View Source Evision.XImgProc.EdgeAwareInterpolator (Evision v0.2.9)
Summary
Functions
getFGSLambda
getFGSSigma
getK
getLambda
getSigma
getUsePostProcessing
Interface to provide a more elaborated cost map, i.e. edge map, for the edge-aware term. This implementation is based on a rather simple gradient-based edge map estimation. To used more complex edge map estimator (e.g. StructuredEdgeDetection that has been used in the original publication) that may lead to improved accuracies, the internal edge map estimation can be bypassed here.
Sets the respective fastGlobalSmootherFilter() parameter.
setFGSSigma
K is a number of nearest-neighbor matches considered, when fitting a locally affine model. Usually it should be around 128. However, lower values would make the interpolation noticeably faster.
Lambda is a parameter defining the weight of the edge-aware term in geodesic distance, should be in the range of 0 to 1000.
Sigma is a parameter defining how fast the weights decrease in the locally-weighted affine fitting. Higher values can help preserve fine details, lower values can help to get rid of noise in the output flow.
Sets whether the fastGlobalSmootherFilter() post-processing is employed. It is turned on by default.
Types
@type t() :: %Evision.XImgProc.EdgeAwareInterpolator{ref: reference()}
Type that represents an XImgProc.EdgeAwareInterpolator
struct.
ref.
reference()
The underlying erlang resource variable.
Functions
@spec getFGSLambda(Keyword.t()) :: any() | {:error, String.t()}
@spec getFGSLambda(t()) :: number() | {:error, String.t()}
getFGSLambda
Positional Arguments
- self:
Evision.XImgProc.EdgeAwareInterpolator.t()
Return
- retval:
float
@see setFGSLambda/2
Python prototype (for reference only):
getFGSLambda() -> retval
@spec getFGSSigma(Keyword.t()) :: any() | {:error, String.t()}
@spec getFGSSigma(t()) :: number() | {:error, String.t()}
getFGSSigma
Positional Arguments
- self:
Evision.XImgProc.EdgeAwareInterpolator.t()
Return
- retval:
float
@see setFGSLambda/2
Python prototype (for reference only):
getFGSSigma() -> retval
@spec getK(Keyword.t()) :: any() | {:error, String.t()}
@spec getK(t()) :: integer() | {:error, String.t()}
getK
Positional Arguments
- self:
Evision.XImgProc.EdgeAwareInterpolator.t()
Return
- retval:
integer()
@see setK/2
Python prototype (for reference only):
getK() -> retval
@spec getLambda(Keyword.t()) :: any() | {:error, String.t()}
@spec getLambda(t()) :: number() | {:error, String.t()}
getLambda
Positional Arguments
- self:
Evision.XImgProc.EdgeAwareInterpolator.t()
Return
- retval:
float
@see setLambda/2
Python prototype (for reference only):
getLambda() -> retval
@spec getSigma(Keyword.t()) :: any() | {:error, String.t()}
@spec getSigma(t()) :: number() | {:error, String.t()}
getSigma
Positional Arguments
- self:
Evision.XImgProc.EdgeAwareInterpolator.t()
Return
- retval:
float
@see setSigma/2
Python prototype (for reference only):
getSigma() -> retval
@spec getUsePostProcessing(Keyword.t()) :: any() | {:error, String.t()}
@spec getUsePostProcessing(t()) :: boolean() | {:error, String.t()}
getUsePostProcessing
Positional Arguments
- self:
Evision.XImgProc.EdgeAwareInterpolator.t()
Return
- retval:
bool
Python prototype (for reference only):
getUsePostProcessing() -> retval
@spec setCostMap(t(), Evision.Mat.maybe_mat_in()) :: t() | {:error, String.t()}
Interface to provide a more elaborated cost map, i.e. edge map, for the edge-aware term. This implementation is based on a rather simple gradient-based edge map estimation. To used more complex edge map estimator (e.g. StructuredEdgeDetection that has been used in the original publication) that may lead to improved accuracies, the internal edge map estimation can be bypassed here.
Positional Arguments
self:
Evision.XImgProc.EdgeAwareInterpolator.t()
costMap:
Evision.Mat
.a type CV_32FC1 Mat is required.
@see cv::ximgproc::createSuperpixelSLIC
Python prototype (for reference only):
setCostMap(_costMap) -> None
Sets the respective fastGlobalSmootherFilter() parameter.
Positional Arguments
- self:
Evision.XImgProc.EdgeAwareInterpolator.t()
- lambda:
float
Python prototype (for reference only):
setFGSLambda(_lambda) -> None
setFGSSigma
Positional Arguments
- self:
Evision.XImgProc.EdgeAwareInterpolator.t()
- sigma:
float
@see setFGSLambda/2
Python prototype (for reference only):
setFGSSigma(_sigma) -> None
K is a number of nearest-neighbor matches considered, when fitting a locally affine model. Usually it should be around 128. However, lower values would make the interpolation noticeably faster.
Positional Arguments
- self:
Evision.XImgProc.EdgeAwareInterpolator.t()
- k:
integer()
Python prototype (for reference only):
setK(_k) -> None
Lambda is a parameter defining the weight of the edge-aware term in geodesic distance, should be in the range of 0 to 1000.
Positional Arguments
- self:
Evision.XImgProc.EdgeAwareInterpolator.t()
- lambda:
float
Python prototype (for reference only):
setLambda(_lambda) -> None
Sigma is a parameter defining how fast the weights decrease in the locally-weighted affine fitting. Higher values can help preserve fine details, lower values can help to get rid of noise in the output flow.
Positional Arguments
- self:
Evision.XImgProc.EdgeAwareInterpolator.t()
- sigma:
float
Python prototype (for reference only):
setSigma(_sigma) -> None
Sets whether the fastGlobalSmootherFilter() post-processing is employed. It is turned on by default.
Positional Arguments
- self:
Evision.XImgProc.EdgeAwareInterpolator.t()
- use_post_proc:
bool
Python prototype (for reference only):
setUsePostProcessing(_use_post_proc) -> None