cv::hfs::HfsSegment Class Reference

#include <opencv2/hfs.hpp>

Collaboration diagram for cv::hfs::HfsSegment:

Public Member Functions
virtual int	getMinRegionSizeI ()=0
virtual int	getMinRegionSizeII ()=0
virtual int	getNumSlicIter ()=0
virtual float	getSegEgbThresholdI ()=0
virtual float	getSegEgbThresholdII ()=0
virtual int	getSlicSpixelSize ()=0
virtual float	getSpatialWeight ()=0
virtual Mat	performSegmentCpu (InputArray src, bool ifDraw=true)=0
	do segmentation with cpu This method is only implemented for reference. It is highly NOT recommanded to use it.
virtual Mat	performSegmentGpu (InputArray src, bool ifDraw=true)=0
	do segmentation gpu
virtual void	setMinRegionSizeI (int n)=0
	: set and get the parameter minRegionSizeI. This parameter is used in the second stage mentioned above. After the EGB segmentation, regions that have fewer pixels then this parameter will be merged into it's adjacent region.
virtual void	setMinRegionSizeII (int n)=0
	: set and get the parameter minRegionSizeII. This parameter is used in the third stage mentioned above. It serves the same purpose as minRegionSizeI
virtual void	setNumSlicIter (int n)=0
	: set and get the parameter numSlicIter. This parameter is used in the first stage. It describes how many iteration to perform when executing SLIC.
virtual void	setSegEgbThresholdI (float c)=0
	: set and get the parameter segEgbThresholdI. This parameter is used in the second stage mentioned above. It is a constant used to threshold weights of the edge when merging adjacent nodes when applying EGB algorithm. The segmentation result tends to have more regions remained if this value is large and vice versa.
virtual void	setSegEgbThresholdII (float c)=0
	: set and get the parameter segEgbThresholdII. This parameter is used in the third stage mentioned above. It serves the same purpose as segEgbThresholdI. The segmentation result tends to have more regions remained if this value is large and vice versa.
virtual void	setSlicSpixelSize (int n)=0
	: set and get the parameter slicSpixelSize. This parameter is used in the first stage mentioned above(the SLIC stage). It describes the size of each superpixel when initializing SLIC. Every superpixel approximately has \(slicSpixelSize \times slicSpixelSize\) pixels in the beginning.
virtual void	setSpatialWeight (float w)=0
	: set and get the parameter spatialWeight. This parameter is used in the first stage mentioned above(the SLIC stage). It describes how important is the role of position when calculating the distance between each pixel and it's center. The exact formula to calculate the distance is \(colorDistance + spatialWeight \times spatialDistance\). The segmentation result tends to have more local consistency if this value is larger.
Public Member Functions inherited from cv::Algorithm
	Algorithm ()
virtual	~Algorithm ()
virtual void	clear ()
	Clears the algorithm state.
virtual bool	empty () const
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
virtual String	getDefaultName () const
virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage.
virtual void	save (const String &filename) const
void	write (const Ptr< FileStorage > &fs, const String &name=String()) const
virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage.
void	write (FileStorage &fs, const String &name) const

Static Public Member Functions
static Ptr< HfsSegment >	create (int height, int width, float segEgbThresholdI=0.08f, int minRegionSizeI=100, float segEgbThresholdII=0.28f, int minRegionSizeII=200, float spatialWeight=0.6f, int slicSpixelSize=8, int numSlicIter=5)
	: create a hfs object
Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file.
template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String.
template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node.

Additional Inherited Members
Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

Member Function Documentation

create()

static Ptr< HfsSegment > cv::hfs::HfsSegment::create ( int  height, int  width, float  segEgbThresholdI = 0.08f, int  minRegionSizeI = 100, float  segEgbThresholdII = 0.28f, int  minRegionSizeII = 200, float  spatialWeight = 0.6f, int  slicSpixelSize = 8, int  numSlicIter = 5  )

static

Python:
	cv.hfs.HfsSegment.create(	height, width[, segEgbThresholdI[, minRegionSizeI[, segEgbThresholdII[, minRegionSizeII[, spatialWeight[, slicSpixelSize[, numSlicIter]]]]]]]	) ->	retval
	cv.hfs.HfsSegment_create(	height, width[, segEgbThresholdI[, minRegionSizeI[, segEgbThresholdII[, minRegionSizeII[, spatialWeight[, slicSpixelSize[, numSlicIter]]]]]]]	) ->	retval

: create a hfs object

Parameters

height	the height of the input image
width	the width of the input image
segEgbThresholdI	parameter segEgbThresholdI
minRegionSizeI	parameter minRegionSizeI
segEgbThresholdII	parameter segEgbThresholdII
minRegionSizeII	parameter minRegionSizeII
spatialWeight	parameter spatialWeight
slicSpixelSize	parameter slicSpixelSize
numSlicIter	parameter numSlicIter

getMinRegionSizeI()

virtual int cv::hfs::HfsSegment::getMinRegionSizeI ( )

pure virtual

Python:
	cv.hfs.HfsSegment.getMinRegionSizeI(		) ->	retval

getMinRegionSizeII()

virtual int cv::hfs::HfsSegment::getMinRegionSizeII ( )

pure virtual

Python:
	cv.hfs.HfsSegment.getMinRegionSizeII(		) ->	retval

getNumSlicIter()

