Examples with ImageChannel qupath.lib.images.servers.ImageChannel used on opensource projects

Example 1 with ImageChannel

use of qupath.lib.images.servers.ImageChannel in project qupath by qupath.

the class CreateChannelTrainingImagesCommand method run.

@Override
public void run() {
    var project = qupath.getProject();
    var imageData = qupath.getImageData();
    var entry = project != null && imageData != null ? project.getEntry(imageData) : null;
    if (entry == null) {
        Dialogs.showErrorMessage(title, "This command requires an open image within a project!");
        return;
    }
    var channels = new ArrayList<>(imageData.getServer().getMetadata().getChannels());
    var list = new CheckListView<ImageChannel>();
    list.getItems().setAll(channels);
    list.getCheckModel().checkAll();
    list.setCellFactory(v -> new CheckBoxListCell<>(item -> list.getItemBooleanProperty(item)) {

        @Override
        public void updateItem(ImageChannel item, boolean empty) {
            super.updateItem(item, empty);
            if (item == null || empty) {
                setText(null);
                return;
            }
            setText(item.getName());
        }
    });
    var label = new Label("Create duplicate images for each of the following channels?");
    if (channels.size() == 1)
        label.setText("Create a duplicate image for the one and only channel?");
    var labelName = new Label("Root name");
    var tfName = new TextField(entry.getImageName().trim());
    labelName.setLabelFor(tfName);
    String namePrompt = "Enter root image name for duplicate images";
    tfName.setPromptText(namePrompt);
    tfName.setTooltip(new Tooltip(namePrompt));
    var cbInitializePoints = new CheckBox("Initialize Points annotations");
    var pane = new GridPane();
    int row = 0;
    PaneTools.addGridRow(pane, row++, 0, null, label, label);
    PaneTools.addGridRow(pane, row++, 0, namePrompt, labelName, tfName);
    PaneTools.addGridRow(pane, row++, 0, "Channels to duplicate", list, list);
    PaneTools.addGridRow(pane, row++, 0, "Create Points annotations for the corresponding channel", cbInitializePoints, cbInitializePoints);
    PaneTools.setFillWidth(Boolean.TRUE, label, tfName, list, cbInitializePoints);
    PaneTools.setHGrowPriority(Priority.ALWAYS, label, tfName, list, cbInitializePoints);
    PaneTools.setVGrowPriority(Priority.ALWAYS, list);
    PaneTools.setMaxWidth(Double.MAX_VALUE, label, tfName, list, cbInitializePoints);
    list.setPrefHeight(240);
    pane.setHgap(5.0);
    pane.setVgap(5.0);
    if (!Dialogs.showConfirmDialog(title, pane))
        return;
    var name = tfName.getText().trim();
    boolean initializePoints = cbInitializePoints.isSelected();
    try {
        for (var channel : list.getCheckModel().getCheckedItems()) {
            var entry2 = project.addDuplicate(entry, true);
            String channelName = channel.getName();
            entry2.setImageName(name.trim() + " - " + channelName);
            if (initializePoints) {
                var imageData2 = entry2.readImageData();
                imageData2.getHierarchy().addPathObjects(Arrays.asList(PathObjects.createAnnotationObject(ROIs.createPointsROI(ImagePlane.getDefaultPlane()), PathClassFactory.getPathClass(channelName, channel.getColor())), PathObjects.createAnnotationObject(ROIs.createPointsROI(ImagePlane.getDefaultPlane()), PathClassFactory.getPathClass(PathClassFactory.StandardPathClasses.IGNORE))));
                entry2.saveImageData(imageData2);
            }
        }
        project.syncChanges();
    } catch (Exception e) {
        Dialogs.showErrorMessage(title, e);
    }
    qupath.refreshProject();
}

Example 2 with ImageChannel

use of qupath.lib.images.servers.ImageChannel in project qupath by qupath.

the class DensityMapDataOp method buildOpAndChannels.

private void buildOpAndChannels() {
    logger.trace("Building density map op with type {}", densityType);
    String baseChannelName;
    ImageChannel lastChannel = null;
    List<ImageOp> sequentialOps = new ArrayList<>();
    switch(densityType) {
        case GAUSSIAN:
            if (radius > 0) {
                double sigma = radius;
                sequentialOps.add(ImageOps.Filters.gaussianBlur(sigma));
                // Scale so that central value ~1 - this is a closer match to the alternative sum filter
                sequentialOps.add(ImageOps.Core.multiply(2 * Math.PI * sigma * sigma));
            }
            baseChannelName = "Gaussian weighted counts ";
            break;
        case PERCENT:
            int[] extractInds = IntStream.range(0, primaryObjects.size()).toArray();
            if (radius > 0) {
                sequentialOps.add(ImageOps.Filters.sum(radius));
                sequentialOps.add(ImageOps.Core.round());
            }
            if (extractInds.length > 0) {
                // Duplicate the image, then
                // - On the first duplicate, remove the last channel and divide all the other channels by its values
                // - On the second duplicate, extract the last channel (so as to retain its values)
                // Finally merge the last channel back
                // The outcome should be that the last channel is unchanged, while the other channels are divided by the last
                // channel elementwise.
                sequentialOps.addAll(Arrays.asList(ImageOps.Core.splitMerge(ImageOps.Core.sequential(ImageOps.Core.splitDivide(ImageOps.Channels.extract(extractInds), ImageOps.Core.sequential(ImageOps.Channels.extract(extractInds.length), ImageOps.Channels.repeat(extractInds.length))), // Convert to percent
                ImageOps.Core.multiply(100.0)), ImageOps.Channels.extract(extractInds.length))));
            } else {
                // Values will be 1 and NaN (a possibly-zero value being divided by itself)
                sequentialOps.add(ImageOps.Core.splitDivide(null, null));
            }
            baseChannelName = "";
            if (!primaryObjects.isEmpty())
                lastChannel = ImageChannel.getInstance(DensityMaps.CHANNEL_ALL_OBJECTS, null);
            break;
        case SUM:
        default:
            if (radius > 0) {
                sequentialOps.add(ImageOps.Filters.sum(radius));
                sequentialOps.add(ImageOps.Core.round());
            }
            baseChannelName = "";
    }
    var channelNames = primaryObjects.keySet().stream().map(n -> baseChannelName + n).toArray(String[]::new);
    var channels = new ArrayList<>(ImageChannel.getChannelList(channelNames));
    if (lastChannel != null)
        channels.add(lastChannel);
    if (channels.isEmpty())
        this.channels = Collections.singletonList(ImageChannel.getInstance(DensityMaps.CHANNEL_ALL_OBJECTS, null));
    else
        this.channels = Collections.unmodifiableList(channels);
    sequentialOps.add(ImageOps.Core.ensureType(PixelType.FLOAT32));
    this.op = ImageOps.Core.sequential(sequentialOps);
}

Example 3 with ImageChannel

use of qupath.lib.images.servers.ImageChannel in project qupath by qupath.

the class WatershedCellDetection method buildParameterList.

private ParameterList buildParameterList(final ImageData<BufferedImage> imageData) {
    ParameterList params = new ParameterList();
    // TODO: Use a better way to determining if pixel size is available in microns
    // params.addEmptyParameter("detectionParameters", "Detection parameters", true);
    String microns = IJ.micronSymbol + "m";
    params.addTitleParameter("Setup parameters");
    String defaultChannel = null;
    List<String> channelNames = new ArrayList<>();
    String[] nucleusGuesses = new String[] { "dapi", "hoechst", "nucleus", "nuclei", "nuclear", "hematoxylin", "haematoxylin" };
    for (ImageChannel channel : imageData.getServer().getMetadata().getChannels()) {
        String name = channel.getName();
        channelNames.add(name);
        if (defaultChannel == null) {
            String lower = name.toLowerCase();
            for (String guess : nucleusGuesses) {
                if (lower.contains(guess))
                    defaultChannel = name;
            }
        }
    }
    if (defaultChannel == null)
        defaultChannel = channelNames.get(0);
    if (channelNames.size() != new HashSet<>(channelNames).size())
        logger.warn("Image contains duplicate channel names! This may be confusing for detection and analysis.");
    params.addChoiceParameter("detectionImage", "Detection channel", defaultChannel, channelNames, "Choose the channel that should be used for nucleus detection (e.g. DAPI)");
    params.addChoiceParameter("detectionImageBrightfield", "Detection image", IMAGE_HEMATOXYLIN, Arrays.asList(IMAGE_HEMATOXYLIN, IMAGE_OPTICAL_DENSITY), "Transformed image to which to apply the detection");
    params.addDoubleParameter("requestedPixelSizeMicrons", "Requested pixel size", .5, microns, "Choose pixel size at which detection will be performed - higher values are likely to be faster, but may be less accurate; set <= 0 to use the full image resolution");
    // params.addDoubleParameter("requestedPixelSize", "Requested downsample factor", 1, "");
    params.addTitleParameter("Nucleus parameters");
    params.addDoubleParameter("backgroundRadiusMicrons", "Background radius", 8, microns, "Radius for background estimation, should be > the largest nucleus radius, or <= 0 to turn off background subtraction");
    params.addDoubleParameter("medianRadiusMicrons", "Median filter radius", 0, microns, "Radius of median filter used to reduce image texture (optional)");
    params.addDoubleParameter("sigmaMicrons", "Sigma", 1.5, microns, "Sigma value for Gaussian filter used to reduce noise; increasing the value stops nuclei being fragmented, but may reduce the accuracy of boundaries");
    params.addDoubleParameter("minAreaMicrons", "Minimum area", 10, microns + "^2", "Detected nuclei with an area < minimum area will be discarded");
    params.addDoubleParameter("maxAreaMicrons", "Maximum area", 400, microns + "^2", "Detected nuclei with an area > maximum area will be discarded");
    params.addDoubleParameter("backgroundRadius", "Background radius", 15, "px", "Radius for background estimation, should be > the largest nucleus radius, or <= 0 to turn off background subtraction");
    params.addDoubleParameter("medianRadius", "Median filter radius", 0, "px", "Radius of median filter used to reduce image texture (optional)");
    params.addDoubleParameter("sigma", "Sigma", 3, "px", "Sigma value for Gaussian filter used to reduce noise; increasing the value stops nuclei being fragmented, but may reduce the accuracy of boundaries");
    params.addDoubleParameter("minArea", "Minimum area", 10, "px^2", "Detected nuclei with an area < minimum area will be discarded");
    params.addDoubleParameter("maxArea", "Maximum area", 1000, "px^2", "Detected nuclei with an area > maximum area will be discarded");
    params.addTitleParameter("Intensity parameters");
    params.addDoubleParameter("threshold", "Threshold", 0.1, null, "Intensity threshold - detected nuclei must have a mean intensity >= threshold");
    // params.addDoubleParameter("threshold", "Threshold", 0.1, null, 0, 2.5,
    // "Intensity threshold - detected nuclei must have a mean intensity >= threshold");
    params.addDoubleParameter("maxBackground", "Max background intensity", 2, null, "If background radius > 0, detected nuclei occurring on a background > max background intensity will be discarded");
    // params.addBooleanParameter("mergeAll", "Merge all", true);
    params.addBooleanParameter("watershedPostProcess", "Split by shape", true, "Split merged detected nuclei based on shape ('roundness')");
    params.addBooleanParameter("excludeDAB", "Exclude DAB (membrane staining)", false, "Set to 'true' if regions of high DAB staining should not be considered nuclei; useful if DAB stains cell membranes");
    params.addTitleParameter("Cell parameters");
    params.addDoubleParameter("cellExpansionMicrons", "Cell expansion", 5, microns, 0, 25, "Amount by which to expand detected nuclei to approximate the full cell area");
    params.addDoubleParameter("cellExpansion", "Cell expansion", 5, "px", "Amount by which to expand detected nuclei to approximate the full cell area");
    // params.addBooleanParameter("limitExpansionByNucleusSize", "Limit cell expansion by nucleus size", false, "If checked, nuclei will not be expanded by more than their (estimated) smallest diameter in any direction - may give more realistic results for smaller, or 'thinner' nuclei");
    params.addBooleanParameter("includeNuclei", "Include cell nucleus", true, "If cell expansion is used, optionally include/exclude the nuclei within the detected cells");
    params.addTitleParameter("General parameters");
    params.addBooleanParameter("smoothBoundaries", "Smooth boundaries", true, "Smooth the detected nucleus/cell boundaries");
    params.addBooleanParameter("makeMeasurements", "Make measurements", true, "Add default shape & intensity measurements during detection");
    return params;
}