Eliminate discrepancies between signatures in documentation and OpenCV headers

All errors were found by doc/check_docs2.py

diff --git a/modules/contrib/doc/openfabmap.rst b/modules/contrib/doc/openfabmap.rst
index d93b4ee..6e8a9f6 100644 (file)
--- a/modules/contrib/doc/openfabmap.rst
+++ b/modules/contrib/doc/openfabmap.rst
@@ -9,10 +9,10 @@ FAB-MAP is an approach to appearance-based place recognition. FAB-MAP compares i
 
 openFABMAP requires training data (e.g. a collection of images from a similar but not identical environment) to construct a visual vocabulary for the visual bag-of-words model, along with a Chow-Liu tree representation of feature likelihood and for use in the Sampled new place method (see below).
 
-FabMap
+of2::FabMap
 --------------------
 
-.. ocv:class:: FabMap
+.. ocv:class:: of2::FabMap
 
 The main FabMap class performs the comparison between visual bags-of-words extracted from one or more images. The FabMap class is instantiated as one of the four inherited FabMap classes (FabMap1, FabMapLUT, FabMapFBO, FabMap2). Each inherited class performs the comparison differently based on algorithm iterations as published (see each class below for specifics). A Chow-Liu tree, detector model parameters and some option flags are common to all Fabmap variants and are supplied on class creation. Training data (visual bag-of-words) is supplied to the class if using the SAMPLED new place method. Test data (visual bag-of-words) is supplied as images to which query bag-of-words are compared against. The common flags are listed below: ::
 
@@ -149,10 +149,10 @@ The inverted index FAB-MAP as in [IJRR2010]_. This version of FAB-MAP is the fas
 
 .. [ICRA2011] A. Glover, et al., "OpenFABMAP: An Open Source Toolbox for Appearance-based Loop Closure Detection," in IEEE International Conference on Robotics and Automation, St Paul, Minnesota, 2011
 
-ImageMatch
+of2::IMatch
 --------------------
 
-.. ocv:struct:: IMatch
+.. ocv:struct:: of2::IMatch
 
 FAB-MAP comparison results are stored in a vector of IMatch structs. Each IMatch structure provides the index of the provided query bag-of-words, the index of the test bag-of-words, the raw log-likelihood of the match (independent of other comparisons), and the match probability (normalised over other comparison likelihoods).
 
@@ -180,48 +180,48 @@ FAB-MAP comparison results are stored in a vector of IMatch structs. Each IMatch
 
     };
 
-Chow-Liu Tree
+of2::ChowLiuTree
 --------------------
 
-.. ocv:class:: ChowLiuTree
+.. ocv:class:: of2::ChowLiuTree
 
 The Chow-Liu tree is a probabilistic model of the environment in terms of feature occurance and co-occurance. The Chow-Liu tree is a form of Bayesian network. FAB-MAP uses the model when calculating bag-of-words similarity by taking into account feature saliency. Training data is provided to the ChowLiuTree class in the form of bag-of-words image descriptors. The make function produces a cv::Mat that encodes the tree structure.
 
-.. ocv:function:: ChowLiuTree::ChowLiuTree()
+.. ocv:function:: of2::ChowLiuTree::ChowLiuTree()
 
-.. ocv:function:: void add(const Mat& imgDescriptor)
+.. ocv:function:: void of2::ChowLiuTree::add(const Mat& imgDescriptor)
 
     :param imgDescriptor:  bag-of-words image descriptors stored as rows in a Mat
 
-.. ocv:function:: void add(const vector<Mat>& imgDescriptors)
+.. ocv:function:: void of2::ChowLiuTree::add(const vector<Mat>& imgDescriptors)
 
     :param imgDescriptors: a vector containing multiple bag-of-words image descriptors
 
-.. ocv:function:: const vector<Mat>& getImgDescriptors() const
+.. ocv:function:: const vector<Mat>& of2::ChowLiuTree::getImgDescriptors() const
 
     Returns a vector containing multiple bag-of-words image descriptors
 
-.. ocv:function:: Mat make(double infoThreshold = 0.0)
+.. ocv:function:: Mat of2::ChowLiuTree::make(double infoThreshold = 0.0)
 
     :param infoThreshold: a threshold can be set to reduce the amount of memory used when making the Chow-Liu tree, which can occur with large vocabulary sizes. This function can fail if the threshold is set too high. If memory is an issue the value must be set by trial and error (~0.0005)
 
 
-BOWMSCTrainer
+of2::BOWMSCTrainer
 --------------------
 
-.. ocv:class:: BOWMSCTrainer : public BOWTrainer
+.. ocv:class:: of2::BOWMSCTrainer : public of2::BOWTrainer
 
 BOWMSCTrainer is a custom clustering algorithm used to produce the feature vocabulary required to create bag-of-words representations. The algorithm is an implementation of [AVC2007]_. Arguments against using K-means for the FAB-MAP algorithm are discussed in [IJRR2010]_. The BOWMSCTrainer inherits from the cv::BOWTrainer class, overwriting the cluster function.
 
-.. ocv:function::   BOWMSCTrainer::BOWMSCTrainer(double clusterSize = 0.4)
+.. ocv:function::   of2::BOWMSCTrainer::BOWMSCTrainer(double clusterSize = 0.4)
 
     :param clusterSize: the specificity of the vocabulary produced. A smaller cluster size will instigate a larger vocabulary.
 
-.. ocv:function::  virtual Mat cluster() const
+.. ocv:function::  virtual Mat of2::BOWMSCTrainer::cluster() const
 
 Cluster using features added to the class
 
-.. ocv:function:: virtual Mat cluster(const Mat& descriptors) const
+.. ocv:function:: virtual Mat of2::BOWMSCTrainer::cluster(const Mat& descriptors) const
 
     :param descriptors: feature descriptors provided as rows of the Mat.
 

diff --git a/modules/core/doc/operations_on_arrays.rst b/modules/core/doc/operations_on_arrays.rst
index 1489334..371c541 100644 (file)
--- a/modules/core/doc/operations_on_arrays.rst
+++ b/modules/core/doc/operations_on_arrays.rst
@@ -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 )
 

diff --git a/modules/gpu/doc/feature_detection_and_description.rst b/modules/gpu/doc/feature_detection_and_description.rst
index 4129ba8..5a6f85c 100644 (file)
--- a/modules/gpu/doc/feature_detection_and_description.rst
+++ b/modules/gpu/doc/feature_detection_and_description.rst
@@ -350,7 +350,7 @@ gpu::ORB_GPU::downloadKeyPoints
 -------------------------------------
 Download keypoints from GPU to CPU memory.
 
-.. ocv:function:: void gpu::ORB_GPU::downloadKeyPoints( GpuMat& d_keypoints, std::vector<KeyPoint>& keypoints )
+.. ocv:function:: static void gpu::ORB_GPU::downloadKeyPoints( const GpuMat& d_keypoints, std::vector<KeyPoint>& keypoints )
 
 
 
@@ -358,7 +358,7 @@ gpu::ORB_GPU::convertKeyPoints
 -------------------------------------
 Converts keypoints from GPU representation to vector of ``KeyPoint``.
 
-.. ocv:function:: void gpu::ORB_GPU::convertKeyPoints( Mat& d_keypoints, std::vector<KeyPoint>& keypoints )
+.. ocv:function:: static void gpu::ORB_GPU::convertKeyPoints( const Mat& d_keypoints, std::vector<KeyPoint>& keypoints )
 
 
 

diff --git a/modules/gpu/doc/image_processing.rst b/modules/gpu/doc/image_processing.rst
index ce05089..69b171e 100644 (file)
--- a/modules/gpu/doc/image_processing.rst
+++ b/modules/gpu/doc/image_processing.rst
@@ -824,7 +824,7 @@ gpu::bilateralFilter
 --------------------
 Performs bilateral filtering of passed image
 
-.. ocv:function:: void gpu::bilateralFilter(const GpuMat& src, GpuMat& dst, int kernel_size, float sigma_color, float sigma_spatial, int borderMode, Stream& stream = Stream::Null())
+.. ocv:function:: void gpu::bilateralFilter( const GpuMat& src, GpuMat& dst, int kernel_size, float sigma_color, float sigma_spatial, int borderMode=BORDER_DEFAULT, Stream& stream=Stream::Null() )
 
     :param src: Source image. Supports only (channles != 2 && depth() != CV_8S && depth() != CV_32S && depth() != CV_64F).
 
@@ -849,7 +849,7 @@ gpu::nonLocalMeans
 -------------------
 Performs pure non local means denoising without any simplification, and thus it is not fast.
 
-.. ocv:function:: void nonLocalMeans(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, int borderMode = BORDER_DEFAULT, Stream& s = Stream::Null())
+.. ocv:function:: void gpu::nonLocalMeans(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, int borderMode = BORDER_DEFAULT, Stream& s = Stream::Null())
 
     :param src: Source image. Supports only CV_8UC1, CV_8UC2 and CV_8UC3.
 
@@ -877,10 +877,10 @@ gpu::FastNonLocalMeansDenoising
     {
     public:
         //! Simple method, recommended for grayscale images (though it supports multichannel images)
-        void simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream& s = Stream::Null());
+        void simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream& s = Stream::Null())
 
         //! Processes luminance and color components separatelly
-        void labMethod(const GpuMat& src, GpuMat& dst, float h_luminance, float h_color, int search_window = 21, int block_size = 7, Stream& s = Stream::Null());
+        void labMethod(const GpuMat& src, GpuMat& dst, float h_luminance, float h_color, int search_window = 21, int block_size = 7, Stream& s = Stream::Null())
     };
 
 The class implements fast approximate Non Local Means Denoising algorithm.
@@ -889,7 +889,7 @@ gpu::FastNonLocalMeansDenoising::simpleMethod()
 -------------------------------------
 Perform image denoising using Non-local Means Denoising algorithm http://www.ipol.im/pub/algo/bcm_non_local_means_denoising with several computational optimizations. Noise expected to be a gaussian white noise
 
-.. ocv:function:: void gpu::FastNonLocalMeansDenoising::simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream& s = Stream::Null());
+.. ocv:function:: void gpu::FastNonLocalMeansDenoising::simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream& s = Stream::Null())
 
     :param src: Input 8-bit 1-channel, 2-channel or 3-channel image.
 
@@ -913,7 +913,7 @@ gpu::FastNonLocalMeansDenoising::labMethod()
 -------------------------------------
 Modification of ``FastNonLocalMeansDenoising::simpleMethod`` for color images
 
-.. ocv:function:: void gpu::FastNonLocalMeansDenoising::labMethod(const GpuMat& src, GpuMat& dst, float h_luminance, float h_color, int search_window = 21, int block_size = 7, Stream& s = Stream::Null());
+.. ocv:function:: void gpu::FastNonLocalMeansDenoising::labMethod(const GpuMat& src, GpuMat& dst, float h_luminance, float h_color, int search_window = 21, int block_size = 7, Stream& s = Stream::Null())
 
     :param src: Input 8-bit 3-channel image.
 

diff --git a/modules/gpu/doc/video.rst b/modules/gpu/doc/video.rst
index 31d24d1..dbfd93a 100644 (file)
--- a/modules/gpu/doc/video.rst
+++ b/modules/gpu/doc/video.rst
@@ -739,7 +739,7 @@ gpu::GMG_GPU::operator()
 ------------------------
 Updates the background model and returns the foreground mask
 
-.. ocv:function:: void gpu::GMG_GPU::operator()(const GpuMat& frame, GpuMat& fgmask, Stream& stream = Stream::Null())
+.. ocv:function:: void gpu::GMG_GPU::operator ()( const GpuMat& frame, GpuMat& fgmask, float learningRate=-1.0f, Stream& stream=Stream::Null() )
 
     :param frame: Next video frame.
 

diff --git a/modules/nonfree/doc/feature_detection.rst b/modules/nonfree/doc/feature_detection.rst
index 5d25c1e..e4ac357 100644 (file)
--- a/modules/nonfree/doc/feature_detection.rst
+++ b/modules/nonfree/doc/feature_detection.rst
@@ -104,8 +104,7 @@ Detects keypoints and computes SURF descriptors for them.
 .. ocv:function:: void SURF::operator()(InputArray img, InputArray mask, vector<KeyPoint>& keypoints) const
 .. ocv:function:: void SURF::operator()(InputArray img, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false)
 
-.. ocv:pyfunction:: cv2.SURF.detect(img, mask) -> keypoints
-.. ocv:pyfunction:: cv2.SURF.detect(img, mask[, descriptors[, useProvidedKeypoints]]) -> keypoints, descriptors
+.. ocv:pyfunction:: cv2.SURF.detect(image[, mask]) -> keypoints
 
 .. ocv:cfunction:: void cvExtractSURF( const CvArr* image, const CvArr* mask, CvSeq** keypoints, CvSeq** descriptors, CvMemStorage* storage, CvSURFParams params )
 

diff --git a/modules/ocl/doc/structures_and_functions.rst b/modules/ocl/doc/structures_and_functions.rst
index 474558f..7ac30b6 100644 (file)
--- a/modules/ocl/doc/structures_and_functions.rst
+++ b/modules/ocl/doc/structures_and_functions.rst
@@ -13,7 +13,7 @@ ocl::getDevice
 ------------------
 Returns the list of devices
 
