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.
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;
};
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;
...
};
Adds descriptors to train a descriptor collection. If the collection trainDescCollectionis is not empty, the new descriptors are added to existing train descriptors.
Parameters: |
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Returns a constant link to the train descriptor collection trainDescCollection .
Clears the train descriptor collection.
Returns true if there are no train descriptors in the collection.
Returns true if the descriptor matcher supports masking permissible matches.
Trains a descriptor matcher
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 ).
Finds the best match for each descriptor from a query set.
Parameters: |
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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.
Finds the k best matches for each descriptor from a query set.
Parameters: |
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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.
For each query descriptor, finds the training descriptors not farther than the specified distance.
Parameters: |
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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.
Clones the matcher.
Parameters: |
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Creates a descriptor matcher of a given type with the default parameters (using default constructor).
Parameters: |
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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.
template<class Distance>
class BruteForceMatcher : public DescriptorMatcher
{
public:
BruteForceMatcher( Distance d = Distance() );
virtual ~BruteForceMatcher();
virtual bool isMaskSupported() const;
virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const;
protected:
...
}
For efficiency, BruteForceMatcher is used as a template parameterized with the distance type. For float descriptors, L2<float> is a common choice. The following distances are supported:
template<typename T>
struct Accumulator
{
typedef T Type;
};
template<> struct Accumulator<unsigned char> { typedef unsigned int Type; };
template<> struct Accumulator<unsigned short> { typedef unsigned int Type; };
template<> struct Accumulator<char> { typedef int Type; };
template<> struct Accumulator<short> { typedef int Type; };
/*
* Euclidean distance functor
*/
template<class T>
struct L2
{
typedef T ValueType;
typedef typename Accumulator<T>::Type ResultType;
ResultType operator()( const T* a, const T* b, int size ) const;
};
/*
* Squared Euclidean distance functor
*/
template<class T>
struct SL2
{
typedef T ValueType;
typedef typename Accumulator<T>::Type ResultType;
ResultType operator()( const T* a, const T* b, int size ) const;
};
// Note: in case of SL2 distance a parameter maxDistance in the method DescriptorMatcher::radiusMatch
// is a squared maximum distance in L2.
/*
* Manhattan distance (city block distance) functor
*/
template<class T>
struct CV_EXPORTS L1
{
typedef T ValueType;
typedef typename Accumulator<T>::Type ResultType;
ResultType operator()( const T* a, const T* b, int size ) const;
};
/*
* Hamming distance functor
*/
struct HammingLUT
{
typedef unsigned char ValueType;
typedef int ResultType;
ResultType operator()( const unsigned char* a, const unsigned char* b,
int size ) const;
...
};
struct Hamming
{
typedef unsigned char ValueType;
typedef int ResultType;
ResultType operator()( const unsigned char* a, const unsigned char* b,
int size ) const;
};
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:
...
};