boofcv.factory.filter.kernel

Class FactoryKernelGaussian

java.lang.Object

boofcv.factory.filter.kernel.FactoryKernelGaussian

public class FactoryKernelGaussian
extends Object

Author:

Peter Abeles

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

Methods

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(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(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 normalize) Creates a floating point Gaussian kernel with the sigma and radius.
protected static Kernel1D_F64	gaussian1D_F64(double sigma, int radius, boolean normalize)
static <T extends ImageSingleBand,K extends Kernel1D> K	gaussian1D(Class<T> imageType, double sigma, int radius) Creates a 1D Gaussian kernel of the specified type.
protected static Kernel2D_F32	gaussian2D_F32(double sigma, int radius, boolean normalize) Creates a kernel for a 2D convolution.
protected static Kernel2D_F64	gaussian2D_F64(double sigma, int radius, boolean normalize)
static <T extends ImageSingleBand,K extends Kernel2D> K	gaussian2D(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) NOTE: If trying to determine radius for gaussian derivative +1 to returned value..
static double	sigmaForRadius(double radius, int order) Given the the radius of a Gaussian distribution, 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(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(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(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(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 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.

normalize - If the kernel should be normalized to one or not.

gaussian1D_F64

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

gaussian2D_F32

protected static Kernel2D_F32 gaussian2D_F32(double sigma,
                          int radius,
                          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.

normalize - If the kernel should be normalized to one or not.

gaussian2D_F64

protected static Kernel2D_F64 gaussian2D_F64(double sigma,
                          int radius,
                          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, choose an appropriate sigma.

NOTE: If trying to determine sigma for gaussian derivative -1 from radius.

Parameters:

radius -

Returns:

radiusForSigma

public static int radiusForSigma(double sigma,
                 int order)

NOTE: If trying to determine radius for gaussian derivative +1 to returned value..

Parameters:

sigma -

Returns:

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.