View Source Evision.Ft (Evision v0.2.9)
Summary
Functions
Creates kernel from basic functions.
Creates kernel from basic functions.
Creates kernel from general functions.
Creates kernel from general functions.
Image filtering
Image filtering
Image inpainting
Image inpainting
Enumerator
Types
Functions
@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
@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
@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
@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
@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
@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
@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
@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