Examples with TextWindow ij.text.TextWindow used on opensource projects

Example 56 with TextWindow

use of ij.text.TextWindow in project GDSC-SMLM by aherbert.

the class BenchmarkSmartSpotRanking method summariseResults.

private void summariseResults(TIntObjectHashMap<RankResults> rankResults, CandidateData candidateData) {
    // Summarise the ranking results.
    final StringBuilder sb = new StringBuilder(filterResult.resultPrefix);
    // countPositive and countNegative is the fractional score of the spot candidates
    addCount(sb, (double) candidateData.countPositive + candidateData.countNegative);
    addCount(sb, candidateData.countPositive);
    addCount(sb, candidateData.countNegative);
    addCount(sb, candidateData.fractionPositive);
    addCount(sb, candidateData.fractionNegative);
    final double[] counter1 = new double[2];
    final int[] counter2 = new int[2];
    candidateData.filterCandidates.forEachValue(result -> {
        counter1[0] += result.np;
        counter1[1] += result.nn;
        counter2[0] += result.pos;
        counter2[1] += result.neg;
        return true;
    });
    final int countTp = counter2[0];
    final int countFp = counter2[1];
    // The fraction of positive and negative candidates that were included
    add(sb, (100.0 * countTp) / candidateData.countPositive);
    add(sb, (100.0 * countFp) / candidateData.countNegative);
    // Add counts of the the candidates
    add(sb, countTp + countFp);
    add(sb, countTp);
    add(sb, countFp);
    // Add fractional counts of the the candidates
    double tp = counter1[0];
    double fp = counter1[1];
    add(sb, tp + fp);
    add(sb, tp);
    add(sb, fp);
    // Materialise rankResults
    final int[] frames = new int[rankResults.size()];
    final RankResults[] rankResultsArray = new RankResults[rankResults.size()];
    final int[] counter = new int[1];
    rankResults.forEachEntry((frame, result) -> {
        frames[counter[0]] = frame;
        rankResultsArray[counter[0]] = result;
        counter[0]++;
        return true;
    });
    // Summarise actual and candidate spots per frame
    final Statistics actual = new Statistics();
    final Statistics candidates = new Statistics();
    for (final RankResults rr : rankResultsArray) {
        actual.add(rr.zPosition.length);
        candidates.add(rr.spots.length);
    }
    add(sb, actual.getMean());
    add(sb, actual.getStandardDeviation());
    add(sb, candidates.getMean());
    add(sb, candidates.getStandardDeviation());
    final String resultPrefix = sb.toString();
    // ---
    // TODO: Further explore pre-filtering of spot candidates.
    // Add good label to spot candidates and have the benchmark spot filter respect this before
    // applying the fail count limit.
    // Correlation between intensity and SNR ...
    // SNR is very good at low density
    // SNR fails at high density. The SNR estimate is probably wrong for high intensity spots.
    // Triangle is very good when there are a large number of good spots in a region of the image
    // (e.g. a mask is used).
    // Triangle is poor when there are few good spots in an image.
    // Perhaps we can estimate the density of the spots and choose the correct thresholding method?
    // ---
    // Do a full benchmark through different Spot SNR, image sizes, densities and mask structures
    // and see if there are patterns for a good threshold method.
    // ---
    // Allow using the fitted results from benchmark spot fit. Will it make a difference if we fit
    // the candidates (some will fail if weak).
    // Can this be done by allowing the user to select the input (spot candidates or fitted
    // positions)?
    // Perhaps I need to produce a precision estimate for all simulated spots and then only use
    // those that achieve a certain precision, i.e. are reasonably in focus. Can this be done?
    // Does the image PSF have a width estimate for the entire stack?
    // Perhaps I should filter, fit and then filter all spots using no fail count. These then become
    // the spots to work with for creating a smart fail count filter.
    // ---
    // Pre-compute the results and have optional sort
    final ArrayList<ScoredResult> list = new ArrayList<>(methodNames.length);
    for (int i = 0; i < methodNames.length; i++) {
        tp = 0;
        fp = 0;
        double tn = 0;
        int itp = 0;
        int ifp = 0;
        int itn = 0;
        final Statistics s = new Statistics();
        long time = 0;
        for (final RankResults rr : rankResultsArray) {
            final RankResult r = rr.results.get(i);
            // Some results will not have a threshold
            if (!Float.isInfinite(r.threshold)) {
                s.add(r.threshold);
            }
            time += r.time;
            tp += r.fresult.getTruePositives();
            fp += r.fresult.getFalsePositives();
            tn += r.fresult.getTrueNegatives();
            itp += r.cresult.getTruePositives();
            ifp += r.cresult.getFalsePositives();
            itn += r.cresult.getTrueNegatives();
        }
        sb.setLength(0);
        sb.append(resultPrefix);
        add(sb, methodNames[i]);
        if (methodNames[i].startsWith("SNR")) {
            sb.append('\t');
        } else {
            add(sb, settings.compactBins);
        }
        add(sb, s.getMean());
        add(sb, s.getStandardDeviation());
        add(sb, TextUtils.nanosToString(time));
        // TP are all accepted candidates that can be matched to a spot
        // FP are all accepted candidates that cannot be matched to a spot
        // TN are all accepted candidates that cannot be matched to a spot
        // FN = The number of missed spots
        // Raw counts of match or no-match
        final FractionClassificationResult f1 = new FractionClassificationResult(itp, ifp, itn, simulationParameters.molecules - itp);
        final double s1 = addScores(sb, f1);
        // Fractional scoring
        final FractionClassificationResult f2 = new FractionClassificationResult(tp, fp, tn, simulationParameters.molecules - tp);
        final double s2 = addScores(sb, f2);
        // Store for sorting
        list.add(new ScoredResult(i, (settings.useFractionScores) ? s2 : s1, sb.toString()));
    }
    if (list.isEmpty()) {
        return;
    }
    Collections.sort(list, ScoredResult::compare);
    final TextWindow summaryTable = createTable();
    if (summaryTable.getTextPanel().getLineCount() > 0) {
        summaryTable.append("");
    }
    try (BufferedTextWindow tw = new BufferedTextWindow(summaryTable)) {
        tw.setIncrement(0);
        for (final ScoredResult r : list) {
            tw.append(r.result);
        }
    }
    if (settings.showOverlay) {
        final int bestMethod = list.get(0).index;
        final Overlay o = new Overlay();
        for (int j = 0; j < rankResultsArray.length; j++) {
            final int frame = frames[j];
            final RankResults rr = rankResultsArray[j];
            final RankResult r = rr.results.get(bestMethod);
            final int[] x1 = new int[r.good.length];
            final int[] y1 = new int[r.good.length];
            int c1 = 0;
            final int[] x2 = new int[r.good.length];
            final int[] y2 = new int[r.good.length];
            int c2 = 0;
            final int[] x3 = new int[r.good.length];
            final int[] y3 = new int[r.good.length];
            int c3 = 0;
            final int[] x4 = new int[r.good.length];
            final int[] y4 = new int[r.good.length];
            int c4 = 0;
            for (int i = 0; i < x1.length; i++) {
                if (r.good[i] == TP) {
                    x1[c1] = rr.spots[i].spot.x;
                    y1[c1] = rr.spots[i].spot.y;
                    c1++;
                } else if (r.good[i] == FP) {
                    x2[c2] = rr.spots[i].spot.x;
                    y2[c2] = rr.spots[i].spot.y;
                    c2++;
                } else if (r.good[i] == TN) {
                    x3[c3] = rr.spots[i].spot.x;
                    y3[c3] = rr.spots[i].spot.y;
                    c3++;
                } else if (r.good[i] == FN) {
                    x4[c4] = rr.spots[i].spot.x;
                    y4[c4] = rr.spots[i].spot.y;
                    c4++;
                }
            }
            addToOverlay(o, frame, x1, y1, c1, Color.green);
            addToOverlay(o, frame, x2, y2, c2, Color.red);
            if (IJ.debugMode) {
                // light green
                addToOverlay(o, frame, x3, y3, c3, new Color(153, 255, 153));
            }
            // light red
            addToOverlay(o, frame, x4, y4, c4, new Color(255, 153, 153));
        }
        imp.setOverlay(o);
    }
}

