Common Interfaces of Generic Descriptor Matchers ================================================ .. highlight:: cpp Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to easily switch between different algorithms solving the same problem. This section is devoted to matching descriptors 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. Every descriptor with the :ocv:class:`DescriptorExtractor` interface has a wrapper with the ``GenericDescriptorMatcher`` interface (see :ocv:class:`VectorDescriptorMatcher` ). There are descriptors such as the One-way descriptor and Ferns that have the ``GenericDescriptorMatcher`` interface implemented but do not support ``DescriptorExtractor``. .. note:: * An example explaining keypoint description can be found at opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp * An example on descriptor matching evaluation can be found at opencv_source_code/samples/cpp/detector_descriptor_matcher_evaluation.cpp * An example on one to many image matching can be found at opencv_source_code/samples/cpp/matching_to_many_images.cpp GenericDescriptorMatcher ------------------------ .. ocv:class:: GenericDescriptorMatcher 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. :: class GenericDescriptorMatcher { public: GenericDescriptorMatcher(); virtual ~GenericDescriptorMatcher(); virtual void add( const vector& images, vector >& keypoints ); const vector& getTrainImages() const; const vector >& getTrainKeypoints() const; virtual void clear(); virtual void train() = 0; virtual bool isMaskSupported() = 0; void classify( const Mat& queryImage, vector& queryKeypoints, const Mat& trainImage, vector& trainKeypoints ) const; void classify( const Mat& queryImage, vector& queryKeypoints ); /* * Group of methods to match keypoints from an image pair. */ void match( const Mat& queryImage, vector& queryKeypoints, const Mat& trainImage, vector& trainKeypoints, vector& matches, const Mat& mask=Mat() ) const; void knnMatch( const Mat& queryImage, vector& queryKeypoints, const Mat& trainImage, vector& trainKeypoints, vector >& matches, int k, const Mat& mask=Mat(), bool compactResult=false ) const; void radiusMatch( const Mat& queryImage, vector& queryKeypoints, const Mat& trainImage, vector& trainKeypoints, vector >& matches, float maxDistance, const Mat& mask=Mat(), bool compactResult=false ) const; /* * Group of methods to match keypoints from one image to an image set. */ void match( const Mat& queryImage, vector& queryKeypoints, vector& matches, const vector& masks=vector() ); void knnMatch( const Mat& queryImage, vector& queryKeypoints, vector >& matches, int k, const vector& masks=vector(), bool compactResult=false ); void radiusMatch( const Mat& queryImage, vector& queryKeypoints, vector >& matches, float maxDistance, const vector& masks=vector(), bool compactResult=false ); virtual void read( const FileNode& ); virtual void write( FileStorage& ) const; virtual Ptr clone( bool emptyTrainData=false ) const = 0; protected: ... }; GenericDescriptorMatcher::add --------------------------------- Adds images and their keypoints to the training collection stored in the class instance. .. ocv:function:: void GenericDescriptorMatcher::add( const vector& images, vector >& keypoints ) :param images: Image collection. :param keypoints: Point collection. It is assumed that ``keypoints[i]`` are keypoints detected in the image ``images[i]`` . GenericDescriptorMatcher::getTrainImages -------------------------------------------- Returns a train image collection. .. ocv:function:: const vector& GenericDescriptorMatcher::getTrainImages() const GenericDescriptorMatcher::getTrainKeypoints ----------------------------------------------- Returns a train keypoints collection. .. ocv:function:: const vector >& GenericDescriptorMatcher::getTrainKeypoints() const GenericDescriptorMatcher::clear ----------------------------------- Clears a train collection (images and keypoints). .. ocv:function:: void GenericDescriptorMatcher::clear() GenericDescriptorMatcher::train ----------------------------------- Trains descriptor matcher .. ocv:function:: void GenericDescriptorMatcher::train() Prepares descriptor matcher, for example, creates a tree-based structure, to extract descriptors or to optimize descriptors matching. GenericDescriptorMatcher::isMaskSupported --------------------------------------------- Returns ``true`` if a generic descriptor matcher supports masking permissible matches. .. ocv:function:: bool GenericDescriptorMatcher::isMaskSupported() GenericDescriptorMatcher::classify -------------------------------------- Classifies keypoints from a query set. .. ocv:function:: void GenericDescriptorMatcher::classify( const Mat& queryImage, vector& queryKeypoints, const Mat& trainImage, vector& trainKeypoints ) const .. ocv:function:: void GenericDescriptorMatcher::classify( const Mat& queryImage, vector& queryKeypoints ) :param queryImage: Query image. :param queryKeypoints: Keypoints from a query image. :param trainImage: Train image. :param trainKeypoints: Keypoints from a train image. 