virtual int cv::hfs::HfsSegment::getNumSlicIter ( )

pure virtual

Python:
	cv.hfs.HfsSegment.getNumSlicIter(		) ->	retval

getSegEgbThresholdI()

virtual float cv::hfs::HfsSegment::getSegEgbThresholdI ( )

pure virtual

Python:
	cv.hfs.HfsSegment.getSegEgbThresholdI(		) ->	retval

getSegEgbThresholdII()

virtual float cv::hfs::HfsSegment::getSegEgbThresholdII ( )

pure virtual

Python:
	cv.hfs.HfsSegment.getSegEgbThresholdII(		) ->	retval

getSlicSpixelSize()

virtual int cv::hfs::HfsSegment::getSlicSpixelSize ( )

pure virtual

Python:
	cv.hfs.HfsSegment.getSlicSpixelSize(		) ->	retval

getSpatialWeight()

virtual float cv::hfs::HfsSegment::getSpatialWeight ( )

pure virtual

Python:
	cv.hfs.HfsSegment.getSpatialWeight(		) ->	retval

performSegmentCpu()

virtual Mat cv::hfs::HfsSegment::performSegmentCpu ( InputArray  src, bool  ifDraw = true  )

pure virtual

Python:
	cv.hfs.HfsSegment.performSegmentCpu(	src[, ifDraw]	) ->	retval

do segmentation with cpu This method is only implemented for reference. It is highly NOT recommanded to use it.

performSegmentGpu()

virtual Mat cv::hfs::HfsSegment::performSegmentGpu ( InputArray  src, bool  ifDraw = true  )

pure virtual

Python:
	cv.hfs.HfsSegment.performSegmentGpu(	src[, ifDraw]	) ->	retval

do segmentation gpu

Parameters

src	the input image
ifDraw	if draw the image in the returned Mat. if this parameter is false, then the content of the returned Mat is a matrix of index, describing the region each pixel belongs to. And it's data type is CV_16U. If this parameter is true, then the returned Mat is a segmented picture, and color of each region is the average color of all pixels in that region. And it's data type is the same as the input image

setMinRegionSizeI()

virtual void cv::hfs::HfsSegment::setMinRegionSizeI ( int  n )

pure virtual

Python:
	cv.hfs.HfsSegment.setMinRegionSizeI(	n	) ->	None

: set and get the parameter minRegionSizeI. This parameter is used in the second stage mentioned above. After the EGB segmentation, regions that have fewer pixels then this parameter will be merged into it's adjacent region.

setMinRegionSizeII()

virtual void cv::hfs::HfsSegment::setMinRegionSizeII ( int  n )

pure virtual

Python:
	cv.hfs.HfsSegment.setMinRegionSizeII(	n	) ->	None

: set and get the parameter minRegionSizeII. This parameter is used in the third stage mentioned above. It serves the same purpose as minRegionSizeI

setNumSlicIter()

virtual void cv::hfs::HfsSegment::setNumSlicIter ( int  n )

pure virtual

Python:
	cv.hfs.HfsSegment.setNumSlicIter(	n	) ->	None

: set and get the parameter numSlicIter. This parameter is used in the first stage. It describes how many iteration to perform when executing SLIC.

setSegEgbThresholdI()

virtual void cv::hfs::HfsSegment::setSegEgbThresholdI ( float  c )

pure virtual

Python:
	cv.hfs.HfsSegment.setSegEgbThresholdI(	c	) ->	None

: set and get the parameter segEgbThresholdI. This parameter is used in the second stage mentioned above. It is a constant used to threshold weights of the edge when merging adjacent nodes when applying EGB algorithm. The segmentation result tends to have more regions remained if this value is large and vice versa.

setSegEgbThresholdII()

virtual void cv::hfs::HfsSegment::setSegEgbThresholdII ( float  c )

pure virtual

Python:
	cv.hfs.HfsSegment.setSegEgbThresholdII(	c	) ->	None

: set and get the parameter segEgbThresholdII. This parameter is used in the third stage mentioned above. It serves the same purpose as segEgbThresholdI. The segmentation result tends to have more regions remained if this value is large and vice versa.

setSlicSpixelSize()

virtual void cv::hfs::HfsSegment::setSlicSpixelSize ( int  n )

pure virtual

Python:
	cv.hfs.HfsSegment.setSlicSpixelSize(	n	) ->	None

: set and get the parameter slicSpixelSize. This parameter is used in the first stage mentioned above(the SLIC stage). It describes the size of each superpixel when initializing SLIC. Every superpixel approximately has \(slicSpixelSize \times slicSpixelSize\) pixels in the beginning.

setSpatialWeight()

virtual void cv::hfs::HfsSegment::setSpatialWeight ( float  w )

pure virtual

Python:
	cv.hfs.HfsSegment.setSpatialWeight(	w	) ->	None

: set and get the parameter spatialWeight. This parameter is used in the first stage mentioned above(the SLIC stage). It describes how important is the role of position when calculating the distance between each pixel and it's center. The exact formula to calculate the distance is \(colorDistance + spatialWeight \times spatialDistance\). The segmentation result tends to have more local consistency if this value is larger.

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The documentation for this class was generated from the following file:

• opencv2/hfs.hpp