Detailed Description

Classes
struct	cv::Accumulator< T >

struct	cv::Accumulator< char >

struct	cv::Accumulator< short >

struct	cv::Accumulator< unsigned char >

struct	cv::Accumulator< unsigned short >

class	cv::AffineFeature
	Class for implementing the wrapper which makes detectors and extractors to be affine invariant, described as ASIFT in [313] . More...

class	cv::FastFeatureDetector
	Wrapping class for feature detection using the FAST method. More...

class	cv::Feature2D
	Abstract base class for 2D image feature detectors and descriptor extractors. More...

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

class	cv::KeyPointsFilter
	A class filters a vector of keypoints. More...

struct	cv::L1< T >

struct	cv::L2< T >

class	cv::MSER
	Maximally stable extremal region extractor. More...

class	cv::ORB
	Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor. More...

class	cv::SIFT
	Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe [174] . More...

class	cv::SimpleBlobDetector
	Class for extracting blobs from an image. : More...

struct	cv::SL2< T >

Typedefs
typedef AffineFeature	cv::AffineDescriptorExtractor

typedef AffineFeature	cv::AffineFeatureDetector

typedef Feature2D	cv::DescriptorExtractor

typedef Feature2D	cv::FeatureDetector

typedef SIFT	cv::SiftDescriptorExtractor

typedef SIFT	cv::SiftFeatureDetector

Functions
void	cv::computeRecallPrecisionCurve (const std::vector< std::vector< DMatch > > &matches1to2, const std::vector< std::vector< uchar > > &correctMatches1to2Mask, std::vector< Point2f > &recallPrecisionCurve)

void	cv::evaluateFeatureDetector (const Mat &img1, const Mat &img2, const Mat &H1to2, std::vector< KeyPoint > keypoints1, std::vector< KeyPoint > keypoints2, float &repeatability, int &correspCount, const Ptr< FeatureDetector > &fdetector=Ptr< FeatureDetector >())

void	cv::FAST (InputArray image, std::vector< KeyPoint > &keypoints, int threshold, bool nonmaxSuppression=true, FastFeatureDetector::DetectorType type=FastFeatureDetector::TYPE_9_16)
	Detects corners using the FAST algorithm.

int	cv::getNearestPoint (const std::vector< Point2f > &recallPrecisionCurve, float l_precision)

float	cv::getRecall (const std::vector< Point2f > &recallPrecisionCurve, float l_precision)

Typedef Documentation

◆ AffineDescriptorExtractor

typedef AffineFeature cv::AffineDescriptorExtractor

#include <opencv2/features.hpp>

◆ AffineFeatureDetector

typedef AffineFeature cv::AffineFeatureDetector

#include <opencv2/features.hpp>

◆ DescriptorExtractor

typedef Feature2D cv::DescriptorExtractor

#include <opencv2/features.hpp>

Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to easily switch between different algorithms solving the same problem. This section is devoted to computing descriptors represented as vectors in a multidimensional space. All objects that implement the vector descriptor extractors inherit the DescriptorExtractor interface.

◆ FeatureDetector

typedef Feature2D cv::FeatureDetector

#include <opencv2/features.hpp>

Feature detectors in OpenCV have wrappers with a common interface that enables you to easily switch between different algorithms solving the same problem. All objects that implement keypoint detectors inherit the FeatureDetector interface.

◆ SiftDescriptorExtractor

typedef SIFT cv::SiftDescriptorExtractor

#include <opencv2/features.hpp>

◆ SiftFeatureDetector

typedef SIFT cv::SiftFeatureDetector

#include <opencv2/features.hpp>

Function Documentation

◆ computeRecallPrecisionCurve()

void cv::computeRecallPrecisionCurve	(	const std::vector< std::vector< DMatch > > &	matches1to2,
		const std::vector< std::vector< uchar > > &	correctMatches1to2Mask,
		std::vector< Point2f > &	recallPrecisionCurve
	)

#include <opencv2/features.hpp>

◆ evaluateFeatureDetector()

void cv::evaluateFeatureDetector	(	const Mat &	img1,
		const Mat &	img2,
		const Mat &	H1to2,
		std::vector< KeyPoint > *	keypoints1,
		std::vector< KeyPoint > *	keypoints2,
		float &	repeatability,
		int &	correspCount,
		const Ptr< FeatureDetector > &	fdetector = `Ptr< FeatureDetector >()`
	)

#include <opencv2/features.hpp>

◆ FAST()

void cv::FAST	(	InputArray	image,
		std::vector< KeyPoint > &	keypoints,
		int	threshold,
		bool	nonmaxSuppression = `true`,
		FastFeatureDetector::DetectorType	type = `FastFeatureDetector::TYPE_9_16`
	)

#include <opencv2/features.hpp>

Detects corners using the FAST algorithm.

Parameters

image	grayscale image where keypoints (corners) are detected.
keypoints	keypoints detected on the image.
threshold	threshold on difference between intensity of the central pixel and pixels of a circle around this pixel.
nonmaxSuppression	if true, non-maximum suppression is applied to detected keypoints (corners).
type	one of the three neighborhoods as defined in the paper: FastFeatureDetector::TYPE_9_16, FastFeatureDetector::TYPE_7_12, FastFeatureDetector::TYPE_5_8