Merge pull request #3638 from mshabunin:doc-upgrade

Documentation transition to fresh Doxygen #3638 Merge with https://github.com/opencv/opencv/pull/25042
1 year ago · 1aaf6e1c8b
31 changed files with 214 additions and 228 deletions
--- a/modules/bioinspired/include/opencv2/bioinspired/retina.hpp
+++ b/modules/bioinspired/include/opencv2/bioinspired/retina.hpp
@ -94,57 +94,12 @@ enum {
				@@ -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
--- a/modules/bioinspired/samples/default_retina_config.xml
+++ b/modules/bioinspired/samples/default_retina_config.xml
@ -0,0 +1,24 @@
				@@ -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>
--- a/modules/bioinspired/samples/realistic_retina_config.xml
+++ b/modules/bioinspired/samples/realistic_retina_config.xml
@ -0,0 +1,24 @@
				@@ -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>
--- a/modules/bioinspired/tutorials/retina_model/retina_model.markdown
+++ b/modules/bioinspired/tutorials/retina_model/retina_model.markdown
@ -1,6 +1,8 @@
				@@ -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
				@@ -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
				@@ -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.
				@@ -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
				@@ -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.
				@@ -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
--- a/modules/cannops/include/opencv2/cann.hpp
+++ b/modules/cannops/include/opencv2/cann.hpp
@ -8,12 +8,12 @@
				@@ -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
    @}
  @}
 */
--- a/modules/cannops/include/opencv2/cann_interface.hpp
+++ b/modules/cannops/include/opencv2/cann_interface.hpp
@ -13,9 +13,9 @@ namespace cann
				@@ -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
--- a/modules/cudaimgproc/include/opencv2/cudaimgproc.hpp
+++ b/modules/cudaimgproc/include/opencv2/cudaimgproc.hpp
@ -844,7 +844,6 @@ cv::Moments cvMoments = convertSpatialMoments<float>(spatialMoments, order);
				@@ -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());
--- a/modules/dnn_superres/tutorials/benchmark/sr_benchmark.markdown
+++ b/modules/dnn_superres/tutorials/benchmark/sr_benchmark.markdown
@ -50,14 +50,9 @@ Explanation
				@@ -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
				@@ -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
				@@ -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
				@@ -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
				@@ -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
				@@ -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
				@@ -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
				@@ -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>
--- a/modules/face/include/opencv2/face/facemark.hpp
+++ b/modules/face/include/opencv2/face/facemark.hpp
@ -12,12 +12,6 @@ Mentor: Delia Passalacqua
				@@ -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
				@@ -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();
				@@ -88,6 +84,7 @@ CV_EXPORTS_W Ptr<Facemark> createFacemarkLBF();
 //! construct a Kazemi facemark detector
 CV_EXPORTS_W Ptr<Facemark> createFacemarkKazemi();

+//! @}

 } // face
 } // cv
--- a/modules/face/include/opencv2/face/facemark_train.hpp
+++ b/modules/face/include/opencv2/face/facemark_train.hpp
@ -12,12 +12,6 @@ Mentor: Delia Passalacqua
				@@ -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>
--- a/modules/face/tutorials/face_landmark/face_landmark_trainer.markdown
+++ b/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
				@@ -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
				@@ -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
				@@ -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)
--- a/modules/fuzzy/include/opencv2/fuzzy.hpp
+++ b/modules/fuzzy/include/opencv2/fuzzy.hpp
@ -52,19 +52,19 @@
				@@ -52,19 +52,19 @@

 Namespace for all functions is `ft`. The module brings implementation of the last image processing algorithms based on fuzzy mathematics. Method are named based on the pattern `FT`_degree_dimension`_`method.

-  @{
+@{
    @defgroup f0_math Math with F0-transform support

-Fuzzy transform (\f$F^0\f$-transform) of the 0th degree transforms whole image to a matrix of its components. These components are used in latter computation where each of them represents average color of certain subarea.
+    Fuzzy transform (\f$F^0\f$-transform) of the 0th degree transforms whole image to a matrix of its components. These components are used in latter computation where each of them represents average color of certain subarea.

    @defgroup f1_math Math with F1-transform support

-Fuzzy transform (\f$F^1\f$-transform) of the 1th degree transforms whole image to a matrix of its components. Each component is polynomial of the 1th degree carrying information about average color and average gradient of certain subarea.
+    Fuzzy transform (\f$F^1\f$-transform) of the 1th degree transforms whole image to a matrix of its components. Each component is polynomial of the 1th degree carrying information about average color and average gradient of certain subarea.

    @defgroup f_image Fuzzy image processing

-Image proceesing based on fuzzy mathematics namely F-transform.
-   @}
+    Image proceesing based on fuzzy mathematics namely F-transform.
+@}

 */

--- a/modules/hdf/include/opencv2/hdf.hpp
+++ b/modules/hdf/include/opencv2/hdf.hpp
@ -41,17 +41,15 @@
				@@ -41,17 +41,15 @@

 This module provides storage routines for Hierarchical Data Format objects.

-  @{
+@{
    @defgroup hdf5 Hierarchical Data Format version 5

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

 #endif
--- a/modules/mcc/include/opencv2/mcc/checker_model.hpp
+++ b/modules/mcc/include/opencv2/mcc/checker_model.hpp
@ -116,7 +116,6 @@ public:
				@@ -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.
--- a/modules/rgbd/include/opencv2/rgbd/dynafu.hpp
+++ b/modules/rgbd/include/opencv2/rgbd/dynafu.hpp
@ -114,7 +114,6 @@ public:
				@@ -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__
--- a/modules/sfm/include/opencv2/sfm.hpp
+++ b/modules/sfm/include/opencv2/sfm.hpp
@ -75,7 +75,7 @@ This module has been originally developed as a project for Google Summer of Code
				@@ -75,7 +75,7 @@ This module has been originally developed as a project for Google Summer of Code
  - Notice that it is compiled only when Eigen, GLog and GFlags are correctly installed.\n
    Check installation instructions in the following tutorial: @ref tutorial_sfm_installation

-  @{
+@{
    @defgroup conditioning Conditioning
    @defgroup fundamental Fundamental
    @defgroup io Input/Output
@ -85,18 +85,17 @@ This module has been originally developed as a project for Google Summer of Code
				@@ -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

+    @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
-
-  @}

+    @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
--- a/modules/stereo/include/opencv2/stereo/quasi_dense_stereo.hpp
+++ b/modules/stereo/include/opencv2/stereo/quasi_dense_stereo.hpp
@ -18,6 +18,7 @@ namespace cv
				@@ -18,6 +18,7 @@ namespace cv
 {
 namespace stereo
 {
+
 /** \addtogroup stereo
 *  @{
 */
@ -190,9 +191,8 @@ public:
				@@ -190,9 +191,8 @@ public:
    CV_PROP_RW PropagationParameters Param;
 };

-} //namespace cv
-} //namespace stereo
-
 /** @}*/

+} //namespace cv
+} //namespace stereo
 #endif // __OPENCV_QUASI_DENSE_STEREO_H__
--- a/modules/text/include/opencv2/text.hpp
+++ b/modules/text/include/opencv2/text.hpp
@ -52,49 +52,49 @@ scene images.
				@@ -52,49 +52,49 @@ scene images.
  @{
    @defgroup text_detect Scene Text Detection

-Class-specific Extremal Regions for Scene Text Detection
--------------------------------------------------------
-
-The scene text detection algorithm described below has been initially proposed by Lukás Neumann &
-Jiri Matas @cite Neumann11. The main idea behind Class-specific Extremal Regions is similar to the MSER
-in that suitable Extremal Regions (ERs) are selected from the whole component tree of the image.
-However, this technique differs from MSER in that selection of suitable ERs is done by a sequential
-classifier trained for character detection, i.e. dropping the stability requirement of MSERs and
-selecting class-specific (not necessarily stable) regions.
-
-The component tree of an image is constructed by thresholding by an increasing value step-by-step
-from 0 to 255 and then linking the obtained connected components from successive levels in a
-hierarchy by their inclusion relation:
-
-![image](pics/component_tree.png)
-
-The component tree may contain a huge number of regions even for a very simple image as shown in
-the previous image. This number can easily reach the order of 1 x 10\^6 regions for an average 1
-Megapixel image. In order to efficiently select suitable regions among all the ERs the algorithm
-make use of a sequential classifier with two differentiated stages.
-
-In the first stage incrementally computable descriptors (area, perimeter, bounding box, and Euler's
-number) are computed (in O(1)) for each region r and used as features for a classifier which
-estimates the class-conditional probability p(r|character). Only the ERs which correspond to local
-maximum of the probability p(r|character) are selected (if their probability is above a global limit
-p_min and the difference between local maximum and local minimum is greater than a delta_min
-value).
-
-In the second stage, the ERs that passed the first stage are classified into character and
-non-character classes using more informative but also more computationally expensive features. (Hole
-area ratio, convex hull ratio, and the number of outer boundary inflexion points).
-
-This ER filtering process is done in different single-channel projections of the input image in
-order to increase the character localization recall.
-
-After the ER filtering is done on each input channel, character candidates must be grouped in
-high-level text blocks (i.e. words, text lines, paragraphs, ...). The opencv_text module implements
-two different grouping algorithms: the Exhaustive Search algorithm proposed in @cite Neumann12 for
-grouping horizontally aligned text, and the method proposed by Lluis Gomez and Dimosthenis Karatzas
-in @cite Gomez13 @cite Gomez14 for grouping arbitrary oriented text (see erGrouping).
-
-To see the text detector at work, have a look at the textdetection demo:
-<https://github.com/opencv/opencv_contrib/blob/master/modules/text/samples/textdetection.cpp>
+    Class-specific Extremal Regions for Scene Text Detection
+    --------------------------------------------------------
+
+    The scene text detection algorithm described below has been initially proposed by Lukás Neumann &
+    Jiri Matas @cite Neumann11. The main idea behind Class-specific Extremal Regions is similar to the MSER
+    in that suitable Extremal Regions (ERs) are selected from the whole component tree of the image.
+    However, this technique differs from MSER in that selection of suitable ERs is done by a sequential
+    classifier trained for character detection, i.e. dropping the stability requirement of MSERs and
+    selecting class-specific (not necessarily stable) regions.
+
+    The component tree of an image is constructed by thresholding by an increasing value step-by-step
+    from 0 to 255 and then linking the obtained connected components from successive levels in a
+    hierarchy by their inclusion relation:
+
+    ![image](pics/component_tree.png)
+
+    The component tree may contain a huge number of regions even for a very simple image as shown in
+    the previous image. This number can easily reach the order of 1 x 10\^6 regions for an average 1
+    Megapixel image. In order to efficiently select suitable regions among all the ERs the algorithm
+    make use of a sequential classifier with two differentiated stages.
+
+    In the first stage incrementally computable descriptors (area, perimeter, bounding box, and Euler's
+    number) are computed (in O(1)) for each region r and used as features for a classifier which
+    estimates the class-conditional probability p(r|character). Only the ERs which correspond to local
+    maximum of the probability p(r|character) are selected (if their probability is above a global limit
+    p_min and the difference between local maximum and local minimum is greater than a delta_min
+    value).
+
+    In the second stage, the ERs that passed the first stage are classified into character and
+    non-character classes using more informative but also more computationally expensive features. (Hole
+    area ratio, convex hull ratio, and the number of outer boundary inflexion points).
+
+    This ER filtering process is done in different single-channel projections of the input image in
+    order to increase the character localization recall.
+
+    After the ER filtering is done on each input channel, character candidates must be grouped in
+    high-level text blocks (i.e. words, text lines, paragraphs, ...). The opencv_text module implements
+    two different grouping algorithms: the Exhaustive Search algorithm proposed in @cite Neumann12 for
+    grouping horizontally aligned text, and the method proposed by Lluis Gomez and Dimosthenis Karatzas
+    in @cite Gomez13 @cite Gomez14 for grouping arbitrary oriented text (see erGrouping).
+
+    To see the text detector at work, have a look at the textdetection demo:
+    <https://github.com/opencv/opencv_contrib/blob/master/modules/text/samples/textdetection.cpp>

    @defgroup text_recognize Scene Text Recognition
  @}
--- a/modules/text/include/opencv2/text/ocr.hpp
+++ b/modules/text/include/opencv2/text/ocr.hpp
@ -363,7 +363,6 @@ CV_EXPORTS_W Ptr<OCRHMMDecoder::ClassifierCallback> loadOCRHMMClassifierCNN(cons
				@@ -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).
 *
--- a/modules/videostab/include/opencv2/videostab.hpp
+++ b/modules/videostab/include/opencv2/videostab.hpp
@ -44,7 +44,7 @@
				@@ -44,7 +44,7 @@
 #define OPENCV_VIDEOSTAB_HPP

 /**
-  @defgroup videostab Video Stabilization
+@defgroup videostab Video Stabilization

 The video stabilization module contains a set of functions and classes that can be used to solve the
 problem of video stabilization. There are a few methods implemented, most of them are described in
@ -53,26 +53,24 @@ paper methods.
				@@ -53,26 +53,24 @@ paper methods.

 ### References

- 1. "Full-Frame Video Stabilization with Motion Inpainting"
-     Yasuyuki Matsushita, Eyal Ofek, Weina Ge, Xiaoou Tang, Senior Member, and Heung-Yeung Shum
- 2. "Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths"
-     Matthias Grundmann, Vivek Kwatra, Irfan Essa
+1. "Full-Frame Video Stabilization with Motion Inpainting"
+    Yasuyuki Matsushita, Eyal Ofek, Weina Ge, Xiaoou Tang, Senior Member, and Heung-Yeung Shum
+2. "Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths"
+    Matthias Grundmann, Vivek Kwatra, Irfan Essa

-     @{
-         @defgroup videostab_motion Global Motion Estimation
+@{
+    @defgroup videostab_motion Global Motion Estimation

-The video stabilization module contains a set of functions and classes for global motion estimation
-between point clouds or between images. In the last case features are extracted and matched
-internally. For the sake of convenience the motion estimation functions are wrapped into classes.
-Both the functions and the classes are available.
+    The video stabilization module contains a set of functions and classes for global motion estimation
+    between point clouds or between images. In the last case features are extracted and matched
+    internally. For the sake of convenience the motion estimation functions are wrapped into classes.
+    Both the functions and the classes are available.

-         @defgroup videostab_marching Fast Marching Method
-
-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.
-
-     @}
+    @defgroup videostab_marching Fast Marching Method

+    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"
--- a/modules/viz/include/opencv2/viz.hpp
+++ b/modules/viz/include/opencv2/viz.hpp
@ -60,25 +60,24 @@ interact with it.
				@@ -60,25 +60,24 @@ interact with it.
 3D visualization window (see Viz3d) is used to display widgets (see Widget), and it provides several
 methods to interact with scene and widgets.

-  @{
+@{
    @defgroup viz_widget Widget

-In this section, the widget framework is explained. Widgets represent 2D or 3D objects, varying from
-simple ones such as lines to complex ones such as point clouds and meshes.
+    In this section, the widget framework is explained. Widgets represent 2D or 3D objects, varying from
+    simple ones such as lines to complex ones such as point clouds and meshes.

-Widgets are **implicitly shared**. Therefore, one can add a widget to the scene, and modify the
-widget without re-adding the widget.
+    Widgets are **implicitly shared**. Therefore, one can add a widget to the scene, and modify the
+    widget without re-adding the widget.

-@code
-// Create a cloud widget
-viz::WCloud cw(cloud, viz::Color::red());
-// Display it in a window
-myWindow.showWidget("CloudWidget1", cw);
-// Modify it, and it will be modified in the window.
-cw.setColor(viz::Color::yellow());
-@endcode
-
-  @}
+    @code
+    // Create a cloud widget
+    viz::WCloud cw(cloud, viz::Color::red());
+    // Display it in a window
+    myWindow.showWidget("CloudWidget1", cw);
+    // Modify it, and it will be modified in the window.
+    cw.setColor(viz::Color::yellow());
+    @endcode
+@}
 */

 #endif /* OPENCV_VIZ_HPP */
--- a/modules/xfeatures2d/include/opencv2/xfeatures2d.hpp
+++ b/modules/xfeatures2d/include/opencv2/xfeatures2d.hpp
@ -46,19 +46,18 @@ the use of this software, even if advised of the possibility of such damage.
				@@ -46,19 +46,18 @@ the use of this software, even if advised of the possibility of such damage.
@{
    @defgroup xfeatures2d_experiment Experimental 2D Features Algorithms

-This section describes experimental algorithms for 2d feature detection.
+    This section describes experimental algorithms for 2d feature detection.

    @defgroup xfeatures2d_nonfree Non-free 2D Features Algorithms

-This section describes two popular algorithms for 2d feature detection, SIFT and SURF, that are
-known to be patented. You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk.
+    This section describes two popular algorithms for 2d feature detection, SIFT and SURF, that are
+    known to be patented. You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk.

    @defgroup xfeatures2d_match Experimental 2D Features Matching Algorithm

-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
-
+    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
@}
 */

--- a/modules/xfeatures2d/include/opencv2/xfeatures2d/nonfree.hpp
+++ b/modules/xfeatures2d/include/opencv2/xfeatures2d/nonfree.hpp
@ -50,6 +50,9 @@ namespace cv
				@@ -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:
--- a/modules/ximgproc/include/opencv2/ximgproc.hpp
+++ b/modules/ximgproc/include/opencv2/ximgproc.hpp
@ -65,12 +65,13 @@
				@@ -65,12 +65,13 @@
 #include "ximgproc/find_ellipses.hpp"


-/** @defgroup ximgproc Extended Image Processing
-  @{
+/**
+@defgroup ximgproc Extended Image Processing
+@{
    @defgroup ximgproc_edge Structured forests for fast edge detection

-This module contains implementations of modern structured edge detection algorithms,
-i.e. algorithms which somehow takes into account pixel affinities in natural images.
+    This module contains implementations of modern structured edge detection algorithms,
+    i.e. algorithms which somehow takes into account pixel affinities in natural images.

    @defgroup ximgproc_edgeboxes EdgeBoxes

@ -84,16 +85,16 @@ i.e. algorithms which somehow takes into account pixel affinities in natural ima
				@@ -84,16 +85,16 @@ i.e. algorithms which somehow takes into account pixel affinities in natural ima

    @defgroup ximgproc_edge_drawing EdgeDrawing

-EDGE DRAWING LIBRARY FOR GEOMETRIC FEATURE EXTRACTION AND VALIDATION
+    EDGE DRAWING LIBRARY FOR GEOMETRIC FEATURE EXTRACTION AND VALIDATION

-Edge Drawing (ED) algorithm is an proactive approach on edge detection problem. In contrast to many other existing edge detection algorithms which follow a subtractive
-approach (i.e. after applying gradient filters onto an image eliminating pixels w.r.t. several rules, e.g. non-maximal suppression and hysteresis in Canny), ED algorithm
-works via an additive strategy, i.e. it picks edge pixels one by one, hence the name Edge Drawing. Then we process those random shaped edge segments to extract higher level
-edge features, i.e. lines, circles, ellipses, etc. The popular method of extraction edge pixels from the thresholded gradient magnitudes is non-maximal supression that tests
-every pixel whether it has the maximum gradient response along its gradient direction and eliminates if it does not. However, this method does not check status of the
-neighboring pixels, and therefore might result low quality (in terms of edge continuity, smoothness, thinness, localization) edge segments. Instead of non-maximal supression,
-ED points a set of edge pixels and join them by maximizing the total gradient response of edge segments. Therefore it can extract high quality edge segments without need for
-an additional hysteresis step.
+    Edge Drawing (ED) algorithm is an proactive approach on edge detection problem. In contrast to many other existing edge detection algorithms which follow a subtractive
+    approach (i.e. after applying gradient filters onto an image eliminating pixels w.r.t. several rules, e.g. non-maximal suppression and hysteresis in Canny), ED algorithm
+    works via an additive strategy, i.e. it picks edge pixels one by one, hence the name Edge Drawing. Then we process those random shaped edge segments to extract higher level
+    edge features, i.e. lines, circles, ellipses, etc. The popular method of extraction edge pixels from the thresholded gradient magnitudes is non-maximal supression that tests
+    every pixel whether it has the maximum gradient response along its gradient direction and eliminates if it does not. However, this method does not check status of the
+    neighboring pixels, and therefore might result low quality (in terms of edge continuity, smoothness, thinness, localization) edge segments. Instead of non-maximal supression,
+    ED points a set of edge pixels and join them by maximizing the total gradient response of edge segments. Therefore it can extract high quality edge segments without need for
+    an additional hysteresis step.

