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Core functionality
Data Structures | |
| class | Affine3< T > |
| Affine transform. More... | |
| class | WImage< T > |
| Image class which provides a thin layer around an IplImage. More... | |
| class | WImageC< T, C > |
| Image class when both the pixel type and number of channels are known at compile time. More... | |
| class | WImageBuffer< T > |
| Image class which owns the data, so it can be allocated and is always freed. More... | |
| class | WImageBufferC< T, C > |
| Like a WImageBuffer class but when the number of channels is known at compile time. More... | |
| class | WImageView< T > |
| View into an image class which allows treating a subimage as an image or treating external data as an image. More... | |
Modules | |
| Basic structures | |
| C structures and operations | |
| Operations on arrays | |
| XML/YAML Persistence | |
XML/YAML file storages. | |
| Clustering | |
| Utility and system functions and macros | |
| OpenGL interoperability | |
This section describes OpenGL interoperability. | |
| Intel IPP Asynchronous C/C++ Converters | |
This section describes conversion between OpenCV and [Intel® IPP Asynchronous C/C++](http://software.intel.com/en-us/intel-ipp-preview) library. | |
| Optimization Algorithms | |
The algorithms in this section minimize or maximize function value within specified constraints or without any constraints. | |
| DirectX interoperability | |
| Eigen support | |
| OpenCL support | |
| Intel VA-API/OpenCL (CL-VA) interoperability | |
This section describes Intel VA-API/OpenCL (CL-VA) interoperability. | |
| Hardware Acceleration Layer | |
Enumerations | |
| enum | CovarFlags { COVAR_SCRAMBLED = 0, COVAR_NORMAL = 1, COVAR_USE_AVG = 2, COVAR_SCALE = 4, COVAR_ROWS = 8, COVAR_COLS = 16 } |
Covariation flags. More... | |
| enum | KmeansFlags { KMEANS_RANDOM_CENTERS = 0, KMEANS_PP_CENTERS = 2, KMEANS_USE_INITIAL_LABELS = 1 } |
k-Means flags More... | |
| enum | LineTypes { , LINE_4 = 4, LINE_8 = 8, LINE_AA = 16 } |
type of line More... | |
| enum | HersheyFonts { FONT_HERSHEY_SIMPLEX = 0, FONT_HERSHEY_PLAIN = 1, FONT_HERSHEY_DUPLEX = 2, FONT_HERSHEY_COMPLEX = 3, FONT_HERSHEY_TRIPLEX = 4, FONT_HERSHEY_COMPLEX_SMALL = 5, FONT_HERSHEY_SCRIPT_SIMPLEX = 6, FONT_HERSHEY_SCRIPT_COMPLEX = 7, FONT_ITALIC = 16 } |
Only a subset of Hershey fonts <http://sources.isc.org/utils/misc/hershey-font.txt> are supported. More... | |
| enum | ReduceTypes { REDUCE_SUM = 0, REDUCE_AVG = 1, REDUCE_MAX = 2, REDUCE_MIN = 3 } |
Functions | |
| void | swap (Mat &a, Mat &b) |
| Swaps two matrices. | |
| void | swap (UMat &a, UMat &b) |
Enumeration Type Documentation
| enum CovarFlags |
Covariation flags.
- Enumerator:
COVAR_SCRAMBLED The output covariance matrix is calculated as:
![\[\texttt{scale} \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...]^T \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...],\]](form_166.png)
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 The output covariance matrix is calculated as:
![\[\texttt{scale} \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...] \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...]^T,\]](form_167.png)
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 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 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 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 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 HersheyFonts |
Only a subset of Hershey fonts <http://sources.isc.org/utils/misc/hershey-font.txt> are supported.
- Enumerator:
| enum KmeansFlags |
k-Means flags
- Enumerator:
| enum LineTypes |
| enum ReduceTypes |
- Enumerator:
Function Documentation
| CV_EXPORTS void swap | ( | Mat & | a, |
| Mat & | b | ||
| ) |
Swaps two matrices.
Definition at line 244 of file matrix.cpp.
| CV_EXPORTS void swap | ( | UMat & | a, |
| UMat & | b | ||
| ) |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
Definition at line 150 of file umatrix.cpp.
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