Math with F1-transform support

Functions
void	cv::ft::FT12D_components (InputArray matrix, InputArray kernel, OutputArray components)
	Computes components of the array using direct \(F^1\)-transform. More...
void	cv::ft::FT12D_createPolynomMatrixHorizontal (int radius, OutputArray matrix, const int chn)
	Creates horizontal matrix for \(F^1\)-transform computation. More...
void	cv::ft::FT12D_createPolynomMatrixVertical (int radius, OutputArray matrix, const int chn)
	Creates vertical matrix for \(F^1\)-transform computation. More...
void	cv::ft::FT12D_inverseFT (InputArray components, InputArray kernel, OutputArray output, int width, int height)
	Computes inverse \(F^1\)-transfrom. More...
void	cv::ft::FT12D_polynomial (InputArray matrix, InputArray kernel, OutputArray c00, OutputArray c10, OutputArray c01, OutputArray components, InputArray mask=noArray())
	Computes elements of \(F^1\)-transform components. More...
void	cv::ft::FT12D_process (InputArray matrix, InputArray kernel, OutputArray output, InputArray mask=noArray())
	Computes \(F^1\)-transfrom and inverse \(F^1\)-transfrom at once. More...

Detailed Description

Fuzzy transform ( \(F^1\)-transform) of the 1th degree transforms whole image to a matrix of its components. Each component is polynomial of the 1th degree carrying information about average color and average gradient of certain subarea.

Function Documentation

FT12D_components()

void cv::ft::FT12D_components	(	InputArray	matrix,
		InputArray	kernel,
		OutputArray	components
	)

Python:
	components	=	cv.ft.FT12D_components(	matrix, kernel[, components]	)

Computes components of the array using direct \(F^1\)-transform.

Parameters

matrix	Input array.
kernel	Kernel used for processing. Function ft::createKernel can be used.
components	Output 32-bit float array for the components.

The function computes linear components using predefined kernel.

FT12D_createPolynomMatrixHorizontal()

void cv::ft::FT12D_createPolynomMatrixHorizontal	(	int	radius,
		OutputArray	matrix,
		const int	chn
	)

Python:
	matrix	=	cv.ft.FT12D_createPolynomMatrixHorizontal(	radius, chn[, matrix]	)

Creates horizontal matrix for \(F^1\)-transform computation.

Parameters

radius	Radius of the basic function.
matrix	The horizontal matrix.
chn	Number of channels.

The function creates helper horizontal matrix for \(F^1\)-transfrom processing. It is used for gradient computation.

FT12D_createPolynomMatrixVertical()

void cv::ft::FT12D_createPolynomMatrixVertical	(	int	radius,
		OutputArray	matrix,
		const int	chn
	)

Python:
	matrix	=	cv.ft.FT12D_createPolynomMatrixVertical(	radius, chn[, matrix]	)

Creates vertical matrix for \(F^1\)-transform computation.

Parameters

radius	Radius of the basic function.
matrix	The vertical matrix.
chn	Number of channels.

The function creates helper vertical matrix for \(F^1\)-transfrom processing. It is used for gradient computation.

FT12D_inverseFT()

void cv::ft::FT12D_inverseFT	(	InputArray	components,
		InputArray	kernel,
		OutputArray	output,
		int	width,
		int	height
	)

Python:
	output	=	cv.ft.FT12D_inverseFT(	components, kernel, width, height[, output]	)

Computes inverse \(F^1\)-transfrom.

Parameters

components	Input 32-bit float single channel array for the components.
kernel	Kernel used for processing. The same kernel as for components computation must be used.
output	Output 32-bit float array.
width	Width of the output array.
height	Height of the output array.

Computation of inverse \(F^1\)-transform.

FT12D_polynomial()

void cv::ft::FT12D_polynomial	(	InputArray	matrix,
		InputArray	kernel,
		OutputArray	c00,
		OutputArray	c10,
		OutputArray	c01,
		OutputArray	components,
		InputArray	mask = noArray()
	)

Python:
	c00, c10, c01, components	=	cv.ft.FT12D_polynomial(	matrix, kernel[, c00[, c10[, c01[, components[, mask]]]]]	)

Computes elements of \(F^1\)-transform components.

Parameters

matrix	Input array.
kernel	Kernel used for processing. Function ft::createKernel can be used.
c00	Elements represent average color.
c10	Elements represent average vertical gradient.
c01	Elements represent average horizontal gradient.
components	Output 32-bit float array for the components.
mask	Mask can be used for unwanted area marking.

The function computes components and its elements using predefined kernel and mask.

FT12D_process()

void cv::ft::FT12D_process	(	InputArray	matrix,
		InputArray	kernel,
		OutputArray	output,
		InputArray	mask = noArray()
	)

Python:
	output	=	cv.ft.FT12D_process(	matrix, kernel[, output[, mask]]	)

Computes \(F^1\)-transfrom and inverse \(F^1\)-transfrom at once.

Parameters

matrix	Input matrix.
kernel	Kernel used for processing. Function ft::createKernel can be used.
output	Output 32-bit float array.
mask	Mask used for unwanted area marking.

This function computes \(F^1\)-transfrom and inverse \(F^1\)-transfotm in one step. It is fully sufficient and optimized for cv::Mat.

Note

F-transform technique of first degreee is described in paper [208].