Cv2Kmeans Method

public static double Kmeans(
	InputArray data,
	int k,
	InputOutputArray bestLabels,
	TermCriteria criteria,
	int attempts,
	KMeansFlags flags,
	OutputArray centers = null
)

Public Shared Function Kmeans ( 
	data As InputArray,
	k As Integer,
	bestLabels As InputOutputArray,
	criteria As TermCriteria,
	attempts As Integer,
	flags As KMeansFlags,
	Optional centers As OutputArray = Nothing
) As Double

public:
static double Kmeans(
	InputArray^ data, 
	int k, 
	InputOutputArray^ bestLabels, 
	TermCriteria criteria, 
	int attempts, 
	KMeansFlags flags, 
	OutputArray^ centers = nullptr
)

static member Kmeans : 
        data : InputArray * 
        k : int * 
        bestLabels : InputOutputArray * 
        criteria : TermCriteria * 
        attempts : int * 
        flags : KMeansFlags * 
        ?centers : OutputArray 
(* Defaults:
        let _centers = defaultArg centers null
*)
-> float 

Parameters

data

Type: OpenCvSharpInputArray
Data for clustering. An array of N-Dimensional points with float coordinates is needed.

k

Type: SystemInt32
Number of clusters to split the set by.

bestLabels

Type: OpenCvSharpInputOutputArray
Input/output integer array that stores the cluster indices for every sample.

criteria

Type: OpenCvSharpTermCriteria
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

Type: SystemInt32
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

Type: OpenCvSharpKMeansFlags
Flag that can take values of cv::KmeansFlags

centers (Optional)

Type: OpenCvSharpOutputArray
Output matrix of the cluster centers, one row per each cluster center.

Return Value

Reference