-.. ocv:function:: int ocl::getDevice(std::vector<Info>& oclinfo, int devicetype = CVCL_DEVICE_TYPE_GPU)
+.. ocv:function:: int ocl::getDevice( std::vector<Info> & oclinfo, int devicetype=CVCL_DEVICE_TYPE_GPU )
 
     :param oclinfo: Output vector of ``ocl::Info`` structures
 

diff --git a/modules/photo/doc/denoising.rst b/modules/photo/doc/denoising.rst
index 8e53e5a..97625d3 100644 (file)
--- a/modules/photo/doc/denoising.rst
+++ b/modules/photo/doc/denoising.rst
@@ -4,11 +4,11 @@ Denoising
 .. highlight:: cpp
 
 fastNlMeansDenoising
------------
+--------------------
 Perform image denoising using Non-local Means Denoising algorithm http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/
 with several computational optimizations. Noise expected to be a gaussian white noise
 
-.. ocv:function:: void fastNlMeansDenoising( Mat& src, Mat& dst, int templateWindowSize, int searchWindowSize, int h )
+.. ocv:function:: void fastNlMeansDenoising( InputArray src, OutputArray dst, float h=3, int templateWindowSize=7, int searchWindowSize=21 )
 
     :param src: Input 8-bit 1-channel, 2-channel or 3-channel image.
 
@@ -25,10 +25,10 @@ Advanced usage of this functions can be manual denoising of colored image in dif
 Such approach is used in ``fastNlMeansDenoisingColored`` by converting image to CIELAB colorspace and then separately denoise L and AB components with different h parameter.
 
 fastNlMeansDenoisingColored
------------
+---------------------------
 Modification of ``fastNlMeansDenoising`` function for colored images
 
-.. ocv:function:: void fastNlMeansDenoisingColored( Mat& src, Mat& dst, int templateWindowSize, int searchWindowSize, int h, int hForColorComponents )
+.. ocv:function:: void fastNlMeansDenoisingColored( InputArray src, OutputArray dst, float h=3, float hColor=3, int templateWindowSize=7, int searchWindowSize=21 )
 
     :param src: Input 8-bit 3-channel image.
 
@@ -45,11 +45,11 @@ Modification of ``fastNlMeansDenoising`` function for colored images
 The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using ``fastNlMeansDenoising`` function.
 
 fastNlMeansDenoisingMulti
------------
+-------------------------
 Modification of ``fastNlMeansDenoising`` function for images sequence where consequtive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces.
 For more details see http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394
 
-.. ocv:function:: void fastNlMeansDenoisingMulti( const std::vector<Mat>& srcImgs, int imgToDenoiseIndex, int temporalWindowSize, Mat& dst, int templateWindowSize, int searchWindowSize, int h)
+.. ocv:function:: void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst, int imgToDenoiseIndex, int temporalWindowSize, float h=3, int templateWindowSize=7, int searchWindowSize=21 )
 
     :param srcImgs: Input 8-bit 1-channel, 2-channel or 3-channel images sequence. All images should have the same type and size.
 
@@ -66,10 +66,10 @@ For more details see http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131
     :param h: Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
 
 fastNlMeansDenoisingColoredMulti
------------
+--------------------------------
 Modification of ``fastNlMeansDenoisingMulti`` function for colored images sequences
 
-.. ocv:function:: void fastNlMeansDenoisingColoredMulti( const std::vector<Mat>& srcImgs, int imgToDenoiseIndex, int temporalWindowSize, Mat& dst, int templateWindowSize, int searchWindowSize, int h, int hForColorComponents)
+.. ocv:function:: void fastNlMeansDenoisingColoredMulti( InputArrayOfArrays srcImgs, OutputArray dst, int imgToDenoiseIndex, int temporalWindowSize, float h=3, float hColor=3, int templateWindowSize=7, int searchWindowSize=21 )
 
     :param srcImgs: Input 8-bit 3-channel images sequence. All images should have the same type and size.
 

diff --git a/modules/video/doc/motion_analysis_and_object_tracking.rst b/modules/video/doc/motion_analysis_and_object_tracking.rst
index 53b2a70..ca5f005 100644 (file)
--- a/modules/video/doc/motion_analysis_and_object_tracking.rst
+++ b/modules/video/doc/motion_analysis_and_object_tracking.rst
@@ -598,12 +598,12 @@ See :ocv:func:`BackgroundSubtractor::getBackgroundImage`.
 
 
 calcOpticalFlowSF
------------
+-----------------
 Calculate an optical flow using "SimpleFlow" algorithm.
 
-.. ocv:function:: void calcOpticalFlowSF( Mat& prev, Mat& next, Mat& flowX, Mat& flowY, int layers, int averaging_block_size, int max_flow)
+.. ocv:function:: void calcOpticalFlowSF( Mat& from, Mat& to, Mat& flow, int layers, int averaging_block_size, int max_flow )
 
-.. ocv:function:: void calcOpticalFlowSF( Mat& prev, Mat& next, Mat& flowX, Mat& flowY, int layers, int averaging_block_size, int max_flow, double sigma_dist, double sigma_color, int postprocess_window, double sigma_dist_fix, double sigma_color_fix, double occ_thr, int upscale_averaging_radiud, double upscale_sigma_dist, double upscale_sigma_color, double speed_up_thr)
+.. ocv:function:: calcOpticalFlowSF( Mat& from, Mat& to, Mat& flow, int layers, int averaging_block_size, int max_flow, double sigma_dist, double sigma_color, int postprocess_window, double sigma_dist_fix, double sigma_color_fix, double occ_thr, int upscale_averaging_radius, double upscale_sigma_dist, double upscale_sigma_color, double speed_up_thr )
 
     :param prev: First 8-bit 3-channel image.
 

diff --git a/modules/videostab/doc/fast_marching.rst b/modules/videostab/doc/fast_marching.rst
index 7332302..5df4a72 100644 (file)
--- a/modules/videostab/doc/fast_marching.rst
+++ b/modules/videostab/doc/fast_marching.rst
@@ -39,7 +39,7 @@ videostab::FastMarchingMethod::run
 
 Template method that runs the Fast Marching Method.
 
-.. ocv:function:: Inpaint FastMarchingMethod::run(const Mat &mask, Inpaint inpaint)
+.. ocv:function:: template<typename Inpaint> Inpaint videostab::FastMarchingMethod::run(const Mat &mask, Inpaint inpaint)
 
     :param mask: Image mask. ``0`` value indicates that the pixel value must be inpainted, ``255`` indicates that the pixel value is known, other values aren't acceptable.
 

diff --git a/modules/videostab/doc/global_motion.rst b/modules/videostab/doc/global_motion.rst
index c4c764f..f33e904 100644 (file)
--- a/modules/videostab/doc/global_motion.rst
+++ b/modules/videostab/doc/global_motion.rst
@@ -8,8 +8,6 @@ The video stabilization module contains a set of functions and classes for globa
 videostab::MotionModel
 ----------------------
 
-.. ocv:class:: videostab::MotionModel
-
 Describes motion model between two point clouds.
 
 ::
@@ -30,7 +28,7 @@ Describes motion model between two point clouds.
 videostab::RansacParams
 -----------------------
 
-.. ocv:class:: videostab::RansacParams
+.. ocv:struct:: videostab::RansacParams
 
 Describes RANSAC method parameters.
 
@@ -55,7 +53,7 @@ Describes RANSAC method parameters.
 videostab::RansacParams::RansacParams
 -------------------------------------
 
-.. ocv:function:: RansacParams::RansacParams()
+.. ocv:function:: videostab::RansacParams::RansacParams()
 
     :return: RANSAC method empty parameters object.
 
@@ -63,7 +61,7 @@ videostab::RansacParams::RansacParams
 videostab::RansacParams::RansacParams
 -------------------------------------
 
-.. ocv:function:: RansacParams::RansacParams(int size, float thresh, float eps, float prob)
+.. ocv:function:: videostab::RansacParams::RansacParams(int size, float thresh, float eps, float prob)
 
     :param size: Subset size.
 
@@ -79,7 +77,7 @@ videostab::RansacParams::RansacParams
 videostab::RansacParams::niters
 -------------------------------
 
-.. ocv:function:: int RansacParams::niters() const
+.. ocv:function:: int videostab::RansacParams::niters() const
 
     :return: Number of iterations that'll be performed by RANSAC method.
 
@@ -87,7 +85,7 @@ videostab::RansacParams::niters
 videostab::RansacParams::default2dMotion
 ----------------------------------------
 
-.. ocv:function:: static RansacParams RansacParams::default2dMotion(MotionModel model)
+.. ocv:function:: static RansacParams videostab::RansacParams::default2dMotion(MotionModel model)
 
     :param model: Motion model. See :ocv:class:`videostab::MotionModel`.
 
