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Eliminate discrepancies between signatures in documentation and OpenCV headers

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All errors were found by doc/check_docs2.py
This commit is contained in:
Andrey Kamaev
2012-10-17 21:24:55 +04:00
parent 04384a71e4
commit 461b69f6ee
14 changed files with 124 additions and 123 deletions
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2
modules/core/doc/drawing_functions.rst
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@@ -452,7 +452,7 @@ polylines
-------------
Draws several polygonal curves.
.. ocv:function:: void polylines( Mat& img, const Point* const* pts, const int* npts, int ncontours, bool isClosed, const Scalar& color, int thickness=1, int lineType=8, int shift=0 )
.. ocv:function:: void polylines( Mat& img, const Point** pts, const int* npts, int ncontours, bool isClosed, const Scalar& color, int thickness=1, int lineType=8, int shift=0 )
.. ocv:function:: void polylines( InputOutputArray img, InputArrayOfArrays pts, bool isClosed, const Scalar& color, int thickness=1, int lineType=8, int shift=0 )

10
modules/core/doc/operations_on_arrays.rst
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@@ -1085,7 +1085,7 @@ Calculates eigenvalues and eigenvectors of a symmetric matrix.
.. ocv:function:: bool eigen(InputArray src, OutputArray eigenvalues, OutputArray eigenvectors, int lowindex=-1,int highindex=-1)
.. ocv:pyfunction:: cv2.eigen(src, calculateEigenvectors[, eigenvalues[, eigenvectors]]) -> retval, eigenvalues, eigenvectors
.. ocv:pyfunction:: cv2.eigen(src, computeEigenvectors[, eigenvalues[, eigenvectors]]) -> retval, eigenvalues, eigenvectors
.. ocv:cfunction:: void cvEigenVV( CvArr* mat, CvArr* evects, CvArr* evals, double eps=0, int lowindex=-1, int highindex=-1 )
@@ -2283,7 +2283,7 @@ PCA constructors
* **CV_PCA_DATA_AS_COL** indicates that the input samples are stored as matrix columns.
:param maxComponents: maximum number of components that PCA should retain; by default, all the components are retained.
:param retainedVariance: Percentage of variance that PCA should retain. Using this parameter will let the PCA decided how many components to retain but it will always keep at least 2.
The default constructor initializes an empty PCA structure. The other constructors initialize the structure and call
@@ -2297,10 +2297,12 @@ Performs Principal Component Analysis of the supplied dataset.
.. ocv:function:: PCA& PCA::operator()(InputArray data, InputArray mean, int flags, int maxComponents=0)
.. ocv:function:: PCA& PCA::operator()(InputArray data, InputArray mean, int flags, double retainedVariance)
.. ocv:function:: PCA& PCA::computeVar(InputArray data, InputArray mean, int flags, double retainedVariance)
.. ocv:pyfunction:: cv2.PCACompute(data[, mean[, eigenvectors[, maxComponents]]]) -> mean, eigenvectors
.. ocv:pyfunction:: cv2.PCAComputeVar(data, retainedVariance[, mean[, eigenvectors]]) -> mean, eigenvectors
:param data: input samples stored as the matrix rows or as the matrix columns.
:param mean: optional mean value; if the matrix is empty (``noArray()``), the mean is computed from the data.
@@ -2312,7 +2314,7 @@ Performs Principal Component Analysis of the supplied dataset.
* **CV_PCA_DATA_AS_COL** indicates that the input samples are stored as matrix columns.
:param maxComponents: maximum number of components that PCA should retain; by default, all the components are retained.
:param retainedVariance: Percentage of variance that PCA should retain. Using this parameter will let the PCA decided how many components to retain but it will always keep at least 2.
The operator performs PCA of the supplied dataset. It is safe to reuse the same PCA structure for multiple datasets. That is, if the structure has been previously used with another dataset, the existing internal data is reclaimed and the new ``eigenvalues``, ``eigenvectors`` , and ``mean`` are allocated and computed.