View Source Evision.XImgProc.AdaptiveManifoldFilter (Evision v0.1.26-rc0)
Link to this section Summary
Functions
Clears the algorithm state
collectGarbage
create
Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read
Apply high-dimensional filtering using adaptive manifolds.
Apply high-dimensional filtering using adaptive manifolds.
getDefaultName
Reads algorithm parameters from a file storage
save
Stores algorithm parameters in a file storage
write
Link to this section Types
@type t() :: %Evision.XImgProc.AdaptiveManifoldFilter{ref: reference()}
Type that represents an XImgProc.AdaptiveManifoldFilter
struct.
ref.
reference()
The underlying erlang resource variable.
Link to this section Functions
Clears the algorithm state
Positional Arguments
- self:
Evision.XImgProc.Evision.XImgProc.AdaptiveManifoldFilter.t()
Python prototype (for reference only):
clear() -> None
collectGarbage
Positional Arguments
- self:
Evision.XImgProc.Evision.XImgProc.AdaptiveManifoldFilter.t()
Python prototype (for reference only):
collectGarbage() -> None
create
Return
- retval:
Evision.XImgProc.Evision.XImgProc.AdaptiveManifoldFilter
Python prototype (for reference only):
create() -> retval
Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read
Positional Arguments
- self:
Evision.XImgProc.Evision.XImgProc.AdaptiveManifoldFilter.t()
Return
- retval:
bool
Python prototype (for reference only):
empty() -> retval
@spec filter(t(), Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
Apply high-dimensional filtering using adaptive manifolds.
Positional Arguments
self:
Evision.XImgProc.Evision.XImgProc.AdaptiveManifoldFilter.t()
src:
Evision.Mat
.filtering image with any numbers of channels.
Keyword Arguments
joint:
Evision.Mat
.optional joint (also called as guided) image with any numbers of channels.
Return
dst:
Evision.Mat
.output image.
Python prototype (for reference only):
filter(src[, dst[, joint]]) -> dst
@spec filter(t(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
Apply high-dimensional filtering using adaptive manifolds.
Positional Arguments
self:
Evision.XImgProc.Evision.XImgProc.AdaptiveManifoldFilter.t()
src:
Evision.Mat
.filtering image with any numbers of channels.
Keyword Arguments
joint:
Evision.Mat
.optional joint (also called as guided) image with any numbers of channels.
Return
dst:
Evision.Mat
.output image.
Python prototype (for reference only):
filter(src[, dst[, joint]]) -> dst
getDefaultName
Positional Arguments
- self:
Evision.XImgProc.Evision.XImgProc.AdaptiveManifoldFilter.t()
Return
- retval:
String
Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.
Python prototype (for reference only):
getDefaultName() -> retval
@spec read(t(), Evision.FileNode.t()) :: t() | {:error, String.t()}
Reads algorithm parameters from a file storage
Positional Arguments
- self:
Evision.XImgProc.Evision.XImgProc.AdaptiveManifoldFilter.t()
- fn_:
Evision.FileNode
Python prototype (for reference only):
read(fn_) -> None
save
Positional Arguments
- self:
Evision.XImgProc.Evision.XImgProc.AdaptiveManifoldFilter.t()
- filename:
String
Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).
Python prototype (for reference only):
save(filename) -> None
@spec write(t(), Evision.FileStorage.t()) :: t() | {:error, String.t()}
Stores algorithm parameters in a file storage
Positional Arguments
- self:
Evision.XImgProc.Evision.XImgProc.AdaptiveManifoldFilter.t()
- fs:
Evision.FileStorage
Python prototype (for reference only):
write(fs) -> None
@spec write(t(), Evision.FileStorage.t(), binary()) :: t() | {:error, String.t()}
write
Positional Arguments
- self:
Evision.XImgProc.Evision.XImgProc.AdaptiveManifoldFilter.t()
- fs:
Evision.FileStorage
- name:
String
Has overloading in C++
Python prototype (for reference only):
write(fs, name) -> None