removed the stuff that's now in xfeatures2d; temporarily added dummy definition of SIFT to make doc builder pass (will remove it later)
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Vadim Pisarevsky
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Common Interfaces of Generic Descriptor Matchers
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================================================
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.. highlight:: cpp
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Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to easily switch
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between different algorithms solving the same problem. This section is devoted to matching descriptors
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that cannot be represented as vectors in a multidimensional space. ``GenericDescriptorMatcher`` is a more generic interface for descriptors. It does not make any assumptions about descriptor representation.
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Every descriptor with the
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:ocv:class:`DescriptorExtractor` interface has a wrapper with the ``GenericDescriptorMatcher`` interface (see
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:ocv:class:`VectorDescriptorMatcher` ).
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There are descriptors such as the One-way descriptor and Ferns that have the ``GenericDescriptorMatcher`` interface implemented but do not support ``DescriptorExtractor``.
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.. note::
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* An example explaining keypoint description can be found at opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp
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* An example on descriptor matching evaluation can be found at opencv_source_code/samples/cpp/detector_descriptor_matcher_evaluation.cpp
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* An example on one to many image matching can be found at opencv_source_code/samples/cpp/matching_to_many_images.cpp
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GenericDescriptorMatcher
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------------------------
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.. ocv:class:: GenericDescriptorMatcher
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Abstract interface for extracting and matching a keypoint descriptor. There are also :ocv:class:`DescriptorExtractor` and :ocv:class:`DescriptorMatcher` for these purposes but their interfaces are intended for descriptors represented as vectors in a multidimensional space. ``GenericDescriptorMatcher`` is a more generic interface for descriptors. ``DescriptorMatcher`` and ``GenericDescriptorMatcher`` have two groups of match methods: for matching keypoints of an image with another image or with an image set. ::
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class GenericDescriptorMatcher
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{
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public:
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GenericDescriptorMatcher();
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virtual ~GenericDescriptorMatcher();
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virtual void add( InputArrayOfArrays images,
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vector<vector<KeyPoint> >& keypoints );
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const vector<Mat>& getTrainImages() const;
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const vector<vector<KeyPoint> >& getTrainKeypoints() const;
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virtual void clear();
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virtual void train() = 0;
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virtual bool isMaskSupported() = 0;
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void classify( InputArray queryImage,
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vector<KeyPoint>& queryKeypoints,
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InputArray trainImage,
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vector<KeyPoint>& trainKeypoints ) const;
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void classify( InputArray queryImage,
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vector<KeyPoint>& queryKeypoints );
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/*
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* Group of methods to match keypoints from an image pair.
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*/
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void match( InputArray queryImage, vector<KeyPoint>& queryKeypoints,
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InputArray trainImage, vector<KeyPoint>& trainKeypoints,
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vector<DMatch>& matches, InputArray mask=noArray() ) const;
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void knnMatch( InputArray queryImage, vector<KeyPoint>& queryKeypoints,
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InputArray trainImage, vector<KeyPoint>& trainKeypoints,
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vector<vector<DMatch> >& matches, int k,
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InputArray mask=noArray(), bool compactResult=false ) const;
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void radiusMatch( InputArray queryImage, vector<KeyPoint>& queryKeypoints,
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InputArray trainImage, vector<KeyPoint>& trainKeypoints,
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vector<vector<DMatch> >& matches, float maxDistance,
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InputArray mask=noArray(), bool compactResult=false ) const;
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/*
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* Group of methods to match keypoints from one image to an image set.
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*/
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void match( InputArray queryImage, vector<KeyPoint>& queryKeypoints,
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vector<DMatch>& matches, InputArrayOfArrays masks=noArray() );
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void knnMatch( InputArray queryImage, vector<KeyPoint>& queryKeypoints,
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vector<vector<DMatch> >& matches, int k,
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InputArrayOfArrays masks=noArray(), bool compactResult=false );
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void radiusMatch( InputArray queryImage, vector<KeyPoint>& queryKeypoints,
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vector<vector<DMatch> >& matches, float maxDistance,
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InputArrayOfArrays masks=noArray(), bool compactResult=false );
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virtual void read( const FileNode& );
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virtual void write( FileStorage& ) const;
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virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const = 0;
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protected:
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...
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};
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GenericDescriptorMatcher::add
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---------------------------------
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Adds images and their keypoints to the training collection stored in the class instance.
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.. ocv:function:: void GenericDescriptorMatcher::add( InputArrayOfArrays images, vector<vector<KeyPoint> >& keypoints )
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:param images: Image collection.
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:param keypoints: Point collection. It is assumed that ``keypoints[i]`` are keypoints detected in the image ``images[i]`` .
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GenericDescriptorMatcher::getTrainImages
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--------------------------------------------
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Returns a train image collection.
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.. ocv:function:: const vector<Mat>& GenericDescriptorMatcher::getTrainImages() const
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GenericDescriptorMatcher::getTrainKeypoints
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-----------------------------------------------
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Returns a train keypoints collection.
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.. ocv:function:: const vector<vector<KeyPoint> >& GenericDescriptorMatcher::getTrainKeypoints() const
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GenericDescriptorMatcher::clear
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-----------------------------------
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Clears a train collection (images and keypoints).
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.. ocv:function:: void GenericDescriptorMatcher::clear()
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GenericDescriptorMatcher::train
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-----------------------------------
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Trains descriptor matcher
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.. ocv:function:: void GenericDescriptorMatcher::train()
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Prepares descriptor matcher, for example, creates a tree-based structure, to extract descriptors or to optimize descriptors matching.
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GenericDescriptorMatcher::isMaskSupported
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---------------------------------------------
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Returns ``true`` if a generic descriptor matcher supports masking permissible matches.
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.. ocv:function:: bool GenericDescriptorMatcher::isMaskSupported()
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GenericDescriptorMatcher::classify
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--------------------------------------
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Classifies keypoints from a query set.
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.. ocv:function:: void GenericDescriptorMatcher::classify( InputArray queryImage, vector<KeyPoint>& queryKeypoints, InputArray trainImage, vector<KeyPoint>& trainKeypoints ) const
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.. ocv:function:: void GenericDescriptorMatcher::classify( InputArray queryImage, vector<KeyPoint>& queryKeypoints )
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:param queryImage: Query image.
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:param queryKeypoints: Keypoints from a query image.
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:param trainImage: Train image.
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:param trainKeypoints: Keypoints from a train image.
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The method classifies each keypoint from a query set. The first variant of the method takes a train image and its keypoints as an input argument. The second variant uses the internally stored training collection that can be built using the ``GenericDescriptorMatcher::add`` method.
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The methods do the following:
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#.
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Call the ``GenericDescriptorMatcher::match`` method to find correspondence between the query set and the training set.
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#.
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Set the ``class_id`` field of each keypoint from the query set to ``class_id`` of the corresponding keypoint from the training set.
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GenericDescriptorMatcher::match
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-----------------------------------
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Finds the best match in the training set for each keypoint from the query set.
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.. ocv:function:: void GenericDescriptorMatcher::match(InputArray queryImage, vector<KeyPoint>& queryKeypoints, InputArray trainImage, vector<KeyPoint>& trainKeypoints, vector<DMatch>& matches, InputArray mask=noArray() ) const
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.. ocv:function:: void GenericDescriptorMatcher::match( InputArray queryImage, vector<KeyPoint>& queryKeypoints, vector<DMatch>& matches, InputArrayOfArrays masks=noArray() )
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:param queryImage: Query image.
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:param queryKeypoints: Keypoints detected in ``queryImage`` .
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:param trainImage: Train image. It is not added to a train image collection stored in the class object.
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:param trainKeypoints: Keypoints detected in ``trainImage`` . They are not added to a train points collection stored in the class object.
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:param matches: Matches. If a query descriptor (keypoint) is masked out in ``mask`` , match is added for this descriptor. So, ``matches`` size may be smaller than the query keypoints count.
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:param mask: Mask specifying permissible matches between an input query and train keypoints.
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:param masks: Set of masks. Each ``masks[i]`` specifies permissible matches between input query keypoints and stored train keypoints from the i-th image.
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The methods find the best match for each query keypoint. In the first variant of the method, a train image and its keypoints are the input arguments. In the second variant, query keypoints are matched to the internally stored training collection that can be built using the ``GenericDescriptorMatcher::add`` method. Optional mask (or masks) can be passed to specify which query and training descriptors can be matched. Namely, ``queryKeypoints[i]`` can be matched with ``trainKeypoints[j]`` only if ``mask.at<uchar>(i,j)`` is non-zero.
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GenericDescriptorMatcher::knnMatch
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--------------------------------------
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Finds the ``k`` best matches for each query keypoint.
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.. ocv:function:: void GenericDescriptorMatcher::knnMatch( InputArray queryImage, vector<KeyPoint>& queryKeypoints, InputArray trainImage, vector<KeyPoint>& trainKeypoints, vector<vector<DMatch> >& matches, int k, InputArray mask=noArray(), bool compactResult=false ) const
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.. ocv:function:: void GenericDescriptorMatcher::knnMatch( InputArray queryImage, vector<KeyPoint>& queryKeypoints, vector<vector<DMatch> >& matches, int k, InputArrayOfArrays masks=noArray(), bool compactResult=false )
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The methods are extended variants of ``GenericDescriptorMatch::match``. The parameters are similar, and the semantics is similar to ``DescriptorMatcher::knnMatch``. But this class does not require explicitly computed keypoint descriptors.
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GenericDescriptorMatcher::radiusMatch
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-----------------------------------------
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For each query keypoint, finds the training keypoints not farther than the specified distance.
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.. ocv:function:: void GenericDescriptorMatcher::radiusMatch( InputArray queryImage, vector<KeyPoint>& queryKeypoints, InputArray trainImage, vector<KeyPoint>& trainKeypoints, vector<vector<DMatch> >& matches, float maxDistance, InputArray mask=noArray(), bool compactResult=false ) const
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.. ocv:function:: void GenericDescriptorMatcher::radiusMatch( InputArray queryImage, vector<KeyPoint>& queryKeypoints, vector<vector<DMatch> >& matches, float maxDistance, InputArrayOfArrays masks=noArray(), bool compactResult=false )
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The methods are similar to ``DescriptorMatcher::radius``. But this class does not require explicitly computed keypoint descriptors.
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GenericDescriptorMatcher::read
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----------------------------------
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Reads a matcher object from a file node.
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.. ocv:function:: void GenericDescriptorMatcher::read( const FileNode& fn )
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GenericDescriptorMatcher::write
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-----------------------------------
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Writes a match object to a file storage.
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.. ocv:function:: void GenericDescriptorMatcher::write( FileStorage& fs ) const
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GenericDescriptorMatcher::clone
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-----------------------------------
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Clones the matcher.
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.. ocv:function:: Ptr<GenericDescriptorMatcher> GenericDescriptorMatcher::clone( bool emptyTrainData=false ) const
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:param emptyTrainData: If ``emptyTrainData`` is false, the method creates a deep copy of the object, that is, copies
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both parameters and train data. If ``emptyTrainData`` is true, the method creates an object copy with the current parameters
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but with empty train data.
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VectorDescriptorMatcher
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-----------------------
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.. ocv:class:: VectorDescriptorMatcher : public GenericDescriptorMatcher
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Class used for matching descriptors that can be described as vectors in a finite-dimensional space. ::
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class CV_EXPORTS VectorDescriptorMatcher : public GenericDescriptorMatcher
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{
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public:
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VectorDescriptorMatcher( const Ptr<DescriptorExtractor>& extractor, const Ptr<DescriptorMatcher>& matcher );
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virtual ~VectorDescriptorMatcher();
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virtual void add( InputArrayOfArrays imgCollection,
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vector<vector<KeyPoint> >& pointCollection );
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virtual void clear();
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virtual void train();
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virtual bool isMaskSupported();
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virtual void read( const FileNode& fn );
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virtual void write( FileStorage& fs ) const;
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virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
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protected:
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...
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};
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Example: ::
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VectorDescriptorMatcher matcher( new SurfDescriptorExtractor,
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new BruteForceMatcher<L2<float> > );
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