OpenCV
3.4.5
Open Source Computer Vision

furnishes all functionalities for querying a dataset provided by user or internal to class (that user must, anyway, populate) on the model of Descriptor Matchers More...
#include "descriptor.hpp"
Public Member Functions  
BinaryDescriptorMatcher ()  
Constructor. More...  
~BinaryDescriptorMatcher ()  
void  add (const std::vector< Mat > &descriptors) 
Store locally new descriptors to be inserted in dataset, without updating dataset. More...  
void  clear () CV_OVERRIDE 
Clear dataset and internal data. More...  
void  knnMatch (const Mat &queryDescriptors, const Mat &trainDescriptors, std::vector< std::vector< DMatch > > &matches, int k, const Mat &mask=Mat(), bool compactResult=false) const 
For every input query descriptor, retrieve the best k matching ones from a dataset provided from user or from the one internal to class. More...  
void  knnMatch (const Mat &queryDescriptors, std::vector< std::vector< DMatch > > &matches, int k, const std::vector< Mat > &masks=std::vector< Mat >(), bool compactResult=false) 
void  match (const Mat &queryDescriptors, const Mat &trainDescriptors, std::vector< DMatch > &matches, const Mat &mask=Mat()) const 
For every input query descriptor, retrieve the best matching one from a dataset provided from user or from the one internal to class. More...  
void  match (const Mat &queryDescriptors, std::vector< DMatch > &matches, const std::vector< Mat > &masks=std::vector< Mat >()) 
void  radiusMatch (const Mat &queryDescriptors, const Mat &trainDescriptors, std::vector< std::vector< DMatch > > &matches, float maxDistance, const Mat &mask=Mat(), bool compactResult=false) const 
For every input query descriptor, retrieve, from a dataset provided from user or from the one internal to class, all the descriptors that are not further than maxDist from input query. More...  
void  radiusMatch (const Mat &queryDescriptors, std::vector< std::vector< DMatch > > &matches, float maxDistance, const std::vector< Mat > &masks=std::vector< Mat >(), bool compactResult=false) 
void  train () 
Update dataset by inserting into it all descriptors that were stored locally by add function. More...  
Public Member Functions inherited from cv::Algorithm  
Algorithm ()  
virtual  ~Algorithm () 
virtual bool  empty () const 
Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...  
virtual String  getDefaultName () const 
virtual void  read (const FileNode &fn) 
Reads algorithm parameters from a file storage. More...  
virtual void  save (const String &filename) const 
virtual void  write (FileStorage &fs) const 
Stores algorithm parameters in a file storage. More...  
void  write (const Ptr< FileStorage > &fs, const String &name=String()) const 
simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. More...  
Static Public Member Functions  
static Ptr< BinaryDescriptorMatcher >  createBinaryDescriptorMatcher () 
Create a BinaryDescriptorMatcher object and return a smart pointer to it. More...  
Static Public Member Functions inherited from cv::Algorithm  
template<typename _Tp >  
static Ptr< _Tp >  load (const String &filename, const String &objname=String()) 
Loads algorithm from the file. More...  
template<typename _Tp >  
static Ptr< _Tp >  loadFromString (const String &strModel, const String &objname=String()) 
Loads algorithm from a String. More...  
template<typename _Tp >  
static Ptr< _Tp >  read (const FileNode &fn) 
Reads algorithm from the file node. More...  
Additional Inherited Members  
Protected Member Functions inherited from cv::Algorithm  
void  writeFormat (FileStorage &fs) const 
furnishes all functionalities for querying a dataset provided by user or internal to class (that user must, anyway, populate) on the model of Descriptor Matchers
Once descriptors have been extracted from an image (both they represent lines and points), it becomes interesting to be able to match a descriptor with another one extracted from a different image and representing the same line or point, seen from a differente perspective or on a different scale. In reaching such goal, the main headache is designing an efficient search algorithm to associate a query descriptor to one extracted from a dataset. In the following, a matching modality based on MultiIndex Hashing (MiHashing) will be described.
The theory described in this section is based on [149] . Given a dataset populated with binary codes, each code is indexed m times into m different hash tables, according to m substrings it has been divided into. Thus, given a query code, all the entries close to it at least in one substring are returned by search as neighbor candidates. Returned entries are then checked for validity by verifying that their full codes are not distant (in Hamming space) more than r bits from query code. In details, each binary code h composed of b bits is divided into m disjoint substrings \(\mathbf{h}^{(1)}, ..., \mathbf{h}^{(m)}\), each with length \(\lfloor b/m \rfloor\) or \(\lceil b/m \rceil\) bits. Formally, when two codes h and g differ by at the most r bits, in at the least one of their m substrings they differ by at the most \(\lfloor r/m \rfloor\) bits. In particular, when \(\mathbf{h}\mathbf{g}_H \le r\) (where \(._H\) is the Hamming norm), there must exist a substring k (with \(1 \le k \le m\)) such that
\[\mathbf{h}^{(k)}  \mathbf{g}^{(k)}_H \le \left\lfloor \frac{r}{m} \right\rfloor .\]
That means that if Hamming distance between each of the m substring is strictly greater than \(\lfloor r/m \rfloor\), then \(\mathbf{h}\mathbf{g}_H\) must be larger that r and that is a contradiction. If the codes in dataset are divided into m substrings, then m tables will be built. Given a query q with substrings \(\{\mathbf{q}^{(i)}\}^m_{i=1}\), ith hash table is searched for entries distant at the most \(\lfloor r/m \rfloor\) from \(\mathbf{q}^{(i)}\) and a set of candidates \(\mathcal{N}_i(\mathbf{q})\) is obtained. The union of sets \(\mathcal{N}(\mathbf{q}) = \bigcup_i \mathcal{N}_i(\mathbf{q})\) is a superset of the rneighbors of q. Then, last step of algorithm is computing the Hamming distance between q and each element in \(\mathcal{N}(\mathbf{q})\), deleting the codes that are distant more that r from q.
cv::line_descriptor::BinaryDescriptorMatcher::BinaryDescriptorMatcher  (  ) 
Constructor.
The BinaryDescriptorMatcher constructed is able to store and manage 256bits long entries.

