use of gdsc.smlm.results.InterlacedImageSource in project GDSC-SMLM by aherbert.
the class PeakFit method showDialog.
@SuppressWarnings("unchecked")
private int showDialog(ImagePlus imp) {
// Executing as an ImageJ plugin.
// Override the defaults with those in the configuration file
final String filename = SettingsManager.getSettingsFilename();
if (simpleFit) {
return showSimpleDialog(filename);
}
GlobalSettings settings = SettingsManager.loadSettings(filename);
calibration = settings.getCalibration();
config = settings.getFitEngineConfiguration();
fitConfig = config.getFitConfiguration();
resultsSettings = settings.getResultsSettings();
boolean isCrop = (bounds != null && imp != null && (bounds.width < imp.getWidth() || bounds.height < imp.getHeight()));
if (!extraOptions) {
integrateFrames = 1;
resultsSettings.imageRollingWindow = 0;
fitConfig.setBackgroundFitting(true);
fitConfig.setMinIterations(0);
fitConfig.setNoise(0);
config.setNoiseMethod(Method.QUICK_RESIDUALS_LEAST_MEAN_OF_SQUARES);
showProcessedFrames = false;
}
ExtendedGenericDialog gd = new ExtendedGenericDialog(TITLE);
gd.addHelp(About.HELP_URL);
gd.addMessage((maximaIdentification) ? "Identify candidate maxima" : "Fit 2D Gaussian to identified maxima");
String[] templates = ConfigurationTemplate.getTemplateNames(true);
gd.addChoice("Template", templates, templates[0]);
gd.addStringField("Config_file", filename, 40);
gd.addNumericField("Calibration (nm/px)", calibration.getNmPerPixel(), 2);
gd.addNumericField("Gain (ADU/photon)", calibration.getGain(), 2);
gd.addCheckbox("EM-CCD", calibration.isEmCCD());
gd.addNumericField("Exposure_time (ms)", calibration.getExposureTime(), 2);
if (isCrop)
gd.addCheckbox("Ignore_bounds_for_noise", optionIgnoreBoundsForNoise);
// This is already set to false before the dialog is displayed
//else
// ignoreBoundsForNoise = false;
gd.addNumericField("Initial_StdDev0", fitConfig.getInitialPeakStdDev0(), 3);
if (!maximaIdentification) {
gd.addNumericField("Initial_StdDev1", fitConfig.getInitialPeakStdDev1(), 3);
gd.addNumericField("Initial_Angle", fitConfig.getInitialAngle(), 3);
}
String[] filterTypes = SettingsManager.getNames((Object[]) DataFilterType.values());
gd.addChoice("Spot_filter_type", filterTypes, filterTypes[config.getDataFilterType().ordinal()]);
String[] filterNames = SettingsManager.getNames((Object[]) DataFilter.values());
gd.addChoice("Spot_filter", filterNames, filterNames[config.getDataFilter(0).ordinal()]);
gd.addSlider("Smoothing", 0, 2.5, config.getSmooth(0));
gd.addSlider("Search_width", 0.5, 2.5, config.getSearch());
gd.addSlider("Border", 0.5, 2.5, config.getBorder());
gd.addSlider("Fitting_width", 2, 4.5, config.getFitting());
if (extraOptions && !fitMaxima) {
gd.addCheckbox("Interlaced_data", optionInterlacedData);
gd.addSlider("Integrate_frames", 1, 5, optionIntegrateFrames);
}
Component discardLabel = null;
if (!maximaIdentification) {
gd.addMessage("--- Gaussian fitting ---");
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()]);
if (extraOptions)
gd.addCheckbox("Fit_background", fitConfig.isBackgroundFitting());
// Parameters specific to each Fit solver are collected in a second dialog
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.addSlider("Duplicate_distance", 0, 1.5, fitConfig.getDuplicateDistance());
gd.addMessage("--- Peak filtering ---\nDiscard fits that shift; are too low; or expand/contract");
discardLabel = gd.getMessage();
gd.addCheckbox("Smart_filter", fitConfig.isSmartFilter());
gd.addCheckbox("Disable_simple_filter", fitConfig.isDisableSimpleFilter());
gd.addSlider("Shift_factor", 0.01, 2, fitConfig.getCoordinateShiftFactor());
gd.addNumericField("Signal_strength", fitConfig.getSignalStrength(), 2);
gd.addNumericField("Min_photons", fitConfig.getMinPhotons(), 0);
if (extraOptions) {
gd.addNumericField("Noise", fitConfig.getNoise(), 2);
String[] noiseMethodNames = SettingsManager.getNames((Object[]) Method.values());
gd.addChoice("Noise_method", noiseMethodNames, noiseMethodNames[config.getNoiseMethod().ordinal()]);
}
gd.addSlider("Min_width_factor", 0, 0.99, fitConfig.getMinWidthFactor());
gd.addSlider("Width_factor", 1.01, 5, fitConfig.getWidthFactor());
gd.addNumericField("Precision", fitConfig.getPrecisionThreshold(), 2);
}
gd.addMessage("--- Results ---");
gd.addCheckbox("Log_progress", resultsSettings.logProgress);
if (!maximaIdentification) {
gd.addCheckbox("Show_deviations", resultsSettings.showDeviations);
}
String[] tableNames = SettingsManager.getNames((Object[]) ResultsTable.values());
gd.addChoice("Results_table", tableNames, tableNames[resultsSettings.getResultsTable().ordinal()]);
String[] imageNames = SettingsManager.getNames((Object[]) ResultsImage.values());
gd.addMessage("--- Image output ---");
gd.addChoice("Image", imageNames, imageNames[resultsSettings.getResultsImage().ordinal()]);
gd.addCheckbox("Weighted", resultsSettings.weightedImage);
gd.addCheckbox("Equalised", resultsSettings.equalisedImage);
gd.addSlider("Image_Precision (nm)", 5, 30, resultsSettings.precision);
gd.addSlider("Image_Scale", 1, 15, resultsSettings.imageScale);
if (extraOptions) {
gd.addNumericField("Image_window", resultsSettings.imageRollingWindow, 0);
gd.addCheckbox("Show_processed_frames", optionShowProcessedFrames);
}
gd.addMessage("--- File output ---");
gd.addStringField("Results_dir", resultsSettings.resultsDirectory);
String[] formatNames = SettingsManager.getNames((Object[]) ResultsFileFormat.values());
gd.addChoice("Results_format", formatNames, formatNames[resultsSettings.getResultsFileFormat().ordinal()]);
gd.addMessage(" ");
gd.addCheckbox("Results_in_memory", resultsSettings.resultsInMemory);
if (extraOptions) {
gd.addMessage("--- Misc ---");
gd.addSlider("Fraction_of_threads", 0.1, 1, fractionOfThreads);
}
if (gd.getLayout() != null) {
GridBagLayout grid = (GridBagLayout) gd.getLayout();
int xOffset = 0, yOffset = 0;
int lastY = -1, rowCount = 0;
for (Component comp : gd.getComponents()) {
// Check if this should be the second major column
if (comp == discardLabel) {
xOffset += 2;
yOffset -= rowCount;
}
// Reposition the field
GridBagConstraints c = grid.getConstraints(comp);
if (lastY != c.gridy)
rowCount++;
lastY = c.gridy;
c.gridx = c.gridx + xOffset;
c.gridy = c.gridy + yOffset;
c.insets.left = c.insets.left + 10 * xOffset;
c.insets.top = 0;
c.insets.bottom = 0;
grid.setConstraints(comp, c);
}
if (IJ.isLinux())
gd.setBackground(new Color(238, 238, 238));
}
// Add a mouse listener to the config file field
if (Utils.isShowGenericDialog()) {
Vector<TextField> texts = (Vector<TextField>) gd.getStringFields();
Vector<TextField> numerics = (Vector<TextField>) gd.getNumericFields();
Vector<Checkbox> checkboxes = (Vector<Checkbox>) gd.getCheckboxes();
Vector<Choice> choices = (Vector<Choice>) gd.getChoices();
int n = 0;
int t = 0;
int b = 0;
int ch = 0;
Choice textTemplate = choices.get(ch++);
textTemplate.addItemListener(this);
textConfigFile = texts.get(t++);
textConfigFile.addMouseListener(this);
textConfigFile.addTextListener(this);
// TODO: add a value changed listener to detect when typing a new file
textNmPerPixel = numerics.get(n++);
textGain = numerics.get(n++);
textEMCCD = checkboxes.get(b++);
textExposure = numerics.get(n++);
textInitialPeakStdDev0 = numerics.get(n++);
if (!maximaIdentification) {
textInitialPeakStdDev1 = numerics.get(n++);
textInitialAngleD = numerics.get(n++);
}
textDataFilterType = choices.get(ch++);
textDataFilter = choices.get(ch++);
textSmooth = numerics.get(n++);
textSearch = numerics.get(n++);
textBorder = numerics.get(n++);
textFitting = numerics.get(n++);
if (extraOptions && !fitMaxima) {
// Skip over the interlaced data option
b++;
// Skip over the integrate frames option
n++;
}
if (!maximaIdentification) {
textFitSolver = choices.get(ch++);
textFitFunction = choices.get(ch++);
if (extraOptions)
textFitBackground = checkboxes.get(b++);
textFailuresLimit = numerics.get(n++);
textIncludeNeighbours = checkboxes.get(b++);
textNeighbourHeightThreshold = numerics.get(n++);
textResidualsThreshold = numerics.get(n++);
textDuplicateDistance = numerics.get(n++);
textSmartFilter = checkboxes.get(b++);
textDisableSimpleFilter = checkboxes.get(b++);
