Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor [詳解]

#include <features2d.hpp>

cv::Feature2Dを継承しています。

公開型
enum	ScoreType { HARRIS_SCORE =0 , FAST_SCORE =1 }

公開メンバ関数
virtual CV_WRAP void	setMaxFeatures (int maxFeatures)=0

virtual CV_WRAP int	getMaxFeatures () const =0

virtual CV_WRAP void	setScaleFactor (double scaleFactor)=0

virtual CV_WRAP double	getScaleFactor () const =0

virtual CV_WRAP void	setNLevels (int nlevels)=0

virtual CV_WRAP int	getNLevels () const =0

virtual CV_WRAP void	setEdgeThreshold (int edgeThreshold)=0

virtual CV_WRAP int	getEdgeThreshold () const =0

virtual CV_WRAP void	setFirstLevel (int firstLevel)=0

virtual CV_WRAP int	getFirstLevel () const =0

virtual CV_WRAP void	setWTA_K (int wta_k)=0

virtual CV_WRAP int	getWTA_K () const =0

virtual CV_WRAP void	setScoreType (ORB::ScoreType scoreType)=0

virtual CV_WRAP ORB::ScoreType	getScoreType () const =0

virtual CV_WRAP void	setPatchSize (int patchSize)=0

virtual CV_WRAP int	getPatchSize () const =0

virtual CV_WRAP void	setFastThreshold (int fastThreshold)=0

virtual CV_WRAP int	getFastThreshold () const =0

virtual CV_WRAP String	getDefaultName () const CV_OVERRIDE

基底クラス cv::Feature2D に属する継承公開メンバ関数
virtual CV_WRAP void	detect (InputArray image, CV_OUT std::vector< KeyPoint > &keypoints, InputArray mask=noArray())
	Detects keypoints in an image (first variant) or image set (second variant). [詳解]

virtual CV_WRAP void	detect (InputArrayOfArrays images, CV_OUT std::vector< std::vector< KeyPoint > > &keypoints, InputArrayOfArrays masks=noArray())

virtual CV_WRAP void	compute (InputArray image, CV_OUT CV_IN_OUT 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). [詳解]

virtual CV_WRAP void	compute (InputArrayOfArrays images, CV_OUT CV_IN_OUT std::vector< std::vector< KeyPoint > > &keypoints, OutputArrayOfArrays descriptors)

virtual CV_WRAP void	detectAndCompute (InputArray image, InputArray mask, CV_OUT std::vector< KeyPoint > &keypoints, OutputArray descriptors, bool useProvidedKeypoints=false)

virtual CV_WRAP int	descriptorSize () const

virtual CV_WRAP int	descriptorType () const

virtual CV_WRAP int	defaultNorm () const

CV_WRAP void	write (const String &fileName) const

CV_WRAP void	read (const String &fileName)

virtual void	write (FileStorage &) const CV_OVERRIDE
	Stores algorithm parameters in a file storage [詳解]

virtual CV_WRAP void	read (const FileNode &) CV_OVERRIDE
	Reads algorithm parameters from a file storage [詳解]

virtual CV_WRAP bool	empty () const CV_OVERRIDE
	Return true if detector object is empty [詳解]

CV_WRAP void	write (const Ptr< FileStorage > &fs, const String &name=String()) const

基底クラス cv::Algorithm に属する継承公開メンバ関数
virtual CV_WRAP void	clear ()
	Clears the algorithm state [詳解]

CV_WRAP void	write (const Ptr< FileStorage > &fs, const String &name=String()) const
	simplified API for language bindings これはオーバーロードされたメンバ関数です。利便性のために用意されています。元の関数との違いは引き数のみです。

virtual CV_WRAP void	save (const String &filename) const

静的公開メンバ関数
static CV_WRAP Ptr< ORB >	create (int nfeatures=500, float scaleFactor=1.2f, int nlevels=8, int edgeThreshold=31, int firstLevel=0, int WTA_K=2, ORB::ScoreType scoreType=ORB::HARRIS_SCORE, int patchSize=31, int fastThreshold=20)
	The ORB constructor [詳解]

基底クラス cv::Algorithm に属する継承静的公開メンバ関数
template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node [詳解]

template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file [詳解]

template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String [詳解]

静的公開変数類
static const int	kBytes = 32

その他の継承メンバ
基底クラス cv::Algorithm に属する継承限定公開メンバ関数
void	writeFormat (FileStorage &fs) const

詳解

Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor

described in [RRKB11] . The algorithm uses FAST in pyramids to detect stable keypoints, selects the strongest features using FAST or Harris response, finds their orientation using first-order moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or k-tuples) are rotated according to the measured orientation).

関数詳解

◆ create()

static CV_WRAP Ptr< ORB > cv::ORB::create	(	int	nfeatures = `500`,
		float	scaleFactor = `1.2f`,
		int	nlevels = `8`,
		int	edgeThreshold = `31`,
		int	firstLevel = `0`,
		int	WTA_K = `2`,
		ORB::ScoreType	scoreType = `ORB::HARRIS_SCORE`,
		int	patchSize = `31`,
		int	fastThreshold = `20`
	)

static

The ORB constructor

引数

nfeatures	The maximum number of features to retain.
scaleFactor	Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor will mean that to cover certain scale range you will need more pyramid levels and so the speed will suffer.
nlevels	The number of pyramid levels. The smallest level will have linear size equal to input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
edgeThreshold	This is size of the border where the features are not detected. It should roughly match the patchSize parameter.
firstLevel	The level of pyramid to put source image to. Previous layers are filled with upscaled source image.
WTA_K	The number of points that produce each element of the oriented BRIEF descriptor. The default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 random points (of course, those point coordinates are random, but they are generated from the pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
scoreType	The default HARRIS_SCORE means that Harris algorithm is used to rank features (the score is written to KeyPoint::score and is used to retain best nfeatures features); FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, but it is a little faster to compute.
patchSize	size of the patch used by the oriented BRIEF descriptor. Of course, on smaller pyramid layers the perceived image area covered by a feature will be larger.
fastThreshold	the fast threshold

◆ getDefaultName()

virtual CV_WRAP String cv::ORB::getDefaultName ( ) const

virtual

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.

cv::Feature2Dを再実装しています。

このクラス詳解は次のファイルから抽出されました:

features2d.hpp

公開型

公開メンバ関数

静的公開メンバ関数

静的公開変数類

その他の継承メンバ

詳解

関数詳解

◆ create()

◆ getDefaultName()