OpenCV
4.5.0
Open Source Computer Vision
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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) |
typedef Affine3<double> cv::Affine3d |
#include <opencv2/core/affine.hpp>
typedef Affine3<float> cv::Affine3f |
#include <opencv2/core/affine.hpp>
enum cv::CovarFlags |
#include <opencv2/core.hpp>
Covariation flags.
Enumerator | |
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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. |
enum cv::KmeansFlags |
#include <opencv2/core.hpp>
k-Means flags
enum cv::ReduceTypes |
#include <opencv2/core.hpp>
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static |
#include <opencv2/core/affine.hpp>
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static |
#include <opencv2/core/affine.hpp>
V is a 3-element vector with member fields x, y and z.
#include <opencv2/core/affine.hpp>
#include <opencv2/core/affine.hpp>
#include <opencv2/core.hpp>
Swaps two matrices.
#include <opencv2/core.hpp>
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.