Merge pull request #3638 from mshabunin:doc-upgrade

Documentation transition to fresh Doxygen #3638

Merge with https://github.com/opencv/opencv/pull/25042

modules/bioinspired/include/opencv2/bioinspired/retina.hpp

@@ -94,57 +94,12 @@ enum {
     Here is the default configuration file of the retina module. It gives results such as the first
     retina output shown on the top of this page.
 
-    @code{xml}
-    <?xml version="1.0"?>
-    <opencv_storage>
-    <OPLandIPLparvo>
-        <colorMode>1</colorMode>
-        <normaliseOutput>1</normaliseOutput>
-        <photoreceptorsLocalAdaptationSensitivity>7.5e-01</photoreceptorsLocalAdaptationSensitivity>
-        <photoreceptorsTemporalConstant>9.0e-01</photoreceptorsTemporalConstant>
-        <photoreceptorsSpatialConstant>5.3e-01</photoreceptorsSpatialConstant>
-        <horizontalCellsGain>0.01</horizontalCellsGain>
-        <hcellsTemporalConstant>0.5</hcellsTemporalConstant>
-        <hcellsSpatialConstant>7.</hcellsSpatialConstant>
-        <ganglionCellsSensitivity>7.5e-01</ganglionCellsSensitivity></OPLandIPLparvo>
-    <IPLmagno>
-        <normaliseOutput>1</normaliseOutput>
-        <parasolCells_beta>0.</parasolCells_beta>
-        <parasolCells_tau>0.</parasolCells_tau>
-        <parasolCells_k>7.</parasolCells_k>
-        <amacrinCellsTemporalCutFrequency>2.0e+00</amacrinCellsTemporalCutFrequency>
-        <V0CompressionParameter>9.5e-01</V0CompressionParameter>
-        <localAdaptintegration_tau>0.</localAdaptintegration_tau>
-        <localAdaptintegration_k>7.</localAdaptintegration_k></IPLmagno>
-    </opencv_storage>
-    @endcode
+    @include default_retina_config.xml
 
     Here is the 'realistic" setup used to obtain the second retina output shown on the top of this page.
 
-    @code{xml}
-    <?xml version="1.0"?>
-    <opencv_storage>
-    <OPLandIPLparvo>
-      <colorMode>1</colorMode>
-      <normaliseOutput>1</normaliseOutput>
-      <photoreceptorsLocalAdaptationSensitivity>8.9e-01</photoreceptorsLocalAdaptationSensitivity>
-      <photoreceptorsTemporalConstant>9.0e-01</photoreceptorsTemporalConstant>
-      <photoreceptorsSpatialConstant>5.3e-01</photoreceptorsSpatialConstant>
-      <horizontalCellsGain>0.3</horizontalCellsGain>
-      <hcellsTemporalConstant>0.5</hcellsTemporalConstant>
-      <hcellsSpatialConstant>7.</hcellsSpatialConstant>
-      <ganglionCellsSensitivity>8.9e-01</ganglionCellsSensitivity></OPLandIPLparvo>
-    <IPLmagno>
-      <normaliseOutput>1</normaliseOutput>
-      <parasolCells_beta>0.</parasolCells_beta>
-      <parasolCells_tau>0.</parasolCells_tau>
-      <parasolCells_k>7.</parasolCells_k>
-      <amacrinCellsTemporalCutFrequency>2.0e+00</amacrinCellsTemporalCutFrequency>
-      <V0CompressionParameter>9.5e-01</V0CompressionParameter>
-      <localAdaptintegration_tau>0.</localAdaptintegration_tau>
-      <localAdaptintegration_k>7.</localAdaptintegration_k></IPLmagno>
-    </opencv_storage>
-    @endcode
+    @include realistic_retina_config.xml
+
       */
     struct RetinaParameters{
         //! Outer Plexiform Layer (OPL) and Inner Plexiform Layer Parvocellular (IplParvo) parameters

modules/bioinspired/samples/default_retina_config.xml

@@ -0,0 +1,24 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<OPLandIPLparvo>
+    <colorMode>1</colorMode>
+    <normaliseOutput>1</normaliseOutput>
+    <photoreceptorsLocalAdaptationSensitivity>7.5e-01</photoreceptorsLocalAdaptationSensitivity>
+    <photoreceptorsTemporalConstant>9.0e-01</photoreceptorsTemporalConstant>
+    <photoreceptorsSpatialConstant>5.3e-01</photoreceptorsSpatialConstant>
+    <horizontalCellsGain>0.01</horizontalCellsGain>
+    <hcellsTemporalConstant>0.5</hcellsTemporalConstant>
+    <hcellsSpatialConstant>7.</hcellsSpatialConstant>
+    <ganglionCellsSensitivity>7.5e-01</ganglionCellsSensitivity>
+</OPLandIPLparvo>
+<IPLmagno>
+    <normaliseOutput>1</normaliseOutput>
+    <parasolCells_beta>0.</parasolCells_beta>
+    <parasolCells_tau>0.</parasolCells_tau>
+    <parasolCells_k>7.</parasolCells_k>
+    <amacrinCellsTemporalCutFrequency>2.0e+00</amacrinCellsTemporalCutFrequency>
+    <V0CompressionParameter>9.5e-01</V0CompressionParameter>
+    <localAdaptintegration_tau>0.</localAdaptintegration_tau>
+    <localAdaptintegration_k>7.</localAdaptintegration_k>
+</IPLmagno>
+</opencv_storage>

modules/bioinspired/samples/realistic_retina_config.xml

@@ -0,0 +1,24 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<OPLandIPLparvo>
+    <colorMode>1</colorMode>
+    <normaliseOutput>1</normaliseOutput>
+    <photoreceptorsLocalAdaptationSensitivity>8.9e-01</photoreceptorsLocalAdaptationSensitivity>
+    <photoreceptorsTemporalConstant>9.0e-01</photoreceptorsTemporalConstant>
+    <photoreceptorsSpatialConstant>5.3e-01</photoreceptorsSpatialConstant>
+    <horizontalCellsGain>0.3</horizontalCellsGain>
+    <hcellsTemporalConstant>0.5</hcellsTemporalConstant>
+    <hcellsSpatialConstant>7.</hcellsSpatialConstant>
+    <ganglionCellsSensitivity>8.9e-01</ganglionCellsSensitivity>
+</OPLandIPLparvo>
+<IPLmagno>
+    <normaliseOutput>1</normaliseOutput>
+    <parasolCells_beta>0.</parasolCells_beta>
+    <parasolCells_tau>0.</parasolCells_tau>
+    <parasolCells_k>7.</parasolCells_k>
+    <amacrinCellsTemporalCutFrequency>2.0e+00</amacrinCellsTemporalCutFrequency>
+    <V0CompressionParameter>9.5e-01</V0CompressionParameter>
+    <localAdaptintegration_tau>0.</localAdaptintegration_tau>
+    <localAdaptintegration_k>7.</localAdaptintegration_k>
+</IPLmagno>
+</opencv_storage>

modules/bioinspired/tutorials/retina_model/retina_model.markdown

@@ -1,6 +1,8 @@
 Retina and real-world vision {#tutorial_bioinspired_retina_model}
 =============================================================
 
+@tableofcontents
+
 Goal
 ----
 
@@ -382,7 +384,7 @@ need to know if mean luminance information is required or not. If not, the the r
 significantly reduce its energy thus giving more visibility to higher spatial frequency details.
 
 
-#### Basic parameters
+## Basic parameters
 
 The simplest parameters are as follows :
 
@@ -397,7 +399,7 @@ processing. You can expect much faster processing using gray levels : it would r
 product per pixel for all of the retina processes and it has recently been parallelized for multicore
 architectures.
 
-#### Photo-receptors parameters
+## Photo-receptors parameters
 
 The following parameters act on the entry point of the retina - photo-receptors - and has impact on all
  of the following processes. These sensors are low pass spatio-temporal filters that smooth temporal and
@@ -421,7 +423,7 @@ and high frequency noise canceling.
     A good compromise for color images is a 0.53 value since such choice won't affect too much the color spectrum.
     Higher values would lead to gray and blurred output images.
 
-#### Horizontal cells parameters
+## Horizontal cells parameters
 
 This parameter set tunes the neural network connected to the photo-receptors, the horizontal cells.
 It modulates photo-receptors sensitivity and completes the processing for final spectral whitening
@@ -446,7 +448,7 @@ It modulates photo-receptors sensitivity and completes the processing for final
 and luminance is already partly enhanced. The following parameters act on the last processing stages
 of the two outing retina signals.
 
-#### Parvo (details channel) dedicated parameter
+## Parvo (details channel) dedicated parameter
 
 -   **ganglionCellsSensitivity** specifies the strength of the final local adaptation occurring at
     the output of this details' dedicated channel. Parameter values remain between 0 and 1. Low value
@@ -455,7 +457,7 @@ of the two outing retina signals.
 **Note :** this parameter can correct eventual burned images by favoring low energetic details of
 the visual scene, even in bright areas.
 
-#### IPL Magno (motion/transient channel) parameters
+## IPL Magno (motion/transient channel) parameters
 
 Once image's information are cleaned, this channel acts as a high pass temporal filter that
 selects only the signals related to transient signals (events, motion, etc.). A low pass spatial filter

modules/cannops/include/opencv2/cann.hpp

@@ -8,12 +8,12 @@
 #include "opencv2/core.hpp"
 
 /**
-  @defgroup cann Ascend-accelerated Computer Vision
+  @defgroup cannops Ascend-accelerated Computer Vision
   @{
     @defgroup canncore Core part
     @{
       @defgroup cann_struct Data Structures
-      @defgroup cann_init Initializeation and Information
+      @defgroup cann_init Initialization and Information
     @}
   @}
  */

modules/cannops/include/opencv2/cann_interface.hpp

@@ -13,9 +13,9 @@ namespace cann
 {
 
 /**
-  @addtogroup cann
+  @addtogroup cannops
   @{
-    @defgroup cannops Operations for Ascend Backend.
+    @defgroup cannops_ops Operations for Ascend Backend.
     @{
         @defgroup cannops_elem Per-element Operations
         @defgroup cannops_core Core Operations on Matrices

modules/cudaimgproc/include/opencv2/cudaimgproc.hpp

@@ -844,7 +844,6 @@ cv::Moments cvMoments = convertSpatialMoments<float>(spatialMoments, order);
 ```
 
 see the \a CUDA_TEST_P(Moments, Async) test inside opencv_contrib_source_code/modules/cudaimgproc/test/test_moments.cpp for an example.
-@returns cv::Moments.
 @sa cuda::moments, cuda::convertSpatialMoments, cuda::numMoments, cuda::MomentsOrder
 */
 CV_EXPORTS_W void spatialMoments(InputArray src, OutputArray moments, const bool binaryImage = false, const MomentsOrder order = MomentsOrder::THIRD_ORDER_MOMENTS, const int momentsType = CV_64F, Stream& stream = Stream::Null());

modules/dnn_superres/tutorials/benchmark/sr_benchmark.markdown

@@ -50,14 +50,9 @@ Explanation
 Benchmarking results
 -----------
 
-Dataset benchmarking
-----
-
-###General100 dataset
-
-<center>
+## General100 dataset
 
-#####2x scaling factor
+### 2x scaling factor
 
 
 |               | Avg inference time in sec (CPU)| Avg PSNR | Avg SSIM |
@@ -70,7 +65,7 @@ Dataset benchmarking
 | Nearest neighbor | 0.000114 | 29.1665 | 0.9049 |
 | Lanczos          | 0.001094 | 32.4687 | 0.9327 |
 
-#####3x scaling factor
+### 3x scaling factor
 
 |               | Avg inference time in sec (CPU)| Avg PSNR | Avg SSIM |
 | ------------- |:-------------------:| ---------:|--------:|
@@ -83,7 +78,7 @@ Dataset benchmarking
 | Lanczos          | 0.001012  |25.9115  |0.8706  |
 
 
-#####4x scaling factor
+### 4x scaling factor
 
 |               | Avg inference time in sec (CPU)| Avg PSNR | Avg SSIM |
 | ------------- |:-------------------:| ---------:|--------:|
@@ -96,14 +91,10 @@ Dataset benchmarking
 | Lanczos          | 0.001012  |25.9115  |0.8706  |
 
 
-</center>
 
-Images
-----
-
-<center>
+## Images
 
-####2x scaling factor
+### 2x scaling factor
 
 |Set5: butterfly.png | size: 256x256 | ||
 |:-------------:|:-------------------:|:-------------:|:----:|
@@ -112,7 +103,7 @@ Images
 ![ESPCN](images/espcn_butterfly.jpg)| ![FSRCNN](images/fsrcnn_butterfly.jpg) | ![LapSRN](images/lapsrn_butterfly.jpg) | ![EDSR](images/edsr_butterfly.jpg)
 |29.0341 / 0.9354 / **0.004157**| 29.0077 / 0.9345 / 0.006325 | 27.8212 / 0.9230 / 0.037937 | **30.0347** / **0.9453** / 2.077280 |
 
-####3x scaling factor
+### 3x scaling factor
 
 |Urban100: img_001.png | size: 1024x644 | ||
 |:-------------:|:-------------------:|:-------------:|:----:|
@@ -122,7 +113,7 @@ Images
 |28.0118 / 0.8588 / **0.030748**| 28.0184 / 0.8597 / 0.094173 |  | **30.5671** / **0.9019** / 9.517580 |
 
 
-####4x scaling factor
+### 4x scaling factor
 
 |Set14: comic.png | size: 250x361 | ||
 |:-------------:|:-------------------:|:-------------:|:----:|
@@ -131,7 +122,7 @@ Images
 |![ESPCN](images/espcn_comic.jpg)| ![FSRCNN](images/fsrcnn_comic.jpg) | ![LapSRN](images/lapsrn_comic.jpg) | ![EDSR](images/edsr_comic.jpg)
 |20.0417 / 0.6302 / **0.001894**| 20.0885 / 0.6384 / 0.002103 | 20.0676 / 0.6339 / 0.061640 | **20.5233** / **0.6901** / 0.665876 |
 
-####8x scaling factor
+### 8x scaling factor
 
 |Div2K: 0006.png | size: 1356x2040 | |
 |:-------------:|:-------------------:|:-------------:|
@@ -139,5 +130,3 @@ Images
 |PSRN / SSIM / Speed (CPU)| 26.3139 / **0.8033** / 0.001107| 23.8291 / 0.7340 / **0.000611** |
 |![Lanczos interpolation](images/lanczos_div2k.jpg)| ![LapSRN](images/lapsrn_div2k.jpg) | |
 |26.1565 / 0.7962 / 0.004782| **26.7046** / 0.7987 / 2.274290 | |
-
-</center>

modules/face/include/opencv2/face/facemark.hpp

@@ -12,12 +12,6 @@ Mentor: Delia Passalacqua
 #ifndef __OPENCV_FACELANDMARK_HPP__
 #define __OPENCV_FACELANDMARK_HPP__
 
-/**
-@defgroup face Face Analysis
-- @ref tutorial_table_of_content_facemark
-- The Facemark API
-*/
-
 #include "opencv2/core.hpp"
 #include <vector>
 
@@ -25,6 +19,8 @@ Mentor: Delia Passalacqua
 namespace cv {
 namespace face {
 
+//! @addtogroup face
+//! @{
 
 /** @brief Abstract base class for all facemark models
 
@@ -88,6 +84,7 @@ CV_EXPORTS_W Ptr<Facemark> createFacemarkLBF();
 //! construct a Kazemi facemark detector
 CV_EXPORTS_W Ptr<Facemark> createFacemarkKazemi();
 
+//! @}
 
 } // face
 } // cv

modules/face/include/opencv2/face/facemark_train.hpp

@@ -12,12 +12,6 @@ Mentor: Delia Passalacqua
 #ifndef __OPENCV_FACELANDMARKTRAIN_HPP__
 #define __OPENCV_FACELANDMARKTRAIN_HPP__
 
-/**
-@defgroup face Face Analysis
-- @ref tutorial_table_of_content_facemark
-- The Facemark API
-*/
-
 #include "opencv2/face/facemark.hpp"
 #include "opencv2/objdetect.hpp"
 #include <vector>

modules/face/tutorials/face_landmark/face_landmark_trainer.markdown

@@ -21,7 +21,7 @@ The above format is similar to HELEN dataset which is used for training the mode
 ./sample_train_landmark_detector -annotations=/home/sukhad/Downloads/code/trainset/ -config=config.xml -face_cascade=lbpcascadefrontalface.xml -model=trained_model.dat -width=460 -height=460
 ```
 
-### Description of command parameters
+## Description of command parameters
 
 > * **annotations** a : (REQUIRED) Path to annotations txt file [example - /data/annotations.txt]
 > * **config** c : (REQUIRED) Path to configuration xml file containing parameters for training.[ example - /data/config.xml]
@@ -30,7 +30,7 @@ The above format is similar to HELEN dataset which is used for training the mode
 > * **height** h : (OPTIONAL) The height which you want all images to get to scale the annotations. Large images are slow to process [default = 460]
 > * **face_cascade** f (REQUIRED) Path to the face cascade xml file which you want to use as a detector.
 
-### Description of training parameters
+## Description of training parameters
 
 
 The configuration file described above which is used while training contains the training parameters which are required for training.
@@ -49,7 +49,7 @@ The configuration file described above which is used while training contains the
 
 To get more detailed description about the training parameters you can refer to the [Research paper](https://pdfs.semanticscholar.org/d78b/6a5b0dcaa81b1faea5fb0000045a62513567.pdf).
 
-### Understanding code
+## Understanding code
 
 
 ![](images/3.jpg)

modules/hdf/include/opencv2/hdf.hpp

@@ -49,8 +49,6 @@ Hierarchical Data Format version 5
 
     In order to use it, the hdf5 library has to be installed, which
     means cmake should find it using `find_package(HDF5)`.
-
-
 @}
 */
 

modules/mcc/include/opencv2/mcc/checker_model.hpp

@@ -116,7 +116,6 @@ public:
     virtual ~CCheckerDraw() {}
     /** \brief Draws the checker to the given image.
     * \param img image in color space BGR
-    * \return void
     */
     CV_WRAP virtual void draw(InputOutputArray img) = 0;
     /** \brief Create a new CCheckerDraw object.

modules/rgbd/include/opencv2/rgbd/dynafu.hpp

@@ -114,7 +114,6 @@ public:
     virtual void renderSurface(OutputArray depthImage, OutputArray vertImage, OutputArray normImage, bool warp=true) = 0;
 };
 
-//! @}
-}
-}
-#endif
+} // dynafu::
+} // cv::
+#endif // __OPENCV_RGBD_DYNAFU_HPP__

modules/sfm/include/opencv2/sfm.hpp

@@ -85,18 +85,17 @@ This module has been originally developed as a project for Google Summer of Code
     @defgroup triangulation Triangulation
 
     @defgroup reconstruction Reconstruction
+
     @note
     - Notice that it is compiled only when Ceres Solver is correctly installed.\n
         Check installation instructions in the following tutorial: @ref tutorial_sfm_installation
 
-
     @defgroup simple_pipeline Simple Pipeline
+
     @note
         - Notice that it is compiled only when Ceres Solver is correctly installed.\n
         Check installation instructions in the following tutorial: @ref tutorial_sfm_installation
-
 @}
-
 */
 
 #endif

modules/stereo/include/opencv2/stereo/quasi_dense_stereo.hpp

@@ -18,6 +18,7 @@ namespace cv
 {
 namespace stereo
 {
+
 /** \addtogroup stereo
  *  @{
  */
@@ -190,9 +191,8 @@ public:
     CV_PROP_RW PropagationParameters Param;
 };
 
-} //namespace cv
-} //namespace stereo
-
 /** @}*/
 
+} //namespace cv
+} //namespace stereo
 #endif // __OPENCV_QUASI_DENSE_STEREO_H__

modules/text/include/opencv2/text/ocr.hpp

@@ -363,7 +363,6 @@ CV_EXPORTS_W Ptr<OCRHMMDecoder::ClassifierCallback> loadOCRHMMClassifierCNN(cons
 
  */
 CV_EXPORTS_W Ptr<OCRHMMDecoder::ClassifierCallback> loadOCRHMMClassifier(const String& filename, int classifier);
-//! @}
 
 /** @brief Utility function to create a tailored language model transitions table from a given list of words (lexicon).
  *

modules/videostab/include/opencv2/videostab.hpp

@@ -70,9 +70,7 @@ Both the functions and the classes are available.
 
     The Fast Marching Method @cite Telea04 is used in of the video stabilization routines to do motion and
     color inpainting. The method is implemented is a flexible way and it's made public for other users.
-
 @}
-
 */
 
 #include "opencv2/videostab/stabilizer.hpp"

modules/viz/include/opencv2/viz.hpp

@@ -77,7 +77,6 @@ myWindow.showWidget("CloudWidget1", cw);
     // Modify it, and it will be modified in the window.
     cw.setColor(viz::Color::yellow());
     @endcode
-
 @}
 */
 

modules/xfeatures2d/include/opencv2/xfeatures2d.hpp

@@ -58,7 +58,6 @@ known to be patented. You need to set the OPENCV_ENABLE_NONFREE option in cmake
     This section describes the following matching strategies:
         - GMS: Grid-based Motion Statistics, @cite Bian2017gms
         - LOGOS: Local geometric support for high-outlier spatial verification, @cite Lowry2018LOGOSLG
-
 @}
 */
 

modules/xfeatures2d/include/opencv2/xfeatures2d/nonfree.hpp

@@ -50,6 +50,9 @@ namespace cv
 namespace xfeatures2d
 {
 
+//! @addtogroup xfeatures2d_nonfree
+//! @{
+
 /** @brief Class for extracting Speeded Up Robust Features from an image @cite Bay06 .
 
