Examples with GrayS32 boofcv.struct.image.GrayS32 used on opensource projects

Example 61 with GrayS32

use of boofcv.struct.image.GrayS32 in project BoofCV by lessthanoptimal.

the class GenericBinaryContourFinder method maxContour.

@Test
public void maxContour() {
    GrayU8 input = TEST3.clone();
    GrayS32 labeled = input.createSameShape(GrayS32.class);
    BinaryContourFinder alg = create();
    alg.setMaxContour(1);
    alg.process(input, labeled);
    assertEquals(1, alg.getContours().size());
    checkExternalSize(alg, 0, 0);
}

Example 62 with GrayS32

use of boofcv.struct.image.GrayS32 in project BoofCV by lessthanoptimal.

the class TestImplIntegralImageOps method block_unsafe.

public void block_unsafe(Method m) throws InvocationTargetException, IllegalAccessException {
    Class[] paramType = m.getParameterTypes();
    Class inputType = paramType[0];
    Class origType = inputType == GrayS32.class ? GrayU8.class : inputType;
    ImageGray input = GeneralizedImageOps.createSingleBand(origType, width, height);
    ImageGray integral = GeneralizedImageOps.createSingleBand(inputType, width, height);
    GImageMiscOps.fill(input, 1);
    GIntegralImageOps.transform(input, integral);
    double found0 = ((Number) m.invoke(null, integral, 4, 5, 8, 8)).doubleValue();
    assertEquals(12, found0, 1e-4f);
}

Example 63 with GrayS32

use of boofcv.struct.image.GrayS32 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 64 with GrayS32

use of boofcv.struct.image.GrayS32 in project BoofCV by lessthanoptimal.

the class TestClusterLabeledImage method case0_connect4.

@Test
public void case0_connect4() {
    GrayS32 input = new GrayS32(5, 5);
    input.data = case0;
    int[] expected = new int[] { 0, 0, 2, 3, 4, 0, 0, 0, 4, 4, 5, 6, 1, 8, 4, 5, 6, 1, 1, 7, 5, 5, 7, 7, 7 };
    GrayS32 output = new GrayS32(5, 5);
    ClusterLabeledImage alg = new ClusterLabeledImage(ConnectRule.FOUR);
    alg.process(input, output, counts);
    int[] convert = new int[9];
    convert[0] = output.get(0, 0);
    convert[1] = output.get(2, 2);
    convert[2] = output.get(2, 0);
    convert[3] = output.get(3, 0);
    convert[4] = output.get(4, 0);
    convert[5] = output.get(0, 2);
    convert[6] = output.get(1, 2);
    convert[7] = output.get(4, 3);
    convert[8] = output.get(3, 2);
    assertEquals(convert.length, counts.size);
    int sum = 0;
    for (int i = 0; i < counts.size; i++) sum += counts.get(i);
    assertEquals(25, sum);
    for (int j = 0; j < output.data.length; j++) {
        assertEquals(convert[expected[j]], output.data[j]);
    }
}

Example 65 with GrayS32

use of boofcv.struct.image.GrayS32 in project BoofCV by lessthanoptimal.

the class TestMergeSmallRegions method process.

/**
 * Runs everything to remove the small patches. This test hsa been designed to take multiple
 * passes to complete.
 */
@Test
public void process() {
    GrayU8 image = new GrayU8(10, 9);
    image.data = new byte[] { 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 7, 8, 0, 0, 0, 0, 0, 5, 6, 5, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 7, 0, 0, 0, 5, 5, 4, 4, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 9, 9, 0, 0, 0, 0, 0, 0, 9, 9, 9, 9, 0, 0, 0, 0, 0, 0, 9, 9, 6, 7 };
    GrayS32 pixelToRegion = new GrayS32(10, 9);
    pixelToRegion.data = new int[] { 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 4, 5, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 9, 0, 0, 0, 1, 1, 3, 3, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 6, 6, 7, 8 };
    GrowQueue_I32 memberCount = new GrowQueue_I32();
    memberCount.resize(10);
    for (int i = 0; i < pixelToRegion.data.length; i++) {
        memberCount.data[pixelToRegion.data[i]]++;
    }
    FastQueue<float[]> regionColor = new FastQueue<float[]>(float[].class, true) {

        protected float[] createInstance() {
            return new float[1];
        }
    };
    regionColor.resize(10);
    ComputeRegionMeanColor<GrayU8> mean = new ComputeRegionMeanColor.U8();
    mean.process(image, pixelToRegion, memberCount, regionColor);
    MergeSmallRegions<GrayU8> alg = new MergeSmallRegions<>(3, ConnectRule.FOUR, mean);
    alg.process(image, pixelToRegion, memberCount, regionColor);
    // check the results.  Should only be three regions
    assertEquals(3, memberCount.size);
    assertEquals(3, regionColor.size);
    GrowQueue_I32 memberExpected = new GrowQueue_I32(3);
    memberExpected.resize(3);
    for (int i = 0; i < pixelToRegion.data.length; i++) {
        memberExpected.data[pixelToRegion.data[i]]++;
    }
    for (int i = 0; i < 3; i++) assertEquals(memberExpected.get(i), memberCount.get(i));
    // simple sanity check
    assertTrue(memberExpected.get(0) > memberExpected.get(1));
}