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opencv2/features2d.hpp@0:ea44dc9ed014, 2016-03-31 (annotated)

Committer:: joeverbout
Date:: Thu Mar 31 21:16:38 2016 +0000
Revision:: 0:ea44dc9ed014

OpenCV on mbed attempt

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joeverbout	0:ea44dc9ed014	1	/*M///////////////////////////////////////////////////////////////////////////////////////
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joeverbout	0:ea44dc9ed014	42
joeverbout	0:ea44dc9ed014	43	#ifndef __OPENCV_FEATURES_2D_HPP__
joeverbout	0:ea44dc9ed014	44	#define __OPENCV_FEATURES_2D_HPP__
joeverbout	0:ea44dc9ed014	45
joeverbout	0:ea44dc9ed014	46	#include "opencv2/core.hpp"
joeverbout	0:ea44dc9ed014	47	#include "opencv2/flann/miniflann.hpp"
joeverbout	0:ea44dc9ed014	48
joeverbout	0:ea44dc9ed014	49	/**
joeverbout	0:ea44dc9ed014	50	@defgroup features2d 2D Features Framework
joeverbout	0:ea44dc9ed014	51	@{
joeverbout	0:ea44dc9ed014	52	@defgroup features2d_main Feature Detection and Description
joeverbout	0:ea44dc9ed014	53	@defgroup features2d_match Descriptor Matchers
joeverbout	0:ea44dc9ed014	54
joeverbout	0:ea44dc9ed014	55	Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to
joeverbout	0:ea44dc9ed014	56	easily switch between different algorithms solving the same problem. This section is devoted to
joeverbout	0:ea44dc9ed014	57	matching descriptors that are represented as vectors in a multidimensional space. All objects that
joeverbout	0:ea44dc9ed014	58	implement vector descriptor matchers inherit the DescriptorMatcher interface.
joeverbout	0:ea44dc9ed014	59
joeverbout	0:ea44dc9ed014	60	@note
joeverbout	0:ea44dc9ed014	61	- An example explaining keypoint matching can be found at
joeverbout	0:ea44dc9ed014	62	opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp
joeverbout	0:ea44dc9ed014	63	- An example on descriptor matching evaluation can be found at
joeverbout	0:ea44dc9ed014	64	opencv_source_code/samples/cpp/detector_descriptor_matcher_evaluation.cpp
joeverbout	0:ea44dc9ed014	65	- An example on one to many image matching can be found at
joeverbout	0:ea44dc9ed014	66	opencv_source_code/samples/cpp/matching_to_many_images.cpp
joeverbout	0:ea44dc9ed014	67
joeverbout	0:ea44dc9ed014	68	@defgroup features2d_draw Drawing Function of Keypoints and Matches
joeverbout	0:ea44dc9ed014	69	@defgroup features2d_category Object Categorization
joeverbout	0:ea44dc9ed014	70
joeverbout	0:ea44dc9ed014	71	This section describes approaches based on local 2D features and used to categorize objects.
joeverbout	0:ea44dc9ed014	72
joeverbout	0:ea44dc9ed014	73	@note
joeverbout	0:ea44dc9ed014	74	- A complete Bag-Of-Words sample can be found at
joeverbout	0:ea44dc9ed014	75	opencv_source_code/samples/cpp/bagofwords_classification.cpp
joeverbout	0:ea44dc9ed014	76	- (Python) An example using the features2D framework to perform object categorization can be
joeverbout	0:ea44dc9ed014	77	found at opencv_source_code/samples/python/find_obj.py
joeverbout	0:ea44dc9ed014	78
joeverbout	0:ea44dc9ed014	79	@}
joeverbout	0:ea44dc9ed014	80	*/
joeverbout	0:ea44dc9ed014	81
joeverbout	0:ea44dc9ed014	82	namespace cv
joeverbout	0:ea44dc9ed014	83	{
joeverbout	0:ea44dc9ed014	84
joeverbout	0:ea44dc9ed014	85	//! @addtogroup features2d
joeverbout	0:ea44dc9ed014	86	//! @{
joeverbout	0:ea44dc9ed014	87
joeverbout	0:ea44dc9ed014	88	// //! writes vector of keypoints to the file storage
joeverbout	0:ea44dc9ed014	89	// CV_EXPORTS void write(FileStorage& fs, const String& name, const std::vector<KeyPoint>& keypoints);
joeverbout	0:ea44dc9ed014	90	// //! reads vector of keypoints from the specified file storage node
joeverbout	0:ea44dc9ed014	91	// CV_EXPORTS void read(const FileNode& node, CV_OUT std::vector<KeyPoint>& keypoints);
joeverbout	0:ea44dc9ed014	92
joeverbout	0:ea44dc9ed014	93	/** @brief A class filters a vector of keypoints.
joeverbout	0:ea44dc9ed014	94
joeverbout	0:ea44dc9ed014	95	Because now it is difficult to provide a convenient interface for all usage scenarios of the
joeverbout	0:ea44dc9ed014	96	keypoints filter class, it has only several needed by now static methods.
joeverbout	0:ea44dc9ed014	97	*/
joeverbout	0:ea44dc9ed014	98	class CV_EXPORTS KeyPointsFilter
joeverbout	0:ea44dc9ed014	99	{
joeverbout	0:ea44dc9ed014	100	public:
joeverbout	0:ea44dc9ed014	101	KeyPointsFilter(){}
joeverbout	0:ea44dc9ed014	102
joeverbout	0:ea44dc9ed014	103	/*
joeverbout	0:ea44dc9ed014	104	* Remove keypoints within borderPixels of an image edge.
joeverbout	0:ea44dc9ed014	105	*/
joeverbout	0:ea44dc9ed014	106	static void runByImageBorder( std::vector<KeyPoint>& keypoints, Size imageSize, int borderSize );
joeverbout	0:ea44dc9ed014	107	/*
joeverbout	0:ea44dc9ed014	108	* Remove keypoints of sizes out of range.
joeverbout	0:ea44dc9ed014	109	*/
joeverbout	0:ea44dc9ed014	110	static void runByKeypointSize( std::vector<KeyPoint>& keypoints, float minSize,
joeverbout	0:ea44dc9ed014	111	float maxSize=FLT_MAX );
joeverbout	0:ea44dc9ed014	112	/*
joeverbout	0:ea44dc9ed014	113	* Remove keypoints from some image by mask for pixels of this image.
joeverbout	0:ea44dc9ed014	114	*/
joeverbout	0:ea44dc9ed014	115	static void runByPixelsMask( std::vector<KeyPoint>& keypoints, const Mat& mask );
joeverbout	0:ea44dc9ed014	116	/*
joeverbout	0:ea44dc9ed014	117	* Remove duplicated keypoints.
joeverbout	0:ea44dc9ed014	118	*/
joeverbout	0:ea44dc9ed014	119	static void removeDuplicated( std::vector<KeyPoint>& keypoints );
joeverbout	0:ea44dc9ed014	120
joeverbout	0:ea44dc9ed014	121	/*
joeverbout	0:ea44dc9ed014	122	* Retain the specified number of the best keypoints (according to the response)
joeverbout	0:ea44dc9ed014	123	*/
joeverbout	0:ea44dc9ed014	124	static void retainBest( std::vector<KeyPoint>& keypoints, int npoints );
joeverbout	0:ea44dc9ed014	125	};
joeverbout	0:ea44dc9ed014	126
joeverbout	0:ea44dc9ed014	127
joeverbout	0:ea44dc9ed014	128	/********************************** Base Classes **********************************/
joeverbout	0:ea44dc9ed014	129
joeverbout	0:ea44dc9ed014	130	/** @brief Abstract base class for 2D image feature detectors and descriptor extractors
joeverbout	0:ea44dc9ed014	131	*/
joeverbout	0:ea44dc9ed014	132	class CV_EXPORTS_W Feature2D : public virtual Algorithm
joeverbout	0:ea44dc9ed014	133	{
joeverbout	0:ea44dc9ed014	134	public:
joeverbout	0:ea44dc9ed014	135	virtual ~Feature2D();
joeverbout	0:ea44dc9ed014	136
joeverbout	0:ea44dc9ed014	137	/** @brief Detects keypoints in an image (first variant) or image set (second variant).
joeverbout	0:ea44dc9ed014	138
joeverbout	0:ea44dc9ed014	139	@param image Image.
joeverbout	0:ea44dc9ed014	140	@param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set
joeverbout	0:ea44dc9ed014	141	of keypoints detected in images[i] .
joeverbout	0:ea44dc9ed014	142	@param mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer
joeverbout	0:ea44dc9ed014	143	matrix with non-zero values in the region of interest.
joeverbout	0:ea44dc9ed014	144	*/
joeverbout	0:ea44dc9ed014	145	CV_WRAP virtual void detect( InputArray image,
joeverbout	0:ea44dc9ed014	146	CV_OUT std::vector<KeyPoint>& keypoints,
joeverbout	0:ea44dc9ed014	147	InputArray mask=noArray() );
joeverbout	0:ea44dc9ed014	148
joeverbout	0:ea44dc9ed014	149	/** @overload
joeverbout	0:ea44dc9ed014	150	@param images Image set.
joeverbout	0:ea44dc9ed014	151	@param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set
joeverbout	0:ea44dc9ed014	152	of keypoints detected in images[i] .
joeverbout	0:ea44dc9ed014	153	@param masks Masks for each input image specifying where to look for keypoints (optional).
joeverbout	0:ea44dc9ed014	154	masks[i] is a mask for images[i].
joeverbout	0:ea44dc9ed014	155	*/
joeverbout	0:ea44dc9ed014	156	virtual void detect( InputArrayOfArrays images,
joeverbout	0:ea44dc9ed014	157	std::vector<std::vector<KeyPoint> >& keypoints,
joeverbout	0:ea44dc9ed014	158	InputArrayOfArrays masks=noArray() );
joeverbout	0:ea44dc9ed014	159
joeverbout	0:ea44dc9ed014	160	/** @brief Computes the descriptors for a set of keypoints detected in an image (first variant) or image set
joeverbout	0:ea44dc9ed014	161	(second variant).
joeverbout	0:ea44dc9ed014	162
joeverbout	0:ea44dc9ed014	163	@param image Image.
joeverbout	0:ea44dc9ed014	164	@param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be
joeverbout	0:ea44dc9ed014	165	computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint
joeverbout	0:ea44dc9ed014	166	with several dominant orientations (for each orientation).
joeverbout	0:ea44dc9ed014	167	@param descriptors Computed descriptors. In the second variant of the method descriptors[i] are
joeverbout	0:ea44dc9ed014	168	descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the
joeverbout	0:ea44dc9ed014	169	descriptor for keypoint j-th keypoint.
joeverbout	0:ea44dc9ed014	170	*/
joeverbout	0:ea44dc9ed014	171	CV_WRAP virtual void compute( InputArray image,
joeverbout	0:ea44dc9ed014	172	CV_OUT CV_IN_OUT std::vector<KeyPoint>& keypoints,
joeverbout	0:ea44dc9ed014	173	OutputArray descriptors );
joeverbout	0:ea44dc9ed014	174
joeverbout	0:ea44dc9ed014	175	/** @overload
joeverbout	0:ea44dc9ed014	176
joeverbout	0:ea44dc9ed014	177	@param images Image set.
joeverbout	0:ea44dc9ed014	178	@param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be
joeverbout	0:ea44dc9ed014	179	computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint
joeverbout	0:ea44dc9ed014	180	with several dominant orientations (for each orientation).
joeverbout	0:ea44dc9ed014	181	@param descriptors Computed descriptors. In the second variant of the method descriptors[i] are
joeverbout	0:ea44dc9ed014	182	descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the
joeverbout	0:ea44dc9ed014	183	descriptor for keypoint j-th keypoint.
joeverbout	0:ea44dc9ed014	184	*/
joeverbout	0:ea44dc9ed014	185	virtual void compute( InputArrayOfArrays images,
joeverbout	0:ea44dc9ed014	186	std::vector<std::vector<KeyPoint> >& keypoints,
joeverbout	0:ea44dc9ed014	187	OutputArrayOfArrays descriptors );
joeverbout	0:ea44dc9ed014	188
joeverbout	0:ea44dc9ed014	189	/** Detects keypoints and computes the descriptors */
joeverbout	0:ea44dc9ed014	190	CV_WRAP virtual void detectAndCompute( InputArray image, InputArray mask,
joeverbout	0:ea44dc9ed014	191	CV_OUT std::vector<KeyPoint>& keypoints,
joeverbout	0:ea44dc9ed014	192	OutputArray descriptors,
joeverbout	0:ea44dc9ed014	193	bool useProvidedKeypoints=false );
joeverbout	0:ea44dc9ed014	194
joeverbout	0:ea44dc9ed014	195	CV_WRAP virtual int descriptorSize() const;
joeverbout	0:ea44dc9ed014	196	CV_WRAP virtual int descriptorType() const;
joeverbout	0:ea44dc9ed014	197	CV_WRAP virtual int defaultNorm() const;
joeverbout	0:ea44dc9ed014	198
joeverbout	0:ea44dc9ed014	199	//! Return true if detector object is empty
joeverbout	0:ea44dc9ed014	200	CV_WRAP virtual bool empty() const;
joeverbout	0:ea44dc9ed014	201	};
joeverbout	0:ea44dc9ed014	202
joeverbout	0:ea44dc9ed014	203	/** Feature detectors in OpenCV have wrappers with a common interface that enables you to easily switch
joeverbout	0:ea44dc9ed014	204	between different algorithms solving the same problem. All objects that implement keypoint detectors
joeverbout	0:ea44dc9ed014	205	inherit the FeatureDetector interface. */
joeverbout	0:ea44dc9ed014	206	typedef Feature2D FeatureDetector;
joeverbout	0:ea44dc9ed014	207
joeverbout	0:ea44dc9ed014	208	/** Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you
joeverbout	0:ea44dc9ed014	209	to easily switch between different algorithms solving the same problem. This section is devoted to
joeverbout	0:ea44dc9ed014	210	computing descriptors represented as vectors in a multidimensional space. All objects that implement
joeverbout	0:ea44dc9ed014	211	the vector descriptor extractors inherit the DescriptorExtractor interface.
joeverbout	0:ea44dc9ed014	212	*/
joeverbout	0:ea44dc9ed014	213	typedef Feature2D DescriptorExtractor;
joeverbout	0:ea44dc9ed014	214
joeverbout	0:ea44dc9ed014	215	//! @addtogroup features2d_main
joeverbout	0:ea44dc9ed014	216	//! @{
joeverbout	0:ea44dc9ed014	217
joeverbout	0:ea44dc9ed014	218	/** @brief Class implementing the BRISK keypoint detector and descriptor extractor, described in @cite LCS11 .
joeverbout	0:ea44dc9ed014	219	*/
joeverbout	0:ea44dc9ed014	220	class CV_EXPORTS_W BRISK : public Feature2D
joeverbout	0:ea44dc9ed014	221	{
joeverbout	0:ea44dc9ed014	222	public:
joeverbout	0:ea44dc9ed014	223	/** @brief The BRISK constructor
joeverbout	0:ea44dc9ed014	224
joeverbout	0:ea44dc9ed014	225	@param thresh AGAST detection threshold score.
joeverbout	0:ea44dc9ed014	226	@param octaves detection octaves. Use 0 to do single scale.
joeverbout	0:ea44dc9ed014	227	@param patternScale apply this scale to the pattern used for sampling the neighbourhood of a
joeverbout	0:ea44dc9ed014	228	keypoint.
joeverbout	0:ea44dc9ed014	229	*/
joeverbout	0:ea44dc9ed014	230	CV_WRAP static Ptr<BRISK> create(int thresh=30, int octaves=3, float patternScale=1.0f);
joeverbout	0:ea44dc9ed014	231
joeverbout	0:ea44dc9ed014	232	/** @brief The BRISK constructor for a custom pattern
joeverbout	0:ea44dc9ed014	233
joeverbout	0:ea44dc9ed014	234	@param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for
joeverbout	0:ea44dc9ed014	235	keypoint scale 1).
joeverbout	0:ea44dc9ed014	236	@param numberList defines the number of sampling points on the sampling circle. Must be the same
joeverbout	0:ea44dc9ed014	237	size as radiusList..
joeverbout	0:ea44dc9ed014	238	@param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint
joeverbout	0:ea44dc9ed014	239	scale 1).
joeverbout	0:ea44dc9ed014	240	@param dMin threshold for the long pairings used for orientation determination (in pixels for
joeverbout	0:ea44dc9ed014	241	keypoint scale 1).
joeverbout	0:ea44dc9ed014	242	@param indexChange index remapping of the bits. */
joeverbout	0:ea44dc9ed014	243	CV_WRAP static Ptr<BRISK> create(const std::vector<float> &radiusList, const std::vector<int> &numberList,
joeverbout	0:ea44dc9ed014	244	float dMax=5.85f, float dMin=8.2f, const std::vector<int>& indexChange=std::vector<int>());
joeverbout	0:ea44dc9ed014	245	};
joeverbout	0:ea44dc9ed014	246
joeverbout	0:ea44dc9ed014	247	/** @brief Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor
joeverbout	0:ea44dc9ed014	248
joeverbout	0:ea44dc9ed014	249	described in @cite RRKB11 . The algorithm uses FAST in pyramids to detect stable keypoints, selects
joeverbout	0:ea44dc9ed014	250	the strongest features using FAST or Harris response, finds their orientation using first-order
joeverbout	0:ea44dc9ed014	251	moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or
joeverbout	0:ea44dc9ed014	252	k-tuples) are rotated according to the measured orientation).
joeverbout	0:ea44dc9ed014	253	*/
joeverbout	0:ea44dc9ed014	254	class CV_EXPORTS_W ORB : public Feature2D
joeverbout	0:ea44dc9ed014	255	{
joeverbout	0:ea44dc9ed014	256	public:
joeverbout	0:ea44dc9ed014	257	enum { kBytes = 32, HARRIS_SCORE=0, FAST_SCORE=1 };
joeverbout	0:ea44dc9ed014	258
joeverbout	0:ea44dc9ed014	259	/** @brief The ORB constructor
joeverbout	0:ea44dc9ed014	260
joeverbout	0:ea44dc9ed014	261	@param nfeatures The maximum number of features to retain.
joeverbout	0:ea44dc9ed014	262	@param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
joeverbout	0:ea44dc9ed014	263	pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
joeverbout	0:ea44dc9ed014	264	will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
joeverbout	0:ea44dc9ed014	265	will mean that to cover certain scale range you will need more pyramid levels and so the speed
joeverbout	0:ea44dc9ed014	266	will suffer.
joeverbout	0:ea44dc9ed014	267	@param nlevels The number of pyramid levels. The smallest level will have linear size equal to
joeverbout	0:ea44dc9ed014	268	input_image_linear_size/pow(scaleFactor, nlevels).
joeverbout	0:ea44dc9ed014	269	@param edgeThreshold This is size of the border where the features are not detected. It should
joeverbout	0:ea44dc9ed014	270	roughly match the patchSize parameter.
joeverbout	0:ea44dc9ed014	271	@param firstLevel It should be 0 in the current implementation.
joeverbout	0:ea44dc9ed014	272	@param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The
joeverbout	0:ea44dc9ed014	273	default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
joeverbout	0:ea44dc9ed014	274	so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
joeverbout	0:ea44dc9ed014	275	random points (of course, those point coordinates are random, but they are generated from the
joeverbout	0:ea44dc9ed014	276	pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
joeverbout	0:ea44dc9ed014	277	rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
joeverbout	0:ea44dc9ed014	278	output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
joeverbout	0:ea44dc9ed014	279	denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
joeverbout	0:ea44dc9ed014	280	bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
joeverbout	0:ea44dc9ed014	281	@param scoreType The default HARRIS_SCORE means that Harris algorithm is used to rank features
joeverbout	0:ea44dc9ed014	282	(the score is written to KeyPoint::score and is used to retain best nfeatures features);
joeverbout	0:ea44dc9ed014	283	FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
joeverbout	0:ea44dc9ed014	284	but it is a little faster to compute.
joeverbout	0:ea44dc9ed014	285	@param patchSize size of the patch used by the oriented BRIEF descriptor. Of course, on smaller
joeverbout	0:ea44dc9ed014	286	pyramid layers the perceived image area covered by a feature will be larger.
joeverbout	0:ea44dc9ed014	287	@param fastThreshold
joeverbout	0:ea44dc9ed014	288	*/
joeverbout	0:ea44dc9ed014	289	CV_WRAP static Ptr<ORB> create(int nfeatures=500, float scaleFactor=1.2f, int nlevels=8, int edgeThreshold=31,
joeverbout	0:ea44dc9ed014	290	int firstLevel=0, int WTA_K=2, int scoreType=ORB::HARRIS_SCORE, int patchSize=31, int fastThreshold=20);
joeverbout	0:ea44dc9ed014	291
joeverbout	0:ea44dc9ed014	292	CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0;
joeverbout	0:ea44dc9ed014	293	CV_WRAP virtual int getMaxFeatures() const = 0;
joeverbout	0:ea44dc9ed014	294
joeverbout	0:ea44dc9ed014	295	CV_WRAP virtual void setScaleFactor(double scaleFactor) = 0;
joeverbout	0:ea44dc9ed014	296	CV_WRAP virtual double getScaleFactor() const = 0;
joeverbout	0:ea44dc9ed014	297
joeverbout	0:ea44dc9ed014	298	CV_WRAP virtual void setNLevels(int nlevels) = 0;
joeverbout	0:ea44dc9ed014	299	CV_WRAP virtual int getNLevels() const = 0;
joeverbout	0:ea44dc9ed014	300
joeverbout	0:ea44dc9ed014	301	CV_WRAP virtual void setEdgeThreshold(int edgeThreshold) = 0;
joeverbout	0:ea44dc9ed014	302	CV_WRAP virtual int getEdgeThreshold() const = 0;
joeverbout	0:ea44dc9ed014	303
joeverbout	0:ea44dc9ed014	304	CV_WRAP virtual void setFirstLevel(int firstLevel) = 0;
joeverbout	0:ea44dc9ed014	305	CV_WRAP virtual int getFirstLevel() const = 0;
joeverbout	0:ea44dc9ed014	306
joeverbout	0:ea44dc9ed014	307	CV_WRAP virtual void setWTA_K(int wta_k) = 0;
joeverbout	0:ea44dc9ed014	308	CV_WRAP virtual int getWTA_K() const = 0;
joeverbout	0:ea44dc9ed014	309
joeverbout	0:ea44dc9ed014	310	CV_WRAP virtual void setScoreType(int scoreType) = 0;
joeverbout	0:ea44dc9ed014	311	CV_WRAP virtual int getScoreType() const = 0;
joeverbout	0:ea44dc9ed014	312
joeverbout	0:ea44dc9ed014	313	CV_WRAP virtual void setPatchSize(int patchSize) = 0;
joeverbout	0:ea44dc9ed014	314	CV_WRAP virtual int getPatchSize() const = 0;
joeverbout	0:ea44dc9ed014	315
joeverbout	0:ea44dc9ed014	316	CV_WRAP virtual void setFastThreshold(int fastThreshold) = 0;
joeverbout	0:ea44dc9ed014	317	CV_WRAP virtual int getFastThreshold() const = 0;
joeverbout	0:ea44dc9ed014	318	};
joeverbout	0:ea44dc9ed014	319
joeverbout	0:ea44dc9ed014	320	/** @brief Maximally stable extremal region extractor
joeverbout	0:ea44dc9ed014	321
joeverbout	0:ea44dc9ed014	322	The class encapsulates all the parameters of the %MSER extraction algorithm (see [wiki
joeverbout	0:ea44dc9ed014	323	article](http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions)).
joeverbout	0:ea44dc9ed014	324
joeverbout	0:ea44dc9ed014	325	- there are two different implementation of %MSER: one for grey image, one for color image
joeverbout	0:ea44dc9ed014	326
joeverbout	0:ea44dc9ed014	327	- the grey image algorithm is taken from: @cite nister2008linear ; the paper claims to be faster
joeverbout	0:ea44dc9ed014	328	than union-find method; it actually get 1.5~2m/s on my centrino L7200 1.2GHz laptop.
joeverbout	0:ea44dc9ed014	329
joeverbout	0:ea44dc9ed014	330	- the color image algorithm is taken from: @cite forssen2007maximally ; it should be much slower
joeverbout	0:ea44dc9ed014	331	than grey image method ( 3~4 times ); the chi_table.h file is taken directly from paper's source
joeverbout	0:ea44dc9ed014	332	code which is distributed under GPL.
joeverbout	0:ea44dc9ed014	333
joeverbout	0:ea44dc9ed014	334	- (Python) A complete example showing the use of the %MSER detector can be found at samples/python/mser.py
joeverbout	0:ea44dc9ed014	335	*/
joeverbout	0:ea44dc9ed014	336	class CV_EXPORTS_W MSER : public Feature2D
joeverbout	0:ea44dc9ed014	337	{
joeverbout	0:ea44dc9ed014	338	public:
joeverbout	0:ea44dc9ed014	339	/** @brief Full consturctor for %MSER detector
joeverbout	0:ea44dc9ed014	340
joeverbout	0:ea44dc9ed014	341	@param _delta it compares \f$(size_{i}-size_{i-delta})/size_{i-delta}\f$
joeverbout	0:ea44dc9ed014	342	@param _min_area prune the area which smaller than minArea
joeverbout	0:ea44dc9ed014	343	@param _max_area prune the area which bigger than maxArea
joeverbout	0:ea44dc9ed014	344	@param _max_variation prune the area have simliar size to its children
joeverbout	0:ea44dc9ed014	345	@param _min_diversity for color image, trace back to cut off mser with diversity less than min_diversity
joeverbout	0:ea44dc9ed014	346	@param _max_evolution for color image, the evolution steps
joeverbout	0:ea44dc9ed014	347	@param _area_threshold for color image, the area threshold to cause re-initialize
joeverbout	0:ea44dc9ed014	348	@param _min_margin for color image, ignore too small margin
joeverbout	0:ea44dc9ed014	349	@param _edge_blur_size for color image, the aperture size for edge blur
joeverbout	0:ea44dc9ed014	350	*/
joeverbout	0:ea44dc9ed014	351	CV_WRAP static Ptr<MSER> create( int _delta=5, int _min_area=60, int _max_area=14400,
joeverbout	0:ea44dc9ed014	352	double _max_variation=0.25, double _min_diversity=.2,
joeverbout	0:ea44dc9ed014	353	int _max_evolution=200, double _area_threshold=1.01,
joeverbout	0:ea44dc9ed014	354	double _min_margin=0.003, int _edge_blur_size=5 );
joeverbout	0:ea44dc9ed014	355
joeverbout	0:ea44dc9ed014	356	/** @brief Detect %MSER regions
joeverbout	0:ea44dc9ed014	357
joeverbout	0:ea44dc9ed014	358	@param image input image (8UC1, 8UC3 or 8UC4)
joeverbout	0:ea44dc9ed014	359	@param msers resulting list of point sets
joeverbout	0:ea44dc9ed014	360	@param bboxes resulting bounding boxes
joeverbout	0:ea44dc9ed014	361	*/
joeverbout	0:ea44dc9ed014	362	CV_WRAP virtual void detectRegions( InputArray image,
joeverbout	0:ea44dc9ed014	363	CV_OUT std::vector<std::vector<Point> >& msers,
joeverbout	0:ea44dc9ed014	364	std::vector<Rect>& bboxes ) = 0;
joeverbout	0:ea44dc9ed014	365
joeverbout	0:ea44dc9ed014	366	CV_WRAP virtual void setDelta(int delta) = 0;
joeverbout	0:ea44dc9ed014	367	CV_WRAP virtual int getDelta() const = 0;
joeverbout	0:ea44dc9ed014	368
joeverbout	0:ea44dc9ed014	369	CV_WRAP virtual void setMinArea(int minArea) = 0;
joeverbout	0:ea44dc9ed014	370	CV_WRAP virtual int getMinArea() const = 0;
joeverbout	0:ea44dc9ed014	371
joeverbout	0:ea44dc9ed014	372	CV_WRAP virtual void setMaxArea(int maxArea) = 0;
joeverbout	0:ea44dc9ed014	373	CV_WRAP virtual int getMaxArea() const = 0;
joeverbout	0:ea44dc9ed014	374
joeverbout	0:ea44dc9ed014	375	CV_WRAP virtual void setPass2Only(bool f) = 0;
joeverbout	0:ea44dc9ed014	376	CV_WRAP virtual bool getPass2Only() const = 0;
joeverbout	0:ea44dc9ed014	377	};
joeverbout	0:ea44dc9ed014	378
joeverbout	0:ea44dc9ed014	379	/** @overload */
joeverbout	0:ea44dc9ed014	380	CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
joeverbout	0:ea44dc9ed014	381	int threshold, bool nonmaxSuppression=true );
joeverbout	0:ea44dc9ed014	382
joeverbout	0:ea44dc9ed014	383	/** @brief Detects corners using the FAST algorithm
joeverbout	0:ea44dc9ed014	384
joeverbout	0:ea44dc9ed014	385	@param image grayscale image where keypoints (corners) are detected.
joeverbout	0:ea44dc9ed014	386	@param keypoints keypoints detected on the image.
joeverbout	0:ea44dc9ed014	387	@param threshold threshold on difference between intensity of the central pixel and pixels of a
joeverbout	0:ea44dc9ed014	388	circle around this pixel.
joeverbout	0:ea44dc9ed014	389	@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners
joeverbout	0:ea44dc9ed014	390	(keypoints).
joeverbout	0:ea44dc9ed014	391	@param type one of the three neighborhoods as defined in the paper:
joeverbout	0:ea44dc9ed014	392	FastFeatureDetector::TYPE_9_16, FastFeatureDetector::TYPE_7_12,
joeverbout	0:ea44dc9ed014	393	FastFeatureDetector::TYPE_5_8
joeverbout	0:ea44dc9ed014	394
joeverbout	0:ea44dc9ed014	395	Detects corners using the FAST algorithm by @cite Rosten06 .
joeverbout	0:ea44dc9ed014	396
joeverbout	0:ea44dc9ed014	397	@note In Python API, types are given as cv2.FAST_FEATURE_DETECTOR_TYPE_5_8,
joeverbout	0:ea44dc9ed014	398	cv2.FAST_FEATURE_DETECTOR_TYPE_7_12 and cv2.FAST_FEATURE_DETECTOR_TYPE_9_16. For corner
joeverbout	0:ea44dc9ed014	399	detection, use cv2.FAST.detect() method.
joeverbout	0:ea44dc9ed014	400	*/
joeverbout	0:ea44dc9ed014	401	CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
joeverbout	0:ea44dc9ed014	402	int threshold, bool nonmaxSuppression, int type );
joeverbout	0:ea44dc9ed014	403
joeverbout	0:ea44dc9ed014	404	//! @} features2d_main
joeverbout	0:ea44dc9ed014	405
joeverbout	0:ea44dc9ed014	406	//! @addtogroup features2d_main
joeverbout	0:ea44dc9ed014	407	//! @{
joeverbout	0:ea44dc9ed014	408
joeverbout	0:ea44dc9ed014	409	/** @brief Wrapping class for feature detection using the FAST method. :
joeverbout	0:ea44dc9ed014	410	*/
joeverbout	0:ea44dc9ed014	411	class CV_EXPORTS_W FastFeatureDetector : public Feature2D
joeverbout	0:ea44dc9ed014	412	{
joeverbout	0:ea44dc9ed014	413	public:
joeverbout	0:ea44dc9ed014	414	enum
joeverbout	0:ea44dc9ed014	415	{
joeverbout	0:ea44dc9ed014	416	TYPE_5_8 = 0, TYPE_7_12 = 1, TYPE_9_16 = 2,
joeverbout	0:ea44dc9ed014	417	THRESHOLD = 10000, NONMAX_SUPPRESSION=10001, FAST_N=10002,
joeverbout	0:ea44dc9ed014	418	};
joeverbout	0:ea44dc9ed014	419
joeverbout	0:ea44dc9ed014	420	CV_WRAP static Ptr<FastFeatureDetector> create( int threshold=10,
joeverbout	0:ea44dc9ed014	421	bool nonmaxSuppression=true,
joeverbout	0:ea44dc9ed014	422	int type=FastFeatureDetector::TYPE_9_16 );
joeverbout	0:ea44dc9ed014	423
joeverbout	0:ea44dc9ed014	424	CV_WRAP virtual void setThreshold(int threshold) = 0;
joeverbout	0:ea44dc9ed014	425	CV_WRAP virtual int getThreshold() const = 0;
joeverbout	0:ea44dc9ed014	426
joeverbout	0:ea44dc9ed014	427	CV_WRAP virtual void setNonmaxSuppression(bool f) = 0;
joeverbout	0:ea44dc9ed014	428	CV_WRAP virtual bool getNonmaxSuppression() const = 0;
joeverbout	0:ea44dc9ed014	429
joeverbout	0:ea44dc9ed014	430	CV_WRAP virtual void setType(int type) = 0;
joeverbout	0:ea44dc9ed014	431	CV_WRAP virtual int getType() const = 0;
joeverbout	0:ea44dc9ed014	432	};
joeverbout	0:ea44dc9ed014	433
joeverbout	0:ea44dc9ed014	434	/** @overload */
joeverbout	0:ea44dc9ed014	435	CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
joeverbout	0:ea44dc9ed014	436	int threshold, bool nonmaxSuppression=true );
joeverbout	0:ea44dc9ed014	437
joeverbout	0:ea44dc9ed014	438	/** @brief Detects corners using the AGAST algorithm
joeverbout	0:ea44dc9ed014	439
joeverbout	0:ea44dc9ed014	440	@param image grayscale image where keypoints (corners) are detected.
joeverbout	0:ea44dc9ed014	441	@param keypoints keypoints detected on the image.
joeverbout	0:ea44dc9ed014	442	@param threshold threshold on difference between intensity of the central pixel and pixels of a
joeverbout	0:ea44dc9ed014	443	circle around this pixel.
joeverbout	0:ea44dc9ed014	444	@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners
joeverbout	0:ea44dc9ed014	445	(keypoints).
joeverbout	0:ea44dc9ed014	446	@param type one of the four neighborhoods as defined in the paper:
joeverbout	0:ea44dc9ed014	447	AgastFeatureDetector::AGAST_5_8, AgastFeatureDetector::AGAST_7_12d,
joeverbout	0:ea44dc9ed014	448	AgastFeatureDetector::AGAST_7_12s, AgastFeatureDetector::OAST_9_16
joeverbout	0:ea44dc9ed014	449
joeverbout	0:ea44dc9ed014	450	For non-Intel platforms, there is a tree optimised variant of AGAST with same numerical results.
joeverbout	0:ea44dc9ed014	451	The 32-bit binary tree tables were generated automatically from original code using perl script.
joeverbout	0:ea44dc9ed014	452	The perl script and examples of tree generation are placed in features2d/doc folder.
joeverbout	0:ea44dc9ed014	453	Detects corners using the AGAST algorithm by @cite mair2010_agast .
joeverbout	0:ea44dc9ed014	454
joeverbout	0:ea44dc9ed014	455	*/
joeverbout	0:ea44dc9ed014	456	CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
joeverbout	0:ea44dc9ed014	457	int threshold, bool nonmaxSuppression, int type );
joeverbout	0:ea44dc9ed014	458	//! @} features2d_main
joeverbout	0:ea44dc9ed014	459
joeverbout	0:ea44dc9ed014	460	//! @addtogroup features2d_main
joeverbout	0:ea44dc9ed014	461	//! @{
joeverbout	0:ea44dc9ed014	462
joeverbout	0:ea44dc9ed014	463	/** @brief Wrapping class for feature detection using the AGAST method. :
joeverbout	0:ea44dc9ed014	464	*/
joeverbout	0:ea44dc9ed014	465	class CV_EXPORTS_W AgastFeatureDetector : public Feature2D
joeverbout	0:ea44dc9ed014	466	{
joeverbout	0:ea44dc9ed014	467	public:
joeverbout	0:ea44dc9ed014	468	enum
joeverbout	0:ea44dc9ed014	469	{
joeverbout	0:ea44dc9ed014	470	AGAST_5_8 = 0, AGAST_7_12d = 1, AGAST_7_12s = 2, OAST_9_16 = 3,
joeverbout	0:ea44dc9ed014	471	THRESHOLD = 10000, NONMAX_SUPPRESSION = 10001,
joeverbout	0:ea44dc9ed014	472	};
joeverbout	0:ea44dc9ed014	473
joeverbout	0:ea44dc9ed014	474	CV_WRAP static Ptr<AgastFeatureDetector> create( int threshold=10,
joeverbout	0:ea44dc9ed014	475	bool nonmaxSuppression=true,
joeverbout	0:ea44dc9ed014	476	int type=AgastFeatureDetector::OAST_9_16 );
joeverbout	0:ea44dc9ed014	477
joeverbout	0:ea44dc9ed014	478	CV_WRAP virtual void setThreshold(int threshold) = 0;
joeverbout	0:ea44dc9ed014	479	CV_WRAP virtual int getThreshold() const = 0;
joeverbout	0:ea44dc9ed014	480
joeverbout	0:ea44dc9ed014	481	CV_WRAP virtual void setNonmaxSuppression(bool f) = 0;
joeverbout	0:ea44dc9ed014	482	CV_WRAP virtual bool getNonmaxSuppression() const = 0;
joeverbout	0:ea44dc9ed014	483
joeverbout	0:ea44dc9ed014	484	CV_WRAP virtual void setType(int type) = 0;
joeverbout	0:ea44dc9ed014	485	CV_WRAP virtual int getType() const = 0;
joeverbout	0:ea44dc9ed014	486	};
joeverbout	0:ea44dc9ed014	487
joeverbout	0:ea44dc9ed014	488	/** @brief Wrapping class for feature detection using the goodFeaturesToTrack function. :
joeverbout	0:ea44dc9ed014	489	*/
joeverbout	0:ea44dc9ed014	490	class CV_EXPORTS_W GFTTDetector : public Feature2D
joeverbout	0:ea44dc9ed014	491	{
joeverbout	0:ea44dc9ed014	492	public:
joeverbout	0:ea44dc9ed014	493	CV_WRAP static Ptr<GFTTDetector> create( int maxCorners=1000, double qualityLevel=0.01, double minDistance=1,
joeverbout	0:ea44dc9ed014	494	int blockSize=3, bool useHarrisDetector=false, double k=0.04 );
joeverbout	0:ea44dc9ed014	495	CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0;
joeverbout	0:ea44dc9ed014	496	CV_WRAP virtual int getMaxFeatures() const = 0;
joeverbout	0:ea44dc9ed014	497
joeverbout	0:ea44dc9ed014	498	CV_WRAP virtual void setQualityLevel(double qlevel) = 0;
joeverbout	0:ea44dc9ed014	499	CV_WRAP virtual double getQualityLevel() const = 0;
joeverbout	0:ea44dc9ed014	500
joeverbout	0:ea44dc9ed014	501	CV_WRAP virtual void setMinDistance(double minDistance) = 0;
joeverbout	0:ea44dc9ed014	502	CV_WRAP virtual double getMinDistance() const = 0;
joeverbout	0:ea44dc9ed014	503
joeverbout	0:ea44dc9ed014	504	CV_WRAP virtual void setBlockSize(int blockSize) = 0;
joeverbout	0:ea44dc9ed014	505	CV_WRAP virtual int getBlockSize() const = 0;
joeverbout	0:ea44dc9ed014	506
joeverbout	0:ea44dc9ed014	507	CV_WRAP virtual void setHarrisDetector(bool val) = 0;
joeverbout	0:ea44dc9ed014	508	CV_WRAP virtual bool getHarrisDetector() const = 0;
joeverbout	0:ea44dc9ed014	509
joeverbout	0:ea44dc9ed014	510	CV_WRAP virtual void setK(double k) = 0;
joeverbout	0:ea44dc9ed014	511	CV_WRAP virtual double getK() const = 0;
joeverbout	0:ea44dc9ed014	512	};
joeverbout	0:ea44dc9ed014	513
joeverbout	0:ea44dc9ed014	514	/** @brief Class for extracting blobs from an image. :
joeverbout	0:ea44dc9ed014	515
joeverbout	0:ea44dc9ed014	516	The class implements a simple algorithm for extracting blobs from an image:
joeverbout	0:ea44dc9ed014	517
joeverbout	0:ea44dc9ed014	518	1. Convert the source image to binary images by applying thresholding with several thresholds from
joeverbout	0:ea44dc9ed014	519	minThreshold (inclusive) to maxThreshold (exclusive) with distance thresholdStep between
joeverbout	0:ea44dc9ed014	520	neighboring thresholds.
joeverbout	0:ea44dc9ed014	521	2. Extract connected components from every binary image by findContours and calculate their
joeverbout	0:ea44dc9ed014	522	centers.
joeverbout	0:ea44dc9ed014	523	3. Group centers from several binary images by their coordinates. Close centers form one group that
joeverbout	0:ea44dc9ed014	524	corresponds to one blob, which is controlled by the minDistBetweenBlobs parameter.
joeverbout	0:ea44dc9ed014	525	4. From the groups, estimate final centers of blobs and their radiuses and return as locations and
joeverbout	0:ea44dc9ed014	526	sizes of keypoints.
joeverbout	0:ea44dc9ed014	527
joeverbout	0:ea44dc9ed014	528	This class performs several filtrations of returned blobs. You should set filterBy\* to true/false
joeverbout	0:ea44dc9ed014	529	to turn on/off corresponding filtration. Available filtrations:
joeverbout	0:ea44dc9ed014	530
joeverbout	0:ea44dc9ed014	531	- By color. This filter compares the intensity of a binary image at the center of a blob to
joeverbout	0:ea44dc9ed014	532	blobColor. If they differ, the blob is filtered out. Use blobColor = 0 to extract dark blobs
joeverbout	0:ea44dc9ed014	533	and blobColor = 255 to extract light blobs.
joeverbout	0:ea44dc9ed014	534	- By area. Extracted blobs have an area between minArea (inclusive) and maxArea (exclusive).
joeverbout	0:ea44dc9ed014	535	- By circularity. Extracted blobs have circularity
joeverbout	0:ea44dc9ed014	536	(\f$\frac{4\piArea}{perimeter * perimeter}\f$) between minCircularity (inclusive) and
joeverbout	0:ea44dc9ed014	537	maxCircularity (exclusive).
joeverbout	0:ea44dc9ed014	538	- By ratio of the minimum inertia to maximum inertia. Extracted blobs have this ratio
joeverbout	0:ea44dc9ed014	539	between minInertiaRatio (inclusive) and maxInertiaRatio (exclusive).
joeverbout	0:ea44dc9ed014	540	- By convexity. Extracted blobs have convexity (area / area of blob convex hull) between
joeverbout	0:ea44dc9ed014	541	minConvexity (inclusive) and maxConvexity (exclusive).
joeverbout	0:ea44dc9ed014	542
joeverbout	0:ea44dc9ed014	543	Default values of parameters are tuned to extract dark circular blobs.
joeverbout	0:ea44dc9ed014	544	*/
joeverbout	0:ea44dc9ed014	545	class CV_EXPORTS_W SimpleBlobDetector : public Feature2D
joeverbout	0:ea44dc9ed014	546	{
joeverbout	0:ea44dc9ed014	547	public:
joeverbout	0:ea44dc9ed014	548	struct CV_EXPORTS_W_SIMPLE Params
joeverbout	0:ea44dc9ed014	549	{
joeverbout	0:ea44dc9ed014	550	CV_WRAP Params();
joeverbout	0:ea44dc9ed014	551	CV_PROP_RW float thresholdStep;
joeverbout	0:ea44dc9ed014	552	CV_PROP_RW float minThreshold;
joeverbout	0:ea44dc9ed014	553	CV_PROP_RW float maxThreshold;
joeverbout	0:ea44dc9ed014	554	CV_PROP_RW size_t minRepeatability;
joeverbout	0:ea44dc9ed014	555	CV_PROP_RW float minDistBetweenBlobs;
joeverbout	0:ea44dc9ed014	556
joeverbout	0:ea44dc9ed014	557	CV_PROP_RW bool filterByColor;
joeverbout	0:ea44dc9ed014	558	CV_PROP_RW uchar blobColor;
joeverbout	0:ea44dc9ed014	559
joeverbout	0:ea44dc9ed014	560	CV_PROP_RW bool filterByArea;
joeverbout	0:ea44dc9ed014	561	CV_PROP_RW float minArea, maxArea;
joeverbout	0:ea44dc9ed014	562
joeverbout	0:ea44dc9ed014	563	CV_PROP_RW bool filterByCircularity;
joeverbout	0:ea44dc9ed014	564	CV_PROP_RW float minCircularity, maxCircularity;
joeverbout	0:ea44dc9ed014	565
joeverbout	0:ea44dc9ed014	566	CV_PROP_RW bool filterByInertia;
joeverbout	0:ea44dc9ed014	567	CV_PROP_RW float minInertiaRatio, maxInertiaRatio;
joeverbout	0:ea44dc9ed014	568
joeverbout	0:ea44dc9ed014	569	CV_PROP_RW bool filterByConvexity;
joeverbout	0:ea44dc9ed014	570	CV_PROP_RW float minConvexity, maxConvexity;
joeverbout	0:ea44dc9ed014	571
joeverbout	0:ea44dc9ed014	572	void read( const FileNode& fn );
joeverbout	0:ea44dc9ed014	573	void write( FileStorage& fs ) const;
joeverbout	0:ea44dc9ed014	574	};
joeverbout	0:ea44dc9ed014	575
joeverbout	0:ea44dc9ed014	576	CV_WRAP static Ptr<SimpleBlobDetector>
joeverbout	0:ea44dc9ed014	577	create(const SimpleBlobDetector::Params &parameters = SimpleBlobDetector::Params());
joeverbout	0:ea44dc9ed014	578	};
joeverbout	0:ea44dc9ed014	579
joeverbout	0:ea44dc9ed014	580	//! @} features2d_main
joeverbout	0:ea44dc9ed014	581
joeverbout	0:ea44dc9ed014	582	//! @addtogroup features2d_main
joeverbout	0:ea44dc9ed014	583	//! @{
joeverbout	0:ea44dc9ed014	584
joeverbout	0:ea44dc9ed014	585	/** @brief Class implementing the KAZE keypoint detector and descriptor extractor, described in @cite ABD12 .
joeverbout	0:ea44dc9ed014	586
joeverbout	0:ea44dc9ed014	587	@note AKAZE descriptor can only be used with KAZE or AKAZE keypoints .. [ABD12] KAZE Features. Pablo
joeverbout	0:ea44dc9ed014	588	F. Alcantarilla, Adrien Bartoli and Andrew J. Davison. In European Conference on Computer Vision
joeverbout	0:ea44dc9ed014	589	(ECCV), Fiorenze, Italy, October 2012.
joeverbout	0:ea44dc9ed014	590	*/
joeverbout	0:ea44dc9ed014	591	class CV_EXPORTS_W KAZE : public Feature2D
joeverbout	0:ea44dc9ed014	592	{
joeverbout	0:ea44dc9ed014	593	public:
joeverbout	0:ea44dc9ed014	594	enum
joeverbout	0:ea44dc9ed014	595	{
joeverbout	0:ea44dc9ed014	596	DIFF_PM_G1 = 0,
joeverbout	0:ea44dc9ed014	597	DIFF_PM_G2 = 1,
joeverbout	0:ea44dc9ed014	598	DIFF_WEICKERT = 2,
joeverbout	0:ea44dc9ed014	599	DIFF_CHARBONNIER = 3
joeverbout	0:ea44dc9ed014	600	};
joeverbout	0:ea44dc9ed014	601
joeverbout	0:ea44dc9ed014	602	/** @brief The KAZE constructor
joeverbout	0:ea44dc9ed014	603
joeverbout	0:ea44dc9ed014	604	@param extended Set to enable extraction of extended (128-byte) descriptor.
joeverbout	0:ea44dc9ed014	605	@param upright Set to enable use of upright descriptors (non rotation-invariant).
joeverbout	0:ea44dc9ed014	606	@param threshold Detector response threshold to accept point
joeverbout	0:ea44dc9ed014	607	@param nOctaves Maximum octave evolution of the image
joeverbout	0:ea44dc9ed014	608	@param nOctaveLayers Default number of sublevels per scale level
joeverbout	0:ea44dc9ed014	609	@param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or
joeverbout	0:ea44dc9ed014	610	DIFF_CHARBONNIER
joeverbout	0:ea44dc9ed014	611	*/
joeverbout	0:ea44dc9ed014	612	CV_WRAP static Ptr<KAZE> create(bool extended=false, bool upright=false,
joeverbout	0:ea44dc9ed014	613	float threshold = 0.001f,
joeverbout	0:ea44dc9ed014	614	int nOctaves = 4, int nOctaveLayers = 4,
joeverbout	0:ea44dc9ed014	615	int diffusivity = KAZE::DIFF_PM_G2);
joeverbout	0:ea44dc9ed014	616
joeverbout	0:ea44dc9ed014	617	CV_WRAP virtual void setExtended(bool extended) = 0;
joeverbout	0:ea44dc9ed014	618	CV_WRAP virtual bool getExtended() const = 0;
joeverbout	0:ea44dc9ed014	619
joeverbout	0:ea44dc9ed014	620	CV_WRAP virtual void setUpright(bool upright) = 0;
joeverbout	0:ea44dc9ed014	621	CV_WRAP virtual bool getUpright() const = 0;
joeverbout	0:ea44dc9ed014	622
joeverbout	0:ea44dc9ed014	623	CV_WRAP virtual void setThreshold(double threshold) = 0;
joeverbout	0:ea44dc9ed014	624	CV_WRAP virtual double getThreshold() const = 0;
joeverbout	0:ea44dc9ed014	625
joeverbout	0:ea44dc9ed014	626	CV_WRAP virtual void setNOctaves(int octaves) = 0;
joeverbout	0:ea44dc9ed014	627	CV_WRAP virtual int getNOctaves() const = 0;
joeverbout	0:ea44dc9ed014	628
joeverbout	0:ea44dc9ed014	629	CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0;
joeverbout	0:ea44dc9ed014	630	CV_WRAP virtual int getNOctaveLayers() const = 0;
joeverbout	0:ea44dc9ed014	631
joeverbout	0:ea44dc9ed014	632	CV_WRAP virtual void setDiffusivity(int diff) = 0;
joeverbout	0:ea44dc9ed014	633	CV_WRAP virtual int getDiffusivity() const = 0;
joeverbout	0:ea44dc9ed014	634	};
joeverbout	0:ea44dc9ed014	635
joeverbout	0:ea44dc9ed014	636	/** @brief Class implementing the AKAZE keypoint detector and descriptor extractor, described in @cite ANB13 . :
joeverbout	0:ea44dc9ed014	637
joeverbout	0:ea44dc9ed014	638	@note AKAZE descriptors can only be used with KAZE or AKAZE keypoints. Try to avoid using extract
joeverbout	0:ea44dc9ed014	639	and detect instead of operator() due to performance reasons. .. [ANB13] Fast Explicit Diffusion
joeverbout	0:ea44dc9ed014	640	for Accelerated Features in Nonlinear Scale Spaces. Pablo F. Alcantarilla, Jesús Nuevo and Adrien
joeverbout	0:ea44dc9ed014	641	Bartoli. In British Machine Vision Conference (BMVC), Bristol, UK, September 2013.
joeverbout	0:ea44dc9ed014	642	*/
joeverbout	0:ea44dc9ed014	643	class CV_EXPORTS_W AKAZE : public Feature2D
joeverbout	0:ea44dc9ed014	644	{
joeverbout	0:ea44dc9ed014	645	public:
joeverbout	0:ea44dc9ed014	646	// AKAZE descriptor type
joeverbout	0:ea44dc9ed014	647	enum
joeverbout	0:ea44dc9ed014	648	{
joeverbout	0:ea44dc9ed014	649	DESCRIPTOR_KAZE_UPRIGHT = 2, ///< Upright descriptors, not invariant to rotation
joeverbout	0:ea44dc9ed014	650	DESCRIPTOR_KAZE = 3,
joeverbout	0:ea44dc9ed014	651	DESCRIPTOR_MLDB_UPRIGHT = 4, ///< Upright descriptors, not invariant to rotation
joeverbout	0:ea44dc9ed014	652	DESCRIPTOR_MLDB = 5
joeverbout	0:ea44dc9ed014	653	};
joeverbout	0:ea44dc9ed014	654
joeverbout	0:ea44dc9ed014	655	/** @brief The AKAZE constructor
joeverbout	0:ea44dc9ed014	656
joeverbout	0:ea44dc9ed014	657	@param descriptor_type Type of the extracted descriptor: DESCRIPTOR_KAZE,
joeverbout	0:ea44dc9ed014	658	DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT.
joeverbout	0:ea44dc9ed014	659	@param descriptor_size Size of the descriptor in bits. 0 -\> Full size
joeverbout	0:ea44dc9ed014	660	@param descriptor_channels Number of channels in the descriptor (1, 2, 3)
joeverbout	0:ea44dc9ed014	661	@param threshold Detector response threshold to accept point
joeverbout	0:ea44dc9ed014	662	@param nOctaves Maximum octave evolution of the image
joeverbout	0:ea44dc9ed014	663	@param nOctaveLayers Default number of sublevels per scale level
joeverbout	0:ea44dc9ed014	664	@param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or
joeverbout	0:ea44dc9ed014	665	DIFF_CHARBONNIER
joeverbout	0:ea44dc9ed014	666	*/
joeverbout	0:ea44dc9ed014	667	CV_WRAP static Ptr<AKAZE> create(int descriptor_type=AKAZE::DESCRIPTOR_MLDB,
joeverbout	0:ea44dc9ed014	668	int descriptor_size = 0, int descriptor_channels = 3,
joeverbout	0:ea44dc9ed014	669	float threshold = 0.001f, int nOctaves = 4,
joeverbout	0:ea44dc9ed014	670	int nOctaveLayers = 4, int diffusivity = KAZE::DIFF_PM_G2);
joeverbout	0:ea44dc9ed014	671
joeverbout	0:ea44dc9ed014	672	CV_WRAP virtual void setDescriptorType(int dtype) = 0;
joeverbout	0:ea44dc9ed014	673	CV_WRAP virtual int getDescriptorType() const = 0;
joeverbout	0:ea44dc9ed014	674
joeverbout	0:ea44dc9ed014	675	CV_WRAP virtual void setDescriptorSize(int dsize) = 0;
joeverbout	0:ea44dc9ed014	676	CV_WRAP virtual int getDescriptorSize() const = 0;
joeverbout	0:ea44dc9ed014	677
joeverbout	0:ea44dc9ed014	678	CV_WRAP virtual void setDescriptorChannels(int dch) = 0;
joeverbout	0:ea44dc9ed014	679	CV_WRAP virtual int getDescriptorChannels() const = 0;
joeverbout	0:ea44dc9ed014	680
joeverbout	0:ea44dc9ed014	681	CV_WRAP virtual void setThreshold(double threshold) = 0;
joeverbout	0:ea44dc9ed014	682	CV_WRAP virtual double getThreshold() const = 0;
joeverbout	0:ea44dc9ed014	683
joeverbout	0:ea44dc9ed014	684	CV_WRAP virtual void setNOctaves(int octaves) = 0;
joeverbout	0:ea44dc9ed014	685	CV_WRAP virtual int getNOctaves() const = 0;
joeverbout	0:ea44dc9ed014	686
joeverbout	0:ea44dc9ed014	687	CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0;
joeverbout	0:ea44dc9ed014	688	CV_WRAP virtual int getNOctaveLayers() const = 0;
joeverbout	0:ea44dc9ed014	689
joeverbout	0:ea44dc9ed014	690	CV_WRAP virtual void setDiffusivity(int diff) = 0;
joeverbout	0:ea44dc9ed014	691	CV_WRAP virtual int getDiffusivity() const = 0;
joeverbout	0:ea44dc9ed014	692	};
joeverbout	0:ea44dc9ed014	693
joeverbout	0:ea44dc9ed014	694	//! @} features2d_main
joeverbout	0:ea44dc9ed014	695
joeverbout	0:ea44dc9ed014	696	/****************************************************************************************\
joeverbout	0:ea44dc9ed014	697	* Distance *
joeverbout	0:ea44dc9ed014	698	\****************************************************************************************/
joeverbout	0:ea44dc9ed014	699
joeverbout	0:ea44dc9ed014	700	template<typename T>
joeverbout	0:ea44dc9ed014	701	struct CV_EXPORTS Accumulator
joeverbout	0:ea44dc9ed014	702	{
joeverbout	0:ea44dc9ed014	703	typedef T Type;
joeverbout	0:ea44dc9ed014	704	};
joeverbout	0:ea44dc9ed014	705
joeverbout	0:ea44dc9ed014	706	template<> struct Accumulator<unsigned char> { typedef float Type; };
joeverbout	0:ea44dc9ed014	707	template<> struct Accumulator<unsigned short> { typedef float Type; };
joeverbout	0:ea44dc9ed014	708	template<> struct Accumulator<char> { typedef float Type; };
joeverbout	0:ea44dc9ed014	709	template<> struct Accumulator<short> { typedef float Type; };
joeverbout	0:ea44dc9ed014	710
joeverbout	0:ea44dc9ed014	711	/*
joeverbout	0:ea44dc9ed014	712	* Squared Euclidean distance functor
joeverbout	0:ea44dc9ed014	713	*/
joeverbout	0:ea44dc9ed014	714	template<class T>
joeverbout	0:ea44dc9ed014	715	struct CV_EXPORTS SL2
joeverbout	0:ea44dc9ed014	716	{
joeverbout	0:ea44dc9ed014	717	enum { normType = NORM_L2SQR };
joeverbout	0:ea44dc9ed014	718	typedef T ValueType;
joeverbout	0:ea44dc9ed014	719	typedef typename Accumulator<T>::Type ResultType;
joeverbout	0:ea44dc9ed014	720
joeverbout	0:ea44dc9ed014	721	ResultType operator()( const T* a, const T* b, int size ) const
joeverbout	0:ea44dc9ed014	722	{
joeverbout	0:ea44dc9ed014	723	return normL2Sqr<ValueType, ResultType>(a, b, size);
joeverbout	0:ea44dc9ed014	724	}
joeverbout	0:ea44dc9ed014	725	};
joeverbout	0:ea44dc9ed014	726
joeverbout	0:ea44dc9ed014	727	/*
joeverbout	0:ea44dc9ed014	728	* Euclidean distance functor
joeverbout	0:ea44dc9ed014	729	*/
joeverbout	0:ea44dc9ed014	730	template<class T>
joeverbout	0:ea44dc9ed014	731	struct CV_EXPORTS L2
joeverbout	0:ea44dc9ed014	732	{
joeverbout	0:ea44dc9ed014	733	enum { normType = NORM_L2 };
joeverbout	0:ea44dc9ed014	734	typedef T ValueType;
joeverbout	0:ea44dc9ed014	735	typedef typename Accumulator<T>::Type ResultType;
joeverbout	0:ea44dc9ed014	736
joeverbout	0:ea44dc9ed014	737	ResultType operator()( const T* a, const T* b, int size ) const
joeverbout	0:ea44dc9ed014	738	{
joeverbout	0:ea44dc9ed014	739	return (ResultType)std::sqrt((double)normL2Sqr<ValueType, ResultType>(a, b, size));
joeverbout	0:ea44dc9ed014	740	}
joeverbout	0:ea44dc9ed014	741	};
joeverbout	0:ea44dc9ed014	742
joeverbout	0:ea44dc9ed014	743	/*
joeverbout	0:ea44dc9ed014	744	* Manhattan distance (city block distance) functor
joeverbout	0:ea44dc9ed014	745	*/
joeverbout	0:ea44dc9ed014	746	template<class T>
joeverbout	0:ea44dc9ed014	747	struct CV_EXPORTS L1
joeverbout	0:ea44dc9ed014	748	{
joeverbout	0:ea44dc9ed014	749	enum { normType = NORM_L1 };
joeverbout	0:ea44dc9ed014	750	typedef T ValueType;
joeverbout	0:ea44dc9ed014	751	typedef typename Accumulator<T>::Type ResultType;
joeverbout	0:ea44dc9ed014	752
joeverbout	0:ea44dc9ed014	753	ResultType operator()( const T* a, const T* b, int size ) const
joeverbout	0:ea44dc9ed014	754	{
joeverbout	0:ea44dc9ed014	755	return normL1<ValueType, ResultType>(a, b, size);
joeverbout	0:ea44dc9ed014	756	}
joeverbout	0:ea44dc9ed014	757	};
joeverbout	0:ea44dc9ed014	758
joeverbout	0:ea44dc9ed014	759	/****************************************************************************************\
joeverbout	0:ea44dc9ed014	760	* DescriptorMatcher *
joeverbout	0:ea44dc9ed014	761	\****************************************************************************************/
joeverbout	0:ea44dc9ed014	762
joeverbout	0:ea44dc9ed014	763	//! @addtogroup features2d_match
joeverbout	0:ea44dc9ed014	764	//! @{
joeverbout	0:ea44dc9ed014	765
joeverbout	0:ea44dc9ed014	766	/** @brief Abstract base class for matching keypoint descriptors.
joeverbout	0:ea44dc9ed014	767
joeverbout	0:ea44dc9ed014	768	It has two groups of match methods: for matching descriptors of an image with another image or with
joeverbout	0:ea44dc9ed014	769	an image set.
joeverbout	0:ea44dc9ed014	770	*/
joeverbout	0:ea44dc9ed014	771	class CV_EXPORTS_W DescriptorMatcher : public Algorithm
joeverbout	0:ea44dc9ed014	772	{
joeverbout	0:ea44dc9ed014	773	public:
joeverbout	0:ea44dc9ed014	774	virtual ~DescriptorMatcher();
joeverbout	0:ea44dc9ed014	775
joeverbout	0:ea44dc9ed014	776	/** @brief Adds descriptors to train a CPU(trainDescCollectionis) or GPU(utrainDescCollectionis) descriptor
joeverbout	0:ea44dc9ed014	777	collection.
joeverbout	0:ea44dc9ed014	778
joeverbout	0:ea44dc9ed014	779	If the collection is not empty, the new descriptors are added to existing train descriptors.
joeverbout	0:ea44dc9ed014	780
joeverbout	0:ea44dc9ed014	781	@param descriptors Descriptors to add. Each descriptors[i] is a set of descriptors from the same
joeverbout	0:ea44dc9ed014	782	train image.
joeverbout	0:ea44dc9ed014	783	*/
joeverbout	0:ea44dc9ed014	784	CV_WRAP virtual void add( InputArrayOfArrays descriptors );
joeverbout	0:ea44dc9ed014	785
joeverbout	0:ea44dc9ed014	786	/** @brief Returns a constant link to the train descriptor collection trainDescCollection .
joeverbout	0:ea44dc9ed014	787	*/
joeverbout	0:ea44dc9ed014	788	CV_WRAP const std::vector<Mat>& getTrainDescriptors() const;
joeverbout	0:ea44dc9ed014	789
joeverbout	0:ea44dc9ed014	790	/** @brief Clears the train descriptor collections.
joeverbout	0:ea44dc9ed014	791	*/
joeverbout	0:ea44dc9ed014	792	CV_WRAP virtual void clear();
joeverbout	0:ea44dc9ed014	793
joeverbout	0:ea44dc9ed014	794	/** @brief Returns true if there are no train descriptors in the both collections.
joeverbout	0:ea44dc9ed014	795	*/
joeverbout	0:ea44dc9ed014	796	CV_WRAP virtual bool empty() const;
joeverbout	0:ea44dc9ed014	797
joeverbout	0:ea44dc9ed014	798	/** @brief Returns true if the descriptor matcher supports masking permissible matches.
joeverbout	0:ea44dc9ed014	799	*/
joeverbout	0:ea44dc9ed014	800	CV_WRAP virtual bool isMaskSupported() const = 0;
joeverbout	0:ea44dc9ed014	801
joeverbout	0:ea44dc9ed014	802	/** @brief Trains a descriptor matcher
joeverbout	0:ea44dc9ed014	803
joeverbout	0:ea44dc9ed014	804	Trains a descriptor matcher (for example, the flann index). In all methods to match, the method
joeverbout	0:ea44dc9ed014	805	train() is run every time before matching. Some descriptor matchers (for example, BruteForceMatcher)
joeverbout	0:ea44dc9ed014	806	have an empty implementation of this method. Other matchers really train their inner structures (for
joeverbout	0:ea44dc9ed014	807	example, FlannBasedMatcher trains flann::Index ).
joeverbout	0:ea44dc9ed014	808	*/
joeverbout	0:ea44dc9ed014	809	CV_WRAP virtual void train();
joeverbout	0:ea44dc9ed014	810
joeverbout	0:ea44dc9ed014	811	/** @brief Finds the best match for each descriptor from a query set.
joeverbout	0:ea44dc9ed014	812
joeverbout	0:ea44dc9ed014	813	@param queryDescriptors Query set of descriptors.
joeverbout	0:ea44dc9ed014	814	@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
joeverbout	0:ea44dc9ed014	815	collection stored in the class object.
joeverbout	0:ea44dc9ed014	816	@param matches Matches. If a query descriptor is masked out in mask , no match is added for this
joeverbout	0:ea44dc9ed014	817	descriptor. So, matches size may be smaller than the query descriptors count.
joeverbout	0:ea44dc9ed014	818	@param mask Mask specifying permissible matches between an input query and train matrices of
joeverbout	0:ea44dc9ed014	819	descriptors.
joeverbout	0:ea44dc9ed014	820
joeverbout	0:ea44dc9ed014	821	In the first variant of this method, the train descriptors are passed as an input argument. In the
joeverbout	0:ea44dc9ed014	822	second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is
joeverbout	0:ea44dc9ed014	823	used. Optional mask (or masks) can be passed to specify which query and training descriptors can be
joeverbout	0:ea44dc9ed014	824	matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if
joeverbout	0:ea44dc9ed014	825	mask.at\<uchar\>(i,j) is non-zero.
joeverbout	0:ea44dc9ed014	826	*/
joeverbout	0:ea44dc9ed014	827	CV_WRAP void match( InputArray queryDescriptors, InputArray trainDescriptors,
joeverbout	0:ea44dc9ed014	828	CV_OUT std::vector<DMatch>& matches, InputArray mask=noArray() ) const;
joeverbout	0:ea44dc9ed014	829
joeverbout	0:ea44dc9ed014	830	/** @brief Finds the k best matches for each descriptor from a query set.
joeverbout	0:ea44dc9ed014	831
joeverbout	0:ea44dc9ed014	832	@param queryDescriptors Query set of descriptors.
joeverbout	0:ea44dc9ed014	833	@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
joeverbout	0:ea44dc9ed014	834	collection stored in the class object.
joeverbout	0:ea44dc9ed014	835	@param mask Mask specifying permissible matches between an input query and train matrices of
joeverbout	0:ea44dc9ed014	836	descriptors.
joeverbout	0:ea44dc9ed014	837	@param matches Matches. Each matches[i] is k or less matches for the same query descriptor.
joeverbout	0:ea44dc9ed014	838	@param k Count of best matches found per each query descriptor or less if a query descriptor has
joeverbout	0:ea44dc9ed014	839	less than k possible matches in total.
joeverbout	0:ea44dc9ed014	840	@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
joeverbout	0:ea44dc9ed014	841	false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
joeverbout	0:ea44dc9ed014	842	the matches vector does not contain matches for fully masked-out query descriptors.
joeverbout	0:ea44dc9ed014	843
joeverbout	0:ea44dc9ed014	844	These extended variants of DescriptorMatcher::match methods find several best matches for each query
joeverbout	0:ea44dc9ed014	845	descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match
joeverbout	0:ea44dc9ed014	846	for the details about query and train descriptors.
joeverbout	0:ea44dc9ed014	847	*/
joeverbout	0:ea44dc9ed014	848	CV_WRAP void knnMatch( InputArray queryDescriptors, InputArray trainDescriptors,
joeverbout	0:ea44dc9ed014	849	CV_OUT std::vector<std::vector<DMatch> >& matches, int k,
joeverbout	0:ea44dc9ed014	850	InputArray mask=noArray(), bool compactResult=false ) const;
joeverbout	0:ea44dc9ed014	851
joeverbout	0:ea44dc9ed014	852	/** @brief For each query descriptor, finds the training descriptors not farther than the specified distance.
joeverbout	0:ea44dc9ed014	853
joeverbout	0:ea44dc9ed014	854	@param queryDescriptors Query set of descriptors.
joeverbout	0:ea44dc9ed014	855	@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
joeverbout	0:ea44dc9ed014	856	collection stored in the class object.
joeverbout	0:ea44dc9ed014	857	@param matches Found matches.
joeverbout	0:ea44dc9ed014	858	@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
joeverbout	0:ea44dc9ed014	859	false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
joeverbout	0:ea44dc9ed014	860	the matches vector does not contain matches for fully masked-out query descriptors.
joeverbout	0:ea44dc9ed014	861	@param maxDistance Threshold for the distance between matched descriptors. Distance means here
joeverbout	0:ea44dc9ed014	862	metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured
joeverbout	0:ea44dc9ed014	863	in Pixels)!
joeverbout	0:ea44dc9ed014	864	@param mask Mask specifying permissible matches between an input query and train matrices of
joeverbout	0:ea44dc9ed014	865	descriptors.
joeverbout	0:ea44dc9ed014	866
joeverbout	0:ea44dc9ed014	867	For each query descriptor, the methods find such training descriptors that the distance between the
joeverbout	0:ea44dc9ed014	868	query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are
joeverbout	0:ea44dc9ed014	869	returned in the distance increasing order.
joeverbout	0:ea44dc9ed014	870	*/
joeverbout	0:ea44dc9ed014	871	void radiusMatch( InputArray queryDescriptors, InputArray trainDescriptors,
joeverbout	0:ea44dc9ed014	872	std::vector<std::vector<DMatch> >& matches, float maxDistance,
joeverbout	0:ea44dc9ed014	873	InputArray mask=noArray(), bool compactResult=false ) const;
joeverbout	0:ea44dc9ed014	874
joeverbout	0:ea44dc9ed014	875	/** @overload
joeverbout	0:ea44dc9ed014	876	@param queryDescriptors Query set of descriptors.
joeverbout	0:ea44dc9ed014	877	@param matches Matches. If a query descriptor is masked out in mask , no match is added for this
joeverbout	0:ea44dc9ed014	878	descriptor. So, matches size may be smaller than the query descriptors count.
joeverbout	0:ea44dc9ed014	879	@param masks Set of masks. Each masks[i] specifies permissible matches between the input query
joeverbout	0:ea44dc9ed014	880	descriptors and stored train descriptors from the i-th image trainDescCollection[i].
joeverbout	0:ea44dc9ed014	881	*/
joeverbout	0:ea44dc9ed014	882	CV_WRAP void match( InputArray queryDescriptors, CV_OUT std::vector<DMatch>& matches,
joeverbout	0:ea44dc9ed014	883	InputArrayOfArrays masks=noArray() );
joeverbout	0:ea44dc9ed014	884	/** @overload
joeverbout	0:ea44dc9ed014	885	@param queryDescriptors Query set of descriptors.
joeverbout	0:ea44dc9ed014	886	@param matches Matches. Each matches[i] is k or less matches for the same query descriptor.
joeverbout	0:ea44dc9ed014	887	@param k Count of best matches found per each query descriptor or less if a query descriptor has
joeverbout	0:ea44dc9ed014	888	less than k possible matches in total.
joeverbout	0:ea44dc9ed014	889	@param masks Set of masks. Each masks[i] specifies permissible matches between the input query
joeverbout	0:ea44dc9ed014	890	descriptors and stored train descriptors from the i-th image trainDescCollection[i].
joeverbout	0:ea44dc9ed014	891	@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
joeverbout	0:ea44dc9ed014	892	false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
joeverbout	0:ea44dc9ed014	893	the matches vector does not contain matches for fully masked-out query descriptors.
joeverbout	0:ea44dc9ed014	894	*/
joeverbout	0:ea44dc9ed014	895	CV_WRAP void knnMatch( InputArray queryDescriptors, CV_OUT std::vector<std::vector<DMatch> >& matches, int k,
joeverbout	0:ea44dc9ed014	896	InputArrayOfArrays masks=noArray(), bool compactResult=false );
joeverbout	0:ea44dc9ed014	897	/** @overload
joeverbout	0:ea44dc9ed014	898	@param queryDescriptors Query set of descriptors.
joeverbout	0:ea44dc9ed014	899	@param matches Found matches.
joeverbout	0:ea44dc9ed014	900	@param maxDistance Threshold for the distance between matched descriptors. Distance means here
joeverbout	0:ea44dc9ed014	901	metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured
joeverbout	0:ea44dc9ed014	902	in Pixels)!
joeverbout	0:ea44dc9ed014	903	@param masks Set of masks. Each masks[i] specifies permissible matches between the input query
joeverbout	0:ea44dc9ed014	904	descriptors and stored train descriptors from the i-th image trainDescCollection[i].
joeverbout	0:ea44dc9ed014	905	@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
joeverbout	0:ea44dc9ed014	906	false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
joeverbout	0:ea44dc9ed014	907	the matches vector does not contain matches for fully masked-out query descriptors.
joeverbout	0:ea44dc9ed014	908	*/
joeverbout	0:ea44dc9ed014	909	void radiusMatch( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
joeverbout	0:ea44dc9ed014	910	InputArrayOfArrays masks=noArray(), bool compactResult=false );
joeverbout	0:ea44dc9ed014	911
joeverbout	0:ea44dc9ed014	912	// Reads matcher object from a file node
joeverbout	0:ea44dc9ed014	913	virtual void read( const FileNode& );
joeverbout	0:ea44dc9ed014	914	// Writes matcher object to a file storage
joeverbout	0:ea44dc9ed014	915	virtual void write( FileStorage& ) const;
joeverbout	0:ea44dc9ed014	916
joeverbout	0:ea44dc9ed014	917	/** @brief Clones the matcher.
joeverbout	0:ea44dc9ed014	918
joeverbout	0:ea44dc9ed014	919	@param emptyTrainData If emptyTrainData is false, the method creates a deep copy of the object,
joeverbout	0:ea44dc9ed014	920	that is, copies both parameters and train data. If emptyTrainData is true, the method creates an
joeverbout	0:ea44dc9ed014	921	object copy with the current parameters but with empty train data.
joeverbout	0:ea44dc9ed014	922	*/
joeverbout	0:ea44dc9ed014	923	virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const = 0;
joeverbout	0:ea44dc9ed014	924
joeverbout	0:ea44dc9ed014	925	/** @brief Creates a descriptor matcher of a given type with the default parameters (using default
joeverbout	0:ea44dc9ed014	926	constructor).
joeverbout	0:ea44dc9ed014	927
joeverbout	0:ea44dc9ed014	928	@param descriptorMatcherType Descriptor matcher type. Now the following matcher types are
joeverbout	0:ea44dc9ed014	929	supported:
joeverbout	0:ea44dc9ed014	930	- `BruteForce` (it uses L2 )
joeverbout	0:ea44dc9ed014	931	- `BruteForce-L1`
joeverbout	0:ea44dc9ed014	932	- `BruteForce-Hamming`
joeverbout	0:ea44dc9ed014	933	- `BruteForce-Hamming(2)`
joeverbout	0:ea44dc9ed014	934	- `FlannBased`
joeverbout	0:ea44dc9ed014	935	*/
joeverbout	0:ea44dc9ed014	936	CV_WRAP static Ptr<DescriptorMatcher> create( const String& descriptorMatcherType );
joeverbout	0:ea44dc9ed014	937	protected:
joeverbout	0:ea44dc9ed014	938	/**
joeverbout	0:ea44dc9ed014	939	* Class to work with descriptors from several images as with one merged matrix.
joeverbout	0:ea44dc9ed014	940	* It is used e.g. in FlannBasedMatcher.
joeverbout	0:ea44dc9ed014	941	*/
joeverbout	0:ea44dc9ed014	942	class CV_EXPORTS DescriptorCollection
joeverbout	0:ea44dc9ed014	943	{
joeverbout	0:ea44dc9ed014	944	public:
joeverbout	0:ea44dc9ed014	945	DescriptorCollection();
joeverbout	0:ea44dc9ed014	946	DescriptorCollection( const DescriptorCollection& collection );
joeverbout	0:ea44dc9ed014	947	virtual ~DescriptorCollection();
joeverbout	0:ea44dc9ed014	948
joeverbout	0:ea44dc9ed014	949	// Vector of matrices "descriptors" will be merged to one matrix "mergedDescriptors" here.
joeverbout	0:ea44dc9ed014	950	void set( const std::vector<Mat>& descriptors );
joeverbout	0:ea44dc9ed014	951	virtual void clear();
joeverbout	0:ea44dc9ed014	952
joeverbout	0:ea44dc9ed014	953	const Mat& getDescriptors() const;
joeverbout	0:ea44dc9ed014	954	const Mat getDescriptor( int imgIdx, int localDescIdx ) const;
joeverbout	0:ea44dc9ed014	955	const Mat getDescriptor( int globalDescIdx ) const;
joeverbout	0:ea44dc9ed014	956	void getLocalIdx( int globalDescIdx, int& imgIdx, int& localDescIdx ) const;
joeverbout	0:ea44dc9ed014	957
joeverbout	0:ea44dc9ed014	958	int size() const;
joeverbout	0:ea44dc9ed014	959
joeverbout	0:ea44dc9ed014	960	protected:
joeverbout	0:ea44dc9ed014	961	Mat mergedDescriptors;
joeverbout	0:ea44dc9ed014	962	std::vector<int> startIdxs;
joeverbout	0:ea44dc9ed014	963	};
joeverbout	0:ea44dc9ed014	964
joeverbout	0:ea44dc9ed014	965	//! In fact the matching is implemented only by the following two methods. These methods suppose
joeverbout	0:ea44dc9ed014	966	//! that the class object has been trained already. Public match methods call these methods
joeverbout	0:ea44dc9ed014	967	//! after calling train().
joeverbout	0:ea44dc9ed014	968	virtual void knnMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k,
joeverbout	0:ea44dc9ed014	969	InputArrayOfArrays masks=noArray(), bool compactResult=false ) = 0;
joeverbout	0:ea44dc9ed014	970	virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
joeverbout	0:ea44dc9ed014	971	InputArrayOfArrays masks=noArray(), bool compactResult=false ) = 0;
joeverbout	0:ea44dc9ed014	972
joeverbout	0:ea44dc9ed014	973	static bool isPossibleMatch( InputArray mask, int queryIdx, int trainIdx );
joeverbout	0:ea44dc9ed014	974	static bool isMaskedOut( InputArrayOfArrays masks, int queryIdx );
joeverbout	0:ea44dc9ed014	975
joeverbout	0:ea44dc9ed014	976	static Mat clone_op( Mat m ) { return m.clone(); }
joeverbout	0:ea44dc9ed014	977	void checkMasks( InputArrayOfArrays masks, int queryDescriptorsCount ) const;
joeverbout	0:ea44dc9ed014	978
joeverbout	0:ea44dc9ed014	979	//! Collection of descriptors from train images.
joeverbout	0:ea44dc9ed014	980	std::vector<Mat> trainDescCollection;
joeverbout	0:ea44dc9ed014	981	std::vector<UMat> utrainDescCollection;
joeverbout	0:ea44dc9ed014	982	};
joeverbout	0:ea44dc9ed014	983
joeverbout	0:ea44dc9ed014	984	/** @brief Brute-force descriptor matcher.
joeverbout	0:ea44dc9ed014	985
joeverbout	0:ea44dc9ed014	986	For each descriptor in the first set, this matcher finds the closest descriptor in the second set
joeverbout	0:ea44dc9ed014	987	by trying each one. This descriptor matcher supports masking permissible matches of descriptor
joeverbout	0:ea44dc9ed014	988	sets.
joeverbout	0:ea44dc9ed014	989	*/
joeverbout	0:ea44dc9ed014	990	class CV_EXPORTS_W BFMatcher : public DescriptorMatcher
joeverbout	0:ea44dc9ed014	991	{
joeverbout	0:ea44dc9ed014	992	public:
joeverbout	0:ea44dc9ed014	993	/** @brief Brute-force matcher constructor.
joeverbout	0:ea44dc9ed014	994
joeverbout	0:ea44dc9ed014	995	@param normType One of NORM_L1, NORM_L2, NORM_HAMMING, NORM_HAMMING2. L1 and L2 norms are
joeverbout	0:ea44dc9ed014	996	preferable choices for SIFT and SURF descriptors, NORM_HAMMING should be used with ORB, BRISK and
joeverbout	0:ea44dc9ed014	997	BRIEF, NORM_HAMMING2 should be used with ORB when WTA_K==3 or 4 (see ORB::ORB constructor
joeverbout	0:ea44dc9ed014	998	description).
joeverbout	0:ea44dc9ed014	999	@param crossCheck If it is false, this is will be default BFMatcher behaviour when it finds the k
joeverbout	0:ea44dc9ed014	1000	nearest neighbors for each query descriptor. If crossCheck==true, then the knnMatch() method with
joeverbout	0:ea44dc9ed014	1001	k=1 will only return pairs (i,j) such that for i-th query descriptor the j-th descriptor in the
joeverbout	0:ea44dc9ed014	1002	matcher's collection is the nearest and vice versa, i.e. the BFMatcher will only return consistent
joeverbout	0:ea44dc9ed014	1003	pairs. Such technique usually produces best results with minimal number of outliers when there are
joeverbout	0:ea44dc9ed014	1004	enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper.
