OpenCV  4.10.0
Open Source Computer Vision
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Classes | Public Member Functions | Static Public Member Functions | List of all members
cv::optflow::GPCForest< T > Class Template Reference

#include <opencv2/optflow/sparse_matching_gpc.hpp>

Collaboration diagram for cv::optflow::GPCForest< T >:

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.
 
void read (const FileNode &fn) CV_OVERRIDE
 Reads algorithm parameters from a file storage.
 
void train (const std::vector< String > &imagesFrom, const std::vector< String > &imagesTo, const std::vector< String > &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.
 
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.
 
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.
 
- Public Member Functions inherited from cv::Algorithm
 Algorithm ()
 
virtual ~Algorithm ()
 
virtual void clear ()
 Clears the algorithm state.
 
virtual bool empty () const
 Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
 
virtual String getDefaultName () const
 
virtual void save (const String &filename) const
 
void write (const Ptr< FileStorage > &fs, const String &name=String()) const
 
void write (FileStorage &fs, const String &name) const
 

Static Public Member Functions

static Ptr< GPCForestcreate ()
 
- Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tpload (const String &filename, const String &objname=String())
 Loads algorithm from the file.
 
template<typename _Tp >
static Ptr< _TploadFromString (const String &strModel, const String &objname=String())
 Loads algorithm from a String.
 
template<typename _Tp >
static Ptr< _Tpread (const FileNode &fn)
 Reads algorithm from the file node.
 

Additional Inherited Members

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

Member Function Documentation

◆ create()

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

◆ read()

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

Reads algorithm parameters from a file storage.

Reimplemented from cv::Algorithm.

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◆ train() [1/3]

template<int T>
void cv::optflow::GPCForest< T >::train ( const std::vector< String > &  imagesFrom,
const std::vector< String > &  imagesTo,
const std::vector< String > &  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.

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◆ train() [2/3]

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.

Here is the call graph for this function:

◆ train() [3/3]

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

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

Stores algorithm parameters in a file storage.

Reimplemented from cv::Algorithm.

Here is the call graph for this function:

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