Class cv::GFTTDetector#

Wrapping class for feature detection using the goodFeaturesToTrack function. :

#include <opencv2/features.hpp>

Collaboration diagram for cv::GFTTDetector:

Public Member Functions#

Public Member Functions inherited from cv::Feature2D

Return	Name	Description
	`~Feature2D()`
`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).
`void`	`compute(` `InputArrayOfArrays images,` `std::vector< std::vector< KeyPoint > > & keypoints,` `OutputArrayOfArrays descriptors )`
`int`	`defaultNorm()`
`int`	`descriptorSize()`
`int`	`descriptorType()`
`void`	`detect(` `InputArray image,` `std::vector< KeyPoint > & keypoints,` `InputArray mask = noArray() )`	Detects keypoints in an image (first variant) or image set (second variant).
`void`	`detect(` `InputArrayOfArrays images,` `std::vector< std::vector< KeyPoint > > & keypoints,` `InputArrayOfArrays masks = noArray() )`
`void`	`detectAndCompute(` `InputArray image,` `InputArray mask,` `std::vector< KeyPoint > & keypoints,` `OutputArray descriptors,` `bool useProvidedKeypoints = false )`
`bool`	`empty()`	Return true if detector object is empty.
`String`	`getDefaultName()`
`void`	`read(const FileNode & fn)`	Reads algorithm parameters from a file storage.
`void`	`read(const String & fileName)`
`void`	`write(const String & fileName)`
`void`	`write(FileStorage & fs)`	Stores algorithm parameters in a file storage.
`void`	`write(` `FileStorage & fs,` `const String & name )`

Public Member Functions inherited from cv::Algorithm

Return	Name	Description
	`Algorithm()`
	`~Algorithm()`
`void`	`clear()`	Clears the algorithm state.
`bool`	`empty()`	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
`String`	`getDefaultName()`
`void`	`read(const FileNode & fn)`	Reads algorithm parameters from a file storage.
`void`	`save(const String & filename)`
`void`	`write(` `const Ptr< FileStorage > & fs,` `const String & name = String() )`
`void`	`write(FileStorage & fs)`	Stores algorithm parameters in a file storage.
`void`	`write(` `FileStorage & fs,` `const String & name )`

Static Public Member Functions#

Static Public Member Functions inherited from cv::Algorithm

Return	Name	Description
`static Ptr< _Tp >`	`load(` `const String & filename,` `const String & objname = String() )`	Loads algorithm from the file.
`static Ptr< _Tp >`	`loadFromString(` `const String & strModel,` `const String & objname = String() )`	Loads algorithm from a String.
`static Ptr< _Tp >`	`read(const FileNode & fn)`	Reads algorithm from the file node.

Additional Inherited Members#

Detailed Description#

Wrapping class for feature detection using the goodFeaturesToTrack function. :

Member Function Documentation#

create()#

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

Python:

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

cv.GFTTDetector.create(maxCorners, qualityLevel, minDistance, blockSize, gradientSize[, useHarrisDetector[, k]]) -> retval

cv.GFTTDetector_create([, maxCorners[, qualityLevel[, minDistance[, blockSize[, useHarrisDetector[, k]]]]]]) -> retval

cv.GFTTDetector_create(maxCorners, qualityLevel, minDistance, blockSize, gradientSize[, useHarrisDetector[, k]]) -> retval

create()#

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 )

Python:

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

cv.GFTTDetector.create(maxCorners, qualityLevel, minDistance, blockSize, gradientSize[, useHarrisDetector[, k]]) -> retval

cv.GFTTDetector_create([, maxCorners[, qualityLevel[, minDistance[, blockSize[, useHarrisDetector[, k]]]]]]) -> retval

cv.GFTTDetector_create(maxCorners, qualityLevel, minDistance, blockSize, gradientSize[, useHarrisDetector[, k]]) -> retval

getBlockSize()#

int cv::GFTTDetector::getBlockSize()

Python:

cv.GFTTDetector.getBlockSize() -> retval

getDefaultName()#

String cv::GFTTDetector::getDefaultName()

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.

getGradientSize()#

int cv::GFTTDetector::getGradientSize()

Python:

cv.GFTTDetector.getGradientSize() -> retval

getHarrisDetector()#

bool cv::GFTTDetector::getHarrisDetector()

Python:

cv.GFTTDetector.getHarrisDetector() -> retval

getK()#

double cv::GFTTDetector::getK()

Python:

cv.GFTTDetector.getK() -> retval

getMaxFeatures()#

int cv::GFTTDetector::getMaxFeatures()

Python:

cv.GFTTDetector.getMaxFeatures() -> retval

getMinDistance()#

double cv::GFTTDetector::getMinDistance()

Python:

cv.GFTTDetector.getMinDistance() -> retval

getQualityLevel()#

double cv::GFTTDetector::getQualityLevel()

Python:

cv.GFTTDetector.getQualityLevel() -> retval

setBlockSize()#

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

Python:

cv.GFTTDetector.setBlockSize(blockSize)

setGradientSize()#

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

Python:

cv.GFTTDetector.setGradientSize(gradientSize_)

setHarrisDetector()#

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

Python:

cv.GFTTDetector.setHarrisDetector(val)

setK()#

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

Python:

cv.GFTTDetector.setK(k)

setMaxFeatures()#

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

Python:

cv.GFTTDetector.setMaxFeatures(maxFeatures)

setMinDistance()#

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

Python:

cv.GFTTDetector.setMinDistance(minDistance)

setQualityLevel()#

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

Python:

cv.GFTTDetector.setQualityLevel(qlevel)

Source file#

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

opencv2/features.hpp