Examples with Gaussian2DFitter gdsc.smlm.fitting.Gaussian2DFitter used on opensource projects

Example 1 with Gaussian2DFitter

use of gdsc.smlm.fitting.Gaussian2DFitter in project GDSC-SMLM by aherbert.

the class GaussianFit method fitMultiple.

/**
	 * Fits a 2D Gaussian to the given data. Fits all the specified peaks.
	 * <p>
	 * Data must be arranged in yx block order, i.e. height rows of width.
	 * <p>
	 * Note: The fit coordinates should be offset by 0.5 if the input data represents pixels
	 * 
	 * @param data
	 * @param width
	 * @param height
	 * @param maxIndices
	 *            Indices of the data to fit
	 * @param estimatedHeights
	 *            Estimated heights for the peaks (input from smoothed data)
	 * @return Array containing the fitted curve data: The first value is the Background. The remaining values are
	 *         Amplitude, PosX, PosY, StdDevX, StdDevY for each fitted peak. If elliptical fitting is performed
	 *         the values are Amplitude, Angle, PosX, PosY, StdDevX, StdDevY for each fitted peak
	 *         <p>
	 *         Null if no fit is possible.
	 */
private double[] fitMultiple(float[] data, int width, int height, int[] maxIndices, double[] estimatedHeights) {
    if (data == null || data.length != width * height)
        return null;
    if (maxIndices == null || maxIndices.length == 0)
        return null;
    Gaussian2DFitter gf = createGaussianFitter(false);
    this.fitResult = gf.fit(Utils.toDouble(data), width, height, maxIndices, estimatedHeights);
    if (fitResult.getStatus() == FitStatus.OK) {
        chiSquared = fitResult.getError();
        double[] params = fitResult.getParameters();
        convertParameters(params);
        return params;
    }
    return null;
}

Example 2 with Gaussian2DFitter

use of gdsc.smlm.fitting.Gaussian2DFitter in project GDSC-SMLM by aherbert.

the class GaussianFit method fit.

/**
	 * Fits a single 2D Gaussian to the data. The fit is initialised at the highest
	 * value and then optimised.
	 * <p>
	 * Data must be arranged in yx block order, i.e. height rows of width.
	 * <p>
	 * The angle parameter is only returned if using elliptical Gaussian fitting.
	 * <p>
	 * Note: The fit coordinates should be offset by 0.5 if the input data represents pixels
	 * 
	 * @return Array containing the fitted curve data: Background, Amplitude, PosX, PosY, StdDevX, StdDevY, Angle. Null
	 *         if no fit is possible.
	 */
public double[] fit(float[] data, int width, int height) {
    if (data == null || data.length != width * height)
        return null;
    // Get the limits
    float max = Float.MIN_VALUE;
    int maxIndex = -1;
    for (int i = data.length; i-- > 0; ) {
        float f = data[i];
        if (max < f) {
            max = f;
            maxIndex = i;
        }
    }
    if (maxIndex < 0) {
        return null;
    }
    Gaussian2DFitter gf = createGaussianFitter(false);
    FitResult fitResult = gf.fit(Utils.toDouble(data), width, height, new int[] { maxIndex });
    if (fitResult.getStatus() == FitStatus.OK) {
        chiSquared = fitResult.getError();
        double[] params = fitResult.getParameters();
        // Check bounds
        if (params[3] < 0 || params[3] >= width || params[4] < 0 || params[4] >= height)
            return null;
        // Re-arrange order for backwards compatibility with old code.
        return new double[] { params[0], params[1], params[3], params[4], Gaussian2DFitter.fwhm2sd(params[5]), Gaussian2DFitter.fwhm2sd(params[6]), params[2] };
    }
    return null;
}

Example 3 with Gaussian2DFitter

use of gdsc.smlm.fitting.Gaussian2DFitter in project GDSC-SMLM by aherbert.

the class SpotAnalysis method updateCurrentSlice.

private void updateCurrentSlice(int slice) {
    if (slice != currentSlice) {
        currentSlice = slice;
        double signal = getSignal(slice);
        double noise = smoothSd[slice - 1];
        currentLabel.setText(String.format("Frame %d: Signal = %s, SNR = %s", slice, Utils.rounded(signal, 4), Utils.rounded(signal / noise, 3)));
        drawProfiles();
        // Fit the PSF using a Gaussian
        float[] data2 = (float[]) rawImp.getImageStack().getProcessor(slice).getPixels();
        double[] data = Utils.toDouble(data2);
        FitConfiguration fitConfiguration = new FitConfiguration();
        fitConfiguration.setFitFunction(FitFunction.FIXED);
        fitConfiguration.setBackgroundFitting(true);
        fitConfiguration.setSignalStrength(0);
        fitConfiguration.setCoordinateShift(rawImp.getWidth() / 4.0f);
        fitConfiguration.setComputeResiduals(false);
        fitConfiguration.setComputeDeviations(false);
        Gaussian2DFitter gf = new Gaussian2DFitter(fitConfiguration);
        double[] params = new double[7];
        double psfWidth = Double.parseDouble(widthTextField.getText());
        params[Gaussian2DFunction.BACKGROUND] = smoothMean[slice - 1];
        params[Gaussian2DFunction.SIGNAL] = (gain * signal);
        params[Gaussian2DFunction.X_POSITION] = rawImp.getWidth() / 2.0f;
        params[Gaussian2DFunction.Y_POSITION] = rawImp.getHeight() / 2.0f;
        params[Gaussian2DFunction.X_SD] = params[Gaussian2DFunction.Y_SD] = psfWidth;
        FitResult fitResult = gf.fit(data, rawImp.getWidth(), rawImp.getHeight(), 1, params, new boolean[1]);
        if (fitResult.getStatus() == FitStatus.OK) {
            params = fitResult.getParameters();
            final double spotSignal = params[Gaussian2DFunction.SIGNAL] / gain;
            rawFittedLabel.setText(String.format("Raw fit: Signal = %s, SNR = %s", Utils.rounded(spotSignal, 4), Utils.rounded(spotSignal / noise, 3)));
            ImageROIPainter.addRoi(rawImp, slice, new PointRoi(params[Gaussian2DFunction.X_POSITION], params[Gaussian2DFunction.Y_POSITION]));
        } else {
            rawFittedLabel.setText("");
            rawImp.setOverlay(null);
        }
        // Fit the PSF using a Gaussian
        if (blurImp == null)
            return;
        data2 = (float[]) blurImp.getImageStack().getProcessor(slice).getPixels();
        data = Utils.toDouble(data2);
        params = new double[7];
        //float psfWidth = Float.parseFloat(widthTextField.getText());
        params[Gaussian2DFunction.BACKGROUND] = (float) smoothMean[slice - 1];
        params[Gaussian2DFunction.SIGNAL] = (float) (gain * signal);
        params[Gaussian2DFunction.X_POSITION] = rawImp.getWidth() / 2.0f;
        params[Gaussian2DFunction.Y_POSITION] = rawImp.getHeight() / 2.0f;
        params[Gaussian2DFunction.X_SD] = params[Gaussian2DFunction.Y_SD] = psfWidth;
        fitResult = gf.fit(data, rawImp.getWidth(), rawImp.getHeight(), 1, params, new boolean[1]);
        if (fitResult.getStatus() == FitStatus.OK) {
            params = fitResult.getParameters();
            final double spotSignal = params[Gaussian2DFunction.SIGNAL] / gain;
            blurFittedLabel.setText(String.format("Blur fit: Signal = %s, SNR = %s", Utils.rounded(spotSignal, 4), Utils.rounded(spotSignal / noise, 3)));
            ImageROIPainter.addRoi(blurImp, slice, new PointRoi(params[Gaussian2DFunction.X_POSITION], params[Gaussian2DFunction.Y_POSITION]));
        } else {
            blurFittedLabel.setText("");
            blurImp.setOverlay(null);
        }
    }
}

Example 4 with Gaussian2DFitter

use of gdsc.smlm.fitting.Gaussian2DFitter in project GDSC-SMLM by aherbert.

the class GaussianFit method runFinal.

/**
	 * Perform fitting using the chosen maxima. Update the overlay if successful.
	 * 
	 * @param ip
	 *            The input image
	 */
private void runFinal(ImageProcessor ip) {
    ip.reset();
    Rectangle bounds = ip.getRoi();
    // Crop to the ROI
    float[] data = ImageConverter.getData(ip);
    int width = bounds.width;
    int height = bounds.height;
    // Sort the maxima
    float[] smoothData = data;
    if (getSmooth() > 0) {
        // Smoothing destructively modifies the data so create a copy
        smoothData = Arrays.copyOf(data, width * height);
        AverageFilter filter = new AverageFilter();
        //filter.blockAverage(smoothData, width, height, smooth);
        if (smooth <= border)
            filter.stripedBlockAverageInternal(smoothData, width, height, (float) smooth);
        else
            filter.stripedBlockAverage(smoothData, width, height, (float) smooth);
    }
    Sort.sort(maxIndices, smoothData);
    // Show the candidate peaks
    if (maxIndices.length > 0) {
        String message = String.format("Identified %d peaks", maxIndices.length);
        if (isLogProgress()) {
            IJ.log(message);
            for (int index : maxIndices) {
                IJ.log(String.format("  %.2f @ [%d,%d]", data[index], bounds.x + index % width, bounds.y + index / width));
            }
        }
        // Check whether to run if the number of peaks is large
        if (maxIndices.length > 10) {
            GenericDialog gd = new GenericDialog("Warning");
            gd.addMessage(message + "\nDo you want to fit?");
            gd.showDialog();
            if (gd.wasCanceled())
                return;
        }
    } else {
        IJ.log("No maxima identified");
        return;
    }
    results = new IJTablePeakResults(showDeviations, imp.getTitle() + " [" + imp.getCurrentSlice() + "]");
    results.begin();
    // Perform the Gaussian fit
    long ellapsed = 0;
    if (!singleFit) {
        if (isLogProgress())
            IJ.log("Combined fit");
        // Estimate height from smoothed data
        double[] estimatedHeights = new double[maxIndices.length];
        for (int i = 0; i < estimatedHeights.length; i++) estimatedHeights[i] = smoothData[maxIndices[i]];
        FitConfiguration config = new FitConfiguration();
        setupPeakFiltering(config);
        long time = System.nanoTime();
        double[] params = fitMultiple(data, width, height, maxIndices, estimatedHeights);
        ellapsed = System.nanoTime() - time;
        if (params != null) {
            // Copy all the valid parameters into a new array
            double[] validParams = new double[params.length];
            int c = 0;
            int validPeaks = 0;
            validParams[c++] = params[0];
            double[] initialParams = convertParameters(fitResult.getInitialParameters());
            double[] paramsDev = convertParameters(fitResult.getParameterStdDev());
            Rectangle regionBounds = new Rectangle();
            int[] xpoints = new int[maxIndices.length];
            int[] ypoints = new int[maxIndices.length];
            int nMaxima = 0;
            for (int i = 1, n = 0; i < params.length; i += 6, n++) {
                int y = maxIndices[n] / width;
                int x = maxIndices[n] % width;
                // Check the peak is a good fit
                if (filterResults && config.validatePeak(n, initialParams, params) != FitStatus.OK)
                    continue;
                if (showFit) {
                    // Copy the valid parameters
                    validPeaks++;
                    for (int ii = i, j = 0; j < 6; ii++, j++) validParams[c++] = params[ii];
                }
                double[] peakParams = extractParams(params, i);
                double[] peakParamsDev = extractParams(paramsDev, i);
                addResult(bounds, regionBounds, data, peakParams, peakParamsDev, nMaxima, x, y, data[maxIndices[n]]);
                // Add fit result to the overlay - Coords are updated with the region offsets in addResult
                double xf = peakParams[3];
                double yf = peakParams[4];
                xpoints[nMaxima] = (int) (xf + 0.5);
                ypoints[nMaxima] = (int) (yf + 0.5);
                nMaxima++;
            }
            setOverlay(nMaxima, xpoints, ypoints);
            // Draw the fit
            if (showFit && validPeaks != 0) {
                double[] pixels = new double[data.length];
                EllipticalGaussian2DFunction f = new EllipticalGaussian2DFunction(validPeaks, width, height);
                invertParameters(validParams);
                f.initialise(validParams);
                for (int x = 0; x < pixels.length; x++) pixels[x] = f.eval(x);
                FloatProcessor fp = new FloatProcessor(width, height, pixels);
                // Insert into a full size image
                FloatProcessor fp2 = new FloatProcessor(ip.getWidth(), ip.getHeight());
                fp2.insert(fp, bounds.x, bounds.y);
                Utils.display(TITLE, fp2);
            }
        } else {
            if (isLogProgress()) {
                IJ.log("Failed to fit " + Utils.pleural(maxIndices.length, "peak") + getReason(fitResult));
            }
            imp.setOverlay(null);
        }
    } else {
        if (isLogProgress())
            IJ.log("Individual fit");
        int nMaxima = 0;
        int[] xpoints = new int[maxIndices.length];
        int[] ypoints = new int[maxIndices.length];
        // Extract each peak and fit individually
        ImageExtractor ie = new ImageExtractor(data, width, height);
        float[] region = null;
        Gaussian2DFitter gf = createGaussianFitter(filterResults);
        for (int n = 0; n < maxIndices.length; n++) {
            int y = maxIndices[n] / width;
            int x = maxIndices[n] % width;
            long time = System.nanoTime();
            Rectangle regionBounds = ie.getBoxRegionBounds(x, y, singleRegionSize);
            region = ie.crop(regionBounds, region);
            int newIndex = (y - regionBounds.y) * regionBounds.width + x - regionBounds.x;
            if (isLogProgress()) {
                IJ.log("Fitting peak " + (n + 1));
            }
            double[] peakParams = fitSingle(gf, region, regionBounds.width, regionBounds.height, newIndex, smoothData[maxIndices[n]]);
            ellapsed += System.nanoTime() - time;
            // Output fit result
            if (peakParams != null) {
                double[] peakParamsDev = null;
                if (showDeviations) {
                    peakParamsDev = convertParameters(fitResult.getParameterStdDev());
                }
                addResult(bounds, regionBounds, data, peakParams, peakParamsDev, n, x, y, data[maxIndices[n]]);
                // Add fit result to the overlay - Coords are updated with the region offsets in addResult
                double xf = peakParams[3];
                double yf = peakParams[4];
                xpoints[nMaxima] = (int) (xf + 0.5);
                ypoints[nMaxima] = (int) (yf + 0.5);
                nMaxima++;
            } else {
                if (isLogProgress()) {
                    IJ.log("Failed to fit peak " + (n + 1) + getReason(fitResult));
                }
            }
        }
        // Update the overlay
        if (nMaxima > 0)
            setOverlay(nMaxima, xpoints, ypoints);
        else
            imp.setOverlay(null);
    }
    results.end();
    if (isLogProgress())
        IJ.log("Time = " + (ellapsed / 1000000.0) + "ms");
}