textCoordinateShiftFactor = numerics.get(n++);
textSignalStrength = numerics.get(n++);
textMinPhotons = numerics.get(n++);
if (extraOptions) {
textNoise = numerics.get(n++);
textNoiseMethod = choices.get(ch++);
}
textMinWidthFactor = numerics.get(n++);
textWidthFactor = numerics.get(n++);
textPrecisionThreshold = numerics.get(n++);
updateFilterInput();
textSmartFilter.addItemListener(this);
textDisableSimpleFilter.addItemListener(this);
}
textLogProgress = checkboxes.get(b++);
if (!maximaIdentification)
textShowDeviations = checkboxes.get(b++);
textResultsTable = choices.get(ch++);
textResultsImage = choices.get(ch++);
textWeightedImage = checkboxes.get(b++);
textEqualisedImage = checkboxes.get(b++);
textPrecision = numerics.get(n++);
textImageScale = numerics.get(n++);
if (extraOptions) {
textImageRollingWindow = numerics.get(n++);
// Skip over show processed frames option
b++;
}
textResultsDirectory = texts.get(t++);
textResultsDirectory.addMouseListener(this);
textBinaryResults = choices.get(ch++);
textResultsInMemory = checkboxes.get(b++);
}
gd.showDialog();
// The refreshSettings method can be called by the dialog listener.
// This updates the Calibration, FitEngineConfiguration, and ResultsSettings so set these
// back in the GlobalSettings object.
settings.setCalibration(this.calibration);
settings.setFitEngineConfiguration(this.config);
settings.setResultsSettings(this.resultsSettings);
if (gd.wasCanceled() || !readDialog(settings, gd, isCrop))
return DONE;
if (imp != null) {
// Store whether the user selected to process all the images.
int flags = IJ.setupDialog(imp, plugin_flags);
// Check if cancelled
if ((flags & DONE) != 0)
return DONE;
if ((flags & DOES_STACKS) == 0) {
// Save the slice number for the overlay
singleFrame = imp.getCurrentSlice();
// Account for interlaced data
if (interlacedData) {
int start = singleFrame;
// Calculate the first frame that is not skipped
while (ignoreFrame(start) && start > dataStart) start--;
if (start < dataStart) {
log("The current frame (%d) is before the start of the interlaced data", singleFrame);
return DONE;
}
if (start != singleFrame)
log("Updated the current frame (%d) to a valid interlaced data frame (%d)", singleFrame, start);
singleFrame = start;
}
// Account for integrated frames
int endFrame = singleFrame;
if (integrateFrames > 1) {
int totalFrames = 1;
while (totalFrames < integrateFrames) {
endFrame++;
if (!ignoreFrame(endFrame))
totalFrames++;
}
log("Updated the image end frame (%d) to %d allow %d integrated frames", singleFrame, endFrame, integrateFrames);
}
// Create a new image source with the correct frames
setSource(new IJImageSource(imp, singleFrame, endFrame - singleFrame));
// Store the image so the results can be added as an overlay
this.imp = imp;
this.imp.setOverlay(null);
}
}
// Allow interlaced data by wrapping the image source
if (interlacedData) {
setSource(new InterlacedImageSource(this.source, dataStart, dataBlock, dataSkip));
}
// Allow frame aggregation by wrapping the image source
if (integrateFrames > 1) {
setSource(new AggregatedImageSource(this.source, integrateFrames));
}
// Ask if the user wants to log progress on multiple frame images
if (resultsSettings.logProgress && source.getFrames() > 1) {
gd = new ExtendedGenericDialog(TITLE);
gd.addMessage("Warning: Log progress on multiple-frame image will be slow");
gd.addCheckbox("Log_progress", resultsSettings.logProgress);
gd.showDialog();
if (gd.wasCanceled())
return DONE;
resultsSettings.logProgress = gd.getNextBoolean();
if (!resultsSettings.logProgress)
SettingsManager.saveSettings(settings, filename);
}
// Get a bias if required
if (resultsSettings.getResultsTable() == ResultsTable.CALIBRATED && calibration.getBias() == 0) {
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()));
if (calibration.getBias() > 0)
SettingsManager.saveSettings(settings, filename);
}
}
// single call to be made.
return plugin_flags;
}
use of gdsc.smlm.results.InterlacedImageSource 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;
}
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