View Source Evision.XImgProc.RICInterpolator (Evision v0.1.34)
Summary
Functions
getAlpha
getFGSLambda
getFGSSigma
getK
getMaxFlow
getModelIter
getRefineModels
getSuperpixelMode
getSuperpixelNNCnt
getSuperpixelRuler
getSuperpixelSize
getUseGlobalSmootherFilter
getUseVariationalRefinement
Alpha is a parameter defining a global weight for transforming geodesic distance into weight.
Alpha is a parameter defining a global weight for transforming geodesic distance into weight.
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.
Sets the respective fastGlobalSmootherFilter() parameter.
Sets the respective fastGlobalSmootherFilter() parameter.
Sets the respective fastGlobalSmootherFilter() parameter.
K is a number of nearest-neighbor matches considered, when fitting a locally affine model for a superpixel segment. However, lower values would make the interpolation noticeably faster. The original implementation of @cite Hu2017 uses 32.
K is a number of nearest-neighbor matches considered, when fitting a locally affine model for a superpixel segment. However, lower values would make the interpolation noticeably faster. The original implementation of @cite Hu2017 uses 32.
MaxFlow is a threshold to validate the predictions using a certain piece-wise affine model. If the prediction exceeds the treshold the translational model will be applied instead.
MaxFlow is a threshold to validate the predictions using a certain piece-wise affine model. If the prediction exceeds the treshold the translational model will be applied instead.
Parameter defining the number of iterations for piece-wise affine model estimation.
Parameter defining the number of iterations for piece-wise affine model estimation.
Parameter to choose wether additional refinement of the piece-wise affine models is employed.
Parameter to choose wether additional refinement of the piece-wise affine models is employed.
Parameter to choose superpixel algorithm variant to use
Parameter to choose superpixel algorithm variant to use
Parameter defines the number of nearest-neighbor matches for each superpixel considered, when fitting a locally affine model.
Parameter defines the number of nearest-neighbor matches for each superpixel considered, when fitting a locally affine model.
Parameter to tune enforcement of superpixel smoothness factor used for oversegmentation.
Parameter to tune enforcement of superpixel smoothness factor used for oversegmentation.
Get the internal cost, i.e. edge map, used for estimating the edge-aware term.
Get the internal cost, i.e. edge map, used for estimating the edge-aware term.
Sets whether the fastGlobalSmootherFilter() post-processing is employed.
Sets whether the fastGlobalSmootherFilter() post-processing is employed.
Parameter to choose wether the VariationalRefinement post-processing is employed.
Parameter to choose wether the VariationalRefinement post-processing is employed.
Types
@type t() :: %Evision.XImgProc.RICInterpolator{ref: reference()}
Type that represents an XImgProc.RICInterpolator
struct.
ref.
reference()
The underlying erlang resource variable.
Functions
getAlpha
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Return
- retval:
float
@copybrief setAlpha
@see setAlpha/2
Python prototype (for reference only):
getAlpha() -> retval
getFGSLambda
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Return
- retval:
float
@copybrief setFGSLambda
@see setFGSLambda/2
Python prototype (for reference only):
getFGSLambda() -> retval
getFGSSigma
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Return
- retval:
float
@copybrief setFGSSigma
@see setFGSSigma/2
Python prototype (for reference only):
getFGSSigma() -> retval
getK
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Return
- retval:
int
@copybrief setK
@see setK/2
Python prototype (for reference only):
getK() -> retval
getMaxFlow
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Return
- retval:
float
@copybrief setMaxFlow
@see setMaxFlow/2
Python prototype (for reference only):
getMaxFlow() -> retval
getModelIter
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Return
- retval:
int
@copybrief setModelIter
@see setModelIter/2
Python prototype (for reference only):
getModelIter() -> retval
getRefineModels
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Return
- retval:
bool
@copybrief setRefineModels
@see setRefineModels/2
Python prototype (for reference only):
getRefineModels() -> retval
getSuperpixelMode
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Return
- retval:
int
@copybrief setSuperpixelMode
@see setSuperpixelMode/2
Python prototype (for reference only):
getSuperpixelMode() -> retval
getSuperpixelNNCnt
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Return
- retval:
int
@copybrief setSuperpixelNNCnt
@see setSuperpixelNNCnt/2
Python prototype (for reference only):
getSuperpixelNNCnt() -> retval
getSuperpixelRuler
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Return
- retval:
float
@copybrief setSuperpixelRuler
@see setSuperpixelRuler/2
Python prototype (for reference only):
getSuperpixelRuler() -> retval
getSuperpixelSize
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Return
- retval:
int
@copybrief setSuperpixelSize
@see setSuperpixelSize/2
Python prototype (for reference only):
getSuperpixelSize() -> retval
getUseGlobalSmootherFilter
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Return
- retval:
bool
@copybrief setUseGlobalSmootherFilter
@see setUseGlobalSmootherFilter/2
Python prototype (for reference only):
getUseGlobalSmootherFilter() -> retval
getUseVariationalRefinement
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Return
- retval:
bool
@copybrief setUseVariationalRefinement
@see setUseVariationalRefinement/2
Python prototype (for reference only):
getUseVariationalRefinement() -> retval
Alpha is a parameter defining a global weight for transforming geodesic distance into weight.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- alpha:
float
.
Python prototype (for reference only):
setAlpha([, alpha]) -> None
Alpha is a parameter defining a global weight for transforming geodesic distance into weight.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- alpha:
float
.
Python prototype (for reference only):
setAlpha([, alpha]) -> None
@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.RICInterpolator.t()
costMap:
Evision.Mat.t()
.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.RICInterpolator.t()
Keyword Arguments
- lambda:
float
.
Python prototype (for reference only):
setFGSLambda([, lambda]) -> None
Sets the respective fastGlobalSmootherFilter() parameter.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- lambda:
float
.
Python prototype (for reference only):
setFGSLambda([, lambda]) -> None
Sets the respective fastGlobalSmootherFilter() parameter.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- sigma:
float
.
Python prototype (for reference only):
setFGSSigma([, sigma]) -> None
Sets the respective fastGlobalSmootherFilter() parameter.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- sigma:
float
.
Python prototype (for reference only):
setFGSSigma([, sigma]) -> None
K is a number of nearest-neighbor matches considered, when fitting a locally affine model for a superpixel segment. However, lower values would make the interpolation noticeably faster. The original implementation of @cite Hu2017 uses 32.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- k:
int
.
Python prototype (for reference only):
setK([, k]) -> None
K is a number of nearest-neighbor matches considered, when fitting a locally affine model for a superpixel segment. However, lower values would make the interpolation noticeably faster. The original implementation of @cite Hu2017 uses 32.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- k:
int
.
Python prototype (for reference only):
setK([, k]) -> None
MaxFlow is a threshold to validate the predictions using a certain piece-wise affine model. If the prediction exceeds the treshold the translational model will be applied instead.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- maxFlow:
float
.
Python prototype (for reference only):
setMaxFlow([, maxFlow]) -> None
MaxFlow is a threshold to validate the predictions using a certain piece-wise affine model. If the prediction exceeds the treshold the translational model will be applied instead.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- maxFlow:
float
.
Python prototype (for reference only):
setMaxFlow([, maxFlow]) -> None
Parameter defining the number of iterations for piece-wise affine model estimation.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- modelIter:
int
.
Python prototype (for reference only):
setModelIter([, modelIter]) -> None
Parameter defining the number of iterations for piece-wise affine model estimation.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- modelIter:
int
.
Python prototype (for reference only):
setModelIter([, modelIter]) -> None
Parameter to choose wether additional refinement of the piece-wise affine models is employed.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- refineModles:
bool
.
Python prototype (for reference only):
setRefineModels([, refineModles]) -> None
Parameter to choose wether additional refinement of the piece-wise affine models is employed.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- refineModles:
bool
.
Python prototype (for reference only):
setRefineModels([, refineModles]) -> None
Parameter to choose superpixel algorithm variant to use:
- cv::ximgproc::SLICType SLIC segments image using a desired region_size (value: 100)
- cv::ximgproc::SLICType SLICO will optimize using adaptive compactness factor (value: 101)
- cv::ximgproc::SLICType MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels (value: 102).
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- mode:
int
.
@see cv::ximgproc::createSuperpixelSLIC
Python prototype (for reference only):
setSuperpixelMode([, mode]) -> None
Parameter to choose superpixel algorithm variant to use:
- cv::ximgproc::SLICType SLIC segments image using a desired region_size (value: 100)
- cv::ximgproc::SLICType SLICO will optimize using adaptive compactness factor (value: 101)
- cv::ximgproc::SLICType MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels (value: 102).
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- mode:
int
.
@see cv::ximgproc::createSuperpixelSLIC
Python prototype (for reference only):
setSuperpixelMode([, mode]) -> None
Parameter defines the number of nearest-neighbor matches for each superpixel considered, when fitting a locally affine model.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- spNN:
int
.
Python prototype (for reference only):
setSuperpixelNNCnt([, spNN]) -> None
Parameter defines the number of nearest-neighbor matches for each superpixel considered, when fitting a locally affine model.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- spNN:
int
.
Python prototype (for reference only):
setSuperpixelNNCnt([, spNN]) -> None
Parameter to tune enforcement of superpixel smoothness factor used for oversegmentation.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- ruler:
float
.
@see cv::ximgproc::createSuperpixelSLIC
Python prototype (for reference only):
setSuperpixelRuler([, ruler]) -> None
Parameter to tune enforcement of superpixel smoothness factor used for oversegmentation.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- ruler:
float
.
@see cv::ximgproc::createSuperpixelSLIC
Python prototype (for reference only):
setSuperpixelRuler([, ruler]) -> None
Get the internal cost, i.e. edge map, used for estimating the edge-aware term.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- spSize:
int
.
@see setCostMap/2
Python prototype (for reference only):
setSuperpixelSize([, spSize]) -> None
Get the internal cost, i.e. edge map, used for estimating the edge-aware term.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- spSize:
int
.
@see setCostMap/2
Python prototype (for reference only):
setSuperpixelSize([, spSize]) -> None
Sets whether the fastGlobalSmootherFilter() post-processing is employed.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- use_FGS:
bool
.
Python prototype (for reference only):
setUseGlobalSmootherFilter([, use_FGS]) -> None
Sets whether the fastGlobalSmootherFilter() post-processing is employed.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- use_FGS:
bool
.
Python prototype (for reference only):
setUseGlobalSmootherFilter([, use_FGS]) -> None
Parameter to choose wether the VariationalRefinement post-processing is employed.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- use_variational_refinement:
bool
.
Python prototype (for reference only):
setUseVariationalRefinement([, use_variational_refinement]) -> None
Parameter to choose wether the VariationalRefinement post-processing is employed.
Positional Arguments
- self:
Evision.XImgProc.RICInterpolator.t()
Keyword Arguments
- use_variational_refinement:
bool
.
Python prototype (for reference only):
setUseVariationalRefinement([, use_variational_refinement]) -> None