@@ -101,7 +99,7 @@ Estimates best global motion between two 2D point clouds in the least-squares se
 
 .. note:: Works in-place and changes input point arrays.
 
-.. ocv:function:: Mat estimateGlobalMotionLeastSquares(InputOutputArray points0, InputOutputArray points1, int model = MM_AFFINE, float *rmse = 0)
+.. ocv:function:: Mat videostab::estimateGlobalMotionLeastSquares(InputOutputArray points0, InputOutputArray points1, int model = MM_AFFINE, float *rmse = 0)
 
     :param points0: Source set of 2D points (``32F``).
 
@@ -119,7 +117,7 @@ videostab::estimateGlobalMotionRansac
 
 Estimates best global motion between two 2D point clouds robustly (using RANSAC method).
 
-.. ocv:function:: Mat estimateGlobalMotionRansac(InputArray points0, InputArray points1, int model = MM_AFFINE, const RansacParams &params = RansacParams::default2dMotion(MM_AFFINE), float *rmse = 0, int *ninliers = 0)
+.. ocv:function:: Mat videostab::estimateGlobalMotionRansac(InputArray points0, InputArray points1, int model = MM_AFFINE, const RansacParams &params = RansacParams::default2dMotion(MM_AFFINE), float *rmse = 0, int *ninliers = 0)
 
     :param points0: Source set of 2D points (``32F``).
 
@@ -139,7 +137,7 @@ videostab::getMotion
 
 Computes motion between two frames assuming that all the intermediate motions are known.
 
-.. ocv:function:: Mat getMotion(int from, int to, const std::vector<Mat> &motions)
+.. ocv:function:: Mat videostab::getMotion(int from, int to, const std::vector<Mat> &motions)
 
     :param from: Source frame index.
 
@@ -176,7 +174,7 @@ videostab::MotionEstimatorBase::setMotionModel
 
 Sets motion model.
 
-.. ocv:function:: void MotionEstimatorBase::setMotionModel(MotionModel val)
+.. ocv:function:: void videostab::MotionEstimatorBase::setMotionModel(MotionModel val)
 
     :param val: Motion model. See :ocv:class:`videostab::MotionModel`.
 
@@ -185,7 +183,7 @@ Sets motion model.
 videostab::MotionEstimatorBase::motionModel
 ----------------------------------------------
 
-.. ocv:function:: MotionModel MotionEstimatorBase::motionModel() const
+.. ocv:function:: MotionModel videostab::MotionEstimatorBase::motionModel() const
 
     :return: Motion model. See :ocv:class:`videostab::MotionModel`.
 
@@ -195,7 +193,7 @@ videostab::MotionEstimatorBase::estimate
 
 Estimates global motion between two 2D point clouds.
 
-.. ocv:function:: Mat MotionEstimatorBase::estimate(InputArray points0, InputArray points1, bool *ok = 0)
+.. ocv:function:: Mat videostab::MotionEstimatorBase::estimate(InputArray points0, InputArray points1, bool *ok = 0)
 
     :param points0: Source set of 2D points (``32F``).
 
@@ -209,7 +207,7 @@ Estimates global motion between two 2D point clouds.
 videostab::MotionEstimatorRansacL2
 ----------------------------------
 
-.. ocv:class:: videostab::MotionEstimatorRansacL2
+.. ocv:class:: videostab::MotionEstimatorRansacL2 : public videostab::MotionEstimatorBase
 
 Describes a robust RANSAC-based global 2D motion estimation method which minimizes L2 error.
 
@@ -233,7 +231,7 @@ Describes a robust RANSAC-based global 2D motion estimation method which minimiz
 videostab::MotionEstimatorL1
 ----------------------------
 
-.. ocv:class:: videostab::MotionEstimatorL1
+.. ocv:class:: videostab::MotionEstimatorL1 : public videostab::MotionEstimatorBase
 
 Describes a global 2D motion estimation method which minimizes L1 error.
 
@@ -274,7 +272,7 @@ Base class for global 2D motion estimation methods which take frames as input.
 videostab::KeypointBasedMotionEstimator
 ---------------------------------------
 
-.. ocv:class:: videostab::KeypointBasedMotionEstimator
+.. ocv:class:: videostab::KeypointBasedMotionEstimator : public videostab::ImageMotionEstimatorBase
 
 Describes a global 2D motion estimation method which uses keypoints detection and optical flow for matching.