cv::optflow::GPCForest< T > Class Template Reference Optical Flow Algorithms


#include <opencv2/optflow/sparse_matching_gpc.hpp>

Inheritance diagram for cv::optflow::GPCForest< T >:
cv::Algorithm

Public Member Functions

void  findCorrespondences ( InputArray imgFrom, InputArray imgTo, std::vector< std::pair< Point2i , Point2i > > &corr, const GPCMatchingParams params= GPCMatchingParams ()) const
  Find correspondences between two images. 更多...
 
void  read (const FileNode &fn) CV_OVERRIDE
  Reads algorithm parameters from a file storage. 更多...
 
void  train ( GPCTrainingSamples &samples, const GPCTrainingParams params= GPCTrainingParams ())
  Train the forest using one sample set for every tree. Please, consider using the next method instead of this one for better quality. 更多...
 
void  train (const std::vector< 字符串 > &imagesFrom, const std::vector< 字符串 > &imagesTo, const std::vector< 字符串 > &gt, const GPCTrainingParams params= GPCTrainingParams ())
  Train the forest using individual samples for each tree. It is generally better to use this instead of the first method. 更多...
 
void  train ( InputArrayOfArrays imagesFrom, InputArrayOfArrays imagesTo, InputArrayOfArrays gt, const GPCTrainingParams params= GPCTrainingParams ())
 
void  write ( FileStorage &fs) const CV_OVERRIDE
  Stores algorithm parameters in a file storage. 更多...
 
-  Public Member Functions inherited from cv::Algorithm
  Algorithm ()
 
virtual  ~Algorithm ()
 
virtual void  clear ()
  Clears the algorithm state. 更多...
 
virtual bool  empty () const
  返回 true 若 Algorithm is empty (e.g. in the very beginning or after unsuccessful read. 更多...
 
virtual 字符串   getDefaultName () const
 
virtual void  save (const 字符串 &filename) const
 
void  write (const Ptr < FileStorage > &fs, const 字符串 &name= 字符串 ()) 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. 更多...
 

Static Public Member Functions

static Ptr < GPCForest create ()
 
-  Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr < _Tp >  load (const 字符串 &filename, const 字符串 &objname= 字符串 ())
  Loads algorithm from the file. 更多...
 
template<typename _Tp >
static Ptr < _Tp >  loadFromString (const 字符串 &strModel, const 字符串 &objname= 字符串 ())
  Loads algorithm from a String. 更多...
 
template<typename _Tp >
static Ptr < _Tp >  read (const FileNode &fn)
  Reads algorithm from the file node. 更多...
 

额外继承成员

-  Protected Member Functions inherited from cv::Algorithm
void  writeFormat ( FileStorage &fs) const
 

成员函数文档编制

◆  create()

template<int T>
static Ptr < GPCForest > cv::optflow::GPCForest < T >::create ( )
inline static

◆  read()

template<int T>
void cv::optflow::GPCForest < T >::read ( const FileNode fn )
inline virtual

Reads algorithm parameters from a file storage.

Reimplemented from cv::Algorithm .

◆  train() [1/3]

template<int T>
void cv::optflow::GPCForest < T >::train ( GPCTrainingSamples samples ,
const GPCTrainingParams   params = GPCTrainingParams ()  
)
inline

Train the forest using one sample set for every tree. Please, consider using the next method instead of this one for better quality.

◆  train() [2/3]

template<int T>
void cv::optflow::GPCForest < T >::train ( const std::vector< 字符串 > &  imagesFrom ,
const std::vector< 字符串 > &  imagesTo ,
const std::vector< 字符串 > &  gt ,
const GPCTrainingParams   params = GPCTrainingParams ()  
)
inline

Train the forest using individual samples for each tree. It is generally better to use this instead of the first method.

◆  train() [3/3]

template<int T>
void cv::optflow::GPCForest < T >::train ( InputArrayOfArrays   imagesFrom ,
InputArrayOfArrays   imagesTo ,
InputArrayOfArrays   gt ,
const GPCTrainingParams   params = GPCTrainingParams ()  
)
inline

◆  write()

template<int T>
void cv::optflow::GPCForest < T >::write ( FileStorage fs ) const
inline virtual

Stores algorithm parameters in a file storage.

Reimplemented from cv::Algorithm .


The documentation for this class was generated from the following file: