OpenCV  3.4.6
Open Source Computer Vision
Modules | Namespaces | Classes | Typedefs | Enumerations | Functions
Core functionality

Modules

 Basic structures
 
 C structures and operations
 
 Operations on arrays
 
 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
 
class  cv::WImage< T >
 Image class which provides a thin layer around an IplImage. More...
 
class  cv::WImageBuffer< T >
 
class  cv::WImageBufferC< T, C >
 
class  cv::WImageC< T, C >
 
class  cv::WImageView< T >
 
class  cv::WImageViewC< T, C >
 

Typedefs

typedef Affine3< double > cv::Affine3d
 
typedef Affine3< float > cv::Affine3f
 
typedef WImageC< short, 1 > cv::WImage1_16s
 
typedef WImageC< ushort, 1 > cv::WImage1_16u
 
typedef WImageC< uchar, 1 > cv::WImage1_b
 
typedef WImageC< float, 1 > cv::WImage1_f
 
typedef WImageC< short, 3 > cv::WImage3_16s
 
typedef WImageC< ushort, 3 > cv::WImage3_16u
 
typedef WImageC< uchar, 3 > cv::WImage3_b
 
typedef WImageC< float, 3 > cv::WImage3_f
 
typedef WImage< short > cv::WImage_16s
 
typedef WImage< ushortcv::WImage_16u
 
typedef WImage< ucharcv::WImage_b
 
typedef WImage< float > cv::WImage_f
 
typedef WImageBufferC< short, 1 > cv::WImageBuffer1_16s
 
typedef WImageBufferC< ushort, 1 > cv::WImageBuffer1_16u
 
typedef WImageBufferC< uchar, 1 > cv::WImageBuffer1_b
 
typedef WImageBufferC< float, 1 > cv::WImageBuffer1_f
 
typedef WImageBufferC< short, 3 > cv::WImageBuffer3_16s
 
typedef WImageBufferC< ushort, 3 > cv::WImageBuffer3_16u
 
typedef WImageBufferC< uchar, 3 > cv::WImageBuffer3_b
 
typedef WImageBufferC< float, 3 > cv::WImageBuffer3_f
 
typedef WImageBuffer< short > cv::WImageBuffer_16s
 
typedef WImageBuffer< ushortcv::WImageBuffer_16u
 
typedef WImageBuffer< ucharcv::WImageBuffer_b
 
typedef WImageBuffer< float > cv::WImageBuffer_f
 
typedef WImageViewC< short, 1 > cv::WImageView1_16s
 
typedef WImageViewC< ushort, 1 > cv::WImageView1_16u
 
typedef WImageViewC< uchar, 1 > cv::WImageView1_b
 
typedef WImageViewC< float, 1 > cv::WImageView1_f
 
typedef WImageViewC< short, 3 > cv::WImageView3_16s
 
typedef WImageViewC< ushort, 3 > cv::WImageView3_16u
 
typedef WImageViewC< uchar, 3 > cv::WImageView3_b
 
typedef WImageViewC< float, 3 > cv::WImageView3_f
 
typedef WImageView< short > cv::WImageView_16s
 
typedef WImageView< ushortcv::WImageView_16u
 
typedef WImageView< ucharcv::WImageView_b
 
typedef WImageView< float > cv::WImageView_f
 

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::HersheyFonts {
  cv::FONT_HERSHEY_SIMPLEX = 0,
  cv::FONT_HERSHEY_PLAIN = 1,
  cv::FONT_HERSHEY_DUPLEX = 2,
  cv::FONT_HERSHEY_COMPLEX = 3,
  cv::FONT_HERSHEY_TRIPLEX = 4,
  cv::FONT_HERSHEY_COMPLEX_SMALL = 5,
  cv::FONT_HERSHEY_SCRIPT_SIMPLEX = 6,
  cv::FONT_HERSHEY_SCRIPT_COMPLEX = 7,
  cv::FONT_ITALIC = 16
}
 Only a subset of Hershey fonts https://en.wikipedia.org/wiki/Hershey_fonts are supported. 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::LineTypes {
  cv::FILLED = -1,
  cv::LINE_4 = 4,
  cv::LINE_8 = 8,
  cv::LINE_AA = 16
}
 type of line More...
 
enum  cv::ReduceTypes {
  cv::REDUCE_SUM = 0,
  cv::REDUCE_AVG = 1,
  cv::REDUCE_MAX = 2,
  cv::REDUCE_MIN = 3
}
 

Functions

 cv::WImageView< T >::WImageView (WImage< T > *img, int c, int r, int width, int height)
 
 cv::WImageView< T >::WImageView (T *data, int width, int height, int channels, int width_step=-1)
 
 cv::WImageViewC< T, C >::WImageViewC ()
 
 cv::WImageViewC< T, C >::WImageViewC (WImageC< T, C > *img, int c, int r, int width, int height)
 
 cv::WImageViewC< T, C >::WImageViewC (T *data, int width, int height, int width_step=-1)
 
virtual cv::WImage< T >::~WImage ()=0
 
virtual cv::WImageC< T, C >::~WImageC ()=0
 
void cv::WImageBuffer< T >::Allocate (int width, int height, int nchannels)
 
void cv::WImageBufferC< T, C >::Allocate (int width, int height)
 
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)
 
WImageView< T > cv::WImage< T >::View (int c, int r, int width, int height)
 
WImageViewC< T, C > cv::WImageC< T, C >::View (int c, int r, int width, int height)
 

Detailed Description

Typedef Documentation

§ Affine3d

typedef Affine3<double> cv::Affine3d

§ Affine3f

typedef Affine3<float> cv::Affine3f

§ WImage1_16s

typedef WImageC<short, 1> cv::WImage1_16s

§ WImage1_16u

§ WImage1_b

§ WImage1_f

typedef WImageC<float, 1> cv::WImage1_f

§ WImage3_16s

typedef WImageC<short, 3> cv::WImage3_16s

§ WImage3_16u

§ WImage3_b

§ WImage3_f

typedef WImageC<float, 3> cv::WImage3_f

§ WImage_16s

typedef WImage<short> cv::WImage_16s

§ WImage_16u

§ WImage_b

§ WImage_f

typedef WImage<float> cv::WImage_f

§ WImageBuffer1_16s

§ WImageBuffer1_16u

§ WImageBuffer1_b

§ WImageBuffer1_f

§ WImageBuffer3_16s

§ WImageBuffer3_16u

§ WImageBuffer3_b

§ WImageBuffer3_f

§ WImageBuffer_16s

§ WImageBuffer_16u

§ WImageBuffer_b

§ WImageBuffer_f

§ WImageView1_16s

typedef WImageViewC<short, 1> cv::WImageView1_16s

§ WImageView1_16u

§ WImageView1_b

§ WImageView1_f

typedef WImageViewC<float, 1> cv::WImageView1_f

§ WImageView3_16s

typedef WImageViewC<short, 3> cv::WImageView3_16s

§ WImageView3_16u

§ WImageView3_b

§ WImageView3_f

typedef WImageViewC<float, 3> cv::WImageView3_f

§ WImageView_16s

§ WImageView_16u

§ WImageView_b

§ WImageView_f

typedef WImageView<float> cv::WImageView_f

Enumeration Type Documentation

§ 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.

§ HersheyFonts

#include <opencv2/core.hpp>

Only a subset of Hershey fonts https://en.wikipedia.org/wiki/Hershey_fonts are supported.

