| OpenCV 3.4.1 Open Source Computer Vision

Public Member Functions | Protected Attributes | List of all members
cv::BOWKMeansTrainer Class Reference
2D Features Framework » Object Categorization

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

`#include "features2d.hpp"`

Inheritance diagram for cv::BOWKMeansTrainer:

## Public Member Functions
| BOWKMeansTrainer (int clusterCount, const TermCriteria &termcrit=TermCriteria(), int attempts=3, int flags=KMEANS_PP_CENTERS)
| The constructor. More...
virtual | ~BOWKMeansTrainer ()
virtual Mat | cluster () const
virtual Mat | cluster (const Mat &descriptors) const
| Clusters train descriptors. More...
Public Member Functions inherited from cv::BOWTrainer
| BOWTrainer ()
virtual | ~BOWTrainer ()
void | add (const Mat &descriptors)
| Adds descriptors to a training set. More...
virtual void | clear ()
int | descriptorsCount () const
| Returns the count of all descriptors stored in the training set. More...
const std::vector< Mat > & | getDescriptors () const
| Returns a training set of descriptors. More...

## Protected Attributes
int | attempts
int | clusterCount
int | flags
TermCriteria | termcrit
Protected Attributes inherited from cv::BOWTrainer
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::BOWKMeansTrainer::BOWKMeansTrainer | ( | int | clusterCount,
|  | const TermCriteria & | termcrit = `TermCriteria()`,
|  | int | attempts = `3`,
|  | int | flags = `KMEANS_PP_CENTERS`
| ) |  |

Python:
| <BOWKMeansTrainer object> | = | cv.BOWKMeansTrainer( | clusterCount[, termcrit[, attempts[, flags]]] | )

The constructor.

See also
  cv::kmeans

## § ~BOWKMeansTrainer()

virtual cv::BOWKMeansTrainer::~BOWKMeansTrainer | ( |  | ) |
| virtual

## Member Function Documentation

## § cluster() [1/2]

virtual Mat cv::BOWKMeansTrainer::cluster | ( |  | ) | const
| virtual

Python:
| retval | = | cv.BOWKMeansTrainer.cluster( |  | )
| retval | = | cv.BOWKMeansTrainer.cluster( | descriptors | )

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

Implements cv::BOWTrainer.

## § cluster() [2/2]

virtual Mat cv::BOWKMeansTrainer::cluster | ( | const Mat & | descriptors | ) | const
| virtual

Python:
| retval | = | cv.BOWKMeansTrainer.cluster( |  | )
| retval | = | cv.BOWKMeansTrainer.cluster( | descriptors | )

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.

Implements cv::BOWTrainer.

## Member Data Documentation

## § attempts

int cv::BOWKMeansTrainer::attempts
| protected

## § clusterCount

int cv::BOWKMeansTrainer::clusterCount
| protected

## § flags

int cv::BOWKMeansTrainer::flags
| protected

## § termcrit

TermCriteria cv::BOWKMeansTrainer::termcrit
| protected

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

- features2d/include/opencv2/features2d.hpp