inline 
destructor
void cv::line_descriptor::BinaryDescriptorMatcher::add  (  const std::vector< Mat > &  descriptors  ) 
Store locally new descriptors to be inserted in dataset, without updating dataset.
descriptors  matrices containing descriptors to be inserted into dataset 

virtual 
Clear dataset and internal data.
Reimplemented from cv::Algorithm.

static 
Create a BinaryDescriptorMatcher object and return a smart pointer to it.
void cv::line_descriptor::BinaryDescriptorMatcher::knnMatch  (  const Mat &  queryDescriptors, 
const Mat &  trainDescriptors,  
std::vector< std::vector< DMatch > > &  matches,  
int  k,  
const Mat &  mask = Mat() , 

bool  compactResult = false 

)  const 
For every input query descriptor, retrieve the best k matching ones from a dataset provided from user or from the one internal to class.
queryDescriptors  query descriptors 
trainDescriptors  dataset of descriptors furnished by user 
matches  vector to host retrieved matches 
k  number of the closest descriptors to be returned for every input query 
mask  mask to select which input descriptors must be matched to ones in dataset 
compactResult  flag to obtain a compact result (if true, a vector that doesn't contain any matches for a given query is not inserted in final result) 
void cv::line_descriptor::BinaryDescriptorMatcher::knnMatch  (  const Mat &  queryDescriptors, 
std::vector< std::vector< DMatch > > &  matches,  
int  k,  
const std::vector< Mat > &  masks = std::vector< Mat >() , 

bool  compactResult = false 

) 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
queryDescriptors  query descriptors 
matches  vector to host retrieved matches 
k  number of the closest descriptors to be returned for every input query 
masks  vector of masks to select which input descriptors must be matched to ones in dataset (the ith mask in vector indicates whether each input query can be matched with descriptors in dataset relative to ith image) 
compactResult  flag to obtain a compact result (if true, a vector that doesn't contain any matches for a given query is not inserted in final result) 
void cv::line_descriptor::BinaryDescriptorMatcher::match  (  const Mat &  queryDescriptors, 
const Mat &  trainDescriptors,  
std::vector< DMatch > &  matches,  
const Mat &  mask = Mat() 

)  const 
For every input query descriptor, retrieve the best matching one from a dataset provided from user or from the one internal to class.
queryDescriptors  query descriptors 
trainDescriptors  dataset of descriptors furnished by user 
matches  vector to host retrieved matches 
mask  mask to select which input descriptors must be matched to one in dataset 
void cv::line_descriptor::BinaryDescriptorMatcher::match  (  const Mat &  queryDescriptors, 
std::vector< DMatch > &  matches,  
const std::vector< Mat > &  masks = std::vector< Mat >() 

) 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
queryDescriptors  query descriptors 
matches  vector to host retrieved matches 
masks  vector of masks to select which input descriptors must be matched to one in dataset (the ith mask in vector indicates whether each input query can be matched with descriptors in dataset relative to ith image) 
void cv::line_descriptor::BinaryDescriptorMatcher::radiusMatch  (  const Mat &  queryDescriptors, 
const Mat &  trainDescriptors,  
std::vector< std::vector< DMatch > > &  matches,  
float  maxDistance,  
const Mat &  mask = Mat() , 

bool  compactResult = false 

)  const 
For every input query descriptor, retrieve, from a dataset provided from user or from the one internal to class, all the descriptors that are not further than maxDist from input query.
queryDescriptors  query descriptors 
trainDescriptors  dataset of descriptors furnished by user 
matches  vector to host retrieved matches 
maxDistance  search radius 
mask  mask to select which input descriptors must be matched to ones in dataset 
compactResult  flag to obtain a compact result (if true, a vector that doesn't contain any matches for a given query is not inserted in final result) 
void cv::line_descriptor::BinaryDescriptorMatcher::radiusMatch  (  const Mat &  queryDescriptors, 
std::vector< std::vector< DMatch > > &  matches,  
float  maxDistance,  
const std::vector< Mat > &  masks = std::vector< Mat >() , 

bool  compactResult = false 

) 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
queryDescriptors  query descriptors 
matches  vector to host retrieved matches 
maxDistance  search radius 
masks  vector of masks to select which input descriptors must be matched to ones in dataset (the ith mask in vector indicates whether each input query can be matched with descriptors in dataset relative to ith image) 
compactResult  flag to obtain a compact result (if true, a vector that doesn't contain any matches for a given query is not inserted in final result) 
void cv::line_descriptor::BinaryDescriptorMatcher::train  (  ) 
Update dataset by inserting into it all descriptors that were stored locally by add function.