Enumerator
FONT_HERSHEY_SIMPLEX 
Python: cv.FONT_HERSHEY_SIMPLEX

normal size sans-serif font

FONT_HERSHEY_PLAIN 
Python: cv.FONT_HERSHEY_PLAIN

small size sans-serif font

FONT_HERSHEY_DUPLEX 
Python: cv.FONT_HERSHEY_DUPLEX

normal size sans-serif font (more complex than FONT_HERSHEY_SIMPLEX)

FONT_HERSHEY_COMPLEX 
Python: cv.FONT_HERSHEY_COMPLEX

normal size serif font

FONT_HERSHEY_TRIPLEX 
Python: cv.FONT_HERSHEY_TRIPLEX

normal size serif font (more complex than FONT_HERSHEY_COMPLEX)

FONT_HERSHEY_COMPLEX_SMALL 
Python: cv.FONT_HERSHEY_COMPLEX_SMALL

smaller version of FONT_HERSHEY_COMPLEX

FONT_HERSHEY_SCRIPT_SIMPLEX 
Python: cv.FONT_HERSHEY_SCRIPT_SIMPLEX

hand-writing style font

FONT_HERSHEY_SCRIPT_COMPLEX 
Python: cv.FONT_HERSHEY_SCRIPT_COMPLEX

more complex variant of FONT_HERSHEY_SCRIPT_SIMPLEX

FONT_ITALIC 
Python: cv.FONT_ITALIC

flag for italic font

§ 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.

§ LineTypes

#include <opencv2/core.hpp>

type of line

Enumerator
FILLED 
Python: cv.FILLED
LINE_4 
Python: cv.LINE_4

4-connected line

LINE_8 
Python: cv.LINE_8

8-connected line

LINE_AA 
Python: cv.LINE_AA

antialiased line

§ 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.

Function Documentation

§ WImageView() [1/2]

template<typename T >
cv::WImageView< T >::WImageView ( WImage< T > *  img,
int  c,
int  r,
int  width,
int  height 
)

§ WImageView() [2/2]

template<typename T >
cv::WImageView< T >::WImageView ( T *  data,
int  width,
int  height,
int  channels,
int  width_step = -1 
)

§ WImageViewC() [1/3]

template<typename T , int C>
cv::WImageViewC< T, C >::WImageViewC ( )

§ WImageViewC() [2/3]

template<typename T , int C>
cv::WImageViewC< T, C >::WImageViewC ( WImageC< T, C > *  img,
int  c,
int  r,
int  width,
int  height 
)

§ WImageViewC() [3/3]

template<typename T , int C>
cv::WImageViewC< T, C >::WImageViewC ( T *  data,
int  width,
int  height,
int  width_step = -1 
)

§ ~WImage()

template<typename T >
cv::WImage< T >::~WImage ( )
inlinepure virtual

§ ~WImageC()

template<typename T , int C>
cv::WImageC< T, C >::~WImageC ( )
inlinepure virtual

§ Allocate() [1/2]

template<typename T >
void cv::WImageBuffer< T >::Allocate ( int  width,
int  height,
int  nchannels 
)
inline

§ Allocate() [2/2]

template<typename T , int C>
void cv::WImageBufferC< T, C >::Allocate ( int  width,
int  height 
)
inline

§ operator*() [1/4]

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

§ operator*() [2/4]

template<typename T , typename V >
static V cv::operator* ( const Affine3< T > &  affine,
const V &  vector 
)
static

#include <opencv2/core/affine.hpp>

V is a 3-element vector with member fields x, y and z.

§ operator*() [3/4]

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

§ operator*() [4/4]

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

§ swap() [1/2]

void cv::swap ( Mat a,
Mat b 
)

#include <opencv2/core.hpp>

Swaps two matrices.

§ swap() [2/2]

void cv::swap ( UMat a,
UMat b 
)

#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.

§ View() [1/2]

template<typename T >
WImageView< T > cv::WImage< T >::View ( int  c,
int  r,
int  width,
int  height 
)

§ View() [2/2]

template<typename T , int C>
WImageViewC< T, C > cv::WImageC< T, C >::View ( int  c,
int  r,
int  width,
int  height 
)