Modules
	Basic structures

	C structures and operations

	Operations on arrays

	Asynchronous API

	XML/YAML Persistence

	Clustering

	Utility and system functions and macros

	OpenGL interoperability

	Intel IPP Asynchronous C/C++ Converters

	Optimization Algorithms

	DirectX interoperability

	Eigen support

	OpenCL support

	Intel VA-API/OpenCL (CL-VA) interoperability

	Hardware Acceleration Layer

Namespaces
	cv::traits

Classes
class	cv::Affine3< T >
	Affine transform. More...

class	cv::BufferPoolController

Typedefs
typedef Affine3< double >	cv::Affine3d

typedef Affine3< float >	cv::Affine3f

Enumerations
enum	cv::CovarFlags { cv::COVAR_SCRAMBLED = 0, cv::COVAR_NORMAL = 1, cv::COVAR_USE_AVG = 2, cv::COVAR_SCALE = 4, cv::COVAR_ROWS = 8, cv::COVAR_COLS = 16 }
	Covariation flags. More...

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

enum	cv::ReduceTypes { cv::REDUCE_SUM = 0, cv::REDUCE_AVG = 1, cv::REDUCE_MAX = 2, cv::REDUCE_MIN = 3 }

Functions
template<typename T >
static Affine3< T >	cv::operator* (const Affine3< T > &affine1, const Affine3< T > &affine2)

template<typename T , typename V >
static V	cv::operator* (const Affine3< T > &affine, const V &vector)
	V is a 3-element vector with member fields x, y and z. More...

static Vec3f	cv::operator* (const Affine3f &affine, const Vec3f &vector)

static Vec3d	cv::operator* (const Affine3d &affine, const Vec3d &vector)

void	cv::swap (Mat &a, Mat &b)
	Swaps two matrices. More...

void	cv::swap (UMat &a, UMat &b)

Detailed Description

Typedef Documentation

◆ Affine3d

typedef Affine3<double> cv::Affine3d

#include <opencv2/core/affine.hpp>

◆ Affine3f

typedef Affine3<float> cv::Affine3f

#include <opencv2/core/affine.hpp>

Enumeration Type Documentation

◆ CovarFlags

enum cv::CovarFlags

#include <opencv2/core.hpp>

Covariation flags.

Enumerator
COVAR_SCRAMBLED Python: cv.COVAR_SCRAMBLED	The output covariance matrix is calculated as: \[\texttt{scale} \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...]^T \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...],\] The covariance matrix will be nsamples x nsamples. Such an unusual covariance matrix is used for fast PCA of a set of very large vectors (see, for example, the EigenFaces technique for face recognition). Eigenvalues of this "scrambled" matrix match the eigenvalues of the true covariance matrix. The "true" eigenvectors can be easily calculated from the eigenvectors of the "scrambled" covariance matrix.
COVAR_NORMAL Python: cv.COVAR_NORMAL	The output covariance matrix is calculated as: \[\texttt{scale} \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...] \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...]^T,\] covar will be a square matrix of the same size as the total number of elements in each input vector. One and only one of COVAR_SCRAMBLED and COVAR_NORMAL must be specified.
COVAR_USE_AVG Python: cv.COVAR_USE_AVG	If the flag is specified, the function does not calculate mean from the input vectors but, instead, uses the passed mean vector. This is useful if mean has been pre-calculated or known in advance, or if the covariance matrix is calculated by parts. In this case, mean is not a mean vector of the input sub-set of vectors but rather the mean vector of the whole set.
COVAR_SCALE Python: cv.COVAR_SCALE	If the flag is specified, the covariance matrix is scaled. In the "normal" mode, scale is 1./nsamples . In the "scrambled" mode, scale is the reciprocal of the total number of elements in each input vector. By default (if the flag is not specified), the covariance matrix is not scaled ( scale=1 ).
COVAR_ROWS Python: cv.COVAR_ROWS	If the flag is specified, all the input vectors are stored as rows of the samples matrix. mean should be a single-row vector in this case.
COVAR_COLS Python: cv.COVAR_COLS	If the flag is specified, all the input vectors are stored as columns of the samples matrix. mean should be a single-column vector in this case.

◆ KmeansFlags

enum cv::KmeansFlags

#include <opencv2/core.hpp>

k-Means flags

Enumerator
KMEANS_RANDOM_CENTERS Python: cv.KMEANS_RANDOM_CENTERS	Select random initial centers in each attempt.
KMEANS_PP_CENTERS Python: cv.KMEANS_PP_CENTERS	Use kmeans++ center initialization by Arthur and Vassilvitskii [Arthur2007].
KMEANS_USE_INITIAL_LABELS Python: cv.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.

◆ ReduceTypes

enum cv::ReduceTypes

#include <opencv2/core.hpp>

Enumerator
REDUCE_SUM Python: cv.REDUCE_SUM	the output is the sum of all rows/columns of the matrix.
REDUCE_AVG Python: cv.REDUCE_AVG	the output is the mean vector of all rows/columns of the matrix.
REDUCE_MAX Python: cv.REDUCE_MAX	the output is the maximum (column/row-wise) of all rows/columns of the matrix.
REDUCE_MIN Python: cv.REDUCE_MIN	the output is the minimum (column/row-wise) of all rows/columns of the matrix.