Class cv::xfeatures2d::BOWKMeansTrainer#

kmeans -based class to train visual vocabulary using the bag of visual words approach. :

#include <opencv2/xfeatures2d.hpp>

Collaboration diagram for cv::xfeatures2d::BOWKMeansTrainer:

cv::xfeatures2d::BOWKMeansTrainer Node1 cv::xfeatures2d::BOWKMeans Trainer   + BOWKMeansTrainer() + ~BOWKMeansTrainer() + cluster() + cluster() Node2 cv::xfeatures2d::BOWTrainer   + BOWTrainer() + ~BOWTrainer() + add() + clear() + cluster() + cluster() + descriptorsCount() + getDescriptors() Node2->Node1 Node3 std::vector< cv::Mat >     Node3->Node2 #descriptors Node4 cv::Mat   + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() and 152 more... + diag() + eye() + eye() + getDefaultAllocator() + getStdAllocator() + ones() + ones() + ones() + ones() + setDefaultAllocator() + zeros() + zeros() + zeros() + zeros() # forEach_impl() Node4->Node3 +elements Node5 int     Node5->Node1 #attempts #clusterCount #flags Node5->Node2 #size Node5->Node4 +cols +dims +dummy +flags +rows Node13 cv::TermCriteria   + TermCriteria() + TermCriteria() + TermCriteria() + isValid() Node5->Node13 +maxCount +type Node6 uint8_t     Node6->Node4 +data +dataend +datalimit +datastart Node7 cv::MatAllocator   + MatAllocator() + ~MatAllocator() + allocate() + allocate() + copy() + deallocate() + download() + getBufferPoolController() + map() + unmap() + upload() Node7->Node4 +allocator Node8 UMatData *     Node8->Node4 +u Node9 MatSize     Node9->Node4 +size Node10 MatStep     Node10->Node4 +step Node11 std::vector< T >     Node11->Node3 < cv::Mat > Node12 T     Node12->Node11 +elements Node13->Node1 #termcrit Node14 double     Node14->Node13 +epsilon

cv::xfeatures2d::BOWKMeansTrainer Node1 cv::xfeatures2d::BOWKMeans Trainer   + BOWKMeansTrainer() + ~BOWKMeansTrainer() + cluster() + cluster() Node2 cv::xfeatures2d::BOWTrainer   + BOWTrainer() + ~BOWTrainer() + add() + clear() + cluster() + cluster() + descriptorsCount() + getDescriptors() Node2->Node1 Node3 std::vector< cv::Mat >     Node3->Node2 #descriptors Node4 cv::Mat   + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() and 152 more... + diag() + eye() + eye() + getDefaultAllocator() + getStdAllocator() + ones() + ones() + ones() + ones() + setDefaultAllocator() + zeros() + zeros() + zeros() + zeros() # forEach_impl() Node4->Node3 +elements Node5 int     Node5->Node1 #attempts #clusterCount #flags Node5->Node2 #size Node5->Node4 +cols +dims +dummy +flags +rows Node13 cv::TermCriteria   + TermCriteria() + TermCriteria() + TermCriteria() + isValid() Node5->Node13 +maxCount +type Node6 uint8_t     Node6->Node4 +data +dataend +datalimit +datastart Node7 cv::MatAllocator   + MatAllocator() + ~MatAllocator() + allocate() + allocate() + copy() + deallocate() + download() + getBufferPoolController() + map() + unmap() + upload() Node7->Node4 +allocator Node8 UMatData *     Node8->Node4 +u Node9 MatSize     Node9->Node4 +size Node10 MatStep     Node10->Node4 +step Node11 std::vector< T >     Node11->Node3 < cv::Mat > Node12 T     Node12->Node11 +elements Node13->Node1 #termcrit Node14 double     Node14->Node13 +epsilon

Public Member Functions#

Public Member Functions inherited from cv::xfeatures2d::BOWTrainer

Return

Name

Description

BOWTrainer()

~BOWTrainer()

void

add(const Mat & descriptors)

Adds descriptors to a training set.

void

clear()

Mat

cluster()

Mat

cluster(const Mat & descriptors)

Clusters train descriptors.

int

descriptorsCount()

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

const std::vector< Mat > &

getDescriptors()

Returns a training set of descriptors.

Additional Inherited Members#

Protected Attributes inherited from cv::xfeatures2d::BOWTrainer

Return

Name

Description

std::vector< Mat >

descriptors

int

size

Detailed Description#

kmeans -based class to train visual vocabulary using the bag of visual words approach. :

Constructor & Destructor Documentation#

BOWKMeansTrainer()#

cv::xfeatures2d::BOWKMeansTrainer::BOWKMeansTrainer(
int clusterCount,
const TermCriteria & termcrit = TermCriteria(),
int attempts = 3,
int flags = KMEANS_PP_CENTERS )

The constructor.

See also

cv::kmeans

~BOWKMeansTrainer()#

cv::xfeatures2d::BOWKMeansTrainer::~BOWKMeansTrainer()

Member Function Documentation#

cluster()#

Mat cv::xfeatures2d::BOWKMeansTrainer::cluster()

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

cluster()#

Mat cv::xfeatures2d::BOWKMeansTrainer::cluster(const Mat & descriptors)

Clusters train descriptors.

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.

Parameters

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

Member Data Documentation#

attempts#

int cv::xfeatures2d::BOWKMeansTrainer::attempts

clusterCount#

int cv::xfeatures2d::BOWKMeansTrainer::clusterCount

flags#

int cv::xfeatures2d::BOWKMeansTrainer::flags

termcrit#

TermCriteria cv::xfeatures2d::BOWKMeansTrainer::termcrit

Source file#

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