Example 57 with TextWindow

use of ij.text.TextWindow in project GDSC-SMLM by aherbert.

the class BenchmarkSpotFilter method getTable.

private static TextWindow getTable(boolean batchSummary) {
    AtomicReference<TextWindow> tableRef;
    String title;
    if (batchSummary) {
        tableRef = batchSummaryTable;
        title = TITLE + " Batch";
    } else {
        tableRef = summaryTable;
        title = TITLE;
    }
    return ImageJUtils.refresh(tableRef, () -> new TextWindow(title, createHeader(), "", 1000, 300));
}

Example 58 with TextWindow

use of ij.text.TextWindow in project GDSC-SMLM by aherbert.

the class BenchmarkFilterAnalysis method createGaWindow.

private void createGaWindow() {
    if (isHeadless) {
        String header = createResultsHeader(false);
        header += "\tIteration";
        IJ.log(header);
        gaWindow = IJ::log;
    } else {
        final TextWindow window = ImageJUtils.refresh(gaWindowRef, () -> {
            String header = createResultsHeader(false);
            header += "\tIteration";
            return new TextWindow(TITLE + " Evolution", header, "", 900, 300);
        });
        if (settings.clearTables) {
            window.getTextPanel().clear();
        }
        gaWindow = window::append;
    }
}

Example 59 with TextWindow

use of ij.text.TextWindow in project GDSC-SMLM by aherbert.

the class PsfEstimator method estimatePsf.

private int estimatePsf() {
    log("Estimating PSF ... Press escape to abort");
    final PeakFit fitter = createFitter();
    // Use the fit configuration to generate a Gaussian function to test what is being evaluated
    final Gaussian2DFunction gf = config.getFitConfiguration().createGaussianFunction(1, 1, 1);
    ignore[ANGLE] = !gf.evaluatesAngle();
    ignore[X] = !gf.evaluatesSD0();
    ignore[Y] = !gf.evaluatesSD1();
    final double[] params = new double[] { gf.evaluatesAngle() ? initialPeakAngle : 0, gf.evaluatesSD0() ? initialPeakStdDev0 : 0, gf.evaluatesSD1() ? initialPeakStdDev1 : 0, 0, 0 };
    final double[] paramsDev = new double[3];
    final boolean[] identical = new boolean[4];
    final double[] p = new double[] { Double.NaN, Double.NaN, Double.NaN, Double.NaN };
    final TextWindow resultsWindow = createResultsWindow();
    addToResultTable(resultsWindow, 0, 0, params, paramsDev, p);
    if (!calculateStatistics(fitter, params, paramsDev)) {
        return (ImageJUtils.isInterrupted()) ? ABORTED : INSUFFICIENT_PEAKS;
    }
    if (!addToResultTable(resultsWindow, 1, size(), params, paramsDev, p)) {
        return BAD_ESTIMATE;
    }
    boolean tryAgain = false;
    int iteration = 2;
    for (; ; ) {
        if (!calculateStatistics(fitter, params, paramsDev)) {
            return (ImageJUtils.isInterrupted()) ? ABORTED : INSUFFICIENT_PEAKS;
        }
        try {
            for (int i = 0; i < 3; i++) {
                getP(i, p, identical);
            }
            if (!ignore[Y]) {
                getPairedP(sampleNew[X], sampleNew[Y], XY, p, identical);
            }
            if (!addToResultTable(resultsWindow, iteration++, size(), params, paramsDev, p)) {
                return BAD_ESTIMATE;
            }
            if ((ignore[ANGLE] || identical[ANGLE] || identical[XY]) && (ignore[X] || identical[X]) && (ignore[Y] || identical[Y])) {
                tryAgain = checkAngleSignificance() || checkXySignificance(identical);
                // Update recommended values. Only use if significant
                params[X] = sampleNew[X].getMean();
                params[Y] = (!ignore[Y] && !identical[XY]) ? sampleNew[Y].getMean() : params[X];
                params[ANGLE] = (!ignore[ANGLE]) ? sampleNew[ANGLE].getMean() : 0;
                // update starting configuration
                initialPeakAngle = (float) params[ANGLE];
                initialPeakStdDev0 = (float) params[X];
                initialPeakStdDev1 = (float) params[Y];
                if (settings.getUpdatePreferences()) {
                    config.getFitConfiguration().setInitialPeakStdDev0((float) params[X]);
                    try {
                        config.getFitConfiguration().setInitialPeakStdDev1((float) params[Y]);
                        config.getFitConfiguration().setInitialAngle((float) params[ANGLE]);
                    } catch (final IllegalStateException | ConfigurationException ex) {
                    // Ignore this as the current PSF is not a 2 axis and theta Gaussian PSF
                    }
                }
                break;
            }
            if (IJ.escapePressed()) {
                IJ.beep();
                IJ.showStatus("Aborted");
                return ABORTED;
            }
        } catch (final Exception ex) {
            Logger.getLogger(getClass().getName()).log(Level.WARNING, "Error while estimating the PSF", ex);
            return EXCEPTION;
        }
    }
    return (tryAgain) ? TRY_AGAIN : COMPLETE;
}

Example 60 with TextWindow

use of ij.text.TextWindow in project GDSC-SMLM by aherbert.

the class Noise method showResults.

private void showResults() {
    // Sort on slice number
    Collections.sort(results, (o1, o2) -> Double.compare(o1[0], o2[0]));
    // Slow when there are lots of results ... Could change the output options in the future
    final TextWindow tw = new TextWindow(imp.getTitle() + " Noise", createHeader(), "", 800, 400);
    for (final double[] result : results) {
        tw.append(createResult(result));
    }
}