (MemoryPeakResults.isMemoryEmpty()) {
        IJ.error(TITLE, "No localisations in memory");
        return;
    }
    if (!showDialog())
        return;
    // Load the results
    results = ResultsManager.loadInputResults(inputOption, false);
    if (results == null || results.size() == 0) {
        IJ.error(TITLE, "No results could be loaded");
        IJ.showStatus("");
        return;
    }
    // Check if the original image is open
    ImageSource source = results.getSource();
    if (source == null) {
        IJ.error(TITLE, "Unknown original source image");
        return;
    }
    source = source.getOriginal();
    if (!source.open()) {
        IJ.error(TITLE, "Cannot open original source image: " + source.toString());
        return;
    }
    final float stdDevMax = getStandardDeviation(results);
    if (stdDevMax < 0) {
        // TODO - Add dialog to get the initial peak width
        IJ.error(TITLE, "Fitting configuration (for initial peak width) is not available");
        return;
    }
    // Rank spots
    rankedResults = new ArrayList<PeakResultRank>(results.size());
    final double a = results.getNmPerPixel();
    final double gain = results.getGain();
    final boolean emCCD = results.isEMCCD();
    for (PeakResult r : results.getResults()) {
        float[] score = getScore(r, a, gain, emCCD, stdDevMax);
        rankedResults.add(new PeakResultRank(r, score[0], score[1]));
    }
    Collections.sort(rankedResults);
    // Prepare results table. Get bias if necessary
    if (showCalibratedValues) {
        // Get a bias if required
        Calibration calibration = results.getCalibration();
        if (calibration.getBias() == 0) {
            ExtendedGenericDialog gd = new ExtendedGenericDialog(TITLE);
            gd.addMessage("Calibrated results requires a camera bias");
            gd.addNumericField("Camera_bias (ADUs)", calibration.getBias(), 2);
            gd.showDialog();
            if (!gd.wasCanceled()) {
                calibration.setBias(Math.abs(gd.getNextNumber()));
            }
        }
    }
    IJTablePeakResults table = new IJTablePeakResults(false, results.getName(), true);
    table.copySettings(results);
    table.setTableTitle(TITLE);
    table.setAddCounter(true);
    table.setShowCalibratedValues(showCalibratedValues);
    table.begin();
    // Add a mouse listener to jump to the frame for the clicked line
    textPanel = table.getResultsWindow().getTextPanel();
    // We must ignore old instances of this class from the mouse listeners
    id = ++currentId;
    textPanel.addMouseListener(this);
    // Add results to the table
    int n = 0;
    for (PeakResultRank rank : rankedResults) {
        rank.rank = n++;
        PeakResult r = rank.peakResult;
        table.add(r.getFrame(), r.origX, r.origY, r.origValue, r.error, r.noise, r.params, r.paramsStdDev);
    }
    table.end();
    if (plotScore || plotHistogram) {
        // Get values for the plots
        float[] xValues = null, yValues = null;
        double yMin, yMax;
        int spotNumber = 0;
        xValues = new float[rankedResults.size()];
        yValues = new float[xValues.length];
        for (PeakResultRank rank : rankedResults) {
            xValues[spotNumber] = spotNumber + 1;
            yValues[spotNumber++] = recoverScore(rank.score);
        }
        // Set the min and max y-values using 1.5 x IQR 
        DescriptiveStatistics stats = new DescriptiveStatistics();
        for (float v : yValues) stats.addValue(v);
        if (removeOutliers) {
            double lower = stats.getPercentile(25);
            double upper = stats.getPercentile(75);
            double iqr = upper - lower;
            yMin = FastMath.max(lower - iqr, stats.getMin());
            yMax = FastMath.min(upper + iqr, stats.getMax());
            IJ.log(String.format("Data range: %f - %f. Plotting 1.5x IQR: %f - %f", stats.getMin(), stats.getMax(), yMin, yMax));
        } else {
            yMin = stats.getMin();
            yMax = stats.getMax();
            IJ.log(String.format("Data range: %f - %f", yMin, yMax));
        }
        plotScore(xValues, yValues, yMin, yMax);
        plotHistogram(yValues, yMin, yMax);
    }
    // Extract spots into a stack
    final int w = source.getWidth();
    final int h = source.getHeight();
    final int size = 2 * radius + 1;
    ImageStack spots = new ImageStack(size, size, rankedResults.size());
    // To assist the extraction of data from the image source, process them in time order to allow 
    // frame caching. Then set the appropriate slice in the result stack
    Collections.sort(rankedResults, new Comparator<PeakResultRank>() {

