cv::GFTTDetector Class Reference

Wrapping class for feature detection using the goodFeaturesToTrack function. : More...

#include <opencv2/features.hpp>

Collaboration diagram for cv::GFTTDetector:

Public Member Functions
virtual int	getBlockSize () const =0
virtual String	getDefaultName () const CV_OVERRIDE
virtual int	getGradientSize ()=0
virtual bool	getHarrisDetector () const =0
virtual double	getK () const =0
virtual int	getMaxFeatures () const =0
virtual double	getMinDistance () const =0
virtual double	getQualityLevel () const =0
virtual void	setBlockSize (int blockSize)=0
virtual void	setGradientSize (int gradientSize_)=0
virtual void	setHarrisDetector (bool val)=0
virtual void	setK (double k)=0
virtual void	setMaxFeatures (int maxFeatures)=0
virtual void	setMinDistance (double minDistance)=0
virtual void	setQualityLevel (double qlevel)=0
Public Member Functions inherited from cv::Feature2D
virtual	~Feature2D ()
virtual void	compute (InputArray image, std::vector< KeyPoint > &keypoints, OutputArray descriptors)
	Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).
virtual void	compute (InputArrayOfArrays images, std::vector< std::vector< KeyPoint > > &keypoints, OutputArrayOfArrays descriptors)
virtual int	defaultNorm () const
virtual int	descriptorSize () const
virtual int	descriptorType () const
virtual void	detect (InputArray image, std::vector< KeyPoint > &keypoints, InputArray mask=noArray())
	Detects keypoints in an image (first variant) or image set (second variant).
virtual void	detect (InputArrayOfArrays images, std::vector< std::vector< KeyPoint > > &keypoints, InputArrayOfArrays masks=noArray())
virtual void	detectAndCompute (InputArray image, InputArray mask, std::vector< KeyPoint > &keypoints, OutputArray descriptors, bool useProvidedKeypoints=false)
virtual bool	empty () const CV_OVERRIDE
	Return true if detector object is empty.
virtual void	read (const FileNode &) CV_OVERRIDE
	Reads algorithm parameters from a file storage.
void	read (const String &fileName)
void	write (const String &fileName) const
virtual void	write (FileStorage &) const CV_OVERRIDE
	Stores algorithm parameters in a file storage.
void	write (FileStorage &fs, const String &name) const
Public Member Functions inherited from cv::Algorithm
	Algorithm ()
virtual	~Algorithm ()
virtual void	clear ()
	Clears the algorithm state.
virtual void	save (const String &filename) const
void	write (FileStorage &fs, const String &name) const

Static Public Member Functions
static Ptr< GFTTDetector >	create (int maxCorners, double qualityLevel, double minDistance, int blockSize, int gradiantSize, bool useHarrisDetector=false, double k=0.04)
static Ptr< GFTTDetector >	create (int maxCorners=1000, double qualityLevel=0.01, double minDistance=1, int blockSize=3, bool useHarrisDetector=false, double k=0.04)
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

Detailed Description

Wrapping class for feature detection using the goodFeaturesToTrack function. :

Member Function Documentation

create() [1/2]

static Ptr< GFTTDetector > cv::GFTTDetector::create ( int maxCorners, double qualityLevel, double minDistance, int blockSize, int gradiantSize, bool useHarrisDetector = false, double k = 0.04 )

static

Python:
	cv.GFTTDetector.create(	[, maxCorners[, qualityLevel[, minDistance[, blockSize[, useHarrisDetector[, k]]]]]]	) ->	retval
	cv.GFTTDetector.create(	maxCorners, qualityLevel, minDistance, blockSize, gradiantSize[, useHarrisDetector[, k]]	) ->	retval
	cv.GFTTDetector_create(	[, maxCorners[, qualityLevel[, minDistance[, blockSize[, useHarrisDetector[, k]]]]]]	) ->	retval
	cv.GFTTDetector_create(	maxCorners, qualityLevel, minDistance, blockSize, gradiantSize[, useHarrisDetector[, k]]	) ->	retval

