Common Interfaces of Generic Descriptor Matchers — OpenCV 2.4.10.0 documentation

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# Common Interfaces of Generic Descriptor Matchers

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 `DescriptorExtractor` interface has a wrapper with the `GenericDescriptorMatcher` interface (see `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

class `GenericDescriptorMatcher`

Abstract interface for extracting and matching a keypoint descriptor. There are also `DescriptorExtractor` and `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.

C++:`` void `GenericDescriptorMatcher::``add`(const vector<Mat>& images, vector<vector<KeyPoint>>& keypoints)

  Parameters: | images – Image collection. keypoints – Point collection. It is assumed that `keypoints[i]` are keypoints detected in the image `images[i]` .

## GenericDescriptorMatcher::getTrainImages

Returns a train image collection.

C++:`` const vector<Mat>& `GenericDescriptorMatcher::``getTrainImages`()`const`

## GenericDescriptorMatcher::getTrainKeypoints

Returns a train keypoints collection.

C++:`` const vector<vector<KeyPoint>>& `GenericDescriptorMatcher::``getTrainKeypoints`()`const`

## GenericDescriptorMatcher::clear

Clears a train collection (images and keypoints).

C++:`` void `GenericDescriptorMatcher::``clear`()

## GenericDescriptorMatcher::train

Trains descriptor matcher

C++:`` 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.

C++:`` bool `GenericDescriptorMatcher::``isMaskSupported`()

## GenericDescriptorMatcher::classify

Classifies keypoints from a query set.

C++:`` void `GenericDescriptorMatcher::``classify`(const Mat& queryImage, vector<KeyPoint>& queryKeypoints, const Mat& trainImage, vector<KeyPoint>& trainKeypoints)`const`

C++:`` void `GenericDescriptorMatcher::``classify`(const Mat& queryImage, vector<KeyPoint>& queryKeypoints)

  Parameters: | queryImage – Query image. queryKeypoints – Keypoints from a query image. trainImage – Train image. 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:

1. Call the `GenericDescriptorMatcher::match` method to find correspondence between the query set and the training set.
2. 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.

C++:`` void `GenericDescriptorMatcher::``match`(const Mat& queryImage, vector<KeyPoint>& queryKeypoints, const Mat& trainImage, vector<KeyPoint>& trainKeypoints, vector<DMatch>& matches, const Mat& mask=Mat() )`const`

C++:`` void `GenericDescriptorMatcher::``match`(const Mat& queryImage, vector<KeyPoint>& queryKeypoints, vector<DMatch>& matches, const vector<Mat>& masks=vector<Mat>() )

  Parameters: | queryImage – Query image. queryKeypoints – Keypoints detected in `queryImage` . trainImage – Train image. It is not added to a train image collection stored in the class object. trainKeypoints – Keypoints detected in `trainImage` . They are not added to a train points collection stored in the class object. 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. mask – Mask specifying permissible matches between an input query and train keypoints. 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.

C++:`` 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`

C++:`` 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.

C++:`` 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`

C++:`` 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.

C++:`` void `GenericDescriptorMatcher::``read`(const FileNode& fn)

## GenericDescriptorMatcher::write

Writes a match object to a file storage.

C++:`` void `GenericDescriptorMatcher::``write`(FileStorage& fs)`const`

## GenericDescriptorMatcher::clone

Clones the matcher.

C++:`` Ptr<GenericDescriptorMatcher> `GenericDescriptorMatcher::``clone`(bool emptyTrainData=false )`const`

  Parameters: | 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

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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