#include <opencv2/optflow/sparse_matching_gpc.hpp>
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| void | findCorrespondences (InputArray imgFrom, InputArray imgTo, std::vector< std::pair< Point2i, Point2i > > &corr, const GPCMatchingParams params=GPCMatchingParams()) const | 
|  | Find correspondences between two images.  More... 
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| void | read (const FileNode &fn) CV_OVERRIDE | 
|  | Reads algorithm parameters from a file storage.  More... 
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| 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.  More... 
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| void | train (const std::vector< String > &imagesFrom, const std::vector< String > &imagesTo, const std::vector< String > >, 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.  More... 
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| void | train (InputArrayOfArrays imagesFrom, InputArrayOfArrays imagesTo, InputArrayOfArrays gt, const GPCTrainingParams params=GPCTrainingParams()) | 
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| void | write (FileStorage &fs) const CV_OVERRIDE | 
|  | Stores algorithm parameters in a file storage.  More... 
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|  | Algorithm () | 
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| virtual | ~Algorithm () | 
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| virtual void | clear () | 
|  | Clears the algorithm state.  More... 
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| virtual bool | empty () const | 
|  | Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.  More... 
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| virtual String | getDefaultName () const | 
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| virtual void | save (const String &filename) const | 
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| 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... 
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◆ create()
◆ read()
Reads algorithm parameters from a file storage. 
Reimplemented from cv::Algorithm.
 
 
◆ train() [1/3]
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]
Train the forest using individual samples for each tree. It is generally better to use this instead of the first method. 
 
 
◆ train() [3/3]
◆ write()
Stores algorithm parameters in a file storage. 
Reimplemented from cv::Algorithm.
 
 
The documentation for this class was generated from the following file: