removed the stuff that's now in xfeatures2d; temporarily added dummy definition of SIFT to make doc builder pass (will remove it later)
Vadim Pisarevsky
11 years ago
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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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