Examples with MultiPathFilter gdsc.smlm.results.filter.MultiPathFilter used on opensource projects

Example 1 with MultiPathFilter

use of gdsc.smlm.results.filter.MultiPathFilter in project GDSC-SMLM by aherbert.

the class BenchmarkFilterAnalysis method getAssignments.

/**
	 * Score the filter using the results list and the configured fail count.
	 *
	 * @param filter
	 *            the filter
	 * @param resultsList
	 *            the results list
	 * @param allAssignments
	 *            all the assignments
	 * @return The score
	 */
private ArrayList<FractionalAssignment[]> getAssignments(DirectFilter filter) {
    final MultiPathFilter multiPathFilter = createMPF(filter, minimalFilter);
    ArrayList<FractionalAssignment[]> allAssignments = new ArrayList<FractionalAssignment[]>(resultsList.length);
    multiPathFilter.fractionScoreSubset(resultsList, failCount, nActual, allAssignments, null, coordinateStore);
    return allAssignments;
}

Example 2 with MultiPathFilter

use of gdsc.smlm.results.filter.MultiPathFilter in project GDSC-SMLM by aherbert.

the class BenchmarkFilterAnalysis method createResults.

/**
	 * Create peak results.
	 *
	 * @param filterResults
	 *            The results from running the filter (or null)
	 * @param filter
	 *            the filter
	 */
private MemoryPeakResults createResults(PreprocessedPeakResult[] filterResults, DirectFilter filter, boolean withBorder) {
    if (filterResults == null) {
        final MultiPathFilter multiPathFilter = createMPF(filter, minimalFilter);
        //multiPathFilter.setDebugFile("/tmp/filter.txt");
        filterResults = filterResults(multiPathFilter);
    }
    MemoryPeakResults results = new MemoryPeakResults();
    results.copySettings(this.results);
    results.setName(TITLE);
    if (withBorder) {
        // To produce the same results as the PeakFit plugin we must implement the border
        // functionality used in the FitWorker. This respects the border of the spot filter.
        FitEngineConfiguration config = new FitEngineConfiguration(new FitConfiguration());
        updateAllConfiguration(config);
        MaximaSpotFilter spotFilter = config.createSpotFilter(true);
        final int border = spotFilter.getBorder();
        int[] bounds = getBounds();
        final int borderLimitX = bounds[0] - border;
        final int borderLimitY = bounds[1] - border;
        for (PreprocessedPeakResult spot : filterResults) {
            if (spot.getX() > border && spot.getX() < borderLimitX && spot.getY() > border && spot.getY() < borderLimitY) {
                double[] p = spot.toGaussian2DParameters();
                float[] params = new float[p.length];
                for (int j = 0; j < p.length; j++) params[j] = (float) p[j];
                int frame = spot.getFrame();
                int origX = (int) p[Gaussian2DFunction.X_POSITION];
                int origY = (int) p[Gaussian2DFunction.Y_POSITION];
                results.addf(frame, origX, origY, 0, 0, spot.getNoise(), params, null);
            }
        }
    } else {
        for (PreprocessedPeakResult spot : filterResults) {
            double[] p = spot.toGaussian2DParameters();
            float[] params = new float[p.length];
            for (int j = 0; j < p.length; j++) params[j] = (float) p[j];
            int frame = spot.getFrame();
            int origX = (int) p[Gaussian2DFunction.X_POSITION];
            int origY = (int) p[Gaussian2DFunction.Y_POSITION];
            results.addf(frame, origX, origY, 0, 0, spot.getNoise(), params, null);
        }
    }
    return results;
}

Example 3 with MultiPathFilter

use of gdsc.smlm.results.filter.MultiPathFilter in project GDSC-SMLM by aherbert.

the class BenchmarkSpotFit method showDialog.

@SuppressWarnings("unchecked")
private boolean showDialog() {
    GenericDialog gd = new GenericDialog(TITLE);
    gd.addHelp(About.HELP_URL);
    gd.addMessage(String.format("Fit candidate spots in the benchmark image created by " + CreateData.TITLE + " plugin\nand identified by the " + BenchmarkSpotFilter.TITLE + " plugin.\nPSF width = %s nm (Square pixel adjustment = %s nm)\n \nConfigure the fitting:", Utils.rounded(simulationParameters.s), Utils.rounded(getSa())));
    gd.addSlider("Fraction_positives", 50, 100, fractionPositives);
    gd.addSlider("Fraction_negatives_after_positives", 0, 100, fractionNegativesAfterAllPositives);
    gd.addSlider("Min_negatives_after_positives", 0, 10, negativesAfterAllPositives);
    gd.addSlider("Match_distance", 0.5, 3.5, distance);
    gd.addSlider("Lower_distance", 0, 3.5, lowerDistance);
    gd.addSlider("Match_signal", 0, 3.5, signalFactor);
    gd.addSlider("Lower_signal", 0, 3.5, lowerSignalFactor);
    // Collect options for fitting
    final double sa = getSa();
    gd.addNumericField("Initial_StdDev", Maths.round(sa / simulationParameters.a), 3);
    gd.addSlider("Fitting_width", 2, 4.5, config.getFitting());
    String[] solverNames = SettingsManager.getNames((Object[]) FitSolver.values());
    gd.addChoice("Fit_solver", solverNames, solverNames[fitConfig.getFitSolver().ordinal()]);
    String[] functionNames = SettingsManager.getNames((Object[]) FitFunction.values());
    gd.addChoice("Fit_function", functionNames, functionNames[fitConfig.getFitFunction().ordinal()]);
    gd.addMessage("Multi-path filter (used to pick optimum results during fitting)");
    // Allow loading the best filter fot these results
    boolean benchmarkSettingsCheckbox = fitResultsId == BenchmarkFilterAnalysis.lastId;
    // This should always be an opt-in decision. Otherwise the user cannot use the previous settings
    useBenchmarkSettings = false;
    if (benchmarkSettingsCheckbox)
        gd.addCheckbox("Benchmark_settings", useBenchmarkSettings);
    gd.addTextAreas(XmlUtils.convertQuotes(multiFilter.toXML()), null, 6, 60);
    gd.addNumericField("Fail_limit", config.getFailuresLimit(), 0);
    gd.addCheckbox("Include_neighbours", config.isIncludeNeighbours());
    gd.addSlider("Neighbour_height", 0.01, 1, config.getNeighbourHeightThreshold());
    //gd.addSlider("Residuals_threshold", 0.01, 1, config.getResidualsThreshold());
    gd.addCheckbox("Compute_doublets", computeDoublets);
    gd.addNumericField("Duplicate_distance", fitConfig.getDuplicateDistance(), 2);
    gd.addCheckbox("Show_score_histograms", showFilterScoreHistograms);
    gd.addCheckbox("Show_correlation", showCorrelation);
    gd.addCheckbox("Plot_rank_by_intensity", rankByIntensity);
    gd.addCheckbox("Save_filter_range", saveFilterRange);
    if (extraOptions) {
    }
    // Add a mouse listener to the config file field
    if (benchmarkSettingsCheckbox && Utils.isShowGenericDialog()) {
        Vector<TextField> numerics = (Vector<TextField>) gd.getNumericFields();
        Vector<Checkbox> checkboxes = (Vector<Checkbox>) gd.getCheckboxes();
        taFilterXml = gd.getTextArea1();
        Checkbox b = checkboxes.get(0);
        b.addItemListener(this);
        textFailLimit = numerics.get(9);
        cbIncludeNeighbours = checkboxes.get(1);
        textNeighbourHeight = numerics.get(10);
        cbComputeDoublets = checkboxes.get(2);
        if (useBenchmarkSettings) {
            FitConfiguration tmpFitConfig = new FitConfiguration();
            FitEngineConfiguration tmp = new FitEngineConfiguration(tmpFitConfig);
            // Collect the residuals threshold
            tmpFitConfig.setComputeResiduals(true);
            if (BenchmarkFilterAnalysis.updateConfiguration(tmp, false)) {
                textFailLimit.setText("" + tmp.getFailuresLimit());
                cbIncludeNeighbours.setState(tmp.isIncludeNeighbours());
                textNeighbourHeight.setText(Utils.rounded(tmp.getNeighbourHeightThreshold()));
                cbComputeDoublets.setState(tmp.getResidualsThreshold() < 1);
                final DirectFilter primaryFilter = tmpFitConfig.getSmartFilter();
                final double residualsThreshold = tmp.getResidualsThreshold();
                taFilterXml.setText(new MultiPathFilter(primaryFilter, minimalFilter, residualsThreshold).toXML());
            }
        }
    }
    gd.showDialog();
    if (gd.wasCanceled())
        return false;
    fractionPositives = Math.abs(gd.getNextNumber());
    fractionNegativesAfterAllPositives = Math.abs(gd.getNextNumber());
    negativesAfterAllPositives = (int) Math.abs(gd.getNextNumber());
    distance = Math.abs(gd.getNextNumber());
    lowerDistance = Math.abs(gd.getNextNumber());
    signalFactor = Math.abs(gd.getNextNumber());
    lowerSignalFactor = Math.abs(gd.getNextNumber());
    fitConfig.setInitialPeakStdDev(gd.getNextNumber());
    config.setFitting(gd.getNextNumber());
    fitConfig.setFitSolver(gd.getNextChoiceIndex());
    fitConfig.setFitFunction(gd.getNextChoiceIndex());
    boolean myUseBenchmarkSettings = false;
    if (benchmarkSettingsCheckbox)
        //useBenchmarkSettings = 
        myUseBenchmarkSettings = gd.getNextBoolean();
    // Read dialog settings
    String xml = gd.getNextText();
    int failLimit = (int) gd.getNextNumber();
    boolean includeNeighbours = gd.getNextBoolean();
    double neighbourHeightThreshold = gd.getNextNumber();
    boolean myComputeDoublets = gd.getNextBoolean();
    double myDuplicateDistance = gd.getNextNumber();
    MultiPathFilter myMultiFilter = null;
    if (myUseBenchmarkSettings && !Utils.isShowGenericDialog()) {
        // Only copy the benchmark settings if not interactive
        FitConfiguration tmpFitConfig = new FitConfiguration();
        FitEngineConfiguration tmp = new FitEngineConfiguration(tmpFitConfig);
        // Collect the residuals threshold
        tmpFitConfig.setComputeResiduals(true);
        if (BenchmarkFilterAnalysis.updateConfiguration(tmp, false)) {
            config.setFailuresLimit(tmp.getFailuresLimit());
            config.setIncludeNeighbours(tmp.isIncludeNeighbours());
            config.setNeighbourHeightThreshold(tmp.getNeighbourHeightThreshold());
            computeDoublets = (tmp.getResidualsThreshold() < 1);
            fitConfig.setDuplicateDistance(tmpFitConfig.getDuplicateDistance());
            final DirectFilter primaryFilter = tmpFitConfig.getSmartFilter();
            final double residualsThreshold = tmp.getResidualsThreshold();
            myMultiFilter = new MultiPathFilter(primaryFilter, minimalFilter, residualsThreshold);
        }
    } else {
        myMultiFilter = MultiPathFilter.fromXML(xml);
        config.setFailuresLimit(failLimit);
        config.setIncludeNeighbours(includeNeighbours);
        config.setNeighbourHeightThreshold(neighbourHeightThreshold);
        computeDoublets = myComputeDoublets;
        fitConfig.setDuplicateDistance(myDuplicateDistance);
    }
    if (myMultiFilter == null) {
        gd = new GenericDialog(TITLE);
        gd.addMessage("The multi-path filter was invalid.\n \nContinue with a default filter?");
        gd.enableYesNoCancel();
        gd.hideCancelButton();
        gd.showDialog();
        if (!gd.wasOKed())
            return false;
    } else {
        multiFilter = myMultiFilter;
    }
    if (computeDoublets) {
        //config.setComputeResiduals(true);
        config.setResidualsThreshold(0);
        fitConfig.setComputeResiduals(true);
    } else {
        config.setResidualsThreshold(1);
        fitConfig.setComputeResiduals(false);
    }
    showFilterScoreHistograms = gd.getNextBoolean();
    showCorrelation = gd.getNextBoolean();
    rankByIntensity = gd.getNextBoolean();
    saveFilterRange = gd.getNextBoolean();
    // Avoid stupidness, i.e. things that move outside the fit window and are bad widths
    // TODO - Fix this for simple or smart filter...
    fitConfig.setDisableSimpleFilter(false);
    // Realistically we cannot fit lower than this
    fitConfig.setMinPhotons(15);
    // Disable shift as candidates may be re-mapped to alternative candidates so the initial position is wrong.
    fitConfig.setCoordinateShiftFactor(0);
    fitConfig.setMinWidthFactor(1.0 / 5);
    fitConfig.setWidthFactor(5);
    // Disable the direct filter
    fitConfig.setDirectFilter(null);
    if (extraOptions) {
    }
    if (gd.invalidNumber())
        return false;
    if (lowerDistance > distance)
        lowerDistance = distance;
    if (lowerSignalFactor > signalFactor)
        lowerSignalFactor = signalFactor;
    // Distances relative to sa (not s) as this is the same as the BenchmarkSpotFilter plugin 
    distanceInPixels = distance * sa / simulationParameters.a;
    lowerDistanceInPixels = lowerDistance * sa / simulationParameters.a;
    GlobalSettings settings = new GlobalSettings();
    settings.setFitEngineConfiguration(config);
    settings.setCalibration(cal);
    // Copy simulation defaults if a new simulation
    if (lastId != simulationParameters.id) {
        cal.setNmPerPixel(simulationParameters.a);
        cal.setGain(simulationParameters.gain);
        cal.setAmplification(simulationParameters.amplification);
        cal.setExposureTime(100);
        cal.setReadNoise(simulationParameters.readNoise);
        cal.setBias(simulationParameters.bias);
        cal.setEmCCD(simulationParameters.emCCD);
        // This is needed to configure the fit solver
        fitConfig.setNmPerPixel(Maths.round(cal.getNmPerPixel()));
        fitConfig.setGain(Maths.round(cal.getGain()));
        fitConfig.setBias(Maths.round(cal.getBias()));
        fitConfig.setReadNoise(Maths.round(cal.getReadNoise()));
        fitConfig.setAmplification(Maths.round(cal.getAmplification()));
        fitConfig.setEmCCD(cal.isEmCCD());
    }
    if (!PeakFit.configureFitSolver(settings, null, extraOptions))
        return false;
    return true;
}

Example 4 with MultiPathFilter

use of gdsc.smlm.results.filter.MultiPathFilter in project GDSC-SMLM by aherbert.

the class BenchmarkSpotFit method itemStateChanged.

public void itemStateChanged(ItemEvent e) {
    if (e.getSource() instanceof Checkbox) {
        Checkbox checkbox = (Checkbox) e.getSource();
        int failLimit;
        boolean includeNeighbours;
        double neighbourHeightThrehsold;
        boolean computeDoublets;
        MultiPathFilter myMultiFilter;
        if (checkbox.getState()) {
            FitConfiguration tmpFitConfig = new FitConfiguration();
            FitEngineConfiguration tmp = new FitEngineConfiguration(tmpFitConfig);
            // Collect residuals threshold
            tmpFitConfig.setComputeResiduals(true);
            if (BenchmarkFilterAnalysis.updateConfiguration(tmp, false)) {
                failLimit = tmp.getFailuresLimit();
                includeNeighbours = tmp.isIncludeNeighbours();
                neighbourHeightThrehsold = tmp.getNeighbourHeightThreshold();
                computeDoublets = tmp.getResidualsThreshold() < 1;
                final DirectFilter primaryFilter = tmpFitConfig.getSmartFilter();
                final double residualsThreshold = tmp.getResidualsThreshold();
                myMultiFilter = new MultiPathFilter(primaryFilter, minimalFilter, residualsThreshold);
            } else {
                IJ.log("Failed to update settings using the filter analysis");
                checkbox.setState(false);
                return;
            }
        } else {
            failLimit = config.getFailuresLimit();
            includeNeighbours = config.isIncludeNeighbours();
            neighbourHeightThrehsold = config.getNeighbourHeightThreshold();
            computeDoublets = BenchmarkSpotFit.computeDoublets;
            myMultiFilter = multiFilter;
        }
        // Update the dialog
        taFilterXml.setText(myMultiFilter.toXML());
        textFailLimit.setText("" + failLimit);
        cbIncludeNeighbours.setState(includeNeighbours);
        textNeighbourHeight.setText(Utils.rounded(neighbourHeightThrehsold));
        cbComputeDoublets.setState(computeDoublets);
    }
}

Example 5 with MultiPathFilter

use of gdsc.smlm.results.filter.MultiPathFilter in project GDSC-SMLM by aherbert.

the class FitWorker method run.