        public int compare(PeakResultRank o1, PeakResultRank o2) {
            if (o1.peakResult.getFrame() < o2.peakResult.getFrame())
                return -1;
            if (o1.peakResult.getFrame() > o2.peakResult.getFrame())
                return 1;
            return 0;
        }
    });
    for (PeakResultRank rank : rankedResults) {
        PeakResult r = rank.peakResult;
        // Extract image
        // Note that the coordinates are relative to the middle of the pixel (0.5 offset)
        // so do not round but simply convert to int
        final int x = (int) (r.params[Gaussian2DFunction.X_POSITION]);
        final int y = (int) (r.params[Gaussian2DFunction.Y_POSITION]);
        // Extract a region but crop to the image bounds
        int minX = x - radius;
        int minY = y - radius;
        int maxX = FastMath.min(x + radius + 1, w);
        int maxY = FastMath.min(y + radius + 1, h);
        int padX = 0, padY = 0;
        if (minX < 0) {
            padX = -minX;
            minX = 0;
        }
        if (minY < 0) {
            padY = -minY;
            minY = 0;
        }
        int sizeX = maxX - minX;
        int sizeY = maxY - minY;
        float[] data = source.get(r.getFrame(), new Rectangle(minX, minY, sizeX, sizeY));
        // Prevent errors with missing data
        if (data == null)
            data = new float[sizeX * sizeY];
        ImageProcessor spotIp = new FloatProcessor(sizeX, sizeY, data, null);
        // Pad if necessary, i.e. the crop is too small for the stack
        if (padX > 0 || padY > 0 || sizeX < size || sizeY < size) {
            ImageProcessor spotIp2 = spotIp.createProcessor(size, size);
            spotIp2.insert(spotIp, padX, padY);
            spotIp = spotIp2;
        }
        int slice = rank.rank + 1;
        spots.setPixels(spotIp.getPixels(), slice);
        spots.setSliceLabel(Utils.rounded(rank.originalScore), slice);
    }
    source.close();
    ImagePlus imp = Utils.display(TITLE, spots);
    imp.setRoi((PointRoi) null);
    // Make bigger		
    for (int i = 10; i-- > 0; ) imp.getWindow().getCanvas().zoomIn(imp.getWidth() / 2, imp.getHeight() / 2);
}

Example 97 with FloatProcessor

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

the class SmoothImage method run.

/*
	 * (non-Javadoc)
	 * 
	 * @see ij.plugin.filter.PlugInFilter#run(ij.process.ImageProcessor)
	 */
public void run(ImageProcessor ip) {
    Rectangle bounds = ip.getRoi();
    // Crop to the ROI
    FloatProcessor fp = ip.crop().toFloat(0, null);
    float[] data = (float[]) fp.getPixels();
    MaximaSpotFilter filter = createSpotFilter();
    int width = fp.getWidth();
    int height = fp.getHeight();
    data = filter.preprocessData(data, width, height);
    //System.out.println(filter.getDescription());
    fp = new FloatProcessor(width, height, data);
    ip.insert(fp, bounds.x, bounds.y);
    //ip.resetMinAndMax();
    ip.setMinAndMax(fp.getMin(), fp.getMax());
}

Example 98 with FloatProcessor

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

the class PCPALMAnalysis method normaliseCorrelation.

private FloatProcessor normaliseCorrelation(FloatProcessor corrIm, FloatProcessor corrW, double density) {
    float[] data = new float[corrIm.getWidth() * corrIm.getHeight()];
    float[] dataIm = (float[]) corrIm.getPixels();
    float[] dataW = (float[]) corrW.getPixels();
    // Square the density for normalisation
    density *= density;
    for (int i = 0; i < data.length; i++) {
        data[i] = (float) ((double) dataIm[i] / (density * dataW[i]));
    }
    FloatProcessor correlation = new FloatProcessor(corrIm.getWidth(), corrIm.getHeight(), data, null);
    return correlation;
}

Example 99 with FloatProcessor

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

the class TraceMolecules method buildCombinedImage.

