Package boofcv.factory.filter.kernel

Class FactoryKernelGaussian

java.lang.Object
boofcv.factory.filter.kernel.FactoryKernelGaussian
public class FactoryKernelGaussian extends Object
Factory for creating Gaussian kernels for convolution.

  • Field Summary

    Fields
    Modifier and Type
    Field
    Description
    static float
    MIN_FRAC
     
    static double
    MIN_FRACD
     

  • Constructor Summary

    Constructors
    Constructor
    Description
    FactoryKernelGaussian()
     

  • Method Summary

    Modifier and Type
    Method
    Description
    static <T extends Kernel1D>
    T
    derivative(int order, boolean isFloat, double sigma, int radius)
    Creates a 1D Gaussian kernel with the specified properties.
    protected static Kernel1D_F32
    derivative1D_F32(int order, double sigma, int radius, boolean normalize)
    Computes the derivative of a Gaussian kernel.
    static <T extends ImageGray<T>, K extends Kernel1D>
    K
    derivativeI(Class<T> imageType, int order, double sigma, int radius)
     
    static <T extends Kernel1D>
    T
    derivativeK(Class<T> kernelType, int order, double sigma, int radius)
     
    static <T extends KernelBase>
    T
    gaussian(int DOF, boolean isFloat, int numBits, double sigma, int radius)
    Creates a Gaussian kernel with the specified properties.
    static <T extends KernelBase>
    T
    gaussian(Class<T> kernelType, double sigma, int radius)
    Creates a Gaussian kernel of the specified type.
    static <T extends ImageGray<T>, K extends Kernel1D>
    K
    gaussian1D(Class<T> imageType, double sigma, int radius)
    Creates a 1D Gaussian kernel of the specified type.
    protected static Kernel1D_F32
    gaussian1D_F32(double sigma, int radius, boolean odd, boolean normalize)
    Creates a floating point Gaussian kernel with the sigma and radius.
    protected static Kernel1D_F64
    gaussian1D_F64(double sigma, int radius, boolean odd, boolean normalize)
     
    static <T extends ImageGray<T>, K extends Kernel2D>
    K
    gaussian2D(ImageDataType imageType, double sigma, int radius)
     
    static <T extends ImageGray<T>, K extends Kernel2D>
    K
    gaussian2D(Class<T> imageType, double sigma, int radius)
    Creates a 2D Gaussian kernel of the specified type.
    static Kernel2D_F32
    gaussian2D_F32(double sigma, int radius, boolean odd, boolean normalize)
    Creates a kernel for a 2D convolution.
    static Kernel2D_F32
    gaussian2D_F32(double sigmaX, int radiusX, boolean oddX, double sigmaY, int radiusY, boolean oddY, boolean normalize)
     
    static Kernel2D_F64
    gaussian2D_F64(double sigma, int radius, boolean odd, boolean normalize)
     
    static Kernel2D_F64
    gaussian2D_F64(double sigmaX, int radiusX, boolean oddX, double sigmaY, int radiusY, boolean oddY, boolean normalize)
     
    static Kernel2D_F64
    gaussianWidth(double sigma, int width)
    Create a gaussian kernel based on its width.
    static int
    radiusForSigma(double sigma, int order)
    Given the the sigma of a Gaussian distribution and the order of its derivative, choose an appropriate radius.
    static double
    sigmaForRadius(double radius, int order)
    Given the the radius of a Gaussian distribution and the order of its derivative, choose an appropriate sigma.

    Methods inherited from class java.lang.Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

  • Field Details

    • MIN_FRAC

      public static float MIN_FRAC

    • MIN_FRACD

      public static double MIN_FRACD

  • Constructor Details

    • FactoryKernelGaussian

      public FactoryKernelGaussian()

  • Method Details

    • gaussian

      public static <T extends KernelBase> T gaussian(Class<T> kernelType, double sigma, int radius)
      Creates a Gaussian kernel of the specified type.
      Parameters:
      kernelType - The type of kernel which is to be created.
      sigma - The distributions stdev. If ≤ 0 then the sigma will be computed from the radius.
      radius - Number of pixels in the kernel's radius. If ≤ 0 then the sigma will be computed from the sigma.
      Returns:
      The computed Gaussian kernel.

    • gaussian1D

      public static <T extends ImageGray<T>, K extends Kernel1D> K gaussian1D(Class<T> imageType, double sigma, int radius)
      Creates a 1D Gaussian kernel of the specified type.
      Parameters:
      imageType - The type of image which is to be convolved by this kernel.
      sigma - The distributions stdev. If ≤ 0 then the sigma will be computed from the radius.
      radius - Number of pixels in the kernel's radius. If ≤ 0 then the sigma will be computed from the sigma.
      Returns:
      The computed Gaussian kernel.

