View Source Evision.XImgProc.EdgeAwareInterpolator (Evision v0.1.28)

Link to this section Summary

Types

t()

Type that represents an XImgProc.EdgeAwareInterpolator struct.

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.

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.

Link to this section Types

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

Type that represents an XImgProc.EdgeAwareInterpolator struct.

  • ref. reference()

    The underlying erlang resource variable.

Link to this section Functions

@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(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(t()) :: integer() | {:error, String.t()}

getK

Positional Arguments
  • self: Evision.XImgProc.EdgeAwareInterpolator.t()
Return
  • retval: int

@see setK/2

Python prototype (for reference only):

getK() -> retval
@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(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
Link to this function

getUsePostProcessing(self)

View Source
@spec getUsePostProcessing(t()) :: boolean() | {:error, String.t()}

getUsePostProcessing

Positional Arguments
  • self: Evision.XImgProc.EdgeAwareInterpolator.t()
Return
  • retval: bool

@see setUsePostProcessing/2

Python prototype (for reference only):

getUsePostProcessing() -> retval
Link to this function

setCostMap(self, costMap)

View Source
@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.t().

    a type CV_32FC1 Mat is required.

@see cv::ximgproc::createSuperpixelSLIC

Python prototype (for reference only):

setCostMap(_costMap) -> None
Link to this function

setFGSLambda(self, lambda)

View Source
@spec setFGSLambda(t(), number()) :: t() | {:error, String.t()}

Sets the respective fastGlobalSmootherFilter() parameter.

Positional Arguments
  • self: Evision.XImgProc.EdgeAwareInterpolator.t()
  • lambda: float

Python prototype (for reference only):

setFGSLambda(_lambda) -> None
Link to this function

setFGSSigma(self, sigma)

View Source
@spec setFGSSigma(t(), number()) :: t() | {:error, String.t()}

setFGSSigma

Positional Arguments
  • self: Evision.XImgProc.EdgeAwareInterpolator.t()
  • sigma: float

@see setFGSLambda/2

Python prototype (for reference only):

setFGSSigma(_sigma) -> None
@spec setK(t(), integer()) :: t() | {:error, String.t()}

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: int

Python prototype (for reference only):

setK(_k) -> None
@spec setLambda(t(), number()) :: t() | {:error, String.t()}

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
@spec setSigma(t(), number()) :: t() | {:error, String.t()}

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
Link to this function

setUsePostProcessing(self, use_post_proc)

View Source
@spec setUsePostProcessing(t(), boolean()) :: t() | {:error, String.t()}

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