private float[] buildCombinedImage(ImageSource source, Trace trace, float fitWidth, Rectangle bounds, double[] combinedNoise, boolean createStack) {
    final int w = source.getWidth();
    final int h = source.getHeight();
    // Get the coordinates and the spot bounds
    float[] centre = trace.getCentroid(CentroidMethod.SIGNAL_WEIGHTED);
    int minX = (int) Math.floor(centre[0] - fitWidth);
    int maxX = (int) Math.ceil(centre[0] + fitWidth);
    int minY = (int) Math.floor(centre[1] - fitWidth);
    int maxY = (int) Math.ceil(centre[1] + fitWidth);
    // Account for crops at the edge of the image
    minX = FastMath.max(0, minX);
    maxX = FastMath.min(w, maxX);
    minY = FastMath.max(0, minY);
    maxY = FastMath.min(h, maxY);
    int width = maxX - minX;
    int height = maxY - minY;
    if (width <= 0 || height <= 0) {
        // The centre must be outside the image width and height
        return null;
    }
    bounds.x = minX;
    bounds.y = minY;
    bounds.width = width;
    bounds.height = height;
    if (createStack)
        slices = new ImageStack(width, height);
    // Combine the images. Subtract the fitted background to zero the image.
    float[] data = new float[width * height];
    float sumBackground = 0;
    double noise = 0;
    for (PeakResult result : trace.getPoints()) {
        noise += result.noise * result.noise;
        float[] sourceData = source.get(result.getFrame(), bounds);
        final float background = result.getBackground();
        sumBackground += background;
        for (int i = 0; i < data.length; i++) {
            data[i] += sourceData[i] - background;
        }
        if (createStack)
            slices.addSlice(new FloatProcessor(width, height, sourceData, null));
    }
    if (createStack) {
        // Add a final image that is the average of the individual slices. This allows
        // it to be visualised in the same intensity scale.
        float[] data2 = Arrays.copyOf(data, data.length);
        final int size = slices.getSize();
        sumBackground /= size;
        for (int i = 0; i < data2.length; i++) data2[i] = sumBackground + data2[i] / size;
        slices.addSlice(new FloatProcessor(width, height, data2, null));
    }
    // Combined noise is the sqrt of the sum-of-squares
    combinedNoise[0] = Math.sqrt(noise);
    return data;
}

Example 100 with FloatProcessor

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

the class TraceMolecules method createBilinearPlot.

private FloatProcessor createBilinearPlot(List<double[]> results, int w, int h) {
    FloatProcessor fp = new FloatProcessor(w, h);
    // Create lookup table that map the tested threshold values to a position in the image
    int[] xLookup = createLookup(tThresholds, settings.minTimeThreshold, w);
    int[] yLookup = createLookup(dThresholds, settings.minDistanceThreshold, h);
    origX = (settings.minTimeThreshold != 0) ? xLookup[1] : 0;
    origY = (settings.minDistanceThreshold != 0) ? yLookup[1] : 0;
    int gridWidth = tThresholds.length;
    int gridHeight = dThresholds.length;
    for (int y = 0, prevY = 0; y < gridHeight; y++) {
        for (int x = 0, prevX = 0; x < gridWidth; x++) {
            // Get the 4 flanking values
            double x1y1 = results.get(prevY * gridWidth + prevX)[2];
            double x1y2 = results.get(y * gridWidth + prevX)[2];
            double x2y1 = results.get(prevY * gridWidth + x)[2];
            double x2y2 = results.get(y * gridWidth + x)[2];
            // Pixel range
            int x1 = xLookup[x];
            int x2 = xLookup[x + 1];
            int y1 = yLookup[y];
            int y2 = yLookup[y + 1];
            double xRange = x2 - x1;
            double yRange = y2 - y1;
            for (int yy = y1; yy < y2; yy++) {
                double yFraction = (yy - y1) / yRange;
                for (int xx = x1; xx < x2; xx++) {
                    // Interpolate
                    double xFraction = (xx - x1) / xRange;
                    double v1 = x1y1 * (1 - xFraction) + x2y1 * xFraction;
                    double v2 = x1y2 * (1 - xFraction) + x2y2 * xFraction;
                    double value = v1 * (1 - yFraction) + v2 * yFraction;
                    fp.setf(xx, yy, (float) value);
                }
            }
            prevX = x;
        }
        prevY = y;
    }
    // Convert to absolute for easier visualisation
    float[] data = (float[]) fp.getPixels();
    for (int i = 0; i < data.length; i++) data[i] = Math.abs(data[i]);
    return fp;
}