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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<Mat>& images,
vector<vector<KeyPoint> >& keypoints );
const vector<Mat>& getTrainImages() const;
const vector<vector<KeyPoint> >& getTrainKeypoints() const;
virtual void clear();
virtual void train() = 0;
virtual bool isMaskSupported() = 0;
void classify( const Mat& queryImage,
vector<KeyPoint>& queryKeypoints,
const Mat& trainImage,
vector<KeyPoint>& trainKeypoints ) const;
void classify( const Mat& queryImage,
vector<KeyPoint>& queryKeypoints );
/*
* Group of methods to match keypoints from an image pair.
*/
void match( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
const Mat& trainImage, vector<KeyPoint>& trainKeypoints,
vector<DMatch>& matches, const Mat& mask=Mat() ) const;
void knnMatch( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
const Mat& trainImage, vector<KeyPoint>& trainKeypoints,
vector<vector<DMatch> >& matches, int k,
const Mat& mask=Mat(), bool compactResult=false ) const;
void radiusMatch( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
const Mat& trainImage, vector<KeyPoint>& trainKeypoints,
vector<vector<DMatch> >& 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<KeyPoint>& queryKeypoints,
vector<DMatch>& matches, const vector<Mat>& masks=vector<Mat>() );
void knnMatch( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
vector<vector<DMatch> >& matches, int k,
const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );
void radiusMatch( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
vector<vector<DMatch> >& matches, float maxDistance,
const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );
virtual void read( const FileNode& );
virtual void write( FileStorage& ) const;
virtual Ptr<GenericDescriptorMatcher> 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<Mat>& images, vector<vector<KeyPoint> >& 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<Mat>& GenericDescriptorMatcher::getTrainImages() const
GenericDescriptorMatcher::getTrainKeypoints
-----------------------------------------------
Returns a train keypoints collection.
.. ocv:function:: const vector<vector<KeyPoint> >& 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<KeyPoint>& queryKeypoints, const Mat& trainImage, vector<KeyPoint>& trainKeypoints ) const
.. ocv:function:: void GenericDescriptorMatcher::classify( const Mat& queryImage, vector<KeyPoint>& 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<KeyPoint>& queryKeypoints, const Mat& trainImage, vector<KeyPoint>& trainKeypoints, vector<DMatch>& matches, const Mat& mask=Mat() ) const
.. ocv:function:: void GenericDescriptorMatcher::match( const Mat& queryImage, vector<KeyPoint>& queryKeypoints, vector<DMatch>& matches, const vector<Mat>& masks=vector<Mat>() )
: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<uchar>(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<KeyPoint>& queryKeypoints, const Mat& trainImage, vector<KeyPoint>& trainKeypoints, vector<vector<DMatch> >& matches, int k, const Mat& mask=Mat(), bool compactResult=false ) const
.. ocv:function:: void GenericDescriptorMatcher::knnMatch( const Mat& queryImage, vector<KeyPoint>& queryKeypoints, vector<vector<DMatch> >& matches, int k, const vector<Mat>& masks=vector<Mat>(), 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<KeyPoint>& queryKeypoints, const Mat& trainImage, vector<KeyPoint>& trainKeypoints, vector<vector<DMatch> >& matches, float maxDistance, const Mat& mask=Mat(), bool compactResult=false ) const
.. ocv:function:: void GenericDescriptorMatcher::radiusMatch( const Mat& queryImage, vector<KeyPoint>& queryKeypoints, vector<vector<DMatch> >& matches, float maxDistance, const vector<Mat>& masks=vector<Mat>(), 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> 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<DescriptorExtractor>& extractor, const Ptr<DescriptorMatcher>& matcher );
virtual ~VectorDescriptorMatcher();
virtual void add( const vector<Mat>& imgCollection,
vector<vector<KeyPoint> >& pointCollection );
virtual void clear();
virtual void train();
virtual bool isMaskSupported();
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
protected:
...
};
Example: ::
VectorDescriptorMatcher matcher( new SurfDescriptorExtractor,
new BruteForceMatcher<L2<float> > );
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