boofcv.factory.filter.kernel

Class FactoryKernelGaussian

java.lang.Object

boofcv.factory.filter.kernel.FactoryKernelGaussian

public class FactoryKernelGaussian
extends java.lang.Object

Field Summary

Fields

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

Constructor Summary

Constructors

Constructor and Description
FactoryKernelGaussian()

Method Summary

Modifier and Type	Method and 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 ImageSingleBand,K extends Kernel1D> K	derivativeI(java.lang.Class<T> imageType, int order, double sigma, int radius)
static <T extends Kernel1D> T	derivativeK(java.lang.Class<T> kernelType, int order, double sigma, int radius)
static <T extends KernelBase> T	gaussian(java.lang.Class<T> kernelType, double sigma, int radius) Creates a Gaussian kernel of the specified type.
static <T extends KernelBase> T	gaussian(int DOF, boolean isFloat, int numBits, double sigma, int radius) Creates a Gaussian kernel with the specified properties.
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 ImageSingleBand,K extends Kernel1D> K	gaussian1D(java.lang.Class<T> imageType, double sigma, int radius) Creates a 1D 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_F64	gaussian2D_F64(double sigma, int radius, boolean odd, boolean normalize)
static <T extends ImageSingleBand,K extends Kernel2D> K	gaussian2D(java.lang.Class<T> imageType, double sigma, int radius) Creates a 2D Gaussian kernel of the specified type.
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 Detail

MIN_FRAC

public static float MIN_FRAC

MIN_FRACD

public static double MIN_FRACD

Constructor Detail

FactoryKernelGaussian

public FactoryKernelGaussian()

Method Detail

gaussian

public static <T extends KernelBase> T gaussian(java.lang.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 ImageSingleBand,K extends Kernel1D> K gaussian1D(java.lang.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 ImageSingleBand,K extends Kernel2D> K gaussian2D(java.lang.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.

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 ImageSingleBand,K extends Kernel1D> K derivativeI(java.lang.Class<T> imageType,
                                                                           int order,
                                                                           double sigma,
                                                                           int radius)

derivativeK

public static <T extends Kernel1D> T derivativeK(java.lang.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_F64

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

normalize -

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.