    • gaussian2D

      public static <T extends ImageGray<T>, K extends Kernel2D> K gaussian2D(Class<T> imageType, double sigma, int radius)
      Creates a 2D Gaussian kernel of the specified type.
      Parameters:
      imageType - The type of image which is to be convolved by this kernel.
      sigma - The distributions stdev. If ≤ 0 then the sigma will be computed from the radius.
      radius - Number of pixels in the kernel's radius. If ≤ 0 then the sigma will be computed from the sigma.
      Returns:
      The computed Gaussian kernel.

    • gaussian2D

      public static <T extends ImageGray<T>, K extends Kernel2D> K gaussian2D(ImageDataType imageType, double sigma, int radius)

    • gaussian

      public static <T extends KernelBase> T gaussian(int DOF, boolean isFloat, int numBits, double sigma, int radius)
      Creates a Gaussian kernel with the specified properties.
      Parameters:
      DOF - 1 for 1D kernel and 2 for 2D kernel.
      isFloat - True for F32 kernel and false for I32.
      numBits - Number of bits in each data element. 32 or 64
      sigma - The distributions stdev. If ≤ 0 then the sigma will be computed from the radius.
      radius - Number of pixels in the kernel's radius. If ≤ 0 then the sigma will be computed from the sigma. @return The computed Gaussian kernel.

    • derivativeI

      public static <T extends ImageGray<T>, K extends Kernel1D> K derivativeI(Class<T> imageType, int order, double sigma, int radius)

    • derivativeK

      public static <T extends Kernel1D> T derivativeK(Class<T> kernelType, int order, double sigma, int radius)

    • derivative

      public static <T extends Kernel1D> T derivative(int order, boolean isFloat, double sigma, int radius)
      Creates a 1D Gaussian kernel with the specified properties.
      Parameters:
      order - The order of the gaussian derivative.
      isFloat - True for F32 kernel and false for I32.
      sigma - The distributions stdev. If ≤ 0 then the sigma will be computed from the radius.
      radius - Number of pixels in the kernel's radius. If ≤ 0 then the sigma will be computed from the sigma.
      Returns:
      The computed Gaussian kernel.

    • gaussian1D_F32

      protected static Kernel1D_F32 gaussian1D_F32(double sigma, int radius, boolean odd, boolean normalize)

      Creates a floating point Gaussian kernel with the sigma and radius. If normalized is set to true then the elements in the kernel will sum up to one.

      Parameters:
      sigma - Distributions standard deviation.
      radius - Kernel's radius.
      odd - Does the kernel have an even or add width
      normalize - If the kernel should be normalized to one or not.

    • gaussian1D_F64

      protected static Kernel1D_F64 gaussian1D_F64(double sigma, int radius, boolean odd, boolean normalize)

    • gaussian2D_F32

      public static Kernel2D_F32 gaussian2D_F32(double sigma, int radius, boolean odd, boolean normalize)
      Creates a kernel for a 2D convolution. This should only be used for validation purposes.
      Parameters:
      sigma - Distributions standard deviation.
      radius - Kernel's radius.
      odd - Does the kernel have an even or add width
      normalize - If the kernel should be normalized to one or not.

    • gaussian2D_F32

      public static Kernel2D_F32 gaussian2D_F32(double sigmaX, int radiusX, boolean oddX, double sigmaY, int radiusY, boolean oddY, boolean normalize)

    • gaussian2D_F64

      public static Kernel2D_F64 gaussian2D_F64(double sigma, int radius, boolean odd, boolean normalize)

    • gaussian2D_F64

      public static Kernel2D_F64 gaussian2D_F64(double sigmaX, int radiusX, boolean oddX, double sigmaY, int radiusY, boolean oddY, boolean normalize)

    • derivative1D_F32

      protected static Kernel1D_F32 derivative1D_F32(int order, double sigma, int radius, boolean normalize)
      Computes the derivative of a Gaussian kernel.
      Parameters:
      sigma - Distributions standard deviation.
      radius - Kernel's radius.
      Returns:
      The derivative of the gaussian

    • sigmaForRadius

      public static double sigmaForRadius(double radius, int order)

      Given the the radius of a Gaussian distribution and the order of its derivative, choose an appropriate sigma.

      Parameters:
      radius - Kernel's radius
      order - Order of the derivative. 0 original distribution
      Returns:
      Default sigma

    • radiusForSigma

      public static int radiusForSigma(double sigma, int order)

      Given the the sigma of a Gaussian distribution and the order of its derivative, choose an appropriate radius.

      Parameters:
      sigma - Distribution's sigma
      order - Order of the derivative. 0 original distribution
      Returns:
      Default sigma

    • gaussianWidth

      public static Kernel2D_F64 gaussianWidth(double sigma, int width)
      Create a gaussian kernel based on its width. Supports kernels of even or odd widths .
      Parameters:
      sigma - Sigma of the Gaussian distribution. If ≤ 0 then the width will be used.
      width - How wide the kernel is. Can be even or odd.
      Returns:
      Gaussian convolution kernel.