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Documentation: fixed class/struc members documentation; added warning on incorrectly documented member

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This commit is contained in:
Andrey Kamaev
2012-05-29 16:55:46 +00:00
parent aaf043e7ba
commit a20248977c
14 changed files with 367 additions and 364 deletions
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3
doc/ocv.py
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@@ -1301,6 +1301,9 @@ class OCVMemberObject(OCVObject):
return ''
def parse_definition(self, parser):
parent_class = self.env.temp_data.get('ocv:parent')
if parent_class is None:
parser.fail("missing parent structure/class")
return parser.parse_member_object()
def describe_signature(self, signode, obj):

4
modules/gpu/doc/image_processing.rst
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@@ -282,7 +282,7 @@ Computes a convolution (or cross-correlation) of two images.
:param ccorr: Flags to evaluate cross-correlation instead of convolution.
:param buf: Optional buffer to avoid extra memory allocations and to adjust some specific parameters. See :ocv:class:`gpu::ConvolveBuf`.
:param buf: Optional buffer to avoid extra memory allocations and to adjust some specific parameters. See :ocv:struct:`gpu::ConvolveBuf`.
:param stream: Stream for the asynchronous version.
@@ -321,7 +321,7 @@ Computes a proximity map for a raster template and an image where the template i
:param method: Specifies the way to compare the template with the image.
:param buf: Optional buffer to avoid extra memory allocations and to adjust some specific parameters. See :ocv:class:`gpu::MatchTemplateBuf`.
:param buf: Optional buffer to avoid extra memory allocations and to adjust some specific parameters. See :ocv:struct:`gpu::MatchTemplateBuf`.
:param stream: Stream for the asynchronous version.

22
modules/ml/doc/boosting.rst
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@@ -69,6 +69,17 @@ CvBoostParams
Boosting training parameters.
There is one structure member that you can set directly:
.. ocv:member:: int split_criteria
Splitting criteria used to choose optimal splits during a weak tree construction. Possible values are:
* **CvBoost::DEFAULT** Use the default for the particular boosting method, see below.
* **CvBoost::GINI** Use Gini index. This is default option for Real AdaBoost; may be also used for Discrete AdaBoost.
* **CvBoost::MISCLASS** Use misclassification rate. This is default option for Discrete AdaBoost; may be also used for Real AdaBoost.
* **CvBoost::SQERR** Use least squares criteria. This is default and the only option for LogitBoost and Gentle AdaBoost.
The structure is derived from :ocv:class:`CvDTreeParams` but not all of the decision tree parameters are supported. In particular, cross-validation is not supported.
All parameters are public. You can initialize them by a constructor and then override some of them directly if you want.
@@ -96,17 +107,6 @@ The constructors.
See :ocv:func:`CvDTreeParams::CvDTreeParams` for description of other parameters.
Also there is one structure member that you can set directly:
.. ocv:member:: int split_criteria
Splitting criteria used to choose optimal splits during a weak tree construction. Possible values are:
* **CvBoost::DEFAULT** Use the default for the particular boosting method, see below.
* **CvBoost::GINI** Use Gini index. This is default option for Real AdaBoost; may be also used for Discrete AdaBoost.
* **CvBoost::MISCLASS** Use misclassification rate. This is default option for Discrete AdaBoost; may be also used for Real AdaBoost.
* **CvBoost::SQERR** Use least squares criteria. This is default and the only option for LogitBoost and Gentle AdaBoost.
Default parameters are:
::

2
modules/ml/doc/ertrees.rst
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@@ -12,4 +12,4 @@ CvERTrees
----------
.. ocv:class:: CvERTrees : public CvRTrees
The class implements the Extremely randomized trees algorithm. ``CvERTrees`` is inherited from :ocv:class:`CvRTrees` and has the same interface, so see description of :ocv:class:`CvRTrees` class to get details. To set the training parameters of Extremely randomized trees the same class :ocv:class:`CvRTParams` is used.
The class implements the Extremely randomized trees algorithm. ``CvERTrees`` is inherited from :ocv:class:`CvRTrees` and has the same interface, so see description of :ocv:class:`CvRTrees` class to get details. To set the training parameters of Extremely randomized trees the same class :ocv:struct:`CvRTParams` is used.