OpenCV  4.10.0-dev
Open Source Computer Vision
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Public Member Functions | Static Public Member Functions | List of all members
cv::GFTTDetector Class Referenceabstract

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

#include <opencv2/features2d.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 Ptr< FileStorage > &fs, const String &name) const
 
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 (const Ptr< FileStorage > &fs, const String &name=String()) const
 
void write (FileStorage &fs, const String &name) const
 

Static Public Member Functions

static Ptr< GFTTDetectorcreate (int maxCorners, double qualityLevel, double minDistance, int blockSize, int gradiantSize, bool useHarrisDetector=false, double k=0.04)
 
static Ptr< GFTTDetectorcreate (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< _Tpload (const String &filename, const String &objname=String())
 Loads algorithm from the file.
 
template<typename _Tp >
static Ptr< _TploadFromString (const String &strModel, const String &objname=String())
 Loads algorithm from a String.
 
template<typename _Tp >
static Ptr< _Tpread (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: