Enumerations
enum	cv::KmeansFlags { cv::KMEANS_RANDOM_CENTERS = 0 , cv::KMEANS_PP_CENTERS = 2 , cv::KMEANS_USE_INITIAL_LABELS = 1 }
	k-Means flags More...

Functions
double	cv::kmeans (InputArray data, int K, InputOutputArray bestLabels, TermCriteria criteria, int attempts, int flags, OutputArray centers=noArray())
	Finds centers of clusters and groups input samples around the clusters.

template<typename _Tp , class _EqPredicate >
int	cv::partition (const std::vector< _Tp > &_vec, std::vector< int > &labels, _EqPredicate predicate=_EqPredicate())
	Splits an element set into equivalency classes.

Detailed Description

Enumeration Type Documentation

◆ KmeansFlags

enum cv::KmeansFlags

#include <opencv2/core.hpp>

k-Means flags

Enumerator
KMEANS_RANDOM_CENTERS	Select random initial centers in each attempt.
KMEANS_PP_CENTERS	Use kmeans++ center initialization by Arthur and Vassilvitskii [Arthur2007].
KMEANS_USE_INITIAL_LABELS	During the first (and possibly the only) attempt, use the user-supplied labels instead of computing them from the initial centers. For the second and further attempts, use the random or semi-random centers. Use one of KMEANS_*_CENTERS flag to specify the exact method.

Function Documentation

◆ kmeans()

double cv::kmeans	(	InputArray	data,
		int	K,
		InputOutputArray	bestLabels,
		TermCriteria	criteria,
		int	attempts,
		int	flags,
		OutputArray	centers = `noArray()`
	)

#include <opencv2/core.hpp>

Finds centers of clusters and groups input samples around the clusters.

The function kmeans implements a k-means algorithm that finds the centers of cluster_count clusters and groups the input samples around the clusters. As an output, \(\texttt{bestLabels}_i\) contains a 0-based cluster index for the sample stored in the \(i^{th}\) row of the samples matrix.

Note

(Python) An example on K-means clustering can be found at opencv_source_code/samples/python/kmeans.py

Parameters

data	Data for clustering. An array of N-Dimensional points with float coordinates is needed. Examples of this array can be: Mat points(count, 2, CV_32F); Mat points(count, 1, CV_32FC2); Mat points(1, count, CV_32FC2); std::vector<cv::Point2f> points(sampleCount);
K	Number of clusters to split the set by.
bestLabels	Input/output integer array that stores the cluster indices for every sample.
criteria	The algorithm termination criteria, that is, the maximum number of iterations and/or the desired accuracy. The accuracy is specified as criteria.epsilon. As soon as each of the cluster centers moves by less than criteria.epsilon on some iteration, the algorithm stops.
attempts	Flag to specify the number of times the algorithm is executed using different initial labellings. The algorithm returns the labels that yield the best compactness (see the last function parameter).
flags	Flag that can take values of cv::KmeansFlags
centers	Output matrix of the cluster centers, one row per each cluster center.

Returns: The function returns the compactness measure that is computed as
\[\sum _i \| \texttt{samples} _i - \texttt{centers} _{ \texttt{labels} _i} \| ^2\]
after every attempt. The best (minimum) value is chosen and the corresponding labels and the compactness value are returned by the function. Basically, you can use only the core of the function, set the number of attempts to 1, initialize labels each time using a custom algorithm, pass them with the ( flags = KMEANS_USE_INITIAL_LABELS ) flag, and then choose the best (most-compact) clustering.

◆ partition()

template<typename _Tp , class _EqPredicate >

int cv::partition	(	const std::vector< _Tp > &	_vec,
		std::vector< int > &	labels,
		_EqPredicate	predicate = `_EqPredicate()`
	)

#include <opencv2/core/operations.hpp>

Splits an element set into equivalency classes.

The generic function partition implements an \(O(N^2)\) algorithm for splitting a set of \(N\) elements into one or more equivalency classes, as described in http://en.wikipedia.org/wiki/Disjoint-set_data_structure . The function returns the number of equivalency classes.