Evision
v0.1.38

View Source Evision.Ft (Evision v0.1.38)

Summary

Types

t()

Type that represents an Ft struct.

Functions

createKernel1(a, b, chn)

Creates kernel from basic functions.

createKernel1(a, b, chn, opts)

Creates kernel from basic functions.

createKernel(function, radius, chn)

Creates kernel from general functions.

createKernel(function, radius, chn, opts)

Creates kernel from general functions.

filter(image, kernel)

Image filtering

filter(image, kernel, opts)

Image filtering

inpaint(image, mask, radius, function, algorithm)

Image inpainting

inpaint(image, mask, radius, function, algorithm, opts)

Image inpainting

Types

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t()

View Source 
@type t() :: %Evision.Ft{ref: reference()}

Type that represents an Ft struct.

  • ref. reference()

    The underlying erlang resource variable.

Functions

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createKernel1(a, b, chn)

View Source 
@spec createKernel1(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), integer()) ::
  Evision.Mat.t() | {:error, String.t()}

Creates kernel from basic functions.

Positional Arguments

  • a: Evision.Mat.t().

    Basic function used in axis x.

  • b: Evision.Mat.t().

    Basic function used in axis y.

  • chn: int.

    Number of kernel channels.

Return

  • kernel: Evision.Mat.t().

    Final 32-bit kernel derived from A and B.

The function creates kernel usable for latter fuzzy image processing.

Python prototype (for reference only):

createKernel1(A, B, chn[, kernel]) -> kernel
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createKernel1(a, b, chn, opts)

View Source 
@spec createKernel1(
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  integer(),
  [{atom(), term()}, ...] | nil
) :: Evision.Mat.t() | {:error, String.t()}

Creates kernel from basic functions.

Positional Arguments

  • a: Evision.Mat.t().

    Basic function used in axis x.

  • b: Evision.Mat.t().

    Basic function used in axis y.

  • chn: int.

    Number of kernel channels.

Return

  • kernel: Evision.Mat.t().

    Final 32-bit kernel derived from A and B.

The function creates kernel usable for latter fuzzy image processing.

Python prototype (for reference only):

createKernel1(A, B, chn[, kernel]) -> kernel
Link to this function

createKernel(function, radius, chn)

View Source 
@spec createKernel(integer(), integer(), integer()) ::
  Evision.Mat.t() | {:error, String.t()}

Creates kernel from general functions.

Positional Arguments

  • function: int.

    Function type could be one of the following:

    • LINEAR Linear basic function.

  • radius: int.

    Radius of the basic function.

  • chn: int.

    Number of kernel channels.

Return

  • kernel: Evision.Mat.t().

    Final 32-bit kernel.

The function creates kernel from predefined functions.

Python prototype (for reference only):

createKernel(function, radius, chn[, kernel]) -> kernel
Link to this function

createKernel(function, radius, chn, opts)

View Source 
@spec createKernel(integer(), integer(), integer(), [{atom(), term()}, ...] | nil) ::
  Evision.Mat.t() | {:error, String.t()}

Creates kernel from general functions.

Positional Arguments

  • function: int.

    Function type could be one of the following:

    • LINEAR Linear basic function.

  • radius: int.

    Radius of the basic function.

  • chn: int.

    Number of kernel channels.

Return

  • kernel: Evision.Mat.t().

    Final 32-bit kernel.

The function creates kernel from predefined functions.

Python prototype (for reference only):

createKernel(function, radius, chn[, kernel]) -> kernel
Link to this function

filter(image, kernel)

View Source 
@spec filter(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) ::
  Evision.Mat.t() | {:error, String.t()}

Image filtering

Positional Arguments

  • image: Evision.Mat.t().

    Input image.

  • kernel: Evision.Mat.t().

    Final 32-bit kernel.

Return

  • output: Evision.Mat.t().

    Output 32-bit image.

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

Python prototype (for reference only):

filter(image, kernel[, output]) -> output
Link to this function

filter(image, kernel, opts)

View Source 
@spec filter(
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  [{atom(), term()}, ...] | nil
) ::
  Evision.Mat.t() | {:error, String.t()}

Image filtering

Positional Arguments

  • image: Evision.Mat.t().

    Input image.

  • kernel: Evision.Mat.t().

    Final 32-bit kernel.

Return

  • output: Evision.Mat.t().

    Output 32-bit image.

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

Python prototype (for reference only):

filter(image, kernel[, output]) -> output
Link to this function

inpaint(image, mask, radius, function, algorithm)

View Source 
@spec inpaint(
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  integer(),
  integer(),
  integer()
) ::
  Evision.Mat.t() | {:error, String.t()}

Image inpainting

Positional Arguments

  • image: Evision.Mat.t().

    Input image.

  • mask: Evision.Mat.t().

    Mask used for unwanted area marking.

  • radius: int.

    Radius of the basic function.

  • function: int.

    Function type could be one of the following:

    • ft::LINEAR Linear basic function.

  • algorithm: int.

    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.
Return

  • output: Evision.Mat.t().

    Output 32-bit image.

This function provides inpainting technique based on the fuzzy mathematic. Note: The algorithms are described in paper @cite Perf:rec.

Python prototype (for reference only):

inpaint(image, mask, radius, function, algorithm[, output]) -> output
Link to this function

inpaint(image, mask, radius, function, algorithm, opts)

View Source 
@spec inpaint(
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  integer(),
  integer(),
  integer(),
  [{atom(), term()}, ...] | nil
) :: Evision.Mat.t() | {:error, String.t()}

Image inpainting

Positional Arguments

  • image: Evision.Mat.t().

    Input image.

  • mask: Evision.Mat.t().

    Mask used for unwanted area marking.

  • radius: int.

    Radius of the basic function.

  • function: int.

    Function type could be one of the following:

    • ft::LINEAR Linear basic function.

  • algorithm: int.

    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.
Return

  • output: Evision.Mat.t().

    Output 32-bit image.

This function provides inpainting technique based on the fuzzy mathematic. Note: The algorithms are described in paper @cite Perf:rec.

Python prototype (for reference only):

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