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. The methods do the following: #. Call the ``GenericDescriptorMatcher::match`` method to find correspondence between the query set and the training set. #. Set the ``class_id`` field of each keypoint from the query set to ``class_id`` of the corresponding keypoint from the training set. GenericDescriptorMatcher::match ----------------------------------- Finds the best match in the training set for each keypoint from the query set. .. ocv:function:: void GenericDescriptorMatcher::match( const Mat& queryImage, vector& queryKeypoints, const Mat& trainImage, vector& trainKeypoints, vector& matches, const Mat& mask=Mat() ) const .. ocv:function:: void GenericDescriptorMatcher::match( const Mat& queryImage, vector& queryKeypoints, vector& matches, const vector& masks=vector() ) :param queryImage: Query image. :param queryKeypoints: Keypoints detected in ``queryImage`` . :param trainImage: Train image. It is not added to a train image collection stored in the class object. :param trainKeypoints: Keypoints detected in ``trainImage`` . They are not added to a train points collection stored in the class object. :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. :param mask: Mask specifying permissible matches between an input query and train keypoints. :param masks: Set of masks. Each ``masks[i]`` specifies permissible matches between input query keypoints and stored train keypoints from the i-th image. 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(i,j)`` is non-zero. GenericDescriptorMatcher::knnMatch -------------------------------------- Finds the ``k`` best matches for each query keypoint. .. ocv:function:: void GenericDescriptorMatcher::knnMatch( const Mat& queryImage, vector& queryKeypoints, const Mat& trainImage, vector& trainKeypoints, vector >& matches, int k, const Mat& mask=Mat(), bool compactResult=false ) const .. ocv:function:: void GenericDescriptorMatcher::knnMatch( const Mat& queryImage, vector& queryKeypoints, vector >& matches, int k, const vector& masks=vector(), bool compactResult=false ) 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. GenericDescriptorMatcher::radiusMatch ----------------------------------------- For each query keypoint, finds the training keypoints not farther than the specified distance. .. ocv:function:: void GenericDescriptorMatcher::radiusMatch( const Mat& queryImage, vector& queryKeypoints, const Mat& trainImage, vector& trainKeypoints, vector >& matches, float maxDistance, const Mat& mask=Mat(), bool compactResult=false ) const .. ocv:function:: void GenericDescriptorMatcher::radiusMatch( const Mat& queryImage, vector& queryKeypoints, vector >& matches, float maxDistance, const vector& masks=vector(), bool compactResult=false ) The methods are similar to ``DescriptorMatcher::radius``. But this class does not require explicitly computed keypoint descriptors. GenericDescriptorMatcher::read ---------------------------------- Reads a matcher object from a file node. .. ocv:function:: void GenericDescriptorMatcher::read( const FileNode& fn ) GenericDescriptorMatcher::write ----------------------------------- Writes a match object to a file storage. .. ocv:function:: void GenericDescriptorMatcher::write( FileStorage& fs ) const GenericDescriptorMatcher::clone ----------------------------------- Clones the matcher. .. ocv:function:: Ptr GenericDescriptorMatcher::clone( bool emptyTrainData=false ) const :param emptyTrainData: If ``emptyTrainData`` is false, the method creates a deep copy of the object, that is, copies both parameters and train data. If ``emptyTrainData`` is true, the method creates an object copy with the current parameters but with empty train data. VectorDescriptorMatcher ----------------------- .. ocv:class:: VectorDescriptorMatcher : public GenericDescriptorMatcher Class used for matching descriptors that can be described as vectors in a finite-dimensional space. :: class CV_EXPORTS VectorDescriptorMatcher : public GenericDescriptorMatcher { public: VectorDescriptorMatcher( const Ptr& extractor, const Ptr& matcher ); virtual ~VectorDescriptorMatcher(); virtual void add( const vector& imgCollection, vector >& pointCollection ); virtual void clear(); virtual void train(); virtual bool isMaskSupported(); virtual void read( const FileNode& fn ); virtual void write( FileStorage& fs ) const; virtual Ptr clone( bool emptyTrainData=false ) const; protected: ... }; Example: :: VectorDescriptorMatcher matcher( new SurfDescriptorExtractor, new BruteForceMatcher > );