create() [2/2]

static Ptr< GFTTDetector > cv::GFTTDetector::create ( int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 1, int blockSize = 3, bool useHarrisDetector = false, double k = 0.04 )

static

Python:
	cv.GFTTDetector.create(	[, maxCorners[, qualityLevel[, minDistance[, blockSize[, useHarrisDetector[, k]]]]]]	) ->	retval
	cv.GFTTDetector.create(	maxCorners, qualityLevel, minDistance, blockSize, gradiantSize[, useHarrisDetector[, k]]	) ->	retval
	cv.GFTTDetector_create(	[, maxCorners[, qualityLevel[, minDistance[, blockSize[, useHarrisDetector[, k]]]]]]	) ->	retval
	cv.GFTTDetector_create(	maxCorners, qualityLevel, minDistance, blockSize, gradiantSize[, useHarrisDetector[, k]]	) ->	retval

getBlockSize()

virtual int cv::GFTTDetector::getBlockSize ( ) const

pure virtual

Python:
	cv.GFTTDetector.getBlockSize(		) ->	retval

getDefaultName()

virtual String cv::GFTTDetector::getDefaultName ( ) const

virtual

Python:
	cv.GFTTDetector.getDefaultName(		) ->	retval

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.

Reimplemented from cv::Feature2D.

getGradientSize()

virtual int cv::GFTTDetector::getGradientSize ( )

pure virtual

Python:
	cv.GFTTDetector.getGradientSize(		) ->	retval

getHarrisDetector()

virtual bool cv::GFTTDetector::getHarrisDetector ( ) const

pure virtual

Python:
	cv.GFTTDetector.getHarrisDetector(		) ->	retval

getK()

virtual double cv::GFTTDetector::getK ( ) const

pure virtual

Python:
	cv.GFTTDetector.getK(		) ->	retval

getMaxFeatures()

virtual int cv::GFTTDetector::getMaxFeatures ( ) const

pure virtual

Python:
	cv.GFTTDetector.getMaxFeatures(		) ->	retval

getMinDistance()

virtual double cv::GFTTDetector::getMinDistance ( ) const

pure virtual

Python:
	cv.GFTTDetector.getMinDistance(		) ->	retval

getQualityLevel()

virtual double cv::GFTTDetector::getQualityLevel ( ) const

pure virtual

Python:
	cv.GFTTDetector.getQualityLevel(		) ->	retval

setBlockSize()

virtual void cv::GFTTDetector::setBlockSize ( int blockSize )

pure virtual

Python:
	cv.GFTTDetector.setBlockSize(	blockSize	) ->	None

setGradientSize()

virtual void cv::GFTTDetector::setGradientSize ( int gradientSize_ )

pure virtual

Python:
	cv.GFTTDetector.setGradientSize(	gradientSize_	) ->	None

setHarrisDetector()

virtual void cv::GFTTDetector::setHarrisDetector ( bool val )

pure virtual

Python:
	cv.GFTTDetector.setHarrisDetector(	val	) ->	None

setK()

virtual void cv::GFTTDetector::setK ( double k )

pure virtual

Python:
	cv.GFTTDetector.setK(	k	) ->	None

setMaxFeatures()

virtual void cv::GFTTDetector::setMaxFeatures ( int maxFeatures )

pure virtual

Python:
	cv.GFTTDetector.setMaxFeatures(	maxFeatures	) ->	None

setMinDistance()

virtual void cv::GFTTDetector::setMinDistance ( double minDistance )

pure virtual

Python:
	cv.GFTTDetector.setMinDistance(	minDistance	) ->	None

setQualityLevel()

virtual void cv::GFTTDetector::setQualityLevel ( double qlevel )

pure virtual

Python:
	cv.GFTTDetector.setQualityLevel(	qlevel	) ->	None

---

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

• opencv2/features.hpp