Examples with Kernel2D boofcv.struct.convolve.Kernel2D used on opensource projects

Example 6 with Kernel2D

use of boofcv.struct.convolve.Kernel2D in project BoofCV by lessthanoptimal.

the class TestSteerableKernel_I32 method checkCombining.

/**
 * Checks to see if the basis kernels are correctly combined together.
 */
@Test
public void checkCombining() {
    double[] c = new double[] { 0.1, 0.2, 0.8 };
    DummySteerableCoefficients coef = new DummySteerableCoefficients(c);
    Kernel2D[] basis = new Kernel2D[3];
    basis[0] = FactoryKernel.random2D_I32(width, width / 2, 0, 30, rand);
    basis[1] = FactoryKernel.random2D_I32(width, width / 2, 0, 30, rand);
    basis[2] = FactoryKernel.random2D_I32(width, width / 2, 0, 30, rand);
    Kernel2D_S32 expected = new Kernel2D_S32(width);
    for (int y = 0; y < width; y++) {
        for (int x = 0; x < width; x++) {
            int total = 0;
            for (int i = 0; i < c.length; i++) {
                total += c[i] * ((Kernel2D_S32) basis[i]).get(x, y);
            }
            expected.set(x, y, total);
        }
    }
    SteerableKernel_I32 alg = new SteerableKernel_I32();
    alg.setBasis(coef, basis);
    Kernel2D_S32 found = alg.compute(60.0);
    assertTrue(KernelMath.isEquals(expected.data, found.data, width * width));
}

Example 7 with Kernel2D

use of boofcv.struct.convolve.Kernel2D in project BoofCV by lessthanoptimal.

the class FactorySteerable method gaussian.

/**
 * Steerable filter for 2D Gaussian derivatives.  The basis is composed of a set of rotated kernels.
 *
 * @param kernelType Specifies which type of 2D kernel should be generated.
 * @param orderX Order of the derivative in the x-axis.
 * @param orderY Order of the derivative in the y-axis.
 *
 * @param sigma
 *@param radius Radius of the kernel.  @return Steerable kernel generator for the specified gaussian derivative.
 */
public static <K extends Kernel2D> SteerableKernel<K> gaussian(Class<K> kernelType, int orderX, int orderY, double sigma, int radius) {
    if (orderX < 0 || orderX > 4)
        throw new IllegalArgumentException("derivX must be from 0 to 4 inclusive.");
    if (orderY < 0 || orderY > 4)
        throw new IllegalArgumentException("derivT must be from 0 to 4 inclusive.");
    int order = orderX + orderY;
    if (order > 4) {
        throw new IllegalArgumentException("The total order of x and y can't be greater than 4");
    }
    int maxOrder = Math.max(orderX, orderY);
    if (sigma <= 0)
        sigma = (float) FactoryKernelGaussian.sigmaForRadius(radius, maxOrder);
    else if (radius <= 0)
        radius = FactoryKernelGaussian.radiusForSigma(sigma, maxOrder);
    Class kernel1DType = FactoryKernel.get1DType(kernelType);
    Kernel1D kerX = FactoryKernelGaussian.derivativeK(kernel1DType, orderX, sigma, radius);
    Kernel1D kerY = FactoryKernelGaussian.derivativeK(kernel1DType, orderY, sigma, radius);
    Kernel2D kernel = GKernelMath.convolve(kerY, kerX);
    Kernel2D[] basis = new Kernel2D[order + 1];
    // convert it into an image which can be rotated
    ImageGray image = GKernelMath.convertToImage(kernel);
    ImageGray imageRotated = (ImageGray) image.createNew(image.width, image.height);
    basis[0] = kernel;
    // form the basis by created rotated versions of the kernel
    double angleStep = Math.PI / basis.length;
    for (int index = 1; index <= order; index++) {
        float angle = (float) (angleStep * index);
        GImageMiscOps.fill(imageRotated, 0);
        new FDistort(image, imageRotated).rotate(angle).apply();
        basis[index] = GKernelMath.convertToKernel(imageRotated);
    }
    SteerableKernel<K> ret;
    if (kernelType == Kernel2D_F32.class)
        ret = (SteerableKernel<K>) new SteerableKernel_F32();
    else
        ret = (SteerableKernel<K>) new SteerableKernel_I32();
    ret.setBasis(FactorySteerCoefficients.polynomial(order), basis);
    return ret;
}

