View Source Evision.Ft (Evision v0.2.9)

Summary

Enumerator

cv_ITERATIVE()

cv_LINEAR()

cv_MULTI_STEP()

cv_ONE_STEP()

cv_SINUS()

Types

enum()

t()

Type that represents an Ft struct.

Functions

createKernel1(named_args)

createKernel1(a, b, chn)

Creates kernel from basic functions.

createKernel1(a, b, chn, opts)

Creates kernel from basic functions.

createKernel(named_args)

createKernel(function, radius, chn)

Creates kernel from general functions.

createKernel(function, radius, chn, opts)

Creates kernel from general functions.

filter(named_args)

filter(image, kernel)

Image filtering

filter(image, kernel, opts)

Image filtering

inpaint(named_args)

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

Image inpainting

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

Image inpainting

Enumerator

cv_ITERATIVE()

cv_LINEAR()

cv_MULTI_STEP()

cv_ONE_STEP()

cv_SINUS()

Types

enum()

@type enum() :: integer()

t()

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

Type that represents an Ft struct.

ref. reference()
The underlying erlang resource variable.

Functions

createKernel1(named_args)

@spec createKernel1(Keyword.t()) :: any() | {:error, String.t()}

createKernel1(a, b, chn)

@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.
Basic function used in axis x.
b: Evision.Mat.
Basic function used in axis y.
chn: integer().
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

createKernel1(a, b, chn, opts)

@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.
Basic function used in axis x.
b: Evision.Mat.
Basic function used in axis y.
chn: integer().
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

createKernel(named_args)

@spec createKernel(Keyword.t()) :: any() | {:error, String.t()}

createKernel(function, radius, chn)

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

Creates kernel from general functions.

Positional Arguments

function: integer().
Function type could be one of the following:
- LINEAR Linear basic function.
radius: integer().
Radius of the basic function.
chn: integer().
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

createKernel(function, radius, chn, opts)

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

Creates kernel from general functions.

Positional Arguments

function: integer().
Function type could be one of the following:
- LINEAR Linear basic function.
radius: integer().
Radius of the basic function.
chn: integer().
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

filter(named_args)

@spec filter(Keyword.t()) :: any() | {:error, String.t()}

filter(image, kernel)

@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.
Input image.
kernel: Evision.Mat.
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

filter(image, kernel, opts)

@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.
Input image.
kernel: Evision.Mat.
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

inpaint(named_args)

@spec inpaint(Keyword.t()) :: any() | {:error, String.t()}

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

@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.
Input image.
mask: Evision.Mat.
Mask used for unwanted area marking.
radius: integer().
Radius of the basic function.
function: integer().
Function type could be one of the following:
- ft::LINEAR Linear basic function.
algorithm: integer().
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

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

@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.
Input image.
mask: Evision.Mat.
Mask used for unwanted area marking.
radius: integer().
Radius of the basic function.
function: integer().
Function type could be one of the following:
- ft::LINEAR Linear basic function.
algorithm: integer().
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