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Conflicts: modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst modules/features2d/doc/common_interfaces_of_descriptor_extractors.rst modules/features2d/doc/object_categorization.rst modules/gpu/doc/camera_calibration_and_3d_reconstruction.rst modules/gpu/doc/image_filtering.rst modules/gpu/doc/image_processing.rst modules/gpu/doc/video.rst modules/imgproc/doc/miscellaneous_transformations.rst modules/imgproc/doc/object_detection.rst modules/imgproc/doc/structural_analysis_and_shape_descriptors.rst modules/imgproc/src/samplers.cpp modules/ml/doc/k_nearest_neighbors.rst modules/nonfree/doc/feature_detection.rst modules/ocl/include/opencv2/ocl/ocl.hpp modules/photo/doc/inpainting.rst modules/ts/include/opencv2/ts.hpp platforms/scripts/camera_build.conf samples/android/camera-calibration/AndroidManifest.xml
94 lines
4.7 KiB
ReStructuredText
94 lines
4.7 KiB
ReStructuredText
Clustering
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==========
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.. highlight:: cpp
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kmeans
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------
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Finds centers of clusters and groups input samples around the clusters.
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.. ocv:function:: double kmeans( InputArray data, int K, InputOutputArray bestLabels, TermCriteria criteria, int attempts, int flags, OutputArray centers=noArray() )
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.. ocv:pyfunction:: cv2.kmeans(data, K, bestLabels, criteria, attempts, flags[, centers]) -> retval, bestLabels, centers
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.. ocv:cfunction:: int cvKMeans2( const CvArr* samples, int cluster_count, CvArr* labels, CvTermCriteria termcrit, int attempts=1, CvRNG* rng=0, int flags=0, CvArr* _centers=0, double* compactness=0 )
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:param samples: Floating-point matrix of input samples, one row per sample.
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:param data: Data for clustering.
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:param cluster_count: Number of clusters to split the set by.
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:param K: Number of clusters to split the set by.
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:param labels: Input/output integer array that stores the cluster indices for every sample.
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:param 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.
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:param termcrit: The algorithm termination criteria, that is, the maximum number of iterations and/or the desired accuracy.
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:param 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).
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:param rng: CvRNG state initialized by RNG().
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:param flags: Flag that can take the following values:
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* **KMEANS_RANDOM_CENTERS** Select random initial centers in each attempt.
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* **KMEANS_PP_CENTERS** Use ``kmeans++`` center initialization by Arthur and Vassilvitskii [Arthur2007].
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* **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.
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:param centers: Output matrix of the cluster centers, one row per each cluster center.
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:param _centers: Output matrix of the cluster centers, one row per each cluster center.
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:param compactness: The returned value that is described below.
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The function ``kmeans`` implements a k-means algorithm that finds the
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centers of ``cluster_count`` clusters and groups the input samples
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around the clusters. As an output,
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:math:`\texttt{labels}_i` contains a 0-based cluster index for
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the sample stored in the
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:math:`i^{th}` row of the ``samples`` matrix.
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The function returns the compactness measure that is computed as
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.. math::
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\sum _i \| \texttt{samples} _i - \texttt{centers} _{ \texttt{labels} _i} \| ^2
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after every attempt. The best (minimum) value is chosen and the
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corresponding labels and the compactness value are returned by the function.
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Basically, you can use only the core of the function, set the number of
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attempts to 1, initialize labels each time using a custom algorithm, pass them with the
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( ``flags`` = ``KMEANS_USE_INITIAL_LABELS`` ) flag, and then choose the best (most-compact) clustering.
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.. note::
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* An example on K-means clustering can be found at opencv_source_code/samples/cpp/kmeans.cpp
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* (Python) An example on K-means clustering can be found at opencv_source_code/samples/python2/kmeans.py
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partition
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-------------
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Splits an element set into equivalency classes.
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.. ocv:function:: template<typename _Tp, class _EqPredicate> int partition( const vector<_Tp>& vec, vector<int>& labels, _EqPredicate predicate=_EqPredicate())
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:param vec: Set of elements stored as a vector.
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:param labels: Output vector of labels. It contains as many elements as ``vec``. Each label ``labels[i]`` is a 0-based cluster index of ``vec[i]`` .
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:param predicate: Equivalence predicate (pointer to a boolean function of two arguments or an instance of the class that has the method ``bool operator()(const _Tp& a, const _Tp& b)`` ). The predicate returns ``true`` when the elements are certainly in the same class, and returns ``false`` if they may or may not be in the same class.
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The generic function ``partition`` implements an
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:math:`O(N^2)` algorithm for
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splitting a set of
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:math:`N` elements into one or more equivalency classes, as described in
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http://en.wikipedia.org/wiki/Disjoint-set_data_structure
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. The function
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returns the number of equivalency classes.
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.. [Arthur2007] Arthur and S. Vassilvitskii. k-means++: the advantages of careful seeding, Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 2007
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