/**
	 * Locate all the peaks in the image specified by the fit job
	 * <p>
	 * WARNING: The FitWorker fits a sub-region of the data for each maxima. It then updates the FitResult parameters
	 * with an offset reflecting the position. The initialParameters are not updated with this offset unless configured.
	 * 
	 * @param job
	 *            The fit job
	 */
public void run(FitJob job) {
    final long start = System.nanoTime();
    job.start();
    this.job = job;
    benchmarking = false;
    this.slice = job.slice;
    // Used for debugging
    //if (logger == null) logger = new gdsc.fitting.logging.ConsoleLogger();
    // Crop to the ROI
    cc = new CoordinateConverter(job.bounds);
    final int width = cc.dataBounds.width;
    final int height = cc.dataBounds.height;
    borderLimitX = width - border;
    borderLimitY = height - border;
    data = job.data;
    FitParameters params = job.getFitParameters();
    this.endT = (params != null) ? params.endT : -1;
    candidates = indentifySpots(job, width, height, params);
    if (candidates.size == 0) {
        finish(job, start);
        return;
    }
    fittedBackground = 0;
    // Always get the noise and store it with the results.
    if (params != null && !Float.isNaN(params.noise)) {
        noise = params.noise;
        fitConfig.setNoise(noise);
    } else if (calculateNoise) {
        noise = estimateNoise(width, height);
        fitConfig.setNoise(noise);
    }
    //System.out.printf("Slice %d : Noise = %g\n", slice, noise);
    if (logger != null)
        logger.info("Slice %d: Noise = %f", slice, noise);
    final ImageExtractor ie = new ImageExtractor(data, width, height);
    double[] region = null;
    if (params != null && params.fitTask == FitTask.MAXIMA_IDENITIFICATION) {
        final float sd0 = (float) fitConfig.getInitialPeakStdDev0();
        final float sd1 = (float) fitConfig.getInitialPeakStdDev1();
        for (int n = 0; n < candidates.getSize(); n++) {
            // Find the background using the perimeter of the data.
            // TODO - Perhaps the Gaussian Fitter should be used to produce the initial estimates but no actual fit done.
            // This would produce coords using the centre-of-mass.
            final Candidate candidate = candidates.get(n);
            final int x = candidate.x;
            final int y = candidate.y;
            final Rectangle regionBounds = ie.getBoxRegionBounds(x, y, fitting);
            region = ie.crop(regionBounds, region);
            final float b = (float) Gaussian2DFitter.getBackground(region, regionBounds.width, regionBounds.height, 1);
            // Offset the coords to the centre of the pixel. Note the bounds will be added later.
            // Subtract the background to get the amplitude estimate then convert to signal.
            final float amplitude = candidate.intensity - ((relativeIntensity) ? 0 : b);
            final float signal = (float) (amplitude * 2.0 * Math.PI * sd0 * sd1);
            final float[] peakParams = new float[] { b, signal, 0, x + 0.5f, y + 0.5f, sd0, sd1 };
            final int index = y * width + x;
            sliceResults.add(createResult(cc.fromDataToGlobalX(x), cc.fromDataToGlobalY(y), data[index], 0, noise, peakParams, null, n));
        }
    } else {
        initialiseFitting();
        // Smooth the data to provide initial background estimates
        final BlockAverageDataProcessor processor = new BlockAverageDataProcessor(1, 1);
        final float[] smoothedData = processor.process(data, width, height);
        final ImageExtractor ie2 = new ImageExtractor(smoothedData, width, height);
        // Allow the results to be filtered for certain peaks
        if (params != null && params.filter != null) {
            resultFilter = new DistanceResultFilter(params.filter, params.distanceThreshold, candidates.getlength());
        //filter = new OptimumDistanceResultFilter(params.filter, params.distanceThreshold, maxIndices.length);
        }
        // The SpotFitter is used to create a dynamic MultiPathFitResult object.
        // This is then passed to a multi-path filter. Thus the same fitting decision process 
        // is used when benchmarking and when running on actual data.
        // Note: The SpotFitter labels each PreprocessedFitResult using the offset in the FitResult object.