joeverbout	0:ea44dc9ed014	1005	*/
joeverbout	0:ea44dc9ed014	1006	CV_WRAP BFMatcher( int normType=NORM_L2, bool crossCheck=false );
joeverbout	0:ea44dc9ed014	1007	virtual ~BFMatcher() {}
joeverbout	0:ea44dc9ed014	1008
joeverbout	0:ea44dc9ed014	1009	virtual bool isMaskSupported() const { return true; }
joeverbout	0:ea44dc9ed014	1010
joeverbout	0:ea44dc9ed014	1011	virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const;
joeverbout	0:ea44dc9ed014	1012	protected:
joeverbout	0:ea44dc9ed014	1013	virtual void knnMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k,
joeverbout	0:ea44dc9ed014	1014	InputArrayOfArrays masks=noArray(), bool compactResult=false );
joeverbout	0:ea44dc9ed014	1015	virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
joeverbout	0:ea44dc9ed014	1016	InputArrayOfArrays masks=noArray(), bool compactResult=false );
joeverbout	0:ea44dc9ed014	1017
joeverbout	0:ea44dc9ed014	1018	int normType;
joeverbout	0:ea44dc9ed014	1019	bool crossCheck;
joeverbout	0:ea44dc9ed014	1020	};
joeverbout	0:ea44dc9ed014	1021
joeverbout	0:ea44dc9ed014	1022
joeverbout	0:ea44dc9ed014	1023	/** @brief Flann-based descriptor matcher.
joeverbout	0:ea44dc9ed014	1024
joeverbout	0:ea44dc9ed014	1025	This matcher trains flann::Index_ on a train descriptor collection and calls its nearest search
joeverbout	0:ea44dc9ed014	1026	methods to find the best matches. So, this matcher may be faster when matching a large train
joeverbout	0:ea44dc9ed014	1027	collection than the brute force matcher. FlannBasedMatcher does not support masking permissible
joeverbout	0:ea44dc9ed014	1028	matches of descriptor sets because flann::Index does not support this. :
joeverbout	0:ea44dc9ed014	1029	*/
joeverbout	0:ea44dc9ed014	1030	class CV_EXPORTS_W FlannBasedMatcher : public DescriptorMatcher
joeverbout	0:ea44dc9ed014	1031	{
joeverbout	0:ea44dc9ed014	1032	public:
joeverbout	0:ea44dc9ed014	1033	CV_WRAP FlannBasedMatcher( const Ptr<flann::IndexParams>& indexParams=makePtr<flann::KDTreeIndexParams>(),
joeverbout	0:ea44dc9ed014	1034	const Ptr<flann::SearchParams>& searchParams=makePtr<flann::SearchParams>() );
joeverbout	0:ea44dc9ed014	1035
joeverbout	0:ea44dc9ed014	1036	virtual void add( InputArrayOfArrays descriptors );
joeverbout	0:ea44dc9ed014	1037	virtual void clear();
joeverbout	0:ea44dc9ed014	1038
joeverbout	0:ea44dc9ed014	1039	// Reads matcher object from a file node
joeverbout	0:ea44dc9ed014	1040	virtual void read( const FileNode& );
joeverbout	0:ea44dc9ed014	1041	// Writes matcher object to a file storage
joeverbout	0:ea44dc9ed014	1042	virtual void write( FileStorage& ) const;
joeverbout	0:ea44dc9ed014	1043
joeverbout	0:ea44dc9ed014	1044	virtual void train();
joeverbout	0:ea44dc9ed014	1045	virtual bool isMaskSupported() const;
joeverbout	0:ea44dc9ed014	1046
joeverbout	0:ea44dc9ed014	1047	virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const;
joeverbout	0:ea44dc9ed014	1048	protected:
joeverbout	0:ea44dc9ed014	1049	static void convertToDMatches( const DescriptorCollection& descriptors,
joeverbout	0:ea44dc9ed014	1050	const Mat& indices, const Mat& distances,
joeverbout	0:ea44dc9ed014	1051	std::vector<std::vector<DMatch> >& matches );
joeverbout	0:ea44dc9ed014	1052
joeverbout	0:ea44dc9ed014	1053	virtual void knnMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k,
joeverbout	0:ea44dc9ed014	1054	InputArrayOfArrays masks=noArray(), bool compactResult=false );
joeverbout	0:ea44dc9ed014	1055	virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
joeverbout	0:ea44dc9ed014	1056	InputArrayOfArrays masks=noArray(), bool compactResult=false );
joeverbout	0:ea44dc9ed014	1057
joeverbout	0:ea44dc9ed014	1058	Ptr<flann::IndexParams> indexParams;
joeverbout	0:ea44dc9ed014	1059	Ptr<flann::SearchParams> searchParams;
joeverbout	0:ea44dc9ed014	1060	Ptr<flann::Index> flannIndex;
joeverbout	0:ea44dc9ed014	1061
joeverbout	0:ea44dc9ed014	1062	DescriptorCollection mergedDescriptors;
joeverbout	0:ea44dc9ed014	1063	int addedDescCount;
joeverbout	0:ea44dc9ed014	1064	};
joeverbout	0:ea44dc9ed014	1065
joeverbout	0:ea44dc9ed014	1066	//! @} features2d_match
joeverbout	0:ea44dc9ed014	1067
joeverbout	0:ea44dc9ed014	1068	/****************************************************************************************\
joeverbout	0:ea44dc9ed014	1069	* Drawing functions *
joeverbout	0:ea44dc9ed014	1070	\****************************************************************************************/
joeverbout	0:ea44dc9ed014	1071
joeverbout	0:ea44dc9ed014	1072	//! @addtogroup features2d_draw
joeverbout	0:ea44dc9ed014	1073	//! @{
joeverbout	0:ea44dc9ed014	1074
joeverbout	0:ea44dc9ed014	1075	struct CV_EXPORTS DrawMatchesFlags
joeverbout	0:ea44dc9ed014	1076	{
joeverbout	0:ea44dc9ed014	1077	enum{ DEFAULT = 0, //!< Output image matrix will be created (Mat::create),
joeverbout	0:ea44dc9ed014	1078	//!< i.e. existing memory of output image may be reused.
joeverbout	0:ea44dc9ed014	1079	//!< Two source image, matches and single keypoints will be drawn.
joeverbout	0:ea44dc9ed014	1080	//!< For each keypoint only the center point will be drawn (without
joeverbout	0:ea44dc9ed014	1081	//!< the circle around keypoint with keypoint size and orientation).
joeverbout	0:ea44dc9ed014	1082	DRAW_OVER_OUTIMG = 1, //!< Output image matrix will not be created (Mat::create).
joeverbout	0:ea44dc9ed014	1083	//!< Matches will be drawn on existing content of output image.
joeverbout	0:ea44dc9ed014	1084	NOT_DRAW_SINGLE_POINTS = 2, //!< Single keypoints will not be drawn.
joeverbout	0:ea44dc9ed014	1085	DRAW_RICH_KEYPOINTS = 4 //!< For each keypoint the circle around keypoint with keypoint size and
joeverbout	0:ea44dc9ed014	1086	//!< orientation will be drawn.
joeverbout	0:ea44dc9ed014	1087	};
joeverbout	0:ea44dc9ed014	1088	};
joeverbout	0:ea44dc9ed014	1089
joeverbout	0:ea44dc9ed014	1090	/** @brief Draws keypoints.
joeverbout	0:ea44dc9ed014	1091
joeverbout	0:ea44dc9ed014	1092	@param image Source image.
joeverbout	0:ea44dc9ed014	1093	@param keypoints Keypoints from the source image.
joeverbout	0:ea44dc9ed014	1094	@param outImage Output image. Its content depends on the flags value defining what is drawn in the
joeverbout	0:ea44dc9ed014	1095	output image. See possible flags bit values below.
joeverbout	0:ea44dc9ed014	1096	@param color Color of keypoints.
joeverbout	0:ea44dc9ed014	1097	@param flags Flags setting drawing features. Possible flags bit values are defined by
joeverbout	0:ea44dc9ed014	1098	DrawMatchesFlags. See details above in drawMatches .
joeverbout	0:ea44dc9ed014	1099
joeverbout	0:ea44dc9ed014	1100	@note
joeverbout	0:ea44dc9ed014	1101	For Python API, flags are modified as cv2.DRAW_MATCHES_FLAGS_DEFAULT,
joeverbout	0:ea44dc9ed014	1102	cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS, cv2.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG,
joeverbout	0:ea44dc9ed014	1103	cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS
joeverbout	0:ea44dc9ed014	1104	*/
joeverbout	0:ea44dc9ed014	1105	CV_EXPORTS_W void drawKeypoints( InputArray image, const std::vector<KeyPoint>& keypoints, InputOutputArray outImage,
joeverbout	0:ea44dc9ed014	1106	const Scalar& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT );
joeverbout	0:ea44dc9ed014	1107
joeverbout	0:ea44dc9ed014	1108	/** @brief Draws the found matches of keypoints from two images.
joeverbout	0:ea44dc9ed014	1109
joeverbout	0:ea44dc9ed014	1110	@param img1 First source image.
joeverbout	0:ea44dc9ed014	1111	@param keypoints1 Keypoints from the first source image.
joeverbout	0:ea44dc9ed014	1112	@param img2 Second source image.
joeverbout	0:ea44dc9ed014	1113	@param keypoints2 Keypoints from the second source image.
joeverbout	0:ea44dc9ed014	1114	@param matches1to2 Matches from the first image to the second one, which means that keypoints1[i]
joeverbout	0:ea44dc9ed014	1115	has a corresponding point in keypoints2[matches[i]] .
joeverbout	0:ea44dc9ed014	1116	@param outImg Output image. Its content depends on the flags value defining what is drawn in the
joeverbout	0:ea44dc9ed014	1117	output image. See possible flags bit values below.
joeverbout	0:ea44dc9ed014	1118	@param matchColor Color of matches (lines and connected keypoints). If matchColor==Scalar::all(-1)
joeverbout	0:ea44dc9ed014	1119	, the color is generated randomly.
joeverbout	0:ea44dc9ed014	1120	@param singlePointColor Color of single keypoints (circles), which means that keypoints do not
joeverbout	0:ea44dc9ed014	1121	have the matches. If singlePointColor==Scalar::all(-1) , the color is generated randomly.
joeverbout	0:ea44dc9ed014	1122	@param matchesMask Mask determining which matches are drawn. If the mask is empty, all matches are
joeverbout	0:ea44dc9ed014	1123	drawn.
joeverbout	0:ea44dc9ed014	1124	@param flags Flags setting drawing features. Possible flags bit values are defined by
joeverbout	0:ea44dc9ed014	1125	DrawMatchesFlags.
joeverbout	0:ea44dc9ed014	1126
joeverbout	0:ea44dc9ed014	1127	This function draws matches of keypoints from two images in the output image. Match is a line
joeverbout	0:ea44dc9ed014	1128	connecting two keypoints (circles). See cv::DrawMatchesFlags.
joeverbout	0:ea44dc9ed014	1129	*/
joeverbout	0:ea44dc9ed014	1130	CV_EXPORTS_W void drawMatches( InputArray img1, const std::vector<KeyPoint>& keypoints1,
joeverbout	0:ea44dc9ed014	1131	InputArray img2, const std::vector<KeyPoint>& keypoints2,
joeverbout	0:ea44dc9ed014	1132	const std::vector<DMatch>& matches1to2, InputOutputArray outImg,
joeverbout	0:ea44dc9ed014	1133	const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1),
joeverbout	0:ea44dc9ed014	1134	const std::vector<char>& matchesMask=std::vector<char>(), int flags=DrawMatchesFlags::DEFAULT );
joeverbout	0:ea44dc9ed014	1135
joeverbout	0:ea44dc9ed014	1136	/** @overload */
joeverbout	0:ea44dc9ed014	1137	CV_EXPORTS_AS(drawMatchesKnn) void drawMatches( InputArray img1, const std::vector<KeyPoint>& keypoints1,
joeverbout	0:ea44dc9ed014	1138	InputArray img2, const std::vector<KeyPoint>& keypoints2,
joeverbout	0:ea44dc9ed014	1139	const std::vector<std::vector<DMatch> >& matches1to2, InputOutputArray outImg,
joeverbout	0:ea44dc9ed014	1140	const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1),
joeverbout	0:ea44dc9ed014	1141	const std::vector<std::vector<char> >& matchesMask=std::vector<std::vector<char> >(), int flags=DrawMatchesFlags::DEFAULT );
joeverbout	0:ea44dc9ed014	1142
joeverbout	0:ea44dc9ed014	1143	//! @} features2d_draw
joeverbout	0:ea44dc9ed014	1144
joeverbout	0:ea44dc9ed014	1145	/****************************************************************************************\
joeverbout	0:ea44dc9ed014	1146	* Functions to evaluate the feature detectors and [generic] descriptor extractors *
joeverbout	0:ea44dc9ed014	1147	\****************************************************************************************/
joeverbout	0:ea44dc9ed014	1148
joeverbout	0:ea44dc9ed014	1149	CV_EXPORTS void evaluateFeatureDetector( const Mat& img1, const Mat& img2, const Mat& H1to2,
joeverbout	0:ea44dc9ed014	1150	std::vector<KeyPoint>* keypoints1, std::vector<KeyPoint>* keypoints2,
joeverbout	0:ea44dc9ed014	1151	float& repeatability, int& correspCount,
joeverbout	0:ea44dc9ed014	1152	const Ptr<FeatureDetector>& fdetector=Ptr<FeatureDetector>() );
joeverbout	0:ea44dc9ed014	1153
joeverbout	0:ea44dc9ed014	1154	CV_EXPORTS void computeRecallPrecisionCurve( const std::vector<std::vector<DMatch> >& matches1to2,
joeverbout	0:ea44dc9ed014	1155	const std::vector<std::vector<uchar> >& correctMatches1to2Mask,
joeverbout	0:ea44dc9ed014	1156	std::vector<Point2f>& recallPrecisionCurve );
joeverbout	0:ea44dc9ed014	1157
joeverbout	0:ea44dc9ed014	1158	CV_EXPORTS float getRecall( const std::vector<Point2f>& recallPrecisionCurve, float l_precision );
joeverbout	0:ea44dc9ed014	1159	CV_EXPORTS int getNearestPoint( const std::vector<Point2f>& recallPrecisionCurve, float l_precision );
joeverbout	0:ea44dc9ed014	1160
joeverbout	0:ea44dc9ed014	1161	/****************************************************************************************\
joeverbout	0:ea44dc9ed014	1162	* Bag of visual words *
joeverbout	0:ea44dc9ed014	1163	\****************************************************************************************/
joeverbout	0:ea44dc9ed014	1164
joeverbout	0:ea44dc9ed014	1165	//! @addtogroup features2d_category
joeverbout	0:ea44dc9ed014	1166	//! @{
joeverbout	0:ea44dc9ed014	1167
joeverbout	0:ea44dc9ed014	1168	/** @brief Abstract base class for training the bag of visual words vocabulary from a set of descriptors.
