_operator_re = re.compile(r'''(?x)
\[\s*\]
| \(\s*\)
+ | (<<|>>)=?
| [!<>=/*%+|&^-]=?
| \+\+ | --
- | (<<|>>)=? | ~ | && | \| | \|\|
+ | ~ | && | \| | \|\|
| ->\*? | \,
''')
theid = sig#obj.get_id()
theid = re.sub(r" +", " ", theid)
theid = re.sub(r"=[^,()]+\([^)]*?\)[^,)]*(,|\))", "\\1", theid)
- theid = re.sub(r"=[^,)]+(,|\))", "\\1", theid)
+ theid = re.sub(r"=\w*[^,)(]+(,|\))", "\\1", theid)
theid = theid.replace("( ", "(").replace(" )", ")")
name = unicode(sigobj.name)
if theid not in self.state.document.ids:
'func' : OCVXRefRole(fix_parens=True),
'funcx' : OCVXRefRole(),
'cfunc' : OCVXRefRole(fix_parens=True),
- 'cfunc' : OCVXRefRole(),
+ 'cfuncx' : OCVXRefRole(),
'jfunc' : OCVXRefRole(fix_parens=True),
- 'jfunc' : OCVXRefRole(),
+ 'jfuncx' : OCVXRefRole(),
'pyfunc' : OCVPyXRefRole(),
'pyoldfunc' : OCVPyXRefRole(),
'member': OCVXRefRole(),
obj = dict[name]
if obj[1] not in self.objtypes_for_role(typ):
return None
+ title = obj[2]
+ if "class" in self.objtypes_for_role(typ):
+ title = u"class " + title
+ elif "struct" in self.objtypes_for_role(typ):
+ title = u"struct " + title
return make_refnode(builder, fromdocname, obj[0], obj[2],
- contnode, name)
+ contnode, title)
parser = DefinitionParser(target)
try:
:param Q: :math:`4 \times 4` perspective transformation matrix that can be obtained with :ocv:func:`stereoRectify`.
- :param handleMissingValues: Indicates, whether the function should handle missing values (i.e. points where the disparity was not computed). If ``handleMissingValues=true``, then pixels with the minimal disparity that corresponds to the outliers (see :ocv:func:`StereoBM::operator()` ) are transformed to 3D points with a very large Z value (currently set to 10000).
+ :param handleMissingValues: Indicates, whether the function should handle missing values (i.e. points where the disparity was not computed). If ``handleMissingValues=true``, then pixels with the minimal disparity that corresponds to the outliers (see :ocv:funcx:`StereoBM::operator()` ) are transformed to 3D points with a very large Z value (currently set to 10000).
:param ddepth: The optional output array depth. If it is ``-1``, the output image will have ``CV_32F`` depth. ``ddepth`` can also be set to ``CV_16S``, ``CV_32S`` or ``CV_32F``.
Ptr<CvStereoBMState> state;
};
-The class is a C++ wrapper for the associated functions. In particular, :ocv:func:`StereoBM::operator()` is the wrapper for
+The class is a C++ wrapper for the associated functions. In particular, :ocv:funcx:`StereoBM::operator()` is the wrapper for
:ocv:cfunc:`cvFindStereoCorrespondenceBM`.
* Mutual information cost function is not implemented. Instead, a simpler Birchfield-Tomasi sub-pixel metric from BT96 is used. Though, the color images are supported as well.
- * Some pre- and post- processing steps from K. Konolige algorithm :ocv:func:`StereoBM::operator()` are included, for example: pre-filtering (``CV_STEREO_BM_XSOBEL`` type) and post-filtering (uniqueness check, quadratic interpolation and speckle filtering).
+ * Some pre- and post- processing steps from K. Konolige algorithm :ocv:funcx:`StereoBM::operator()` are included, for example: pre-filtering (``CV_STEREO_BM_XSOBEL`` type) and post-filtering (uniqueness check, quadratic interpolation and speckle filtering).
:param maxComponents: Maximum number of components that PCA should retain. By default, all the components are retained.
The default constructor initializes an empty PCA structure. The second constructor initializes the structure and calls
-:ocv:func:`PCA::operator()` .
+:ocv:funcx:`PCA::operator()` .
* **SVD::FULL_UV** When the matrix is not square, by default the algorithm produces ``u`` and ``vt`` matrices of sufficiently large size for the further ``A`` reconstruction. If, however, ``FULL_UV`` flag is specified, ``u`` and ``vt`` will be full-size square orthogonal matrices.
The first constructor initializes an empty ``SVD`` structure. The second constructor initializes an empty ``SVD`` structure and then calls
-:ocv:func:`SVD::operator()` .
+:ocv:funcx:`SVD::operator()` .
SVD::operator ()
.. ocv:member:: int[] subset
- Bit array indicating the value subset in case of split on a categorical variable. The rule is:
+ Bit array indicating the value subset in case of split on a categorical variable. The rule is: ::
-::
-
- if var_value in subset
- then next_node <- left
- else next_node <- right
+ if var_value in subset
+ then next_node <- left
+ else next_node <- right
-.. ocv:member:: float ord.c
+.. ocv:member:: float ord::c
The threshold value in case of split on an ordered variable. The rule is: ::
- if var_value < c
+ if var_value < ord.c
then next_node<-left
else next_node<-right
-.. ocv:member:: int ord.split_point
+.. ocv:member:: int ord::split_point
Used internally by the training algorithm.
size reaches a limit. Besides, the larger networks are trained much
longer than the smaller ones, so it is reasonable to pre-process the data,
using
-:ocv:func:`PCA::operator()` or similar technique, and train a smaller network
+:ocv:funcx:`PCA::operator()` or similar technique, and train a smaller network
on only essential features.
Another MPL feature is an inability to handle categorical
.. ocv:function:: void BackgroundSubtractorMOG::operator()(InputArray image, OutputArray fgmask, double learningRate=0)
-Parameters are the same as in ``BackgroundSubtractor::operator()``
+Parameters are the same as in :ocv:funcx:`BackgroundSubtractor::operator()`
BackgroundSubtractorMOG2
.. ocv:function:: void BackgroundSubtractorMOG2::operator()(InputArray image, OutputArray fgmask, double learningRate=-1)
- See :ocv:func:`BackgroundSubtractor::operator()`.
+ See :ocv:funcx:`BackgroundSubtractor::operator()`.
BackgroundSubtractorMOG2::getBackgroundImage
projects / platform / upstream / opencv.git / commitdiff