Examples with ListDisplayPanel boofcv.gui.ListDisplayPanel used on opensource projects

Example 21 with ListDisplayPanel

use of boofcv.gui.ListDisplayPanel in project BoofCV by lessthanoptimal.

the class ExampleWatershedWithSeeds method main.

public static void main(String[] args) {
    BufferedImage image = UtilImageIO.loadImage(UtilIO.pathExample("particles01.jpg"));
    GrayU8 input = ConvertBufferedImage.convertFromSingle(image, null, GrayU8.class);
    // declare working data
    GrayU8 binary = new GrayU8(input.width, input.height);
    GrayS32 label = new GrayS32(input.width, input.height);
    // Try using the mean pixel value to create a binary image then erode it to separate the particles from
    // each other
    double mean = ImageStatistics.mean(input);
    ThresholdImageOps.threshold(input, binary, (int) mean, true);
    GrayU8 filtered = BinaryImageOps.erode8(binary, 2, null);
    int numRegions = BinaryImageOps.contour(filtered, ConnectRule.EIGHT, label).size() + 1;
    // +1 to regions because contour only counts blobs and not the background
    // The labeled image can be used as is.  A precondition for seeded watershed is that all seeds have an
    // ID > 0.  Luckily, a value of 0 was used for background pixels in the contour algorithm.
    WatershedVincentSoille1991 watershed = FactorySegmentationAlg.watershed(ConnectRule.FOUR);
    watershed.process(input, label);
    GrayS32 output = watershed.getOutput();
    BufferedImage outLabeled = VisualizeBinaryData.renderLabeledBG(label, numRegions, null);
    VisualizeRegions.watersheds(output, image, 1);
    // Removing the watersheds and update the region count
    // NOTE: watershed.getTotalRegions() does not return correct results if seeds are used!
    watershed.removeWatersheds();
    numRegions -= 1;
    BufferedImage outRegions = VisualizeRegions.regions(output, numRegions, null);
    ListDisplayPanel gui = new ListDisplayPanel();
    gui.addImage(image, "Watersheds");
    gui.addImage(outRegions, "Regions");
    gui.addImage(outLabeled, "Seeds");
    ShowImages.showWindow(gui, "Watershed", true);
// Additional processing would be needed for this example to be really useful.
// The watersheds can be used to characterize the background while the seed binary image the particles
// From this the particles could be more accurately classified by assigning each pixel one of the two
// just mentioned groups based distance
}

Example 22 with ListDisplayPanel

use of boofcv.gui.ListDisplayPanel in project BoofCV by lessthanoptimal.

the class ExampleFourierTransform method applyBoxFilter.

/**
 * Demonstration of how to apply a box filter in the frequency domain and compares the results
 * to a box filter which has been applied in the spatial domain
 */
public static void applyBoxFilter(GrayF32 input) {
    // declare storage
    GrayF32 boxImage = new GrayF32(input.width, input.height);
    InterleavedF32 boxTransform = new InterleavedF32(input.width, input.height, 2);
    InterleavedF32 transform = new InterleavedF32(input.width, input.height, 2);
    GrayF32 blurredImage = new GrayF32(input.width, input.height);
    GrayF32 spatialBlur = new GrayF32(input.width, input.height);
    DiscreteFourierTransform<GrayF32, InterleavedF32> dft = DiscreteFourierTransformOps.createTransformF32();
    // Make the image scaled from 0 to 1 to reduce overflow issues
    PixelMath.divide(input, 255.0f, input);
    // compute the Fourier Transform
    dft.forward(input, transform);
    // the image edges
    for (int y = 0; y < 15; y++) {
        int yy = y - 7 < 0 ? boxImage.height + (y - 7) : y - 7;
        for (int x = 0; x < 15; x++) {
            int xx = x - 7 < 0 ? boxImage.width + (x - 7) : x - 7;
            // Set the value such that it doesn't change the image intensity
            boxImage.set(xx, yy, 1.0f / (15 * 15));
        }
    }
    // compute the DFT for the box filter
    dft.forward(boxImage, boxTransform);
    // Visualize the Fourier Transform for the input image and the box filter
    displayTransform(transform, "Input Image");
    displayTransform(boxTransform, "Box Filter");
    // apply the filter. convolution in spacial domain is the same as multiplication in the frequency domain
    DiscreteFourierTransformOps.multiplyComplex(transform, boxTransform, transform);
    // convert the image back and display the results
    dft.inverse(transform, blurredImage);
    // undo change of scale
    PixelMath.multiply(blurredImage, 255.0f, blurredImage);
    PixelMath.multiply(input, 255.0f, input);
    // For sake of comparison, let's compute the box blur filter in the spatial domain
    // NOTE: The image border will be different since the frequency domain wraps around and this implementation
    // of the spacial domain adapts the kernel size
    BlurImageOps.mean(input, spatialBlur, 7, null);
    // Convert to BufferedImage for output
    BufferedImage originOut = ConvertBufferedImage.convertTo(input, null);
    BufferedImage spacialOut = ConvertBufferedImage.convertTo(spatialBlur, null);
    BufferedImage blurredOut = ConvertBufferedImage.convertTo(blurredImage, null);
    ListDisplayPanel listPanel = new ListDisplayPanel();
    listPanel.addImage(originOut, "Original Image");
    listPanel.addImage(spacialOut, "Spacial Domain Box");
    listPanel.addImage(blurredOut, "Frequency Domain Box");
    ShowImages.showWindow(listPanel, "Box Blur in Spacial and Frequency Domain of Input Image");
}

Example 23 with ListDisplayPanel

use of boofcv.gui.ListDisplayPanel in project BoofCV by lessthanoptimal.

the class ExampleImageConvert method convert.

void convert() {
    // Converting between BoofCV image types is easy with ConvertImage.  ConvertImage copies
    // the value of a pixel in one image into another image.  When doing so you need to take
    // in account the storage capabilities of these different class types.
    // Going from an unsigned 8-bit image to unsigned 16-bit image is no problem
    GrayU16 imageU16 = new GrayU16(gray.width, gray.height);
    ConvertImage.convert(gray, imageU16);
    // You can convert back into the 8-bit image from the 16-bit image with no problem
    // in this situation because imageU16 does not use the full range of 16-bit values
    ConvertImage.convert(imageU16, gray);
    // Here is an example where you over flow the image after converting
    // There won't be an exception or any error messages but the output image will be corrupted
    GrayU8 imageBad = new GrayU8(derivX.width, derivX.height);
    ConvertImage.convert(derivX, imageBad);
    // One way to get around this problem rescale and adjust the pixel values so that they
    // will be within a valid range.
    GrayS16 scaledAbs = new GrayS16(derivX.width, derivX.height);
    GPixelMath.abs(derivX, scaledAbs);
    GPixelMath.multiply(scaledAbs, 255.0 / ImageStatistics.max(scaledAbs), scaledAbs);
    // If you just want to see the values of a 16-bit image there are built in utility functions
    // for visualizing their values too
    BufferedImage colorX = VisualizeImageData.colorizeSign(derivX, null, -1);
    // Let's see what all the bad image looks like
    // ConvertBufferedImage is similar to ImageConvert in that it does a direct coversion with out
    // adjusting the pixel's value
    BufferedImage outBad = new BufferedImage(imageBad.width, imageBad.height, BufferedImage.TYPE_INT_RGB);
    BufferedImage outScaled = new BufferedImage(imageBad.width, imageBad.height, BufferedImage.TYPE_INT_RGB);
    ListDisplayPanel panel = new ListDisplayPanel();
    panel.addImage(ConvertBufferedImage.convertTo(scaledAbs, outScaled), "Scaled");
    panel.addImage(colorX, "Visualized");
    panel.addImage(ConvertBufferedImage.convertTo(imageBad, outBad), "Bad");
    ShowImages.showWindow(panel, "Image Convert", true);
}

Example 24 with ListDisplayPanel

use of boofcv.gui.ListDisplayPanel in project BoofCV by lessthanoptimal.

the class ExampleImageDerivative method main.

public static void main(String[] args) {
    BufferedImage input = UtilImageIO.loadImage(UtilIO.pathExample("simple_objects.jpg"));
    // We will use floating point images here, but GrayU8 with GrayS16 for derivatives also works
    GrayF32 grey = new GrayF32(input.getWidth(), input.getHeight());
    ConvertBufferedImage.convertFrom(input, grey);
    // First order derivative, also known as the gradient
    GrayF32 derivX = new GrayF32(grey.width, grey.height);
    GrayF32 derivY = new GrayF32(grey.width, grey.height);
    GImageDerivativeOps.gradient(DerivativeType.SOBEL, grey, derivX, derivY, BorderType.EXTENDED);
    // Second order derivative, also known as the Hessian
    GrayF32 derivXX = new GrayF32(grey.width, grey.height);
    GrayF32 derivXY = new GrayF32(grey.width, grey.height);
    GrayF32 derivYY = new GrayF32(grey.width, grey.height);
    GImageDerivativeOps.hessian(DerivativeType.SOBEL, derivX, derivY, derivXX, derivXY, derivYY, BorderType.EXTENDED);
    // There's also a built in function for computing arbitrary derivatives
    AnyImageDerivative<GrayF32, GrayF32> derivative = GImageDerivativeOps.createAnyDerivatives(DerivativeType.SOBEL, GrayF32.class, GrayF32.class);
    // the boolean sequence indicates if its an X or Y derivative
    derivative.setInput(grey);
    GrayF32 derivXYX = derivative.getDerivative(true, false, true);
    // Visualize the results
    ListDisplayPanel gui = new ListDisplayPanel();
    gui.addImage(ConvertBufferedImage.convertTo(grey, null), "Input Grey");
    gui.addImage(VisualizeImageData.colorizeSign(derivX, null, -1), "Sobel X");
    gui.addImage(VisualizeImageData.colorizeSign(derivY, null, -1), "Sobel Y");
    // Use colors to show X and Y derivatives in one image.  Looks pretty.
    gui.addImage(VisualizeImageData.colorizeGradient(derivX, derivY, -1), "Sobel X and Y");
    gui.addImage(VisualizeImageData.colorizeSign(derivXX, null, -1), "Sobel XX");
    gui.addImage(VisualizeImageData.colorizeSign(derivXY, null, -1), "Sobel XY");
    gui.addImage(VisualizeImageData.colorizeSign(derivYY, null, -1), "Sobel YY");
    gui.addImage(VisualizeImageData.colorizeSign(derivXYX, null, -1), "Sobel XYX");
    ShowImages.showWindow(gui, "Image Derivatives", true);
}

Example 25 with ListDisplayPanel

use of boofcv.gui.ListDisplayPanel in project BoofCV by lessthanoptimal.

the class CommonFitPolygonChecks method renderPolygons.

public void renderPolygons(List<Polygon2D_F64> polygons, Class imageType) {
    BufferedImage work = new BufferedImage(width, height, BufferedImage.TYPE_INT_RGB);
    Graphics2D g2 = work.createGraphics();
    g2.setColor(Color.WHITE);
    g2.fillRect(0, 0, width, height);
    g2.setColor(Color.BLACK);
    distorted.clear();
    for (int i = 0; i < polygons.size(); i++) {
        Polygon2D_F64 orig = polygons.get(i);
        int[] x = new int[orig.size()];
        int[] y = new int[orig.size()];
        for (int j = 0; j < orig.size(); j++) {
            x[j] = (int) orig.get(j).x;
            y[j] = (int) orig.get(j).y;
        }
        g2.fillPolygon(x, y, orig.size());
        distorted.add(orig);
    }
    orig = null;
    image = GeneralizedImageOps.createSingleBand(imageType, width, height);
    ConvertBufferedImage.convertFrom(work, image, true);
    if (showRendered) {
        ListDisplayPanel panel = new ListDisplayPanel();
        panel.addImage(work, "Work");
        panel.addImage(image, "Image");
        try {
            Thread.sleep(4000);
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
    }
}