Fix warnings from documentation build

modules/core/doc/command_line_parser.rst

@@ -4,24 +4,24 @@ Command Line Parser
 .. highlight:: cpp
 
 CommandLineParser
---------
+-----------------
 .. ocv:class:: CommandLineParser
 
 The CommandLineParser class is designed for command line arguments parsing
 
 
-    .. ocv:function:: CommandLineParser::CommandLineParser(int argc, const char * const argv[], const std::string keys)
+    .. ocv:function:: CommandLineParser::CommandLineParser( int argc, const char* const argv[], const string& keys )
 
         :param argc:
         :param argv:
         :param keys:
 
-    .. ocv:function:: T CommandLineParser::get<T>(const std::string& name, bool space_delete = true)
+    .. ocv:function:: template<typename T> T CommandLineParser::get<T>(const std::string& name, bool space_delete = true)
 
         :param name:
         :param space_delete:
 
-    .. ocv:function:: T CommandLineParser::get<T>(int index, bool space_delete = true)
+    .. ocv:function:: template<typename T> T CommandLineParser::get<T>(int index, bool space_delete = true)
 
         :param index:
         :param space_delete:
@@ -33,7 +33,7 @@ The CommandLineParser class is designed for command line arguments parsing
     .. ocv:function:: bool CommandLineParser::check()
 
 
-    .. ocv:function:: void CommandLineParser::about(std::string message)
+    .. ocv:function:: void CommandLineParser::about( const string& message )
 
         :param message:
 

modules/gpu/doc/object_detection.rst

@@ -220,10 +220,6 @@ After each weak classifier evaluation, the sample trace at the point :math:`t` i
 
 The sample has been rejected if it fall rejection threshold. So stageless cascade allows to reject not-object sample as soon as possible. Another meaning of the sample trace is a confidence with that sample recognized as desired object. At each :math:`t` that confidence depend on all previous weak classifier. This feature of soft cascade is resulted in more accurate detection. The original formulation of soft cascade can be found in [BJ05]_.
 
-.. [BJ05] Lubomir Bourdev and Jonathan Brandt. tRobust Object Detection Via Soft Cascade. IEEE CVPR, 2005.
-.. [BMTG12] Rodrigo Benenson, Markus Mathias, Radu Timofte and Luc Van Gool. Pedestrian detection at 100 frames per second. IEEE CVPR, 2012.
-
-
 gpu::SCascade
 -----------------------------------------------
 .. ocv:class:: gpu::SCascade : public Algorithm

modules/softcascade/doc/softcascade_detector.rst

@@ -26,8 +26,8 @@ The sample has been rejected if it fall rejection threshold. So stageless cascad
 
 
 softcascade::Detector
--------------------
+---------------------
-.. ocv:class:: Detector : public Algorithm
+.. ocv:class:: softcascade::Detector : public Algorithm
 
 Implementation of soft (stageless) cascaded detector. ::
 

modules/videostab/doc/global_motion.rst

@@ -7,22 +7,18 @@ The video stabilization module contains a set of functions and classes for globa
 
 videostab::MotionModel
 ----------------------
-
 Describes motion model between two point clouds.
 
-::
+.. ocv:enum:: videostab::MotionModel
 
-    enum MotionModel
+  .. ocv:emember:: MM_TRANSLATION = 0
-    {
+  .. ocv:emember:: MM_TRANSLATION_AND_SCALE = 1
-        MM_TRANSLATION = 0,
+  .. ocv:emember:: MM_ROTATION = 2
-        MM_TRANSLATION_AND_SCALE = 1,
+  .. ocv:emember:: MM_RIGID = 3
-        MM_ROTATION = 2,
+  .. ocv:emember:: MM_SIMILARITY = 4
-        MM_RIGID = 3,
+  .. ocv:emember:: MM_AFFINE = 5
-        MM_SIMILARITY = 4,
+  .. ocv:emember:: MM_HOMOGRAPHY = 6
-        MM_AFFINE = 5,
+  .. ocv:emember:: MM_UNKNOWN = 7
-        MM_HOMOGRAPHY = 6,
-        MM_UNKNOWN = 7
-    };
 
 
 videostab::RansacParams
@@ -34,7 +30,7 @@ Describes RANSAC method parameters.
 
 ::
 
-    struct CV_EXPORTS RansacParams
+    struct RansacParams
     {
         int size; // subset size
         float thresh; // max error to classify as inlier
@@ -87,7 +83,7 @@ videostab::RansacParams::default2dMotion
 
 .. ocv:function:: static RansacParams videostab::RansacParams::default2dMotion(MotionModel model)
 
-    :param model: Motion model. See :ocv:class:`videostab::MotionModel`.
+    :param model: Motion model. See :ocv:enum:`videostab::MotionModel`.
 
     :return: Default RANSAC method parameters for the given motion model.
 
@@ -123,9 +119,9 @@ Estimates best global motion between two 2D point clouds robustly (using RANSAC
 
     :param points1: Destination set of 2D points (``32F``).
 
-    :param model: Motion model. See :ocv:class:`videostab::MotionModel`.
+    :param model: Motion model. See :ocv:enum:`videostab::MotionModel`.
 
-    :param params: RANSAC method parameters. See :ocv:class:`videostab::RansacParams`.
+    :param params: RANSAC method parameters. See :ocv:struct:`videostab::RansacParams`.
 
     :param rmse: Final root-mean-square error.
 
@@ -157,7 +153,7 @@ Base class for all global motion estimation methods.
 
 ::
 
-    class CV_EXPORTS MotionEstimatorBase
+    class MotionEstimatorBase
     {
     public:
         virtual ~MotionEstimatorBase();
@@ -176,16 +172,16 @@ Sets motion model.
 
 .. ocv:function:: void videostab::MotionEstimatorBase::setMotionModel(MotionModel val)
 
-    :param val: Motion model. See :ocv:class:`videostab::MotionModel`.
+    :param val: Motion model. See :ocv:enum:`videostab::MotionModel`.
 
 
 
 videostab::MotionEstimatorBase::motionModel
-----------------------------------------------
+-------------------------------------------
 
 .. ocv:function:: MotionModel videostab::MotionEstimatorBase::motionModel() const
 
-    :return: Motion model. See :ocv:class:`videostab::MotionModel`.
+    :return: Motion model. See :ocv:enum:`videostab::MotionModel`.
 
 
 videostab::MotionEstimatorBase::estimate
@@ -213,7 +209,7 @@ Describes a robust RANSAC-based global 2D motion estimation method which minimiz
 
 ::
 
-    class CV_EXPORTS MotionEstimatorRansacL2 : public MotionEstimatorBase
+    class MotionEstimatorRansacL2 : public MotionEstimatorBase
     {
     public:
         MotionEstimatorRansacL2(MotionModel model = MM_AFFINE);
@@ -239,7 +235,7 @@ Describes a global 2D motion estimation method which minimizes L1 error.
 
 ::
 
-    class CV_EXPORTS MotionEstimatorL1 : public MotionEstimatorBase
+    class MotionEstimatorL1 : public MotionEstimatorBase
     {
     public:
         MotionEstimatorL1(MotionModel model = MM_AFFINE);
@@ -257,7 +253,7 @@ Base class for global 2D motion estimation methods which take frames as input.
 
 ::
 
-    class CV_EXPORTS ImageMotionEstimatorBase
+    class ImageMotionEstimatorBase
     {
     public:
         virtual ~ImageMotionEstimatorBase();
@@ -278,7 +274,7 @@ Describes a global 2D motion estimation method which uses keypoints detection an
 
 ::
 
-    class CV_EXPORTS KeypointBasedMotionEstimator : public ImageMotionEstimatorBase
+    class KeypointBasedMotionEstimator : public ImageMotionEstimatorBase
     {
     public:
         KeypointBasedMotionEstimator(Ptr<MotionEstimatorBase> estimator);