joeverbout	0:ea44dc9ed014	1169
joeverbout	0:ea44dc9ed014	1170	For details, see, for example, Visual Categorization with Bags of Keypoints by Gabriella Csurka,
joeverbout	0:ea44dc9ed014	1171	Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. :
joeverbout	0:ea44dc9ed014	1172	*/
joeverbout	0:ea44dc9ed014	1173	class CV_EXPORTS_W BOWTrainer
joeverbout	0:ea44dc9ed014	1174	{
joeverbout	0:ea44dc9ed014	1175	public:
joeverbout	0:ea44dc9ed014	1176	BOWTrainer();
joeverbout	0:ea44dc9ed014	1177	virtual ~BOWTrainer();
joeverbout	0:ea44dc9ed014	1178
joeverbout	0:ea44dc9ed014	1179	/** @brief Adds descriptors to a training set.
joeverbout	0:ea44dc9ed014	1180
joeverbout	0:ea44dc9ed014	1181	@param descriptors Descriptors to add to a training set. Each row of the descriptors matrix is a
joeverbout	0:ea44dc9ed014	1182	descriptor.
joeverbout	0:ea44dc9ed014	1183
joeverbout	0:ea44dc9ed014	1184	The training set is clustered using clustermethod to construct the vocabulary.
joeverbout	0:ea44dc9ed014	1185	*/
joeverbout	0:ea44dc9ed014	1186	CV_WRAP void add( const Mat& descriptors );
joeverbout	0:ea44dc9ed014	1187
joeverbout	0:ea44dc9ed014	1188	/** @brief Returns a training set of descriptors.
joeverbout	0:ea44dc9ed014	1189	*/
joeverbout	0:ea44dc9ed014	1190	CV_WRAP const std::vector<Mat>& getDescriptors() const;
joeverbout	0:ea44dc9ed014	1191
joeverbout	0:ea44dc9ed014	1192	/** @brief Returns the count of all descriptors stored in the training set.
joeverbout	0:ea44dc9ed014	1193	*/
joeverbout	0:ea44dc9ed014	1194	CV_WRAP int descriptorsCount() const;
joeverbout	0:ea44dc9ed014	1195
joeverbout	0:ea44dc9ed014	1196	CV_WRAP virtual void clear();
joeverbout	0:ea44dc9ed014	1197
joeverbout	0:ea44dc9ed014	1198	/** @overload */
joeverbout	0:ea44dc9ed014	1199	CV_WRAP virtual Mat cluster() const = 0;
joeverbout	0:ea44dc9ed014	1200
joeverbout	0:ea44dc9ed014	1201	/** @brief Clusters train descriptors.
joeverbout	0:ea44dc9ed014	1202
joeverbout	0:ea44dc9ed014	1203	@param descriptors Descriptors to cluster. Each row of the descriptors matrix is a descriptor.
joeverbout	0:ea44dc9ed014	1204	Descriptors are not added to the inner train descriptor set.
joeverbout	0:ea44dc9ed014	1205
joeverbout	0:ea44dc9ed014	1206	The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first
joeverbout	0:ea44dc9ed014	1207	variant of the method, train descriptors stored in the object are clustered. In the second variant,
joeverbout	0:ea44dc9ed014	1208	input descriptors are clustered.
joeverbout	0:ea44dc9ed014	1209	*/
joeverbout	0:ea44dc9ed014	1210	CV_WRAP virtual Mat cluster( const Mat& descriptors ) const = 0;
joeverbout	0:ea44dc9ed014	1211
joeverbout	0:ea44dc9ed014	1212	protected:
joeverbout	0:ea44dc9ed014	1213	std::vector<Mat> descriptors;
joeverbout	0:ea44dc9ed014	1214	int size;
joeverbout	0:ea44dc9ed014	1215	};
joeverbout	0:ea44dc9ed014	1216
joeverbout	0:ea44dc9ed014	1217	/** @brief kmeans -based class to train visual vocabulary using the bag of visual words approach. :
joeverbout	0:ea44dc9ed014	1218	*/
joeverbout	0:ea44dc9ed014	1219	class CV_EXPORTS_W BOWKMeansTrainer : public BOWTrainer
joeverbout	0:ea44dc9ed014	1220	{
joeverbout	0:ea44dc9ed014	1221	public:
joeverbout	0:ea44dc9ed014	1222	/** @brief The constructor.
joeverbout	0:ea44dc9ed014	1223
joeverbout	0:ea44dc9ed014	1224	@see cv::kmeans
joeverbout	0:ea44dc9ed014	1225	*/
joeverbout	0:ea44dc9ed014	1226	CV_WRAP BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(),
joeverbout	0:ea44dc9ed014	1227	int attempts=3, int flags=KMEANS_PP_CENTERS );
joeverbout	0:ea44dc9ed014	1228	virtual ~BOWKMeansTrainer();
joeverbout	0:ea44dc9ed014	1229
joeverbout	0:ea44dc9ed014	1230	// Returns trained vocabulary (i.e. cluster centers).
joeverbout	0:ea44dc9ed014	1231	CV_WRAP virtual Mat cluster() const;
joeverbout	0:ea44dc9ed014	1232	CV_WRAP virtual Mat cluster( const Mat& descriptors ) const;
joeverbout	0:ea44dc9ed014	1233
joeverbout	0:ea44dc9ed014	1234	protected:
joeverbout	0:ea44dc9ed014	1235
joeverbout	0:ea44dc9ed014	1236	int clusterCount;
joeverbout	0:ea44dc9ed014	1237	TermCriteria termcrit;
joeverbout	0:ea44dc9ed014	1238	int attempts;
joeverbout	0:ea44dc9ed014	1239	int flags;
joeverbout	0:ea44dc9ed014	1240	};
joeverbout	0:ea44dc9ed014	1241
joeverbout	0:ea44dc9ed014	1242	/** @brief Class to compute an image descriptor using the bag of visual words.
joeverbout	0:ea44dc9ed014	1243
joeverbout	0:ea44dc9ed014	1244	Such a computation consists of the following steps:
joeverbout	0:ea44dc9ed014	1245
joeverbout	0:ea44dc9ed014	1246	1. Compute descriptors for a given image and its keypoints set.
joeverbout	0:ea44dc9ed014	1247	2. Find the nearest visual words from the vocabulary for each keypoint descriptor.
joeverbout	0:ea44dc9ed014	1248	3. Compute the bag-of-words image descriptor as is a normalized histogram of vocabulary words
joeverbout	0:ea44dc9ed014	1249	encountered in the image. The i-th bin of the histogram is a frequency of i-th word of the
joeverbout	0:ea44dc9ed014	1250	vocabulary in the given image.
joeverbout	0:ea44dc9ed014	1251	*/
joeverbout	0:ea44dc9ed014	1252	class CV_EXPORTS_W BOWImgDescriptorExtractor
joeverbout	0:ea44dc9ed014	1253	{
joeverbout	0:ea44dc9ed014	1254	public:
joeverbout	0:ea44dc9ed014	1255	/** @brief The constructor.
joeverbout	0:ea44dc9ed014	1256
joeverbout	0:ea44dc9ed014	1257	@param dextractor Descriptor extractor that is used to compute descriptors for an input image and
joeverbout	0:ea44dc9ed014	1258	its keypoints.
joeverbout	0:ea44dc9ed014	1259	@param dmatcher Descriptor matcher that is used to find the nearest word of the trained vocabulary
joeverbout	0:ea44dc9ed014	1260	for each keypoint descriptor of the image.
joeverbout	0:ea44dc9ed014	1261	*/
joeverbout	0:ea44dc9ed014	1262	CV_WRAP BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>& dextractor,
joeverbout	0:ea44dc9ed014	1263	const Ptr<DescriptorMatcher>& dmatcher );
joeverbout	0:ea44dc9ed014	1264	/** @overload */
joeverbout	0:ea44dc9ed014	1265	BOWImgDescriptorExtractor( const Ptr<DescriptorMatcher>& dmatcher );
joeverbout	0:ea44dc9ed014	1266	virtual ~BOWImgDescriptorExtractor();
joeverbout	0:ea44dc9ed014	1267
joeverbout	0:ea44dc9ed014	1268	/** @brief Sets a visual vocabulary.
joeverbout	0:ea44dc9ed014	1269
joeverbout	0:ea44dc9ed014	1270	@param vocabulary Vocabulary (can be trained using the inheritor of BOWTrainer ). Each row of the
joeverbout	0:ea44dc9ed014	1271	vocabulary is a visual word (cluster center).
joeverbout	0:ea44dc9ed014	1272	*/
joeverbout	0:ea44dc9ed014	1273	CV_WRAP void setVocabulary( const Mat& vocabulary );
joeverbout	0:ea44dc9ed014	1274
joeverbout	0:ea44dc9ed014	1275	/** @brief Returns the set vocabulary.
joeverbout	0:ea44dc9ed014	1276	*/
joeverbout	0:ea44dc9ed014	1277	CV_WRAP const Mat& getVocabulary() const;
joeverbout	0:ea44dc9ed014	1278
joeverbout	0:ea44dc9ed014	1279	/** @brief Computes an image descriptor using the set visual vocabulary.
joeverbout	0:ea44dc9ed014	1280
joeverbout	0:ea44dc9ed014	1281	@param image Image, for which the descriptor is computed.
joeverbout	0:ea44dc9ed014	1282	@param keypoints Keypoints detected in the input image.
joeverbout	0:ea44dc9ed014	1283	@param imgDescriptor Computed output image descriptor.
joeverbout	0:ea44dc9ed014	1284	@param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that
joeverbout	0:ea44dc9ed014	1285	pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary)
joeverbout	0:ea44dc9ed014	1286	returned if it is non-zero.
joeverbout	0:ea44dc9ed014	1287	@param descriptors Descriptors of the image keypoints that are returned if they are non-zero.
joeverbout	0:ea44dc9ed014	1288	*/
joeverbout	0:ea44dc9ed014	1289	void compute( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray imgDescriptor,
joeverbout	0:ea44dc9ed014	1290	std::vector<std::vector<int> >* pointIdxsOfClusters=0, Mat* descriptors=0 );
joeverbout	0:ea44dc9ed014	1291	/** @overload
joeverbout	0:ea44dc9ed014	1292	@param keypointDescriptors Computed descriptors to match with vocabulary.
joeverbout	0:ea44dc9ed014	1293	@param imgDescriptor Computed output image descriptor.
joeverbout	0:ea44dc9ed014	1294	@param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that
joeverbout	0:ea44dc9ed014	1295	pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary)
joeverbout	0:ea44dc9ed014	1296	returned if it is non-zero.
joeverbout	0:ea44dc9ed014	1297	*/
joeverbout	0:ea44dc9ed014	1298	void compute( InputArray keypointDescriptors, OutputArray imgDescriptor,
joeverbout	0:ea44dc9ed014	1299	std::vector<std::vector<int> >* pointIdxsOfClusters=0 );
joeverbout	0:ea44dc9ed014	1300	// compute() is not constant because DescriptorMatcher::match is not constant
joeverbout	0:ea44dc9ed014	1301
joeverbout	0:ea44dc9ed014	1302	CV_WRAP_AS(compute) void compute2( const Mat& image, std::vector<KeyPoint>& keypoints, CV_OUT Mat& imgDescriptor )
joeverbout	0:ea44dc9ed014	1303	{ compute(image,keypoints,imgDescriptor); }
joeverbout	0:ea44dc9ed014	1304
joeverbout	0:ea44dc9ed014	1305	/** @brief Returns an image descriptor size if the vocabulary is set. Otherwise, it returns 0.
joeverbout	0:ea44dc9ed014	1306	*/
joeverbout	0:ea44dc9ed014	1307	CV_WRAP int descriptorSize() const;
joeverbout	0:ea44dc9ed014	1308
joeverbout	0:ea44dc9ed014	1309	/** @brief Returns an image descriptor type.
joeverbout	0:ea44dc9ed014	1310	*/
joeverbout	0:ea44dc9ed014	1311	CV_WRAP int descriptorType() const;
joeverbout	0:ea44dc9ed014	1312
joeverbout	0:ea44dc9ed014	1313	protected:
joeverbout	0:ea44dc9ed014	1314	Mat vocabulary;
joeverbout	0:ea44dc9ed014	1315	Ptr<DescriptorExtractor> dextractor;
joeverbout	0:ea44dc9ed014	1316	Ptr<DescriptorMatcher> dmatcher;
joeverbout	0:ea44dc9ed014	1317	};
joeverbout	0:ea44dc9ed014	1318
joeverbout	0:ea44dc9ed014	1319	//! @} features2d_category
joeverbout	0:ea44dc9ed014	1320
joeverbout	0:ea44dc9ed014	1321	//! @} features2d
joeverbout	0:ea44dc9ed014	1322
joeverbout	0:ea44dc9ed014	1323	} /* namespace cv */
joeverbout	0:ea44dc9ed014	1324
joeverbout	0:ea44dc9ed014	1325	#endif
joeverbout	0:ea44dc9ed014	1326

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Type:	Program
Mbed OS support:	Mbed 2 deprecated
Created:	31 Mar 2016
Imports:	152
Forks:	0
Commits:	1
Dependents:	0
Dependencies:	1
Followers:	2

opencv2/features2d.hpp@0:ea44dc9ed014, 2016-03-31 (annotated)

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