        // The initial params and deviations can then be extracted for the results that pass the filter.
        MultiPathFilter filter;
        IMultiPathFitResults multiPathResults = this;
        SelectedResultStore store = this;
        coordinateStore = coordinateStore.resize(width, height);
        if (params != null && params.fitTask == FitTask.BENCHMARKING) {
            // Run filtering as normal. However in the event that a candidate is missed or some
            // results are not generated we must generate them. This is done in the complete(int)
            // method if we set the benchmarking flag.
            benchmarking = true;
            // Filter using the benchmark filter
            filter = params.benchmarkFilter;
            if (filter == null) {
                // Create a default filter using the standard FitConfiguration to ensure sensible fits
                // are stored as the current slice results.
                // Note the current fit configuration for benchmarking may have minimal filtering settings
                // so we do not use that object.
                final FitConfiguration tmp = new FitConfiguration();
                final double residualsThreshold = 0.4;
                filter = new MultiPathFilter(tmp, createMinimalFilter(), residualsThreshold);
            }
        } else {
            // Filter using the configuration
            filter = new MultiPathFilter(fitConfig, createMinimalFilter(), config.getResidualsThreshold());
        }
        // If we are benchmarking then do not generate results dynamically since we will store all 
        // results in the fit job
        dynamicMultiPathFitResult = new DynamicMultiPathFitResult(ie, ie2, !benchmarking);
        // Debug where the fit config may be different between benchmarking and fitting
        if (slice == -1) {
            SettingsManager.saveFitEngineConfiguration(config, String.format("/tmp/config.%b.xml", benchmarking));
            Utils.write(String.format("/tmp/filter.%b.xml", benchmarking), filter.toXML());
            //filter.setDebugFile(String.format("/tmp/fitWorker.%b.txt", benchmarking));
            StringBuilder sb = new StringBuilder();
            sb.append((benchmarking) ? ((gdsc.smlm.results.filter.Filter) filter.getFilter()).toXML() : fitConfig.getSmartFilter().toXML()).append("\n");
            sb.append(((gdsc.smlm.results.filter.Filter) filter.getMinimalFilter()).toXML()).append("\n");
            sb.append(filter.residualsThreshold).append("\n");
            sb.append(config.getFailuresLimit()).append("\n");
            sb.append(fitConfig.getDuplicateDistance()).append("\n");
            if (spotFilter != null)
                sb.append(spotFilter.getDescription()).append("\n");
            sb.append("MaxCandidate = ").append(candidates.getSize()).append("\n");
            for (int i = 0; i < candidates.list.length; i++) {
                sb.append(String.format("Fit %d [%d,%d = %.1f]\n", i, candidates.get(i).x, candidates.get(i).y, candidates.get(i).intensity));
            }
            Utils.write(String.format("/tmp/candidates.%b.xml", benchmarking), sb.toString());
        }
        filter.select(multiPathResults, config.getFailuresLimit(), true, store, coordinateStore);
        if (logger != null)
            logger.info("Slice %d: %d / %d", slice, success, candidates.getSize());
        // Result filter post-processing
        if (resultFilter != null) {
            resultFilter.finalise();
            job.setResults(resultFilter.getResults());
            job.setIndices(resultFilter.getMaxIndices());
            for (int i = 0; i < resultFilter.getFilteredCount(); i++) {
                job.setFitResult(i, resultFilter.getFitResults()[i]);
            }
            sliceResults.clear();
            sliceResults.addAll(resultFilter.getResults());
        }
    }
    // Add the ROI bounds to the fitted peaks
    final float offsetx = cc.dataBounds.x;
    final float offsety = cc.dataBounds.y;
    for (int i = 0; i < sliceResults.size(); i++) {
        final PeakResult result = sliceResults.get(i);
        result.params[Gaussian2DFunction.X_POSITION] += offsetx;
        result.params[Gaussian2DFunction.Y_POSITION] += offsety;
    }
    this.results.addAll(sliceResults);
    finish(job, start);
}