Abstract base class for training the bag of visual words vocabulary from a set of descriptors. More...

#include <opencv2/xfeatures2d.hpp>

Collaboration diagram for cv::xfeatures2d::BOWTrainer:

Public Member Functions
	BOWTrainer ()

virtual	~BOWTrainer ()

void	add (const Mat &descriptors)
	Adds descriptors to a training set.

virtual void	clear ()

virtual Mat	cluster () const =0

virtual Mat	cluster (const Mat &descriptors) const =0
	Clusters train descriptors.

int	descriptorsCount () const
	Returns the count of all descriptors stored in the training set.

const std::vector< Mat > &	getDescriptors () const
	Returns a training set of descriptors.

Protected Attributes
std::vector< Mat >	descriptors

int	size

Detailed Description

Abstract base class for training the bag of visual words vocabulary from a set of descriptors.

For details, see, for example, Visual Categorization with Bags of Keypoints by Gabriella Csurka, Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. :

Constructor & Destructor Documentation

◆ BOWTrainer()

cv::xfeatures2d::BOWTrainer::BOWTrainer ( )

◆ ~BOWTrainer()

virtual cv::xfeatures2d::BOWTrainer::~BOWTrainer ( )

virtual

Member Function Documentation

◆ add()

void cv::xfeatures2d::BOWTrainer::add ( const Mat & descriptors )

Python:
	cv.xfeatures2d.BOWTrainer.add(	descriptors	) ->	None

Adds descriptors to a training set.

Parameters

descriptors Descriptors to add to a training set. Each row of the descriptors matrix is a descriptor.

The training set is clustered using clustermethod to construct the vocabulary.

◆ clear()

virtual void cv::xfeatures2d::BOWTrainer::clear ( )

virtual

Python:
	cv.xfeatures2d.BOWTrainer.clear(		) ->	None

◆ cluster() [1/2]

virtual Mat cv::xfeatures2d::BOWTrainer::cluster ( ) const

pure virtual

Python:
	cv.xfeatures2d.BOWTrainer.cluster(		) ->	retval
	cv.xfeatures2d.BOWTrainer.cluster(	descriptors	) ->	retval

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Implemented in cv::xfeatures2d::BOWKMeansTrainer.

◆ cluster() [2/2]

virtual Mat cv::xfeatures2d::BOWTrainer::cluster ( const Mat & descriptors ) const

pure virtual

Python:
	cv.xfeatures2d.BOWTrainer.cluster(		) ->	retval
	cv.xfeatures2d.BOWTrainer.cluster(	descriptors	) ->	retval

Clusters train descriptors.

Parameters

descriptors Descriptors to cluster. Each row of the descriptors matrix is a descriptor. Descriptors are not added to the inner train descriptor set.

The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first variant of the method, train descriptors stored in the object are clustered. In the second variant, input descriptors are clustered.

Implemented in cv::xfeatures2d::BOWKMeansTrainer.

◆ descriptorsCount()

int cv::xfeatures2d::BOWTrainer::descriptorsCount ( ) const

Python:
	cv.xfeatures2d.BOWTrainer.descriptorsCount(		) ->	retval

Returns the count of all descriptors stored in the training set.

◆ getDescriptors()

const std::vector< Mat > & cv::xfeatures2d::BOWTrainer::getDescriptors ( ) const

Python:
	cv.xfeatures2d.BOWTrainer.getDescriptors(		) ->	retval

Returns a training set of descriptors.

Member Data Documentation

◆ descriptors

std::vector<Mat> cv::xfeatures2d::BOWTrainer::descriptors

protected

◆ size

int cv::xfeatures2d::BOWTrainer::size