Fuzzy image processing#

Detailed Description#

Image proceesing based on fuzzy mathematics namely F-transform.

Function Documentation#

createKernel()#

void cv::ft::createKernel(
InputArray A,
InputArray B,
OutputArray kernel,
const int chn )

#include <opencv2/fuzzy/fuzzy_image.hpp>

Python:

cv.ft.createKernel(function, radius, chn[, kernel]) -> kernel

cv.ft.createKernel1(A, B, chn[, kernel]) -> kernel

Creates kernel from basic functions.

The function creates kernel usable for latter fuzzy image processing.

Parameters

  • A — Basic function used in axis x.

  • B — Basic function used in axis y.

  • kernel — Final 32-bit kernel derived from A and B.

  • chn — Number of kernel channels.

createKernel()#

void cv::ft::createKernel(
int function,
int radius,
OutputArray kernel,
const int chn )

#include <opencv2/fuzzy/fuzzy_image.hpp>

Python:

cv.ft.createKernel(function, radius, chn[, kernel]) -> kernel

cv.ft.createKernel1(A, B, chn[, kernel]) -> kernel

Creates kernel from general functions.

The function creates kernel from predefined functions.

Parameters

  • function — Function type could be one of the following:

    • LINEAR Linear basic function.

  • radius — Radius of the basic function.

  • kernel — Final 32-bit kernel.

  • chn — Number of kernel channels.

filter()#

void cv::ft::filter(
InputArray image,
InputArray kernel,
OutputArray output )

#include <opencv2/fuzzy/fuzzy_image.hpp>

Python:

cv.ft.filter(image, kernel[, output]) -> output

Image filtering.

Filtering of the input image by means of F-transform.

Parameters

  • image — Input image.

  • kernel — Final 32-bit kernel.

  • output — Output 32-bit image.

inpaint()#

void cv::ft::inpaint(
InputArray image,
InputArray mask,
OutputArray output,
int radius,
int function,
int algorithm )

#include <opencv2/fuzzy/fuzzy_image.hpp>

Python:

cv.ft.inpaint(image, mask, radius, function, algorithm[, output]) -> output

Image inpainting.

This function provides inpainting technique based on the fuzzy mathematic.

Note

The algorithms are described in paper [241].

Parameters

  • image — Input image.

  • mask — Mask used for unwanted area marking.

  • output — Output 32-bit image.

  • radius — Radius of the basic function.

  • function — Function type could be one of the following:

    • ft::LINEAR Linear basic function.

  • algorithm — Algorithm could be one of the following:

    • ft::ONE_STEP One step algorithm.

    • ft::MULTI_STEP This algorithm automaticaly increases radius of the basic function.

    • ft::ITERATIVE Iterative algorithm running in more steps using partial computations.