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

doc/ocv.py

@@ -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):

modules/gpu/doc/image_processing.rst

@@ -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.
 

modules/ml/doc/boosting.rst

@@ -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:
 
 ::

modules/ml/doc/ertrees.rst

@@ -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.