    @defgroup ximgproc_fourier Fourier descriptors

@ -115,8 +116,7 @@ an additional hysteresis step.
				@@ -115,8 +116,7 @@ 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".
-
-    @}
+@}
 */

 namespace cv
@ -124,6 +124,9 @@ namespace cv
				@@ -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{
				@@ -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.

--- a/modules/ximgproc/include/opencv2/ximgproc/color_match.hpp
+++ b/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
				@@ -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
--- a/modules/ximgproc/include/opencv2/ximgproc/deriche_filter.hpp
+++ b/modules/ximgproc/include/opencv2/ximgproc/deriche_filter.hpp
@ -71,6 +71,8 @@ CV_EXPORTS_W void GradientDericheY(InputArray op, OutputArray dst, double alpha,
				@@ -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
--- a/modules/ximgproc/include/opencv2/ximgproc/edgepreserving_filter.hpp
+++ b/modules/ximgproc/include/opencv2/ximgproc/edgepreserving_filter.hpp
@ -26,8 +26,8 @@ namespace cv { namespace ximgproc {
				@@ -26,8 +26,8 @@ namespace cv { namespace ximgproc {
    */
    CV_EXPORTS_W void edgePreservingFilter( InputArray src, OutputArray dst, int d, double threshold );

-}} // namespace
-
 //! @}

+}} // namespace
+
 #endif
--- a/modules/ximgproc/include/opencv2/ximgproc/fast_hough_transform.hpp
+++ b/modules/ximgproc/include/opencv2/ximgproc/fast_hough_transform.hpp
@ -82,8 +82,7 @@ enum AngleRangeOption
				@@ -82,8 +82,7 @@ enum AngleRangeOption
 *          two operands. Formally, a binary operation @f$ f @f$ on a set @f$ S @f$
 *          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
+ *          @f[ f: S \times S \to S @f]
 */
 enum HoughOp
 {
--- a/modules/ximgproc/include/opencv2/ximgproc/paillou_filter.hpp
+++ b/modules/ximgproc/include/opencv2/ximgproc/paillou_filter.hpp
@ -61,6 +61,8 @@ namespace ximgproc {
				@@ -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
--- a/modules/ximgproc/include/opencv2/ximgproc/peilin.hpp
+++ b/modules/ximgproc/include/opencv2/ximgproc/peilin.hpp
@ -27,6 +27,8 @@ namespace cv { namespace ximgproc {
				@@ -27,6 +27,8 @@ namespace cv { namespace ximgproc {
    /** @overload */
    CV_EXPORTS_W void PeiLinNormalization ( InputArray I, OutputArray T );

+    //! @}
+
 }} // namespace

 #endif
--- a/modules/ximgproc/include/opencv2/ximgproc/run_length_morphology.hpp
+++ b/modules/ximgproc/include/opencv2/ximgproc/run_length_morphology.hpp
@ -113,6 +113,8 @@ CV_EXPORTS void createRLEImage(const std::vector<cv::Point3i>& runs, OutputArray
				@@ -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));

+//! @}
+
 }
 }
 }