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# Common Interfaces of 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 are represented as vectors in a multidimensional space. All objects that implement `vector` descriptor matchers inherit the `DescriptorMatcher` interface.

Note

- An example explaining keypoint matching 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

## DMatch

struct `DMatch`

Class for matching keypoint descriptors: query descriptor index, train descriptor index, train image index, and distance between descriptors.

    struct DMatch
    {
        DMatch() : queryIdx(-1), trainIdx(-1), imgIdx(-1),
                   distance(std::numeric_limits<float>::max()) {}
        DMatch( int _queryIdx, int _trainIdx, float _distance ) :
                queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(-1),
                distance(_distance) {}
        DMatch( int _queryIdx, int _trainIdx, int _imgIdx, float _distance ) :
                queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(_imgIdx),
                distance(_distance) {}

        int queryIdx; // query descriptor index
        int trainIdx; // train descriptor index
        int imgIdx;   // train image index

        float distance;

        // less is better
        bool operator<( const DMatch &m ) const;
    };

## DescriptorMatcher

class `DescriptorMatcher` : public `Algorithm`

Abstract base class for matching keypoint descriptors. It has two groups of match methods: for matching descriptors of an image with another image or with an image set.

    class DescriptorMatcher
    {
    public:
        virtual ~DescriptorMatcher();

        virtual void add( const vector<Mat>& descriptors );

        const vector<Mat>& getTrainDescriptors() const;
        virtual void clear();
        bool empty() const;
        virtual bool isMaskSupported() const = 0;

        virtual void train();

        /*
         * Group of methods to match descriptors from an image pair.
         */
        void match( const Mat& queryDescriptors, const Mat& trainDescriptors,
                    vector<DMatch>& matches, const Mat& mask=Mat() ) const;
        void knnMatch( const Mat& queryDescriptors, const Mat& trainDescriptors,
                       vector<vector<DMatch> >& matches, int k,
                       const Mat& mask=Mat(), bool compactResult=false ) const;
        void radiusMatch( const Mat& queryDescriptors, const Mat& trainDescriptors,
                          vector<vector<DMatch> >& matches, float maxDistance,
                          const Mat& mask=Mat(), bool compactResult=false ) const;
        /*
         * Group of methods to match descriptors from one image to an image set.
         */
        void match( const Mat& queryDescriptors, vector<DMatch>& matches,
                    const vector<Mat>& masks=vector<Mat>() );
        void knnMatch( const Mat& queryDescriptors, vector<vector<DMatch> >& matches,
                       int k, const vector<Mat>& masks=vector<Mat>(),
                       bool compactResult=false );
        void radiusMatch( const Mat& queryDescriptors, 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<DescriptorMatcher> clone( bool emptyTrainData=false ) const = 0;

        static Ptr<DescriptorMatcher> create( const string& descriptorMatcherType );

    protected:
        vector<Mat> trainDescCollection;
        ...
    };

## DescriptorMatcher::add

Adds descriptors to train a descriptor collection. If the collection `trainDescCollectionis` is not empty, the new descriptors are added to existing train descriptors.

C++:`` void `DescriptorMatcher::``add`(const vector<Mat>& descriptors)

  Parameters:

  - descriptors – Descriptors to add. Each `descriptors[i]` is a set of descriptors from the same train image.

## DescriptorMatcher::getTrainDescriptors

Returns a constant link to the train descriptor collection `trainDescCollection` .

C++:`` const vector<Mat>& `DescriptorMatcher::``getTrainDescriptors`()`const`

## DescriptorMatcher::clear

Clears the train descriptor collection.

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

## DescriptorMatcher::empty

Returns true if there are no train descriptors in the collection.

C++:`` bool `DescriptorMatcher::``empty`()`const`

## DescriptorMatcher::isMaskSupported

Returns true if the descriptor matcher supports masking permissible matches.

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

## DescriptorMatcher::train

Trains a descriptor matcher

C++:`` void `DescriptorMatcher::``train`()

Trains a descriptor matcher (for example, the flann index). In all methods to match, the method `train()` is run every time before matching. Some descriptor matchers (for example, `BruteForceMatcher`) have an empty implementation of this method. Other matchers really train their inner structures (for example, `FlannBasedMatcher` trains `flann::Index` ).

## DescriptorMatcher::match

Finds the best match for each descriptor from a query set.

C++:`` void `DescriptorMatcher::``match`(const Mat& queryDescriptors, const Mat& trainDescriptors, vector<DMatch>& matches, const Mat& mask=Mat() )`const`

C++:`` void `DescriptorMatcher::``match`(const Mat& queryDescriptors, vector<DMatch>& matches, const vector<Mat>& masks=vector<Mat>() )

  Parameters:

  - queryDescriptors – Query set of descriptors.
  - trainDescriptors – Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.
  - matches – Matches. If a query descriptor is masked out in `mask` , no match is added for this descriptor. So, `matches` size may be smaller than the query descriptors count.
  - mask – Mask specifying permissible matches between an input query and train matrices of descriptors.
  - masks – Set of masks. Each `masks[i]` specifies permissible matches between the input query descriptors and stored train descriptors from the i-th image `trainDescCollection[i]`.

In the first variant of this method, the train descriptors are passed as an input argument. In the second variant of the method, train descriptors collection that was set by `DescriptorMatcher::add` is used. Optional mask (or masks) can be passed to specify which query and training descriptors can be matched. Namely, `queryDescriptors[i]` can be matched with `trainDescriptors[j]` only if `mask.at<uchar>(i,j)` is non-zero.

## DescriptorMatcher::knnMatch

Finds the k best matches for each descriptor from a query set.

C++:`` void `DescriptorMatcher::``knnMatch`(const Mat& queryDescriptors, const Mat& trainDescriptors, vector<vector<DMatch>>& matches, int k, const Mat& mask=Mat(), bool compactResult=false )`const`

C++:`` void `DescriptorMatcher::``knnMatch`(const Mat& queryDescriptors, vector<vector<DMatch>>& matches, int k, const vector<Mat>& masks=vector<Mat>(), bool compactResult=false )

  Parameters:

  - queryDescriptors – Query set of descriptors.
  - trainDescriptors – Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.
  - mask – Mask specifying permissible matches between an input query and train matrices of descriptors.
  - masks – Set of masks. Each `masks[i]` specifies permissible matches between the input query descriptors and stored train descriptors from the i-th image `trainDescCollection[i]`.
  - matches – Matches. Each `matches[i]` is k or less matches for the same query descriptor.
  - k – Count of best matches found per each query descriptor or less if a query descriptor has less than k possible matches in total.
  - compactResult – Parameter used when the mask (or masks) is not empty. If `compactResult` is false, the `matches` vector has the same size as `queryDescriptors` rows. If `compactResult` is true, the `matches` vector does not contain matches for fully masked-out query descriptors.

These extended variants of `DescriptorMatcher::match()` methods find several best matches for each query descriptor. The matches are returned in the distance increasing order. See `DescriptorMatcher::match()` for the details about query and train descriptors.

## DescriptorMatcher::radiusMatch

For each query descriptor, finds the training descriptors not farther than the specified distance.

C++:`` void `DescriptorMatcher::``radiusMatch`(const Mat& queryDescriptors, const Mat& trainDescriptors, vector<vector<DMatch>>& matches, float maxDistance, const Mat& mask=Mat(), bool compactResult=false )`const`

C++:`` void `DescriptorMatcher::``radiusMatch`(const Mat& queryDescriptors, vector<vector<DMatch>>& matches, float maxDistance, const vector<Mat>& masks=vector<Mat>(), bool compactResult=false )

  Parameters:

  - queryDescriptors – Query set of descriptors.
  - trainDescriptors – Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.
  - mask – Mask specifying permissible matches between an input query and train matrices of descriptors.
  - masks – Set of masks. Each `masks[i]` specifies permissible matches between the input query descriptors and stored train descriptors from the i-th image `trainDescCollection[i]`.
  - matches – Found matches.
  - compactResult – Parameter used when the mask (or masks) is not empty. If `compactResult` is false, the `matches` vector has the same size as `queryDescriptors` rows. If `compactResult` is true, the `matches` vector does not contain matches for fully masked-out query descriptors.
  - maxDistance – Threshold for the distance between matched descriptors. Distance means here metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured in Pixels)!

For each query descriptor, the methods find such training descriptors that the distance between the query descriptor and the training descriptor is equal or smaller than `maxDistance`. Found matches are returned in the distance increasing order.

## DescriptorMatcher::clone

Clones the matcher.

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

  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.

## DescriptorMatcher::create

Creates a descriptor matcher of a given type with the default parameters (using default constructor).

C++:`` Ptr<DescriptorMatcher> `DescriptorMatcher::``create`(const string& descriptorMatcherType)

  Parameters:

  - descriptorMatcherType –

    Descriptor matcher type. Now the following matcher types are supported:

    - `BruteForce` (it uses `L2` )
    - `BruteForce-L1`
    - `BruteForce-Hamming`
    - `BruteForce-Hamming(2)`
    - `FlannBased`

## BFMatcher

class `BFMatcher` : public `DescriptorMatcher`

Brute-force descriptor matcher. For each descriptor in the first set, this matcher finds the closest descriptor in the second set by trying each one. This descriptor matcher supports masking permissible matches of descriptor sets.

## BFMatcher::BFMatcher

Brute-force matcher constructor.

C++:`` `BFMatcher::``BFMatcher`(int normType=NORM_L2, bool crossCheck=false )

  Parameters:

  - normType – One of `NORM_L1`, `NORM_L2`, `NORM_HAMMING`, `NORM_HAMMING2`. `L1` and `L2` norms are preferable choices for SIFT and SURF descriptors, `NORM_HAMMING` should be used with ORB, BRISK and BRIEF, `NORM_HAMMING2` should be used with ORB when `WTA_K==3` or `4` (see ORB::ORB constructor description).
  - crossCheck – If it is false, this is will be default BFMatcher behaviour when it finds the k nearest neighbors for each query descriptor. If `crossCheck==true`, then the `knnMatch()` method with `k=1` will only return pairs `(i,j)` such that for `i-th` query descriptor the `j-th` descriptor in the matcher’s collection is the nearest and vice versa, i.e. the `BFMatcher` will only return consistent pairs. Such technique usually produces best results with minimal number of outliers when there are enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper.

## FlannBasedMatcher

class `FlannBasedMatcher` : public `DescriptorMatcher`

Flann-based descriptor matcher. This matcher trains `flann::Index_` on a train descriptor collection and calls its nearest search methods to find the best matches. So, this matcher may be faster when matching a large train collection than the brute force matcher. `FlannBasedMatcher` does not support masking permissible matches of descriptor sets because `flann::Index` does not support this.

    class FlannBasedMatcher : public DescriptorMatcher
    {
    public:
        FlannBasedMatcher(
          const Ptr<flann::IndexParams>& indexParams=new flann::KDTreeIndexParams(),
          const Ptr<flann::SearchParams>& searchParams=new flann::SearchParams() );

        virtual void add( const vector<Mat>& descriptors );
        virtual void clear();

        virtual void train();
        virtual bool isMaskSupported() const;

        virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const;
    protected:
        ...
    };

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