Example 8 with Kernel2D

use of boofcv.struct.convolve.Kernel2D in project BoofCV by lessthanoptimal.

the class DisplayGaussianKernelApp method setActiveAlgorithm.

@Override
public void setActiveAlgorithm(String name, Object cookie) {
    DerivType type = (DerivType) cookie;
    panel.reset();
    for (int radius = 1; radius <= 40; radius += 2) {
        int maxOrder = Math.max(type.orderX, type.orderY);
        double sigma = FactoryKernelGaussian.sigmaForRadius(radius, maxOrder);
        Class typeKer1 = FactoryKernel.getKernelType(imageType, 1);
        Kernel1D kerX = FactoryKernelGaussian.derivativeK(typeKer1, type.orderX, sigma, radius);
        Kernel1D kerY = FactoryKernelGaussian.derivativeK(typeKer1, type.orderY, sigma, radius);
        Kernel2D kernel = GKernelMath.convolve(kerY, kerX);
        T smallImg = GKernelMath.convertToImage(kernel);
        new FDistort(smallImg, largeImg).interpNN().scaleExt().apply();
        double maxValue = GImageStatistics.maxAbs(largeImg);
        BufferedImage out = VisualizeImageData.colorizeSign(largeImg, null, maxValue);
        panel.addImage(out, String.format("%5d", radius));
    }
}

Example 9 with Kernel2D

use of boofcv.struct.convolve.Kernel2D in project BoofCV by lessthanoptimal.

the class TestConvolveImageStandard_SB method convolve.

/**
 * Unit test for 2D convolution.
 */
public void convolve(ImageGray img, ImageGray dest) {
    Kernel2D ker = FactoryKernel.createKernelForImage(kernelWidth, kernelRadius, 2, img.getClass());
    // standard symmetric odd kernel shape
    ker = FactoryKernel.random(ker.getClass(), kernelWidth, kernelRadius, 0, maxKernelValue, rand);
    convolve(ker, img, dest);
    // odd non-symmetric kernel shape
    ker = FactoryKernel.random(ker.getClass(), kernelWidth, 0, 0, maxKernelValue, rand);
    convolve(ker, img, dest);
    // even non-symmetric kernel shape
    ker = FactoryKernel.random(ker.getClass(), 2, 1, 0, maxKernelValue, rand);
    convolve(ker, img, dest);
}

Example 10 with Kernel2D

use of boofcv.struct.convolve.Kernel2D in project BoofCV by lessthanoptimal.

the class TestConvolveImageStandard_SB method convolveDiv.

/**
 * Unit test for 2D convolution with division.
 */
public void convolveDiv(ImageGray img, ImageGray dest) {
    Kernel2D ker = FactoryKernel.createKernelForImage(kernelWidth, kernelRadius, 2, img.getClass());
    // standard symmetric odd kernel shape
    ker = FactoryKernel.random(ker.getClass(), kernelWidth, kernelRadius, 0, maxKernelValue, rand);
    convolveDiv(ker, img, dest);
    // odd non-symmetric kernel shape
    ker = FactoryKernel.random(ker.getClass(), kernelWidth, 0, 0, maxKernelValue, rand);
    convolveDiv(ker, img, dest);
    // even non-symmetric kernel shape
    ker = FactoryKernel.random(ker.getClass(), 2, 1, 0, maxKernelValue, rand);
    convolveDiv(ker, img, dest);
}