Example 5 with Gaussian2DFitter

use of gdsc.smlm.fitting.Gaussian2DFitter in project GDSC-SMLM by aherbert.

the class GaussianFit method createGaussianFitter.

private Gaussian2DFitter createGaussianFitter(boolean simpleFiltering) {
    FitConfiguration config = new FitConfiguration();
    config.setMaxIterations(getMaxIterations());
    config.setSignificantDigits(getSignificantDigits());
    config.setDelta(getDelta());
    config.setInitialPeakStdDev(getInitialPeakStdDev());
    config.setComputeDeviations(showDeviations);
    config.setDuplicateDistance(0);
    // Set-up peak filtering only for single fitting
    config.setDisableSimpleFilter(!simpleFiltering);
    setupPeakFiltering(config);
    if (isLogProgress()) {
        config.setLog(new IJLogger());
    }
    if (getFitCriteria() >= 0 && getFitCriteria() < FitCriteria.values().length) {
        config.setFitCriteria(FitCriteria.values()[getFitCriteria()]);
    } else {
        config.setFitCriteria(FitCriteria.LEAST_SQUARED_ERROR);
    }
    if (getFitFunction() >= 0 && getFitFunction() < FitFunction.values().length) {
        config.setFitFunction(FitFunction.values()[getFitFunction()]);
    } else {
        config.setFitFunction(FitFunction.CIRCULAR);
    }
    config.setBackgroundFitting(fitBackground);
    return new Gaussian2DFitter(config);
}