 The algorithm parameters:

modules/ximgproc/include/opencv2/ximgproc.hpp

@@ -65,7 +65,8 @@
 #include "ximgproc/find_ellipses.hpp"
 
 
-/** @defgroup ximgproc Extended Image Processing
+/**
+@defgroup ximgproc Extended Image Processing
 @{
     @defgroup ximgproc_edge Structured forests for fast edge detection
 
@@ -115,7 +116,6 @@ an additional hysteresis step.
 
     The size of the original image is required for compatibility with the imgproc functions when the boundary handling requires that pixel outside the image boundary are
     "on".
-
 @}
 */
 
@@ -124,6 +124,9 @@ namespace cv
 namespace ximgproc
 {
 
+//! @addtogroup ximgproc
+//! @{
+
 enum ThinningTypes{
     THINNING_ZHANGSUEN    = 0, // Thinning technique of Zhang-Suen
     THINNING_GUOHALL      = 1  // Thinning technique of Guo-Hall
@@ -139,9 +142,6 @@ enum LocalBinarizationMethods{
 	BINARIZATION_NICK = 3     //!< NICK technique. See @cite Khurshid2009 .
 };
 
-//! @addtogroup ximgproc
-//! @{
-
 /** @brief Performs thresholding on input images using Niblack's technique or some of the
 popular variations it inspired.
 

modules/ximgproc/include/opencv2/ximgproc/color_match.hpp

@@ -61,6 +61,8 @@ CV_EXPORTS_W void qdft(InputArray img, OutputArray qimg, int  	flags, bool sideL
 */
 CV_EXPORTS_W void colorMatchTemplate(InputArray img, InputArray templ, OutputArray result);
 
+//! @}
+
 }
 }
 #endif

modules/ximgproc/include/opencv2/ximgproc/deriche_filter.hpp

@@ -71,6 +71,8 @@ CV_EXPORTS_W void GradientDericheY(InputArray op, OutputArray dst, double alpha,
 */
 CV_EXPORTS_W void GradientDericheX(InputArray op, OutputArray dst, double alpha,double omega);
 
+//! @}
+
 }
 }
 #endif

modules/ximgproc/include/opencv2/ximgproc/edgepreserving_filter.hpp

@@ -26,8 +26,8 @@ namespace cv { namespace ximgproc {
     */
     CV_EXPORTS_W void edgePreservingFilter( InputArray src, OutputArray dst, int d, double threshold );
 
-}} // namespace
-
 //! @}
 
+}} // namespace
+
 #endif

modules/ximgproc/include/opencv2/ximgproc/fast_hough_transform.hpp

@@ -83,7 +83,6 @@ enum AngleRangeOption
  *          is a binary relation that maps elements of the Cartesian product
  *          @f$ S \times S @f$ to @f$ S @f$:
  *          @f[ f: S \times S \to S @f]
- * @ingroup MinUtils_MathOper
  */
 enum HoughOp
 {

modules/ximgproc/include/opencv2/ximgproc/paillou_filter.hpp

@@ -61,6 +61,8 @@ namespace ximgproc {
 CV_EXPORTS void GradientPaillouY(InputArray op, OutputArray _dst, double alpha, double omega);
 CV_EXPORTS void GradientPaillouX(InputArray op, OutputArray _dst, double alpha, double omega);
 
+//! @}
+
 }
 }
 #endif

modules/ximgproc/include/opencv2/ximgproc/peilin.hpp

@@ -27,6 +27,8 @@ namespace cv { namespace ximgproc {
     /** @overload */
     CV_EXPORTS_W void PeiLinNormalization ( InputArray I, OutputArray T );
 
+    //! @}
+
 }} // namespace
 
 #endif

modules/ximgproc/include/opencv2/ximgproc/run_length_morphology.hpp

@@ -113,6 +113,8 @@ CV_EXPORTS void createRLEImage(const std::vector<cv::Point3i>& runs, OutputArray
 CV_EXPORTS void morphologyEx(InputArray rlSrc, OutputArray rlDest, int op, InputArray rlKernel,
     bool bBoundaryOnForErosion = true, Point anchor = Point(0,0));
 
+//! @}
+
 }
 }
 }